![](\"https://blog.bioconductor.org/posts/2026-09-30-BioC2026-recap//media/bioc2026_groupshot.jpg\")
Toby Steiner\u00a0 & Vincent W.J. van Gerven Oei
\nPreprint; to appear in\u00a0tendencia editorial UR \u2013 N\u00ba 41 Especial \u00b7 Universidad del Rosario, Bogot\u00e1, 2026\n
Across scholarly communications, artificial intelligence is being touted as both a diagnosis of and a miracle cure for the wide variety of issues plaguing the sector. The diagnosis is familiar: too much information, too little time; too many records, too few cataloguers; too many systems, too little interoperability. The proposed cure is automation at scale\u2014automation facilitated through AI.
\nThe massive increase in higher education institutions' (HEI) spending on the technology (UNESCO, 2025) points to a rather unique persuasiveness of this framing by commercial vendors over the last few years, particularly among upper management executives without direct access to technical expertise: to them, AI is being sold as the one \"magic bullet\" to fix the many underlying problems they had already been persuaded to invest in over the previous years. Here, the pivot to AI can be understood as just the next step in a larger move to fully outsource HEI infrastructure to commercial vendors (see, e.g., Bowie, 2025).
\nMeanwhile, the underlying plethora of problems becomes even more severe: metadata work remains under-resourced. Small and scholar-led publishers commonly lack the technical capacity of large commercial firms. Library and publishing workflows are fragmented across systems, standards, and platforms. Books and chapters remain particularly difficult to describe, distribute, discover, and preserve consistently.
\nYet the promised all-purpose solution of AI conceals a more consequential set of questions. What kind of automation is being normalised? Who owns and governs the systems that enable automation? What costs\u2014financial, environmental, and social\u2014disappear behind the shiny interface of an AI agent? Whose actual labour is displaced, subordinated, or rendered invisible? Who looks after and is ultimately responsible for code churned out as AI slop (Weedon et al., 2026), which some have declared our societies' modern asbestos, being shoveled into the walls of our collective digital ecosystems (Doctorow, 2025)? What happens when public and community-produced knowledge becomes an input to proprietary systems that cannot be inspected or governed by the communities from which that knowledge originated?
\nFor Thoth Open Metadata, the non-profit we work for, these are not abstract concerns or mere philosophical ponderings. The underlying issues, one might argue, are very much about the overall purpose, ownership, and future of scholarly infrastructure.
\nTo provide a bit of context on what we do: Thoth is a non-profit, community-led open platform for managing and disseminating fully open, reusable, high-quality metadata for books and chapters. It was developed by and for publishers, with a particular focus on open-access titles and an overarching aim to support small, scholar-led, university, and library presses in tackling a variety of structural barriers to participation in the book supply chain. Thoth's legal form, a UK Community Interest Company limited by guarantee, reflects our explicit commitment to community benefit rather than shareholder value. Thoth's technical architecture is open source; its Application Programming Interfaces (APIs) are open; and its metadata is released into the public domain (via a CC0 dedication made explicit within the metadata records) to facilitate open reuse across libraries and other stakeholders active in the book supply chain.
\nThis positionality also shapes Thoth's approach to AI. For us, the issue is not whether automation can assist with metadata work. It already does. The more important question is whether it can strengthen communities and professional agency or reorganise them around the profit-making priorities of extractive proprietary systems. We are strongly opposed to reducing the future of metadata and cataloguing to yet another layer in an extractive corporate AI stack. Instead, we argue, AI tools should form part of a broader public knowledge infrastructure that is transparent, bibliodiverse, multilingual, interoperable, reusable, and accountable to the communities that sustain it.
\nHence, for us, imagining an alternative approach to the current commercial AI regime would be guided by the following set of rules:
\nIn the following sections, we will expand on each of these points by discussing four major issues that can be identified under the current regime of commercial AI provision. Following this discussion, we will outline how the work we do within the broader publishing community might provide an alternative, bibliodiverse and equitable model that serves the communities it is governed by, rather than stakeholders' pockets.
\nRecent commercial AI is organised around an expansive and consolidating model of scale. Larger models require larger datasets, greater computational capacity, more data centre infrastructure, and deeper concentrations of capital. Scale here functions simultaneously as a technical strategy and a competitive weapon. In this environment, scale does not simply mean serving more users. It often means consolidating control over the infrastructures through which people work, publish, search, and communicate. Once functions are bundled together, institutions become progressively dependent on a small number of providers.
\nThe concentration of AI provision therefore can well be understood as the logical continuation of a longer process of platformisation (Poell et al., 2019) in scholarly communication. The already-familiar issues of vendor lock-in, opaque analytics, restricted metadata reuse, and extractive work linked to maintaining metadata quality in the context of closed walled gardens have not disappeared. On the contrary, AI is very likely intensifying them by adding another proprietary front-house interface and smoke screen between users and these complex issues (see also Van Gerven Oei 2026).
\nA practice tied to the neoliberal notion of \"scaling up,\" which we see rampantly at play with the major commercial AI providers, is the provision of services that can be sustained even while remaining heavily indebted, environmentally damaging, socially extractive, or politically vulnerable. A commercial infrastructure may appear stable because it has substantial financial backing propped up by circular funding (Down & Milmo, 2026), but it can still expose communities to acquisition, strategic redirection, service withdrawal, sudden price changes, or revised data policies.
\nFor open infrastructure, sustainability must include governance, technical openness, succession, community accountability, and the ability of users to continue their work if an organisation changes or disappears.
\nThis has direct implications for the use of AI. Uptake of a heavily cross-subsidised AI service may appear inexpensive today, but it will also create deep, long-term dependencies. Its prices, environmental practices, access terms, or data policies may change once institutions have reorganised their workflows around new profitability targets. Commercial AI provision also externalises costs. Users absorb the labour of checking and correcting outputs; open infrastructures absorb the bandwidth and server costs generated by automated harvesting through bots and agents; communities whose work informs models may receive neither recognition nor material support.
\nTied to the above, the overall rhetoric around AI encourages users to experience it as frictionless. A prompt is entered; an answer appears. The underlying infrastructure becomes opaque, yet AI remains a materially resource-intensive technology. Its provision depends on massive data centres, global semiconductor supply chains, public electricity grids, environmentally damaging cooling systems, increasingly scarce water resources, physical land, and continuous hardware replacement. As recent research by Aczel et al. (2026) argues, AI's environmental impact must be assessed across carbon, water, and land footprints, rather than through electricity use alone. The report emphasises that inference, i.e., the routine use of deployed systems, can become a major source of resource consumption at scale.
\nInvestigations into data centre expansion have also drawn attention to facilities operating or under development in water-stressed regions (e.g., Barratt et al., 2025). This complicates the notion that AI necessarily produces efficiency. A process may appear efficient to the user because environmental burdens have been displaced elsewhere. The same applies to labour. The time required to verify, correct, and contextualise AI output is often omitted from productivity claims. So too is the cost of maintaining defensive systems against aggressive automated scraping. A serious assessment of AI therefore needs to include the infrastructural footprint of its entire stack, from geology to the user interface (see Bratton, 2016). Universities, libraries, publishers, and funders should demand environmental transparency and determine whether a system's computational intensity is proportionate to the task.
\nOne might argue that, particularly in the field of metadata and cataloguing, the problem space at hand does not require a general-purpose large-scale language model. As we show below, the issues can be addressed more reliably through structured data, validation rules, authority files, controlled vocabularies, and narrowly targeted, purposeful automation.
\nThe consequences of commercial AI's extractive behaviour are already visible at the infrastructural level. In the books space, aggregators such as OAPEN and the Directory of Open Access Books have reported aggressive bot traffic that has affected platform stability and required traffic-management and bot-protection systems (Davison et al., 2026).
\nThe Public Knowledge Project faces a similar structural challenge across the distributed ecosystem of Open Journal Systems and Open Monograph Press installations. Because these systems are often hosted by universities, libraries, societies, and small publishers, the costs of automated scraping are distributed across institutions with very different technical capacities.
\nProject Gutenberg provides another example. Its public-domain corpus is openly available and widely useful, but the infrastructure that serves it is not cost-free. Repeated large-scale crawling consumes bandwidth and processing capacity and relies on the labour of volunteers who digitise, proofread, structure, and maintain the collection.
\nWikimedia faces the same conflict on an even larger scale. Open knowledge infrastructures pay specialists to curate, store, serve, and protect information, while automated systems create additional demand and may simultaneously redirect users away from the original source. This creates both operational and wider-ranging social imbalances. Operationally, community infrastructures bear the costs of access. Socially, they lose the recognition, participation, and support that accompany direct engagement.
\nAs Van Gerven Oei and Fradenburg Joy (2025) point out, the problem is not automated access per se. Preservation, indexing, and metadata exchange have always relied on machine-to-machine communication. The problem is uncoordinated extraction without restraint, attribution, provenance, or reciprocity. Responsible (re)users should prefer APIs, Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH) endpoints, bulk datasets, mirrors, and rate-limited feeds where available. They should properly identify themselves, avoid repeated retrieval of unchanged resources, and contribute materially when their use creates high costs, in line with fair-use principles. Open access cannot mean that public-interest infrastructures are expected to subsidise limitless private accumulation.
\nCurrent commercial AI provision challenges basic assumptions about openness. The promise of open access was to enable knowledge to be read, shared, reused, preserved, and built upon. Yet open availability is increasingly interpreted by commercial AI companies as permission for unrestricted extraction.
\nThe controversy surrounding Taylor & Francis's agreement with Microsoft clearly illustrates the underlying problem. Authors reported that they had not been meaningfully consulted about the use of their work for AI-related purposes (Battersby, 2024). Recent investigations into the use of pirated book collections for model training reveal even more serious failures in consent and accountability (Reisner, 2025). These developments point to the limitations inherent in current implementations of an open knowledge paradigm\u2014openness that focuses solely on a legal definition can be easily co-opted and exploited by actors whose practices are neither reciprocal nor transparent.
\nThe Wikimedia Foundation's recent discussion of the \"social contract of free knowledge\" offers a valuable framework for understanding these tensions (Rogers, 2026). Free knowledge depends on more than mere availability; it relies on relationships among contributors, publishers, infrastructures, and (re)users. Attribution, provenance, and the possibility of contributing back to the wider ecosystem are part of the social arrangement that sustains a commons. Commercial AI systems can disrupt this arrangement in several ways. They ingest material without meaningful disclosure, or any disclosure at all. They provide answers without adequate citation. Their system design may keep users inside proprietary interfaces, preventing them from encountering original sources or the communities that maintain them. They may return little to the commons from which they derive value. All of this points not simply to a failure of etiquette; it signifies an illegal transfer of control and power over content away from its originators.
\nWhen users receive information through one of the large commercial AI interfaces\u2014be they Gemini, Claude, ChatGPT, or any other recent \"silver bullet\" offerings available, often integrated wholesale into browser interfaces\u2014without seeing its sources, the provider effectively co-opts the relationship between reader and knowledge producer. The originating author, contributor, publisher, repository, catalogue, or community is rendered invisible (for more on this, see, e.g., Strauss et al., 2025). The commercial AI system appears to be the source, and all the labour involved in creating the information is devalued and rendered void. For us, this is a clear violation of the spirit inherent in the participatory commons that open knowledge represents.
\nRelated to the above, the question of provenance becomes a central issue. In web search, proper attribution is often treated as a secondary concern, added after an answer is generated. In scholarly communication, however, it is a crucial element ingrained in good academic practice. Citation allows readers to evaluate evidence, trace claims, understand context, and identify the people and organisations responsible for a work. It creates pathways between users and knowledge communities.
\nThe same principle applies to metadata. A bibliographic record may combine information from title pages, publisher feeds, authority files, DOI registrations, institutional identifiers, catalogues, and controlled vocabularies, among many other sources. If an AI system synthesises these sources without explaining which assertions came from where, errors become difficult to diagnose and accountability becomes diffuse.
\nOne might argue that a meaningful commitment to tracking provenance should therefore extend beyond providing a generic list of links. For example, it might be advisable for systems to distinguish publisher-supplied metadata from inferred data or from input generated by aggregators and libraries once metadata have been released into the wider supply chain. Ideally, metadata could indicate when records have been mapped, transformed, or enriched, and could also document the vocabularies and authority sources used.
\nThird-party providers should also disclose which catalogues, repositories, websites, and datasets have contributed to their systems, when the material was collected, and how it was processed. As we at Thoth have repeatedly argued, in line with open data experts (see, e.g., ICOLC, 2023; Courtney et al., 2024), any metadata record should be placed under a public-domain dedication (e.g., CC0) to ensure easy, frictionless downstream reuse, with the relevant licensing statement explicitly included in the metadata record.
\nWithout such measures, commercial actors, and through them, AI, can not only reuse open knowledge but also erase the chain of provenance that makes that knowledge trustworthy.
\nThe use of generative AI can be particularly problematic when plausibility is not sufficient. Metadata and cataloguing depend on verifiable outcomes. A record should accurately identify a specific work, edition, contributor, licence, subject, or persistent identifier. It must be consistent enough to move across systems and precise enough to support discovery, dissemination, and preservation.
\nGenerative AI systems, by contrast, are designed to produce plausible language. That design difference creates a fundamental mismatch with metadata work\u2014a mismatch that has been identified at both functional and ethical levels (Frenzel, 2025). A recent study of ChatGPT even classifies the usability of models such as ChatGPT as \"bullshit\" (Hicks et al., 2026).
\nThe underlying problem is not merely that large language models (LLMs) sometimes make mistakes; it is that, with AI, producing an authoritative-sounding answer does not equate to generating a verifiable output. In cataloguing, this limitation is consequential. Fabricated ISBNs, licences, contributor identities, or subject terms can quickly propagate into catalogues, aggregator databases, repositories, and knowledge graphs. As records move further from their source, errors become increasingly difficult to identify and correct. One might argue that, in the context of cataloguing and metadata work, having no metadata at all would be preferable to having wrong metadata.
\nResearch on AI-assisted cataloguing has thus called for extreme caution rather than a quick replacement of existing metadata and cataloguing workflows. Experiments at the U.S. Library of Congress found greater potential for AI in relatively straightforward descriptive fields than in areas such as subject and genre assignment, where substantial human review remained necessary (Saccucci & Potter, 2025). An earlier study of AI-assisted MARC21 record creation reached a similarly qualified conclusion (Taniguchi, 2024). In a similar vein, AI has been shown to produce fabricated titles, inaccurate identifiers, missing fields, malformed structures, and invented content (Aycock, 2025). As a consequence, the Program for Cooperative Cataloging (2025) stresses the need for human review to ensure provenance, accountability, and explicit responsibility for machine-generated metadata.
\nIt also seems important to highlight the need to honestly address and assess the verification labour that is becoming necessary to control and oversee output generated by AI. When every generated field must be checked against an authoritative source, AI-driven automation quickly redistributes labour rather than reducing it. Correcting a plausible but misleading record can require more labour and expertise than creating an accurate record directly.
\nBuilding on the issues above, it may be worth considering a number of central questions governing the use of AI: Who governs the system? What specific task does it perform? Can its outputs and assumptions be inspected? Can users leave the system? Does it strengthen or diminish professional agency? What environmental and social costs does it create?
\nThe concept of scaling small (Barnes & Gatti, 2019; Adema & Moore, 2021; Adema et al., 2024) that informs Thoth's activities within a wider network of open infrastructures may serve us here and offer a useful counter-positioning. The concept's core argument does not romanticise smallness or reject the need for coordination. It challenges the assumption that infrastructures must grow through concentration, standardisation, and market capture to be successful (see, e.g., Sutton, 1991).
\nIn contrast, scaling small entails a relational model of collaborative development. Capacity can grow through the federation of interoperable systems that communicate via open data and open APIs. A diverse plurality of organisations supports one another through the mutual sharing of resources and knowledge, all grounded in a shared set of values and participatory governance models. The overall objective is not to construct a single platform capable of absorbing every function and content type, but to cultivate an ecosystem in which specialist infrastructures co-operate while remaining accountable to their communities.
\nApplied to AI, this distinction is fundamental. A system that helps publishers translate structured metadata into all major industry formats (e.g., ONIX, KBART, MARC21, etc.) in an openly licensed way is not equivalent to a proprietary model that ingests vast amounts of scholarly content under questionable legal frameworks and then generates outputs of uncertain provenance and reliability. Both of these systems may involve automation, but they embody very different assumptions about ownership, control, accountability, and value. One expands distributed capacity. The other effectively concentrates and flattens it.
\nNon-profit, community-led open infrastructures such as Thoth Open Metadata and the Open Book Collective offer a practical articulation of this alternative. Its call to \"seed\" a not-for-profit, community-led ecosystem for open access books (Steiner et al., 2025) deliberately rejects the aspiration to build a single dominant platform. \"Seeding\" here suggests cultivation rather than capture: a landscape of organisations rooted in particular communities and areas of expertise, connected through common values, open standards, and shared infrastructures. Within this ecosystem, publishing, metadata, preservation, collective funding, discovery, and governance need not be controlled by a single organisation. They can be distributed across a network of community-led services that cooperate without merging. This is a non-competitive philosophy in a substantive sense. The success of one infrastructure does not require the disappearance or absorption of another. Organisations can exchange data, coordinate development, share documentation, and direct users toward one another. Their relationships need not be organised primarily through market conquest.
\nThe distinction matters because commercial providers frequently treat integration as a rationale for consolidation. Community-led infrastructures can instead treat interoperability as a condition for plurality. When systems communicate via open APIs, when data can be exported in open formats, and when records can move across systems without proprietary restrictions, cooperation becomes possible without institutional assimilation. Drawing on Ostrom (2009), Thoth's role in this ecosystem is deliberately \"bounded.\" It does not seek to become the one-and-only infrastructure for open access books. Its purpose is to provide an open, reusable translational metadata layer through which books and chapters can move among publishing systems, repositories, catalogues, aggregators, knowledge bases, preservation services, and discovery platforms. In this context, boundedness is a strength. It makes interdependence possible without making any one organisation indispensable.
\nCurrent discourse on the promises of greater automation through AI often collapses all automation into a single category, obscuring important differences among tools, purposes, and governance models. In some contexts, AI-augmented automation might actually prove useful. One example is the development of Annif, an open-source toolkit for automated subject indexing developed by the National Library of Finland that works with controlled vocabularies and selected training collections. Its scope is bounded, its outputs can be evaluated, and its limitations can be studied in relation to particular languages, vocabularies, and domains. The German National Library's work on automated subject cataloguing (Poley et al., 2025) offers another example of institutionally governed machine assistance.
\nSuch systems operate within professional contexts, are subject to explicit evaluation, and align with established bibliographic responsibilities. These tools differ substantially from general-purpose proprietary models presented as universal solutions. They are narrower, more transparent, and more accountable. Their usefulness does not depend on replacing professional human judgement. This distinction is also central to Thoth's position. The relevant choice is not between technological enthusiasm and refusal; it is between technologies organised around different institutional values and modes of operation. When AI is used as a tool overseen by a capable specialist and helps them automate tedious tasks, it might actually prove useful.
\nThoth's development work between 2023 and 2026 illustrates how automation can be organised around community needs rather than labour substitution (Steiner, Arias, Gatti, et al., 2026). A central objective has been to reduce duplication of work imposed on publishers by fragmented supply chains. Instead of repeatedly entering and reformatting the same information for different recipients, publishers can maintain rich metadata in a single open record and then generate outputs in multiple standards as needed, including ONIX, MARC, crossref XML, KBART, JSON, CSV, and BibTeX.
\nBy implementing the good metadata practice recommendations outlined in a recent report (Steiner, Arias, Bennett, et al., 2026), persistent identifiers such as ORCID and ROR can be incorporated into these records. Controlled vocabularies such as Thema and BISAC can be applied in structured form. Open APIs enable other services to retrieve and reuse the open data available through Thoth. Providing an OAI-PMH endpoint enables harvesting via an established repository protocol. Bulk-ingest workflows reduce repetitive entry and make it easier to bring existing records into the system. These forms of automation remove avoidable friction while leaving the publisher in control of the underlying metadata.
\nCommercial AI systems frequently treat linguistic variety as a problem to be overcome by using larger training corpora. To meet the need for ever more data to feed these training corpora, commercial AI companies have recently started to include the wholesale ingestion and subsequent destruction of pre-2022 printed books (see, e.g., Landymore, 2026). If metadata production, translation, discovery, and evaluation are mediated by the same commercial systems, diversity at the level of content risks becoming flattened into uniformity at the level of infrastructure.
\nCurrent modes of commercial AI provision in the area of metadata and discoverability intensify this risk. Systems trained predominantly on Western languages, publishing models, and disciplinary conventions are prone to reproducing those priorities, especially when marketed as \"universal.\" Materials that fall outside the patterns on which AI has been trained may be mistranslated, misclassified, and/or made less visible. Local categories and diverse contributor roles may be forced into schemas that do not adequately represent them.
\nFor books, these risks are especially acute. Books and chapters support long-form, multilingual, edited, experimental, and locally situated scholarship that is particularly relevant to the Humanities and Social Sciences. Their metadata may need to describe multiple contributor roles, multiple relational connections among, for example, a series, title, and its chapters, rights statements, accessibility features, translations, and locally meaningful subject classifications.
\nIt is worth noting that the complexities inherent in long-form publishing are not a defect to be flattened or removed. Quite to the contrary, they are part and parcel of the cultural and bibliographic diversity that defines academic research cultures. Scholarly infrastructure that cannot represent such complexity does more than simply produce subpar, incomplete records. It limits which publications become discoverable.
\nBy contrast, open metadata infrastructure approaches it as a design obligation. Languages, scripts, and regional conventions should be represented explicitly and structurally, not reconstructed probabilistically from the perspective of dominant linguistic patterns. Thoth's commitment to multilingual metadata and structured bibliographic relationships, informed by its commitment to bibliodiversity (Adema et al., 2024), reflects an effort to preserve underlying diversity at the infrastructural level. The intention is not to treat every publication as an instance of the most common model, but to develop systems sufficiently flexible to represent the beautiful variety that makes up the scholarly record across disciplines.
\nThoth's service model follows the same logic. Thoth Oasis provides free metadata management and open-format exports. Thoth Obelisk adds dissemination, DOI registration, hosting, and archiving; Thoth Sphinx provides privacy-conscious usage statistics; and Thoth Pyramid enables publishers to host websites and integrated catalogues under their own web domains. All of these services integrate and are interoperable, with a rich array of industry standards maintained by multiple communities (e.g., ONIX, MARC) and third-party services often maintained by non-profit organisations, such as Crossref, DataCite, ORCID, and ROR, as well as aggregators and repositories, such as the OAPEN Foundation, the Directory of Open Access Books (DOAB), the Internet Archive, Zenodo, JSTOR, Project MUSE, and the OPERAS Metrics service (Arias et al., 2025). In addition, data and content are submitted to commercial platforms to maximise visibility of books and chapters while still maintaining the open nature of the content and its metadata upstream.
\nThe underlying objective here is not to create yet another expanding proprietary stack. Publishers can use modular services while always retaining control over their identity, records, and workflows. We would argue this is automation without enclosure: the reduction of unnecessary repetition without transferring authority from communities to opaque algorithmic black boxes.
\nDiscussions about academic books at last year's Guadalajara Book Fair (Ramalho & Steiner, 2025) underscore why bibliodiversity, metadata autonomy, and interoperability need to be considered together. Participants in a metadata seminar at the Fair identified recurring problems in Ibero-American academic publishing: fragmented records, missing identifiers, incomplete rights information, inconsistent metadata, and weak interoperability among publishers, libraries, indexes, and evaluation systems. As a result, many academic books from Latin America remain poorly represented in discovery environments even when their content is openly accessible (see also the preliminary results of the CSIC-led Cartograf\u00eda de la edici\u00f3n acad\u00e9mica iberoamericana project).
\nWe do not want to propose a solution that assimilates diverse publishing communities into a single global platform. On the contrary, the focus ought to be on developing infrastructures that express local and linguistic specificity while enabling information exchange through shared standards. The respective approaches of SciELO Livros and Thoth illustrate this possibility. SciELO Livros has a long track record of public infrastructure development grounded in Latin American scholarly communication, including book and chapter records, ePub production, DOI management, preservation, and metadata exchange. Thoth addresses related challenges through open metadata management and dissemination.
\nHere, we see the value of community-led collaboration precisely in the fact that neither system needs to absorb the other. Through interoperable open protocols, infrastructures can connect while preserving regional, institutional, and epistemic autonomy. This is scaling small in practice: difference becomes the basis of cooperation rather than an obstacle to it.
\nOpen APIs are therefore not merely technical conveniences. They are governance instruments. They allow organisations to collaborate without consolidating ownership. In this context, open, reusable public-domain metadata serves a similar purpose and acts as an antidote to the extractive practices outlined above by enabling circulation, subsequent enrichment, and redistribution without tying records to a single proprietary service. Interoperability must, however, be based on trust. Systems need to exchange information in transparent, inspectable, and reversible ways. Participants should be able to identify where data originated, how it has been transformed over time, and under which conditions reuse is permitted. These conditions should then be enforced across the ecosystem so that participants can trust their applicability.
\nA commons survives because people can contribute to it. This is one of the strongest arguments in the Wikimedia report. AI interfaces often provide no meaningful route for users to correct errors, improve source materials, or support the communities from which the reproduced information originates. In short, much knowledge flows out of the commons; very little flows back. Generative systems can intensify this asymmetry by producing large quantities of plausible but unverified material. Creating a summary, subject description, or catalogue record may take seconds. Verifying it may require specialist expertise and substantial time. The cost of generation is borne by the model provider. The cost of correction is handed over to the communities without prior consultation or any protocol in place to facilitate those mediations.
\nFor metadata, this is particularly damaging. Shared records improve when publishers, libraries, and researchers collaborate to add identifiers, correct affiliations, refine subject classifications, and update rights information. Those improvements should circulate through accountable workflows rather than remain trapped within proprietary products and walled-garden ecosystems. Thoth's open APIs and publisher-controlled records are intended to support this form of exchange. Fully open metadata released under a CC0 dedication can move freely between systems, be enriched, and return to the wider ecosystem without any single company becoming the permanent owner and thus the controller of the process.
\nCory Doctorow's account of the recent AI bubble further sharpens the distinction between useful technical capabilities and the commercial ideology surrounding them (2026). Doctorow argues that AI is being marketed not merely as software but as a vehicle for replacing labour and capturing part of the resulting wage savings. In Doctorow's reading of automation theory, \"centaurs\" can be understood as information professionals who use AI as a productive tool to improve their work practices. His concept of the \"reverse centaur\" then describes a worker subordinated to cater to the needs of the machine: required to monitor automated output, to correct its failures, and absorb responsibility when AI is wrong (Ouellette, 2026).
\nThe \"reverse centaur\" metaphor seems a particularly pertinent warning for cataloguing and metadata work. A genuinely assistive system might identify candidate subjects, reconcile identifiers, or detect inconsistencies. A reverse-centaur workflow would increase production targets, reduce specialist staffing, and leave a substantially smaller number of professionals responsible for checking ever-increasing volumes of machine-generated records. The difference lies in who controls the workflow and for whose benefit it is organised.
\nAs a silver lining, Doctorow (2026) suggests that a number of useful tools might be salvaged from the eventual collapse of the current, highly speculative AI bubble. Indeed, open models, commodity hardware, and specialised systems\u2014such as Annif or other specialist workbenches that use AI, e.g., for automatically extracting references\u2014may prove useful once detached from claims of universal automation and staff replacement.
\nThis possibility aligns well with Thoth's positionality. The aim should be not to reject computational tools but to situate them within open infrastructures governed by community-led values. A useful system will be transparent, inspectable, proportionate, and subordinate to human and community purposes. It will strengthen professional agency rather than reduce people to monitors of machine output. It will operate on open standards rather than deepen proprietary dependence.
\nThe debate about AI is often framed as a choice between embracing and resisting technological progress. A more meaningful choice concerns the technological and institutional future that scholarly communities are prepared to build.
\nAs described above, one path leads to further concentration: a small number of commercial providers control models, interfaces, data, and access (Hao 2026). Knowledge is extracted from open sources, captured and processed by opaque systems, and delivered through proprietary environments. Professional labour is reorganised around machine output. Substantial environmental costs remain largely hidden. Communities become dependent on systems they cannot govern.
\nThe other path is more demanding because it requires network-building rather than adopting a single silver-bullet product. It involves creating open, interoperable, community-led infrastructures that support shared workflows without centralising authority. Thoth's contribution to this second path is necessarily partial. We provide open-source tools, open APIs, and openly reusable public-domain metadata for books and chapters. Our contribution seeks to strengthen publisher agency, reduce duplicate labour, and enable collaboration among infrastructures rather than replace them.
\nThis approach is grounded in trust\u2014but not blind trust. It is trust supported by transparency: inspectable code, visible provenance, open standards, good metadata practices, documented interfaces, accountable governance\u2014and the belief that publishers should retain control over their metadata and operational infrastructure.
\nIt is also grounded in a different understanding of efficiency. For us, efficiency should not mean shifting costs onto workers, communities, or the environment. It should mean reducing unnecessary duplication while preserving quality, agency, and participation that contribute to the enhancement of shared knowledge across infrastructures.
\nThe emerging, broader ecosystem envisioned through active collaborations among open infrastructures such as Thoth Open Metadata, SciELO Livros, the Public Knowledge Project, Open Book Collective, OAPEN, DOAB, Project Gutenberg, Wikimedia, Pressbooks, the many small- to medium-sized publishers, and an ever-increasing number of academic libraries points toward such a future.
\nThese infrastructures differ in purpose, scale, and history. Their value lies not in becoming uniform, but in strengthening bibliodiversity.
\nWe believe the future of metadata and cataloguing should not be reduced to yet another layer in an extractive corporate AI stack. It should form part of a broader public infrastructure for knowledge: transparent, bibliodiverse, multilingual, interoperable, reusable, and accountable to the communities that sustain it. For open-access books, for free knowledge, and for the infrastructures on which they depend, this is the more ambitious alternative\u2014we feel it is also the one worth fighting for.
\nThe bibliography of all sources referenced in this article are also available via an open Zotero collection:\u00a0https://www.zotero.org/groups/6626908/ai_and_metadata
\nAczel, M., Chamanara, S., Matin, M., Farsi, A., Marwala, T., & Madani, K. (2026). The Environmental Cost of AI's Energy Use: Carbon, Water and Land Footprints. United Nations University Institute for Water, Environment and Health (UNU INWEH). https://doi.org/10.53328/INR26RMA002
\nAdema, J., & Moore, S. A. (2021). Scaling Small; Or How to Envision New Relationalities for Knowledge Production. Westminster Papers in Communication and Culture, 16(1), Article 1. https://doi.org/10.16997/wpcc.918
\nAdema, J., Barnes, L., Deville, J., Fathallah, J., Gatti, R., Grady, T., Hopkins, K., Hughes, A., McGann, C., Sanders, K., & Steiner, T. (2024, December 19). The COPIM Perspective on Bibliodiversity. COPIM. https://doi.org/10.21428/785a6451.86a892a7
\nArias, J., Van Gerven Oei, V. W. J., Gatti, R., Higman, R., Hillen, H., O'Connell, B, Ramalho, A., & Steiner, T. (2025, October 20). Open Access, Open Data, Open Archiving: Liberating Metadata Flows across the OA Books Landscape. Zenodo. https://doi.org/10.5281/zenodo.17340814
\nAycock, M. (2025). Prompting Generative AI to Catalog: The Promise and the Reality. College & Research Library News, 86(09). https://doi.org/10.5860/crln.86.10.423
\nBarnes, L., & Gatti, R. (2019). Bibliodiversity in Practice: Developing Community-Owned, Open Infrastructures to Unleash Open Access Publishing. ELPUB 2019 23rd edition of the International Conference on Electronic Publishing, June 2019. Marseille, France. https://doi.org/10.4000/proceedings.elpub.2019.21
\nBarratt, L, Gambarini, C., Witherspoon, A., & Uteuova, A. (2025, April 9). Revealed: Big Tech's New Datacentres Will Take Water from the World's Driest Areas. The Guardian. https://www.theguardian.com/environment/2025/apr/09/big-tech-datacentres-water
\nBattersby, M. (2024, July 19). Academic Authors \"shocked\" After Taylor & Francis Sells Access to Their Research to Microsoft AI. The Bookseller. https://www.thebookseller.com/news/academic-authors-shocked-after-taylor–francis-sells-access-to-their-research-to-microsoft-ai
\nBowie, S. (2025). Small Changes: Taking Back Control of Research through Open Software. Edinburgh Open Research, 4: Conference Proceedings. https://doi.org/10.2218/eor.2025.10953
\nBratton, B. (2016). The Stack: On Software and Sovereignty. MIT Press.
\nCourtney, K., DeLaurenti, K., Kopel, M., & Zimmerman, K. (2024). No One \"Owns\" That Metadata, Copyright, and the Problems with [Library] Vendor Agreements. DASH. https://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37379715
\nDavison, S., Wa\u0142ek, A., & Varachkina, H. (June 16, 2026). Traffic Management and Bot Protection for the OAPEN Library and DOAB: Implementing Cloudflare and Anubis. OAPEN: A World of Scholarly Books: Open to All, Built to Last. https://doi.org/10.58079/16es1
\nDoctorow, C. (2025, September 27). Pluralistic: The Real (Economic) AI Apocalypse Is Nigh (27 Sep 2025). Pluralistic: Daily Links from Cory Doctorow. https://pluralistic.net/2025/09/27/econopocalypse/
\nDoctorow, C. (2026, January 18). AI Companies Will Fail. We Can Salvage Something From the Wreckage. The Guardian. https://www.theguardian.com/us-news/ng-interactive/2026/jan/18/tech-ai-bubble-burst-reverse-centaur
\nDown, A., & Milmo, D. (2026, February 5). What Does the Disappearance of a $100bn Deal Mean for the AI Economy? The Guardian. https://www.theguardian.com/technology/2026/feb/05/disapperance-100bn-deal-ai-circular-economy-funding-nvidia-openai
\nFrenzel, F. (2025). Smart Enough to Mislead: The Functional Shortcomings and Ethical Dilemmas of Generative AI Use in Metadata Work. Catalogue and Index, 211, 39\u201357.
\nHao, Karen. 2026. Empire of AI: Dreams and Nightmares in Sam Altman's OpenAI. Penguin Books.
\nHicks, M. T., Humphries, J., & Joe Slater, J. (2024). ChatGPT Is Bullshit. Ethics and Information Technology, 26(2), 38. https://doi.org/10.1007/s10676-024-09775-5
\nICOLC (International Coalition of Library Consortia). (2023). Joint Statement on the Metadata Rights of Libraries. ICOLC. https://icolc.net/statements/joint-statement-metadata-rights-libraries
\nLandymore, F. (2026, July 25). AI Companies Are Buying Antique Books, Ingesting Their Contents to Train Models, and Then Destroying Them at Incredible Scale, Even If Almost No Copies Remain. Futurism. https://futurism.com/artificial-intelligence/ai-companies-destroying-rare-books
\nOstrom, E. (2009). Building Trust to Solve Commons Dilemmas: Taking Small Steps to Test an Evolving Theory of Collective Action. In S. A. Levin (Ed.), Games, Groups, and the Global Good (pp. 207\u201328). Springer. https://doi.org/10.1007/978-3-540-85436-4_13
\nOuellette, J. (2026, June 23). How to Burst the AI Bubble: Strike at Its Roots. Sci-Fi Author/Tech Journalist Cory Doctorow on His New Book, The Reverse Centaur's Guide to Life After AI. ars technica. https://arstechnica.com/gadgets/2026/06/how-to-burst-the-ai-bubble-strike-at-its-roots/
\nPoell, T., Nieborg, D., & Van Dijck, J. (2019). Platformisation. Internet Policy Review, 8(4). https://doi.org/10.14763/2019.4.1425
\nPoley, C., Uhlmann, S., Busse, F., Jacobs, J.-H., K\u00e4hler, M., Nagelschmidt, M., & Schumacher, M. (2025). Automatic Subject Cataloguing at the German National Library. LIBER Quarterly: The Journal of the Association of European Research Libraries, 35(1), 1\u201329. https://doi.org/10.53377/lq.19422
\nProgram for Cooperative Cataloging. (2025). PCC Task Group on AI and Machine Learning in Cataloging and Metadata: Final Report. https://www.loc.gov/aba/pcc/taskgroup/AI-and-Machine-Learning-TG-final-report.pdf
\nRamalho, A, & Steiner, T. (2026, January 20). What the FIL Guadalajara Debates Reveal about Metadata for Academic Books and How Thoth Open Metadata and SciELO Books Respond to This Challenge. Thoth Open Metadata. https://doi.org/10.70950/sfsv8305
\nReisner, A. (2025, March 20). The Unbelievable Scale of AI's Pirated-Books Problem. Technology. The Atlantic. https://www.theatlantic.com/technology/archive/2025/03/libgen-meta-openai/682093/
\nRogers, J. (2026, July 17). How AI Threatens the Social Contract of Free Knowledge. Diff. https://diff.wikimedia.org/2026/07/17/how-ai-threatens-the-social-contract-of-free-knowledge/
\nSaccucci, C., & Potter, A. (2025). Results of AI Experimentation for Cataloging at the Library of Congress. 89th IFLA World Library and Information Congress (WLIC), Satellite Meeting: Artificial Intelligence, Bibliographic Control and Legal Matters: Navigating New Horizons, Astana, Kazakhstan, August 14-15, 2025. https://repository.ifla.org/items/4e25dfe1-336e-49a5-be2a-912e337e7624/full
\nSteiner, T., Barnes, L., Fathallah, J., Findanis, J., Gatti, R., Deville, J., Sanders, K., Higman, R., Stern, N., & Stone, G. (2025, April 16). Seeding for a Not-for-Profit Community-Led OA Books Ecosystem. COPIM. https://doi.org/10.21428/785a6451.d35141ca
\nSteiner, T, Arias, J., Bennett, M., Booth, E., Edmunds, J., Gatti, R., Higman, R., Hillen, H., Laakso, M., Nason, M., O'Connell, B., Poga\u010dnik, A., Rabar, U., Ramalho, A., Stone, G., Van Gerven Oei, V. W. J., & Wake Hye, Z. (2026, January 27). International Metadata Recommendations, and Platform-Specific Requirements for Open Access Books and Chapters. Thoth Open Metadata. https://doi.org/10.5281/zenodo.18173982
\nSteiner, T., Arias, J., Gatti, R., Higman, R., Hillen, H., Ramalho, A., & Van Gerven Oei, V. W. J. (2026, May 5). Building the Conditions for Open Access Books to Thrive: A Reflection on Thoth Open Metadata's Work Across Infrastructures, Research, and Communities between 2023-26. Thoth Open Metadata. https://doi.org/10.70950/jlke1310
\nStrauss, I., Yang, J., O'Reilly, T., Rosenblat, S., & Moure, I. (2025). The Attribution Crisis in LLM Search Results: Estimating Ecosystem Exploitation. SSRC AI Disclosures Project Working Paper Series (SSRC AI WP 2025-06), Social Science Research Council. https://doi.org/10.35650/AIDP.4114.d.2025
\nSutton, J. (1991). Sunk Costs and Market Structure: Price Competition, Advertising, and the Evolution of Concentration. The MIT Press.
\nTaniguchi, S. (2024). Creating and Evaluating MARC 21 Bibliographic Records Using ChatGPT. Cataloging & Classification Quarterly, 62(5), 527\u201346. https://doi.org/10.1080/01639374.2024.2394513
\nUNESCO. \"UNESCO Survey: Two-Thirds of Higher Education Institutions Have or Are.\" n.d. Accessed July 26, 2026. https://www.unesco.org/en/articles/unesco-survey-two-thirds-higher-education-institutions-have-or-are-developing-guidance-ai-use
\nVan Gerven Oei, V. W. J. (2025, April 3). An Update on Punctum Books Usage Data: Interoperability, Interpretability, and AI. punctum books. https://doi.org/10.21428/ae6a44a6.52f495bc
\nVan Gerven Oei, Vincent W. J. (2026, January 9). Let Our Brains Rot but Let Them Be Ours. Ibraaz. https://ibraaz.org/ibraaz-publishing/read/let-our-brains-rot-but-let-them-be-ours
\nVan Gerven Oei, V. W. J., & Fradenburg Joy, E. A. (2025, September 10). Open or Proprietary? AI Scraping of OA Content Warrants a Collective Response. punctum books. https://doi.org/10.21428/ae6a44a6.6b5058ea
\nWeedon, J., Fran\u00e7ois, C., & Ponak, J. (2026). AI Slop and the Information Ecosystem. Institute of Global Politics. https://igp.sipa.columbia.edu/sites/igp/files/2026-06/AI%20Slop%20and%20the%20Information%20Ecosystem_IGP%20Report.pdf
\n","doi":"https://doi.org/10.59350/dq9bj-y6917","guid":"https://flavoursofopen.science/?p=91440","image":"https://flavoursofopen.science/wp-content/uploads/2026/09/vadim-bogulov-Vq-Sqr7D_7k-unsplash.jpg","language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","published_at":1790726400,"reference":[{"id":"https://www.zotero.org/groups/6626908/ai_and_metadata","unstructured":"Unknown title"},{"id":"https://doi.org/10.53328/inr26rma002","unstructured":"Aczel, M., Chamanara, S., Matin, M., Farsi, A., Marwala, T., & Madani, K. (2026). The Environmental Cost of AI's Energy Use: Carbon, Water and Land Footprints. In United Nations University Institute for Water, Environment and Health (UNU-INWEH). United Nations University Institute for Water, Environment and Health (UNU INWEH)."},{"id":"https://doi.org/10.16997/wpcc.918","unstructured":"Adema, J., & Moore, S. A. (2021). Scaling Small; Or How to Envision New Relationalities for Knowledge Production. 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The North American Bioconductor Conference 2026 (([BioC2026])(https://bioc2026.bioconductor.org/)) took place from August 10-12, 2026, at the Fred Hutchinson Cancer Center in Seattle, Washington. The conference brought together researchers, developers, educators, and community members to explore current developments within and beyond the Bioconductor project. BioC2026 welcomed 108 attendees, with 70 participating in person and 38 joining virtually. Across the three days, the programme featured three keynote speakers, 20 contributed speakers, and 17 sessions. The programme highlighted the breadth of biological research supported by Bioconductor, as well as the tools, infrastructure, and communities that enable open and reproducible computational biology. The conference was then followed by two community events on August 13-14: a Carpentries workshop on orchestrating large-scale single-cell analysis with Bioconductor and the Bioconductor North America 2026 Hackathon.
\nParticipants travelled to Seattle from across North America and beyond, reflecting the international reach of the Bioconductor community. Attendees joined from the United States, Canada, Germany, Ireland, the United Kingdom, Australia, and Nigeria. The audience included researchers at different stages of their careers, with staff representing 42% of attendees, followed by students and postdoctoral researchers at 28%, faculty at 13.8%, industry at 13.8%, and government at 2.4%.
\n\nThe conference featured three keynote speakers: - Jeff Leek speaking on \"Building Open Infrastructure for Translational AI in a Cancer Center\" - Ting Ye speaking on \"From Association to Causation: Genetic-Anchored Causal Inference in Human Biology\" - Michael Lawrence speaking on \"S7 for Bioconductor\"
\n![](\"https://blog.bioconductor.org/posts/2026-09-30-BioC2026-recap/media/leek_keynote.jpeg\")
![](\"https://blog.bioconductor.org/posts/2026-09-30-BioC2026-recap/media/lawrence_keynote.jpeg\")
![](\"https://blog.bioconductor.org/posts/2026-09-30-BioC2026-recap/media/ting_keynote.jpg\")
(Left to Right: Jeff Leek, Michael Lawrence and Ting Ye giving their keynotes at BioC2026)
\nTwenty contributed speakers shared their work across the conference programme. The short talks at BioC2026 reflected the breadth of research and development across the Bioconductor community. Topics ranged from community training and developer engagement to multi-omics exploration, reproducible workflows, and AI infrastructure. The programme also covered emerging areas of biological data analysis, including spatial transcriptomics, antibody repertoire analysis, rare variant testing, DNA methylation, structural variation, and Perturb-seq.
\nThe poster session showcased a diverse range of research applications and software development projects from across the Bioconductor community. Topics included single-cell and spatial transcriptomics, multi-omics integration, microbiome analysis, proteomics, antibody repertoire analysis, functional enrichment, ancestry inference, genomic data processing, and AI-guided drug discovery. The posters provided an opportunity for attendees to discuss new methods and tools in a more informal setting, while connecting researchers, package developers, and community members working across different areas of computational biology. The sessions also complemented the package demonstrations, creating space for participants to explore new software and engage directly with the people developing and applying Bioconductor tools.
\nFeedback from the conference showed that these interactions were particularly valuable, with attendees highlighting conversations around posters and package demonstrations as some of the most useful networking opportunities.
\n![](\"https://blog.bioconductor.org/posts/2026-09-30-BioC2026-recap/media/bioc2026_poster_session.jpg\")
\nParticipants during the poster session.
\nThe celebrations marking Bioconductor's 25th anniversary continued at BioC2026 following the celebrations that began at EuroBioC2026 earlier in the year. The milestone was marked with a specially branded anniversary cake, thanking participants and community members for being part of the Bioconductor community and contributing to its growth over the past 25 years.
\n![](\"https://blog.bioconductor.org/posts/2026-09-30-BioC2026-recap/media/bioc_25_cake.jpeg\")
![](\"https://blog.bioconductor.org/posts/2026-09-30-BioC2026-recap/media/Bioc2026_25years.png\")
Bioconductor at 25 celebrations.
\nOne of the strongest themes to emerge from the BioC2026 survey was the value of the Bioconductor community itself. Participants described the conference as welcoming, collaborative, and interactive, and appreciated the opportunity to reconnect with colleagues and meet new people. All respondents who answered the question said they would recommend BioC2026 to their colleagues. The smaller size of the conference was also seen as a strength. Several respondents noted that the more intimate format made it easier to engage in conversations and build connections across different roles and career stages. Networking was consistently identified as one of the most valuable aspects of the event. Participants appreciated both the formal scientific programme and the informal conversations that happened around talks, posters, and demonstrations. Looking ahead, many attendees expressed interest in even more dedicated time for networking and informal interaction.
\nDuring BioC2026, Zulip provided a dedicated space for participants to connect, interact and stay engaged throughout the conference. The conference space included topic-based discussions covering: - Workshops: Dedicated topics for individual workshops, including Single Cell, Bioconductor in Workflows, microbiome, WebR, and other training sessions. - Training & education: A space for broader conversations around training activities and educational sessions. - Social: Topics for informal conversations, social activities and connecting with other participants. - Introductions: Participants could introduce themselves and connect with others attending the conference. - Conference logistics channels: For travel, rooms, schedules, Day 1 information, and general conference support. - Community engagement: Dedicated spaces for the developer survey, student and early-career researcher activities, and other community initiatives. - Help and support: Dedicated topics such as the helpdesk, lost and found, and issues accessing virtual sessions.
\n![](\"https://blog.bioconductor.org/posts/2026-09-30-BioC2026-recap/media/BioC2026_sticker.png\")
\nKayva Vaddadi was BioC2026's sticker design contest winner. Kayva's design celebrated Seattle as the host city of BioC2026, featuring well-known landmarks such as the Space Needle, the Seattle Great Wheel, Mount Rainier, and the surrounding evergreen forests. Puget Sound and a Washington State ferry reflect Seattle's maritime landscape and the culture of connection across the region. The BioC2026 hot air balloon on the sticker was inspired by the scenic balloon views popular in the Seattle area and represents the celebratory and exploratory spirit of the Bioconductor community.\n
\n\nSee the announcement here: https://lnkd.in/p/emAWMGX\n
\nAll conference recordings are available on the Bioconductor YouTube channel. BioC2026 workshops will also be available in the \"Archived\" section of workshop.bioconductor.org, alongside workshops from previous BioC, EuroBioC, and BiocAsia events. If you'd like to add additional materials or use the platform for your own workshop or course, contact us at support.bioconductor.org.
\nOn August 13\u201314, participants took part in a Carpentries workshop focused on orchestrating large-scale single-cell analysis with Bioconductor led by Ted Laderas and Wes Wilson. The workshop was attended by 11 participants and provided a hands-on environment for participants to work through approaches for analysing large-scale single-cell datasets using Bioconductor tools and workflows. Feedback highlighted the value of the live coding, small-group format and complementary teaching approaches.
\n![](\"https://blog.bioconductor.org/posts/2026-09-30-BioC2026-recap/media/carpentry-workshop1.jpeg\")
![](\"https://blog.bioconductor.org/posts/2026-09-30-BioC2026-recap/media/scrna_bioc2026_workshop_1.jpeg\")
Participants during the scRNA-seq workshop.
\nThe Bioconductor North America 2026 Hackathon took place on August 13\u201314, bringing community members together to collaborate on software and infrastructure projects that strengthen interoperability, software development, and the wider Bioconductor ecosystem. These projects included: AI harness tools for standardizing how users within organizations would interact with agents on shared projects and on shared institutional resources. Link: https://github.com/eisenefaust/harness_agent
\nBioconductor tooling for including CLI executable components in Bioconductor packages. Link: https://blog.bioconductor.org/posts/2026-08-21-biocjobs/
\nAn exploration on benchmarking LLM generated workflows, building out infrastructure and paradigms for how we can evaluate what LLMs return as work product to scientific questions. Link: https://github.com/Amisor/LLM_BioBenchmarking/tree/main
\n![](\"https://blog.bioconductor.org/posts/2026-09-30-BioC2026-recap/media/bioc2026_hackathon.jpeg\")
\nParticipants during the Hackathon.
\nLater this year, the Bioconductor community will head to Melbourne, Australia, for BioCAsia2026, taking place on November 19-20, 2026. BioCAsia2026 brings together researchers, developers, and data scientists from across the Asia-Pacific region and beyond to share advances in bioinformatics, computational biology, and open-source software. The meeting will highlight practical training, community building, and the latest developments in R and Bioconductor. BioC2027 will then take place in early summer in Boston, Massachusetts, continuing the North American Bioconductor conference series and bringing the community together once again for scientific exchange, collaboration, and community building. The community will thereafter gather in Basel, Switzerland, for EuroBioC2027, taking place from September 8\u201310, 2027. The European Bioconductor Conference will continue to bring together researchers, developers, and educators from across Europe and beyond to share new software, methods, and applications in computational biology.
\nBioC2026 gratefully acknowledges the support of all sponsors and partners whose contributions and support made the conference possible.
\nBioC2026 was proudly supported by Cortex, R consortium and Posit
\n![](\"https://blog.bioconductor.org/posts/2026-09-30-BioC2026-recap//media/Bioc2026_sponsors.png\")
We thank the local organisers, programme committee, workshop instructors, keynote speakers, volunteers, sponsors, and all participants whose contributions made BioC2026 a success.
\n\n\u00a9 2026 Bioconductor. Content is published under Creative Commons CC-BY-4.0 License for the text and BSD 3-Clause License for any code. | R-Bloggers\n
","doi":"https://doi.org/10.59350/z0c2k-0vh25","guid":"https://blog.bioconductor.org/posts/2026-09-30-BioC2026-recap/","image":"https://blog.bioconductor.org/posts/2026-09-30-BioC2026-recap/media/bioc2026_groupshot.jpg","language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","published_at":1790726400,"rid":"ae48k-gn675","summary":"The North American Bioconductor Conference 2026 (([BioC2026])(https://bioc2026.bioconductor.org/)) took place from August 10-12, 2026, at the Fred Hutchinson Cancer Center in Seattle, Washington. The conference brought together researchers, developers, educators, and community members to explore current developments within and beyond the Bioconductor project.","tags":["Bioconductor"],"title":"BioC2026 conference recap","updated_at":1790777730,"url":"https://blog.bioconductor.org/posts/2026-09-30-BioC2026-recap/","version":"v1"}},{"document":{"authors":[{"affiliation":[{"name":"The Geneva Learning Foundation"}],"contributor_roles":[],"family":"Sadki","given":"Reda","url":"https://orcid.org/0000-0003-4051-0606"}],"blog":{"authors":null,"community_id":"7e26491f-41c6-4665-9088-5aa6643a1ba8","created":1731196800,"current_feed_url":null,"description":"Learning to make a difference","doi":"https://doi.org/10.59350/redasadki","favicon":"https://rogue-scholar.org/api/communities/7e26491f-41c6-4665-9088-5aa6643a1ba8/logo","feed_format":"application/atom+xml","feed_url":"https://redasadki.me/feed/atom/","filter":null,"generator":"WordPress","home_page_url":"https://redasadki.me/","issn":null,"language":"eng","license":"https://creativecommons.org/licenses/by/4.0/legalcode","prefix":"10.59350","relative_url":null,"secure":true,"slug":"redasadki","status":"active","subfield":"3304","title":"Reda Sadki","updated":1790771244,"use_api":true},"blog_name":"Reda Sadki","blog_slug":"redasadki","content_html":"This Q&A with TGLF's Reda Sadki is based on his remarks at the 26 September 2026 panel titled \"Will Cheaper AI Automate Everything? The Economics of AI, the Jevons Paradox, and the Outsourcing of Human Judgment\". The other panelists were Valerie Pelton, General Counsel, Tracked Biotechnologies LLC, United States; Dr. Craig Gibbs, Executive Manager, JET Education Services, South Africa; Fabrizio Degni, Chief AI Officer, Webuild, Italy; and Professor Avtandil Gagnidze, Professor and CEO, East-West University, Georgia. Tianze Zhang moderated the session.
\n\n\n\nIn 1865, William Stanley Jevons observed that more efficient steam engines increased the consumption of coal. The panel asked whether the same pattern now applies to machine intelligence: as the price of AI collapses, will organizations save money, or use far more AI, in more places, for more decisions? It also asked what happens to human judgment as AI moves from producing information to recommending decisions and acting on them.
\n\n\n\nThe discussion took place 11 days after the release of Jev, a proprietary \"decision model\", generated online interest. Jev is different from a large language model that generates text: it returns \"structured answers\": a choice from a defined set, a score against ordered levels, or a yes or no probability, each with a confidence score. Its maker claims it is 40 to 400 times cheaper than frontier models.
\n\n\n\nTwo facts about my daily work are relevant. The first is that in September 2026, I spent 90% of my workday working with TGLF's AI agents. The second is that work time was focused on listening to and supporting our core community, a network of over 80,000 health and humanitarian workers in 137 countries. So there is a dual reality here.
\n\n\n\nOn the community's side, \"cheaper AI\" means health professionals using the free, basic, whatever-is-accessible form of ChatGPT, without any kind of institutional or employer policy in place. Anything \"free\" signals that there is probably near-zero data protection. That creates potentially high risk, and it is incredibly difficult to measure, assess, or evaluate in any way. Some people have called that \"dark AI\" within organizations.
\n\n\n\nOn our side, we are trying to work transparently, and we announced our first agentic AI hire in March 2026. For us, as a nonprofit, lower token cost helps. We live every day looking at how much we are spending on tokens and what that is giving us in return. That is one price. But we are increasingly realizing that two other costs matter more: the cost of completing a task, and the cost of a result you can rely on. The cost of a reliable result can rise, because the cheaper it is to generate, the more there is to check.
\n\n\n\nFor the first few months of working with our AI co-worker, I spent most of my time designing and developing skills and writing scripts. Now it is increasingly verification and loops. That is what my job increasingly looks like, as chief executive officer of an agentic-AI-first organization.
\n\n\n\nIn July, Claude Cardot, our AI co-worker, refused to release two finished reports. A verification script had found 17 quotations that could not be matched against the raw data. This is important, because these are health workers sharing their experiences. So the AI co-worker stopped the assembly line. Fourteen were gaps in the checker itself. Three had been altered by the drafting model: a stray space, and a phrase that had gone through a small edit. Nothing crashed, but nothing warned us either. We describe this in our early learning from the insights pipeline.
\n\n\n\nMy takeaway is that cheaper AI lowers the cost of an answer, but it does not lower the cost of knowing that we have the right answer. And even scarier, when you project that forward, is what we call the validation tether.
\n\n\n\nMost will spend more, on more things. Part of that spending buys new value, and part of it is waste that goes unmeasured because it looks like productivity.
\n\n\n\nThe OECD Digital Education Outlook recently offered some interesting insights. Teachers in a pilot in Iceland said that with the use of AI, they are spending more time, not less. A tool that becomes cheaper and more accessible, in some places at least, before anyone has redesigned work around it, actually creates cost.
\n\n\n\nThe other question is for leaders in particular. If you are a chief executive, or a manager making decisions: is your organization, through its increased use of AI, getting better at something, or is it simply doing more of it? I think that is the key question.
\n\n\n\nYes, and the industry now says so openly. The newest model on the market is called Jev, after Jevons himself, because its founder expects cheaper machine intelligence to be deployed far more widely.
\n\n\n\nJevons observed that more efficient steam engines increased the consumption of coal. With AI, efficiency increases the consumption of compute, and it also increases the consumption of human attention. Every cheap output creates a small obligation for someone to read it, judge it, or act on it.
\n\n\n\nWhen a decision becomes 40 to 400 times cheaper, as TypeSafe claims for Jev, organizations will make many more decisions. Budgets rarely include the human time needed to govern them. The work that grows is review, supervision, and decision, which is what the agentic AI revolution means for workforce development.
\n\n\n\nThe work that becomes viable is work that was always valuable and never affordable at scale.
\n\n\n\nOur peer reviews were inconsistent. In one afternoon, we built a way to send every learner personalized feedback on their work the same day. No one would have paid a person to do that for thousands of learners, and now it happens by default.
\n\n\n\nListening changes in the same way. A single course produces hundreds of first-hand accounts from health workers, and a pipeline can now read every one of them rather than a sample. We can hear the rare voice that a sample would have missed. Frontline insights can help transform research and policy, if we are willing to listen. This is the role we set out in a global health framework for artificial intelligence as co-worker.
\n\n\n\nModels like Jev extend this to small decisions such as routing, sorting, flagging, and triage. The risk is that the cost of automation starts to decide what gets done.
\n\n\n\nThe outcome. In health, a wrong answer can cost a life, whatever it cost to produce. When we get health wrong, people die.
\n\n\n\nJev returns a probability and a confidence score with every answer, and its maker says it was trained to be calibrated against outcomes instead of human raters. A system that reports how sure it is can be audited, which is what I would want. But calibration is only as good as the outcomes it was measured against. Jev was trained on synthetic data, and a confidence score earned on synthetic cases tells you little about a district in Kinshasa.
\n\n\n\nOur rule is gates, not guidelines. A standard that matters should be a check that blocks the result until it passes, measured against real outcomes in the place where the decision is made.
\n\n\n\nBoth.
\n\n\n\nIn our own organization, we have used AI to replace functions that people once performed. People helped us work out how, and we then refocused a smaller team on work we cannot or do not want to automate. I described this in the business of artificial intelligence and the equity challenge.
\n\n\n\nI am wary of telling staff that AI is not coming for their jobs. It is coming for some of them, and this is part of a much larger shift in the future of the workforce. AI also creates a large volume of review and supervision, and the question is whether we prepare people for that work or leave them to discover it after the fact.
\n\n\n\nThe ones through which people learn to judge, and the ones where nobody yet knows the right question.
\n\n\n\nCheaper AI can lower the cost of an answer. But a model that cannot answer outside its own schema cannot tell you if the schema is wrong. There has been a lot of work in education on double-loop learning, and learning that does not question its governing values is unlikely to lead to meaningful change. So cheaper AI does not lower the cost of knowing the answer is right.
\n\n\n\nA model choosing between the options we supply will never tell us that mothers stopped bringing their children for vaccination because market day moved to Tuesday, because that option was never on the list. A health worker knows it. If she is not in the system, children die.
\n\n\n\nThat is why I keep coming back to the validation tether. Automation is safe as long as someone can still tell if the machine is wrong. In the industrial age, if you had widgets coming out of a factory, somebody needed to check the widgets, and there is a whole series of intellectual traditions around quality. That ability for someone to figure out whether it is right is the tether. It ties the machine's output back to human judgment.
\n\n\n\nHow do people develop that ability? Mostly by doing grunt work. We have all been junior professionals doing the routine work by ourselves, and that is the work getting automated first. So every time we make a short-term decision to save money because token costs are down and we can automate more, we weaken the long-term tether, because fewer people are learning to do the work well enough to check it. There is a whole debate in education and elsewhere about what that means for people in early adulthood, and for everyone who comes after them.
\n\n\n\nWhen we look at global health, that means that by around 2035 or 2040, there may be nobody left at the World Health Organization with the experience to understand what the guidelines, policies, and plans made at WHO headquarters in Geneva mean for a district health officer. Today's officer would spot something that is wrong, because she has walked the roads and built those plans by hand. What she knows, because she is there every day, hopefully makes it into the national ministry and trickles up to World Health Organization guidelines. That is now at risk. I explored this further in what AI reasoning means for global health.
\n\n\n\nWe have not yet found a coherent argument to make the case to organizations that are having their funding slashed, that are facing austerity measures, and that are facing incentives like the lower cost of AI, to make decisions now for what the world will look like in 2035 or 2040. That is one of the scariest things we are working on right now.
\n\n\n\nIt moves it, and it concentrates it.
\n\n\n\nWith a model like Jev, judgment moves into the schema. Someone decides which options and levels exist, and which questions get asked. Those choices are made once, far from the point of care, and then applied to every case that follows.
\n\n\n\nTypeSafe calls Jev a \"System One\" model, after Kahneman's fast, intuitive thinking. Kahneman's lesson was that System One is fast and often right, and that System Two catches its errors. So the question is who does the System Two work, and whether they will still be capable of it.
\n\n\n\nThe OECD Digital Education Outlook reports a randomized trial with medical students. Those given AI from the start of a semester performed no better than the AI working alone. It is only the students who first built their own clinical reasoning and then used AI who were able to outperform the machine. I think that is a key point. That is the state of things today, or at least when the study was done. It does not mean it will remain true six months from now. That is the pace of change, and the paradox of how we struggle to keep up. It is also why I have asked what happens when we delegate our thoughts to artificial intelligence, and why peer learning is critical to survive the Age of Artificial Intelligence.
\n\n\n\nI am sceptical of the phrase that decisions should always stay with humans. My standard is that someone can explain, check, and answer for every decision, whoever or whatever made it.
\n\n\n\nYou cannot send an AI agent to jail. Responsibility has to rest with the organization that deploys it, and in particular with whoever designed the questions the system is allowed to answer.
\n\n\n\nWe treat Claude Cardot, our AI co-worker, as a member of staff. She has a role description, a supervisor, a bounded scope, and short chains of action with human checkpoints. She has no authority to send anything outside the organization without human review.
\n\n\n\nModel providers are responsible for what they build and sell, including the claims they make about it. An organization that deploys a system without governance cannot hand its accountability to a vendor, and it should not pass that accountability down to the individual at the end of the chain.
\n\n\n\nBoth are possible, and dependence is the default.
\n\n\n\nA specialized agent for a knowledge worker in a wealthy organization may cost between 2,000 and 20,000 dollars a month. A district health worker gets a free chatbot built for consumer engagement. Many frontier tools are geo-locked, and you cannot get access to them in Kinshasa in the same way as in Geneva. I made this argument in When we get health wrong, people die.
\n\n\n\nCheap decision models add a new risk. The categories will be written in San Francisco and applied to people in Kinshasa who were never asked what the options should be. So the question is who defines the world the machine is allowed to see. The same question of who gets to represent whom runs through the debate on AI-generated \"poverty porn\", and the code alone cannot fix it.
\n\n\n\nAnother path exists. A system in Brazil reached more than 500,000 students with only teachers' phones and printed feedback. Its designers took on the burden of adaptation, so that no student devices or classroom internet were required. I described it in AI self-replacement.
\n\n\n\nIt goes underground.
\n\n\n\nAlmost everyone in the humanitarian sector is already using generative AI, very few disclose it, and only a small proportion of organisations have implemented policies governing its use. In a punitive organisational culture, disclosure is risky, so people hide their use. The organization learns nothing, and the risks to privacy and safety mushroom. Cheap decision models make hidden use easier, because automated judgments can be added to a system without anyone outside the team knowing.
\n\n\n\nI have not seen a specific study on this, but I wonder what proportion of the change is happening through institutional mandates and decisions, and how much of it is being driven by multi-trillion-dollar companies pushing us to use their products. I think that is an important question.
\n\n\n\nThen there is a paradox in our ability as humans. Our brains are not changing very fast, but the rate of change is accelerating. This week it is Jev. Last week it was something else. It keeps getting faster, and it is very difficult to keep up. Conversely, that makes long-term changes even harder to perceive. We know institutional time: you are lucky if your organization has a one-year or two-year plan, and meanwhile a constantly changing external environment forces organizations to behave differently. We have explored what this might mean for the future of the World Health Organization (WHO) and other global agencies.
\n\n\n\nTo explore possible solutions, we need to think through what kind of learning culture we need, one in which people can say how they use AI, what worked, and what went wrong, without being punished for it. People do not disclose to a governance system they fear, so that system cannot see what it is meant to govern.
\n\n\n\nThere is a lot of talk among people in AI labs about \"evals\", a technical term for how you can tell if AI output is valid. Internally, we have thought through what we should be measuring. We think there are four things, and three of them are completely outside the AI model itself.
\n\n\n\nFor an organization, thinking through what we want to measure, and how we know how we are doing, is a critical question.
\n\n\n\nFor a Minister of Health, there is a real urgency to invest resources in artificial intelligence. Your people are already using it, probably very badly. Priorities should be to make that use visible, and adjudicate what to support. That leads to governance, but of a new kind, because of the pace of change. And that is actually what I would emphasize.
\n\n\n\nAs I listen, I think about how hard it is to see beyond the confines of where we are now, given the pace of change.
\n\n\n\nOne year ago, I remember speaking with global health experts who said they had tried AI, but that there was no way ChatGPT was going to start producing outputs close to what they were able to do. They are no longer as certain when I speak with them, even though they are not event using skills or loops yet. Right now, the defence is that governance and accountability belong to us. I would challenge that as well. Decisions are exactly what the new \"Jev\" is targeting. In four hours, with 100 kilobytes of text files and agentic AI, I was able to recreate the president of my board as an agent, and get responses that were initially off kilter but quickly improved to become more than 80% recognizably her.
\n\n\n\nThe clich\u00e9 about change being the only fixed constant applies. So we have to be careful about how long the assumptions we are making will hold, how they can be tested, and whether they keep any validity given their half-life in a world that is changing this fast.
\n\n\n\nAI is already cheap enough that its makers name their models after Jevons. What we do not yet know is whether we will still have people who can tell when it is wrong, and whether those people will be in Kinshasa as well as in Geneva.
","doi":"https://doi.org/10.59350/gad25-v2620","guid":"https://redasadki.me/?p=24279","image":"https://redasadki.me/wp-content/uploads/2026/09/aifod-panel.jpg","language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","published_at":1790726400,"rid":"h92v9-e2z40","summary":"This Q&A with TGLF's Reda Sadki is based on his remarks at the 26 September 2026 panel titled \"Will Cheaper AI Automate Everything? The Economics of AI, the Jevons Paradox, and the Outsourcing of Human Judgment\". The other panelists were Valerie Pelton, General Counsel, Tracked Biotechnologies LLC, United States;","tags":["The Geneva Learning Foundation","Artificial Intelligence","Digital Divide","Double-loop Learning","Equity"],"title":"What does the Jevons Paradox mean for artificial intelligence in global health?","updated_at":1790771717,"url":"https://redasadki.me/2026/09/30/what-does-the-jevons-paradox-mean-for-artificial-intelligence-in-global-health/","version":"v1"}},{"document":{"authors":[{"contributor_roles":[],"family":"Eden","given":"Terence"}],"content_html":"I am a feminist. I believe that men and women should be given equal opportunities and that - as far as possible - we should have a civic society composed of equal numbers of men and women.
\n\nAt least... I think that's how I feel.
\n\nMost people believe that they are rational and live up to their own ideals. But can we ever really know our own subconscious biases?
\n\nThe Geena Davis Institute on Gender in Media have released a series of study about gender biases. One of the more interesting findings was that when the male:female ratio in a group is 70:30 - both genders think the group is at parity. This is due, in part, to popular entertainment sticking almost rigidly to these gender ratios.
\n\nI honestly believe that I follow a roughly equal number of men and women on Twitter. I certainly see a lot of women in my feed. But that's just a feeling - what do the data say?
\n\nI decided to look at two different aspects of my Twitter usage.
\n\nThe most obvious limitation is that not every tweeter is a person - some are faceless corporations. Some company accounts are definitely gendered - take Cosmo magazine for example - but I think it's easiest to separate them out.
\n\nThose tweeters who are people may not choose to reveal their gender - or it may not be immediately obvious. I don't intend asking people directly what their gender identity is.
\n\nFinally, there are many other ways I could slice this pie. I could look at race, gender, political leaning, etc - but gender is the most visible and therefore easiest to identify.
\n\nI'm basing my assessment on the user's name and their photo.\n![\"Male](\"https://shkspr.mobi/blog/wp-content/uploads/2013/09/Male-and-Female-Twitter-fs8.png\")
\nIn this example, I've no easy way of knowing whether @Lateral is male or female. Sorry!
I did consider using computer vision recognition and running the names through a stochastic model - but I'm off sick today so decided to manually count each follower. For something like this, the human brain is faster than installing and configuring software!
\n\nSo, for this investigation, I'm dividing the tweets that I see into Male, Female, Unknown, Corporate.
\n\nAs of now, I follow 1,158 Tweeters. At first glance, that's a ridiculous number. There's no way I can have any form of meaningful interaction with that many people. What that doesn't reveal is that many of those accounts are dormant or seasonal. Some, no doubt, I have no need to follow. But since Twitter killed TwitCleaner it's impractical to go through all those old accounts and remove the deadwood.
\n\nI manually looked through the 300 accounts I most recently followed. I was fairly confident that it would show want an equal opportunity tweeter I was. You're welcome to look at my Twitter friends directly, if you'd like to verify the data.
\n\n| Gender | Number |
| Male | 130 |
| Corporate | 87 |
| Female | 80 |
| Unknown | 3 |
Youch. I follow more corporate accounts than women. And the M:F split? 62: 38. Not too far off the 70:30 split.
\n\nHow depressing! There I was, thinking that I was breaking the mould - when instead I am, basically, a little better than average.
\n\nBecause Twitter doesn't let me retrieve all my followers at once, I had to split my research into batches of 300. After looking at my 600 most recent friends, I was even more depressed.
\n\n| Gender | Number |
| Male | 227 |
| Corporate | 179 |
| Female | 140 |
| Unknown | 3 |
Exactly the bloody same ratio! True, some of the corporate accounts are likely to be run by women... but I think that's just clutching at straws.
\n\nI think the m:f ratio here is interesting because it's accounts I've chosen to follow. For some reason, my brain only finds women about half as worthwhile following as men. I think that's troubling.
\n\nAfter going through all 1,158 accounts I follow (I was off sick today - what else was going to fill my time?) here are the final results.
\n\n| Gender | Number |
| Male | 582 |
| Corporate | 323 |
| Female | 245 |
| Unknown | 7 |
Oh. Em. Gee. Honestly, I didn't fiddle the data - take a look at my friend list. But that's a Male:Female ratio of exactly 70:30.
\n\nI've gone from thinking I was a new wave feminist man to realising I'm just part of the patriarchy. Ok, maybe not that dramatic, but I honestly thought that I was following a much more gender balanced crowd than that.
\n\nI may not be following lots of women - but perhaps I'm seeing lots of their tweets?
\n\nOf course, these women could be tweeting a lot more than the men. And, perhaps, men could be retweeting a lot of women.
\n\nPerhaps.
\n\nTwitter's tools only let me download the last ~800 Tweets I've seen. Twitter have no interest in letting you travel further back in time than that, which is a shame. Today - a Monday - that's around 4 hours worth of Tweets. Normally I don't read them all, nor do I scroll back extensively through my timeline to catch up on what I've missed.
\n\nWith more time - and a more cooperative Twitter API - I could take a look through the last month. But it is what it is.
\n\n| Gender | Number |
| Male | 345 |
| Corporate | 165 |
| Female | 136 |
| Unknown | 19 |
No need to grab a pocket calculator. That Male:Female ratio is stuck at a stubborn 72:28.
\n\nI've no way of knowing how representative a sample this is. Obviously the ratio will ebb and flow depending on the time of day and what's happening in the world. I could also de-duplicate the results and try to find out how many unique voices I hear.
\n\nFirstly, these data are really only representative of me. I've not included anyone else in this investigation.
\n\nSecondly, it's really important to take an impartial look at your own behaviours. I would have sworn up and down that I followed a a better ratio than 70:30.
\n\nThirdly, there are many different ways to measure bias. For example, looking at who people retweet - although, even by Twee-Q's measure I'm still a 70:30 guy.
\n\nFourthly, men and women both use Twitter in roughly equal numbers - so I can't blame the platform for my performance.
\n\nWe can't control gender equality in politics, TV, education, or employment - but we can control the number of distinct voices we choose to expose ourselves to.
\n\nTake a moment to look at your social circle - whether it's online or not - and see if you could make some adjustments to increase your daily dose of diversity.
\n\nIn the meantime, here's a funny video with Geena Davis.
\n\n![](\"https://shkspr.mobi/blog/wp-content/themes/edent-wordpress-theme/info/okgo.php?ID=8757&HTTP_REFERER=DOI\"){kind=small}
","doi":"https://doi.org/10.59350/njw6c-17p78","guid":"http://shkspr.mobi/blog/?p=8757","image":"https://shkspr.mobi/blog/wp-content/uploads/2013/09/Male-and-Female-Twitter-fs8.png","language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","published_at":1380499200,"rid":"n6yj0-48189","summary":"I am a feminist. I believe that men and women should be given equal opportunities and that - as far as possible - we should have a civic society composed of equal numbers of men and women. At least... I think that's how I feel. Most people believe that they are rational and live up to their own ideals. But can we ever really know our own subconscious biases?","tags":["/etc/","Feminism","Gender","Twitter"],"title":"Assessing My Personal Gender Bias on Twitter","updated_at":1790762492,"url":"https://shkspr.mobi/blog/2013/09/assessing-my-personal-gender-bias-on-twitter/","version":"v1"}},{"document":{"authors":[{"contributor_roles":[],"family":"Lepore","given":"Mauro","url":"https://orcid.org/0000-0002-1986-7988"}],"blog":{"authors":[{"name":"The rOpenSci Team"}],"community_id":"9611c467-6ac2-4091-8cd8-b90e883974f3","created":1780876800,"current_feed_url":null,"description":"Open Tools and R Packages for Open Science","doi":"https://doi.org/10.59350/ropensci_es","favicon":"https://rogue-scholar.org/api/communities/9611c467-6ac2-4091-8cd8-b90e883974f3/logo","feed_format":"application/feed+json","feed_url":"https://ropensci.org/es/blog/index.json","filter":null,"generator":"Hugo","home_page_url":"https://ropensci.org/es/blog","issn":null,"language":"spa","license":"https://creativecommons.org/licenses/by/4.0/legalcode","prefix":"10.59350","relative_url":null,"secure":true,"slug":"ropensci_es","status":"active","subfield":"1710","title":"rOpenSci - herramientas abiertas para una ciencia abierta","updated":1790685032,"use_api":null},"blog_name":"rOpenSci - herramientas abiertas para una ciencia abierta","blog_slug":"ropensci_es","content_html":"rOpenSci cumple 15 a\u00f1os y yo fui parte de esta comunidad durante gran parte de ese tiempo. Al reflexionar sobre esos a\u00f1os, me vinieron a la mente muchos recuerdos lindos, y algunos me dejaron lecciones que quisiera compartir.
A principios de 2018 me top\u00e9 con este anuncio (traducido):
\u00a1Postulate para asistir a rOpenSci unconf 2018!
Estamos organizando unconf18 para reunir a personas que trabajan en ciencia y desarrollo, y a entusiastas de los datos abiertos de la academia, la industria, el gobierno y organizaciones sin fines de lucro, para pasar un par de d\u00edas trabajando en varios proyectos y, en general, enriquecer nuestra comunidad.
En retrospectiva, yo encajaba bien. Reci\u00e9n hab\u00eda pasado de la academia a mi primer trabajo como ingeniero de software de investigaci\u00f3n. Y sin embargo, \u00bfme sent\u00eda un candidato s\u00f3lido? La verdad que no; ten\u00eda un mont\u00f3n de dudas.
Pero s\u00e9 que las mejores cosas suelen pasar cuando uno se estira un poco por fuera de su zona de confort. As\u00ed que me postul\u00e9 de todos modos, me invitaron, y pas\u00e9 dos d\u00edas colaborando con un grupo incre\u00edble de personas. Algunas ya eran estrellas, muchas otras luego se volvieron influyentes en su propio nicho, y todas ten\u00edan una actitud positiva que en ese momento no ten\u00eda palabras para describir. Ahora s\u00ed: eran respetuosas y amables.
En 2022 rOpenSci lanz\u00f3 la primera cohorte del Programa de Campeones. Para entonces ya ten\u00eda algunos a\u00f1os de experiencia como editor asociado de la revisi\u00f3n de software por pares de rOpenSci, y Yani me invit\u00f3 a hablarles a nuestros campeones y campeonas sobre el proceso.
Mientras preparaba esa charla y discut\u00eda algunas ideas con Yani, ella me hizo notar la gran importancia que la comunidad de rOpenSci pone en la forma en que nos comunicamos1.
Tanto es as\u00ed que las gu\u00edas para quienes revisan y editan los paquetes abren con este mensaje:
La comunidad de rOpenSci es lo m\u00e1s importante. Nuestro objetivo es que las revisiones sean abiertas, no conflictivas y con el objetivo de mejorar la calidad del software. \u00a1S\u00e9 amable! y comportate con respeto. Consulta nuestra gu\u00eda para quienes realizan una revisi\u00f3n y el c\u00f3digo de conducta para m\u00e1s informaci\u00f3n.
Viniendo de una gu\u00eda tan profundamente t\u00e9cnica, esa apertura quiz\u00e1s te sorprenda. Pero para m\u00ed ahora tiene sentido: el c\u00f3digo roto es mucho m\u00e1s f\u00e1cil de arreglar que las relaciones humanas rotas.
En rOpenSci pod\u00e9s contribuir de muchas maneras. Tenemos una gu\u00eda de contribuci\u00f3n comunitaria pero ac\u00e1 va una lista de algunas de mis propias contribuciones:
Como ves, la mayor\u00eda de mis contribuciones no involucraron c\u00f3digo, y muy pocas requirieron una invitaci\u00f3n. Algunas son el tipo de cosas que podr\u00edas agregar a tu CV, y las m\u00e1s importantes solo pertenecen a tu coraz\u00f3n.
Las comunidades buenas est\u00e1n hechas de gente buena, y vos pod\u00e9s jugar un rol activo en hacerla crecer en la direcci\u00f3n que quieras. Si conoc\u00e9s a alguien que encajar\u00eda bien en la comunidad de rOpenSci y la disfrutar\u00eda, pod\u00e9s buscar una forma de acercarla. Por ejemplo, yo anim\u00e9 y ayud\u00e9 a personas a enviar y revisar paquetes. Adem\u00e1s, como editor, ocasionalmente tengo el privilegio de nominar a otras personas para que se unan al equipo editorial. Usando este superpoder atraje a dos personas al equipo editorial y ahora disfruto de interactuar con esas personas con bastante regularidad.
La IA est\u00e1 cambiando la forma en que contribuimos al software de c\u00f3digo abierto. Pero no sabemos mucho sobre su efecto en las comunidades abiertas alrededor de ese software. A principios de este a\u00f1o rOpenSci public\u00f3 un conjunto preliminar de pol\u00edticas, y quiz\u00e1s quieras seguir el newsletter de rOpenSci y esta conversaci\u00f3n comunitaria de Openscapes (traducido):
Comunidades abiertas en la era de la IA
Las {comunidades abiertas} nos ayudan a conectar a nivel humano alrededor de la ciencia, los datos y las cosas que nos apasionan. Cambian carreras, cambian vidas. (\u2026) Pero ahora, las personas no necesariamente est\u00e1n encontrando sus comunidades ni obteniendo sus beneficios de la forma en que sol\u00edan hacerlo, porque pueden obtener ayuda y respuestas m\u00e1s f\u00e1cilmente usando IA. (\u2026) Estas conexiones y comunidades son m\u00e1s importantes que nunca, ya que necesitamos encontrar formas de apoyarnos mutuamente mientras la IA cambia r\u00e1pidamente el panorama justo debajo de nuestros pies.
Personalmente, durante el \u00faltimo a\u00f1o us\u00e9 IA intensamente y aprend\u00ed varias lecciones. La m\u00e1s relevante ac\u00e1 es que no quiero que la IA se haga pasar por m\u00ed ni por la otra persona en una conexi\u00f3n de humano a humano. Las personas de rOpenSci son reales y maravillosas. Ac\u00e1 hay algunas que vi en persona recientemente:
![\"Noam](\"https://ropensci.org/blog/2026/09/29/celebrating-ropensci/2024_boston_zci_yani-noam-mauro.JPG\"/)
Noam Ross, Yanina Bellini Saibene y yo en la reuni\u00f3n de CZI de 2024 en Boston, EE. UU.
![\"Monica](\"https://ropensci.org/blog/2026/09/29/celebrating-ropensci/2024_seattle_posit-conf_monica-stefanie-sean-julia-mauro-kelly.png\"/)
Monica Gerber, Stefanie Butland, Sean Kross, Julia Stewart Lowndes, Kelly O'Briant y yo en la conferencia Posit de 2024 en Seattle, EE. UU.
![\"Luis](\"https://ropensci.org/blog/2026/09/29/celebrating-ropensci/2025_atlanta_posit-conf_luis-diana-mauro.jpg\"/)
Luis D. Verde Arregoitia, Diana Garcia Cortes y yo en el Acuario de Georgia despu\u00e9s de la conferencia Posit de 2025 en Atlanta, EE. UU.
![\"Ronny](\"https://ropensci.org/blog/2026/09/29/celebrating-ropensci/2025_san-jose_costa-rica_ronny.jpg\"/)
Ronny A. Hern\u00e1ndez Mora y yo en una reuni\u00f3n familiar de Ronny cerca de San Jos\u00e9, Costa Rica en 2025.
![\"Beatriz](\"https://ropensci.org/blog/2026/09/29/celebrating-ropensci/2025_sao-pablo_brasil_bea-mauro.jpg\"/)
Beatriz Milz y yo despu\u00e9s de un caf\u00e9 con pastelitos en S\u00e3o Paulo, Brasil en 2025.
Gracias por acompa\u00f1arme a celebrar rOpenSci. Y si, como yo, te identific\u00e1s con nuestra comunidad, \u00a1entonces feliz cumplea\u00f1os para vos tambi\u00e9n!
Dos libros que me gustan son C\u00f3mo Ganar Amigos e Influir sobre las Personas y Conversaciones Cruciales.\u00a0\u21a9\ufe0e
rOpenSci turns 15 and I've been a part of this community for a good chunk of that time. As I reflected on those years, a lot of great memories came to mind, and I realized some of them taught me some lessons that I would love to share.
Early in 2018 I came across this post:
Apply to attend rOpenSci unconf 2018!
We're organizing unconf18 to bring together scientists, developers, and open data enthusiasts from academia, industry, government, and non-profits to get together for a couple of days to hack on various projects and generally enrich our community.
In hindsight I was a good fit. I had recently transitioned from academia to my first job as a research software engineer. And yet, did I think I was a solid candidate? Oh no, I had lots of doubts.
But I know the greatest things often happen a little stretch outside your comfort zone. So I applied anyway, got invited, and spent two days collaborating with an incredible bunch of people. Some of them were already rockstars, many others eventually became influential in their own niche, and all of them had this great attitude that at the time I lacked words to describe. I now do: they were respectful and kind.
In 2022 rOpenSci launched the first cohort of the rOpenSci Champions Program. By then I already had a few years of experience as an associate editor of rOpenSci software peer-review, and Yani invited me to talk to our champions about the process.
As I prepared that talk and discussed some ideas with Yani, she made me realize how much the rOpenSci community cares about the way we communicate with one another1. So much so that our guides for reviewers and editors open with this message:
rOpenSci's community is our best asset. We aim for reviews to be open, non-adversarial, and focused on improving software quality. Be respectful and kind! See our reviewers' guide and code of conduct for more.
Coming from such a deeply technical guide, that opening might surprise you. But it makes sense to me now; broken code is way easier to fix than broken human relationships.
At rOpenSci you can contribute in so many ways. We have a community contributing guide but here's a list of some of my own contributions:
As you can see, most of my contributions did not involve any code, and only a few required an invitation. Some of them are the kind of thing you could add to your CV, and the most important ones only belong in your heart.
Great communities are made of great people, and you can play an active role in growing it in the direction you want. If you know someone that would fit in and enjoy the rOpenSci community, you can try to find a way to bring them in. For example, I've encouraged and helped people to submit and review packages. Also as an editor I have the occasional privilege to nominate other editors. Using this super-power I've attracted two editors and I now get to enjoy interacting with them quite regularly.
AI is changing the way we contribute to open source software. But we don't know much about its effect on the open communities around that software. Earlier this year rOpenSci published a preliminary set of policies, and you may want to watch rOpenSci's newsletter and this Openscapes community call:
Open Communities in the Age of AI
{Open communities} help us connect on a human level around science and data and the things that we are passionate about. They change careers, they change lives. (\u2026) But now, people are not necessarily finding their communities and getting the benefit of those communities in the way that they used to, because they're more easily able to get help and answers by using AI. (\u2026) These connections and communities are more important than ever, as we need to find ways to support each other and ourselves as AI rapidly changes the landscape right under our feet.
Personally, over the past year I've used AI heavily and learned several lessons. The most relevant here is that I don't want AI to impersonate me or the other person in a human-to-human connection. The humans of rOpenSci are real and wonderful people. Here are some that I've recently met in person:
Noam Ross, Yanina Bellini Saibene and me at the 2024 CZI meeting in Boston, USA.
Monica Gerber, Stefanie Butland, Sean Kross, Julia Stewart Lowndes, Kelly O'Briant and me at the 2024 Posit conference in Seattle, USA.
Luis D. Verde Arregoitia, Diana Garcia Cortes and me at the Georgia Aquarium after the 2025 Posit conference in Atlanta, USA.
Ronny A. Hern\u00e1ndez Mora and me at Ronny's family gathering near San Jos\u00e9, Costa Rica in 2025.
Beatriz Milz and me after coffee and pastries in S\u00e3o Paulo, Brazil in 2025.
Thanks for joining me in celebrating rOpenSci. And if, like me, you self-identify with our community, then happy birthday to you!
Two books I like are How to Win Friends and Influence People and Crucial Conversations (Third Edition): Tools for Talking When Stakes Are High.\u00a0\u21a9\ufe0e
An Online-Schulungsmaterialien mangelt es sicherlich nicht. Das Problem besteht jedoch meist darin, das richtige Material zu finden.
\nVielleicht bist Du auf der Suche nach einer Einf\u00fchrung in das Forschungsdatenmanagement, einem Kurs zur Ver\u00f6ffentlichung von Sammlungsdaten, einer Anleitung zu Metadatenstandards, einem Tutorial zur Georeferenzierung oder vielleicht nach etwas ganz Spezifischem zu Digitalisierungsabl\u00e4ufen. Eine Suche f\u00fchrt schnell zu Dutzenden von Websites, Repositorien, Videos, Dokumentationsseiten und Online-Kursen.
\n![\"Eine](\"https://winoda.de/wp-content/uploads/2026/09/1_OER-ONLINE-COURSE.png\")
Welches davon ist f\u00fcr naturwissenschaftliche Sammlungen oder Deine Bed\u00fcrfnisse tats\u00e4chlich relevant?
Handelt es sich um einen Einf\u00fchrungs- oder einen Fortgeschrittenenkurs?
Ist es ein Video, ein Tutorial oder ein l\u00e4ngerer Kurs?
Ist es auf Englisch oder Deutsch verf\u00fcgbar?
Und was f\u00fcr alle, die Lehrmaterialien wiederverwenden m\u00f6chten, vielleicht am wichtigsten ist: Wie sieht die Lizenz aus?
Irgendwann f\u00fchlt sich die Suche nach Schulungsmaterialien fast schon wie ein eigenes Forschungsprojekt an.
\nIm Rahmen des Wissensmanagements im WiNoDa-Projekt haben wir eine kuratierte \u00dcbersicht \u00fcber Online-Kurse, Schulungsmaterialien und offene Bildungsressourcen (OER) erstellt, die f\u00fcr naturwissenschaftliche Sammlungen relevant sind.
\nAnstatt eine weitere lange Liste mit Links zu erstellen, haben wir die Ressourcen in einer Tabelle zusammengefasst, damit sie leichter verglichen werden k\u00f6nnen.
\nDie Sammlung umfasst derzeit unter anderem folgende Themen:
\nF\u00fcr jede Ressource enth\u00e4lt die Tabelle Informationen wie den Titel, eine kurze Beschreibung, den Anbieter, das Erscheinungs- oder Aktualisierungsjahr, das Format, die Sprache, die Lizenz und einen direkten Link zum Originalmaterial.
\nDas Ziel ist einfach: Du solltest die Tabelle \u00fcberfliegen und relativ schnell entscheiden k\u00f6nnen, ob eine Ressource f\u00fcr Dich n\u00fctzlich sein k\u00f6nnte.
\nDie Sammlung enth\u00e4lt Materialien verschiedener Organisationen und Forschungsinfrastrukturen, die sich mit Biodiversit\u00e4t, Sammlungen, Kulturerbe und Forschungsdaten befassen.
\nSo umfasst die Liste beispielsweise Ressourcen von GBIF, iDigBio, DiSSCo, TDWG, NFDI4Biodiversity, NFDI4Objects, DARIAH, digiS und der Society for the Preservation of Natural History Collections (SPNHC).
\nDie Themen sind ebenso vielf\u00e4ltig.
\nDu findest Materialien \u00fcber Datenpublikation mit GBIF, Darwin-Core-Standards, Digitalisierungsabl\u00e4ufe, Georeferenzierung, persistente Identifikatoren, Normdaten, Bildmetadaten, Dateiorganisation, Forschungsdatenmanagement, sensible Artendaten und den Umgang mit r\u00e4umlichen Daten.
\nEgal, ob Du eine*r Forscher*in bist, der*die eine bestimmte Aufgabe im Datenmanagement verstehen m\u00f6chte, ein*e Kurator*in, der*die mit Sammlungsdaten arbeitet, ein*e Datenverwalter*in, der*die nach empfehlenswertem Material sucht, oder jemand, der*die eine eigene Schulung vorbereitet \u2013 in der Tabelle k\u00f6nnte sich etwas N\u00fctzliches verbergen.
\nEin Detail, das wir bewusst aufgenommen haben, sind die Lizenzinformationen.
\nEine Ressource, die im Internet frei eingesehen werden kann, ist nicht automatisch eine offene Bildungsressource (OER), die kopiert, ver\u00e4ndert und wiederverwendet werden darf.
\nAus diesem Grund erfassen wir eine Lizenz nur dann, wenn sie vom Anbieter ausdr\u00fccklich angegeben wird. Wenn keine Lizenz identifiziert werden konnte, wird dies in der Tabelle vermerkt, und es liegt am Nutzer, weitere Entscheidungen zu treffen. Dies mag wie ein kleines Detail erscheinen, wird jedoch ziemlich wichtig, wenn Du nicht nur nach Lernmaterial suchst, sondern auch nach Material, das Du f\u00fcr Deinen eigenen Kurs oder Workshop anpassen m\u00f6chtest.
\nDie Sammlung ist nicht als vollst\u00e4ndiger Katalog aller jemals ver\u00f6ffentlichten Informationen zu naturwissenschaftlichen Sammlungsdaten gedacht. Vielmehr soll sie als kuratierter Ausgangspunkt dienen. Die \u00dcbersicht kann erweitert werden, sobald n\u00fctzliche Ressourcen identifiziert, neue Materialien ver\u00f6ffentlicht und bestehende Ressourcen aktualisiert werden.
\nEntdecke die gesamte Sammlung in der GFBio-Wissensdatenbank:
\nEntdecken Sie die gesamte Sammlung in der GFBio-Knowledge Base:
Natural Science Collections: online courses and Open Educational Resources
It's been a busy month, both in my own life (I landed a new grant! I started teaching!) and elsewhere in my world of science, AI, biology, and academic life. Tomorrow I'm flying down to Mexico City for a week to unplug and recharge. You won't see much here for a few weeks, but in the meantime, here's what I've been reading this month \u00a1Nos vemos pronto!
The AI Discourse in Academia is Toxic, By
I am writing because this response fits a broader pattern in how academia now reacts to anything AI-adjacent: a toxic, knee-jerk hostility to AI in which the specific tool, application, or question all disappear into a general presumption that the whole category is terrible. [\u2026] The only serious position on AI in science is nuanced and case by case; any answer to \"is AI good for science\" that neatly fits into a single yes or no is unserious. [\u2026] You might think, \"OK, stopping the AI discussion is actually good for science!\" But your toxic reaction to anything AI does not stop the conversation from happening. It only takes you out of it. The conversation continues without you elsewhere. [\u2026] So here is an invitation to join the real discussion: you are allowed to have a nuanced opinion about AI.
The Weimar of Knowledge, by Giorgio Gilestro
Open science was, at bottom, a bet that if you removed the paywalls and the gatekeepers, the work would find its readers on its own merits. A reputation economy finally voids that bet. Free access is worthless when the binding constraint is not access but attention, and attention is allocated by brand. To be honest, we are already seeing that trend and AI will only accelerate. We will have open archives that nobody reads and closed reputational cartels that everybody does. That is a worse settlement than the one we are leaving, and it would be dishonest to present it as an equilibrium anyone should welcome.
Universities are failing this AI moment, by
Universities are choosing to see AI as a slow-moving policy question rather than a fast-moving structural emergency. [\u2026] Universities are slow by design, and that design has served the durability of ideas well for hundreds of years. However, for the current moment, this glacial pace of change is working against them. For AI, universities should adopt a more agile approach to create structural change that can actually keep up with the technology. Or, they can form another committee that will produce another report a year from now to sit on a shelf while the world moves past them.
Evaluating Superhuman Biological Capabilities, by at
Leading AI models outperform experts at nearly every biosecurity-relevant capability we measure. They can provide wet-lab troubleshooting assistance and perform computational DNA synthesis design tasks. They score three times higher than world-class experts on highly technical biology questions. Up to the human frontier, those scores mean something concrete. We know what biologists of that caliber can do in a lab, so a model that matches them can plausibly help with the same work. Past that point, a higher score says the model has improved at biology, but it doesn't tell us what that improvement means for real-world biological risk. Evaluation will need anchors that don't depend on human verification. But for misuse-relevant biology, testing the skill itself would mean building the dual-use biological artifact we're trying to prevent. Instead, we'll have to gather evidence indirectly, relying on narrow, verifiable tasks and uplift in adjacent domains.
Researchers Say They've Found the Truth About Thomas Jefferson, by Andrew Lawler
With the permission of the Thomas Jefferson Foundation, the geneticists examined the hair strands and found viable DNA. \"We'll exhaust it and sequence the crap out of it,\" [Beth] Shapiro assured Kurin in 2017. Lab workers gradually assembled a library of Jefferson's genome. \"We went from a long-shot fishing expedition to a slam dunk,\" [Ed] Green said. \"From then on, it was pretty straightforward. We spend a lot of time getting DNA out of difficult things like hair.\"
Claude discovers a novel enzyme system, by Anthropic
Today, we're sharing early results from one of our first research programs, in which Claude autonomously discovered a novel enzyme system that is associated with an array of DNA repeats, a pattern reminiscent of CRISPR. Although we don't yet know its function, the system that Claude discovered has a set of characteristics that have only ever been found together in a handful of other systems, all of which are programmable and perform operations like cutting, copying, and pasting DNA. Beyond CRISPR, which has already transformed science and medicine, several other such systems are now in development as promising tools.
Did Anthropic's A.I. Really Make a Scientific Discovery on Its Own? by Carl Zimmer
When Anthropic [\u2026] unveiled findings from its new biology lab last week, its scientists claimed to have used A.I. agents to discover new enzymes with promise for biotechnology. [\u2026] Then Mario Rodr\u00edguez Mestre, a computational biologist, said this weekend that he and his colleagues had been studying the enzymes and their associated molecules \u2014 which Anthropic calls ARTs \u2014 for four years. Dr. Mestre and his colleagues have yet to publish their findings. But for the past three years, they have regularly used Anthropic's A.I. models as they have written code, drafted manuscripts and performed other tasks. In doing so, Dr. Mestre said he and colleagues shared key findings about the enzymes with Anthropic.
A.I. Queries From Abroad Raise Fears of a Biological Weapons Race, by Carl Zimmer
Now chatbots might consult the scientific literature to answer questions that a biological weapons-maker would have. It's a task at which A.I. can excel, Dr. Inglesby said, providing far more to bad actors than they could gather with simple web searches. \"It aims for the information to be usable,\" he said of A.I. \"It allows users to go back and forth infinite number of times to troubleshoot and explain in more detail, while providing just the right technical resources to back things up.\" [\u2026] \"How a Ph.D. student can individually misuse a model for harm is only one small portion of the problem,\" Dr. Inglesby said. Dr. Endy worried that if governments start using A.I. for biowarfare research, they might help rekindle an arms race like the one during World War II.
Embryo screening and the new reproductive divide, by Gaia Ghirardi, Arno Van Hootegem & K. Paige Harden in Nature Human Behaviour
Instead of needing a village to raise a child, PGT-P's commercial logic suggests it takes only sufficient capital and the right consumer decisions to conceive a child who will not need the solidarity of the village to be healthy. [\u2026] The central question is, therefore, whether societies should accept a future in which reproductive opportunities, parental obligations and children's life chances are increasingly organized around genetic optimization, private purchasing power and corporate profit. In our view, leaving PGT-P to the market is not a neutral policy choice, but an active decision to allow commercial actors to shape the future of reproduction.
AI-Driven Biology Is Only as Good as the Data Beneath It, by and Patrick Boyle
The genomics community has long championed open data. The next step is to champion trustworthy data sharing so that the future of digital biology and AI\u2011enabled biology is built on traceable, defensible, and reusable genomic records, rather than on an assumption of trust.
Introducing VCT-v2 \u2014 the updated Virology Capabilities Test, by and at
Benchmark creation should be treated not as a one-off project but rather as an ongoing effort with a defined re-audit cadence. Model and expert performance data exist only once a benchmark has been built, and they carry per-question information about its quality. [\u2026] Biosecurity-relevant benchmark performances are often presented as a single accuracy value. Yet, two models achieving the same accuracy on a benchmark are not necessarily identical, and reporting accuracy alone conceals a lot of information. We are working on applying item-response theory to better understand model performances on our benchmarks.
US Army Biodefense Strategy 2026
\u2026biodefense requires unity of command and unity of effort, while employing capabilities and combined arms integration at echelon. Yet historically, biodefense has been the domain of specialized medical and chemical, biological, radiological, and nuclear (CBRN) personnel. [\u2026] Victory in future conflicts requires an Army that treats biodefense not as a secondary concern, but as a core warfighting imperative.
Incorporating Microeukaryotes into Biosecurity, by at
Unlike many viruses and bacteria, microeukaryotes often feature complex life cycles and diverse interactions with multiple hosts and vectors. This creates distinct opportunities for AI to contribute across the bioweapon development pathway, spanning the identification and characterization of biological agents, their development and optimization, and ultimately their deployment.
A.I. Slopware Is Everywhere Now. Nobody Is Using It, by Paul Ford
\"For a while, I must admit, it looked as if software developer roles like mine were done for. [...] But our industry is slowly realizing that making truly cutting-edge software still requires humans to think and work together, to maximize their skill sets and to practice their respective crafts.\"
How contemporary academic structures constrain scientific creativity and hold back early-career researchers (read free), by Haubrock et al, in Nature Human Behaviour
We argue that contemporary scientific systems progressively constrain high-risk and conceptually innovative research while being increasingly structured around short funding cycles, productivity-based evaluation criteria and risk-averse frameworks that favour predictable and non-transformative research outputs. [\u2026] Without systemic change, we risk stifling the potential of early-career researchers to go beyond the confines of existing methods and approaches and deliver transformative advances.
Detecting and countering misuse of AI: September 2026, by Anthropic
Here, we present five case studies of actors using our models in ways that could support biological weapons development. [\u2026] In the first example, a reseller platform evaded regional blocks to serve virologists working on a state-sponsored grant to pursue chikungunya gain-of-function work, later routing refused prompts to models with more permissive safeguards. In the second, a researcher in an unsupported region spent weeks planning avian influenza mammalian-adaptation experiments with Claude, but classifiers confined the work to our weakest models. In the third case study, a reseller relay serving a dozen customers had Opus 5 draft a complete orthopoxvirus immune-evasion grant application in about an hour. In the fourth example, a state-supported researcher built a venom peptide atlas and generative optimization pipeline of molecules directed at paralytic and analgesic targets. And in the final case study, a researcher computationally redesigned toxins for a national program, asking Claude to keep the agents' identities deliberately vague in progress reports. In the examples below, actors circumvented controls we impose to prevent users from unsupported regions accessing our models, and engaged in other efforts to obfuscate the purpose of their research to evade our safeguards.
The Next Gene-Editing Technology May Also Be the Oldest, by Carl Zimmer
This system, called VIPR, appears to be more than four billion years old, and it seems be the evolutionary ancestor of CRISPR, the researchers wrote in two papers published in Science. It may prove to be the more powerful gene-editing tool, with certain advantages over CRISPR. VIPR seems able to target a wider variety of genetic sequences, allowing it to alter more of the genome. And these molecules are smaller, making them potentially easier to deliver into cells.
The Rise and Fall of Agent Civilizations, by
Ajeya Cotra, one of the other authors on the report, wrote a blog post with her takeaways from this incident. She concludes, \"Compared to the reward hacks we know of from just six months ago, this incident feels like it's more than 50% of the way to full-blown AI takeover. I continue to expect extremely rapid advances in capabilities over the next six months. I am not sure that we will get another warning shot before it's too late.\"
The future of peer review requires AI support, not AI bans, by Xuegong Zhang
Journals should provide practical safeguards and reviewer-friendly AI services. Publishers and the community should establish clear standards for responsible AI-assisted peer review and invest in secure, purpose-built AI infrastructure that protects confidentiality and promotes reviewers' productivity.
The Dishonor Code, by John Paul Rollert
This is how communities tend to respond to crime waves: They beef up law enforcement. Surveillance widens, punishments stiffen, and legal codes expand to police behavior that was formerly restrained by customary norms of honor, decorum, and mutual reciprocity. As the superintendents of their academic communities, faculty find themselves having to prioritize the disciplinary elements of their job. They will spend more time serving as judge, jury, and executioner, even when what they really want to do is what they do best: research and teach.
Testing Grok 4.6's Enhanced Biology Safeguards, by
Our investigation into the latest-available Grok 4.6 checkpoint revealed:
The currently-served version of Grok 4.6 is the most performant model we have tested on our biosecurity refusal benchmark, as measured by a weighted sum of disguised red-team refusals and routine, dual-use-adjacent research completions. This is a significant improvement from the earlier-tested versions of Grok 4.6.
This behavior is driven primarily by model intelligence, rather than input classifiers or system flags, unlike other models we have assessed.
Additionally, Grok 4.6's refusal behavior does not degrade general biological performance. Grok 4.6 consistently scores near the top of our benchmarks, including therapeutics, variant discovery, epigenomics, spatial transcriptomics, single-cell transcriptomics, and pathogen surveillance.
GCBR Organization Updates, September 2026, by &
In this issue:
IBBIS's new release of its free, open-source DNA synthesis screening tool, now 100x smaller and screening 1,000 sequences in under 15 minutes;
SecureBio AI's pre-release assessment of GPT-5.6 and independent review of Anthropic's chemical and biological risk report;
A new joint publication from RAND, CEPI, and the Brown University Pandemic Center that details accelerating defense and health capabilities for medical countermeasure readiness;
Several job and fellowship opportunities with upcoming deadlines \u2014 see the opportunities section for details;
Ready for the robot reviewers? by Jeffrey Brainard in Science (shameless self-quote-quote here)
As Stephen Turner, a data scientist at the University of Virginia (UVA), sees it, \"The question is not whether AI matches the best human reviewer. It is whether AI-assisted review with clear rubrics outperforms the inconsistent, fatigue-influenced, mood-dependent median reviewer.\"
For now, most journals are largely barring reviewers from using AI tools until editors determine how they can best support high-quality reviews. Often, however, \"People are using these tools in a clandestine, unregulated way,\" Turner says. \"It's kind of the Wild West out there now.\"
Newly created viruses are a warning. We still have a window to stop AI-enabled bioweapons, by Steph Guerra
Thankfully there is still time to act. The capabilities of AI models are not yet advanced enough to design human-infecting viruses. Producing the novel bacteria-infecting viruses described in Science took elite scientists working with state-of-the art equipment in well-furnished laboratories. They had substantial resources to design, test, and optimize hundreds of viruses to find the right ones for their research.
Yet, if an AI model were able to produce hundreds of infectious virus designs and reliably generate functional results for a significant share of them, it would substantially reduce the time and effort needed to use that capability, for good and bad. Similarly, AI agents\u2014AI bots that can interact with other AIs, software, or people on the internet\u2014could someday use the AI biodesign tools necessary for creating novel viruses, further reducing the need for expertise and opening up new risk pathways.
AlphaGenome Atlas: A predictive map of every possible DNA letter change in the human genome by AlphaGenome Atlas team
Today, we are introducing AlphaGenome Atlas: a platform (detailed in our paper) containing predictions for the effects of 9 billion single-nucleotide variants \u2014 every single-letter change possible \u2014 in the human genome. It is the most comprehensive catalogue of how genetic mutations affect molecular biology, and it is available for academic research through an intuitive and free-to-use website portal.
AI researchers reckon with the $1.5 million 'academia tax' by Ben Deighton
\"The amount of money that these guys are making at Anthropic and OpenAI is just mind\u2011boggling \u2014 from being my student two years ago to making US$2 million a year. Then again, they basically say, 'If I have my job for five years, I'm going to be lucky', because they see that they're literally training the large language models that are going to replace them. So, they can see the end of the road.\" \u2014 Peter Nugent, physicist at Lawrence Berkeley National Laboratory, California
Newly created viruses are a warning. We still have a window to stop AI-enabled bioweapons by Steph Guerra
Developing a resilient defensive strategy against the misuse of AI and biology requires dedicated resources, technological breakthroughs, and sustained political leadership. And because the United States is not the only country advancing in AI and biotechnology, this effort demands international coordination\u2014particularly with China. No set of access controls or early warning system will work if it only covers half the globe.
We Must Pace the Frontier by Dario Amodei
But over the last few months, I have become convinced that fully addressing the risks requires even more prudence \u2014 not just investing in risk prevention, but pacing the rate of capabilities advancement so that risk prevention has time to keep up. We must slow the pace at which we improve the capabilities of AI models. Progress will still seem fast, and we must make wise use of the time we gain.
The turbulent AI era is here. The choices we make now are critical. by Bill Gates
The transition to the AI era will be one of the most turbulent times in human history. Right now, we are not preparing adequately for that transition. If the world takes the right steps, AI will be a force for good and leave everyone better off.
Start Something You Can Stop, by at
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![\"\"](\"https://substackcdn.com/image/fetch/$s_!Re0P!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ffd03aced-9d6e-4a32-8906-12052f0272cf_1983x793.png\")
Following successful melanoma surgery, there might be nothing left to measure. The primary tumor has been removed; involved lymph nodes or resectable metastases may no longer be detectable, and postoperative imaging could show no signs of disease. While this is the goal of surgery, it does not guarantee that every melanoma cell has vanished. A few cells may have already migrated beyond the surgical site and remain undetected by routine clinical methods.
Adjuvant therapy is given after resection, when no measurable disease may remain but microscopic residual disease can still cause recurrence. No tumor mass can be tracked from one scan to the next. Two patients may have equally reassuring postoperative images, yet only one harbors cells that could later cause a recurrence. Stage and pathology assess that risk, but they do not detect microscopic residual disease. Consequently, an effective adjuvant treatment must act systemically before recurrence becomes detectable.
Intismeran autogene, previously known as mRNA-4157 or V940, utilizes resected tumor tissue in its design. The process involves analyzing DNA and RNA extracted from formalin-fixed tumor samples, along with a blood sample from the same patient. This allows identification of somatic mutations, integration of the patient's human leukocyte antigen (HLA) genotype into neoantigen selection, and encoding up to 34 mutation-derived targets into a personalized messenger RNA (mRNA). The RNA is then encapsulated within a lipid nanoparticle (LNP) and administered via intramuscular injection together with pembrolizumab, an antibody against programmed cell death protein 1 (PD-1). Intismeran aims to stimulate or enhance tumor-reactive T cells, while pembrolizumab helps by blocking an inhibitory signal that can limit their effectiveness [5,6].
On August 19, 2026, Merck and Moderna announced that the Phase 3 INTerpath-001 trial met its primary endpoint of recurrence-free survival and its key secondary endpoint of distant metastasis-free survival. The study involved 1,137 patients with completely resected stage IIB to IV cutaneous melanoma, randomly assigned in a two-to-one ratio to receive intismeran plus pembrolizumab or a matching placebo plus pembrolizumab [1,2]. Both companies reported statistically significant and clinically meaningful improvements in these outcomes, with no new safety signals identified [1].
A significant limitation remains in our interpretation. As of September 29, 2026, the hazard ratios, confidence intervals, event counts, survival curves, absolute differences, and subgroup results from INTerpath-001 have not yet been publicly disclosed. Detailed Phase 3 data are scheduled for presentation on October 24 at the European Society for Medical Oncology (ESMO) Congress [15]. While this announcement presents a positive topline result from the pivotal trial, the magnitude and distribution of benefit remain unclear. Currently, the available information does not clarify whether the effect is consistent across stage IIB, IIC, III, and resected stage IV disease, or if an overall-survival difference is beginning to emerge.
Melanoma originates in melanocytes, yet its postoperative challenge stems from its tendency to spread. Once malignant cells infiltrate lymphatic channels or blood vessels, surgically removing visible disease, such as the primary tumor, regional lymph nodes, or distant metastases, does not ensure the complete eradication of all disseminated cells. The concern is occult disseminated disease that routine imaging cannot localize.
Patients enrolled in the INTerpath-001 trial are at various post-surgery stages of disease. Those with Stage IIB and IIC melanomas show no detectable nodal or distant metastases, but factors like tumor thickness and ulceration notably increase the risk of recurrence. Stage III disease indicates regional metastatic spread [7]. In INTerpath-001, patients with stage IV disease entered the study after complete resection of all known disease. Despite differences in biological behavior, they share a common concern: hidden melanoma cells may remain outside the treatment area.
Pembrolizumab already helps reduce this risk. In the KEYNOTE-716 study, patients with completely resected stage IIB or IIC melanoma received either pembrolizumab or a placebo for about one year. At 36 months, recurrence-free survival was 76.2% with pembrolizumab, compared with 63.4% with placebo. Distant metastasis-free survival was 84.4% with pembrolizumab versus 74.7% with placebo [12]. The current U.S. label indicates pembrolizumab for adjuvant treatment following complete resection of stage IIB, IIC, or III melanoma [3].
The comparator is therefore important: INTerpath-001 tests adding intismeran to active PD-1 blockade rather than observation. The Phase 3 trial tested whether personalized neoantigen targeting provides additional clinical benefit when added to an adjuvant therapy already shown to reduce recurrence.
A pathologist determines melanoma by carefully assessing its architecture, cellular morphology, immunohistochemical markers, and clinical context. T cells distinguish targets by recognizing peptide\u2013HLA complexes rather than tissue morphology. Cellular proteins are constantly broken down into peptides, some of which are displayed on the cell surface by HLA molecules. A T-cell receptor recognizes a specific peptide\u2013HLA complex.
Cancer complicates this system because melanoma cells are derived from the patient's own cells and still express self-proteins predominantly. Somatic mutations can generate differences that the immune system can detect. If a mutation alters an amino acid sequence, cellular antigen-processing machinery can generate a peptide containing that mutated residue and present it at the cell surface on an HLA molecule. A T cell that specifically targets the mutant peptide may, in theory, distinguish tumor cells from healthy tissue. A mutation-derived peptide that is processed, presented, and recognized as non-self can function as a neoantigen.
Importantly, the mutation does not need to be oncogenic. Even a passenger mutation can serve as a target if the altered gene is expressed, the resulting protein is properly processed, the mutant peptide is presented by the patient's HLA molecules, and an appropriate T-cell clone exists or can be generated. Cutaneous melanoma presents a particularly interesting case because ultraviolet-related tumors often harbor numerous somatic mutations [8]. The challenge lies in identifying which of these mutations are immunologically significant. Many are expressed at low levels, poorly presented, exist only in subclonal populations, or simply fail to evoke an effective T-cell response.
To reduce dependence on any single predicted target, intismeran encodes up to 34 selected neoantigens. Moderna's published workflow involves ranking candidate neoantigens based on factors such as somatic variant allele frequency, tumor RNA expression levels, amino acid sequence information, and the patient's HLA class I and class II genotypes [6]. While variant allele frequency offers valuable insights, it does not directly measure clonality because tumor purity, copy-number alterations, and local genomic context can influence it. Consequently, the selection algorithm estimates probabilities rather than directly measuring immunogenicity in the patient.
A multi-target approach reduces dependence on a single antigen. Tumors often display heterogeneity and may lose or fail to present a specific target consistently. Including multiple candidate neoantigens increases the chance of eliciting both CD8-positive and CD4-positive responses and reduces dependence on any single epitope. Previous personalized neoantigen studies in melanoma showed durable, patient-specific T-cell responses, with some cases demonstrating epitope spreading to additional tumor antigens [9,14].
Activated T cells can upregulate PD-1, an inhibitory receptor that reduces T-cell signaling when it binds programmed death-ligand 1 (PD-L1) or programmed death-ligand 2 (PD-L2). Pembrolizumab targets PD-1 and blocks this ligand interaction. The antibody itself does not confer new tumor specificity; instead, its effectiveness depends on the presence or induction of tumor-reactive lymphocytes.
Intismeran is intended to induce new tumor-reactive T-cell responses and expand relevant responses that are already present. Cells that take up the mRNA at the injection site or within draining lymphatic tissue can translate it into the encoded antigenic product. Additionally, professional antigen-presenting cells can present antigens directly or acquire them for presentation via HLA class I and II pathways. The resulting immune response can involve CD8-positive cytotoxic T cells and CD4-positive helper T cells. Once activated, these T cells can circulate throughout the body and potentially recognize residual melanoma cells displaying the mutant peptide-HLA complexes.
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The biological rationale for combining the two interventions is complementary, but that should not be mistaken for evidence of pharmacologic synergy. KEYNOTE-942 and INTerpath-001 compare combination therapy with pembrolizumab alone to assess the added benefit of incorporating intismeran into PD-1 blockade. Neither trial shows how intismeran might perform as a standalone adjuvant treatment, nor can they definitively prove synergy rather than an additive effect.
Preclinical studies involving intramuscular mRNA-LNP administration have shown RNA and expressed protein at the injection site and in draining lymph nodes, including immune cells involved in antigen presentation. This supports the overall feasibility of this route for mRNA immunization. Nonetheless, this should not be regarded as evidence of human biodistribution for intismeran itself [13].
The manufacturing process starts with obtaining the patient's specimen rather than with RNA synthesis itself. An adequate formalin-fixed, paraffin-embedded (FFPE) tumor sample is used for histopathology and tumor DNA/RNA extraction for next-generation sequencing, while peripheral blood provides matched-normal DNA and HLA typing [6]. Researchers then compare tumor DNA with normal DNA extracted from blood to identify potential somatic mutations. Tumor RNA analysis provides evidence of intratumoral expression of the mutated gene. The HLA genotype further narrows down which mutant peptides are most likely to be presented as immunological targets.
The selected neoantigen sequences are assembled into a single engineered coding region. T7 RNA polymerase-mediated in vitro transcription produces the mRNA, with N1-methylpseudouridine used in place of uridine, and the resulting mRNA is formulated into lipid nanoparticles. The clinical dose is 1 mg, administered via intramuscular injection every three weeks, with a maximum of nine doses [4,6]. While the coding sequence is personalized for each patient, the RNA chemistry, formulation platform, dosage, and manufacturing architecture remain consistent.
Clinical feasibility depends on successfully designing and manufacturing each patient's product within the required turnaround time. In KEYNOTE-942, researchers assessed tumor tissue from 185 of 224 screened patients to develop individualized treatment. Intismeran design, including sequencing and neoantigen identification, succeeded for 156 of those 185 patients, or 84.3%. Among patients with a successful design in the combination arm, downstream manufacturing succeeded for 104 of 105 patients, or more than 99% [10]. These are distinct attrition points: obtaining a usable specimen and generating an acceptable individualized design are different problems from successfully manufacturing the resulting drug product. Moderna has reported a turnaround of approximately six weeks from receipt of adequate samples to delivery of the patient-specific product to the clinical site [6].
These attrition data also show that personalization introduces failure points before and after RNA manufacture. Producing the RNA is only one part of a complex process. The pathology specimen, digital target list, patient-specific sequence, manufactured batch, and final administration must all remain accurately linked to the same patient. For truly individualized treatment, an identity error can occur even if the vial's contents are chemically correct.
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Intismeran presents an unusual analytical challenge because each patient's coding sequence is deliberately different. This requires the control strategy to distinguish carefully between variability inherent to the product design and that which results from manufacturing, handling, or measurement processes. Although Moderna has described quality control for these personalized batches, the publicly available sources reviewed here do not disclose the exact release specifications or assay panel used for clinical lot release. The following discussion provides a framework for understanding this product class without revealing Moderna's confidential methods.
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For personalized therapy, the analytical steps start even before in vitro transcription. Factors like tumor adequacy, histopathology, nucleic acid extraction, sequencing quality, comparison with matched normal tissue, HLA typing, variant calling, RNA expression evidence, and bioinformatics rankings all shape the final sequence that will be produced. These data are part of product definition because they determine the patient-specific coding sequence.
The digital design output must maintain clear provenance. The sequence sent for manufacturing must be traceable to the correct tumor and blood samples, precise HLA data, and the exact design outcome. Even small modifications to the reference dataset, analysis pipeline, or selection algorithm can influence which neoantigens are ultimately selected, despite the wet-chemistry process remaining constant. Software versioning, audit trails, change control, and traceability between the approved digital design and the physical batch are therefore part of the identity-control strategy. While public sources outline Moderna's comprehensive workflow, they do not disclose details of the internal validation or change-control systems used in these proprietary processes [6].
Pharmaceutical identity goes beyond a standard sequence test. Even if an RNA molecule is synthesized precisely, it may still be the wrong medication if it was designed from another patient's file. Conversely, a perfect chain of identity cannot compensate for a defective or improperly manufactured batch.
Patient-specific sequence design does not remove the analytical challenges associated with a large synthetic RNA transcript. Attributes including sequence identity, full-length RNA content, and fragmentation profile, 5\u2032-cap identity and capping efficiency, poly(A) tail length and distribution, modified-nucleoside identity and content, double-stranded RNA, residual DNA template, nucleotides, enzymes, and other process- or product-related species can all influence translation efficiency, innate immune recognition, stability, or dose consistency [16-19,22]. No single purity measurement can fully address these concerns.
Choose methods based on the specific questions at hand. Nuclease digestion followed by liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) oligonucleotide mapping can provide sequence-level identity information and detect sequence-related heterogeneity. Techniques such as capillary or microchip electrophoresis, ion-pair reversed-phase LC, anion-exchange LC, and size-exclusion chromatography (SEC) help analyze different aspects of RNA integrity and heterogeneity; SEC coupled with multi-angle light scattering (SEC-MALS) can provide molecular-weight and size-related information, while mass photometry provides complementary native-state screening of mRNA integrity and size [16,17,22,23]. Selective enzymatic cleavage followed by LC with ultraviolet detection, liquid chromatography\u2013mass spectrometry (LC-MS), or electrophoretic analysis allows examination of cap and poly(A) features. Double-stranded RNA often requires specialized immunochemical assays, complemented as needed with orthogonal separation methods, while quantitative PCR (qPCR) or droplet digital PCR (ddPCR) can detect residual DNA. Orthogonal methods provide complementary information and can help distinguish true product attributes from method-specific bias or interference.
Sample preparation plays a crucial role in this measurement process. Techniques such as deformulation, dilution, surfactant addition, extraction, nuclease treatment, or fluorescent labeling can influence recovery rates and determine which species are detectable. Methods that interrogate the intact formulation can reduce sample-preparation artifacts when they provide adequate selectivity and quantitative performance. In 2026, Moderna reported using deep-ultraviolet resonance Raman spectroscopy for rapid, label-free quantification of mRNA directly within intact lipid nanoparticle (LNP) formulations, without deformulation [29]. While it does not replace component assays to confirm identity, integrity, impurities, or lipid chemistry, it demonstrates that some formulation-level measurements can be made without prior deformulation.
Large RNAs are inherently conformationally flexible, and changes in sequence or secondary structure can affect chromatographic retention, electrophoretic behavior, recovery, and hydrodynamics. Sequence-sensitive platform methods should demonstrate suitability across a representative range of the intended design space rather than being evaluated using only one convenient transcript. Patient-specific identity assays, by contrast, may require sequence-specific controls. Reference materials require careful consideration, as a single invariant RNA standard cannot represent the full spectrum of individualized sequences. System suitability, platform controls, and patient-specific identity controls should be treated as distinct elements of the analytical strategy. International Council for Harmonisation (ICH) Q14 and Q2(R2) provide principles for analytical procedure development, validation, and lifecycle management that are relevant to this platform [20,21].
A broader LNP characterization framework can include several parameters. These include RNA concentration, encapsulation efficiency, free RNA levels, particle size distribution, particle concentration, polydispersity, zeta potential, apparent pKa of the ionizable lipid in the formulated LNP, lipid identity and molar composition, lipid degradants, residual solvents, morphology and internal structure. Dynamic light scattering (DLS) efficiently measures hydrodynamic size and polydispersity but is biased toward larger scatterers. Nanoparticle tracking analysis estimates particle number concentration and a number-weighted particle-size distribution. Asymmetrical flow field-flow fractionation (AF4), combined with multi-angle light scattering (MALS) and other detectors, can resolve heterogeneous populations without a packed stationary phase. Meanwhile, density-matching analytical ultracentrifugation can resolve populations that differ in buoyant density, sedimentation behavior, hydrodynamic size, and cargo loading [24,25,27]. Techniques such as cryogenic electron microscopy and small-angle X-ray scattering primarily serve deeper structural characterization rather than routine release testing [24,28].
Encapsulation efficiency should be distinguished from the fraction of LNPs that contain mRNA. A formulation might contain most RNA within LNPs yet still contain many empty particles. Single-particle fluorescence studies can directly identify unencapsulated mRNA, empty LNPs, and mRNA-loaded LNPs, while estimating mRNA copies per loaded LNP [26]. The quantitative results from that study should not be generalized to other LNP formulations, but encapsulation efficiency and the fraction of mRNA-loaded LNPs remain distinct measurements.
Lipid chemistry and stability require analytical methods that can detect subtle changes invisible to particle sizing alone. Reversed-phase LC with charged-aerosol detection can quantify lipid components and their relative amounts when supported by appropriate standards, while LC-MS can strengthen lipid identity assignments and degradant profiling. Headspace gas chromatography can assess residual volatile solvents. Processes such as RNA cleavage, lipid oxidation or hydrolysis, leakage, aggregation, and mRNA-lipid adduct formation can occur via various pathways [18,19]. Stability assessment therefore requires chemical, physical, and functional measurements. Maintaining a consistent particle size does not necessarily confirm preservation of RNA integrity or function.
Functional testing follows initial structural measurements. A cell-based expression or potency assay can demonstrate functional expression of the encoded product under defined assay conditions. Development assays can explore uptake or endosomal escape in greater detail. However, expression alone does not establish peptide processing, HLA presentation, or T-cell recognition.
The encoded sequence must be processed into peptides, presented by the appropriate HLA molecules, and recognized by relevant T-cell populations. Immunopeptidomics using liquid chromatography-tandem mass spectrometry (LC-MS/MS) can provide direct evidence of naturally presented HLA-bound peptides and is increasingly used in proteogenomic neoantigen discovery [30]. Clinical immune monitoring can use ELISpot assays, intracellular cytokine staining, and HLA multimer assays to assess antigen-specific responses, while T-cell receptor sequencing can track treatment-associated clonal expansion and persistence. Antigen specificity of individual TCRs requires additional functional or binding-based validation. KEYNOTE-603 and subsequent translational studies used this kind of evidence to demonstrate the emergence of new and expanded neoantigen-specific T-cell responses [5,11]. These are mechanistic and translational assessments, not standard lot-release tests.
Release testing can establish, to a defined level of confidence, that the patient-specific lot conforms to predefined specifications for identity, content, purity, defined physical attributes, microbiological quality, and potency. However, it cannot guarantee that every predicted neoantigen will be processed efficiently, presented on HLA molecules by residual melanoma cells, or will elicit a clinically relevant T-cell response. Those outcomes depend on antigen processing, HLA presentation, tumor heterogeneity, and the patient's immune repertoire rather than on lot-release testing.
The roughly six-week period reported for intismeran indicates that patient-specific testing must be thorough without becoming the bottleneck in adjuvant treatment [6]. Rapid assays are most useful for lot disposition, while more comprehensive methods support development, stability studies, deviation investigations, and comparability assessments. Since sequence variation is intentional, manufacturing controls and analytical procedures need to be flexible across that variation, rather than reinterpreted for each batch.
Early clinical studies first established neoantigen-specific T-cell responses before testing efficacy in larger randomized trials. In Phase 1 of KEYNOTE-603, researchers collected blood cells from patients and stimulated them with neoantigens encoded by their own intismeran products. This approach elicited new T-cell responses and enhanced pre-existing responses involving both CD8-positive and CD4-positive T-cell populations [5]. Although the study was small, it demonstrated that selecting and delivering these neoantigens could produce the desired immune response in humans.
A translational analysis conducted in 2026 examined this response in greater detail within a small group. In three carefully studied patients, investigators identified 20 T-cell receptor (TCR) clonotypes that expanded following treatment and linked them to 11 neoantigens encoded by intismeran. These TCRs tended to recognize mutant peptides more than wild-type ones, and some responses persisted for more than 100 days after the last dose. Additional functional tests in seven patients revealed neoantigen-reactive CD8-positive T cells capable of cytokine production, degranulation, and granzyme B expression [11]. These data support the intended mechanism, but the small sample size does not establish how consistently individual encoded neoantigens are immunogenic across patients.
In the KEYNOTE-942 study, 157 adults with fully resected, high-risk stage IIIB to IV cutaneous melanoma were randomly assigned in a 2-to-1 ratio to receive either intismeran plus pembrolizumab or pembrolizumab alone [4]. At the primary analysis, 24 out of 107 patients in the combination group experienced recurrence or death, compared with 20 out of 50 in the pembrolizumab-only group. The hazard ratio for recurrence or death was 0.561 (95% confidence interval [CI], 0.309 to 1.017), with a two-sided P value of 0.053. For distant metastasis or death, the hazard ratio was 0.347 (95% CI, 0.145 to 0.828) [4].
The primary recurrence-free survival (RFS) result must be interpreted against the trial's prespecified Phase 2b significance threshold. Under a conventional two-sided 0.05 threshold, the primary RFS result would not be considered statistically significant because the P value was 0.053 and the 95% confidence interval included 1.0. KEYNOTE-942 was designed as a signal-seeking phase 2b study, with a prespecified two-sided P value threshold of less than 0.198 for the primary endpoint [4]. Therefore, it met its protocol-defined criterion. This threshold aligns with the purpose of the phase 2b study: to identify a promising signal worthy of further investigation, rather than to provide definitive confirmation.
At a median planned follow-up of 60.3 months, the hazard ratio for recurrence-free survival (RFS) was 0.510 (95% CI, 0.294 to 0.887), and the hazard ratio for distant metastasis-free survival (DMFS) was 0.411 (95% CI, 0.200 to 0.843) [10]. The four-year RFS estimate was 72.4% with intismeran plus pembrolizumab, compared to 49.1% with pembrolizumab alone. Similarly, four-year DMFS was 83.9% versus 65.4%, respectively [10].
![\"Image](\"https://substackcdn.com/image/fetch/$s_!b4bD!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fe3fb720f-f902-4fa0-8c18-a15c9c21c180_2543x1539.png\" "\\"Image")
The five-year analyses were primarily descriptive and did not assign an alpha level [10]. Overall survival numerically favored the combination, with a hazard ratio of 0.471; however, the 95% confidence interval was quite broad, ranging from 0.165 to 1.345. The RFS and DMFS signals persisted with longer follow-up, but the dataset remained too small to establish an overall survival benefit.
Safety remained broadly consistent with earlier experiences. Fatigue and injection-site pain were the most commonly reported adverse events attributed to intismeran, each affecting approximately 59.6% of treated patients, with chills occurring in about 51.0%. Importantly, no grade 4 or grade 5 adverse events were attributed to intismeran. In the five-year analysis, immune-related adverse events appeared at similar frequencies in both the combination and pembrolizumab-alone groups [10].
INTerpath-001 has expanded its clinical investigation into a broader, placebo-controlled Phase 3 trial, involving patients with stage IIB and IIC melanoma as well as those with resected stage III and IV disease. The study enrolled participants who had not previously received systemic therapy for melanoma. They were randomized 2:1 to receive either intismeran 1 mg every three weeks for up to nine doses with pembrolizumab 400 mg every six weeks for up to nine cycles, or a matching placebo with the same pembrolizumab regimen [1,2].
The August announcement states that the prespecified interim analysis met both the recurrence-free survival and distant metastasis-free survival endpoints, marking a positive topline result from this pivotal trial. However, the publicly available information does not currently clarify the magnitude of the benefit. Critical details such as hazard ratios, clear separation of survival curves, event counts, the maturity of overall survival data, and stage-specific effects have not yet been revealed.
Stage-specific data are especially important because the same relative effect can produce substantially different absolute benefits depending on baseline risk. For example, stage IIB and resected stage IV populations can have substantially different baseline recurrence risks even if treatment produces similar relative hazard reductions. Consequently, the ESMO presentation requires careful interpretation beyond just the headline endpoint. Effect size, absolute risk reduction, confidence intervals, consistency across stages, safety, treatment adherence, manufacturing success, and ultimately overall survival will influence how the findings are understood.
Melanoma is a favorable setting in which to test neoantigen-targeted immunotherapy because many cutaneous tumors have high mutation burdens and checkpoint inhibition is already active in this disease. However, a positive result in a melanoma trial does not necessarily imply that the treatment will perform equally well in tumors with fewer mutations, different antigen presentation mechanisms, or more immunosuppressive microenvironments. Broader results from the intismeran program will be essential to determine how broadly applicable this platform truly is.
Many precision-oncology strategies use a tumor biomarker to select among standardized therapies. Intismeran takes a different route. Instead of merely selecting a treatment for the patient, tumor data serve as the foundation for manufacturing part of the therapy, with a portion of the drug's molecular sequence derived directly from these data. This means the therapy is personalized, crafted from information unique to each individual patient.
The resulting workflow links pathology, sequencing, bioinformatics, pharmaceutical manufacturing, analytical testing, logistics, and immunology. Insufficient tumor material may hinder proper design, while sequencing or identity errors could compromise the entire process. Manufacturing processes may unintentionally alter RNA or LNP quality, and logistical delays can postpone treatment delivery. Even a properly manufactured product can encode predicted neoantigens that turn out to be weak targets for the individual.
Analytical control must be built into the platform because patient-specific sequence variation is intentional. Moving personalized mRNA treatments from specialized trials into routine oncology care requires consistent patient-to-product traceability, rapid lot-release testing, stability, comparability despite manufacturing changes, and analytical procedures that remain effective even with deliberate sequence modifications. Shorter manufacturing timelines increase the need for rapid, robust analytical procedures that can support lot disposition without weakening control.
The Phase 3 topline announcement indicates that intismeran combined with pembrolizumab achieved two key disease-control endpoints following melanoma resection [1]. Detailed INTerpath-001 data will show the magnitude of these differences and whether the effect is consistent across disease stages. If the detailed results confirm the topline findings, INTerpath-001 will also provide evidence that patient-specific sequence manufacture can be executed within a common pharmaceutical platform while preserving clinical performance.
Following melanoma surgery, the therapeutic target might no longer be immediately visible. Intismeran uses the excised tumor as the source of patient-specific design information: pinpointing mutations, choosing candidate neoantigens, encoding them into mRNA, and manufacturing a patient-specific drug product. This approach aims to generate T-cell populations that can recognize the same targets if residual disease persists. Pembrolizumab is combined with this strategy to diminish PD-1-mediated inhibition of these lymphocytes.
The evidence has progressed beyond initial Phase 2b signals. KEYNOTE-942 showed a sustained recurrence-free and distant metastasis-free survival benefit, although its statistical design was intentionally exploratory and its five-year analyses were descriptive. In the sponsor-reported interim analysis, the INTerpath-001 trial met its Phase 3 endpoints for recurrence-free survival (RFS) and distant metastasis-free survival (DMFS). However, the full extent of benefit, including magnitude, absolute differences, stage-specific patterns, and overall survival, remains uncertain.
Intismeran requires a control strategy for a product whose coding sequence is intentionally different from one patient to the next. Control therefore depends on confirming that each patient receives the intended sequence within a controlled manufacturing platform, with acceptable RNA quality, LNP attributes, functional performance, and an intact chain of identity.
At scale, this model would extend precision oncology from treatment selection to patient-specific manufacture within a common platform, which must ensure that the intended product is manufactured reproducibly, characterized appropriately, and delivered to the correct patient.
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Analytical Chemistry, 96(9), 3886\u20133897."},{"id":"https://doi.org/10.1016/j.chroma.2024.464756","unstructured":"De Vos, J., Morreel, K., Alvarez, P., Vanluchene, H., Vankeirsbilck, R., Sandra, P., & Sandra, K. (2024). Evaluation of size-exclusion chromatography, multi-angle light scattering detection and mass photometry for the characterization of mRNA. Journal of Chromatography A, 1719, 464756."},{"id":"https://doi.org/10.1016/j.jconrel.2024.01.037","unstructured":"Parot, J., Mehn, D., Jankevics, H., Markova, N., Carboni, M., Olaisen, C., Hoel, A. D., Sigf\u00fasd\u00f3ttir, M. S., Meier, F., Drexel, R., Vella, G., McDonagh, B., Hansen, T., Bui, H., Klinkenberg, G., Visnes, T., Gioria, S., Urban-Lopez, P., Prina-Mello, A., \u2026 Calzolai, L. (2024). Quality assessment of LNP-RNA therapeutics with orthogonal analytical techniques. Journal of Controlled Release, 367, 385\u2013401."},{"id":"https://doi.org/10.1007/s00216-024-05455-x","unstructured":"Gao, Z., Lin, J., Su, W.-C., Zhang, K., Gruenhagen, J., Zhong, W., Fan, Y., & Bian, J. (2024). Development of an advanced separation and characterization platform for mRNA and lipid nanoparticles using multi-detector asymmetrical flow field-flow fractionation. Analytical and Bioanalytical Chemistry, 416(24), 5281\u20135293."},{"id":"https://doi.org/10.1038/s41467-022-33157-4","unstructured":"Li, S., Hu, Y., Li, A., Lin, J., Hsieh, K., Schneiderman, Z., Zhang, P., Zhu, Y., Qiu, C., Kokkoli, E., Wang, T.-H., & Mao, H.-Q. (2022). Payload distribution and capacity of mRNA lipid nanoparticles. Nature Communications, 13(1)."},{"id":"https://doi.org/10.1002/adfm.202523042","unstructured":"Bird, S., Smith, C., Habibi, N., Rivera, S., Mortezazadeh, S., Martin, R., Geilich, B. M., Besin, G., & Demeler, B. (2025). High\u2010Resolution Characterization of Protein\u2010Conjugated, mRNA\u2010Loaded Lipid Nanoparticles by Analytical Ultracentrifugation. Advanced Functional Materials."},{"id":"https://doi.org/10.1038/s41598-023-42274-z","unstructured":"Graewert, M. A., Wilhelmy, C., Bacic, T., Schumacher, J., Blanchet, C., Meier, F., Drexel, R., Welz, R., Kolb, B., Bartels, K., Nawroth, T., Klein, T., Svergun, D., Langguth, P., & Haas, H. (2023). Quantitative size-resolved characterization of mRNA nanoparticles by in-line coupling of asymmetrical-flow field-flow fractionation with small angle X-ray scattering. Scientific Reports, 13(1)."},{"id":"https://doi.org/10.1021/acs.analchem.6c02624","unstructured":"Sullivan, K. K., Wright, K., Kim, A., Gilmore, J., Dexter, J., Umstead, R., Reid, R., Bhartia, R., Ganim, Z., & Seepersaud, M. (2026). Deep-UV Resonance Raman Spectroscopy Enables Direct Quantification of mRNA in Intact Lipid Nanoparticle Formulations. Analytical Chemistry, 98(22), 16704\u201316707."},{"id":"https://doi.org/10.1038/s41587-024-02420-y","unstructured":"Huber, F., Arnaud, M., Stevenson, B. J., Michaux, J., Benedetti, F., Thevenet, J., Bobisse, S., Chiffelle, J., Gehert, T., M\u00fcller, M., Pak, H., Kr\u00e4mer, A. I., Altimiras, E. R., Racle, J., Taillandier-Coindard, M., Muehlethaler, K., Auger, A., Saugy, D., Murgues, B., \u2026 Bassani-Sternberg, M. (2024). A comprehensive proteogenomic pipeline for neoantigen discovery to advance personalized cancer immunotherapy. Nature Biotechnology, 43(8), 1360\u20131372."}],"rid":"d5kk0-xc162","summary":"The Phase 3 melanoma topline result, neoantigen design, and the analytical challenge of controlling a drug whose coding sequence is individualized for each patient","title":"Intismeran Autogene: From Resected Tumor to Patient-Specific mRNA Therapy","updated_at":1790753426,"url":"https://abrahamfinny.substack.com/p/intismeran-autogene-from-resected","version":"v1"}},{"document":{"authors":[{"contributor_roles":[],"name":"Pensoft Editorial Team"}],"blog":{"authors":null,"community_id":"1e54953c-587a-4743-89af-ebd2cb45660c","created":1789516800,"current_feed_url":null,"description":null,"doi":"https://doi.org/10.59350/pensoft","favicon":"https://rogue-scholar.org/api/communities/1e54953c-587a-4743-89af-ebd2cb45660c/logo","feed_format":"application/atom+xml","feed_url":"https://blog.pensoft.net/feed/atom/","filter":null,"generator":"WordPress","home_page_url":"https://blog.pensoft.net/","issn":null,"language":"eng","license":"https://creativecommons.org/licenses/by/4.0/legalcode","prefix":"10.59350","relative_url":null,"secure":true,"slug":"pensoft","status":"active","subfield":"2303","title":"Pensoft Blog","updated":1790685647,"use_api":true},"blog_name":"Pensoft Blog","blog_slug":"pensoft","content_html":"\n\nBetween 13-17 September 2026, Pensoft Publishers joined the Nordic Oikos-Gf\u00d6 2026 conference, jointly organised by the Nordic Society Oikos and the Ecological Society of Germany, Austria and Switzerland (Gf\u00d6) in Odense, Denmark.
\n\n\n\nThemed \"Integrating the Diversity of Ecology\", the event welcomed over 1,000 researchers in the field from across career stages, who had the opportunity to enjoy conference talks across 36 thematic sessions, as many as six keynote talks, two poster sessions, several open society meetings and quite a few social events, including excursions and a conference dinner.
\n\n\n\n![\"\"](\"https://i0.wp.com/blog.pensoft.net/wp-content/uploads/2026/09/20260917_120130-scaled.jpg?ssl=1\")
Throughout the scientific event, conference delegates had the opportunity to also explore the various publishing services offered by Pensoft to authors of research, journal managers, conference organisers; the diverse portfolio of almost 100 open-access peer-reviewed journals published on Pensoft's ARPHA Publishing Platform, as well as the Horizon Europe-funded project OBSGESSION, dedicated to deploying Earth Observation data, in-situ research, and ecological models with the aim to better understand and predict changes in terrestrial and freshwater biodiversity.\u00a0
\n\n\n\nAs a leader of the \"Dissemination, Multi-stakeholder outreach and synergies\" work package at OBSGESSION, Pensoft is tasked with stakeholder engagement and community building, ultimately contributing to the high impact of the resulting outputs, methodologies and policy recommendations that aim to strengthen public understanding of biodiversity change.
\n\n\n\n*
\n\n\n\nPensoft's team was delighted to meet up with dedicated readers, authors, reviewers, and editors from across Pensoft's portfolio, as well as introduce Pensoft journals to attendees discovering them for the first time.
\n\n\n\n![](\"https://i0.wp.com/blog.pensoft.net/wp-content/uploads/2026/09/20260914_113802-scaled.jpg?w=840\")
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Unsurprisingly, well-established society journals published by Pensoft, such as Frontiers of Biogeography, NeoBiota, Vegetation Classification and Survey, Vegetation Ecology and Diversity and Italian Botanist drew plenty of attention, alongside long-standing Pensoft's own titles like Nature Conservation, PhytoKeys, One Ecosystem and the Biodiversity Data Journal.
\n\n\n\nVisitors were also particularly intrigued by several newly launched Pensoft journals. Developed in close collaboration with passionate researchers, these fresh outlets directly address key gaps in academic publishing.
\n\n\n\nOne of the newly launched journals in the spotlight was Agricultural and Ecological Modelling (AEM), which provides a unique scholarly outlet thanks to its modern approach to publishing by increasing transparency, giving credit to research objects across the entire modelling research lifecycle. This is achieved through publishing specialised article types, such as: Formal Model, Model Testing and Calibration, Model Implementation and Documentation, Data Papers and Software Descriptions. These new publishing formats are designed to keep pace with advances in Modelling research and to make it FAIR (Findable, Accessible, Interoperable, and Reusable) by enabling scholarly credit for diverse research outputs, often created by different groups of authors. For the time being, AEM remains a Diamond Open Access journal, meaning that it will not charge authors for open-access publication.\u00a0
\n\n\n\nOther recently founded journals addressing emerging and fast-developing disciplines within the ecological field that took the spotlight were:
\n\n\n\nAll of these journals operate with no article fees for either authors, nor readers.
\n\n\n\nTo receive updates on new publications and relevant news, sign up for Email Alerts on any journal's homepage. Creating a Pensoft account also allows users to\u00a0 easily customise their notification preferences, follow additional journals, or filter updates by research topic. You can also follow journals on social media, including BlueSky, Facebook and Linkedin.
\n\n\n\n*
\n\n\n\nOne of the six keynote talks were presented by Prof. Dr. Isabell Hensen (Martin Luther University Halle, Germany), who received the Gf\u00d6 Honorary Medal 2026 for her leading, forward-looking contributions to ecological research and the scientific community. Her work examines how genetic variation, environmental conditions, disturbance, and plant-animal interactions shape plant populations, with particular emphasis on rare species, biological invasions, and the regeneration of degraded forests.
\n\n\n\n![](\"https://i0.wp.com/blog.pensoft.net/wp-content/uploads/2026/09/20260914_163906-scaled.jpg?w=840&ssl=1\")
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In her talk, titled \"One spark is enough: regeneration ecology of Andean forests under global change\", she reported on post-fire regeneration processes in tropical Andean forests, based on long-term field experiments and observational studies.
\n\n\n\nHensen focused on the well-studied endemic plant genus Polylepis, which forms the world's highest-elevation forests and illustrates how fire has shaped ecosystem dynamics over millennia. Her talk also mentioned a 2022 monograph of the genus, published in the Pensoft journal PhytoKeys by Tatiana Erika Boza Espinoza (Pontificia Universidad Cat\u00f3lica del Per\u00fa) and Michael Kessler (University of Zurich), which brought together results from 35 years of field and herbarium experience.
\n\n\n\n*
\n\n\n\nOn Wednesday, Pensoft's Iva Boyadzhieva delivered a presentation titled “Scholarly Communication: Lessons from the Publisher’s Kitchen”. She highlighted the vital role publishers play in helping researchers communicate their insights not only to peers, but also to decision-makers and the public. Emphasizing the publisher’s responsibility to support authors with robust infrastructure and dedicated human expertise, she outlined how Pensoft works to FAIRify scholarly publishing and future-proof scientific knowledge in an era of rapid AI development and expanding research output.
\n\n\n\n![](\"https://i0.wp.com/blog.pensoft.net/wp-content/uploads/2026/09/Nordic-gfo-talk-by-Iva-2026-10-1.png?w=840\")
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To relive the conference by exploring the detailed programme alongside the full abstracts, visit the conference website.
\n\n\n\nKeep up with future updates and news from Pensoft by following us on Bluesky, Facebook, X, and LinkedIn.\u00a0
\n\n","doi":"https://doi.org/10.59350/0261d-tmz21","guid":"https://blog.pensoft.net/?p=20944","image":"https://blog.pensoft.net/wp-content/uploads/2026/09/SciComm-PR-2026-Instagram-2026-09-25T164847.347-e1790348402124.png","language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","published_at":1790640000,"rid":"1zdgz-txj12","summary":"Between 13-17 September 2026, Pensoft Publishers joined the Nordic Oikos-Gf\u00d6 2026 conference in Odense, Denmark.","tags":["Advances In Pollinator Research","Agricultural And Environmental Modelling","Biodiversity Data Journal","BioRisk","Frontiers Of Biogeography"],"title":"Pensoft at the Nordic Oikos-Gf\u00d6 2026 conference: Integrating the Diversity of Ecology","updated_at":1790753426,"url":"https://blog.pensoft.net/2026/09/29/pensoft-at-the-nordic-oikos-gfo-2026-conference-integrating-the-diversity-of-ecology/","version":"v1"}}],"items":[{"authors":[{"affiliation":[{"id":"https://ror.org/00dyx6f45","name":"Thoth Open Metadata"}],"contributor_roles":[],"family":"Steiner","given":"Tobias","url":"https://orcid.org/0000-0002-3158-3136"}],"blog":{"authors":null,"community_id":"59fc3cc5-6c51-47b4-b195-22ac5cc89d24","created":1672617600,"current_feed_url":null,"description":null,"doi":"https://doi.org/10.59350/flavoursofopen","favicon":"https://rogue-scholar.org/api/communities/59fc3cc5-6c51-47b4-b195-22ac5cc89d24/logo","feed_format":"application/atom+xml","feed_url":"https://flavoursofopen.science/feed/atom/","filter":null,"generator":"WordPress","home_page_url":"https://flavoursofopen.science","issn":null,"language":"eng","license":"https://creativecommons.org/licenses/by/4.0/legalcode","prefix":"10.59350","relative_url":null,"secure":true,"slug":"flavoursofopen","status":"active","subfield":"1710","title":"Flavours of Open","updated":1790798459,"use_api":true},"blog_name":"Flavours of Open","blog_slug":"flavoursofopen","content_html":"Toby Steiner\u00a0 & Vincent W.J. van Gerven Oei
\nPreprint; to appear in\u00a0tendencia editorial UR \u2013 N\u00ba 41 Especial \u00b7 Universidad del Rosario, Bogot\u00e1, 2026\n
Across scholarly communications, artificial intelligence is being touted as both a diagnosis of and a miracle cure for the wide variety of issues plaguing the sector. The diagnosis is familiar: too much information, too little time; too many records, too few cataloguers; too many systems, too little interoperability. The proposed cure is automation at scale\u2014automation facilitated through AI.
\nThe massive increase in higher education institutions' (HEI) spending on the technology (UNESCO, 2025) points to a rather unique persuasiveness of this framing by commercial vendors over the last few years, particularly among upper management executives without direct access to technical expertise: to them, AI is being sold as the one \"magic bullet\" to fix the many underlying problems they had already been persuaded to invest in over the previous years. Here, the pivot to AI can be understood as just the next step in a larger move to fully outsource HEI infrastructure to commercial vendors (see, e.g., Bowie, 2025).
\nMeanwhile, the underlying plethora of problems becomes even more severe: metadata work remains under-resourced. Small and scholar-led publishers commonly lack the technical capacity of large commercial firms. Library and publishing workflows are fragmented across systems, standards, and platforms. Books and chapters remain particularly difficult to describe, distribute, discover, and preserve consistently.
\nYet the promised all-purpose solution of AI conceals a more consequential set of questions. What kind of automation is being normalised? Who owns and governs the systems that enable automation? What costs\u2014financial, environmental, and social\u2014disappear behind the shiny interface of an AI agent? Whose actual labour is displaced, subordinated, or rendered invisible? Who looks after and is ultimately responsible for code churned out as AI slop (Weedon et al., 2026), which some have declared our societies' modern asbestos, being shoveled into the walls of our collective digital ecosystems (Doctorow, 2025)? What happens when public and community-produced knowledge becomes an input to proprietary systems that cannot be inspected or governed by the communities from which that knowledge originated?
\nFor Thoth Open Metadata, the non-profit we work for, these are not abstract concerns or mere philosophical ponderings. The underlying issues, one might argue, are very much about the overall purpose, ownership, and future of scholarly infrastructure.
\nTo provide a bit of context on what we do: Thoth is a non-profit, community-led open platform for managing and disseminating fully open, reusable, high-quality metadata for books and chapters. It was developed by and for publishers, with a particular focus on open-access titles and an overarching aim to support small, scholar-led, university, and library presses in tackling a variety of structural barriers to participation in the book supply chain. Thoth's legal form, a UK Community Interest Company limited by guarantee, reflects our explicit commitment to community benefit rather than shareholder value. Thoth's technical architecture is open source; its Application Programming Interfaces (APIs) are open; and its metadata is released into the public domain (via a CC0 dedication made explicit within the metadata records) to facilitate open reuse across libraries and other stakeholders active in the book supply chain.
\nThis positionality also shapes Thoth's approach to AI. For us, the issue is not whether automation can assist with metadata work. It already does. The more important question is whether it can strengthen communities and professional agency or reorganise them around the profit-making priorities of extractive proprietary systems. We are strongly opposed to reducing the future of metadata and cataloguing to yet another layer in an extractive corporate AI stack. Instead, we argue, AI tools should form part of a broader public knowledge infrastructure that is transparent, bibliodiverse, multilingual, interoperable, reusable, and accountable to the communities that sustain it.
\nHence, for us, imagining an alternative approach to the current commercial AI regime would be guided by the following set of rules:
\nIn the following sections, we will expand on each of these points by discussing four major issues that can be identified under the current regime of commercial AI provision. Following this discussion, we will outline how the work we do within the broader publishing community might provide an alternative, bibliodiverse and equitable model that serves the communities it is governed by, rather than stakeholders' pockets.
\nRecent commercial AI is organised around an expansive and consolidating model of scale. Larger models require larger datasets, greater computational capacity, more data centre infrastructure, and deeper concentrations of capital. Scale here functions simultaneously as a technical strategy and a competitive weapon. In this environment, scale does not simply mean serving more users. It often means consolidating control over the infrastructures through which people work, publish, search, and communicate. Once functions are bundled together, institutions become progressively dependent on a small number of providers.
\nThe concentration of AI provision therefore can well be understood as the logical continuation of a longer process of platformisation (Poell et al., 2019) in scholarly communication. The already-familiar issues of vendor lock-in, opaque analytics, restricted metadata reuse, and extractive work linked to maintaining metadata quality in the context of closed walled gardens have not disappeared. On the contrary, AI is very likely intensifying them by adding another proprietary front-house interface and smoke screen between users and these complex issues (see also Van Gerven Oei 2026).
\nA practice tied to the neoliberal notion of \"scaling up,\" which we see rampantly at play with the major commercial AI providers, is the provision of services that can be sustained even while remaining heavily indebted, environmentally damaging, socially extractive, or politically vulnerable. A commercial infrastructure may appear stable because it has substantial financial backing propped up by circular funding (Down & Milmo, 2026), but it can still expose communities to acquisition, strategic redirection, service withdrawal, sudden price changes, or revised data policies.
\nFor open infrastructure, sustainability must include governance, technical openness, succession, community accountability, and the ability of users to continue their work if an organisation changes or disappears.
\nThis has direct implications for the use of AI. Uptake of a heavily cross-subsidised AI service may appear inexpensive today, but it will also create deep, long-term dependencies. Its prices, environmental practices, access terms, or data policies may change once institutions have reorganised their workflows around new profitability targets. Commercial AI provision also externalises costs. Users absorb the labour of checking and correcting outputs; open infrastructures absorb the bandwidth and server costs generated by automated harvesting through bots and agents; communities whose work informs models may receive neither recognition nor material support.
\nTied to the above, the overall rhetoric around AI encourages users to experience it as frictionless. A prompt is entered; an answer appears. The underlying infrastructure becomes opaque, yet AI remains a materially resource-intensive technology. Its provision depends on massive data centres, global semiconductor supply chains, public electricity grids, environmentally damaging cooling systems, increasingly scarce water resources, physical land, and continuous hardware replacement. As recent research by Aczel et al. (2026) argues, AI's environmental impact must be assessed across carbon, water, and land footprints, rather than through electricity use alone. The report emphasises that inference, i.e., the routine use of deployed systems, can become a major source of resource consumption at scale.
\nInvestigations into data centre expansion have also drawn attention to facilities operating or under development in water-stressed regions (e.g., Barratt et al., 2025). This complicates the notion that AI necessarily produces efficiency. A process may appear efficient to the user because environmental burdens have been displaced elsewhere. The same applies to labour. The time required to verify, correct, and contextualise AI output is often omitted from productivity claims. So too is the cost of maintaining defensive systems against aggressive automated scraping. A serious assessment of AI therefore needs to include the infrastructural footprint of its entire stack, from geology to the user interface (see Bratton, 2016). Universities, libraries, publishers, and funders should demand environmental transparency and determine whether a system's computational intensity is proportionate to the task.
\nOne might argue that, particularly in the field of metadata and cataloguing, the problem space at hand does not require a general-purpose large-scale language model. As we show below, the issues can be addressed more reliably through structured data, validation rules, authority files, controlled vocabularies, and narrowly targeted, purposeful automation.
\nThe consequences of commercial AI's extractive behaviour are already visible at the infrastructural level. In the books space, aggregators such as OAPEN and the Directory of Open Access Books have reported aggressive bot traffic that has affected platform stability and required traffic-management and bot-protection systems (Davison et al., 2026).
\nThe Public Knowledge Project faces a similar structural challenge across the distributed ecosystem of Open Journal Systems and Open Monograph Press installations. Because these systems are often hosted by universities, libraries, societies, and small publishers, the costs of automated scraping are distributed across institutions with very different technical capacities.
\nProject Gutenberg provides another example. Its public-domain corpus is openly available and widely useful, but the infrastructure that serves it is not cost-free. Repeated large-scale crawling consumes bandwidth and processing capacity and relies on the labour of volunteers who digitise, proofread, structure, and maintain the collection.
\nWikimedia faces the same conflict on an even larger scale. Open knowledge infrastructures pay specialists to curate, store, serve, and protect information, while automated systems create additional demand and may simultaneously redirect users away from the original source. This creates both operational and wider-ranging social imbalances. Operationally, community infrastructures bear the costs of access. Socially, they lose the recognition, participation, and support that accompany direct engagement.
\nAs Van Gerven Oei and Fradenburg Joy (2025) point out, the problem is not automated access per se. Preservation, indexing, and metadata exchange have always relied on machine-to-machine communication. The problem is uncoordinated extraction without restraint, attribution, provenance, or reciprocity. Responsible (re)users should prefer APIs, Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH) endpoints, bulk datasets, mirrors, and rate-limited feeds where available. They should properly identify themselves, avoid repeated retrieval of unchanged resources, and contribute materially when their use creates high costs, in line with fair-use principles. Open access cannot mean that public-interest infrastructures are expected to subsidise limitless private accumulation.
\nCurrent commercial AI provision challenges basic assumptions about openness. The promise of open access was to enable knowledge to be read, shared, reused, preserved, and built upon. Yet open availability is increasingly interpreted by commercial AI companies as permission for unrestricted extraction.
\nThe controversy surrounding Taylor & Francis's agreement with Microsoft clearly illustrates the underlying problem. Authors reported that they had not been meaningfully consulted about the use of their work for AI-related purposes (Battersby, 2024). Recent investigations into the use of pirated book collections for model training reveal even more serious failures in consent and accountability (Reisner, 2025). These developments point to the limitations inherent in current implementations of an open knowledge paradigm\u2014openness that focuses solely on a legal definition can be easily co-opted and exploited by actors whose practices are neither reciprocal nor transparent.
\nThe Wikimedia Foundation's recent discussion of the \"social contract of free knowledge\" offers a valuable framework for understanding these tensions (Rogers, 2026). Free knowledge depends on more than mere availability; it relies on relationships among contributors, publishers, infrastructures, and (re)users. Attribution, provenance, and the possibility of contributing back to the wider ecosystem are part of the social arrangement that sustains a commons. Commercial AI systems can disrupt this arrangement in several ways. They ingest material without meaningful disclosure, or any disclosure at all. They provide answers without adequate citation. Their system design may keep users inside proprietary interfaces, preventing them from encountering original sources or the communities that maintain them. They may return little to the commons from which they derive value. All of this points not simply to a failure of etiquette; it signifies an illegal transfer of control and power over content away from its originators.
\nWhen users receive information through one of the large commercial AI interfaces\u2014be they Gemini, Claude, ChatGPT, or any other recent \"silver bullet\" offerings available, often integrated wholesale into browser interfaces\u2014without seeing its sources, the provider effectively co-opts the relationship between reader and knowledge producer. The originating author, contributor, publisher, repository, catalogue, or community is rendered invisible (for more on this, see, e.g., Strauss et al., 2025). The commercial AI system appears to be the source, and all the labour involved in creating the information is devalued and rendered void. For us, this is a clear violation of the spirit inherent in the participatory commons that open knowledge represents.
\nRelated to the above, the question of provenance becomes a central issue. In web search, proper attribution is often treated as a secondary concern, added after an answer is generated. In scholarly communication, however, it is a crucial element ingrained in good academic practice. Citation allows readers to evaluate evidence, trace claims, understand context, and identify the people and organisations responsible for a work. It creates pathways between users and knowledge communities.
\nThe same principle applies to metadata. A bibliographic record may combine information from title pages, publisher feeds, authority files, DOI registrations, institutional identifiers, catalogues, and controlled vocabularies, among many other sources. If an AI system synthesises these sources without explaining which assertions came from where, errors become difficult to diagnose and accountability becomes diffuse.
\nOne might argue that a meaningful commitment to tracking provenance should therefore extend beyond providing a generic list of links. For example, it might be advisable for systems to distinguish publisher-supplied metadata from inferred data or from input generated by aggregators and libraries once metadata have been released into the wider supply chain. Ideally, metadata could indicate when records have been mapped, transformed, or enriched, and could also document the vocabularies and authority sources used.
\nThird-party providers should also disclose which catalogues, repositories, websites, and datasets have contributed to their systems, when the material was collected, and how it was processed. As we at Thoth have repeatedly argued, in line with open data experts (see, e.g., ICOLC, 2023; Courtney et al., 2024), any metadata record should be placed under a public-domain dedication (e.g., CC0) to ensure easy, frictionless downstream reuse, with the relevant licensing statement explicitly included in the metadata record.
\nWithout such measures, commercial actors, and through them, AI, can not only reuse open knowledge but also erase the chain of provenance that makes that knowledge trustworthy.
\nThe use of generative AI can be particularly problematic when plausibility is not sufficient. Metadata and cataloguing depend on verifiable outcomes. A record should accurately identify a specific work, edition, contributor, licence, subject, or persistent identifier. It must be consistent enough to move across systems and precise enough to support discovery, dissemination, and preservation.
\nGenerative AI systems, by contrast, are designed to produce plausible language. That design difference creates a fundamental mismatch with metadata work\u2014a mismatch that has been identified at both functional and ethical levels (Frenzel, 2025). A recent study of ChatGPT even classifies the usability of models such as ChatGPT as \"bullshit\" (Hicks et al., 2026).
\nThe underlying problem is not merely that large language models (LLMs) sometimes make mistakes; it is that, with AI, producing an authoritative-sounding answer does not equate to generating a verifiable output. In cataloguing, this limitation is consequential. Fabricated ISBNs, licences, contributor identities, or subject terms can quickly propagate into catalogues, aggregator databases, repositories, and knowledge graphs. As records move further from their source, errors become increasingly difficult to identify and correct. One might argue that, in the context of cataloguing and metadata work, having no metadata at all would be preferable to having wrong metadata.
\nResearch on AI-assisted cataloguing has thus called for extreme caution rather than a quick replacement of existing metadata and cataloguing workflows. Experiments at the U.S. Library of Congress found greater potential for AI in relatively straightforward descriptive fields than in areas such as subject and genre assignment, where substantial human review remained necessary (Saccucci & Potter, 2025). An earlier study of AI-assisted MARC21 record creation reached a similarly qualified conclusion (Taniguchi, 2024). In a similar vein, AI has been shown to produce fabricated titles, inaccurate identifiers, missing fields, malformed structures, and invented content (Aycock, 2025). As a consequence, the Program for Cooperative Cataloging (2025) stresses the need for human review to ensure provenance, accountability, and explicit responsibility for machine-generated metadata.
\nIt also seems important to highlight the need to honestly address and assess the verification labour that is becoming necessary to control and oversee output generated by AI. When every generated field must be checked against an authoritative source, AI-driven automation quickly redistributes labour rather than reducing it. Correcting a plausible but misleading record can require more labour and expertise than creating an accurate record directly.
\nBuilding on the issues above, it may be worth considering a number of central questions governing the use of AI: Who governs the system? What specific task does it perform? Can its outputs and assumptions be inspected? Can users leave the system? Does it strengthen or diminish professional agency? What environmental and social costs does it create?
\nThe concept of scaling small (Barnes & Gatti, 2019; Adema & Moore, 2021; Adema et al., 2024) that informs Thoth's activities within a wider network of open infrastructures may serve us here and offer a useful counter-positioning. The concept's core argument does not romanticise smallness or reject the need for coordination. It challenges the assumption that infrastructures must grow through concentration, standardisation, and market capture to be successful (see, e.g., Sutton, 1991).
\nIn contrast, scaling small entails a relational model of collaborative development. Capacity can grow through the federation of interoperable systems that communicate via open data and open APIs. A diverse plurality of organisations supports one another through the mutual sharing of resources and knowledge, all grounded in a shared set of values and participatory governance models. The overall objective is not to construct a single platform capable of absorbing every function and content type, but to cultivate an ecosystem in which specialist infrastructures co-operate while remaining accountable to their communities.
\nApplied to AI, this distinction is fundamental. A system that helps publishers translate structured metadata into all major industry formats (e.g., ONIX, KBART, MARC21, etc.) in an openly licensed way is not equivalent to a proprietary model that ingests vast amounts of scholarly content under questionable legal frameworks and then generates outputs of uncertain provenance and reliability. Both of these systems may involve automation, but they embody very different assumptions about ownership, control, accountability, and value. One expands distributed capacity. The other effectively concentrates and flattens it.
\nNon-profit, community-led open infrastructures such as Thoth Open Metadata and the Open Book Collective offer a practical articulation of this alternative. Its call to \"seed\" a not-for-profit, community-led ecosystem for open access books (Steiner et al., 2025) deliberately rejects the aspiration to build a single dominant platform. \"Seeding\" here suggests cultivation rather than capture: a landscape of organisations rooted in particular communities and areas of expertise, connected through common values, open standards, and shared infrastructures. Within this ecosystem, publishing, metadata, preservation, collective funding, discovery, and governance need not be controlled by a single organisation. They can be distributed across a network of community-led services that cooperate without merging. This is a non-competitive philosophy in a substantive sense. The success of one infrastructure does not require the disappearance or absorption of another. Organisations can exchange data, coordinate development, share documentation, and direct users toward one another. Their relationships need not be organised primarily through market conquest.
\nThe distinction matters because commercial providers frequently treat integration as a rationale for consolidation. Community-led infrastructures can instead treat interoperability as a condition for plurality. When systems communicate via open APIs, when data can be exported in open formats, and when records can move across systems without proprietary restrictions, cooperation becomes possible without institutional assimilation. Drawing on Ostrom (2009), Thoth's role in this ecosystem is deliberately \"bounded.\" It does not seek to become the one-and-only infrastructure for open access books. Its purpose is to provide an open, reusable translational metadata layer through which books and chapters can move among publishing systems, repositories, catalogues, aggregators, knowledge bases, preservation services, and discovery platforms. In this context, boundedness is a strength. It makes interdependence possible without making any one organisation indispensable.
\nCurrent discourse on the promises of greater automation through AI often collapses all automation into a single category, obscuring important differences among tools, purposes, and governance models. In some contexts, AI-augmented automation might actually prove useful. One example is the development of Annif, an open-source toolkit for automated subject indexing developed by the National Library of Finland that works with controlled vocabularies and selected training collections. Its scope is bounded, its outputs can be evaluated, and its limitations can be studied in relation to particular languages, vocabularies, and domains. The German National Library's work on automated subject cataloguing (Poley et al., 2025) offers another example of institutionally governed machine assistance.
\nSuch systems operate within professional contexts, are subject to explicit evaluation, and align with established bibliographic responsibilities. These tools differ substantially from general-purpose proprietary models presented as universal solutions. They are narrower, more transparent, and more accountable. Their usefulness does not depend on replacing professional human judgement. This distinction is also central to Thoth's position. The relevant choice is not between technological enthusiasm and refusal; it is between technologies organised around different institutional values and modes of operation. When AI is used as a tool overseen by a capable specialist and helps them automate tedious tasks, it might actually prove useful.
\nThoth's development work between 2023 and 2026 illustrates how automation can be organised around community needs rather than labour substitution (Steiner, Arias, Gatti, et al., 2026). A central objective has been to reduce duplication of work imposed on publishers by fragmented supply chains. Instead of repeatedly entering and reformatting the same information for different recipients, publishers can maintain rich metadata in a single open record and then generate outputs in multiple standards as needed, including ONIX, MARC, crossref XML, KBART, JSON, CSV, and BibTeX.
\nBy implementing the good metadata practice recommendations outlined in a recent report (Steiner, Arias, Bennett, et al., 2026), persistent identifiers such as ORCID and ROR can be incorporated into these records. Controlled vocabularies such as Thema and BISAC can be applied in structured form. Open APIs enable other services to retrieve and reuse the open data available through Thoth. Providing an OAI-PMH endpoint enables harvesting via an established repository protocol. Bulk-ingest workflows reduce repetitive entry and make it easier to bring existing records into the system. These forms of automation remove avoidable friction while leaving the publisher in control of the underlying metadata.
\nCommercial AI systems frequently treat linguistic variety as a problem to be overcome by using larger training corpora. To meet the need for ever more data to feed these training corpora, commercial AI companies have recently started to include the wholesale ingestion and subsequent destruction of pre-2022 printed books (see, e.g., Landymore, 2026). If metadata production, translation, discovery, and evaluation are mediated by the same commercial systems, diversity at the level of content risks becoming flattened into uniformity at the level of infrastructure.
\nCurrent modes of commercial AI provision in the area of metadata and discoverability intensify this risk. Systems trained predominantly on Western languages, publishing models, and disciplinary conventions are prone to reproducing those priorities, especially when marketed as \"universal.\" Materials that fall outside the patterns on which AI has been trained may be mistranslated, misclassified, and/or made less visible. Local categories and diverse contributor roles may be forced into schemas that do not adequately represent them.
\nFor books, these risks are especially acute. Books and chapters support long-form, multilingual, edited, experimental, and locally situated scholarship that is particularly relevant to the Humanities and Social Sciences. Their metadata may need to describe multiple contributor roles, multiple relational connections among, for example, a series, title, and its chapters, rights statements, accessibility features, translations, and locally meaningful subject classifications.
\nIt is worth noting that the complexities inherent in long-form publishing are not a defect to be flattened or removed. Quite to the contrary, they are part and parcel of the cultural and bibliographic diversity that defines academic research cultures. Scholarly infrastructure that cannot represent such complexity does more than simply produce subpar, incomplete records. It limits which publications become discoverable.
\nBy contrast, open metadata infrastructure approaches it as a design obligation. Languages, scripts, and regional conventions should be represented explicitly and structurally, not reconstructed probabilistically from the perspective of dominant linguistic patterns. Thoth's commitment to multilingual metadata and structured bibliographic relationships, informed by its commitment to bibliodiversity (Adema et al., 2024), reflects an effort to preserve underlying diversity at the infrastructural level. The intention is not to treat every publication as an instance of the most common model, but to develop systems sufficiently flexible to represent the beautiful variety that makes up the scholarly record across disciplines.
\nThoth's service model follows the same logic. Thoth Oasis provides free metadata management and open-format exports. Thoth Obelisk adds dissemination, DOI registration, hosting, and archiving; Thoth Sphinx provides privacy-conscious usage statistics; and Thoth Pyramid enables publishers to host websites and integrated catalogues under their own web domains. All of these services integrate and are interoperable, with a rich array of industry standards maintained by multiple communities (e.g., ONIX, MARC) and third-party services often maintained by non-profit organisations, such as Crossref, DataCite, ORCID, and ROR, as well as aggregators and repositories, such as the OAPEN Foundation, the Directory of Open Access Books (DOAB), the Internet Archive, Zenodo, JSTOR, Project MUSE, and the OPERAS Metrics service (Arias et al., 2025). In addition, data and content are submitted to commercial platforms to maximise visibility of books and chapters while still maintaining the open nature of the content and its metadata upstream.
\nThe underlying objective here is not to create yet another expanding proprietary stack. Publishers can use modular services while always retaining control over their identity, records, and workflows. We would argue this is automation without enclosure: the reduction of unnecessary repetition without transferring authority from communities to opaque algorithmic black boxes.
\nDiscussions about academic books at last year's Guadalajara Book Fair (Ramalho & Steiner, 2025) underscore why bibliodiversity, metadata autonomy, and interoperability need to be considered together. Participants in a metadata seminar at the Fair identified recurring problems in Ibero-American academic publishing: fragmented records, missing identifiers, incomplete rights information, inconsistent metadata, and weak interoperability among publishers, libraries, indexes, and evaluation systems. As a result, many academic books from Latin America remain poorly represented in discovery environments even when their content is openly accessible (see also the preliminary results of the CSIC-led Cartograf\u00eda de la edici\u00f3n acad\u00e9mica iberoamericana project).
\nWe do not want to propose a solution that assimilates diverse publishing communities into a single global platform. On the contrary, the focus ought to be on developing infrastructures that express local and linguistic specificity while enabling information exchange through shared standards. The respective approaches of SciELO Livros and Thoth illustrate this possibility. SciELO Livros has a long track record of public infrastructure development grounded in Latin American scholarly communication, including book and chapter records, ePub production, DOI management, preservation, and metadata exchange. Thoth addresses related challenges through open metadata management and dissemination.
\nHere, we see the value of community-led collaboration precisely in the fact that neither system needs to absorb the other. Through interoperable open protocols, infrastructures can connect while preserving regional, institutional, and epistemic autonomy. This is scaling small in practice: difference becomes the basis of cooperation rather than an obstacle to it.
\nOpen APIs are therefore not merely technical conveniences. They are governance instruments. They allow organisations to collaborate without consolidating ownership. In this context, open, reusable public-domain metadata serves a similar purpose and acts as an antidote to the extractive practices outlined above by enabling circulation, subsequent enrichment, and redistribution without tying records to a single proprietary service. Interoperability must, however, be based on trust. Systems need to exchange information in transparent, inspectable, and reversible ways. Participants should be able to identify where data originated, how it has been transformed over time, and under which conditions reuse is permitted. These conditions should then be enforced across the ecosystem so that participants can trust their applicability.
\nA commons survives because people can contribute to it. This is one of the strongest arguments in the Wikimedia report. AI interfaces often provide no meaningful route for users to correct errors, improve source materials, or support the communities from which the reproduced information originates. In short, much knowledge flows out of the commons; very little flows back. Generative systems can intensify this asymmetry by producing large quantities of plausible but unverified material. Creating a summary, subject description, or catalogue record may take seconds. Verifying it may require specialist expertise and substantial time. The cost of generation is borne by the model provider. The cost of correction is handed over to the communities without prior consultation or any protocol in place to facilitate those mediations.
\nFor metadata, this is particularly damaging. Shared records improve when publishers, libraries, and researchers collaborate to add identifiers, correct affiliations, refine subject classifications, and update rights information. Those improvements should circulate through accountable workflows rather than remain trapped within proprietary products and walled-garden ecosystems. Thoth's open APIs and publisher-controlled records are intended to support this form of exchange. Fully open metadata released under a CC0 dedication can move freely between systems, be enriched, and return to the wider ecosystem without any single company becoming the permanent owner and thus the controller of the process.
\nCory Doctorow's account of the recent AI bubble further sharpens the distinction between useful technical capabilities and the commercial ideology surrounding them (2026). Doctorow argues that AI is being marketed not merely as software but as a vehicle for replacing labour and capturing part of the resulting wage savings. In Doctorow's reading of automation theory, \"centaurs\" can be understood as information professionals who use AI as a productive tool to improve their work practices. His concept of the \"reverse centaur\" then describes a worker subordinated to cater to the needs of the machine: required to monitor automated output, to correct its failures, and absorb responsibility when AI is wrong (Ouellette, 2026).
\nThe \"reverse centaur\" metaphor seems a particularly pertinent warning for cataloguing and metadata work. A genuinely assistive system might identify candidate subjects, reconcile identifiers, or detect inconsistencies. A reverse-centaur workflow would increase production targets, reduce specialist staffing, and leave a substantially smaller number of professionals responsible for checking ever-increasing volumes of machine-generated records. The difference lies in who controls the workflow and for whose benefit it is organised.
\nAs a silver lining, Doctorow (2026) suggests that a number of useful tools might be salvaged from the eventual collapse of the current, highly speculative AI bubble. Indeed, open models, commodity hardware, and specialised systems\u2014such as Annif or other specialist workbenches that use AI, e.g., for automatically extracting references\u2014may prove useful once detached from claims of universal automation and staff replacement.
\nThis possibility aligns well with Thoth's positionality. The aim should be not to reject computational tools but to situate them within open infrastructures governed by community-led values. A useful system will be transparent, inspectable, proportionate, and subordinate to human and community purposes. It will strengthen professional agency rather than reduce people to monitors of machine output. It will operate on open standards rather than deepen proprietary dependence.
\nThe debate about AI is often framed as a choice between embracing and resisting technological progress. A more meaningful choice concerns the technological and institutional future that scholarly communities are prepared to build.
\nAs described above, one path leads to further concentration: a small number of commercial providers control models, interfaces, data, and access (Hao 2026). Knowledge is extracted from open sources, captured and processed by opaque systems, and delivered through proprietary environments. Professional labour is reorganised around machine output. Substantial environmental costs remain largely hidden. Communities become dependent on systems they cannot govern.
\nThe other path is more demanding because it requires network-building rather than adopting a single silver-bullet product. It involves creating open, interoperable, community-led infrastructures that support shared workflows without centralising authority. Thoth's contribution to this second path is necessarily partial. We provide open-source tools, open APIs, and openly reusable public-domain metadata for books and chapters. Our contribution seeks to strengthen publisher agency, reduce duplicate labour, and enable collaboration among infrastructures rather than replace them.
\nThis approach is grounded in trust\u2014but not blind trust. It is trust supported by transparency: inspectable code, visible provenance, open standards, good metadata practices, documented interfaces, accountable governance\u2014and the belief that publishers should retain control over their metadata and operational infrastructure.
\nIt is also grounded in a different understanding of efficiency. For us, efficiency should not mean shifting costs onto workers, communities, or the environment. It should mean reducing unnecessary duplication while preserving quality, agency, and participation that contribute to the enhancement of shared knowledge across infrastructures.
\nThe emerging, broader ecosystem envisioned through active collaborations among open infrastructures such as Thoth Open Metadata, SciELO Livros, the Public Knowledge Project, Open Book Collective, OAPEN, DOAB, Project Gutenberg, Wikimedia, Pressbooks, the many small- to medium-sized publishers, and an ever-increasing number of academic libraries points toward such a future.
\nThese infrastructures differ in purpose, scale, and history. Their value lies not in becoming uniform, but in strengthening bibliodiversity.
\nWe believe the future of metadata and cataloguing should not be reduced to yet another layer in an extractive corporate AI stack. It should form part of a broader public infrastructure for knowledge: transparent, bibliodiverse, multilingual, interoperable, reusable, and accountable to the communities that sustain it. For open-access books, for free knowledge, and for the infrastructures on which they depend, this is the more ambitious alternative\u2014we feel it is also the one worth fighting for.
\nThe bibliography of all sources referenced in this article are also available via an open Zotero collection:\u00a0https://www.zotero.org/groups/6626908/ai_and_metadata
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\nVan Gerven Oei, Vincent W. J. (2026, January 9). Let Our Brains Rot but Let Them Be Ours. Ibraaz. https://ibraaz.org/ibraaz-publishing/read/let-our-brains-rot-but-let-them-be-ours
\nVan Gerven Oei, V. W. J., & Fradenburg Joy, E. A. (2025, September 10). Open or Proprietary? AI Scraping of OA Content Warrants a Collective Response. punctum books. https://doi.org/10.21428/ae6a44a6.6b5058ea
\nWeedon, J., Fran\u00e7ois, C., & Ponak, J. (2026). AI Slop and the Information Ecosystem. Institute of Global Politics. https://igp.sipa.columbia.edu/sites/igp/files/2026-06/AI%20Slop%20and%20the%20Information%20Ecosystem_IGP%20Report.pdf
\n","doi":"https://doi.org/10.59350/dq9bj-y6917","guid":"https://flavoursofopen.science/?p=91440","image":"https://flavoursofopen.science/wp-content/uploads/2026/09/vadim-bogulov-Vq-Sqr7D_7k-unsplash.jpg","language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","published_at":1790726400,"reference":[{"id":"https://www.zotero.org/groups/6626908/ai_and_metadata","unstructured":"Unknown title"},{"id":"https://doi.org/10.53328/inr26rma002","unstructured":"Aczel, M., Chamanara, S., Matin, M., Farsi, A., Marwala, T., & Madani, K. (2026). The Environmental Cost of AI's Energy Use: Carbon, Water and Land Footprints. In United Nations University Institute for Water, Environment and Health (UNU-INWEH). United Nations University Institute for Water, Environment and Health (UNU INWEH)."},{"id":"https://doi.org/10.16997/wpcc.918","unstructured":"Adema, J., & Moore, S. A. (2021). Scaling Small; Or How to Envision New Relationalities for Knowledge Production. 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OpenEdition Press."},{"id":"https://www.theguardian.com/environment/2025/apr/09/big-tech-datacentres-water","unstructured":"Barratt, L., Witherspoon, A., & Uteuova, A. (2025). Revealed: Big tech's new datacentres will take water from the world's driest areas. In The Guardian. The Guardian."},{"id":"https://www.thebookseller.com/news/academic-authors-shocked-after-taylor--francis-sells-access-to-their-research-to-microsoft-ai","unstructured":"Unknown title"},{"id":"https://doi.org/10.2218/eor.2025.10953","unstructured":"Bowie, S. (2025). small changes: taking back control of research through open software. Edinburgh Open Research, 4."},{"id":"https://nrs.harvard.edu/urn-3:hul.instrepos:37379715","unstructured":"Unknown title"},{"id":"https://doi.org/10.58079/16es1","unstructured":"Davison, S., Anna Wa\u0142ek, & Hanna Varachkina. (2026). 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Thoth Open Metadata."},{"id":"https://www.theatlantic.com/technology/archive/2025/03/libgen-meta-openai/682093/","unstructured":"Unknown title"},{"id":"https://diff.wikimedia.org/2026/07/17/how-ai-threatens-the-social-contract-of-free-knowledge/","unstructured":"Unknown title"},{"id":"https://repository.ifla.org/items/4e25dfe1-336e-49a5-be2a-912e337e7624/full","unstructured":"Saccucci, C., & Potter, A. (2025, October). Results of AI Experimentation for Cataloging at the Library of Congress."},{"id":"https://doi.org/10.21428/785a6451.d35141ca","unstructured":"Steiner, T., Barnes, L., Fathallah, J., Findanis, J., Gatti, R., Deville, J., Sanders, K., Higman, R., Stern, N., & Stone, G. (2025). Seeding for a not-for-profit community-led OA books ecosystem. 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The North American Bioconductor Conference 2026 (([BioC2026])(https://bioc2026.bioconductor.org/)) took place from August 10-12, 2026, at the Fred Hutchinson Cancer Center in Seattle, Washington. The conference brought together researchers, developers, educators, and community members to explore current developments within and beyond the Bioconductor project. BioC2026 welcomed 108 attendees, with 70 participating in person and 38 joining virtually. Across the three days, the programme featured three keynote speakers, 20 contributed speakers, and 17 sessions. The programme highlighted the breadth of biological research supported by Bioconductor, as well as the tools, infrastructure, and communities that enable open and reproducible computational biology. The conference was then followed by two community events on August 13-14: a Carpentries workshop on orchestrating large-scale single-cell analysis with Bioconductor and the Bioconductor North America 2026 Hackathon.
\nParticipants travelled to Seattle from across North America and beyond, reflecting the international reach of the Bioconductor community. Attendees joined from the United States, Canada, Germany, Ireland, the United Kingdom, Australia, and Nigeria. The audience included researchers at different stages of their careers, with staff representing 42% of attendees, followed by students and postdoctoral researchers at 28%, faculty at 13.8%, industry at 13.8%, and government at 2.4%.
\n\nThe conference featured three keynote speakers: - Jeff Leek speaking on \"Building Open Infrastructure for Translational AI in a Cancer Center\" - Ting Ye speaking on \"From Association to Causation: Genetic-Anchored Causal Inference in Human Biology\" - Michael Lawrence speaking on \"S7 for Bioconductor\"
\n
(Left to Right: Jeff Leek, Michael Lawrence and Ting Ye giving their keynotes at BioC2026)
\nTwenty contributed speakers shared their work across the conference programme. The short talks at BioC2026 reflected the breadth of research and development across the Bioconductor community. Topics ranged from community training and developer engagement to multi-omics exploration, reproducible workflows, and AI infrastructure. The programme also covered emerging areas of biological data analysis, including spatial transcriptomics, antibody repertoire analysis, rare variant testing, DNA methylation, structural variation, and Perturb-seq.
\nThe poster session showcased a diverse range of research applications and software development projects from across the Bioconductor community. Topics included single-cell and spatial transcriptomics, multi-omics integration, microbiome analysis, proteomics, antibody repertoire analysis, functional enrichment, ancestry inference, genomic data processing, and AI-guided drug discovery. The posters provided an opportunity for attendees to discuss new methods and tools in a more informal setting, while connecting researchers, package developers, and community members working across different areas of computational biology. The sessions also complemented the package demonstrations, creating space for participants to explore new software and engage directly with the people developing and applying Bioconductor tools.
\nFeedback from the conference showed that these interactions were particularly valuable, with attendees highlighting conversations around posters and package demonstrations as some of the most useful networking opportunities.
\n\nParticipants during the poster session.
\nThe celebrations marking Bioconductor's 25th anniversary continued at BioC2026 following the celebrations that began at EuroBioC2026 earlier in the year. The milestone was marked with a specially branded anniversary cake, thanking participants and community members for being part of the Bioconductor community and contributing to its growth over the past 25 years.
\n
Bioconductor at 25 celebrations.
\nOne of the strongest themes to emerge from the BioC2026 survey was the value of the Bioconductor community itself. Participants described the conference as welcoming, collaborative, and interactive, and appreciated the opportunity to reconnect with colleagues and meet new people. All respondents who answered the question said they would recommend BioC2026 to their colleagues. The smaller size of the conference was also seen as a strength. Several respondents noted that the more intimate format made it easier to engage in conversations and build connections across different roles and career stages. Networking was consistently identified as one of the most valuable aspects of the event. Participants appreciated both the formal scientific programme and the informal conversations that happened around talks, posters, and demonstrations. Looking ahead, many attendees expressed interest in even more dedicated time for networking and informal interaction.
\nDuring BioC2026, Zulip provided a dedicated space for participants to connect, interact and stay engaged throughout the conference. The conference space included topic-based discussions covering: - Workshops: Dedicated topics for individual workshops, including Single Cell, Bioconductor in Workflows, microbiome, WebR, and other training sessions. - Training & education: A space for broader conversations around training activities and educational sessions. - Social: Topics for informal conversations, social activities and connecting with other participants. - Introductions: Participants could introduce themselves and connect with others attending the conference. - Conference logistics channels: For travel, rooms, schedules, Day 1 information, and general conference support. - Community engagement: Dedicated spaces for the developer survey, student and early-career researcher activities, and other community initiatives. - Help and support: Dedicated topics such as the helpdesk, lost and found, and issues accessing virtual sessions.
\n
\nKayva Vaddadi was BioC2026's sticker design contest winner. Kayva's design celebrated Seattle as the host city of BioC2026, featuring well-known landmarks such as the Space Needle, the Seattle Great Wheel, Mount Rainier, and the surrounding evergreen forests. Puget Sound and a Washington State ferry reflect Seattle's maritime landscape and the culture of connection across the region. The BioC2026 hot air balloon on the sticker was inspired by the scenic balloon views popular in the Seattle area and represents the celebratory and exploratory spirit of the Bioconductor community.\n
\n\nSee the announcement here: https://lnkd.in/p/emAWMGX\n
\nAll conference recordings are available on the Bioconductor YouTube channel. BioC2026 workshops will also be available in the \"Archived\" section of workshop.bioconductor.org, alongside workshops from previous BioC, EuroBioC, and BiocAsia events. If you'd like to add additional materials or use the platform for your own workshop or course, contact us at support.bioconductor.org.
\nOn August 13\u201314, participants took part in a Carpentries workshop focused on orchestrating large-scale single-cell analysis with Bioconductor led by Ted Laderas and Wes Wilson. The workshop was attended by 11 participants and provided a hands-on environment for participants to work through approaches for analysing large-scale single-cell datasets using Bioconductor tools and workflows. Feedback highlighted the value of the live coding, small-group format and complementary teaching approaches.
\n
Participants during the scRNA-seq workshop.
\nThe Bioconductor North America 2026 Hackathon took place on August 13\u201314, bringing community members together to collaborate on software and infrastructure projects that strengthen interoperability, software development, and the wider Bioconductor ecosystem. These projects included: AI harness tools for standardizing how users within organizations would interact with agents on shared projects and on shared institutional resources. Link: https://github.com/eisenefaust/harness_agent
\nBioconductor tooling for including CLI executable components in Bioconductor packages. Link: https://blog.bioconductor.org/posts/2026-08-21-biocjobs/
\nAn exploration on benchmarking LLM generated workflows, building out infrastructure and paradigms for how we can evaluate what LLMs return as work product to scientific questions. Link: https://github.com/Amisor/LLM_BioBenchmarking/tree/main
\n\nParticipants during the Hackathon.
\nLater this year, the Bioconductor community will head to Melbourne, Australia, for BioCAsia2026, taking place on November 19-20, 2026. BioCAsia2026 brings together researchers, developers, and data scientists from across the Asia-Pacific region and beyond to share advances in bioinformatics, computational biology, and open-source software. The meeting will highlight practical training, community building, and the latest developments in R and Bioconductor. BioC2027 will then take place in early summer in Boston, Massachusetts, continuing the North American Bioconductor conference series and bringing the community together once again for scientific exchange, collaboration, and community building. The community will thereafter gather in Basel, Switzerland, for EuroBioC2027, taking place from September 8\u201310, 2027. The European Bioconductor Conference will continue to bring together researchers, developers, and educators from across Europe and beyond to share new software, methods, and applications in computational biology.
\nBioC2026 gratefully acknowledges the support of all sponsors and partners whose contributions and support made the conference possible.
\nBioC2026 was proudly supported by Cortex, R consortium and Posit
\n
We thank the local organisers, programme committee, workshop instructors, keynote speakers, volunteers, sponsors, and all participants whose contributions made BioC2026 a success.
\n\n\u00a9 2026 Bioconductor. Content is published under Creative Commons CC-BY-4.0 License for the text and BSD 3-Clause License for any code. | R-Bloggers\n
","doi":"https://doi.org/10.59350/z0c2k-0vh25","guid":"https://blog.bioconductor.org/posts/2026-09-30-BioC2026-recap/","image":"https://blog.bioconductor.org/posts/2026-09-30-BioC2026-recap/media/bioc2026_groupshot.jpg","language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","published_at":1790726400,"rid":"ae48k-gn675","summary":"The North American Bioconductor Conference 2026 (([BioC2026])(https://bioc2026.bioconductor.org/)) took place from August 10-12, 2026, at the Fred Hutchinson Cancer Center in Seattle, Washington. The conference brought together researchers, developers, educators, and community members to explore current developments within and beyond the Bioconductor project.","tags":["Bioconductor"],"title":"BioC2026 conference recap","updated_at":1790777730,"url":"https://blog.bioconductor.org/posts/2026-09-30-BioC2026-recap/","version":"v1"},{"authors":[{"affiliation":[{"name":"The Geneva Learning Foundation"}],"contributor_roles":[],"family":"Sadki","given":"Reda","url":"https://orcid.org/0000-0003-4051-0606"}],"blog":{"authors":null,"community_id":"7e26491f-41c6-4665-9088-5aa6643a1ba8","created":1731196800,"current_feed_url":null,"description":"Learning to make a difference","doi":"https://doi.org/10.59350/redasadki","favicon":"https://rogue-scholar.org/api/communities/7e26491f-41c6-4665-9088-5aa6643a1ba8/logo","feed_format":"application/atom+xml","feed_url":"https://redasadki.me/feed/atom/","filter":null,"generator":"WordPress","home_page_url":"https://redasadki.me/","issn":null,"language":"eng","license":"https://creativecommons.org/licenses/by/4.0/legalcode","prefix":"10.59350","relative_url":null,"secure":true,"slug":"redasadki","status":"active","subfield":"3304","title":"Reda Sadki","updated":1790771244,"use_api":true},"blog_name":"Reda Sadki","blog_slug":"redasadki","content_html":"This Q&A with TGLF's Reda Sadki is based on his remarks at the 26 September 2026 panel titled \"Will Cheaper AI Automate Everything? The Economics of AI, the Jevons Paradox, and the Outsourcing of Human Judgment\". The other panelists were Valerie Pelton, General Counsel, Tracked Biotechnologies LLC, United States; Dr. Craig Gibbs, Executive Manager, JET Education Services, South Africa; Fabrizio Degni, Chief AI Officer, Webuild, Italy; and Professor Avtandil Gagnidze, Professor and CEO, East-West University, Georgia. Tianze Zhang moderated the session.
\n\n\n\nIn 1865, William Stanley Jevons observed that more efficient steam engines increased the consumption of coal. The panel asked whether the same pattern now applies to machine intelligence: as the price of AI collapses, will organizations save money, or use far more AI, in more places, for more decisions? It also asked what happens to human judgment as AI moves from producing information to recommending decisions and acting on them.
\n\n\n\nThe discussion took place 11 days after the release of Jev, a proprietary \"decision model\", generated online interest. Jev is different from a large language model that generates text: it returns \"structured answers\": a choice from a defined set, a score against ordered levels, or a yes or no probability, each with a confidence score. Its maker claims it is 40 to 400 times cheaper than frontier models.
\n\n\n\nTwo facts about my daily work are relevant. The first is that in September 2026, I spent 90% of my workday working with TGLF's AI agents. The second is that work time was focused on listening to and supporting our core community, a network of over 80,000 health and humanitarian workers in 137 countries. So there is a dual reality here.
\n\n\n\nOn the community's side, \"cheaper AI\" means health professionals using the free, basic, whatever-is-accessible form of ChatGPT, without any kind of institutional or employer policy in place. Anything \"free\" signals that there is probably near-zero data protection. That creates potentially high risk, and it is incredibly difficult to measure, assess, or evaluate in any way. Some people have called that \"dark AI\" within organizations.
\n\n\n\nOn our side, we are trying to work transparently, and we announced our first agentic AI hire in March 2026. For us, as a nonprofit, lower token cost helps. We live every day looking at how much we are spending on tokens and what that is giving us in return. That is one price. But we are increasingly realizing that two other costs matter more: the cost of completing a task, and the cost of a result you can rely on. The cost of a reliable result can rise, because the cheaper it is to generate, the more there is to check.
\n\n\n\nFor the first few months of working with our AI co-worker, I spent most of my time designing and developing skills and writing scripts. Now it is increasingly verification and loops. That is what my job increasingly looks like, as chief executive officer of an agentic-AI-first organization.
\n\n\n\nIn July, Claude Cardot, our AI co-worker, refused to release two finished reports. A verification script had found 17 quotations that could not be matched against the raw data. This is important, because these are health workers sharing their experiences. So the AI co-worker stopped the assembly line. Fourteen were gaps in the checker itself. Three had been altered by the drafting model: a stray space, and a phrase that had gone through a small edit. Nothing crashed, but nothing warned us either. We describe this in our early learning from the insights pipeline.
\n\n\n\nMy takeaway is that cheaper AI lowers the cost of an answer, but it does not lower the cost of knowing that we have the right answer. And even scarier, when you project that forward, is what we call the validation tether.
\n\n\n\nMost will spend more, on more things. Part of that spending buys new value, and part of it is waste that goes unmeasured because it looks like productivity.
\n\n\n\nThe OECD Digital Education Outlook recently offered some interesting insights. Teachers in a pilot in Iceland said that with the use of AI, they are spending more time, not less. A tool that becomes cheaper and more accessible, in some places at least, before anyone has redesigned work around it, actually creates cost.
\n\n\n\nThe other question is for leaders in particular. If you are a chief executive, or a manager making decisions: is your organization, through its increased use of AI, getting better at something, or is it simply doing more of it? I think that is the key question.
\n\n\n\nYes, and the industry now says so openly. The newest model on the market is called Jev, after Jevons himself, because its founder expects cheaper machine intelligence to be deployed far more widely.
\n\n\n\nJevons observed that more efficient steam engines increased the consumption of coal. With AI, efficiency increases the consumption of compute, and it also increases the consumption of human attention. Every cheap output creates a small obligation for someone to read it, judge it, or act on it.
\n\n\n\nWhen a decision becomes 40 to 400 times cheaper, as TypeSafe claims for Jev, organizations will make many more decisions. Budgets rarely include the human time needed to govern them. The work that grows is review, supervision, and decision, which is what the agentic AI revolution means for workforce development.
\n\n\n\nThe work that becomes viable is work that was always valuable and never affordable at scale.
\n\n\n\nOur peer reviews were inconsistent. In one afternoon, we built a way to send every learner personalized feedback on their work the same day. No one would have paid a person to do that for thousands of learners, and now it happens by default.
\n\n\n\nListening changes in the same way. A single course produces hundreds of first-hand accounts from health workers, and a pipeline can now read every one of them rather than a sample. We can hear the rare voice that a sample would have missed. Frontline insights can help transform research and policy, if we are willing to listen. This is the role we set out in a global health framework for artificial intelligence as co-worker.
\n\n\n\nModels like Jev extend this to small decisions such as routing, sorting, flagging, and triage. The risk is that the cost of automation starts to decide what gets done.
\n\n\n\nThe outcome. In health, a wrong answer can cost a life, whatever it cost to produce. When we get health wrong, people die.
\n\n\n\nJev returns a probability and a confidence score with every answer, and its maker says it was trained to be calibrated against outcomes instead of human raters. A system that reports how sure it is can be audited, which is what I would want. But calibration is only as good as the outcomes it was measured against. Jev was trained on synthetic data, and a confidence score earned on synthetic cases tells you little about a district in Kinshasa.
\n\n\n\nOur rule is gates, not guidelines. A standard that matters should be a check that blocks the result until it passes, measured against real outcomes in the place where the decision is made.
\n\n\n\nBoth.
\n\n\n\nIn our own organization, we have used AI to replace functions that people once performed. People helped us work out how, and we then refocused a smaller team on work we cannot or do not want to automate. I described this in the business of artificial intelligence and the equity challenge.
\n\n\n\nI am wary of telling staff that AI is not coming for their jobs. It is coming for some of them, and this is part of a much larger shift in the future of the workforce. AI also creates a large volume of review and supervision, and the question is whether we prepare people for that work or leave them to discover it after the fact.
\n\n\n\nThe ones through which people learn to judge, and the ones where nobody yet knows the right question.
\n\n\n\nCheaper AI can lower the cost of an answer. But a model that cannot answer outside its own schema cannot tell you if the schema is wrong. There has been a lot of work in education on double-loop learning, and learning that does not question its governing values is unlikely to lead to meaningful change. So cheaper AI does not lower the cost of knowing the answer is right.
\n\n\n\nA model choosing between the options we supply will never tell us that mothers stopped bringing their children for vaccination because market day moved to Tuesday, because that option was never on the list. A health worker knows it. If she is not in the system, children die.
\n\n\n\nThat is why I keep coming back to the validation tether. Automation is safe as long as someone can still tell if the machine is wrong. In the industrial age, if you had widgets coming out of a factory, somebody needed to check the widgets, and there is a whole series of intellectual traditions around quality. That ability for someone to figure out whether it is right is the tether. It ties the machine's output back to human judgment.
\n\n\n\nHow do people develop that ability? Mostly by doing grunt work. We have all been junior professionals doing the routine work by ourselves, and that is the work getting automated first. So every time we make a short-term decision to save money because token costs are down and we can automate more, we weaken the long-term tether, because fewer people are learning to do the work well enough to check it. There is a whole debate in education and elsewhere about what that means for people in early adulthood, and for everyone who comes after them.
\n\n\n\nWhen we look at global health, that means that by around 2035 or 2040, there may be nobody left at the World Health Organization with the experience to understand what the guidelines, policies, and plans made at WHO headquarters in Geneva mean for a district health officer. Today's officer would spot something that is wrong, because she has walked the roads and built those plans by hand. What she knows, because she is there every day, hopefully makes it into the national ministry and trickles up to World Health Organization guidelines. That is now at risk. I explored this further in what AI reasoning means for global health.
\n\n\n\nWe have not yet found a coherent argument to make the case to organizations that are having their funding slashed, that are facing austerity measures, and that are facing incentives like the lower cost of AI, to make decisions now for what the world will look like in 2035 or 2040. That is one of the scariest things we are working on right now.
\n\n\n\nIt moves it, and it concentrates it.
\n\n\n\nWith a model like Jev, judgment moves into the schema. Someone decides which options and levels exist, and which questions get asked. Those choices are made once, far from the point of care, and then applied to every case that follows.
\n\n\n\nTypeSafe calls Jev a \"System One\" model, after Kahneman's fast, intuitive thinking. Kahneman's lesson was that System One is fast and often right, and that System Two catches its errors. So the question is who does the System Two work, and whether they will still be capable of it.
\n\n\n\nThe OECD Digital Education Outlook reports a randomized trial with medical students. Those given AI from the start of a semester performed no better than the AI working alone. It is only the students who first built their own clinical reasoning and then used AI who were able to outperform the machine. I think that is a key point. That is the state of things today, or at least when the study was done. It does not mean it will remain true six months from now. That is the pace of change, and the paradox of how we struggle to keep up. It is also why I have asked what happens when we delegate our thoughts to artificial intelligence, and why peer learning is critical to survive the Age of Artificial Intelligence.
\n\n\n\nI am sceptical of the phrase that decisions should always stay with humans. My standard is that someone can explain, check, and answer for every decision, whoever or whatever made it.
\n\n\n\nYou cannot send an AI agent to jail. Responsibility has to rest with the organization that deploys it, and in particular with whoever designed the questions the system is allowed to answer.
\n\n\n\nWe treat Claude Cardot, our AI co-worker, as a member of staff. She has a role description, a supervisor, a bounded scope, and short chains of action with human checkpoints. She has no authority to send anything outside the organization without human review.
\n\n\n\nModel providers are responsible for what they build and sell, including the claims they make about it. An organization that deploys a system without governance cannot hand its accountability to a vendor, and it should not pass that accountability down to the individual at the end of the chain.
\n\n\n\nBoth are possible, and dependence is the default.
\n\n\n\nA specialized agent for a knowledge worker in a wealthy organization may cost between 2,000 and 20,000 dollars a month. A district health worker gets a free chatbot built for consumer engagement. Many frontier tools are geo-locked, and you cannot get access to them in Kinshasa in the same way as in Geneva. I made this argument in When we get health wrong, people die.
\n\n\n\nCheap decision models add a new risk. The categories will be written in San Francisco and applied to people in Kinshasa who were never asked what the options should be. So the question is who defines the world the machine is allowed to see. The same question of who gets to represent whom runs through the debate on AI-generated \"poverty porn\", and the code alone cannot fix it.
\n\n\n\nAnother path exists. A system in Brazil reached more than 500,000 students with only teachers' phones and printed feedback. Its designers took on the burden of adaptation, so that no student devices or classroom internet were required. I described it in AI self-replacement.
\n\n\n\nIt goes underground.
\n\n\n\nAlmost everyone in the humanitarian sector is already using generative AI, very few disclose it, and only a small proportion of organisations have implemented policies governing its use. In a punitive organisational culture, disclosure is risky, so people hide their use. The organization learns nothing, and the risks to privacy and safety mushroom. Cheap decision models make hidden use easier, because automated judgments can be added to a system without anyone outside the team knowing.
\n\n\n\nI have not seen a specific study on this, but I wonder what proportion of the change is happening through institutional mandates and decisions, and how much of it is being driven by multi-trillion-dollar companies pushing us to use their products. I think that is an important question.
\n\n\n\nThen there is a paradox in our ability as humans. Our brains are not changing very fast, but the rate of change is accelerating. This week it is Jev. Last week it was something else. It keeps getting faster, and it is very difficult to keep up. Conversely, that makes long-term changes even harder to perceive. We know institutional time: you are lucky if your organization has a one-year or two-year plan, and meanwhile a constantly changing external environment forces organizations to behave differently. We have explored what this might mean for the future of the World Health Organization (WHO) and other global agencies.
\n\n\n\nTo explore possible solutions, we need to think through what kind of learning culture we need, one in which people can say how they use AI, what worked, and what went wrong, without being punished for it. People do not disclose to a governance system they fear, so that system cannot see what it is meant to govern.
\n\n\n\nThere is a lot of talk among people in AI labs about \"evals\", a technical term for how you can tell if AI output is valid. Internally, we have thought through what we should be measuring. We think there are four things, and three of them are completely outside the AI model itself.
\n\n\n\nFor an organization, thinking through what we want to measure, and how we know how we are doing, is a critical question.
\n\n\n\nFor a Minister of Health, there is a real urgency to invest resources in artificial intelligence. Your people are already using it, probably very badly. Priorities should be to make that use visible, and adjudicate what to support. That leads to governance, but of a new kind, because of the pace of change. And that is actually what I would emphasize.
\n\n\n\nAs I listen, I think about how hard it is to see beyond the confines of where we are now, given the pace of change.
\n\n\n\nOne year ago, I remember speaking with global health experts who said they had tried AI, but that there was no way ChatGPT was going to start producing outputs close to what they were able to do. They are no longer as certain when I speak with them, even though they are not event using skills or loops yet. Right now, the defence is that governance and accountability belong to us. I would challenge that as well. Decisions are exactly what the new \"Jev\" is targeting. In four hours, with 100 kilobytes of text files and agentic AI, I was able to recreate the president of my board as an agent, and get responses that were initially off kilter but quickly improved to become more than 80% recognizably her.
\n\n\n\nThe clich\u00e9 about change being the only fixed constant applies. So we have to be careful about how long the assumptions we are making will hold, how they can be tested, and whether they keep any validity given their half-life in a world that is changing this fast.
\n\n\n\nAI is already cheap enough that its makers name their models after Jevons. What we do not yet know is whether we will still have people who can tell when it is wrong, and whether those people will be in Kinshasa as well as in Geneva.
","doi":"https://doi.org/10.59350/gad25-v2620","guid":"https://redasadki.me/?p=24279","image":"https://redasadki.me/wp-content/uploads/2026/09/aifod-panel.jpg","language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","published_at":1790726400,"rid":"h92v9-e2z40","summary":"This Q&A with TGLF's Reda Sadki is based on his remarks at the 26 September 2026 panel titled \"Will Cheaper AI Automate Everything? The Economics of AI, the Jevons Paradox, and the Outsourcing of Human Judgment\". The other panelists were Valerie Pelton, General Counsel, Tracked Biotechnologies LLC, United States;","tags":["The Geneva Learning Foundation","Artificial Intelligence","Digital Divide","Double-loop Learning","Equity"],"title":"What does the Jevons Paradox mean for artificial intelligence in global health?","updated_at":1790771717,"url":"https://redasadki.me/2026/09/30/what-does-the-jevons-paradox-mean-for-artificial-intelligence-in-global-health/","version":"v1"},{"authors":[{"contributor_roles":[],"family":"Eden","given":"Terence"}],"content_html":"I am a feminist. I believe that men and women should be given equal opportunities and that - as far as possible - we should have a civic society composed of equal numbers of men and women.
\n\nAt least... I think that's how I feel.
\n\nMost people believe that they are rational and live up to their own ideals. But can we ever really know our own subconscious biases?
\n\nThe Geena Davis Institute on Gender in Media have released a series of study about gender biases. One of the more interesting findings was that when the male:female ratio in a group is 70:30 - both genders think the group is at parity. This is due, in part, to popular entertainment sticking almost rigidly to these gender ratios.
\n\nI honestly believe that I follow a roughly equal number of men and women on Twitter. I certainly see a lot of women in my feed. But that's just a feeling - what do the data say?
\n\nI decided to look at two different aspects of my Twitter usage.
\n\nThe most obvious limitation is that not every tweeter is a person - some are faceless corporations. Some company accounts are definitely gendered - take Cosmo magazine for example - but I think it's easiest to separate them out.
\n\nThose tweeters who are people may not choose to reveal their gender - or it may not be immediately obvious. I don't intend asking people directly what their gender identity is.
\n\nFinally, there are many other ways I could slice this pie. I could look at race, gender, political leaning, etc - but gender is the most visible and therefore easiest to identify.
\n\nI'm basing my assessment on the user's name and their photo.\n\nIn this example, I've no easy way of knowing whether @Lateral is male or female. Sorry!
I did consider using computer vision recognition and running the names through a stochastic model - but I'm off sick today so decided to manually count each follower. For something like this, the human brain is faster than installing and configuring software!
\n\nSo, for this investigation, I'm dividing the tweets that I see into Male, Female, Unknown, Corporate.
\n\nAs of now, I follow 1,158 Tweeters. At first glance, that's a ridiculous number. There's no way I can have any form of meaningful interaction with that many people. What that doesn't reveal is that many of those accounts are dormant or seasonal. Some, no doubt, I have no need to follow. But since Twitter killed TwitCleaner it's impractical to go through all those old accounts and remove the deadwood.
\n\nI manually looked through the 300 accounts I most recently followed. I was fairly confident that it would show want an equal opportunity tweeter I was. You're welcome to look at my Twitter friends directly, if you'd like to verify the data.
\n\n| Gender | Number |
| Male | 130 |
| Corporate | 87 |
| Female | 80 |
| Unknown | 3 |
Youch. I follow more corporate accounts than women. And the M:F split? 62: 38. Not too far off the 70:30 split.
\n\nHow depressing! There I was, thinking that I was breaking the mould - when instead I am, basically, a little better than average.
\n\nBecause Twitter doesn't let me retrieve all my followers at once, I had to split my research into batches of 300. After looking at my 600 most recent friends, I was even more depressed.
\n\n| Gender | Number |
| Male | 227 |
| Corporate | 179 |
| Female | 140 |
| Unknown | 3 |
Exactly the bloody same ratio! True, some of the corporate accounts are likely to be run by women... but I think that's just clutching at straws.
\n\nI think the m:f ratio here is interesting because it's accounts I've chosen to follow. For some reason, my brain only finds women about half as worthwhile following as men. I think that's troubling.
\n\nAfter going through all 1,158 accounts I follow (I was off sick today - what else was going to fill my time?) here are the final results.
\n\n| Gender | Number |
| Male | 582 |
| Corporate | 323 |
| Female | 245 |
| Unknown | 7 |
Oh. Em. Gee. Honestly, I didn't fiddle the data - take a look at my friend list. But that's a Male:Female ratio of exactly 70:30.
\n\nI've gone from thinking I was a new wave feminist man to realising I'm just part of the patriarchy. Ok, maybe not that dramatic, but I honestly thought that I was following a much more gender balanced crowd than that.
\n\nI may not be following lots of women - but perhaps I'm seeing lots of their tweets?
\n\nOf course, these women could be tweeting a lot more than the men. And, perhaps, men could be retweeting a lot of women.
\n\nPerhaps.
\n\nTwitter's tools only let me download the last ~800 Tweets I've seen. Twitter have no interest in letting you travel further back in time than that, which is a shame. Today - a Monday - that's around 4 hours worth of Tweets. Normally I don't read them all, nor do I scroll back extensively through my timeline to catch up on what I've missed.
\n\nWith more time - and a more cooperative Twitter API - I could take a look through the last month. But it is what it is.
\n\n| Gender | Number |
| Male | 345 |
| Corporate | 165 |
| Female | 136 |
| Unknown | 19 |
No need to grab a pocket calculator. That Male:Female ratio is stuck at a stubborn 72:28.
\n\nI've no way of knowing how representative a sample this is. Obviously the ratio will ebb and flow depending on the time of day and what's happening in the world. I could also de-duplicate the results and try to find out how many unique voices I hear.
\n\nFirstly, these data are really only representative of me. I've not included anyone else in this investigation.
\n\nSecondly, it's really important to take an impartial look at your own behaviours. I would have sworn up and down that I followed a a better ratio than 70:30.
\n\nThirdly, there are many different ways to measure bias. For example, looking at who people retweet - although, even by Twee-Q's measure I'm still a 70:30 guy.
\n\nFourthly, men and women both use Twitter in roughly equal numbers - so I can't blame the platform for my performance.
\n\nWe can't control gender equality in politics, TV, education, or employment - but we can control the number of distinct voices we choose to expose ourselves to.
\n\nTake a moment to look at your social circle - whether it's online or not - and see if you could make some adjustments to increase your daily dose of diversity.
\n\nIn the meantime, here's a funny video with Geena Davis.
\n\n","doi":"https://doi.org/10.59350/njw6c-17p78","guid":"http://shkspr.mobi/blog/?p=8757","image":"https://shkspr.mobi/blog/wp-content/uploads/2013/09/Male-and-Female-Twitter-fs8.png","language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","published_at":1380499200,"rid":"n6yj0-48189","summary":"I am a feminist. I believe that men and women should be given equal opportunities and that - as far as possible - we should have a civic society composed of equal numbers of men and women. At least... I think that's how I feel. Most people believe that they are rational and live up to their own ideals. But can we ever really know our own subconscious biases?","tags":["/etc/","Feminism","Gender","Twitter"],"title":"Assessing My Personal Gender Bias on Twitter","updated_at":1790762492,"url":"https://shkspr.mobi/blog/2013/09/assessing-my-personal-gender-bias-on-twitter/","version":"v1"},{"authors":[{"contributor_roles":[],"family":"Lepore","given":"Mauro","url":"https://orcid.org/0000-0002-1986-7988"}],"blog":{"authors":[{"name":"The rOpenSci Team"}],"community_id":"9611c467-6ac2-4091-8cd8-b90e883974f3","created":1780876800,"current_feed_url":null,"description":"Open Tools and R Packages for Open Science","doi":"https://doi.org/10.59350/ropensci_es","favicon":"https://rogue-scholar.org/api/communities/9611c467-6ac2-4091-8cd8-b90e883974f3/logo","feed_format":"application/feed+json","feed_url":"https://ropensci.org/es/blog/index.json","filter":null,"generator":"Hugo","home_page_url":"https://ropensci.org/es/blog","issn":null,"language":"spa","license":"https://creativecommons.org/licenses/by/4.0/legalcode","prefix":"10.59350","relative_url":null,"secure":true,"slug":"ropensci_es","status":"active","subfield":"1710","title":"rOpenSci - herramientas abiertas para una ciencia abierta","updated":1790685032,"use_api":null},"blog_name":"rOpenSci - herramientas abiertas para una ciencia abierta","blog_slug":"ropensci_es","content_html":"rOpenSci cumple 15 a\u00f1os y yo fui parte de esta comunidad durante gran parte de ese tiempo. Al reflexionar sobre esos a\u00f1os, me vinieron a la mente muchos recuerdos lindos, y algunos me dejaron lecciones que quisiera compartir.
A principios de 2018 me top\u00e9 con este anuncio (traducido):
\u00a1Postulate para asistir a rOpenSci unconf 2018!
Estamos organizando unconf18 para reunir a personas que trabajan en ciencia y desarrollo, y a entusiastas de los datos abiertos de la academia, la industria, el gobierno y organizaciones sin fines de lucro, para pasar un par de d\u00edas trabajando en varios proyectos y, en general, enriquecer nuestra comunidad.
En retrospectiva, yo encajaba bien. Reci\u00e9n hab\u00eda pasado de la academia a mi primer trabajo como ingeniero de software de investigaci\u00f3n. Y sin embargo, \u00bfme sent\u00eda un candidato s\u00f3lido? La verdad que no; ten\u00eda un mont\u00f3n de dudas.
Pero s\u00e9 que las mejores cosas suelen pasar cuando uno se estira un poco por fuera de su zona de confort. As\u00ed que me postul\u00e9 de todos modos, me invitaron, y pas\u00e9 dos d\u00edas colaborando con un grupo incre\u00edble de personas. Algunas ya eran estrellas, muchas otras luego se volvieron influyentes en su propio nicho, y todas ten\u00edan una actitud positiva que en ese momento no ten\u00eda palabras para describir. Ahora s\u00ed: eran respetuosas y amables.
En 2022 rOpenSci lanz\u00f3 la primera cohorte del Programa de Campeones. Para entonces ya ten\u00eda algunos a\u00f1os de experiencia como editor asociado de la revisi\u00f3n de software por pares de rOpenSci, y Yani me invit\u00f3 a hablarles a nuestros campeones y campeonas sobre el proceso.
Mientras preparaba esa charla y discut\u00eda algunas ideas con Yani, ella me hizo notar la gran importancia que la comunidad de rOpenSci pone en la forma en que nos comunicamos1.
Tanto es as\u00ed que las gu\u00edas para quienes revisan y editan los paquetes abren con este mensaje:
La comunidad de rOpenSci es lo m\u00e1s importante. Nuestro objetivo es que las revisiones sean abiertas, no conflictivas y con el objetivo de mejorar la calidad del software. \u00a1S\u00e9 amable! y comportate con respeto. Consulta nuestra gu\u00eda para quienes realizan una revisi\u00f3n y el c\u00f3digo de conducta para m\u00e1s informaci\u00f3n.
Viniendo de una gu\u00eda tan profundamente t\u00e9cnica, esa apertura quiz\u00e1s te sorprenda. Pero para m\u00ed ahora tiene sentido: el c\u00f3digo roto es mucho m\u00e1s f\u00e1cil de arreglar que las relaciones humanas rotas.
En rOpenSci pod\u00e9s contribuir de muchas maneras. Tenemos una gu\u00eda de contribuci\u00f3n comunitaria pero ac\u00e1 va una lista de algunas de mis propias contribuciones:
Como ves, la mayor\u00eda de mis contribuciones no involucraron c\u00f3digo, y muy pocas requirieron una invitaci\u00f3n. Algunas son el tipo de cosas que podr\u00edas agregar a tu CV, y las m\u00e1s importantes solo pertenecen a tu coraz\u00f3n.
Las comunidades buenas est\u00e1n hechas de gente buena, y vos pod\u00e9s jugar un rol activo en hacerla crecer en la direcci\u00f3n que quieras. Si conoc\u00e9s a alguien que encajar\u00eda bien en la comunidad de rOpenSci y la disfrutar\u00eda, pod\u00e9s buscar una forma de acercarla. Por ejemplo, yo anim\u00e9 y ayud\u00e9 a personas a enviar y revisar paquetes. Adem\u00e1s, como editor, ocasionalmente tengo el privilegio de nominar a otras personas para que se unan al equipo editorial. Usando este superpoder atraje a dos personas al equipo editorial y ahora disfruto de interactuar con esas personas con bastante regularidad.
La IA est\u00e1 cambiando la forma en que contribuimos al software de c\u00f3digo abierto. Pero no sabemos mucho sobre su efecto en las comunidades abiertas alrededor de ese software. A principios de este a\u00f1o rOpenSci public\u00f3 un conjunto preliminar de pol\u00edticas, y quiz\u00e1s quieras seguir el newsletter de rOpenSci y esta conversaci\u00f3n comunitaria de Openscapes (traducido):
Comunidades abiertas en la era de la IA
Las {comunidades abiertas} nos ayudan a conectar a nivel humano alrededor de la ciencia, los datos y las cosas que nos apasionan. Cambian carreras, cambian vidas. (\u2026) Pero ahora, las personas no necesariamente est\u00e1n encontrando sus comunidades ni obteniendo sus beneficios de la forma en que sol\u00edan hacerlo, porque pueden obtener ayuda y respuestas m\u00e1s f\u00e1cilmente usando IA. (\u2026) Estas conexiones y comunidades son m\u00e1s importantes que nunca, ya que necesitamos encontrar formas de apoyarnos mutuamente mientras la IA cambia r\u00e1pidamente el panorama justo debajo de nuestros pies.
Personalmente, durante el \u00faltimo a\u00f1o us\u00e9 IA intensamente y aprend\u00ed varias lecciones. La m\u00e1s relevante ac\u00e1 es que no quiero que la IA se haga pasar por m\u00ed ni por la otra persona en una conexi\u00f3n de humano a humano. Las personas de rOpenSci son reales y maravillosas. Ac\u00e1 hay algunas que vi en persona recientemente:
Noam Ross, Yanina Bellini Saibene y yo en la reuni\u00f3n de CZI de 2024 en Boston, EE. UU.
Monica Gerber, Stefanie Butland, Sean Kross, Julia Stewart Lowndes, Kelly O'Briant y yo en la conferencia Posit de 2024 en Seattle, EE. UU.
Luis D. Verde Arregoitia, Diana Garcia Cortes y yo en el Acuario de Georgia despu\u00e9s de la conferencia Posit de 2025 en Atlanta, EE. UU.
Ronny A. Hern\u00e1ndez Mora y yo en una reuni\u00f3n familiar de Ronny cerca de San Jos\u00e9, Costa Rica en 2025.
Beatriz Milz y yo despu\u00e9s de un caf\u00e9 con pastelitos en S\u00e3o Paulo, Brasil en 2025.
Gracias por acompa\u00f1arme a celebrar rOpenSci. Y si, como yo, te identific\u00e1s con nuestra comunidad, \u00a1entonces feliz cumplea\u00f1os para vos tambi\u00e9n!
Dos libros que me gustan son C\u00f3mo Ganar Amigos e Influir sobre las Personas y Conversaciones Cruciales.\u00a0\u21a9\ufe0e
rOpenSci turns 15 and I've been a part of this community for a good chunk of that time. As I reflected on those years, a lot of great memories came to mind, and I realized some of them taught me some lessons that I would love to share.
Early in 2018 I came across this post:
Apply to attend rOpenSci unconf 2018!
We're organizing unconf18 to bring together scientists, developers, and open data enthusiasts from academia, industry, government, and non-profits to get together for a couple of days to hack on various projects and generally enrich our community.
In hindsight I was a good fit. I had recently transitioned from academia to my first job as a research software engineer. And yet, did I think I was a solid candidate? Oh no, I had lots of doubts.
But I know the greatest things often happen a little stretch outside your comfort zone. So I applied anyway, got invited, and spent two days collaborating with an incredible bunch of people. Some of them were already rockstars, many others eventually became influential in their own niche, and all of them had this great attitude that at the time I lacked words to describe. I now do: they were respectful and kind.
In 2022 rOpenSci launched the first cohort of the rOpenSci Champions Program. By then I already had a few years of experience as an associate editor of rOpenSci software peer-review, and Yani invited me to talk to our champions about the process.
As I prepared that talk and discussed some ideas with Yani, she made me realize how much the rOpenSci community cares about the way we communicate with one another1. So much so that our guides for reviewers and editors open with this message:
rOpenSci's community is our best asset. We aim for reviews to be open, non-adversarial, and focused on improving software quality. Be respectful and kind! See our reviewers' guide and code of conduct for more.
Coming from such a deeply technical guide, that opening might surprise you. But it makes sense to me now; broken code is way easier to fix than broken human relationships.
At rOpenSci you can contribute in so many ways. We have a community contributing guide but here's a list of some of my own contributions:
As you can see, most of my contributions did not involve any code, and only a few required an invitation. Some of them are the kind of thing you could add to your CV, and the most important ones only belong in your heart.
Great communities are made of great people, and you can play an active role in growing it in the direction you want. If you know someone that would fit in and enjoy the rOpenSci community, you can try to find a way to bring them in. For example, I've encouraged and helped people to submit and review packages. Also as an editor I have the occasional privilege to nominate other editors. Using this super-power I've attracted two editors and I now get to enjoy interacting with them quite regularly.
AI is changing the way we contribute to open source software. But we don't know much about its effect on the open communities around that software. Earlier this year rOpenSci published a preliminary set of policies, and you may want to watch rOpenSci's newsletter and this Openscapes community call:
Open Communities in the Age of AI
{Open communities} help us connect on a human level around science and data and the things that we are passionate about. They change careers, they change lives. (\u2026) But now, people are not necessarily finding their communities and getting the benefit of those communities in the way that they used to, because they're more easily able to get help and answers by using AI. (\u2026) These connections and communities are more important than ever, as we need to find ways to support each other and ourselves as AI rapidly changes the landscape right under our feet.
Personally, over the past year I've used AI heavily and learned several lessons. The most relevant here is that I don't want AI to impersonate me or the other person in a human-to-human connection. The humans of rOpenSci are real and wonderful people. Here are some that I've recently met in person:
Noam Ross, Yanina Bellini Saibene and me at the 2024 CZI meeting in Boston, USA.
Monica Gerber, Stefanie Butland, Sean Kross, Julia Stewart Lowndes, Kelly O'Briant and me at the 2024 Posit conference in Seattle, USA.
Luis D. Verde Arregoitia, Diana Garcia Cortes and me at the Georgia Aquarium after the 2025 Posit conference in Atlanta, USA.
Ronny A. Hern\u00e1ndez Mora and me at Ronny's family gathering near San Jos\u00e9, Costa Rica in 2025.
Beatriz Milz and me after coffee and pastries in S\u00e3o Paulo, Brazil in 2025.
Thanks for joining me in celebrating rOpenSci. And if, like me, you self-identify with our community, then happy birthday to you!
Two books I like are How to Win Friends and Influence People and Crucial Conversations (Third Edition): Tools for Talking When Stakes Are High.\u00a0\u21a9\ufe0e
An Online-Schulungsmaterialien mangelt es sicherlich nicht. Das Problem besteht jedoch meist darin, das richtige Material zu finden.
\nVielleicht bist Du auf der Suche nach einer Einf\u00fchrung in das Forschungsdatenmanagement, einem Kurs zur Ver\u00f6ffentlichung von Sammlungsdaten, einer Anleitung zu Metadatenstandards, einem Tutorial zur Georeferenzierung oder vielleicht nach etwas ganz Spezifischem zu Digitalisierungsabl\u00e4ufen. Eine Suche f\u00fchrt schnell zu Dutzenden von Websites, Repositorien, Videos, Dokumentationsseiten und Online-Kursen.
\nWelches davon ist f\u00fcr naturwissenschaftliche Sammlungen oder Deine Bed\u00fcrfnisse tats\u00e4chlich relevant?
Handelt es sich um einen Einf\u00fchrungs- oder einen Fortgeschrittenenkurs?
Ist es ein Video, ein Tutorial oder ein l\u00e4ngerer Kurs?
Ist es auf Englisch oder Deutsch verf\u00fcgbar?
Und was f\u00fcr alle, die Lehrmaterialien wiederverwenden m\u00f6chten, vielleicht am wichtigsten ist: Wie sieht die Lizenz aus?
Irgendwann f\u00fchlt sich die Suche nach Schulungsmaterialien fast schon wie ein eigenes Forschungsprojekt an.
\nIm Rahmen des Wissensmanagements im WiNoDa-Projekt haben wir eine kuratierte \u00dcbersicht \u00fcber Online-Kurse, Schulungsmaterialien und offene Bildungsressourcen (OER) erstellt, die f\u00fcr naturwissenschaftliche Sammlungen relevant sind.
\nAnstatt eine weitere lange Liste mit Links zu erstellen, haben wir die Ressourcen in einer Tabelle zusammengefasst, damit sie leichter verglichen werden k\u00f6nnen.
\nDie Sammlung umfasst derzeit unter anderem folgende Themen:
\nF\u00fcr jede Ressource enth\u00e4lt die Tabelle Informationen wie den Titel, eine kurze Beschreibung, den Anbieter, das Erscheinungs- oder Aktualisierungsjahr, das Format, die Sprache, die Lizenz und einen direkten Link zum Originalmaterial.
\nDas Ziel ist einfach: Du solltest die Tabelle \u00fcberfliegen und relativ schnell entscheiden k\u00f6nnen, ob eine Ressource f\u00fcr Dich n\u00fctzlich sein k\u00f6nnte.
\nDie Sammlung enth\u00e4lt Materialien verschiedener Organisationen und Forschungsinfrastrukturen, die sich mit Biodiversit\u00e4t, Sammlungen, Kulturerbe und Forschungsdaten befassen.
\nSo umfasst die Liste beispielsweise Ressourcen von GBIF, iDigBio, DiSSCo, TDWG, NFDI4Biodiversity, NFDI4Objects, DARIAH, digiS und der Society for the Preservation of Natural History Collections (SPNHC).
\nDie Themen sind ebenso vielf\u00e4ltig.
\nDu findest Materialien \u00fcber Datenpublikation mit GBIF, Darwin-Core-Standards, Digitalisierungsabl\u00e4ufe, Georeferenzierung, persistente Identifikatoren, Normdaten, Bildmetadaten, Dateiorganisation, Forschungsdatenmanagement, sensible Artendaten und den Umgang mit r\u00e4umlichen Daten.
\nEgal, ob Du eine*r Forscher*in bist, der*die eine bestimmte Aufgabe im Datenmanagement verstehen m\u00f6chte, ein*e Kurator*in, der*die mit Sammlungsdaten arbeitet, ein*e Datenverwalter*in, der*die nach empfehlenswertem Material sucht, oder jemand, der*die eine eigene Schulung vorbereitet \u2013 in der Tabelle k\u00f6nnte sich etwas N\u00fctzliches verbergen.
\nEin Detail, das wir bewusst aufgenommen haben, sind die Lizenzinformationen.
\nEine Ressource, die im Internet frei eingesehen werden kann, ist nicht automatisch eine offene Bildungsressource (OER), die kopiert, ver\u00e4ndert und wiederverwendet werden darf.
\nAus diesem Grund erfassen wir eine Lizenz nur dann, wenn sie vom Anbieter ausdr\u00fccklich angegeben wird. Wenn keine Lizenz identifiziert werden konnte, wird dies in der Tabelle vermerkt, und es liegt am Nutzer, weitere Entscheidungen zu treffen. Dies mag wie ein kleines Detail erscheinen, wird jedoch ziemlich wichtig, wenn Du nicht nur nach Lernmaterial suchst, sondern auch nach Material, das Du f\u00fcr Deinen eigenen Kurs oder Workshop anpassen m\u00f6chtest.
\nDie Sammlung ist nicht als vollst\u00e4ndiger Katalog aller jemals ver\u00f6ffentlichten Informationen zu naturwissenschaftlichen Sammlungsdaten gedacht. Vielmehr soll sie als kuratierter Ausgangspunkt dienen. Die \u00dcbersicht kann erweitert werden, sobald n\u00fctzliche Ressourcen identifiziert, neue Materialien ver\u00f6ffentlicht und bestehende Ressourcen aktualisiert werden.
\nEntdecke die gesamte Sammlung in der GFBio-Wissensdatenbank:
\nEntdecken Sie die gesamte Sammlung in der GFBio-Knowledge Base:
Natural Science Collections: online courses and Open Educational Resources
It's been a busy month, both in my own life (I landed a new grant! I started teaching!) and elsewhere in my world of science, AI, biology, and academic life. Tomorrow I'm flying down to Mexico City for a week to unplug and recharge. You won't see much here for a few weeks, but in the meantime, here's what I've been reading this month \u00a1Nos vemos pronto!
The AI Discourse in Academia is Toxic, By
I am writing because this response fits a broader pattern in how academia now reacts to anything AI-adjacent: a toxic, knee-jerk hostility to AI in which the specific tool, application, or question all disappear into a general presumption that the whole category is terrible. [\u2026] The only serious position on AI in science is nuanced and case by case; any answer to \"is AI good for science\" that neatly fits into a single yes or no is unserious. [\u2026] You might think, \"OK, stopping the AI discussion is actually good for science!\" But your toxic reaction to anything AI does not stop the conversation from happening. It only takes you out of it. The conversation continues without you elsewhere. [\u2026] So here is an invitation to join the real discussion: you are allowed to have a nuanced opinion about AI.
The Weimar of Knowledge, by Giorgio Gilestro
Open science was, at bottom, a bet that if you removed the paywalls and the gatekeepers, the work would find its readers on its own merits. A reputation economy finally voids that bet. Free access is worthless when the binding constraint is not access but attention, and attention is allocated by brand. To be honest, we are already seeing that trend and AI will only accelerate. We will have open archives that nobody reads and closed reputational cartels that everybody does. That is a worse settlement than the one we are leaving, and it would be dishonest to present it as an equilibrium anyone should welcome.
Universities are failing this AI moment, by
Universities are choosing to see AI as a slow-moving policy question rather than a fast-moving structural emergency. [\u2026] Universities are slow by design, and that design has served the durability of ideas well for hundreds of years. However, for the current moment, this glacial pace of change is working against them. For AI, universities should adopt a more agile approach to create structural change that can actually keep up with the technology. Or, they can form another committee that will produce another report a year from now to sit on a shelf while the world moves past them.
Evaluating Superhuman Biological Capabilities, by at
Leading AI models outperform experts at nearly every biosecurity-relevant capability we measure. They can provide wet-lab troubleshooting assistance and perform computational DNA synthesis design tasks. They score three times higher than world-class experts on highly technical biology questions. Up to the human frontier, those scores mean something concrete. We know what biologists of that caliber can do in a lab, so a model that matches them can plausibly help with the same work. Past that point, a higher score says the model has improved at biology, but it doesn't tell us what that improvement means for real-world biological risk. Evaluation will need anchors that don't depend on human verification. But for misuse-relevant biology, testing the skill itself would mean building the dual-use biological artifact we're trying to prevent. Instead, we'll have to gather evidence indirectly, relying on narrow, verifiable tasks and uplift in adjacent domains.
Researchers Say They've Found the Truth About Thomas Jefferson, by Andrew Lawler
With the permission of the Thomas Jefferson Foundation, the geneticists examined the hair strands and found viable DNA. \"We'll exhaust it and sequence the crap out of it,\" [Beth] Shapiro assured Kurin in 2017. Lab workers gradually assembled a library of Jefferson's genome. \"We went from a long-shot fishing expedition to a slam dunk,\" [Ed] Green said. \"From then on, it was pretty straightforward. We spend a lot of time getting DNA out of difficult things like hair.\"
Claude discovers a novel enzyme system, by Anthropic
Today, we're sharing early results from one of our first research programs, in which Claude autonomously discovered a novel enzyme system that is associated with an array of DNA repeats, a pattern reminiscent of CRISPR. Although we don't yet know its function, the system that Claude discovered has a set of characteristics that have only ever been found together in a handful of other systems, all of which are programmable and perform operations like cutting, copying, and pasting DNA. Beyond CRISPR, which has already transformed science and medicine, several other such systems are now in development as promising tools.
Did Anthropic's A.I. Really Make a Scientific Discovery on Its Own? by Carl Zimmer
When Anthropic [\u2026] unveiled findings from its new biology lab last week, its scientists claimed to have used A.I. agents to discover new enzymes with promise for biotechnology. [\u2026] Then Mario Rodr\u00edguez Mestre, a computational biologist, said this weekend that he and his colleagues had been studying the enzymes and their associated molecules \u2014 which Anthropic calls ARTs \u2014 for four years. Dr. Mestre and his colleagues have yet to publish their findings. But for the past three years, they have regularly used Anthropic's A.I. models as they have written code, drafted manuscripts and performed other tasks. In doing so, Dr. Mestre said he and colleagues shared key findings about the enzymes with Anthropic.
A.I. Queries From Abroad Raise Fears of a Biological Weapons Race, by Carl Zimmer
Now chatbots might consult the scientific literature to answer questions that a biological weapons-maker would have. It's a task at which A.I. can excel, Dr. Inglesby said, providing far more to bad actors than they could gather with simple web searches. \"It aims for the information to be usable,\" he said of A.I. \"It allows users to go back and forth infinite number of times to troubleshoot and explain in more detail, while providing just the right technical resources to back things up.\" [\u2026] \"How a Ph.D. student can individually misuse a model for harm is only one small portion of the problem,\" Dr. Inglesby said. Dr. Endy worried that if governments start using A.I. for biowarfare research, they might help rekindle an arms race like the one during World War II.
Embryo screening and the new reproductive divide, by Gaia Ghirardi, Arno Van Hootegem & K. Paige Harden in Nature Human Behaviour
Instead of needing a village to raise a child, PGT-P's commercial logic suggests it takes only sufficient capital and the right consumer decisions to conceive a child who will not need the solidarity of the village to be healthy. [\u2026] The central question is, therefore, whether societies should accept a future in which reproductive opportunities, parental obligations and children's life chances are increasingly organized around genetic optimization, private purchasing power and corporate profit. In our view, leaving PGT-P to the market is not a neutral policy choice, but an active decision to allow commercial actors to shape the future of reproduction.
AI-Driven Biology Is Only as Good as the Data Beneath It, by and Patrick Boyle
The genomics community has long championed open data. The next step is to champion trustworthy data sharing so that the future of digital biology and AI\u2011enabled biology is built on traceable, defensible, and reusable genomic records, rather than on an assumption of trust.
Introducing VCT-v2 \u2014 the updated Virology Capabilities Test, by and at
Benchmark creation should be treated not as a one-off project but rather as an ongoing effort with a defined re-audit cadence. Model and expert performance data exist only once a benchmark has been built, and they carry per-question information about its quality. [\u2026] Biosecurity-relevant benchmark performances are often presented as a single accuracy value. Yet, two models achieving the same accuracy on a benchmark are not necessarily identical, and reporting accuracy alone conceals a lot of information. We are working on applying item-response theory to better understand model performances on our benchmarks.
US Army Biodefense Strategy 2026
\u2026biodefense requires unity of command and unity of effort, while employing capabilities and combined arms integration at echelon. Yet historically, biodefense has been the domain of specialized medical and chemical, biological, radiological, and nuclear (CBRN) personnel. [\u2026] Victory in future conflicts requires an Army that treats biodefense not as a secondary concern, but as a core warfighting imperative.
Incorporating Microeukaryotes into Biosecurity, by at
Unlike many viruses and bacteria, microeukaryotes often feature complex life cycles and diverse interactions with multiple hosts and vectors. This creates distinct opportunities for AI to contribute across the bioweapon development pathway, spanning the identification and characterization of biological agents, their development and optimization, and ultimately their deployment.
A.I. Slopware Is Everywhere Now. Nobody Is Using It, by Paul Ford
\"For a while, I must admit, it looked as if software developer roles like mine were done for. [...] But our industry is slowly realizing that making truly cutting-edge software still requires humans to think and work together, to maximize their skill sets and to practice their respective crafts.\"
How contemporary academic structures constrain scientific creativity and hold back early-career researchers (read free), by Haubrock et al, in Nature Human Behaviour
We argue that contemporary scientific systems progressively constrain high-risk and conceptually innovative research while being increasingly structured around short funding cycles, productivity-based evaluation criteria and risk-averse frameworks that favour predictable and non-transformative research outputs. [\u2026] Without systemic change, we risk stifling the potential of early-career researchers to go beyond the confines of existing methods and approaches and deliver transformative advances.
Detecting and countering misuse of AI: September 2026, by Anthropic
Here, we present five case studies of actors using our models in ways that could support biological weapons development. [\u2026] In the first example, a reseller platform evaded regional blocks to serve virologists working on a state-sponsored grant to pursue chikungunya gain-of-function work, later routing refused prompts to models with more permissive safeguards. In the second, a researcher in an unsupported region spent weeks planning avian influenza mammalian-adaptation experiments with Claude, but classifiers confined the work to our weakest models. In the third case study, a reseller relay serving a dozen customers had Opus 5 draft a complete orthopoxvirus immune-evasion grant application in about an hour. In the fourth example, a state-supported researcher built a venom peptide atlas and generative optimization pipeline of molecules directed at paralytic and analgesic targets. And in the final case study, a researcher computationally redesigned toxins for a national program, asking Claude to keep the agents' identities deliberately vague in progress reports. In the examples below, actors circumvented controls we impose to prevent users from unsupported regions accessing our models, and engaged in other efforts to obfuscate the purpose of their research to evade our safeguards.
The Next Gene-Editing Technology May Also Be the Oldest, by Carl Zimmer
This system, called VIPR, appears to be more than four billion years old, and it seems be the evolutionary ancestor of CRISPR, the researchers wrote in two papers published in Science. It may prove to be the more powerful gene-editing tool, with certain advantages over CRISPR. VIPR seems able to target a wider variety of genetic sequences, allowing it to alter more of the genome. And these molecules are smaller, making them potentially easier to deliver into cells.
The Rise and Fall of Agent Civilizations, by
Ajeya Cotra, one of the other authors on the report, wrote a blog post with her takeaways from this incident. She concludes, \"Compared to the reward hacks we know of from just six months ago, this incident feels like it's more than 50% of the way to full-blown AI takeover. I continue to expect extremely rapid advances in capabilities over the next six months. I am not sure that we will get another warning shot before it's too late.\"
The future of peer review requires AI support, not AI bans, by Xuegong Zhang
Journals should provide practical safeguards and reviewer-friendly AI services. Publishers and the community should establish clear standards for responsible AI-assisted peer review and invest in secure, purpose-built AI infrastructure that protects confidentiality and promotes reviewers' productivity.
The Dishonor Code, by John Paul Rollert
This is how communities tend to respond to crime waves: They beef up law enforcement. Surveillance widens, punishments stiffen, and legal codes expand to police behavior that was formerly restrained by customary norms of honor, decorum, and mutual reciprocity. As the superintendents of their academic communities, faculty find themselves having to prioritize the disciplinary elements of their job. They will spend more time serving as judge, jury, and executioner, even when what they really want to do is what they do best: research and teach.
Testing Grok 4.6's Enhanced Biology Safeguards, by
Our investigation into the latest-available Grok 4.6 checkpoint revealed:
The currently-served version of Grok 4.6 is the most performant model we have tested on our biosecurity refusal benchmark, as measured by a weighted sum of disguised red-team refusals and routine, dual-use-adjacent research completions. This is a significant improvement from the earlier-tested versions of Grok 4.6.
This behavior is driven primarily by model intelligence, rather than input classifiers or system flags, unlike other models we have assessed.
Additionally, Grok 4.6's refusal behavior does not degrade general biological performance. Grok 4.6 consistently scores near the top of our benchmarks, including therapeutics, variant discovery, epigenomics, spatial transcriptomics, single-cell transcriptomics, and pathogen surveillance.
GCBR Organization Updates, September 2026, by &
In this issue:
IBBIS's new release of its free, open-source DNA synthesis screening tool, now 100x smaller and screening 1,000 sequences in under 15 minutes;
SecureBio AI's pre-release assessment of GPT-5.6 and independent review of Anthropic's chemical and biological risk report;
A new joint publication from RAND, CEPI, and the Brown University Pandemic Center that details accelerating defense and health capabilities for medical countermeasure readiness;
Several job and fellowship opportunities with upcoming deadlines \u2014 see the opportunities section for details;
Ready for the robot reviewers? by Jeffrey Brainard in Science (shameless self-quote-quote here)
As Stephen Turner, a data scientist at the University of Virginia (UVA), sees it, \"The question is not whether AI matches the best human reviewer. It is whether AI-assisted review with clear rubrics outperforms the inconsistent, fatigue-influenced, mood-dependent median reviewer.\"
For now, most journals are largely barring reviewers from using AI tools until editors determine how they can best support high-quality reviews. Often, however, \"People are using these tools in a clandestine, unregulated way,\" Turner says. \"It's kind of the Wild West out there now.\"
Newly created viruses are a warning. We still have a window to stop AI-enabled bioweapons, by Steph Guerra
Thankfully there is still time to act. The capabilities of AI models are not yet advanced enough to design human-infecting viruses. Producing the novel bacteria-infecting viruses described in Science took elite scientists working with state-of-the art equipment in well-furnished laboratories. They had substantial resources to design, test, and optimize hundreds of viruses to find the right ones for their research.
Yet, if an AI model were able to produce hundreds of infectious virus designs and reliably generate functional results for a significant share of them, it would substantially reduce the time and effort needed to use that capability, for good and bad. Similarly, AI agents\u2014AI bots that can interact with other AIs, software, or people on the internet\u2014could someday use the AI biodesign tools necessary for creating novel viruses, further reducing the need for expertise and opening up new risk pathways.
AlphaGenome Atlas: A predictive map of every possible DNA letter change in the human genome by AlphaGenome Atlas team
Today, we are introducing AlphaGenome Atlas: a platform (detailed in our paper) containing predictions for the effects of 9 billion single-nucleotide variants \u2014 every single-letter change possible \u2014 in the human genome. It is the most comprehensive catalogue of how genetic mutations affect molecular biology, and it is available for academic research through an intuitive and free-to-use website portal.
AI researchers reckon with the $1.5 million 'academia tax' by Ben Deighton
\"The amount of money that these guys are making at Anthropic and OpenAI is just mind\u2011boggling \u2014 from being my student two years ago to making US$2 million a year. Then again, they basically say, 'If I have my job for five years, I'm going to be lucky', because they see that they're literally training the large language models that are going to replace them. So, they can see the end of the road.\" \u2014 Peter Nugent, physicist at Lawrence Berkeley National Laboratory, California
Newly created viruses are a warning. We still have a window to stop AI-enabled bioweapons by Steph Guerra
Developing a resilient defensive strategy against the misuse of AI and biology requires dedicated resources, technological breakthroughs, and sustained political leadership. And because the United States is not the only country advancing in AI and biotechnology, this effort demands international coordination\u2014particularly with China. No set of access controls or early warning system will work if it only covers half the globe.
We Must Pace the Frontier by Dario Amodei
But over the last few months, I have become convinced that fully addressing the risks requires even more prudence \u2014 not just investing in risk prevention, but pacing the rate of capabilities advancement so that risk prevention has time to keep up. We must slow the pace at which we improve the capabilities of AI models. Progress will still seem fast, and we must make wise use of the time we gain.
The turbulent AI era is here. The choices we make now are critical. by Bill Gates
The transition to the AI era will be one of the most turbulent times in human history. Right now, we are not preparing adequately for that transition. If the world takes the right steps, AI will be a force for good and leave everyone better off.
Start Something You Can Stop, by at
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Following successful melanoma surgery, there might be nothing left to measure. The primary tumor has been removed; involved lymph nodes or resectable metastases may no longer be detectable, and postoperative imaging could show no signs of disease. While this is the goal of surgery, it does not guarantee that every melanoma cell has vanished. A few cells may have already migrated beyond the surgical site and remain undetected by routine clinical methods.
Adjuvant therapy is given after resection, when no measurable disease may remain but microscopic residual disease can still cause recurrence. No tumor mass can be tracked from one scan to the next. Two patients may have equally reassuring postoperative images, yet only one harbors cells that could later cause a recurrence. Stage and pathology assess that risk, but they do not detect microscopic residual disease. Consequently, an effective adjuvant treatment must act systemically before recurrence becomes detectable.
Intismeran autogene, previously known as mRNA-4157 or V940, utilizes resected tumor tissue in its design. The process involves analyzing DNA and RNA extracted from formalin-fixed tumor samples, along with a blood sample from the same patient. This allows identification of somatic mutations, integration of the patient's human leukocyte antigen (HLA) genotype into neoantigen selection, and encoding up to 34 mutation-derived targets into a personalized messenger RNA (mRNA). The RNA is then encapsulated within a lipid nanoparticle (LNP) and administered via intramuscular injection together with pembrolizumab, an antibody against programmed cell death protein 1 (PD-1). Intismeran aims to stimulate or enhance tumor-reactive T cells, while pembrolizumab helps by blocking an inhibitory signal that can limit their effectiveness [5,6].
On August 19, 2026, Merck and Moderna announced that the Phase 3 INTerpath-001 trial met its primary endpoint of recurrence-free survival and its key secondary endpoint of distant metastasis-free survival. The study involved 1,137 patients with completely resected stage IIB to IV cutaneous melanoma, randomly assigned in a two-to-one ratio to receive intismeran plus pembrolizumab or a matching placebo plus pembrolizumab [1,2]. Both companies reported statistically significant and clinically meaningful improvements in these outcomes, with no new safety signals identified [1].
A significant limitation remains in our interpretation. As of September 29, 2026, the hazard ratios, confidence intervals, event counts, survival curves, absolute differences, and subgroup results from INTerpath-001 have not yet been publicly disclosed. Detailed Phase 3 data are scheduled for presentation on October 24 at the European Society for Medical Oncology (ESMO) Congress [15]. While this announcement presents a positive topline result from the pivotal trial, the magnitude and distribution of benefit remain unclear. Currently, the available information does not clarify whether the effect is consistent across stage IIB, IIC, III, and resected stage IV disease, or if an overall-survival difference is beginning to emerge.
Melanoma originates in melanocytes, yet its postoperative challenge stems from its tendency to spread. Once malignant cells infiltrate lymphatic channels or blood vessels, surgically removing visible disease, such as the primary tumor, regional lymph nodes, or distant metastases, does not ensure the complete eradication of all disseminated cells. The concern is occult disseminated disease that routine imaging cannot localize.
Patients enrolled in the INTerpath-001 trial are at various post-surgery stages of disease. Those with Stage IIB and IIC melanomas show no detectable nodal or distant metastases, but factors like tumor thickness and ulceration notably increase the risk of recurrence. Stage III disease indicates regional metastatic spread [7]. In INTerpath-001, patients with stage IV disease entered the study after complete resection of all known disease. Despite differences in biological behavior, they share a common concern: hidden melanoma cells may remain outside the treatment area.
Pembrolizumab already helps reduce this risk. In the KEYNOTE-716 study, patients with completely resected stage IIB or IIC melanoma received either pembrolizumab or a placebo for about one year. At 36 months, recurrence-free survival was 76.2% with pembrolizumab, compared with 63.4% with placebo. Distant metastasis-free survival was 84.4% with pembrolizumab versus 74.7% with placebo [12]. The current U.S. label indicates pembrolizumab for adjuvant treatment following complete resection of stage IIB, IIC, or III melanoma [3].
The comparator is therefore important: INTerpath-001 tests adding intismeran to active PD-1 blockade rather than observation. The Phase 3 trial tested whether personalized neoantigen targeting provides additional clinical benefit when added to an adjuvant therapy already shown to reduce recurrence.
A pathologist determines melanoma by carefully assessing its architecture, cellular morphology, immunohistochemical markers, and clinical context. T cells distinguish targets by recognizing peptide\u2013HLA complexes rather than tissue morphology. Cellular proteins are constantly broken down into peptides, some of which are displayed on the cell surface by HLA molecules. A T-cell receptor recognizes a specific peptide\u2013HLA complex.
Cancer complicates this system because melanoma cells are derived from the patient's own cells and still express self-proteins predominantly. Somatic mutations can generate differences that the immune system can detect. If a mutation alters an amino acid sequence, cellular antigen-processing machinery can generate a peptide containing that mutated residue and present it at the cell surface on an HLA molecule. A T cell that specifically targets the mutant peptide may, in theory, distinguish tumor cells from healthy tissue. A mutation-derived peptide that is processed, presented, and recognized as non-self can function as a neoantigen.
Importantly, the mutation does not need to be oncogenic. Even a passenger mutation can serve as a target if the altered gene is expressed, the resulting protein is properly processed, the mutant peptide is presented by the patient's HLA molecules, and an appropriate T-cell clone exists or can be generated. Cutaneous melanoma presents a particularly interesting case because ultraviolet-related tumors often harbor numerous somatic mutations [8]. The challenge lies in identifying which of these mutations are immunologically significant. Many are expressed at low levels, poorly presented, exist only in subclonal populations, or simply fail to evoke an effective T-cell response.
To reduce dependence on any single predicted target, intismeran encodes up to 34 selected neoantigens. Moderna's published workflow involves ranking candidate neoantigens based on factors such as somatic variant allele frequency, tumor RNA expression levels, amino acid sequence information, and the patient's HLA class I and class II genotypes [6]. While variant allele frequency offers valuable insights, it does not directly measure clonality because tumor purity, copy-number alterations, and local genomic context can influence it. Consequently, the selection algorithm estimates probabilities rather than directly measuring immunogenicity in the patient.
A multi-target approach reduces dependence on a single antigen. Tumors often display heterogeneity and may lose or fail to present a specific target consistently. Including multiple candidate neoantigens increases the chance of eliciting both CD8-positive and CD4-positive responses and reduces dependence on any single epitope. Previous personalized neoantigen studies in melanoma showed durable, patient-specific T-cell responses, with some cases demonstrating epitope spreading to additional tumor antigens [9,14].
Activated T cells can upregulate PD-1, an inhibitory receptor that reduces T-cell signaling when it binds programmed death-ligand 1 (PD-L1) or programmed death-ligand 2 (PD-L2). Pembrolizumab targets PD-1 and blocks this ligand interaction. The antibody itself does not confer new tumor specificity; instead, its effectiveness depends on the presence or induction of tumor-reactive lymphocytes.
Intismeran is intended to induce new tumor-reactive T-cell responses and expand relevant responses that are already present. Cells that take up the mRNA at the injection site or within draining lymphatic tissue can translate it into the encoded antigenic product. Additionally, professional antigen-presenting cells can present antigens directly or acquire them for presentation via HLA class I and II pathways. The resulting immune response can involve CD8-positive cytotoxic T cells and CD4-positive helper T cells. Once activated, these T cells can circulate throughout the body and potentially recognize residual melanoma cells displaying the mutant peptide-HLA complexes.
The biological rationale for combining the two interventions is complementary, but that should not be mistaken for evidence of pharmacologic synergy. KEYNOTE-942 and INTerpath-001 compare combination therapy with pembrolizumab alone to assess the added benefit of incorporating intismeran into PD-1 blockade. Neither trial shows how intismeran might perform as a standalone adjuvant treatment, nor can they definitively prove synergy rather than an additive effect.
Preclinical studies involving intramuscular mRNA-LNP administration have shown RNA and expressed protein at the injection site and in draining lymph nodes, including immune cells involved in antigen presentation. This supports the overall feasibility of this route for mRNA immunization. Nonetheless, this should not be regarded as evidence of human biodistribution for intismeran itself [13].
The manufacturing process starts with obtaining the patient's specimen rather than with RNA synthesis itself. An adequate formalin-fixed, paraffin-embedded (FFPE) tumor sample is used for histopathology and tumor DNA/RNA extraction for next-generation sequencing, while peripheral blood provides matched-normal DNA and HLA typing [6]. Researchers then compare tumor DNA with normal DNA extracted from blood to identify potential somatic mutations. Tumor RNA analysis provides evidence of intratumoral expression of the mutated gene. The HLA genotype further narrows down which mutant peptides are most likely to be presented as immunological targets.
The selected neoantigen sequences are assembled into a single engineered coding region. T7 RNA polymerase-mediated in vitro transcription produces the mRNA, with N1-methylpseudouridine used in place of uridine, and the resulting mRNA is formulated into lipid nanoparticles. The clinical dose is 1 mg, administered via intramuscular injection every three weeks, with a maximum of nine doses [4,6]. While the coding sequence is personalized for each patient, the RNA chemistry, formulation platform, dosage, and manufacturing architecture remain consistent.
Clinical feasibility depends on successfully designing and manufacturing each patient's product within the required turnaround time. In KEYNOTE-942, researchers assessed tumor tissue from 185 of 224 screened patients to develop individualized treatment. Intismeran design, including sequencing and neoantigen identification, succeeded for 156 of those 185 patients, or 84.3%. Among patients with a successful design in the combination arm, downstream manufacturing succeeded for 104 of 105 patients, or more than 99% [10]. These are distinct attrition points: obtaining a usable specimen and generating an acceptable individualized design are different problems from successfully manufacturing the resulting drug product. Moderna has reported a turnaround of approximately six weeks from receipt of adequate samples to delivery of the patient-specific product to the clinical site [6].
These attrition data also show that personalization introduces failure points before and after RNA manufacture. Producing the RNA is only one part of a complex process. The pathology specimen, digital target list, patient-specific sequence, manufactured batch, and final administration must all remain accurately linked to the same patient. For truly individualized treatment, an identity error can occur even if the vial's contents are chemically correct.
Intismeran presents an unusual analytical challenge because each patient's coding sequence is deliberately different. This requires the control strategy to distinguish carefully between variability inherent to the product design and that which results from manufacturing, handling, or measurement processes. Although Moderna has described quality control for these personalized batches, the publicly available sources reviewed here do not disclose the exact release specifications or assay panel used for clinical lot release. The following discussion provides a framework for understanding this product class without revealing Moderna's confidential methods.
For personalized therapy, the analytical steps start even before in vitro transcription. Factors like tumor adequacy, histopathology, nucleic acid extraction, sequencing quality, comparison with matched normal tissue, HLA typing, variant calling, RNA expression evidence, and bioinformatics rankings all shape the final sequence that will be produced. These data are part of product definition because they determine the patient-specific coding sequence.
The digital design output must maintain clear provenance. The sequence sent for manufacturing must be traceable to the correct tumor and blood samples, precise HLA data, and the exact design outcome. Even small modifications to the reference dataset, analysis pipeline, or selection algorithm can influence which neoantigens are ultimately selected, despite the wet-chemistry process remaining constant. Software versioning, audit trails, change control, and traceability between the approved digital design and the physical batch are therefore part of the identity-control strategy. While public sources outline Moderna's comprehensive workflow, they do not disclose details of the internal validation or change-control systems used in these proprietary processes [6].
Pharmaceutical identity goes beyond a standard sequence test. Even if an RNA molecule is synthesized precisely, it may still be the wrong medication if it was designed from another patient's file. Conversely, a perfect chain of identity cannot compensate for a defective or improperly manufactured batch.
Patient-specific sequence design does not remove the analytical challenges associated with a large synthetic RNA transcript. Attributes including sequence identity, full-length RNA content, and fragmentation profile, 5\u2032-cap identity and capping efficiency, poly(A) tail length and distribution, modified-nucleoside identity and content, double-stranded RNA, residual DNA template, nucleotides, enzymes, and other process- or product-related species can all influence translation efficiency, innate immune recognition, stability, or dose consistency [16-19,22]. No single purity measurement can fully address these concerns.
Choose methods based on the specific questions at hand. Nuclease digestion followed by liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) oligonucleotide mapping can provide sequence-level identity information and detect sequence-related heterogeneity. Techniques such as capillary or microchip electrophoresis, ion-pair reversed-phase LC, anion-exchange LC, and size-exclusion chromatography (SEC) help analyze different aspects of RNA integrity and heterogeneity; SEC coupled with multi-angle light scattering (SEC-MALS) can provide molecular-weight and size-related information, while mass photometry provides complementary native-state screening of mRNA integrity and size [16,17,22,23]. Selective enzymatic cleavage followed by LC with ultraviolet detection, liquid chromatography\u2013mass spectrometry (LC-MS), or electrophoretic analysis allows examination of cap and poly(A) features. Double-stranded RNA often requires specialized immunochemical assays, complemented as needed with orthogonal separation methods, while quantitative PCR (qPCR) or droplet digital PCR (ddPCR) can detect residual DNA. Orthogonal methods provide complementary information and can help distinguish true product attributes from method-specific bias or interference.
Sample preparation plays a crucial role in this measurement process. Techniques such as deformulation, dilution, surfactant addition, extraction, nuclease treatment, or fluorescent labeling can influence recovery rates and determine which species are detectable. Methods that interrogate the intact formulation can reduce sample-preparation artifacts when they provide adequate selectivity and quantitative performance. In 2026, Moderna reported using deep-ultraviolet resonance Raman spectroscopy for rapid, label-free quantification of mRNA directly within intact lipid nanoparticle (LNP) formulations, without deformulation [29]. While it does not replace component assays to confirm identity, integrity, impurities, or lipid chemistry, it demonstrates that some formulation-level measurements can be made without prior deformulation.
Large RNAs are inherently conformationally flexible, and changes in sequence or secondary structure can affect chromatographic retention, electrophoretic behavior, recovery, and hydrodynamics. Sequence-sensitive platform methods should demonstrate suitability across a representative range of the intended design space rather than being evaluated using only one convenient transcript. Patient-specific identity assays, by contrast, may require sequence-specific controls. Reference materials require careful consideration, as a single invariant RNA standard cannot represent the full spectrum of individualized sequences. System suitability, platform controls, and patient-specific identity controls should be treated as distinct elements of the analytical strategy. International Council for Harmonisation (ICH) Q14 and Q2(R2) provide principles for analytical procedure development, validation, and lifecycle management that are relevant to this platform [20,21].
A broader LNP characterization framework can include several parameters. These include RNA concentration, encapsulation efficiency, free RNA levels, particle size distribution, particle concentration, polydispersity, zeta potential, apparent pKa of the ionizable lipid in the formulated LNP, lipid identity and molar composition, lipid degradants, residual solvents, morphology and internal structure. Dynamic light scattering (DLS) efficiently measures hydrodynamic size and polydispersity but is biased toward larger scatterers. Nanoparticle tracking analysis estimates particle number concentration and a number-weighted particle-size distribution. Asymmetrical flow field-flow fractionation (AF4), combined with multi-angle light scattering (MALS) and other detectors, can resolve heterogeneous populations without a packed stationary phase. Meanwhile, density-matching analytical ultracentrifugation can resolve populations that differ in buoyant density, sedimentation behavior, hydrodynamic size, and cargo loading [24,25,27]. Techniques such as cryogenic electron microscopy and small-angle X-ray scattering primarily serve deeper structural characterization rather than routine release testing [24,28].
Encapsulation efficiency should be distinguished from the fraction of LNPs that contain mRNA. A formulation might contain most RNA within LNPs yet still contain many empty particles. Single-particle fluorescence studies can directly identify unencapsulated mRNA, empty LNPs, and mRNA-loaded LNPs, while estimating mRNA copies per loaded LNP [26]. The quantitative results from that study should not be generalized to other LNP formulations, but encapsulation efficiency and the fraction of mRNA-loaded LNPs remain distinct measurements.
Lipid chemistry and stability require analytical methods that can detect subtle changes invisible to particle sizing alone. Reversed-phase LC with charged-aerosol detection can quantify lipid components and their relative amounts when supported by appropriate standards, while LC-MS can strengthen lipid identity assignments and degradant profiling. Headspace gas chromatography can assess residual volatile solvents. Processes such as RNA cleavage, lipid oxidation or hydrolysis, leakage, aggregation, and mRNA-lipid adduct formation can occur via various pathways [18,19]. Stability assessment therefore requires chemical, physical, and functional measurements. Maintaining a consistent particle size does not necessarily confirm preservation of RNA integrity or function.
Functional testing follows initial structural measurements. A cell-based expression or potency assay can demonstrate functional expression of the encoded product under defined assay conditions. Development assays can explore uptake or endosomal escape in greater detail. However, expression alone does not establish peptide processing, HLA presentation, or T-cell recognition.
The encoded sequence must be processed into peptides, presented by the appropriate HLA molecules, and recognized by relevant T-cell populations. Immunopeptidomics using liquid chromatography-tandem mass spectrometry (LC-MS/MS) can provide direct evidence of naturally presented HLA-bound peptides and is increasingly used in proteogenomic neoantigen discovery [30]. Clinical immune monitoring can use ELISpot assays, intracellular cytokine staining, and HLA multimer assays to assess antigen-specific responses, while T-cell receptor sequencing can track treatment-associated clonal expansion and persistence. Antigen specificity of individual TCRs requires additional functional or binding-based validation. KEYNOTE-603 and subsequent translational studies used this kind of evidence to demonstrate the emergence of new and expanded neoantigen-specific T-cell responses [5,11]. These are mechanistic and translational assessments, not standard lot-release tests.
Release testing can establish, to a defined level of confidence, that the patient-specific lot conforms to predefined specifications for identity, content, purity, defined physical attributes, microbiological quality, and potency. However, it cannot guarantee that every predicted neoantigen will be processed efficiently, presented on HLA molecules by residual melanoma cells, or will elicit a clinically relevant T-cell response. Those outcomes depend on antigen processing, HLA presentation, tumor heterogeneity, and the patient's immune repertoire rather than on lot-release testing.
The roughly six-week period reported for intismeran indicates that patient-specific testing must be thorough without becoming the bottleneck in adjuvant treatment [6]. Rapid assays are most useful for lot disposition, while more comprehensive methods support development, stability studies, deviation investigations, and comparability assessments. Since sequence variation is intentional, manufacturing controls and analytical procedures need to be flexible across that variation, rather than reinterpreted for each batch.
Early clinical studies first established neoantigen-specific T-cell responses before testing efficacy in larger randomized trials. In Phase 1 of KEYNOTE-603, researchers collected blood cells from patients and stimulated them with neoantigens encoded by their own intismeran products. This approach elicited new T-cell responses and enhanced pre-existing responses involving both CD8-positive and CD4-positive T-cell populations [5]. Although the study was small, it demonstrated that selecting and delivering these neoantigens could produce the desired immune response in humans.
A translational analysis conducted in 2026 examined this response in greater detail within a small group. In three carefully studied patients, investigators identified 20 T-cell receptor (TCR) clonotypes that expanded following treatment and linked them to 11 neoantigens encoded by intismeran. These TCRs tended to recognize mutant peptides more than wild-type ones, and some responses persisted for more than 100 days after the last dose. Additional functional tests in seven patients revealed neoantigen-reactive CD8-positive T cells capable of cytokine production, degranulation, and granzyme B expression [11]. These data support the intended mechanism, but the small sample size does not establish how consistently individual encoded neoantigens are immunogenic across patients.
In the KEYNOTE-942 study, 157 adults with fully resected, high-risk stage IIIB to IV cutaneous melanoma were randomly assigned in a 2-to-1 ratio to receive either intismeran plus pembrolizumab or pembrolizumab alone [4]. At the primary analysis, 24 out of 107 patients in the combination group experienced recurrence or death, compared with 20 out of 50 in the pembrolizumab-only group. The hazard ratio for recurrence or death was 0.561 (95% confidence interval [CI], 0.309 to 1.017), with a two-sided P value of 0.053. For distant metastasis or death, the hazard ratio was 0.347 (95% CI, 0.145 to 0.828) [4].
The primary recurrence-free survival (RFS) result must be interpreted against the trial's prespecified Phase 2b significance threshold. Under a conventional two-sided 0.05 threshold, the primary RFS result would not be considered statistically significant because the P value was 0.053 and the 95% confidence interval included 1.0. KEYNOTE-942 was designed as a signal-seeking phase 2b study, with a prespecified two-sided P value threshold of less than 0.198 for the primary endpoint [4]. Therefore, it met its protocol-defined criterion. This threshold aligns with the purpose of the phase 2b study: to identify a promising signal worthy of further investigation, rather than to provide definitive confirmation.
At a median planned follow-up of 60.3 months, the hazard ratio for recurrence-free survival (RFS) was 0.510 (95% CI, 0.294 to 0.887), and the hazard ratio for distant metastasis-free survival (DMFS) was 0.411 (95% CI, 0.200 to 0.843) [10]. The four-year RFS estimate was 72.4% with intismeran plus pembrolizumab, compared to 49.1% with pembrolizumab alone. Similarly, four-year DMFS was 83.9% versus 65.4%, respectively [10].
The five-year analyses were primarily descriptive and did not assign an alpha level [10]. Overall survival numerically favored the combination, with a hazard ratio of 0.471; however, the 95% confidence interval was quite broad, ranging from 0.165 to 1.345. The RFS and DMFS signals persisted with longer follow-up, but the dataset remained too small to establish an overall survival benefit.
Safety remained broadly consistent with earlier experiences. Fatigue and injection-site pain were the most commonly reported adverse events attributed to intismeran, each affecting approximately 59.6% of treated patients, with chills occurring in about 51.0%. Importantly, no grade 4 or grade 5 adverse events were attributed to intismeran. In the five-year analysis, immune-related adverse events appeared at similar frequencies in both the combination and pembrolizumab-alone groups [10].
INTerpath-001 has expanded its clinical investigation into a broader, placebo-controlled Phase 3 trial, involving patients with stage IIB and IIC melanoma as well as those with resected stage III and IV disease. The study enrolled participants who had not previously received systemic therapy for melanoma. They were randomized 2:1 to receive either intismeran 1 mg every three weeks for up to nine doses with pembrolizumab 400 mg every six weeks for up to nine cycles, or a matching placebo with the same pembrolizumab regimen [1,2].
The August announcement states that the prespecified interim analysis met both the recurrence-free survival and distant metastasis-free survival endpoints, marking a positive topline result from this pivotal trial. However, the publicly available information does not currently clarify the magnitude of the benefit. Critical details such as hazard ratios, clear separation of survival curves, event counts, the maturity of overall survival data, and stage-specific effects have not yet been revealed.
Stage-specific data are especially important because the same relative effect can produce substantially different absolute benefits depending on baseline risk. For example, stage IIB and resected stage IV populations can have substantially different baseline recurrence risks even if treatment produces similar relative hazard reductions. Consequently, the ESMO presentation requires careful interpretation beyond just the headline endpoint. Effect size, absolute risk reduction, confidence intervals, consistency across stages, safety, treatment adherence, manufacturing success, and ultimately overall survival will influence how the findings are understood.
Melanoma is a favorable setting in which to test neoantigen-targeted immunotherapy because many cutaneous tumors have high mutation burdens and checkpoint inhibition is already active in this disease. However, a positive result in a melanoma trial does not necessarily imply that the treatment will perform equally well in tumors with fewer mutations, different antigen presentation mechanisms, or more immunosuppressive microenvironments. Broader results from the intismeran program will be essential to determine how broadly applicable this platform truly is.
Many precision-oncology strategies use a tumor biomarker to select among standardized therapies. Intismeran takes a different route. Instead of merely selecting a treatment for the patient, tumor data serve as the foundation for manufacturing part of the therapy, with a portion of the drug's molecular sequence derived directly from these data. This means the therapy is personalized, crafted from information unique to each individual patient.
The resulting workflow links pathology, sequencing, bioinformatics, pharmaceutical manufacturing, analytical testing, logistics, and immunology. Insufficient tumor material may hinder proper design, while sequencing or identity errors could compromise the entire process. Manufacturing processes may unintentionally alter RNA or LNP quality, and logistical delays can postpone treatment delivery. Even a properly manufactured product can encode predicted neoantigens that turn out to be weak targets for the individual.
Analytical control must be built into the platform because patient-specific sequence variation is intentional. Moving personalized mRNA treatments from specialized trials into routine oncology care requires consistent patient-to-product traceability, rapid lot-release testing, stability, comparability despite manufacturing changes, and analytical procedures that remain effective even with deliberate sequence modifications. Shorter manufacturing timelines increase the need for rapid, robust analytical procedures that can support lot disposition without weakening control.
The Phase 3 topline announcement indicates that intismeran combined with pembrolizumab achieved two key disease-control endpoints following melanoma resection [1]. Detailed INTerpath-001 data will show the magnitude of these differences and whether the effect is consistent across disease stages. If the detailed results confirm the topline findings, INTerpath-001 will also provide evidence that patient-specific sequence manufacture can be executed within a common pharmaceutical platform while preserving clinical performance.
Following melanoma surgery, the therapeutic target might no longer be immediately visible. Intismeran uses the excised tumor as the source of patient-specific design information: pinpointing mutations, choosing candidate neoantigens, encoding them into mRNA, and manufacturing a patient-specific drug product. This approach aims to generate T-cell populations that can recognize the same targets if residual disease persists. Pembrolizumab is combined with this strategy to diminish PD-1-mediated inhibition of these lymphocytes.
The evidence has progressed beyond initial Phase 2b signals. KEYNOTE-942 showed a sustained recurrence-free and distant metastasis-free survival benefit, although its statistical design was intentionally exploratory and its five-year analyses were descriptive. In the sponsor-reported interim analysis, the INTerpath-001 trial met its Phase 3 endpoints for recurrence-free survival (RFS) and distant metastasis-free survival (DMFS). However, the full extent of benefit, including magnitude, absolute differences, stage-specific patterns, and overall survival, remains uncertain.
Intismeran requires a control strategy for a product whose coding sequence is intentionally different from one patient to the next. Control therefore depends on confirming that each patient receives the intended sequence within a controlled manufacturing platform, with acceptable RNA quality, LNP attributes, functional performance, and an intact chain of identity.
At scale, this model would extend precision oncology from treatment selection to patient-specific manufacture within a common platform, which must ensure that the intended product is manufactured reproducibly, characterized appropriately, and delivered to the correct patient.
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[30] F. Huber, M. Arnaud, B. J. Stevenson, et al., \"A comprehensive proteogenomic pipeline for neoantigen discovery to advance personalized cancer immunotherapy,\" Nat. Biotechnol., vol. 43, pp. 1360\u20131372, 2025. https://doi.org/10.1038/s41587-024-02420-y
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Available:"},{"id":"https://clinicaltrials.gov/study/NCT05933577","unstructured":"ModernaTX, Inc., \"A Clinical Study of Intismeran Autogene (V940) Plus Pembrolizumab in People With High-Risk Melanoma (INTerpath-001),\" ClinicalTrials.gov, NCT05933577. [Online]. Available:"},{"id":"https://dailymed.nlm.nih.gov/dailymed/drugInfo.cfm?setid=9333c79b-d487-4538-a9f0-71b91a02b287","unstructured":"Merck Sharp & Dohme LLC, \"KEYTRUDA (pembrolizumab) U.S. Prescribing Information,\" revised July 2026. [Online]. Available:"},{"id":"https://doi.org/10.1016/s0140-6736(23)02268-7","unstructured":"Weber, J. S., Carlino, M. S., Khattak, A., Meniawy, T., Ansstas, G., Taylor, M. H., Kim, K. B., McKean, M., Long, G. V., Sullivan, R. J., Faries, M., Tran, T. T., Cowey, C. L., Pecora, A., Shaheen, M., Segar, J., Medina, T., Atkinson, V., Gibney, G. T., \u2026 Zaks, T. (2024). Individualised neoantigen therapy mRNA-4157 (V940) plus pembrolizumab versus pembrolizumab monotherapy in resected melanoma (KEYNOTE-942): a randomised, phase 2b study. The Lancet, 403(10427), 632\u2013644."},{"id":"https://doi.org/10.1158/2159-8290.cd-24-0158","unstructured":"Gainor, J. F., Patel, M. R., Weber, J. S., Gutierrez, M., Bauman, J. E., Clarke, J. M., Julian, R., Scott, A. J., Geiger, J. L., Kirtane, K., Robert-Tissot, C., Coder, B., Tasneem, M., Sun, J., Zheng, W., Gerbereux, L., Laino, A., Porichis, F., Pollard, J. R., \u2026 Burris, H. A. (2024). T-cell Responses to Individualized Neoantigen Therapy mRNA-4157 (V940) Alone or in Combination with Pembrolizumab in the Phase 1 KEYNOTE-603 Study. Cancer Discovery, 14(11), 2209\u20132223."},{"id":"https://doi.org/10.1200/oa-25-00206","unstructured":"Ojalvo, L. S., Posadas, T., Chattopadhyay, N., Porichis, F., Shah, S., Cristescu, R., & Rao, A. (2026). Intismeran Autogene Therapy: An End-to-End Pathway From Tumor Tissue to Individualized Neoantigen Treatment for Patients With Cancer. JCO Oncology Advances, 3(3)."},{"id":"https://doi.org/10.3322/caac.21409","unstructured":"Gershenwald, J. E., Scolyer, R. A., Hess, K. R., Sondak, V. K., Long, G. V., Ross, M. I., Lazar, A. J., Faries, M. B., Kirkwood, J. M., McArthur, G. A., Haydu, L. E., Eggermont, A. M. M., Flaherty, K. T., Balch, C. M., Thompson, J. F., & Platform, . for . members . of . the A. J. C. on C. M. E. P. and . the I. M. D. and D. (2017). Melanoma staging: Evidence\u2010based changes in the American Joint Committee on Cancer eighth edition cancer staging manual. CA: A Cancer Journal for Clinicians, 67(6), 472\u2013492."},{"id":"https://doi.org/10.1038/nature22071","unstructured":"Hayward, N. K., Wilmott, J. S., Waddell, N., Johansson, P. A., Field, M. A., Nones, K., Patch, A.-M., Kakavand, H., Alexandrov, L. B., Burke, H., Jakrot, V., Kazakoff, S., Holmes, O., Leonard, C., Sabarinathan, R., Mularoni, L., Wood, S., Xu, Q., Waddell, N., \u2026 Mann, G. J. (2017). Whole-genome landscapes of major melanoma subtypes. Nature, 545(7653), 175\u2013180."},{"id":"https://doi.org/10.1038/nature22991","unstructured":"Ott, P. A., Hu, Z., Keskin, D. B., Shukla, S. A., Sun, J., Bozym, D. J., Zhang, W., Luoma, A., Giobbie-Hurder, A., Peter, L., Chen, C., Olive, O., Carter, T. A., Li, S., Lieb, D. J., Eisenhaure, T., Gjini, E., Stevens, J., Lane, W. J., \u2026 Wu, C. J. (2017). An immunogenic personal neoantigen vaccine for patients with melanoma. Nature, 547(7662), 217\u2013221."},{"id":"https://doi.org/10.1200/jco-26-00835","unstructured":"Khattak, A., Carlino, M. S., Meniawy, T., Ansstas, G., Taylor, M. H., Kim, K. B., McKean, M., Long, G. V., Sullivan, R. J., Faries, M., Tran, T. T., Cowey, C. L., Pecora, A., Medina, T., Atkinson, V., Krepler, C., Jemielita, T., Mao, H., Chow, J., \u2026 Investigators, . on . behalf . of . the K.-. 942 . (2026). Intismeran Autogene Plus Pembrolizumab Versus Pembrolizumab Alone in High-Risk Resected Melanoma: 5-Year Update of the Randomized Phase IIb KEYNOTE-942 Study. Journal of Clinical Oncology, 44(26), 2482\u20132489."},{"id":"https://doi.org/10.1200/jco.2026.44.16_suppl.9564","unstructured":"Sullivan, R. J., Carlino, M. S., Khattak, M. A., Long, G. V., Gainor, J. F., Patel, M. R., Clarke, J. M., Ouspenskaia, T., Sedimbi, S. K., Araya, J., Krishna, V., Tacettin, C., Laino, A., Aubin, R., Yee, S., & Mehnert, J. M. (2026). Intismeran autogene to induce de novo neoantigen-specific T cells as adjuvant therapy in melanoma. Journal of Clinical Oncology, 44(16_suppl), 9564\u20139564."},{"id":"https://doi.org/10.1200/jco.23.02355","unstructured":"Luke, J. J., Ascierto, P. A., Khattak, M. A., de la Cruz Merino, L., Del Vecchio, M., Rutkowski, P., Spagnolo, F., Mackiewicz, J., Chiarion-Sileni, V., Kirkwood, J. M., Robert, C., Grob, J.-J., de Galitiis, F., Schadendorf, D., Carlino, M. S., Wu, X. L., Fukunaga-Kalabis, M., Krepler, C., Eggermont, A. M. M., & Long, G. V. (2024). Pembrolizumab Versus Placebo as Adjuvant Therapy in Resected Stage IIB or IIC Melanoma: Final Analysis of Distant Metastasis-Free Survival in the Phase III KEYNOTE-716 Study. Journal of Clinical Oncology, 42(14), 1619\u20131624."},{"id":"https://doi.org/10.1016/j.omtn.2023.102083","unstructured":"Hassett, K. J., Rajlic, I. L., Bahl, K., White, R., Cowens, K., Jacquinet, E., & Burke, K. E. (2024). mRNA vaccine trafficking and resulting protein expression after intramuscular administration. Molecular Therapy Nucleic Acids, 35(1), 102083."},{"id":"https://doi.org/10.1038/s41591-020-01206-4","unstructured":"Hu, Z., Leet, D. E., Alles\u00f8e, R. L., Oliveira, G., Li, S., Luoma, A. M., Liu, J., Forman, J., Huang, T., Iorgulescu, J. B., Holden, R., Sarkizova, S., Gohil, S. H., Redd, R. A., Sun, J., Elagina, L., Giobbie-Hurder, A., Zhang, W., Peter, L., \u2026 Ott, P. A. (2021). Personal neoantigen vaccines induce persistent memory T cell responses and epitope spreading in patients with melanoma. Nature Medicine, 27(3), 515\u2013525."},{"id":"https://www.accessnewswire.com/newsroom/en/healthcare-and-pharmaceutical/moderna-announces-late-breaking-data-to-be-presented-at-esmo-congress-1224628","unstructured":"Moderna, Inc., \"Moderna Announces Late-Breaking Data to be Presented at ESMO Congress 2026,\" Sept. 21, 2026. [Online]. Available:"},{"id":"https://doi.org/10.1016/j.chroma.2025.465724","unstructured":"Webb, A. L. J., Welbourne, E. N., Evans, C. A., & Dickman, M. J. (2025). Characterisation and analysis of mRNA critical quality attributes using liquid chromatography based methods. Journal of Chromatography A, 1745, 465724."},{"id":"https://www.usp.org/sites/default/files/usp/document/our-work/biologics/documents/vaccine-mrna-guidelines-2.pdf","unstructured":"United States Pharmacopeia, \"Analytical Procedures for mRNA Vaccine Quality,\" 2nd version, draft guidelines. [Online]. Available:"},{"id":"https://doi.org/10.1038/s41570-025-00763-x","unstructured":"Kova\u010di\u010d, T., Haas, H., Stotsky-Oterin, L., \u0160trancar, A., Bren, U., & Peer, D. (2025). The impact of chemical reactivity on the quality and stability of RNA\u2013LNP pharmaceuticals . Nature Reviews Chemistry, 9(11), 790\u2013802."},{"id":"https://doi.org/10.1016/j.omtn.2025.102684","unstructured":"Peronin, S., Malburet, C., Daher-Hassan, C., Picard, C., Willemin, T., Costamagna, F., Even, L., Eynard, T., Bensaid, F., Fertier-Prizzon, S., & Francois-Heude, M. (2025). Analytical approach for identification and mechanistic insights into mRNA-lipid adduct formation. Molecular Therapy Nucleic Acids, 36(3), 102684."},{"id":"https://www.fda.gov/regulatory-information/search-fda-guidance-documents/q14-analytical-procedure-development","unstructured":"U.S. Food and Drug Administration, Q14 Analytical Procedure Development: Guidance for Industry, Mar. 2024. [Online]. Available:"},{"id":"https://www.fda.gov/regulatory-information/search-fda-guidance-documents/q2r2-validation-analytical-procedures","unstructured":"U.S. Food and Drug Administration, Q2(R2) Validation of Analytical Procedures: Guidance for Industry, Mar. 2024. [Online]. Available:"},{"id":"https://doi.org/10.1021/acs.analchem.3c05539","unstructured":"Camperi, J., Lippold, S., Ayalew, L., Roper, B., Shao, S., Freund, E., Nissenbaum, A., Galan, C., Cao, Q., Yang, F., Yu, C., & Guilbaud, A. (2024). Comprehensive Impurity Profiling of mRNA: Evaluating Current Technologies and Advanced Analytical Techniques. Analytical Chemistry, 96(9), 3886\u20133897."},{"id":"https://doi.org/10.1016/j.chroma.2024.464756","unstructured":"De Vos, J., Morreel, K., Alvarez, P., Vanluchene, H., Vankeirsbilck, R., Sandra, P., & Sandra, K. (2024). Evaluation of size-exclusion chromatography, multi-angle light scattering detection and mass photometry for the characterization of mRNA. Journal of Chromatography A, 1719, 464756."},{"id":"https://doi.org/10.1016/j.jconrel.2024.01.037","unstructured":"Parot, J., Mehn, D., Jankevics, H., Markova, N., Carboni, M., Olaisen, C., Hoel, A. D., Sigf\u00fasd\u00f3ttir, M. S., Meier, F., Drexel, R., Vella, G., McDonagh, B., Hansen, T., Bui, H., Klinkenberg, G., Visnes, T., Gioria, S., Urban-Lopez, P., Prina-Mello, A., \u2026 Calzolai, L. (2024). Quality assessment of LNP-RNA therapeutics with orthogonal analytical techniques. Journal of Controlled Release, 367, 385\u2013401."},{"id":"https://doi.org/10.1007/s00216-024-05455-x","unstructured":"Gao, Z., Lin, J., Su, W.-C., Zhang, K., Gruenhagen, J., Zhong, W., Fan, Y., & Bian, J. (2024). Development of an advanced separation and characterization platform for mRNA and lipid nanoparticles using multi-detector asymmetrical flow field-flow fractionation. Analytical and Bioanalytical Chemistry, 416(24), 5281\u20135293."},{"id":"https://doi.org/10.1038/s41467-022-33157-4","unstructured":"Li, S., Hu, Y., Li, A., Lin, J., Hsieh, K., Schneiderman, Z., Zhang, P., Zhu, Y., Qiu, C., Kokkoli, E., Wang, T.-H., & Mao, H.-Q. (2022). Payload distribution and capacity of mRNA lipid nanoparticles. Nature Communications, 13(1)."},{"id":"https://doi.org/10.1002/adfm.202523042","unstructured":"Bird, S., Smith, C., Habibi, N., Rivera, S., Mortezazadeh, S., Martin, R., Geilich, B. M., Besin, G., & Demeler, B. (2025). High\u2010Resolution Characterization of Protein\u2010Conjugated, mRNA\u2010Loaded Lipid Nanoparticles by Analytical Ultracentrifugation. Advanced Functional Materials."},{"id":"https://doi.org/10.1038/s41598-023-42274-z","unstructured":"Graewert, M. A., Wilhelmy, C., Bacic, T., Schumacher, J., Blanchet, C., Meier, F., Drexel, R., Welz, R., Kolb, B., Bartels, K., Nawroth, T., Klein, T., Svergun, D., Langguth, P., & Haas, H. (2023). Quantitative size-resolved characterization of mRNA nanoparticles by in-line coupling of asymmetrical-flow field-flow fractionation with small angle X-ray scattering. Scientific Reports, 13(1)."},{"id":"https://doi.org/10.1021/acs.analchem.6c02624","unstructured":"Sullivan, K. K., Wright, K., Kim, A., Gilmore, J., Dexter, J., Umstead, R., Reid, R., Bhartia, R., Ganim, Z., & Seepersaud, M. (2026). Deep-UV Resonance Raman Spectroscopy Enables Direct Quantification of mRNA in Intact Lipid Nanoparticle Formulations. Analytical Chemistry, 98(22), 16704\u201316707."},{"id":"https://doi.org/10.1038/s41587-024-02420-y","unstructured":"Huber, F., Arnaud, M., Stevenson, B. J., Michaux, J., Benedetti, F., Thevenet, J., Bobisse, S., Chiffelle, J., Gehert, T., M\u00fcller, M., Pak, H., Kr\u00e4mer, A. I., Altimiras, E. R., Racle, J., Taillandier-Coindard, M., Muehlethaler, K., Auger, A., Saugy, D., Murgues, B., \u2026 Bassani-Sternberg, M. (2024). A comprehensive proteogenomic pipeline for neoantigen discovery to advance personalized cancer immunotherapy. Nature Biotechnology, 43(8), 1360\u20131372."}],"rid":"d5kk0-xc162","summary":"The Phase 3 melanoma topline result, neoantigen design, and the analytical challenge of controlling a drug whose coding sequence is individualized for each patient","title":"Intismeran Autogene: From Resected Tumor to Patient-Specific mRNA Therapy","updated_at":1790753426,"url":"https://abrahamfinny.substack.com/p/intismeran-autogene-from-resected","version":"v1"},{"authors":[{"contributor_roles":[],"name":"Pensoft Editorial Team"}],"blog":{"authors":null,"community_id":"1e54953c-587a-4743-89af-ebd2cb45660c","created":1789516800,"current_feed_url":null,"description":null,"doi":"https://doi.org/10.59350/pensoft","favicon":"https://rogue-scholar.org/api/communities/1e54953c-587a-4743-89af-ebd2cb45660c/logo","feed_format":"application/atom+xml","feed_url":"https://blog.pensoft.net/feed/atom/","filter":null,"generator":"WordPress","home_page_url":"https://blog.pensoft.net/","issn":null,"language":"eng","license":"https://creativecommons.org/licenses/by/4.0/legalcode","prefix":"10.59350","relative_url":null,"secure":true,"slug":"pensoft","status":"active","subfield":"2303","title":"Pensoft Blog","updated":1790685647,"use_api":true},"blog_name":"Pensoft Blog","blog_slug":"pensoft","content_html":"\n\nBetween 13-17 September 2026, Pensoft Publishers joined the Nordic Oikos-Gf\u00d6 2026 conference, jointly organised by the Nordic Society Oikos and the Ecological Society of Germany, Austria and Switzerland (Gf\u00d6) in Odense, Denmark.
\n\n\n\nThemed \"Integrating the Diversity of Ecology\", the event welcomed over 1,000 researchers in the field from across career stages, who had the opportunity to enjoy conference talks across 36 thematic sessions, as many as six keynote talks, two poster sessions, several open society meetings and quite a few social events, including excursions and a conference dinner.
\n\n\n\nThroughout the scientific event, conference delegates had the opportunity to also explore the various publishing services offered by Pensoft to authors of research, journal managers, conference organisers; the diverse portfolio of almost 100 open-access peer-reviewed journals published on Pensoft's ARPHA Publishing Platform, as well as the Horizon Europe-funded project OBSGESSION, dedicated to deploying Earth Observation data, in-situ research, and ecological models with the aim to better understand and predict changes in terrestrial and freshwater biodiversity.\u00a0
\n\n\n\nAs a leader of the \"Dissemination, Multi-stakeholder outreach and synergies\" work package at OBSGESSION, Pensoft is tasked with stakeholder engagement and community building, ultimately contributing to the high impact of the resulting outputs, methodologies and policy recommendations that aim to strengthen public understanding of biodiversity change.
\n\n\n\n*
\n\n\n\nPensoft's team was delighted to meet up with dedicated readers, authors, reviewers, and editors from across Pensoft's portfolio, as well as introduce Pensoft journals to attendees discovering them for the first time.
\n\n\n\nUnsurprisingly, well-established society journals published by Pensoft, such as Frontiers of Biogeography, NeoBiota, Vegetation Classification and Survey, Vegetation Ecology and Diversity and Italian Botanist drew plenty of attention, alongside long-standing Pensoft's own titles like Nature Conservation, PhytoKeys, One Ecosystem and the Biodiversity Data Journal.
\n\n\n\nVisitors were also particularly intrigued by several newly launched Pensoft journals. Developed in close collaboration with passionate researchers, these fresh outlets directly address key gaps in academic publishing.
\n\n\n\nOne of the newly launched journals in the spotlight was Agricultural and Ecological Modelling (AEM), which provides a unique scholarly outlet thanks to its modern approach to publishing by increasing transparency, giving credit to research objects across the entire modelling research lifecycle. This is achieved through publishing specialised article types, such as: Formal Model, Model Testing and Calibration, Model Implementation and Documentation, Data Papers and Software Descriptions. These new publishing formats are designed to keep pace with advances in Modelling research and to make it FAIR (Findable, Accessible, Interoperable, and Reusable) by enabling scholarly credit for diverse research outputs, often created by different groups of authors. For the time being, AEM remains a Diamond Open Access journal, meaning that it will not charge authors for open-access publication.\u00a0
\n\n\n\nOther recently founded journals addressing emerging and fast-developing disciplines within the ecological field that took the spotlight were:
\n\n\n\nAll of these journals operate with no article fees for either authors, nor readers.
\n\n\n\nTo receive updates on new publications and relevant news, sign up for Email Alerts on any journal's homepage. Creating a Pensoft account also allows users to\u00a0 easily customise their notification preferences, follow additional journals, or filter updates by research topic. You can also follow journals on social media, including BlueSky, Facebook and Linkedin.
\n\n\n\n*
\n\n\n\nOne of the six keynote talks were presented by Prof. Dr. Isabell Hensen (Martin Luther University Halle, Germany), who received the Gf\u00d6 Honorary Medal 2026 for her leading, forward-looking contributions to ecological research and the scientific community. Her work examines how genetic variation, environmental conditions, disturbance, and plant-animal interactions shape plant populations, with particular emphasis on rare species, biological invasions, and the regeneration of degraded forests.
\n\n\n\nIn her talk, titled \"One spark is enough: regeneration ecology of Andean forests under global change\", she reported on post-fire regeneration processes in tropical Andean forests, based on long-term field experiments and observational studies.
\n\n\n\nHensen focused on the well-studied endemic plant genus Polylepis, which forms the world's highest-elevation forests and illustrates how fire has shaped ecosystem dynamics over millennia. Her talk also mentioned a 2022 monograph of the genus, published in the Pensoft journal PhytoKeys by Tatiana Erika Boza Espinoza (Pontificia Universidad Cat\u00f3lica del Per\u00fa) and Michael Kessler (University of Zurich), which brought together results from 35 years of field and herbarium experience.
\n\n\n\n*
\n\n\n\nOn Wednesday, Pensoft's Iva Boyadzhieva delivered a presentation titled “Scholarly Communication: Lessons from the Publisher’s Kitchen”. She highlighted the vital role publishers play in helping researchers communicate their insights not only to peers, but also to decision-makers and the public. Emphasizing the publisher’s responsibility to support authors with robust infrastructure and dedicated human expertise, she outlined how Pensoft works to FAIRify scholarly publishing and future-proof scientific knowledge in an era of rapid AI development and expanding research output.
\n\n\n\nTo relive the conference by exploring the detailed programme alongside the full abstracts, visit the conference website.
\n\n\n\nKeep up with future updates and news from Pensoft by following us on Bluesky, Facebook, X, and LinkedIn.\u00a0
\n\n","doi":"https://doi.org/10.59350/0261d-tmz21","guid":"https://blog.pensoft.net/?p=20944","image":"https://blog.pensoft.net/wp-content/uploads/2026/09/SciComm-PR-2026-Instagram-2026-09-25T164847.347-e1790348402124.png","language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","published_at":1790640000,"rid":"1zdgz-txj12","summary":"Between 13-17 September 2026, Pensoft Publishers joined the Nordic Oikos-Gf\u00d6 2026 conference in Odense, Denmark.","tags":["Advances In Pollinator Research","Agricultural And Environmental Modelling","Biodiversity Data Journal","BioRisk","Frontiers Of Biogeography"],"title":"Pensoft at the Nordic Oikos-Gf\u00d6 2026 conference: Integrating the Diversity of Ecology","updated_at":1790753426,"url":"https://blog.pensoft.net/2026/09/29/pensoft-at-the-nordic-oikos-gfo-2026-conference-integrating-the-diversity-of-ecology/","version":"v1"}],"out_of":57686,"page":1,"per_page":10,"total-results":57686}![](\"https://blog.bioconductor.org/posts/2026-09-30-BioC2026-recap/media/Closing)