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Yesterday the Rogue Scholar science blog archive started using Crossref schema 5.5 that was released last Friday.
The most relevant change in the new schema for Rogue Scholar is the introduction of a dedicated content type blog post (as type posted_content, subtype blog). This makes it easier to cite blog posts with Crossref DOIs as many citation styles (e.g. APA) format blog posts differently than preprints. And it also makes it easier to recognize the content type of a reference, as previously Rogue Scholar used the generic preprint/posted_content content type.
The other important change in schema 5.5 is the support for CRediT contributor roles. Rogue Scholar already supports a wider range of contributor roles, and this is now reflected in the metadata sent to Crossref.
Rogue Scholar will over time update all more than 50K blog posts to use schema 5.5. Crossref schema 5.5 support is via the commonmeta-py library, which will also provide Crossref DOI registration functionality to other repositories using the InvenioRDM platform in the upcoming v14.0 release.
Please reach out via Slack, email, Mastodon, or Bluesky if you have any questions or comments.
Commonmeta is a set of libraries to convert scholarly metadata into different formats via an intermediary format defined by JSON Schema. The schema is now available as a v1.0 version release candidate, with v1.0 planned to be released in September.
The feature image of this post is from a presentation by Geoffrey Bilder from the early work on the Research Organization Registry (ROR) where we were both involved. The organizational identifier gap has since been addressed, making another important gap visible: the metadata to describe scholarly content, contributors and organizations with persistent identifies (via Crossref, DataCite, ORCID, ROR) and the ways to obtain them via APIs or downloadable annual data files, are fragmented and complicated. Commonmeta tries to address one important aspect of this gap, making these metadata easier to interoperate, e.g. when combining metadata about dissertations or preprints registered with Crossref or DataCite, or finding out more details (e.g. license or language) about the works found in an ORCID profile.
Work on commonmeta started with the bolognese Ruby library that I wrote in 2017 as DataCite Technical Director to support DOI content negotiation and later also DOI registration. In 2023 I announced commonmeta as I had refactored bolognese to be more generic and rewritten in Python. When I launched the Rogue Scholar science blog archive in 2023, I added Crossref DOI registration functionality to commonmeta. And in the last nine months I worked on integrating Crossref DOI registration into the InvenioRDM repository platform, version 14.0 will launch in a few weeks and has Crossref DOI registration built in.
Crossref DOI registration requires submitting XML and XML validation, features supported in commonmeta, included the latest Crossref 5.5 schema announces last week. But commonmeta itself is described in JSON and defined via JSON Schema. JSON is not only the most common serialization format for scholarly metadata (compared to XML, JSON-LD, or older formats such as bibtex or ris), but is also much easier (and faster) to work with, including the important checks for validity and consistency. Commonmeta therefore is described with JSON Schema, but until now the schema was at version 0.x and not stable. Releasing a stable v1.0 of the schema will be an important milestone.
This version v1.0 of the commonmeta schema comes with four important changes:
The latest schema can be found at https://codeberg.org/front-matter/commonmeta-schema/src/branch/main/schemas. Please reach out via email, Mastodon, Bluesky or Rogue Scholar Slack if you have any questions or comments regarding the commometa v1.0 schema release candidate.
We talk about search as though it were one reasonably well-defined activity.
Someone types words into a box. The system finds documents. Hopefully, the right ones appear near the top.
But there is no single professional tradition of search.
Instead, there are multiple communities that look at the same search process and see quite different things. They attend different conferences, read different literatures, use different vocabularies and, most importantly, disagree about what it means for search to work well.
Some are predominantly academic research communities. Some are practitioner professions, often within librarianship. Some are infrastructure ecosystems or engineering practices rather than disciplines in the conventional sense. They are not equivalent categories, and their boundaries are not clean.
This has always been true. But AI search is making it much harder to ignore, because activities that were previously distributed across separate layers and professions are increasingly being rethought, remixed and rebundled.
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In 2012, Lorcan Dempsey published an essay titled Thirteen Ways of Looking at Libraries, Discovery, and the Catalog. He borrowed Wallace Stevens's conceit because the catalogue was, as he put it, a topic about which \"we don't yet have a single story\".
Rather than force everything into one model, he looked at the catalogue from thirteen angles: web scale, the single search box, metadata sources, discovery layers, knowledge organisation, sourcing and scaling, and more.
I have been thinking about something similar for academic search, but from a different angle of expertise.
What happens when ten (or maybe eleven) different kinds of specialists whose work or jobs impact search look at the same search box?
Two caveats before I begin. First, these are traditions, not boxes, and I will return to how much they leak into one another. Second, my descriptions may be somewhat reductive and the representative names under each tradition are merely illustrative, reflecting my own limited understanding of each area as a generalist and my own blind spots. No omission should be read as a judgement.
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For the information literacy tradition, search is primarily something people learn to do.
Can the user turn a vague question into useful search terms? Do they understand the difference between a database and the open web? Can they evaluate authority, recognise bias and avoid accepting the first plausible result? More recently, this has expanded into fact-checking, misinformation and the critical evaluation of AI-generated answers.
This is primarily a practitioner and pedagogical tradition, although it has a substantial academic literature.
Good search: produces a more capable and critical searcher.
Where they gather: LILAC, LOEX, ECIL and the community around the ACRL Framework.
Representative people: Alison Head, Barbara Fister, Alison Hicks, Sheila Webber, Mike Caulfield, Char Booth and Margy MacMillan.
Contribution to the stack: the human layer: evaluation habits, source criticism and the insistence that searching is a learnable intellectual skill rather than merely a product feature.
Potential blind spot: what happens inside the machinery after the user presses search1.
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People rarely begin with a perfectly formed information need. They learn what they are looking for by searching, reading, reformulating and sometimes abandoning their original question.
From this perspective, search is not a single transaction. It is an interaction among a person, a task, an interface and a changing state of knowledge.
This is why a system that produces an impressive answer to the first question is not necessarily supporting the complete information-seeking process.
Unlike information literacy (#1), this is primarily an academic research tradition. Information literacy tends to ask what people should learn to do, with librarians focusing particularly on just the academic context. Information behaviour more often studies what people actually do, why they do it and how their needs change. But both are closely related.
Good search: supports exploration and helps the user make progress, even when the original query is confused or incomplete.
Where they gather: ASIS&T, Information Seeking in Context and the ACM SIGIR Conference on Human Information Interaction and Retrieval (CHIIR), where this tradition and information retrieval (#8) actually talk to one another.
Representative people: Marcia Bates, Carol Kuhlthau, Nicholas Belkin and, among contemporary researchers, Chirag Shah.
Contribution to the stack: models showing that information needs evolve during a search, and that sessions and tasks, rather than isolated queries, are often the real unit of analysis.
Potential blind spot: its insights into evolving needs are frequently ignored by vendors or at best reduced to interface features (e.g. citation trails, facets) rather than shaping the underlying retrieval process.
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For evidence synthesis, search is not merely a convenient way to find several good papers. It is part of the research method.
The search must be sensitive, transparent and defensible. Concepts must be represented adequately. Appropriate databases must be selected. Controlled vocabulary and free-text terms must be combined. The strategy should be reported, reviewed and tested against known relevant studies.
This is mostly a professional and methodological tradition2, especially among information specialists, systematic reviewers and health librarians. But it also has a growing research community studying search methods, automation and the validity of different retrieval approaches3.
Good search: retrieves a sufficiently complete body of evidence and leaves an audit trail.
Where they gather: the Cochrane and Campbell colloquia, ESMARConf, EAHIL and the health library associations.
Representative people: Wichor Bramer, Siw Waffenschmidt, Julie Glanville, Farhad Shokraneh and Dean Giustini.
Contribution to the stack: probably the strongest culture of methodological accountability in search: PRESS peer review, PRISMA-S reporting, validated filters and the radical idea that a search strategy is a citable research object.
Potential blind spot: the field has traditionally focused on auditing search strategies far more closely than the ranking systems applied to their results. This made sense when every retrieved record was screened and ranking affected only the order in which records appeared.
Evidence-synthesis specialists have therefore been understandably cautious about relevance ranking. PubMed's machine-learning Best Match algorithm has existed since 2017 and became the default in 2020, but using it in a systematic review remains controversial even after almost a decade4. As a result, experts from this tradition may underestimate the value of better ranking using more modern retrieval techniques.
That said, the field is becoming more comfortable with ranking for screening prioritisation and technology-assisted review, particularly where every record remains available for screening or where safeguards make the risk of missed studies explicit and testable5.
This is one of the clearest overlaps with the information retrieval community (#8): evidence synthesis imports active learning, screening prioritisation and stopping methods from IR, but subjects them to unusually demanding expectations for recall, transparency and reproducibility.
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Search engines do not retrieve the things themselves. They retrieve representations of things.
Is this the same author or two people with similar names? Are these records different works, different versions or duplicates? How should a subject be expressed? Which relationships among authors, institutions, publications and concepts have been captured?
Embeddings do not make these problems disappear. They may simply hide them until a system confidently merges the wrong author, work, institution or version.
This is predominantly a professional tradition rooted in cataloguing, knowledge organisation and bibliographic control, although it also has a strong theoretical and research wing.
Good search: depends on entities being represented accurately and consistently.
Where they gather: DCMI, SWIB, IFLA cataloguing meetings and the linked-data-for-libraries community.
Representative people: Karen Coyle and Ashleigh Faith6.
Contribution to the stack: identity itself: authority control, work modelling and the disambiguation that stops a system merging two authors who happen to share a name.
Potential blind spot: metadata quality is still too often judged through conformance to standards rather than demonstrated effects on retrieval, discovery and user outcomes.
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Search quality depends partly on what information is available for machines to process. In academic search, this boils down to questions like:
Are abstracts open? Are references available through open citation data? Do publications have DOIs? Are authors connected through ORCID and institutions through ROR? Can repository records be harvested and reused? Can a commercial system legally index and display the relevant content?
A supposedly intelligent search system cannot use signals it cannot access. Famous and well-documented resources tend to perform better partly because information about them is widely available. Content hidden behind contractual, technical and metadata barriers remains harder to discover.
This is less a single profession than an ecosystem. It includes persistent-identifier organisations, standards bodies, librarians, publishers, technologists, policy researchers and advocates for open science.
Good search: requires scholarly information that is machine-readable, interoperable, reusable and sustainably governed.
Where they gather: FORCE11, PIDfest, Crossref events and the communities around ORCID, ROR, DataCite, OpenAlex, OpenCitations and COAR.
Representative people: Geoffrey Bilder, Cameron Neylon, Jason Priem, Bianca Kramer, Martin Fenner, Kathleen Shearer, Ginny Hendricks, Maria Gould and John Chodacki.
Contribution to the stack: the identifiers, relationships and open corpora on which most academic AI search tool silently depends.
Potential blind spot: treating openness as the finish line. Getting data out is not the same as getting it found, ranked or used well.
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The scholarly literature is not merely a bag of documents. It is a network of authors, institutions, citations, journals, concepts and research communities.
Citation links, bibliographic coupling, co-citation and co-authorship patterns can help people navigate the literature, identify related work and locate emerging fields. They can also reproduce existing prestige, language, disciplinary and geographical biases.
This matters increasingly because academic search tools do not rely only on textual similarity. Many also exploit citation graphs, publication venues, author relationships and other scholarly signals.
Bibliometrics and scientometrics are predominantly academic research traditions, although their methods are widely applied in research evaluation, university management, funding and commercial analytics and librarians are involved in these areas. Search is not the field's only or even necessarily its central concern, but its models of the scholarly graph increasingly shape discovery and recommendation.
Good search: uses scholarly relationships intelligently without confusing visibility with relevance or citation counts with quality.
Where they gather: ISSI , STI Conference, LIS-Bibliometrics Conference, Bibliometrics and Research Impact Community (BRIC) Conference ; Scientometrics and Quantitative Science Studies on the journal side.
Representative people: Eugene Garfield, Ludo Waltman, Cassidy Sugimoto, Nees Jan van Eck, Mike Thelwall, Paul Wouters, Loet Leydesdorff and Philipp Mayr.
Contribution to the stack: the graph signals behind citation searching, related-paper recommendation and much of what makes academic AI search feel clever.
Potential blind spot: bibliometric signals developed for analysis or navigation can be absorbed into rankings and recommendations without the cautions that bibliometricians themselves attach to them.
This group also overlaps highly with open scholarly infrastructure (#6) community when they advocate for open references and metadata e.g I4OC, I4OA, Barcelona Declaration on Open Research Information
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Finding the perfect article is not much use if the user cannot open it.
Behind the friendly Full Text Available label lies a chain of machinery comprising knowledge bases, holdings, link resolvers, proxies, federated identity, single sign-on, entitlement checks, open-access routing and publisher platforms.
Retrieval asks:
What should I read?
Fulfilment asks:
Can I actually read it?
Users do not care that these are managed by different systems or departments. A broken link means that, from their perspective, the search failed.
This tradition therefore sits downstream of retrieval in a technical sense but firmly inside the end-to-end discovery experience.
Good search: ends with the user obtaining an appropriate and accessible version of the resource.
Where they gather: ER&L, NASIG, UKSG, the Charleston Conference and communities around Access initiatives and services such as SeamlessAccess, GetFTR and OpenAthens.
Representative people: Herbert Van de Sompel, Todd Carpenter, Heather Flanagan, Roger Schonfeld, Lisa Janicke Hinchliffe, Jill Emery and Graham Stone.
Contribution to the stack: the switching layer: OpenURL, knowledge bases and entitlements and the machinery that turns a citation into a document that opens.
Potential blind spot: it can establish whether a link worked, but not whether the right things were found in the first place.
Many people in this group also overlap with open scholarly infrastructure (#5), including through service on persistent-identifier and standards boards.
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For the information retrieval researcher, search is something that can be modelled and tested.
How should queries and documents be represented? Should the system use BM25, dense embeddings, learned sparse retrieval, late interaction or a hybrid pipeline? Does reranking improve the results? What happens to recall, precision, Mean Average Precision (MAP), Mean Reciprocal Rank (MRR), Normalized Discounted Cumulative Gain (NDCG) on a test collection7?
Good search: performs better against defined relevance judgements under controlled evaluation.
Where they gather: SIGIR, ECIR and CIKM, with The Text Retrieval Conference( TREC) and Conference and Labs of the Evaluation Forum (CLEF) as shared evaluation infrastructures.
Representative people: Karen Sp\u00e4rck Jones, Stephen Robertson and Ellen Voorhees; contemporary researchers include Jimmy Lin, Omar Khattab and Nandan Thakur.
Contribution to the stack: the measurement discipline itself, together with the indexing and ranking models that run from probabilistic retrieval through dense and late-interaction systems.
Potential blind spot: benchmarks rarely reproduce the awkward reality of library discovery: licensed collections, inconsistent metadata, long research tasks and users who cannot clearly state what they need.
TREC belongs firmly to this tradition. Its classic model is almost the canonical expression of academic information retrieval: shared documents and topics, ranked runs submitted by participating systems, pooled relevance judgements and common evaluation measures.
TREC has hosted tasks far beyond traditional document ranking, but it has generally brought them into the IR evaluation paradigm rather than becoming a separate tradition of its own.
Information retrieval also overlaps significantly with information behaviour (#2). Interactive information retrieval studies what happens when the neat benchmark abstraction of one query, one ranked list and one set of relevance judgements meets an actual person with an evolving information need.
CHIIR is one place where information behaviour and information retrieval genuinely meet, although the border between studying the user and optimising the system remains surprisingly durable.
Most librarians no longer have much association with this camp. A typical librarian might remember hearing about TF-IDF once in their MLIS class. There are exceptions, of course, notably in continental Europe.
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This is the practitioner counterpart of information retrieval (#8).
Industry search practitioners ask a somewhat different question from academic information retrieval researchers.
They want to know why a live, usually enterprise-scale system is failing actual users.
Which queries return nothing? Which produce bad rankings? How should relevance be judged? Did the new hybrid pipeline improve results or merely add cost and latency? Can the team diagnose a failure and make the system better next week?
The IR researcher (#8) asks whether a ranking method improves nDCG on a benchmark.
The relevance engineer (#9) asks why users searching for red shoes on Tuesday are seeing nonsense and how to fix it before Friday.
The underlying concepts overlap considerably. The professional settings, constraints and feedback loops do not.
Libraries teach search. We configure search. We procure search. We sometimes survey users about search.
Today, most libraries have much less of a professional culture around relevance engineering: systematically judging, diagnosing and improving the quality of live search results8.
Relevance engineering presupposes access to query logs, relevance judgements and the ranking configuration itself. In most library discovery products, the vendor holds all three.
Libraries should borrow the engineering discipline without blindly adopting its commercial objectives. An e-commerce system may optimise for conversion. A library should not.
Good search: can be measured, diagnosed and continuously improved in production.
Where they gather: Search Solutions, Haystack, Berlin Buzzwords, and the relevance Slack channels where much of the practical craft circulates.
Representative people: Tony Russell-Rose, Martin White, Charlie Hull, Doug Turnbull, Trey Grainger, Daniel Tunkelang, Jo Kristian Bergum and Lettie Y. Conrad.
Contribution to the stack: judgement lists, offline and online evaluation and the discipline of treating a bad result as a debuggable failure rather than a fact of life.
Potential blind spot: its methods often assume query volume, instrumented systems and rapid feedback loops that library search rarely possesses.
Search UX consultants9, publishing product managers and relevance engineers do not form one completely coherent community. Search UX also overlaps with information behaviour (#2), while product managers may sit closer to library discovery (#10). The centre of gravity here is the engineering practice of evaluating and improving a live retrieval system.
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Finally, the discovery librarian or product owner who might try (whether they know it or not) to combine all these elements colliding into library's main search system.
Primo, Summon, EBSCO Discovery Service and similar products10 combine metadata from many sources, retrieve and rank records, present facets, calculate availability and send users through different access pathways.
They are the default owners of the library search stack and often the people most responsible for how users perceive library search through the one search box on the library website.
Unfortunately, they may be expected to integrate components they cannot properly inspect, evaluate or control.
Good search: delivers a coherent end-to-end service.
Where they gather: ELUNA and IGeLU, Code4Lib and vendor platforms such as the Ex Libris Idea Exchange, where enhancement requests go to join a queue.
Representative people: Ken Varnum, Christine Stohn, Lorcan Dempsey and Marshall Breeding.
Contribution to the stack: integration and translation: the tradition whose job is to make the other nine behave as one service.
Potential blind spot: it sees the system through the vendor's administration console. What the console does not expose, the profession gradually stops asking about.
People in this group are mostly library-systems specialists and often overlap with metadata (#4), e-resource management (#7) and to a lesser extent scholarly infrastructure (#5). In my experience, however, they are less likely to be deeply embedded in information literacy (#1) or evidence synthesis (#3).
This creates gaps between librarians who are largely front-of-house and those who are largely back-of-house. The people teaching users how to search may have limited understanding of how the system retrieves and ranks11. The people configuring the system may have limited contact with users even librarians attempting complex searches beyond seeing such queries in a search log.
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There is an obvious problem with dividing search into ten traditions.
The traditions do not stay politely in their assigned boxes.
Some are intellectual descendants of others. Some pair academic research communities with practitioner counterparts. Others overlap so heavily that the placement is partly a matter of convenience.
They are also not all the same kind of entity. Information behaviour is an academic research field. E-resource management is a professional specialism. Open scholarly infrastructure is an ecosystem. Relevance engineering is an engineering practice.
Linked data is the clearest example of why the boxes leak.
It grew largely out of the metadata and knowledge-organisation tradition (#4). Authority files, identifiers and relationships among works, people, organisations and subjects existed long before anyone called them knowledge graphs.
Once those identifiers and relationships travel between catalogues, repositories, publishers, funders and research information systems, linked data becomes scholarly infrastructure (#5). ORCID is not merely a well-formed metadata field. ROR is not merely authority control for institutions. Crossref is not merely a catalogue of DOI records.
And once the resulting network is analysed or exploited for retrieval and recommendation, it overlaps with bibliometrics (#6) and information retrieval (#8).
OpenAlex is difficult to assign to any one tradition. It is simultaneously open scholarly infrastructure (#5), a scholarly knowledge graph and a source for bibliometric analysis (#6).
What unites the traditions is not institutional form. It is that each brings a distinct definition of the search problem and a distinct theory of what counts as success.
Read back through the ten definitions of good search:
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None of these definitions is wrong.
None is sufficient on its own.
The problem is not that librarians and adjacent professionals know nothing about search. There is an extraordinary amount of expertise.
The problem is that this expertise is fragmented across communities that do not share a common vocabulary, evaluation framework or model of the complete system. Often they are not even aware of each other and the lessons they can teach each other.
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This fragmentation was manageable when the search stack was comparatively stable and search primarily returned lists of records.
Different communities could concentrate on different layers. Practitioners worked on teaching query formulation, metadata, fulfilment and the integrated service. Researchers studied information behaviour, scholarly graphs and ranking algorithms.
The boundaries were never perfect, but the division of labour made sense because the layers were reasonably visible and the foundations were stable.
An information behaviour researcher could study how people formulated and reformulated their needs without understanding the details of BM25, a standard lexical retrieval and ranking algorithm used by many systems.
Today, that researcher may need to understand whether the system uses lexical retrieval, dense retrieval, learned sparse retrieval, late interaction or some hybrid (which affects what search queries work), and whether it generates a direct answer rather than simply returning a ranked list. A practitioner teaching information literacy faces the same complexity when trying to work out exactly what \"AI-powered search\" means and which component might affect learning.
AI search does not abolish these layers. Metadata remains metadata. Retrieval remains retrieval. Access remains access.
What AI search does is rebundle them into systems in which interpretation, retrieval, relevance assessment, evidence selection and synthesis repeatedly call one another in novel and different ways rather than in the same standard way.
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The family of technologies that followed the transformer architecture is changing many of these layers simultaneously12.
The major inflection point was the introduction of that architecture in 2017. From 2018 onwards, encoder models such as BERT demonstrated how effectively contextual language models could be applied to retrieval, while GPT-style decoder models showed increasingly capable generation. Retrieval-augmented generation brought the two together explicitly in 2020.
Even into the early 2020s, while academic researchers in information retrieval and NLP (#8), selected industry research teams and a handful of start-ups (#9) were excitedly experimenting with transformer-based approaches to search, the public, including most librarians, remained generally unaware of the scale of the changes coming their way13.
Changes such as Bing and Google incorporating BERT and related transformer models into parts of their query-understanding and ranking systems in 2019 happened largely under the hood and went mostly unnoticed by users, except in the SEO community, because search engines continued to present the familiar list of results.
The launch of ChatGPT on 30 November 2022 triggered a much more visible awakening. The focus was no longer merely on using transformer models to improve matching and ranking, but on using generative models to produce answers directly. That is the point at which every other community became aware that the foundations of search were shaking. More recently, we have seen the rise of \"Deep Research\" and \"agentic search\" in 2024/2025.
A language model may now interpret the user's question, translate it into several searches, assess and rerank what comes back, extract and reconcile evidence, compose an answer, and then decide it lacks enough evidence and search again.
These are not merely improvements to the ranking algorithm. Activities that once occurred before retrieval, during retrieval and after retrieval are now orchestrated inside the same model-and-harness system.
The query is no longer necessarily a query. It may be a conversation, a research objective or an underspecified task that the system must progressively interpret.
The result is no longer necessarily a result list. It may be a direct answer, a literature map, a table of extracted findings, a generated report or a set of claims linked to supporting passages.
Ranking is no longer necessarily a single operation performed once against a fixed corpus. Documents may be retrieved, judged, discarded, searched within, compared against one another and used to generate further searches.
Relevance itself becomes more complicated. A document may be useful not because it directly answers the original question but because it supplies a missing entity, suggests a better query, contradicts an emerging conclusion or points the agent towards another source.
The old layers still exist. But they are now more tightly coupled and recursively invoked.
Everyone sees part of the problem. Nobody automatically owns the complete chain.
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There is now an eleventh community in the room14.
It is not yet a stable professional tradition in the way that information retrieval or knowledge organisation is. It is better understood as an emerging coalition of NLP researchers, IR researchers, model providers, software engineers, framework developers and application builders.
They gather around retrieval-augmented generation, tool use, deep research and agents rather than catalogues and indexes.
They borrow the language of information retrieval, including recall, precision and nDCG. But they also introduce different definitions of success: faithfulness, groundedness, citation correctness, answer relevance and task completion.
Is this eleventh community simply information retrieval (#8) and its industry or enterprise counterpart (#9) in disguise?
Maybe. ColBERT was published at SIGIR. The TREC Deep Learning Track evaluated neural rankers , and TREC inaugurated a separate RAG Track in 2024. Researchers such as Jimmy Lin, Omar Khattab and Nandan Thakur are clearly from the IR tradition.
But at least as much of the inheritance comes from natural language processing. Dense passage retrieval, open-domain question answering and the original RAG paper emerged substantially from NLP research rather than conventional search engineering15. The original RAG work explicitly combined the parametric memory of a pretrained sequence-to-sequence model with a non-parametric dense index.
Good search: produces an answer or research output that is useful, supported by evidence and faithful to the retrieved sources.
Where they gather: arXiv, NeurIPS and ACL on the research side; framework communities such as Haystack's Discord, LangChain meetups and vendor communities such as the OpenAI Developer Community on the industry side.
Representative people: Patrick Lewis from the original RAG paper, together with figures such as Jerry Liu from LlamaIndex as well as many of the same names from Information Retrieval (#8) and relevance engineering (#9)
Contribution to the stack: the generation and orchestration layers, and the reframing of retrieval as one component inside a larger reasoning and tool-use loop.
Potential blind spot: treating retrieval as a solved commodity and repeatedly rediscovering, at considerable cost, lessons that information retrieval researchers and search engineers have accumulated over decades16.
But what does this mean for libraries?
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Nobody can become a deep expert in all eleven traditions17.
That would be an absurd job description18.
So let me be clear about what I am proposing. The discovery architect is not a job title to put in the next vacancy advertisement. It is a capability, and in most libraries it will be distributed across a team rather than embodied in one person.
What the capability requires is that somebody, or most likely several somebodies between them, understands that the different traditions exist, recognises how they fit together and can identify when a particular form of expertise is missing, rather than viewing academic search narrowly through one or two traditions.
Where does this sit organisationally? The obvious home is wherever discovery, systems and collections expertise already meet. In some libraries that is a discovery or systems team or community. In others it may be a research support or scholarly communication unit that has drifted into AI evaluation work. The label matters less than the mandate: someone must own the question of whether search is working, end to end, and no single existing role currently does.
It's fairly common for academic libraries to have a discovery committee or taskforce. But, no matter what the team is called or how it is organized, most libraries will lack expertise that spans all the communities with current staff expertise e.g. Information retrieval (#8) or Relevance Engineering (#9). This is not necessarily a call to go hire such expertise but rather a call to broad one's understanding of such areas.
The conventional discovery professional operates and integrates the present library discovery service.
The discovery architect capability spans the broader AI-mediated scholarly search environment: discovery indexes, scholarly graphs, metadata, access systems, search engines, AI answer systems, retrieval tools and agentic workflows.
Not merely a Primo administrator.
Not merely a literature review search trainer.
Not merely a metadata librarian.
Not merely an AI enthusiast who has learnt to spell RAG.
The discovery architect team as a whole should be able to move across the scholarly search stack, translate among professional communities and ask the questions that individual systems and specialists may overlook.
When an AI search product performs badly, this team should be able to ask:
Is the problem corpus coverage?
Is it missing or poor metadata?
Is it entity disambiguation?
Is it retrieval?
Is it ranking?
Is it the search depth or stopping rule?
Is it a distorted graph signal?
Is it poor evidence selection?
Is the generated claim unsupported?
Is the source inaccessible?
Is the model relying too heavily on its own parametric knowledge?
Are we evaluating the wrong outcome?
They do not need to solve every problem themselves.
They need to recognise what kind of problem it is and which tradition's expertise is required.
There is an important caveat.
Recognising the type of problem is not the same as being allowed to examine it.
Asking whether the fault lies in ranking achieves little if nobody outside the vendor can inspect the ranking.
The discovery architect team therefore carries a procurement agenda as well as a diagnostic one:
access to query logs;
the ability to conduct relevance judgements;
documentation of indexed coverage;
transparency about ranking signals;
visibility into retrieval and evidence-selection stages;
stable interfaces for evaluation;
the ability to export or audit intermediate results.
If vendors will not open these layers, the eleven traditions can meet, exchange vocabulary and produce sophisticated hypotheses.
But they can only speculate together.
There is a question underneath all of this that the eleventh community is only beginning to ask, and that the rest of us should be asking constantly.
When an agentic search system performs well, where does the capability actually live? In the model, in the retriever, or in the harness around them?
In Part 2, I will take up that question, look at what controlled benchmarks such as BrowseComp-Plus are starting to reveal about it, and ask what it means for the practical question libraries face: when a vendor shows us an impressive agentic search system, what exactly are we licensing?
To be fair, I estimate that 30\u201350 per cent of this blog's readership is in this community or tradition, showing eagerness to learn more.
Probably the second-largest community of people reading my blog.
There is growing interest in SWAR (Study Within A Review) methodology , including applying it for AI tools.
PubMed itself says itsellf that PubMed Best Match feature was not designed for comprehensive or systematic searching
The current Cochrane Handbook has a section that recognises a role for relevance ranking in screening prioritisation and other forms of technology-assisted review.
This brief list of people reflects my limited depth of understanding in this area.
Evidence synthesis (#3) often treats searching and screening as binary classification. Sensitivity corresponds to recall and PPV to precision; specificity and NPV measure how reliably irrelevant records are excluded. Other metrics used include F1, and Work Saved over Sampling at 95% recall (WSS@95). Evidence synthesis generally prioritises high sensitivity and safe exclusion, whereas information retrieval (#8) often evaluates ranked order using MAP, MRR (particularly useful when only the first relevant result matters) and NDCG.
To be fair, traditionally many of the systems we manage have traditionally neither allowed nor encouraged us to tweak the parameters that affect relevance ranking. Though Ex Libris's Primo does allow this to some extent, most libraries just accept the default settings.
There is a broader library UX (LibUX) community, most notably Andy Priestner, Aaron Schmidt and Ned Potter, though this is not specific to search. Some members, such as Matt Borg and Matthew Reidsma, have also studied user search behaviour
A minority of academic libraries run open-source solutions such as VuFind and Blacklight.
From personal experience, many information literacy librarians outside my institution have asked me how Primo and Summon work.
Before transformer-based models reshaped retrieval, leading search systems in the 2010s typically combined lexical retrieval with learning-to-rank, enriched by behavioural, entity and sometimes knowledge-graph signals. Neural ranking models were already emerging, but BERT and later transformers produced a much larger shift in how queries and documents could be represented and matched.
I remember seeing such approaches emerge when researchers created search engines over the COVID-19 Open Research Dataset (CORD-19), using BERT-style models for retrieval and ranking alongside early question-answering and generative components. In the early 2020s, some librarians were becoming aware of citation-based mapping services such as Connected Papers and ResearchRabbit, which belong to a different class of tools.
Dempsey had thirteen ways. I have stopped at eleven (ten plus an emerging one), and I am confident readers can supply the rest. Some candidates I considered and left out: archival description and discovery is a genuine tradition of its own, with finding aids, EAD and a rather different idea of what a record represents. Expert search outside health, particularly patent and legal search, shares the evidence-synthesis community's obsession with recall and defensibility but almost never talks to it. And the web SEO community, now busily reinventing itself as generative engine optimisation, looks at search entirely from the supply side. It is only a matter of time before researchers ask about the academic-search version: not how to find things, but how to be found. There are others, of course.
When I was trying to read frontier papers about \"generative search\", I was often confused because the NLP and information retrieval communities seemed to be writing about many of the same technologies from different angles. This was especially clear in work on tasks such as question answering. This has been written about fairly frequently in the literature -see here for historical discussion up to 2020 and here for a more technical and up-to-date comparison of Information retrieval to \"AI\" (mostly NLP).
The clearest example is the rediscovery of lexical baselines. Early RAG builders reached instinctively for dense embeddings alone, only to find that hybrid pipelines including boring old BM25 were hard to beat, a result the BEIR benchmark had already demonstrated for out-of-domain retrieval.
I am a generalist, but my knowledge does not span all these communities. Early in my career, I studied and led the implementation of library discovery systems (#10). Since then, I have developed a largely self-taught interest in information literacy (#1), evidence synthesis (#3), bibliometrics (#6) and information retrieval (#8). I have also been somewhat involved in open scholarly infrastructure (#5) and, to some extent, the access and fulfilment chain (#7).
I know there are librarians with \"discovery\" in their titles, but they are likely to be mostly in community #10 and #4.
Back in June I wrote about going the entire month of July without using AI. I wrote about why I'm doing it and what I hoped to achieve.
I failed.
I made it about halfway through the month before I had to call it off. It started off with some personal / non-work exceptions here and there. Then some exceptions crept into my day job. Then the opportunity costs of not using AI for some of the things I had going on this month became too high.
This is a reflection on the attempt. The good, the bad, where I cheated, and where I'm at now. At >5k words it's the longest thing I've ever written on this newsletter by a large margin.
Positives
Negatives
Exceptions
Conclusions
An admission (I'm not alone): In the nearly 4 years ChatGPT and the like have been on the scene I've taken my fair share of turns writing large chunks of text for proposals, pitch decks for companies I've been involved with, responses to reviewers, and so on. And every time, without exception, I can't really recall what I wrote or easily explain the reasoning behind it. This tracks with that MIT study showing something similar. If I had to answer questions about something I wrote with heavy assistance from AI I would have struggled. Or even just going back to revise largely AI-generated text - good editors have a knack for revising other people's writing, but I hate doing it. When I'm revising my own writing, I remember how this paragraph in the approach connected to that section in the significance, and how it all ties together on the aims page. I have a mental map of where things are, because I did the hard work to consider the scaffolding and move things around during an edit.
When working with large chunks of AI-generated writing it feels like revising someone else's writing. With latest gen frontier models (Opus 4.8, GPT-5.6) the writing is probably really good, but I don't start the process with a map of the throughlines connecting parts of the narrative. The same thing is true with code. Writing code is harder. Reading some else's code (even if it's good, perhaps especially if it's good) can be much harder.
An immediate benefit I got out of my few weeks of not using AI for writing was having a much tighter connection to the text I write. For sure, it takes much longer to get words on the page, but revisions are much easier, and I feel much more confident in follow-up discussion than if I had used AI extensively in ideation and creation.
The same is true with reading. Sure, an AI can summarize a paper for me, pulling out key ideas and such. I wrote about this here recently.
But my understanding and later recall of the paper, the motivations behind the experiments, the design (and its flaws), the execution (and flaws there too), are nowhere near where they'd be if I'd have read the paper (or at least the intro, discussion, and figures). Most importantly, I develop a sense of where the gaps are (and thus where the opportunities are) much more easily when I'm reading a paper than when I'm relying on an AI summary. All things I knew before starting this experiment, but the self-experimentation reinforced my priors.
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Will I go back to never using AI while writing? No. It's a helpful tool, and I'll use it when it serves me, albeit a little more judiciously. However, when I'm using AI for writing I end up spending far more time than I want removing the AI-isms. So much so that I wrote a Claude skill to remove these patterns from the writing I don't really want to do anyway (cover letters for manuscripts, for example - the abstract should suffice, don't require me to write a cover letter!).
It works well but it isn't perfect. Is going back to remove the em-dashes and the \"not x, not Y, but Z\" and the like a good use of my time? Is this the thing I worked so hard for for the past few decades, all the training, all the effort crafting my voice, all the writing I do here to try to become a better writer? Do I want to spend my time doing this at all? No. Not at all. Maybe (surely?) the AIs will get better at not using these bland and tired constructions, but I imagine something else will take the place of the emdashes and tricolon abuse and whatnot.
These past few weeks I was much happier spending more time thinking of how to phrase something or fleshing out an idea, while spending zero time managing AI and correcting all the tells. Was the writing I did myself unassisted better than writing with an AI assist followed by extensive editing? I don't know. I'd be willing to wager it might not be (test your own judgment, you'll be surprised). But I certainly enjoyed the experience of writing a lot more, and stayed in the flow for longer, without the sidebar of all my other recent Claude chats begging to distract me (see below on bugs, bikes, engines, plumbing).
Earlier this year I wrote about token anxiety and how session limits can really upset my work life balance. And I'm not in the camp of using the \"good enough\" faster/cheaper models when a better one exists. The cost of a hallucination or mistake that Kimi makes that Opus doesn't is far more expensive than the tokens I burn on Opus. I'm maxing Opus 4.8 with high or extra high thinking enabled most of the time.
When not using AI, I didn't have this session limit anxiety, and that freed up so much mental bandwidth to focus on a problem on my terms, on my timeline, rather than when my session limit restarted.
The past few weeks I had a better handle on my attention.
With no chat window open there was no sidebar of half-finished conversations begging for my attention, no checking whether the thing I asked for thirty seconds ago had finished. I wrote in longer stretches with deeper flow (being summer and not being as meeting-packed around here helps). A morning that used to splinter into a bunch of prompt-and-wait cycles, these past few weeks became a more cohesive single deep flow session.
The dopamine hit from one-shotting a working prototype is real, and I defend it below. I think that same hit fragments a writing session. Every prompt is a small slot machine, and the pull to keep feeding it competes with the slower payoff of working a on paragraph or whatever until it feels right. A couple weeks of abstinence (or at least moderation) made me notice how often I'd reach for AI without ever deciding to.
I still like writing code. I don't get to do it much in my job any more, but there's still something fun about solving a little puzzle, like figuring out a video game with very cryptic goals. It's not as valuable of a skill as it used to be, but I still enjoy it.
Much ink has been spilled on the risks and benefits of writing code with AI. I'll just note that I was able to discover or rediscover newer features of tools and libraries that the LLMs don't really know about yet. Take dplyr 1.2.0, for instance, released in February. There are some really nice expansions of the filter() function, as well as some nice additions like recode_values(). Same thing with Quarto. Quarto 1.9 has some nice goodies like PDF accessibility features and support for Typst books (which I used for the PDF version of my DSTT book).
I've been building course materials for a new genomics foundation course I'll be teaching this fall to our data science students. As I mentioned in the original post, I'm going to ask my students to struggle through some of the readings and assignments, so it's only fair if I struggle through creating those assignments and course materials.
However, one of the things I've found AI really helpful for is for building one-off web apps to demonstrate a concept. I've found vibe coding a throwaway interactive web app can take <5 minutes and can be far superior to janky diagrams I can build in PowerPoint that take much more time. For example, back in May I vibe coded this little interactive genetic drift simulator in pure HTML+JavaScript deployed to a GitHub pages site. I suppose I could have made an exception for this kind of thing, but I wanted to avoid that dopamine hit you get from one-shotting something like this, afraid of starting a slippery slope to abandon the whole AI Dry July plan (which, I eventually did). I'll probably go back to some of my materials I created over the last few weeks and vibe up a few more tools like this one.
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Another thing I missed was the kind of research that you can do with specialized tools like Consensus (like I talked about here) or even with the generic \"Deep Research\" tools in Claude/ChatGPT. On a few literature searches I ended up spending more time on this and surfaced far fewer papers than if I had used some AI help here. I suppose I could have made an exception for this one too, but it's nearly impossible to separate the \"find me papers around this topic\" from \"summarize all the papers you found on this topic.\" Most of these tools do both at once.
I don't get to write a lot of code these days, but still I had a few small projects in mind that I wanted to do this month. I ended up just not starting some of these projects at all. One involved tinkering around with an nf-core workflow for CRISPR screen analysis, and another involved getting some data from the IUCN Red List API and doing something with it (I started doing this with Claude Science right at the end of June, but put the project down these last few weeks). In both cases, I had a pretty clear picture of what I want to do, but the activation energy was just high enough in both to make me put them off. I haven't written Nextflow code in a while, and I didn't feel like reading the API docs for the IUCN project. I could have spent time on both, but (1) since I don't need to write much Nextflow I don't care that I'm deskilling there, and (2) the LLMs are great at reading API docs and helping me construct the correct call, such that doing this myself would have been more time-consuming with little/no benefit to me.
More generally, the bigger cost to me over the experiment was time, and the things I didn't do. There were a few RFPs with short turnaround times that slipped. I worked a few evenings and a weekend on a side hustle project that probably would have taken me an hour if I wasn't doing this to myself.
Writing/coding/doing/etc without AI is fine when my calendar is open, like it is over the summer with no courses, far fewer meetings, fewer demands on my time. It's a different call when I have every 15 minutes of every day scheduled this week and it's Thursday night and I have a midnight deadline.
I don't want to dress up doing things without AI as something that's always virtuous. Struggling through a complicated topic in a proposal or manuscript or code base is an investment. Struggling through administrative chores or boilerplate I'll never think about again is just slower and an inefficient waste of my time (and my time requires more water and energy usage than a Claude query). Without doubt I lost hours to friction that taught me nothing, nor sharpened any skill I care to retain.
While working up some examples for a Bioconductor lab I want to incorporate I ran into some trouble. When I hit a wall this month I noticed where I used to turn to for help. I previously turned to SeqAnswers or Biostars. Stack Overflow was the reflex for a decade, and it's a shell of what it once was. The good answers are old and the people who wrote them have scattered. It's not 2018 any more. The fallbacks have eroded, and AI is part of why. It handed me a faster tool but it drained the places I used to go when I needed help working something out. The cost of abstaining is higher now.
I had to make a few exceptions along the way. AI meeting notes I can't live without. With some of the other items, could I have done these without genAI? Sure, but it would have taken me much longer, and at great expense. The things here aren't things I care about deskilling in, because I don't really have much skill in anyway. If anything, I've learned an incredible amount from asking follow-up questions, and attempting to DIY things I never would have without AI help.
But one exception leads to another, leads to another, and so on.
I have Zoom AI companion automatically enabled on all of my meetings, even when they're in person. Unapologetically. It's a godsend. I can better engage in a conversation if I'm not scribbling down notes. And I can't write by hand that fast (and typing notes on a computer is distracting to me and my counterparts - I could be writing an essay like the one you're reading now, for all you'd know). And I rely on these notes all the time.
So I didn't turn this feature off, and I'm glad I didn't. Writing legible notes fast by hand with a pen isn't a skill I'm worried about deskilling in. Could I have asked my EA to come to all my meetings and take notes by hand? Sure, but man what an expensive waste of their time this would have been, to absolutely no one's benefit.
I'm working on slides for a genomics course I'll be teaching in the fall. We'll need to meet the federal digital accessibility compliance deadline next Spring, so I'm going ahead and taking care of this now. A big component here is adding alt-text for screen readers to the images. I don't know what PowerPoint is using behind the scenes to do this, but it's pretty good. I manually examine everyone before approving, and I haven't caught one yet that isn't perfect, or at least much better than what I could write with limited time and competing priorities.
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I wrote here a while back about how to do this in Quarto with Claude Code if you're using Quarto for slides or other materials.
I used AI liberally for mechanical/maintenance issues. I needed to troubleshoot my son's mountain bike. I was chasing gears for hours trying to troubleshoot the derailleur indexing when the culprit was actually a bent hanger. My lawnmower self-propulsion stopped working, and I used ChatGPT to help me narrow down whether it was a transmission or worn gears. I also used Chat/Claude to help me with a plumbing issue, and an issue with the rod bearings on my Kia's engine causing excessive oil consumption. Sure I guess I could have spent $100 at a bike shop, $200 at some small engine repair shop, $500 for a plumber, and $1,000+ at the chop shop, but some AI assistance and DIY elbow grease saved me thousands of dollars and many hours of hauling broken gear around to all these places.
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I recently had a conversation with a fellow Software Carpentry instructor about AI in the SWC curriculum. The discussion touched on the possibility of using AI as a learning assistant. I'm torn on this one. Using AI for these things really helped me learn more about bike components, small engine repair, etc. But I think the difference here is that there was a physical barrier. Claude could tell me to how to adjust limit screws and cable tension until things lined up, but couldn't do it for me. I physically had to put my phone down and do something with my hands in the real world. Contrast that to coding - it'll just do it for you, or at minimum be the helpful assistant, asking you, \"would you like me to implement and test this for you?\"
The next class of exception fell into insect, plant, and plant pathogen identification. I found a few really interesting moths in my garden. I wanted to confirm that indeed my hedges are covered in poison ivy. And I figured out that a palm plant in my office is infested with spider mites (and asked for treatment options). I asked what could be done about these damn lanternflies (nothing). Sure, there are other apps out there that let you do this, but don't they all or mostly use genAI behind the scenes? And there are probably local Slack or Discord servers with channels dedicated to this kind of thing, but I bet most real people on those servers are using AI before responding.
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I have a mud room / foyer between my back door and the entrance to the rest of the house. The floors needed stripping, sanding, and painting. Badly. We did a two-color diamond pattern, but before committing I tried a few color combinations by taking a photo of the room (top left) and telling Nano Banana what I had in mind, supplying candidate colors from the Behr paint catalog. This helped me visualize what the room might look like before starting the project (bottom row). We ended up going with black and white (top right).
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It helped me outdoors also. Last year we ripped out a section of the lawn and planted a bunch of natives. Long term we'd love to rip out the entire lawn and make the entire backyard a meadow filled with natives, maybe even get a few honeybee hives. I asked chat for some ideas about what this might look like after going over my preferences, and while the plants shown aren't necessarily central Virginia native species, it helped me visualize what a no-grass meadow in place of a lawn might look like.
More than anything, this exercise convinced me that I need to pay a real landscape architect to come over, in real life, and help me with design. Which, I'm doing. This will be a labor of love for years to come, and I know enough to know when I need a professional. Also, there's something not quite right about using an algorithm to design a more inviting backyard for me to touch grass, literally.
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This exception that really started the slippery slope at work.
I have a very tight deadline for a major proposal that we're leading here at UVA in partnership with several companies and university centers/departments. While I tried to (and ultimately was happy that I did) write most of what I contributed to the pre-proposal without relying on AI, using AI as a reviewer was absolutely necessary.
The solicitation itself suggested to applicants that the sponsor would be reviewing proposals with AI. At least that's what the Proposal Evaluation \u2192 Proposal Review Process \u2192 Handling sensitive information section seems to suggest. I have very reliable intelligence to suggest that other federal funders are also doing this.
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As such, not using AI during prep for continuous review as the role of an evaluator in the sponsor's office wouldn't be some honorable thing to brag about. It would just be stubborn and foolish. And disrespectful to my colleagues - we're all spending hundreds of ours collectively on this proposal, and we should do whatever it takes to maximize the chances this gets funded. This isn't a typical NIH/NSF proposal. It's a behemoth with tons of compliance checks, any one of which could get your proposal that cost thousands of hours collectively, dismissed without review as being nonconforming.
Back in the GPT-4o / Opus 3 days, or even now with most open-weight models, it was the human's job to fact check the AI. Now I'm getting AI to fact check me.
For a few weeks now I've been writing a mini review paper on AI and Biosecurity, summarizing a lot of the truly great work coming out of RAND, NTI, SecureBio, Active Site, CLTR, UK AISI, US CAISI, IGSC, Microsoft, NIST, IBBIS, MIT, Scale AI, Berkeley, National Academies, NSCEB, Johns Hopkins, several of the frontier labs, and many others. The paper is expanding on many of the topics I've covered here.
It started off as another newsletter post here, but became so long and extensive that decided to turn this into a paper I'll try to publish soon. And I did this without using AI beyond some citation network analysis and literature discovery.
I read all these papers I'm citing in the review. Every one of them. And I wrote the review without using AI. However, with a few hundred papers, preprints, whitepapers, model cards, benchmarking websites, conference talks, and so on, and because I'm citing numbers and such in tables, I have a bit of anxiety about publishing this, even in preprint form, for fear that I've misrepresented a benchmarking claim or study design (at best), or misstated a result or numeric value (at worst). This is a very small community, and many authors are colleagues.
So I set up a simple but effective fact-checking agent with Claude Cowork. I exported the Zotero library along with a BiBTeX file of all the references I used in my manuscript. The BiBTeX file pointed to the location of each citation's PDF file on disk. I then got Claude to read every sentence, and where there's a number or claim backed by a citation, to read the actual PDF of the thing I'm citing and ensure that I didn't misrepresent the claim, and to return a table of claims and numbers cited, together with a judgment (verified, questionable, misrepresentation, inaccurate).
This thing burned hundreds of dollars in tokens reading hundreds of PDFs, making notes, and fact-checking my own writing, all using Opus 4.8-extra. But worth it to avoid even a single, simple human slip.
If you've made it this far, thanks for reading. And if you're an expert in the AIxBio / Biosecurity world (especially if you're at one of the organizations and likely one of the authors I've cited in this manuscript), and you want to review it and offer comments on this manuscript even before I publish it as a preprint, please email me (address here). Also, if you're interested in being a peer-reviewer if/when I eventually try to get it published in a journal, let me know that also.
As noted in the Negatives section, the opportunity costs were extraordinarily high, too high for me to take a month of complete abstinence. A few examples:
Anthropic redeployed Fable in the beginning of July, giving everyone on a subscription plan a week to use it before it dropped back to API rates only. I had a sort of work project, sort of side project thing in mind \u2014 something that would comb through the grants.gov and sam.gov postings using their respective APIs, then customize alerts to me based on my interest with some AI thing reading through the solicitation details. Fable one-shotted this thing, perfectly, installable with uv, unit tests with pytest. I still have work to do to add some features I'd like to add before releasing, but it's a great start, one that I wouldn't have started at all without AI.
The UVA AI Upskilling series is still going strong over the summer. I'm attending several of these. Hard to avoid using AI at an AI upskilling workshop.
In designing my upcoming fall course, I just wanted to see how well Claude could complete the assignments.
Answer: perfectly, instantly, and cheaply even with Sonnet 5. For both code and paper discussion assignments.
See also my previous lamentation on this topic, and toward the bottom of this post on why code isn't going to be front and center in this course.
So where does this leave me? Not as an abstainer.
There used to be a time when intelligent people could make a defensible argument that these tools aren't useful, they make too many mistakes, or that they're just worthless next-token predictors. That point has passed, and these arguments are no longer opinions. They're just factually wrong, incorrect statements. Driven by identity more than evidence.
The rule I'm keeping from my AI (semi-) Dry July fortnight is about which productive struggles are worth keeping. Some amount of friction is useful. At least looking at my code while debugging helps me consider how the system fits together, the way fixing one broken derailleur means I can fix the next. Other frictions teach me nothing, and I usually don't feel diminished by handing it off. The category error I'd drifted into was treating every cognitive task like something I could offload, outsourcing the parts that were actually mine to learn and defend.
So the policy, for me (which will evolve, no doubt). Prose I'll have to defend, proposals I'll write, papers in my own field, lectures I'll deliver, and some aspects of software architecture: these are all are mine to do (or at least mine to start off with). One-off demos, first-pass literature scans, editing, and genuinely disposable work I don't care about can and will go to the machine. And as I've mentioned in previous posts, I have never and will never use AI speak for me: if I'm expressing an opinion, or a sentence ever has an \"I\" in it, it's written by me, with zero LLMs speaking for me.
Teaching materials will probably land on a 90/10 split in favor of not using AI. Not because AI can't make good teaching materials, but because I'll be asking students to struggle through the material so it's only fair that I struggle through design, and also for the reasons outlined in Positive #1 (mental maps and recall).
I don't know what higher education will look like in 5 years, but abstinence-only education has never worked for anything, and I don't think that you should only engage in data science if you're in a long-term committed relationship with a card-carrying Data Scientist\u2122. When, how, and to what degree to thoughtfully incorporate AI into my classroom is something I don't have a perfect answer to yet.
These few weeks of self-experimentation didn't change my mind so much as sharpen what I already knew. AI does result in deskilling. Some things I'm okay deskilling in, and others I'm not. I'm going to be more intentional with what I hand off to the LLMs, and I'm no longer handing over wholesale the work I'd miss if I forgot how to do it, or anything I'll ever need to understand, defend, revise, and own.
\u2042
Over 15 years ago I made a terrible life decision. I tried giving up caffeine. I had developed a deep physiological dependency, getting severe headaches without my morning megadose. I traveled and I went out camping and backpacking more than I do now, and carrying coffee making supplies wasn't worth the weight and space and hassle it required. I thought I could rid myself of the addition and live more freely without the drug. It took me months to completely cut caffeine, and even months after having zero caffeine the craving never went away. I always felt like a part of my brain wasn't firing on all cylinders, and the craving for caffeine was still preoccupying.
I started adding caffeine back into my life on a very limited basis, once per week or less, and found the drug absolutely supercharged my productivity, mood, and focus (unsurprisingly). Once a week turned into twice per week, every day, and very soon back to multiple cups daily. The point is, even long after the drug had metabolized out of my system I always felt like I was missing a limb, like some part of my cognition had the brakes engaged, a governor module I couldn't remove. A physical addiction in the literal sense.
Michael Pollan describes addiction in his 2021 book, This Is Your Mind on Plants. In an earlier interview with NPR he notes:
\"I think the word 'addiction' has a lot of moral baggage attached to it,\" he says. \"As [Johns Hopkins researcher] Roland Griffiths told me, if you have a steady supply of something, you can afford it and it's not interfering with your life, there's nothing wrong with being addicted.\"
Is access to generative AI like access to my morning coffee? I have a steady supply. I can afford it (at least for now, as long as my employer keeps footing the bill). Is it interfering with my life? This experiment didn't give me a straight answer to that one.
","doi":"https://doi.org/10.59350/fk81y-9sh42","guid":"200128706","image":"https://substackcdn.com/image/fetch/$s_!PRie!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1e9537f0-ba39-4528-b4b5-9f5f6d6fe0c6_3128x1167.png","language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","published_at":1784332800,"rid":"3wmd0-rb806","summary":"Reflections on trying to go a month without AI. I made it two weeks (sort of). 5.2k words, 23 minutes reading time.","tags":["AI"],"title":"AI Dry July: Aborted","updated_at":1784370435,"url":"https://blog.stephenturner.us/p/ai-dry-july-aborted","version":"v1"}},{"document":{"authors":[{"affiliation":[{"id":"https://ror.org/041kmwe10","name":"Imperial College London"}],"contributor_roles":[],"family":"Rzepa","given":"Henry","url":"https://orcid.org/0000-0002-8635-8390"}],"blog":{"authors":[{"name":"Henry Rzepa","url":"https://orcid.org/0000-0002-8635-8390"}],"community_id":"8fb94c86-e95f-41cf-aac2-a2877ffc1b5f","created":1693094400,"current_feed_url":null,"description":"Chemistry with a twist","favicon":"https://rogue-scholar.org/api/communities/8fb94c86-e95f-41cf-aac2-a2877ffc1b5f/logo","feed_format":"application/atom+xml","feed_url":"https://www.ch.ic.ac.uk/rzepa/blog/?feed=atom","filter":null,"generator":"WordPress","home_page_url":"https://www.ch.ic.ac.uk/rzepa/blog","issn":null,"language":"eng","license":"https://creativecommons.org/licenses/by/4.0/legalcode","prefix":"10.59350","relative_url":null,"secure":true,"slug":"rzepa","status":"active","subfield":"1606","title":"Henry Rzepa's Blog","updated":1784360740,"use_api":true},"blog_name":"Henry Rzepa's Blog","blog_slug":"rzepa","content_html":"(Some) chemists have a strange fascination with bonds between two specified atoms – more exactly how short or how long can such a bond get? I asked a slight different question[1] of a molecule known as nitrosobenzene dimer, noting that both nitrogens were both connected to each other and carried a (formal) positive charge; one might naively imagine that the coulomb effects between two positive atoms might result in a repulsion which would greatly lengthen the bond between them (it does not, but it does weaken it). I moved from this to asking how many examples of such molecules there might be, and whether any exhibited unusual bond lengths. After a search of\u00a0the\u00a0CSD, one (“JEGRAS”) caught my interest, shown in blue below[2],[3] and exhibiting a crystallographic N-N distance of 1.695\u00c5 (to answer the question posed in the title above).
\n![\"\"](\"https://www.ch.ic.ac.uk/rzepa/blog/wp-content/uploads/2026/07/JEGRAS.svg\")
Now this is a long N-N bond, probably the longest such bond ever found! And the Lewis valence bond structure has a remarkable up to three centres containing groups with ionic charge separation.\u00a0The one shown in blue is how it is represented in the literature[2], but many others can be devised. The list above is by no means comprehensive, some with a double bond between the two nitrogens and others with no bond there! Two are aromatic (red) and one has little charge separation (green).\u00a0Some have three centres of charge separation, others with only one. This might be an interesting problem to set students in a tutorial – how many Lewis structures can they devise in a set time?
\nThe first task is to see if this effect might be replicated using DFT theory. Here are some models (again by no means comprehensive). It seems that adding a DCM solvation field\u00a0(to mimic the polar environment of a crystal)\u00a0does make a difference.
\nExpt
\n| Method | \nN-N length | \nDOI | \n
|---|---|---|
| Expt | \n1.695 | \n[3] | \n
| MN15L/Def2-TZVPP | \n1.732 | \n[4] | \n
| MN15L/Def2-TZVPP + water solvation | \n1.678 | \n[5] | \n
| MN15L/Def2-TZVPP + DCM solvation | \n1.686 | \n[6] | \n
| MN15L/Def2-QZVPP | \n1.728 | \n[7] | \n
| r2-SCAN-3c/Def2-mTZVPP + DCM | \n1.787 | \n– | \n
The calculated (MN15L/Def2-TZVPP + DCM solvation[8]) Wiberg bond index of the N-N bond is 0.5594, an unusually low value for a \u03c3-bond. The corresponding NBO localised orbital however has a normal electron occupancy (1.9507e).
\n![\"\"](\"https://www.ch.ic.ac.uk/rzepa/blog/wp-content/uploads/2026/07/NN.jpg\")
So this unusual N-N bond can be added to the list of “longest ever” bonds. However, unlike e.g. ultra long C-C single bonds, which tend to be dominated by steric repulsions, this one achieves its status purely electronically.
\n![\"Banner](\"https://pkp.sfu.ca/wp-content/uploads/2026/07/Latin-America-Regional-feature-V2-1024x576.png\")
\"Diamond Open Access\" is not new. Latin America was publishing free to read and publish scholarship long before it had a name, and before it was emphasized internationally. Learn how Latin America has always been a leader in open access publishing, and how the region has been a major influence on how PKP has developed OJS.\u00a0
Long before the term \"Diamond Open Access\" existed, or became a term international policy bodies reached for, it was simply how most of Latin America published research. Public universities ran their own journals and provided hosting either internally or by partnering with fellow institutions. Editors worked without author fees because the idea of charging researchers to publish, or readers to read, never took hold the way it did elsewhere.\u00a0
\nLatin Americans built a scholar-led publishing culture on their own terms. The adoption of PKP's Open Journal Systems (OJS) emerged naturally as a result of values alignment: institutional ownership, non-commercial governance, and open access.\u00a0
\nPKP did not bring this ecosystem into being, but it found a home in one and has spent two decades being shaped by it\u2014in our software priorities, documentation, training programs, and research.
\nFor thousands of university-run and society-run journals, OJS supplied something the region's publishing culture had not previously had at scale: a standardized, low-cost technical backbone that could handle submission tracking, peer review, layout, and indexing without requiring each journal to build its own system or pay for a commercial platform.\u00a0
\nThis end-to-end platform let small editorial teams at regional public universities run peer-review processes as rigorous and procedurally as those at much larger institutions, and it made journals interoperable with the metadata standards that indexes like DOAJ, Latindex, and Redalyc required for inclusion.
\nThe scale that followed is now one of the most cited facts about OJS worldwide: a large share of the platform's global installations are concentrated in Latin America, with 8,413 journals in the region as of 2025. Much of this growth occurred through institutional adoption rather than individual journals seeking out software independently.\u00a0
\nNational networks such as Redalyc and SciELO encouraged member journals toward common technical standards; university library systems rolled out shared OJS instances that dozens of journals could use without each needing their own server or systems administrators; and national science agencies, in several countries, made a degree of technical standardization a condition of funding or indexing support.
\nThat growth reshaped OJS itself. Multilingual interface support, workflow features tailored to volunteer-run editorial teams, and integrations with regional indexing services all stemmed from demands that surfaced from the community, including Latin American users.\u00a0
\nPKP's own research has documented this dynamic directly, treating the region not as a market to study from the outside but as the source of much of what the organization now understands about sustaining non-commercial publishing.
\nFor those involved with software sustainability and contribution, software is a visible part of this infrastructure, but it is not the largest part. Sustaining thousands of non-commercial journals requires documentation, translation, metadata expertise, and governance\u2014work that rarely appears in adoption statistics but without which the software is unusable.\u00a0
\nIn Latin America, this work has largely been done by the community itself: editors training the editors who come after them, librarians running metadata workshops for smaller institutions, and university presses documenting migrations in Spanish.
\nThis is also where the region's dependence on partner organizations becomes visible, and vice versa. For example, DOAJ's indexing criteria have pushed journals toward more transparent editorial policies, often ahead of national requirements. Crossref and DataCite have made persistent identifiers accessible to journals that could never have afforded commercial DOI registration on their own, frequently through agreements negotiated by regional consortia rather than individual publishers.\u00a0
\nNo single organization runs this infrastructure. It holds together through overlapping contributions from regional networks, national library systems, service providers, and volunteer contributors, with PKP occupying one part of a much larger structure.
\nA significant share of this knowledge work happens in Spanish, and it is a condition for the infrastructure functioning at all. An editor working through partially translated documentation is, in practice, operating with degraded infrastructure regardless of what the software can do.\u00a0
\nPKP brings together a volunteer community from Latin America and Spain to translate the software, adapt, and often originate documentation that reflects how OJS is actually used in Spanish-language contexts. Further, PKP School develops Spanish-language courses, extending training to editors who would otherwise be excluded from professional development available to English-speaking colleagues, and the offers the spanish-speaking community a space to share experiences and solving issues with PKP Forum's Spanish Regional Network. Partner institutions across the region have independently produced guides and workshops that circulate well beyond their institutions of origin, forming a distributed, multilingual knowledge base that no single organization owns (e.g., Seminario Permanente de Editores).
\nAlongside this infrastructure work, the conversation about what counts as a rigorous journal is shifting. Mexico's science and technology authority, SECIHTI, has formally established the Sistema Nacional de Publicaciones Cient\u00edficas y Human\u00edsticas (SNPCyH), a policy instrument aimed at strengthening the country's ecosystem of non-commercial, open-access academic journals. Notably, the system evaluates journals on editorial presentation, editorial policy and management, and content accessibility explicitly without relying on citation metrics or impact-factor-style indicators.\u00a0
\nEarly discussion has centered less on indexing thresholds than on editorial quality, research integrity, and bibliodiversity, acknowledging that a healthy ecosystem needs journals publishing in multiple languages and on regional topics, not only those competing for international citation counts.
\nMexico's process should not be read as a regional template; assessment frameworks vary considerably by country. What appears more broadly is a shift in emphasis, visible in Mexico and in policy conversations elsewhere, away from indexing compliance as an end in itself and toward the editorial practices indexing was always meant to signal.
\nThat conversation will continue this August 2026, when the Second Latin American Open Science Forum (Foro Latinoamericano de Ciencia Abierta) will convene academics, researchers, editors, and PKP software users from across the region together in Heredia, Costa Rica. The forum's second edition follows an inaugural gathering in Quito.\u00a0
\nPKP will contribute workshops and panel sessions, and will spend at least as much time listening: these forums work best as spaces where attendees compare notes across national contexts, and where PKP staff hear directly what is and is not working in the software and documentation it maintains.
\nNone of the above suggests the region's publishing ecosystem is without strain. Editorial teams remain under-resourced, and the pressure to align with international indexing criteria has not disappeared even as its terms are renegotiated.\u00a0
\nBut Latin America's central contribution to global scholarly communication has never been a promise of frictionless success. Rather, Latin America has shown through the decades that non-commercial, scholar-led publishing can work at scale, sustained by a layered infrastructure \u2013 technical, linguistic, editorial, and institutional \u2013 that no single organization builds alone.\u00a0
\nPKP's software plays a critical role in this infrastructure, but it is one among many, including DOAJ, Crossref, DataCite, regional networks, university libraries, and the countless volunteer editors and translators whose names rarely appear in any organization's annual report.\u00a0
\nPKP's engagement with Latin America has brought us a clearer sense of what is needed for scholarly infrastructure to be embedded in the community and to be truly sustainable. PKP's work in the region is a standing reminder that the people who understand this best are usually the ones already doing the work.
\nThank you to Famira Racy, Juan Pablo Alperin, Marc Bria, and Pedro L\u00f3pez Casique for their helpful reviews of this post
\nThe post What Latin America Has Taught Us About Building Scholarly Infrastructure appeared first on Public Knowledge Project.
","doi":"https://doi.org/10.59350/9y1hp-rn072","guid":"https://pkp.sfu.ca/?p=19404","image":"https://pkp.sfu.ca/wp-content/uploads/2026/07/Latin-America-Regional-feature-V2-1024x576.png","language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","published_at":1784332800,"rid":"sj989-9y243","summary":"\"Diamond Open Access\" is not new. Latin America was publishing free to read and publish scholarship long before it had a name, and before it was emphasized internationally. Learn how [\u2026] The post What Latin America Has Taught Us About Building Scholarly Infrastructure appeared first on Public Knowledge Project.","tags":["Community Newsletter","News","Diamond OA","Latin America","Regional Feature"],"title":"What Latin America Has Taught Us About Building Scholarly Infrastructure","updated_at":1784335912,"url":"https://pkp.sfu.ca/2026/07/17/regional-feature-latin-america/","version":"v1"}},{"document":{"authors":[{"contributor_roles":[],"family":"Marcum","given":"Christopher Steven","url":"https://orcid.org/0000-0002-0899-6143"}],"blog":{"authors":null,"community_id":"8bdb1ae7-4621-4fa5-ad1a-3a639417dfd5","created":1768694400,"current_feed_url":null,"description":"Perspectives on science, data, and technology that don't fit anywhere else.","favicon":"https://rogue-scholar.org/api/communities/8bdb1ae7-4621-4fa5-ad1a-3a639417dfd5/logo","feed_format":"application/atom+xml","feed_url":"http://chrismarcum.com/marcum-blog/feed.atom","filter":null,"generator":"Jekyll","home_page_url":"https://www.chrismarcum.com/marcum-blog/","issn":null,"language":"eng","license":"https://creativecommons.org/licenses/by/4.0/legalcode","prefix":"10.59350","relative_url":null,"secure":true,"slug":"chrismarcum","status":"active","subfield":"3312","title":"Open Evidence","updated":1784315682,"use_api":null},"blog_name":"Open Evidence","blog_slug":"chrismarcum","content_html":"\n\nThis post was co-authored with Abigail Haddad and a version of it is cross-posted at Present of Coding.
\n
We analyzed over 50,000 comments posted on the Office of Management and Budget's Proposed Regulation for Federal Financial Assistance (i.e., the Uniform Guidance codified at 2 CFR 200). Many news outlets, including the New York Times, have reported that there were 500,000 comments received and, thus, the posted comments represent only 10% of the true count.
\nThat's probably not true - the count is likely closer to what was actually posted by OMB.
\nWhy? Because there is a major flaw in the way that regulations.gov counts comments received that can be easily gamed to increase that denominator \u2013 and based on the patterns in the data in terms of when OMB has been receiving vs. posting comments, there's no 450,000 backlog of comments they have yet to post.
\n\nA qualification of a viral cartoon graph of the growth in public comments on this topic.
Arbitrarily increasing the comment count is easy for users. As we'll show, we were able to increase the comment count for a different proposed regulation by almost a million comments with just one actual comment submission. This is because the General Services Administration, which runs the site, has built-in a feature that allows gaming by design.
\nHere's how to game the system to increase the count:
\nWhen you add an attachment instead of only using the provided comment field, you're asked: Did you attach files that contain comments from more than one person/entity?
\nIf you check \"Yes\", you're asked to provide a value in a numeric field for the Number of persons/entities represented.
\nThis is where the game can happen. Whatever value you enter in that field is added to the total number of comments received.
\nAccording to the documentation, it takes until 11:59 PM ET of that day for this change to be reflected on the website, which adds to the lack of transparency about what is actually happening because it's not instantaneously reflected in the count.
\nTo illustrate this, we conducted a few repeatable experiments on notices that are currently open for comment. Here we share the most dramatic of those experiments where we incremented the count by 999,999 comments \u2013 this is the biggest number you can enter in that field.
\nWe picked \"Considering Lands with Wilderness Characteristics in the BLM Land Use Planning Process\". As of around 8:00 AM ET on 7/16/2026, the total number of comments received was just 310.
\nWe submitted a simple comment with an attachment and indicated that it represented 999,999 entities:
\nThe next day, the total count went up by roughly that amount, now showing a total of over 1 million comments received, and they had posted 301 of them:
\nWhat does this mean for the 500,000 reported comments on 2 CFR 200? They're likely not real. But it was reasonable for media outlets to assume they were real. The only way to figure out how this number is calculated via the website is to click on \"more details,\" then \"FAQ page,\" then \"How are Comments counted and posted to Regulations.gov?\". There, you'll find an explanation, more or less. But almost no one does that. Neither of us learned it that way; we only know this because it was explained to us.
\nComments have been closed for several days now, and based on the patterns in terms of when OMB is receiving vs. posting comments, we just don't think it's plausible that they have yet to post 450,000 \u2013 we can see when comments were submitted vs. posted, and they've clearly posted the vast majority of them already.
\nThis method is misleading and GSA should fix it. It should be replaced with a count that actually represents what everyone currently assumes it does\u2014the total number of comments that have been submitted. That way, it will be possible for those following public interest in regulations to track submitted comments, even if there's a lag in agency posting.
\nWhen this many people are getting the meaning of a published statistic wrong, the problem is with the statistic, not the people.
","doi":"https://doi.org/10.59350/wgyxn-jne09","guid":"https://www.chrismarcum.com/marcum-blog/2026/07/17/The%20Comment%20Count%20is%20Too%20Damn%20High","image":"https://github.com/user-attachments/assets/072f1800-e85f-439c-9fd5-5e1e034c52f2","language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","published_at":1784246400,"rid":"qrkyn-g0d50","summary":"We analyzed over 50,000 comments posted on the Office of Management and Budget's Proposed Regulation for Federal Financial Assistance (i.e., the Uniform Guidance codified at 2 CFR 200). Many news outlets, including the New York Times, have reported that there were 500,000 comments received and, thus, the posted comments represent only 10% of the true count.","tags":["General","Government"],"title":"It's (probably) Not 500,000 Comments on OMB's Proposed Regulation","updated_at":1784315742,"url":"https://www.chrismarcum.com/marcum-blog/2026/07/17/The-Comment-Count-is-Too-Damn-High.html","version":"v1"}},{"document":{"authors":[{"contributor_roles":[],"family":"Royle","given":"Stephen","url":"https://orcid.org/0000-0001-8927-6967"}],"blog":{"authors":[{"name":"Stephen Royle"}],"community_id":"d1d0a116-fe9c-4f5a-b8c5-c3b69edb8327","created":1673740800,"current_feed_url":null,"description":"x == (s || z). You say it kwontized","favicon":"https://rogue-scholar.org/api/communities/d1d0a116-fe9c-4f5a-b8c5-c3b69edb8327/logo","feed_format":"application/atom+xml","feed_url":"https://quantixed.org/feed/atom/","filter":null,"generator":"WordPress","home_page_url":"https://quantixed.org","issn":null,"language":"eng","license":"https://creativecommons.org/licenses/by/4.0/legalcode","prefix":"10.59350","relative_url":null,"secure":true,"slug":"quantixed","status":"active","subfield":"1307","title":"quantixed","updated":1784299506,"use_api":true},"blog_name":"quantixed","blog_slug":"quantixed","content_html":"Niche tips are the bedrock of quantixed. Here is another. Posted purely because we reran into this problem and had to refigure out the answer.
\n\n\n\nWe have a segmented image. Each object has a unique integer assigned as a pixel value. How do we get a list of the integers corresponding to the objects?
\n\n\n\nLet’s assume the segmented objects are not labelled contiguously, or that we only want the objects in one particular slice/frame or within a certain ROI. We’d like to analyse only a subset of the total objects so we need to know which ones they are.
\nWorking with an image (matrix) in most languages, it is trivial to extract the unique integers in the image directly. But sticking with ImageJ, what built-in options do we have?
\n\n\n\nWe can use getRawStatistics(nPixels, mean, min, max, std, histogram); to get an array (histogram) which contains the number of pixels of each value. Excluding 0, we can find any pixel values that have non-zero counts and then we would have our list of objects.
\n// open file, optionally select ROI clear outside etc.\nname = getTitle;\nf = File.open("path/to/particles.txt");\nprint(f, "filename" + "\\t" + "particle_id" + "\\n");\n// get a list of all unique pixel values\ngetRawStatistics(nPixels, mean, min, max, std, histogram);\n// start at 1 because 0 is background\nfor (i = 1; i < histogram.length; j++) {\n\tif (histogram[i] > 0) {\n\t\tprint(f, name + "\\t" + i + "\\n");\n\t}\n}\nFile.close(f);\nGreat! But there’s a problem:
\n\n\n\nIn our case the segmented objects are in a 32-bit image stack that came from a CLIJ routine ext.CLIJx_voronoiOtsuLabeling()
This strategy relies on the histogram generated by getRawStatistics() which generates arrays as follows:
While this is annoying, it makes sense because 32 bit images are floating point and not integer based. No problem, surely we can just convert to 16-bit and we should be fine… well, yes but here is the other gotcha…
\n\n\n\nIf we convert from 32-bit to 16-bit we can see the following behaviour:
\n\n\n\n![\"\"](\"https://quantixed.org/wp-content/uploads/2026/07/segment1.png\")
![\"\"](\"https://quantixed.org/wp-content/uploads/2026/07/segment3.png\")
![\"\"](\"https://quantixed.org/wp-content/uploads/2026/07/segment2.png\")
In the 32-bit test image, we have a background of 0, and two squares: one with a value of 182 and the other with 221. A Glasbey LUT is used to show the objects. If we convert to 16-bit we see that the first square is now 1462 and the other is 65535!
\n\n\n\nThe downsampling to 16-bit results in a scaling effect. This is the default behaviour and can cause some confusion. There are two ways around this issue:
\n\n\n\nIn Edit > Options > Conversions, there is a checkbox to switch on/off “scaling”Scale when converting”. The default is for it to be on. In a script you can turn it off and then turn it back on again at the end of the script using:
\n\n\n\nsetOption("ScaleConversions", false);\n// do all the steps\nsetOption("ScaleConversions", true);\nIf we set the scale of the image to the 16-bit range and do the conversion we will get the desired result regardless of whether the user has the option checked or not.
\n\n\n\nsetMinAndMax(0, 65535);\nrun("16-bit");\nNow, using getRawStatistics() we get a 222 length histogram with pixel values of 182 and 221 as our objects of interest, as intended (the right image above).
I promised it was a niche topic, but the moral of the story is be careful when downsampling images as it may not result in the intended behaviour.
\n\n\n\n—
\n\n\n\nThis post is part of a\u00a0series of tips.
","doi":"https://doi.org/10.59350/y3tww-1gn97","guid":"https://quantixed.org/?p=3810","image":"https://quantixed.org/wp-content/uploads/2026/07/segment1.png","language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","published_at":1784246400,"rid":"8rs3k-msy71","summary":"Niche tips are the bedrock of quantixed. Here is another. Posted purely because we reran into this problem and had to refigure out the answer. Problem We have a segmented image. Each object has a unique integer assigned as a pixel value. How do we get a list of the integers corresponding to the objects?","tags":["Computing","Cell Biology","Code","FIJI","ImageJ"],"title":"Tips from the Blog XVIII: extracting segmented objects in ImageJ","updated_at":1784301221,"url":"https://quantixed.org/2026/07/17/tips-from-the-blog-xviii-extracting-segmented-objects-in-imagej/","version":"v1"}},{"document":{"authors":[{"contributor_roles":[],"family":"Willighagen","given":"Lars","url":"https://orcid.org/0000-0002-4751-4637"}],"blog":{"authors":null,"community_id":"e0509c2b-3c92-4e55-a306-bb03ddf5f7c8","created":1673136000,"current_feed_url":null,"description":"Thoughts about bibliographic metadata, programming, statistics, taxonomy, and biology.","favicon":"https://rogue-scholar.org/api/communities/e0509c2b-3c92-4e55-a306-bb03ddf5f7c8/logo","feed_format":"application/atom+xml","feed_url":"https://larsgw.blogspot.com/feeds/posts/default","filter":null,"generator":"Blogger","home_page_url":"https://larsgw.blogspot.com/","issn":null,"language":"eng","license":"https://creativecommons.org/licenses/by/4.0/legalcode","prefix":"10.59350","relative_url":null,"secure":true,"slug":"syntaxus_baccata","status":"active","subfield":"1110","title":"Syntaxus baccata","updated":1784296096,"use_api":null},"blog_name":"Syntaxus baccata","blog_slug":"syntaxus_baccata","content_html":"The Library of Identification Resources (LoIR) is a database of identification keys and other publications and resources that assist in the taxonomic identification of animals, plants, and fungi. As a result, the database contains a lot of taxonomic info, which can be roughly divided into two parts. Firstly, the taxonomic scope of every identification key or other type of resource is recorded, like \"hoverflies\" or \"Syrphidae\" in a key to the hoverflies of Europe. Secondly, if feasible, checklists are made for all of the individual species (or genera, etc.) included in each of the identification keys. Both sets of scientific names of taxa are mapped to external databases to allow easier comparisons and, importantly, to power the search engine. Until two days ago, this external database was the GBIF Backbone Taxonomy, maintained by the Global Biodiversity Information Facility (GBIF).
\nHowever, in June of 2026, GBIF replaced the GBIF Backbone Taxonomy with the Catalogue of Life (COL). This comes with many improvements, as the Catalogue of Life gets frequent updates, includes taxa with intermediate ranks, and provides an Extended Release (COL XR). The COL XR integrates additional checklists leading to better coverage of less common taxa, obscure synonyms, and recent taxonomic changes, directly affecting the data quality of GBIF. As opposed to the GBIF Backbone Taxonomy, which used to integrate various datasets as well, there also seems to be a clear path for small data publishers like LoIR to contribute to the COL XR.
\nThis change also means that LoIR needed to move from the GBIF Backbone Taxonomy to COL (XR) before GBIF fully deprecates the old system. This transition came with its own challenges, for both parts of the taxonomic data in LoIR. Back in 2025, we found that the base release of COL missed 14.3% of the 'endpoint' taxa in checklists of identification keys in LoIR, compared to just 3.5% by the GBIF Backbone Taxonomy (Willighagen & Jongejans, 2025). The pressing question for me was whether COL XR could improve on this enough to use it as the main taxonomy in LoIR.
\nNow, after updating the tools used to develop LoIR (see Willighagen, 2026), all identification checklists have been mapped to COL XR instead of COL. This lead to a marked improvement, leaving just 3.5% of endpoint taxa unmapped compared to 5.0% with the GBIF Backbone Taxonomy. Only 1,029 scientific names (0.9%) were mapped to the GBIF Backbone Taxonomy but could not be mapped to COL XR (Fig. 1). This is a good sign!
\n\n\nNote that since 2025, the overall mapping coverage of both COL and the GBIF Backbone Taxonomy decreased, due to the addition of many publications about Heteroptera published between 1880 and 1963. Looking only at checklists added before August 4th, 2025 (see Willighagen, 2025), COL XR misses only 2.4% of endpoint taxa, compared to 3.5% with the GBIF Backbone Taxonomy at the time.
\n
![\"Alluvial](\"https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh4f4U3Y05j42NARhrnG0HpDdaF7Hjh6ggIXOotfNxS4pYPPtO8WVSNX79E1SNfb9BL9dwOyD_-ZhCFCLuljDBhxOa8KCzbRKmYFs50U-eB4NDMUbCqjZrTVFt-zB8pcNs3F1c_RIxvlEigUq8z2v5ElDhDhm8AWon3UkN48itRp5KuggBfY8nfnOqJB-w/s1600/completeness-stats-alluvium2.png\"/)
\nFigure 1: Alluvial diagram of changes in the coverage of mappings of endpoint taxa in checklists between the GBIF Backbone Taxonomy and COL XR.
The other part of the taxonomic info in LoIR, the taxonomic scopes, was more difficult to convert. However, here COL (XR) also offers immense improvements by including taxa with intermediate ranks. For example, say an identification key is intended for all weevils, i.e. the superfamily Curculionoidea. Before, that taxonomic scope had to be mapped to all constituent families, because the GBIF Backbone Taxonomy did not include superfamilies or any other intermediate taxa. Now, it can simply be mapped directly to COL's entry for Curculionoidea: KV7G2.
\nThis was possible for most, but not all taxa. For example, the subfamilies of shield bugs (Pentatomidae) have not been added to COL (XR) yet, and therefore still had to be mapped to all genera separately. The list of taxonomic scopes also contain a lot of taxa that are now considered obsolete, and have to be mapped to multiple current taxa. However, whereas with the GBIF Backbone Taxonomy only 72.5% of taxonomic scopes could be mapped directly, with COL XR this raises to 90.2% (Fig. 2). Additionally, 23 taxa that were not mapped at all in the GBIF Backbone Taxonomy are now mapped to COL (XR). Unfortunately, there were still 5 taxa that could not be mapped to COL (XR) yet, mostly heteropteran genera.
\nCOL XR as a whole does still require some additional curation. An issue with COL's name index meant there are a lot of duplicate entries for the same taxa in the current release of COL XR (26.6). Additionally, some genera have been given an incorrect classification (see the previous link), and as aforementioned, several species, genera, and higher taxa are still missing. This should all improve as COL XR continues to develop though, and now hopefully the data in LoIR could contribute to this!
\n![\"Alluvial](\"https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjfZiqOx7djomKGsa5jkzYO6ULPG0C1zjwZCpvlOozdi_3j7T1dBxNIasPmOfffS4rhjBUCvIJqYBpcYwksZN7KtGyQRbzLuv2FLGiA84UIa2IPbkZRqWBwr-Dp1-uPjK62lnFuXmgeOCwrhTg1zPfGBu0EhbwKfFlZJD8L_4RtQUKAzlNBXnYM2QfHe44/s1600/completeness-stats-alluvium.png\"/)
\nFigure 2: Alluvial diagram of changes in the coverage of mappings of taxonomic scopes between the GBIF Backbone Taxonomy and COL XR.
After both sets of taxonomic info in LoIR were adapted to use COL XR, the search engine had to be updated to use the new mappings. This process was relatively simple, as GBIF has API endpoints for both the GBIF Backbone Taxonomy and COL XR. The API endpoint for COL XR is technically still experimental, but an equivalent, stable version will likely be added in the future.
\nAll in all, I think this is a great succes, and I am excited for future developments.
\nWissenschaftsblogs stellen einen wichtigen Bestandteil der wissenschaftlichen Kommunikationskultur dar (Ochsner and Pampel 2026). Als interaktive Publikationsformen (K\u00f6nig 2015) unterst\u00fctzen Blogs den Austausch unter Forschenden und tragen gleichzeitig zur Dialogf\u00f6rderung zwischen Wissenschaft und Gesellschaft bei. Blogs regen Diskussionen an und erm\u00f6glichen eine direkte, schnelle sowie offene Kommunikation von Forschungsergebnissen (K\u00f6nig 2015; Ochsner and Pampel 2026). In Bezug auf ihre Langzeitverf\u00fcgbarkeit und Zitierf\u00e4higkeit stehen Wissenschaftsblogs jedoch vor besonderen Herausforderungen (Ochsner and Pampel 2026). Um die Sicherung und Auffindbarkeit von Inhalten dauerhaft gew\u00e4hrleisten zu k\u00f6nnen, wurde im Rahmen des von der Deutschen Forschungsgemeinschaft (DFG) gef\u00f6rderten Projekts Infra Wiss Blogs ein Anforderungskatalog entwickelt, der anhand einer kooperativen Informationsinfrastruktur Ma\u00dfnahmen zur langfristigen Verf\u00fcgbarkeit und eindeutigen Zitierbarkeit von Wissenschaftsblogs darstellt (Ochsner and Pampel 2026). Als Teil der [Forschungsgruppe Information Management] (https://www.ibi.hu-berlin.de/de/forschung/infomanagement) am Institut f\u00fcr Bibliotheks- und Informationswissenschaft (IBI) habe ich zur bildlichen Vermittlung der Kataloginhalte ein wissenschaftliches Poster gestaltet.
\n![](\"https://infomgnt.org/posts/2026-07-16-entwicklung-eines-posters-zu-anforderungen-an-die-langzeitverf\u00fcgbarkeit-von-wissenschaftsblogs/poster_datei_rgb.jpg\"/)
F\u00fcr die visuelle Gestaltung des Posters hatte ich zun\u00e4chst mehrere Ideen, die ich anhand von Mind-Maps und kleinen Skizzen auf Papier weiterentwickelt oder ver\u00e4ndert habe. Ich mag Fantasy und Science-Fiction und hatte deshalb den Einfall, die Inhalte des Anforderungskatalogs in ein 'magisches Bild-Setting' zu \u00fcbertragen, das gleichzeitig Spa\u00df beim Anschauen macht und die wissenschaftlichen Inhalte gut transportieren kann. Bisher habe ich vor allem Illustrationen f\u00fcr Veranstaltungs- und Konzertposter angefertigt und fand es spannend, mir die Gestaltung f\u00fcr ein wissenschaftliches Poster \u00fcberlegen zu k\u00f6nnen.
\nIm Prozess der Bildentwicklung habe ich alle Motive per Hand gezeichnet und anschlie\u00dfend am Computer eingef\u00e4rbt. Hierbei entschied ich mich dazu, die Zeichnungen in der Gr\u00f6\u00dfe der final geplanten Druckversion (DIN-A0) anzufertigen. Ich finde, dass man dadurch einen besonders guten Eindruck zur Wirkung einzelner Bildelemente und deren Beziehung zueinander erhalten kann. Weil ich kein Papier in der passenden Gr\u00f6\u00dfe zur Hand hatte, habe ich daf\u00fcr einfach mehrere A3-gro\u00dfe Bl\u00e4tter zusammengeklebt. Darauf konnte ich nun eine vergr\u00f6\u00dferte Bleistiftzeichnung meiner kleinen Skizze \u00fcbertragen. Nach einer erneuten \u00dcberarbeitung dieser Vorzeichnung trennte ich alle Papiere wieder voneinander, um im Anschluss einzelne Bildteile am Schreibtisch mit schwarzer Tusche und Pinsel nachzuzeichnen.
\n![](\"https://infomgnt.org/posts/2026-07-16-entwicklung-eines-posters-zu-anforderungen-an-die-langzeitverf\u00fcgbarkeit-von-wissenschaftsblogs/zeichnung_1.jpg\"/)
![](\"https://infomgnt.org/posts/2026-07-16-entwicklung-eines-posters-zu-anforderungen-an-die-langzeitverf\u00fcgbarkeit-von-wissenschaftsblogs/zeichnung_2.jpg\"/)
![](\"https://infomgnt.org/posts/2026-07-16-entwicklung-eines-posters-zu-anforderungen-an-die-langzeitverf\u00fcgbarkeit-von-wissenschaftsblogs/zeichnung_3.jpg\"/)
Im n\u00e4chsten Schritt habe ich die insgesamt zehn fertig gezeichneten A3-Bl\u00e4tter eingescannt und am Computer mit verschiedenen Bildbearbeitungsprogrammen zu einer einzelnen Datei zusammengef\u00fcgt. Der Einf\u00e4rbungsprozess der bisherigen schwarz-wei\u00dfen Version fand ebenfalls digital statt. Nachdem der fertiggestellte Bildteil des Posters exportiert war, konnte ich mithilfe eines Textbearbeitungsprogramms den schriftlichen Inhalt zum Anforderungskatalog einf\u00fcgen und entsprechend formatieren.
\nDas Wissenschaftsposter war Teil der Poster-Ausstellung auf der 114. BiblioCon (2026) in Berlin (Schielke, Ochsner, and Pampel 2026) und ist derzeit am Institut f\u00fcr Bibliotheks- und Informationswissenschaft (IBI) der Humboldt-Universit\u00e4t zu Berlin ausgestellt.
\nWeitere Informationen zum Projekt InfraWissBlogs und zu der Forschungsgruppe Information Management sind auf unserer offiziellen Webseite zu finden.
\nDieser Text \u2013 mit Ausnahme von Zitaten und anderweitig gekennzeichneten Teilen \u2013 steht unter der CC BY 4.0 DEED.
\n@online{schielke2026,\n author = {Schielke, Johanna},\n title = {Entwicklung Eines {Posters} Zu {Anforderungen} an Die\n {Langzeitverfuegbarkeit} von {Wissenschaftsblogs}},\n date = {2026-07-16},\n url = {https://infomgnt.org/posts/2026-07-16-entwicklung-eines-posters-zu-anforderungen-an-die-langzeitverfuegbarkeit-von-wissenschaftsblogs},\n langid = {en}\n}\nYesterday the Rogue Scholar science blog archive started using Crossref schema 5.5 that was released last Friday.
The most relevant change in the new schema for Rogue Scholar is the introduction of a dedicated content type blog post (as type posted_content, subtype blog). This makes it easier to cite blog posts with Crossref DOIs as many citation styles (e.g. APA) format blog posts differently than preprints. And it also makes it easier to recognize the content type of a reference, as previously Rogue Scholar used the generic preprint/posted_content content type.
The other important change in schema 5.5 is the support for CRediT contributor roles. Rogue Scholar already supports a wider range of contributor roles, and this is now reflected in the metadata sent to Crossref.
Rogue Scholar will over time update all more than 50K blog posts to use schema 5.5. Crossref schema 5.5 support is via the commonmeta-py library, which will also provide Crossref DOI registration functionality to other repositories using the InvenioRDM platform in the upcoming v14.0 release.
Please reach out via Slack, email, Mastodon, or Bluesky if you have any questions or comments.
Commonmeta is a set of libraries to convert scholarly metadata into different formats via an intermediary format defined by JSON Schema. The schema is now available as a v1.0 version release candidate, with v1.0 planned to be released in September.
The feature image of this post is from a presentation by Geoffrey Bilder from the early work on the Research Organization Registry (ROR) where we were both involved. The organizational identifier gap has since been addressed, making another important gap visible: the metadata to describe scholarly content, contributors and organizations with persistent identifies (via Crossref, DataCite, ORCID, ROR) and the ways to obtain them via APIs or downloadable annual data files, are fragmented and complicated. Commonmeta tries to address one important aspect of this gap, making these metadata easier to interoperate, e.g. when combining metadata about dissertations or preprints registered with Crossref or DataCite, or finding out more details (e.g. license or language) about the works found in an ORCID profile.
Work on commonmeta started with the bolognese Ruby library that I wrote in 2017 as DataCite Technical Director to support DOI content negotiation and later also DOI registration. In 2023 I announced commonmeta as I had refactored bolognese to be more generic and rewritten in Python. When I launched the Rogue Scholar science blog archive in 2023, I added Crossref DOI registration functionality to commonmeta. And in the last nine months I worked on integrating Crossref DOI registration into the InvenioRDM repository platform, version 14.0 will launch in a few weeks and has Crossref DOI registration built in.
Crossref DOI registration requires submitting XML and XML validation, features supported in commonmeta, included the latest Crossref 5.5 schema announces last week. But commonmeta itself is described in JSON and defined via JSON Schema. JSON is not only the most common serialization format for scholarly metadata (compared to XML, JSON-LD, or older formats such as bibtex or ris), but is also much easier (and faster) to work with, including the important checks for validity and consistency. Commonmeta therefore is described with JSON Schema, but until now the schema was at version 0.x and not stable. Releasing a stable v1.0 of the schema will be an important milestone.
This version v1.0 of the commonmeta schema comes with four important changes:
The latest schema can be found at https://codeberg.org/front-matter/commonmeta-schema/src/branch/main/schemas. Please reach out via email, Mastodon, Bluesky or Rogue Scholar Slack if you have any questions or comments regarding the commometa v1.0 schema release candidate.
We talk about search as though it were one reasonably well-defined activity.
Someone types words into a box. The system finds documents. Hopefully, the right ones appear near the top.
But there is no single professional tradition of search.
Instead, there are multiple communities that look at the same search process and see quite different things. They attend different conferences, read different literatures, use different vocabularies and, most importantly, disagree about what it means for search to work well.
Some are predominantly academic research communities. Some are practitioner professions, often within librarianship. Some are infrastructure ecosystems or engineering practices rather than disciplines in the conventional sense. They are not equivalent categories, and their boundaries are not clean.
This has always been true. But AI search is making it much harder to ignore, because activities that were previously distributed across separate layers and professions are increasingly being rethought, remixed and rebundled.
In 2012, Lorcan Dempsey published an essay titled Thirteen Ways of Looking at Libraries, Discovery, and the Catalog. He borrowed Wallace Stevens's conceit because the catalogue was, as he put it, a topic about which \"we don't yet have a single story\".
Rather than force everything into one model, he looked at the catalogue from thirteen angles: web scale, the single search box, metadata sources, discovery layers, knowledge organisation, sourcing and scaling, and more.
I have been thinking about something similar for academic search, but from a different angle of expertise.
What happens when ten (or maybe eleven) different kinds of specialists whose work or jobs impact search look at the same search box?
Two caveats before I begin. First, these are traditions, not boxes, and I will return to how much they leak into one another. Second, my descriptions may be somewhat reductive and the representative names under each tradition are merely illustrative, reflecting my own limited understanding of each area as a generalist and my own blind spots. No omission should be read as a judgement.
For the information literacy tradition, search is primarily something people learn to do.
Can the user turn a vague question into useful search terms? Do they understand the difference between a database and the open web? Can they evaluate authority, recognise bias and avoid accepting the first plausible result? More recently, this has expanded into fact-checking, misinformation and the critical evaluation of AI-generated answers.
This is primarily a practitioner and pedagogical tradition, although it has a substantial academic literature.
Good search: produces a more capable and critical searcher.
Where they gather: LILAC, LOEX, ECIL and the community around the ACRL Framework.
Representative people: Alison Head, Barbara Fister, Alison Hicks, Sheila Webber, Mike Caulfield, Char Booth and Margy MacMillan.
Contribution to the stack: the human layer: evaluation habits, source criticism and the insistence that searching is a learnable intellectual skill rather than merely a product feature.
Potential blind spot: what happens inside the machinery after the user presses search1.
People rarely begin with a perfectly formed information need. They learn what they are looking for by searching, reading, reformulating and sometimes abandoning their original question.
From this perspective, search is not a single transaction. It is an interaction among a person, a task, an interface and a changing state of knowledge.
This is why a system that produces an impressive answer to the first question is not necessarily supporting the complete information-seeking process.
Unlike information literacy (#1), this is primarily an academic research tradition. Information literacy tends to ask what people should learn to do, with librarians focusing particularly on just the academic context. Information behaviour more often studies what people actually do, why they do it and how their needs change. But both are closely related.
Good search: supports exploration and helps the user make progress, even when the original query is confused or incomplete.
Where they gather: ASIS&T, Information Seeking in Context and the ACM SIGIR Conference on Human Information Interaction and Retrieval (CHIIR), where this tradition and information retrieval (#8) actually talk to one another.
Representative people: Marcia Bates, Carol Kuhlthau, Nicholas Belkin and, among contemporary researchers, Chirag Shah.
Contribution to the stack: models showing that information needs evolve during a search, and that sessions and tasks, rather than isolated queries, are often the real unit of analysis.
Potential blind spot: its insights into evolving needs are frequently ignored by vendors or at best reduced to interface features (e.g. citation trails, facets) rather than shaping the underlying retrieval process.
For evidence synthesis, search is not merely a convenient way to find several good papers. It is part of the research method.
The search must be sensitive, transparent and defensible. Concepts must be represented adequately. Appropriate databases must be selected. Controlled vocabulary and free-text terms must be combined. The strategy should be reported, reviewed and tested against known relevant studies.
This is mostly a professional and methodological tradition2, especially among information specialists, systematic reviewers and health librarians. But it also has a growing research community studying search methods, automation and the validity of different retrieval approaches3.
Good search: retrieves a sufficiently complete body of evidence and leaves an audit trail.
Where they gather: the Cochrane and Campbell colloquia, ESMARConf, EAHIL and the health library associations.
Representative people: Wichor Bramer, Siw Waffenschmidt, Julie Glanville, Farhad Shokraneh and Dean Giustini.
Contribution to the stack: probably the strongest culture of methodological accountability in search: PRESS peer review, PRISMA-S reporting, validated filters and the radical idea that a search strategy is a citable research object.
Potential blind spot: the field has traditionally focused on auditing search strategies far more closely than the ranking systems applied to their results. This made sense when every retrieved record was screened and ranking affected only the order in which records appeared.
Evidence-synthesis specialists have therefore been understandably cautious about relevance ranking. PubMed's machine-learning Best Match algorithm has existed since 2017 and became the default in 2020, but using it in a systematic review remains controversial even after almost a decade4. As a result, experts from this tradition may underestimate the value of better ranking using more modern retrieval techniques.
That said, the field is becoming more comfortable with ranking for screening prioritisation and technology-assisted review, particularly where every record remains available for screening or where safeguards make the risk of missed studies explicit and testable5.
This is one of the clearest overlaps with the information retrieval community (#8): evidence synthesis imports active learning, screening prioritisation and stopping methods from IR, but subjects them to unusually demanding expectations for recall, transparency and reproducibility.
Search engines do not retrieve the things themselves. They retrieve representations of things.
Is this the same author or two people with similar names? Are these records different works, different versions or duplicates? How should a subject be expressed? Which relationships among authors, institutions, publications and concepts have been captured?
Embeddings do not make these problems disappear. They may simply hide them until a system confidently merges the wrong author, work, institution or version.
This is predominantly a professional tradition rooted in cataloguing, knowledge organisation and bibliographic control, although it also has a strong theoretical and research wing.
Good search: depends on entities being represented accurately and consistently.
Where they gather: DCMI, SWIB, IFLA cataloguing meetings and the linked-data-for-libraries community.
Representative people: Karen Coyle and Ashleigh Faith6.
Contribution to the stack: identity itself: authority control, work modelling and the disambiguation that stops a system merging two authors who happen to share a name.
Potential blind spot: metadata quality is still too often judged through conformance to standards rather than demonstrated effects on retrieval, discovery and user outcomes.
Search quality depends partly on what information is available for machines to process. In academic search, this boils down to questions like:
Are abstracts open? Are references available through open citation data? Do publications have DOIs? Are authors connected through ORCID and institutions through ROR? Can repository records be harvested and reused? Can a commercial system legally index and display the relevant content?
A supposedly intelligent search system cannot use signals it cannot access. Famous and well-documented resources tend to perform better partly because information about them is widely available. Content hidden behind contractual, technical and metadata barriers remains harder to discover.
This is less a single profession than an ecosystem. It includes persistent-identifier organisations, standards bodies, librarians, publishers, technologists, policy researchers and advocates for open science.
Good search: requires scholarly information that is machine-readable, interoperable, reusable and sustainably governed.
Where they gather: FORCE11, PIDfest, Crossref events and the communities around ORCID, ROR, DataCite, OpenAlex, OpenCitations and COAR.
Representative people: Geoffrey Bilder, Cameron Neylon, Jason Priem, Bianca Kramer, Martin Fenner, Kathleen Shearer, Ginny Hendricks, Maria Gould and John Chodacki.
Contribution to the stack: the identifiers, relationships and open corpora on which most academic AI search tool silently depends.
Potential blind spot: treating openness as the finish line. Getting data out is not the same as getting it found, ranked or used well.
The scholarly literature is not merely a bag of documents. It is a network of authors, institutions, citations, journals, concepts and research communities.
Citation links, bibliographic coupling, co-citation and co-authorship patterns can help people navigate the literature, identify related work and locate emerging fields. They can also reproduce existing prestige, language, disciplinary and geographical biases.
This matters increasingly because academic search tools do not rely only on textual similarity. Many also exploit citation graphs, publication venues, author relationships and other scholarly signals.
Bibliometrics and scientometrics are predominantly academic research traditions, although their methods are widely applied in research evaluation, university management, funding and commercial analytics and librarians are involved in these areas. Search is not the field's only or even necessarily its central concern, but its models of the scholarly graph increasingly shape discovery and recommendation.
Good search: uses scholarly relationships intelligently without confusing visibility with relevance or citation counts with quality.
Where they gather: ISSI , STI Conference, LIS-Bibliometrics Conference, Bibliometrics and Research Impact Community (BRIC) Conference ; Scientometrics and Quantitative Science Studies on the journal side.
Representative people: Eugene Garfield, Ludo Waltman, Cassidy Sugimoto, Nees Jan van Eck, Mike Thelwall, Paul Wouters, Loet Leydesdorff and Philipp Mayr.
Contribution to the stack: the graph signals behind citation searching, related-paper recommendation and much of what makes academic AI search feel clever.
Potential blind spot: bibliometric signals developed for analysis or navigation can be absorbed into rankings and recommendations without the cautions that bibliometricians themselves attach to them.
This group also overlaps highly with open scholarly infrastructure (#6) community when they advocate for open references and metadata e.g I4OC, I4OA, Barcelona Declaration on Open Research Information
Finding the perfect article is not much use if the user cannot open it.
Behind the friendly Full Text Available label lies a chain of machinery comprising knowledge bases, holdings, link resolvers, proxies, federated identity, single sign-on, entitlement checks, open-access routing and publisher platforms.
Retrieval asks:
What should I read?
Fulfilment asks:
Can I actually read it?
Users do not care that these are managed by different systems or departments. A broken link means that, from their perspective, the search failed.
This tradition therefore sits downstream of retrieval in a technical sense but firmly inside the end-to-end discovery experience.
Good search: ends with the user obtaining an appropriate and accessible version of the resource.
Where they gather: ER&L, NASIG, UKSG, the Charleston Conference and communities around Access initiatives and services such as SeamlessAccess, GetFTR and OpenAthens.
Representative people: Herbert Van de Sompel, Todd Carpenter, Heather Flanagan, Roger Schonfeld, Lisa Janicke Hinchliffe, Jill Emery and Graham Stone.
Contribution to the stack: the switching layer: OpenURL, knowledge bases and entitlements and the machinery that turns a citation into a document that opens.
Potential blind spot: it can establish whether a link worked, but not whether the right things were found in the first place.
Many people in this group also overlap with open scholarly infrastructure (#5), including through service on persistent-identifier and standards boards.
For the information retrieval researcher, search is something that can be modelled and tested.
How should queries and documents be represented? Should the system use BM25, dense embeddings, learned sparse retrieval, late interaction or a hybrid pipeline? Does reranking improve the results? What happens to recall, precision, Mean Average Precision (MAP), Mean Reciprocal Rank (MRR), Normalized Discounted Cumulative Gain (NDCG) on a test collection7?
Good search: performs better against defined relevance judgements under controlled evaluation.
Where they gather: SIGIR, ECIR and CIKM, with The Text Retrieval Conference( TREC) and Conference and Labs of the Evaluation Forum (CLEF) as shared evaluation infrastructures.
Representative people: Karen Sp\u00e4rck Jones, Stephen Robertson and Ellen Voorhees; contemporary researchers include Jimmy Lin, Omar Khattab and Nandan Thakur.
Contribution to the stack: the measurement discipline itself, together with the indexing and ranking models that run from probabilistic retrieval through dense and late-interaction systems.
Potential blind spot: benchmarks rarely reproduce the awkward reality of library discovery: licensed collections, inconsistent metadata, long research tasks and users who cannot clearly state what they need.
TREC belongs firmly to this tradition. Its classic model is almost the canonical expression of academic information retrieval: shared documents and topics, ranked runs submitted by participating systems, pooled relevance judgements and common evaluation measures.
TREC has hosted tasks far beyond traditional document ranking, but it has generally brought them into the IR evaluation paradigm rather than becoming a separate tradition of its own.
Information retrieval also overlaps significantly with information behaviour (#2). Interactive information retrieval studies what happens when the neat benchmark abstraction of one query, one ranked list and one set of relevance judgements meets an actual person with an evolving information need.
CHIIR is one place where information behaviour and information retrieval genuinely meet, although the border between studying the user and optimising the system remains surprisingly durable.
Most librarians no longer have much association with this camp. A typical librarian might remember hearing about TF-IDF once in their MLIS class. There are exceptions, of course, notably in continental Europe.
This is the practitioner counterpart of information retrieval (#8).
Industry search practitioners ask a somewhat different question from academic information retrieval researchers.
They want to know why a live, usually enterprise-scale system is failing actual users.
Which queries return nothing? Which produce bad rankings? How should relevance be judged? Did the new hybrid pipeline improve results or merely add cost and latency? Can the team diagnose a failure and make the system better next week?
The IR researcher (#8) asks whether a ranking method improves nDCG on a benchmark.
The relevance engineer (#9) asks why users searching for red shoes on Tuesday are seeing nonsense and how to fix it before Friday.
The underlying concepts overlap considerably. The professional settings, constraints and feedback loops do not.
Libraries teach search. We configure search. We procure search. We sometimes survey users about search.
Today, most libraries have much less of a professional culture around relevance engineering: systematically judging, diagnosing and improving the quality of live search results8.
Relevance engineering presupposes access to query logs, relevance judgements and the ranking configuration itself. In most library discovery products, the vendor holds all three.
Libraries should borrow the engineering discipline without blindly adopting its commercial objectives. An e-commerce system may optimise for conversion. A library should not.
Good search: can be measured, diagnosed and continuously improved in production.
Where they gather: Search Solutions, Haystack, Berlin Buzzwords, and the relevance Slack channels where much of the practical craft circulates.
Representative people: Tony Russell-Rose, Martin White, Charlie Hull, Doug Turnbull, Trey Grainger, Daniel Tunkelang, Jo Kristian Bergum and Lettie Y. Conrad.
Contribution to the stack: judgement lists, offline and online evaluation and the discipline of treating a bad result as a debuggable failure rather than a fact of life.
Potential blind spot: its methods often assume query volume, instrumented systems and rapid feedback loops that library search rarely possesses.
Search UX consultants9, publishing product managers and relevance engineers do not form one completely coherent community. Search UX also overlaps with information behaviour (#2), while product managers may sit closer to library discovery (#10). The centre of gravity here is the engineering practice of evaluating and improving a live retrieval system.
Finally, the discovery librarian or product owner who might try (whether they know it or not) to combine all these elements colliding into library's main search system.
Primo, Summon, EBSCO Discovery Service and similar products10 combine metadata from many sources, retrieve and rank records, present facets, calculate availability and send users through different access pathways.
They are the default owners of the library search stack and often the people most responsible for how users perceive library search through the one search box on the library website.
Unfortunately, they may be expected to integrate components they cannot properly inspect, evaluate or control.
Good search: delivers a coherent end-to-end service.
Where they gather: ELUNA and IGeLU, Code4Lib and vendor platforms such as the Ex Libris Idea Exchange, where enhancement requests go to join a queue.
Representative people: Ken Varnum, Christine Stohn, Lorcan Dempsey and Marshall Breeding.
Contribution to the stack: integration and translation: the tradition whose job is to make the other nine behave as one service.
Potential blind spot: it sees the system through the vendor's administration console. What the console does not expose, the profession gradually stops asking about.
People in this group are mostly library-systems specialists and often overlap with metadata (#4), e-resource management (#7) and to a lesser extent scholarly infrastructure (#5). In my experience, however, they are less likely to be deeply embedded in information literacy (#1) or evidence synthesis (#3).
This creates gaps between librarians who are largely front-of-house and those who are largely back-of-house. The people teaching users how to search may have limited understanding of how the system retrieves and ranks11. The people configuring the system may have limited contact with users even librarians attempting complex searches beyond seeing such queries in a search log.
There is an obvious problem with dividing search into ten traditions.
The traditions do not stay politely in their assigned boxes.
Some are intellectual descendants of others. Some pair academic research communities with practitioner counterparts. Others overlap so heavily that the placement is partly a matter of convenience.
They are also not all the same kind of entity. Information behaviour is an academic research field. E-resource management is a professional specialism. Open scholarly infrastructure is an ecosystem. Relevance engineering is an engineering practice.
Linked data is the clearest example of why the boxes leak.
It grew largely out of the metadata and knowledge-organisation tradition (#4). Authority files, identifiers and relationships among works, people, organisations and subjects existed long before anyone called them knowledge graphs.
Once those identifiers and relationships travel between catalogues, repositories, publishers, funders and research information systems, linked data becomes scholarly infrastructure (#5). ORCID is not merely a well-formed metadata field. ROR is not merely authority control for institutions. Crossref is not merely a catalogue of DOI records.
And once the resulting network is analysed or exploited for retrieval and recommendation, it overlaps with bibliometrics (#6) and information retrieval (#8).
OpenAlex is difficult to assign to any one tradition. It is simultaneously open scholarly infrastructure (#5), a scholarly knowledge graph and a source for bibliometric analysis (#6).
What unites the traditions is not institutional form. It is that each brings a distinct definition of the search problem and a distinct theory of what counts as success.
Read back through the ten definitions of good search:
None of these definitions is wrong.
None is sufficient on its own.
The problem is not that librarians and adjacent professionals know nothing about search. There is an extraordinary amount of expertise.
The problem is that this expertise is fragmented across communities that do not share a common vocabulary, evaluation framework or model of the complete system. Often they are not even aware of each other and the lessons they can teach each other.
This fragmentation was manageable when the search stack was comparatively stable and search primarily returned lists of records.
Different communities could concentrate on different layers. Practitioners worked on teaching query formulation, metadata, fulfilment and the integrated service. Researchers studied information behaviour, scholarly graphs and ranking algorithms.
The boundaries were never perfect, but the division of labour made sense because the layers were reasonably visible and the foundations were stable.
An information behaviour researcher could study how people formulated and reformulated their needs without understanding the details of BM25, a standard lexical retrieval and ranking algorithm used by many systems.
Today, that researcher may need to understand whether the system uses lexical retrieval, dense retrieval, learned sparse retrieval, late interaction or some hybrid (which affects what search queries work), and whether it generates a direct answer rather than simply returning a ranked list. A practitioner teaching information literacy faces the same complexity when trying to work out exactly what \"AI-powered search\" means and which component might affect learning.
AI search does not abolish these layers. Metadata remains metadata. Retrieval remains retrieval. Access remains access.
What AI search does is rebundle them into systems in which interpretation, retrieval, relevance assessment, evidence selection and synthesis repeatedly call one another in novel and different ways rather than in the same standard way.
The family of technologies that followed the transformer architecture is changing many of these layers simultaneously12.
The major inflection point was the introduction of that architecture in 2017. From 2018 onwards, encoder models such as BERT demonstrated how effectively contextual language models could be applied to retrieval, while GPT-style decoder models showed increasingly capable generation. Retrieval-augmented generation brought the two together explicitly in 2020.
Even into the early 2020s, while academic researchers in information retrieval and NLP (#8), selected industry research teams and a handful of start-ups (#9) were excitedly experimenting with transformer-based approaches to search, the public, including most librarians, remained generally unaware of the scale of the changes coming their way13.
Changes such as Bing and Google incorporating BERT and related transformer models into parts of their query-understanding and ranking systems in 2019 happened largely under the hood and went mostly unnoticed by users, except in the SEO community, because search engines continued to present the familiar list of results.
The launch of ChatGPT on 30 November 2022 triggered a much more visible awakening. The focus was no longer merely on using transformer models to improve matching and ranking, but on using generative models to produce answers directly. That is the point at which every other community became aware that the foundations of search were shaking. More recently, we have seen the rise of \"Deep Research\" and \"agentic search\" in 2024/2025.
A language model may now interpret the user's question, translate it into several searches, assess and rerank what comes back, extract and reconcile evidence, compose an answer, and then decide it lacks enough evidence and search again.
These are not merely improvements to the ranking algorithm. Activities that once occurred before retrieval, during retrieval and after retrieval are now orchestrated inside the same model-and-harness system.
The query is no longer necessarily a query. It may be a conversation, a research objective or an underspecified task that the system must progressively interpret.
The result is no longer necessarily a result list. It may be a direct answer, a literature map, a table of extracted findings, a generated report or a set of claims linked to supporting passages.
Ranking is no longer necessarily a single operation performed once against a fixed corpus. Documents may be retrieved, judged, discarded, searched within, compared against one another and used to generate further searches.
Relevance itself becomes more complicated. A document may be useful not because it directly answers the original question but because it supplies a missing entity, suggests a better query, contradicts an emerging conclusion or points the agent towards another source.
The old layers still exist. But they are now more tightly coupled and recursively invoked.
Everyone sees part of the problem. Nobody automatically owns the complete chain.
There is now an eleventh community in the room14.
It is not yet a stable professional tradition in the way that information retrieval or knowledge organisation is. It is better understood as an emerging coalition of NLP researchers, IR researchers, model providers, software engineers, framework developers and application builders.
They gather around retrieval-augmented generation, tool use, deep research and agents rather than catalogues and indexes.
They borrow the language of information retrieval, including recall, precision and nDCG. But they also introduce different definitions of success: faithfulness, groundedness, citation correctness, answer relevance and task completion.
Is this eleventh community simply information retrieval (#8) and its industry or enterprise counterpart (#9) in disguise?
Maybe. ColBERT was published at SIGIR. The TREC Deep Learning Track evaluated neural rankers , and TREC inaugurated a separate RAG Track in 2024. Researchers such as Jimmy Lin, Omar Khattab and Nandan Thakur are clearly from the IR tradition.
But at least as much of the inheritance comes from natural language processing. Dense passage retrieval, open-domain question answering and the original RAG paper emerged substantially from NLP research rather than conventional search engineering15. The original RAG work explicitly combined the parametric memory of a pretrained sequence-to-sequence model with a non-parametric dense index.
Good search: produces an answer or research output that is useful, supported by evidence and faithful to the retrieved sources.
Where they gather: arXiv, NeurIPS and ACL on the research side; framework communities such as Haystack's Discord, LangChain meetups and vendor communities such as the OpenAI Developer Community on the industry side.
Representative people: Patrick Lewis from the original RAG paper, together with figures such as Jerry Liu from LlamaIndex as well as many of the same names from Information Retrieval (#8) and relevance engineering (#9)
Contribution to the stack: the generation and orchestration layers, and the reframing of retrieval as one component inside a larger reasoning and tool-use loop.
Potential blind spot: treating retrieval as a solved commodity and repeatedly rediscovering, at considerable cost, lessons that information retrieval researchers and search engineers have accumulated over decades16.
But what does this mean for libraries?
Nobody can become a deep expert in all eleven traditions17.
That would be an absurd job description18.
So let me be clear about what I am proposing. The discovery architect is not a job title to put in the next vacancy advertisement. It is a capability, and in most libraries it will be distributed across a team rather than embodied in one person.
What the capability requires is that somebody, or most likely several somebodies between them, understands that the different traditions exist, recognises how they fit together and can identify when a particular form of expertise is missing, rather than viewing academic search narrowly through one or two traditions.
Where does this sit organisationally? The obvious home is wherever discovery, systems and collections expertise already meet. In some libraries that is a discovery or systems team or community. In others it may be a research support or scholarly communication unit that has drifted into AI evaluation work. The label matters less than the mandate: someone must own the question of whether search is working, end to end, and no single existing role currently does.
It's fairly common for academic libraries to have a discovery committee or taskforce. But, no matter what the team is called or how it is organized, most libraries will lack expertise that spans all the communities with current staff expertise e.g. Information retrieval (#8) or Relevance Engineering (#9). This is not necessarily a call to go hire such expertise but rather a call to broad one's understanding of such areas.
The conventional discovery professional operates and integrates the present library discovery service.
The discovery architect capability spans the broader AI-mediated scholarly search environment: discovery indexes, scholarly graphs, metadata, access systems, search engines, AI answer systems, retrieval tools and agentic workflows.
Not merely a Primo administrator.
Not merely a literature review search trainer.
Not merely a metadata librarian.
Not merely an AI enthusiast who has learnt to spell RAG.
The discovery architect team as a whole should be able to move across the scholarly search stack, translate among professional communities and ask the questions that individual systems and specialists may overlook.
When an AI search product performs badly, this team should be able to ask:
Is the problem corpus coverage?
Is it missing or poor metadata?
Is it entity disambiguation?
Is it retrieval?
Is it ranking?
Is it the search depth or stopping rule?
Is it a distorted graph signal?
Is it poor evidence selection?
Is the generated claim unsupported?
Is the source inaccessible?
Is the model relying too heavily on its own parametric knowledge?
Are we evaluating the wrong outcome?
They do not need to solve every problem themselves.
They need to recognise what kind of problem it is and which tradition's expertise is required.
There is an important caveat.
Recognising the type of problem is not the same as being allowed to examine it.
Asking whether the fault lies in ranking achieves little if nobody outside the vendor can inspect the ranking.
The discovery architect team therefore carries a procurement agenda as well as a diagnostic one:
access to query logs;
the ability to conduct relevance judgements;
documentation of indexed coverage;
transparency about ranking signals;
visibility into retrieval and evidence-selection stages;
stable interfaces for evaluation;
the ability to export or audit intermediate results.
If vendors will not open these layers, the eleven traditions can meet, exchange vocabulary and produce sophisticated hypotheses.
But they can only speculate together.
There is a question underneath all of this that the eleventh community is only beginning to ask, and that the rest of us should be asking constantly.
When an agentic search system performs well, where does the capability actually live? In the model, in the retriever, or in the harness around them?
In Part 2, I will take up that question, look at what controlled benchmarks such as BrowseComp-Plus are starting to reveal about it, and ask what it means for the practical question libraries face: when a vendor shows us an impressive agentic search system, what exactly are we licensing?
To be fair, I estimate that 30\u201350 per cent of this blog's readership is in this community or tradition, showing eagerness to learn more.
Probably the second-largest community of people reading my blog.
There is growing interest in SWAR (Study Within A Review) methodology , including applying it for AI tools.
PubMed itself says itsellf that PubMed Best Match feature was not designed for comprehensive or systematic searching
The current Cochrane Handbook has a section that recognises a role for relevance ranking in screening prioritisation and other forms of technology-assisted review.
This brief list of people reflects my limited depth of understanding in this area.
Evidence synthesis (#3) often treats searching and screening as binary classification. Sensitivity corresponds to recall and PPV to precision; specificity and NPV measure how reliably irrelevant records are excluded. Other metrics used include F1, and Work Saved over Sampling at 95% recall (WSS@95). Evidence synthesis generally prioritises high sensitivity and safe exclusion, whereas information retrieval (#8) often evaluates ranked order using MAP, MRR (particularly useful when only the first relevant result matters) and NDCG.
To be fair, traditionally many of the systems we manage have traditionally neither allowed nor encouraged us to tweak the parameters that affect relevance ranking. Though Ex Libris's Primo does allow this to some extent, most libraries just accept the default settings.
There is a broader library UX (LibUX) community, most notably Andy Priestner, Aaron Schmidt and Ned Potter, though this is not specific to search. Some members, such as Matt Borg and Matthew Reidsma, have also studied user search behaviour
A minority of academic libraries run open-source solutions such as VuFind and Blacklight.
From personal experience, many information literacy librarians outside my institution have asked me how Primo and Summon work.
Before transformer-based models reshaped retrieval, leading search systems in the 2010s typically combined lexical retrieval with learning-to-rank, enriched by behavioural, entity and sometimes knowledge-graph signals. Neural ranking models were already emerging, but BERT and later transformers produced a much larger shift in how queries and documents could be represented and matched.
I remember seeing such approaches emerge when researchers created search engines over the COVID-19 Open Research Dataset (CORD-19), using BERT-style models for retrieval and ranking alongside early question-answering and generative components. In the early 2020s, some librarians were becoming aware of citation-based mapping services such as Connected Papers and ResearchRabbit, which belong to a different class of tools.
Dempsey had thirteen ways. I have stopped at eleven (ten plus an emerging one), and I am confident readers can supply the rest. Some candidates I considered and left out: archival description and discovery is a genuine tradition of its own, with finding aids, EAD and a rather different idea of what a record represents. Expert search outside health, particularly patent and legal search, shares the evidence-synthesis community's obsession with recall and defensibility but almost never talks to it. And the web SEO community, now busily reinventing itself as generative engine optimisation, looks at search entirely from the supply side. It is only a matter of time before researchers ask about the academic-search version: not how to find things, but how to be found. There are others, of course.
When I was trying to read frontier papers about \"generative search\", I was often confused because the NLP and information retrieval communities seemed to be writing about many of the same technologies from different angles. This was especially clear in work on tasks such as question answering. This has been written about fairly frequently in the literature -see here for historical discussion up to 2020 and here for a more technical and up-to-date comparison of Information retrieval to \"AI\" (mostly NLP).
The clearest example is the rediscovery of lexical baselines. Early RAG builders reached instinctively for dense embeddings alone, only to find that hybrid pipelines including boring old BM25 were hard to beat, a result the BEIR benchmark had already demonstrated for out-of-domain retrieval.
I am a generalist, but my knowledge does not span all these communities. Early in my career, I studied and led the implementation of library discovery systems (#10). Since then, I have developed a largely self-taught interest in information literacy (#1), evidence synthesis (#3), bibliometrics (#6) and information retrieval (#8). I have also been somewhat involved in open scholarly infrastructure (#5) and, to some extent, the access and fulfilment chain (#7).
I know there are librarians with \"discovery\" in their titles, but they are likely to be mostly in community #10 and #4.
Back in June I wrote about going the entire month of July without using AI. I wrote about why I'm doing it and what I hoped to achieve.
I failed.
I made it about halfway through the month before I had to call it off. It started off with some personal / non-work exceptions here and there. Then some exceptions crept into my day job. Then the opportunity costs of not using AI for some of the things I had going on this month became too high.
This is a reflection on the attempt. The good, the bad, where I cheated, and where I'm at now. At >5k words it's the longest thing I've ever written on this newsletter by a large margin.
Positives
Negatives
Exceptions
Conclusions
An admission (I'm not alone): In the nearly 4 years ChatGPT and the like have been on the scene I've taken my fair share of turns writing large chunks of text for proposals, pitch decks for companies I've been involved with, responses to reviewers, and so on. And every time, without exception, I can't really recall what I wrote or easily explain the reasoning behind it. This tracks with that MIT study showing something similar. If I had to answer questions about something I wrote with heavy assistance from AI I would have struggled. Or even just going back to revise largely AI-generated text - good editors have a knack for revising other people's writing, but I hate doing it. When I'm revising my own writing, I remember how this paragraph in the approach connected to that section in the significance, and how it all ties together on the aims page. I have a mental map of where things are, because I did the hard work to consider the scaffolding and move things around during an edit.
When working with large chunks of AI-generated writing it feels like revising someone else's writing. With latest gen frontier models (Opus 4.8, GPT-5.6) the writing is probably really good, but I don't start the process with a map of the throughlines connecting parts of the narrative. The same thing is true with code. Writing code is harder. Reading some else's code (even if it's good, perhaps especially if it's good) can be much harder.
An immediate benefit I got out of my few weeks of not using AI for writing was having a much tighter connection to the text I write. For sure, it takes much longer to get words on the page, but revisions are much easier, and I feel much more confident in follow-up discussion than if I had used AI extensively in ideation and creation.
The same is true with reading. Sure, an AI can summarize a paper for me, pulling out key ideas and such. I wrote about this here recently.
But my understanding and later recall of the paper, the motivations behind the experiments, the design (and its flaws), the execution (and flaws there too), are nowhere near where they'd be if I'd have read the paper (or at least the intro, discussion, and figures). Most importantly, I develop a sense of where the gaps are (and thus where the opportunities are) much more easily when I'm reading a paper than when I'm relying on an AI summary. All things I knew before starting this experiment, but the self-experimentation reinforced my priors.
Will I go back to never using AI while writing? No. It's a helpful tool, and I'll use it when it serves me, albeit a little more judiciously. However, when I'm using AI for writing I end up spending far more time than I want removing the AI-isms. So much so that I wrote a Claude skill to remove these patterns from the writing I don't really want to do anyway (cover letters for manuscripts, for example - the abstract should suffice, don't require me to write a cover letter!).
It works well but it isn't perfect. Is going back to remove the em-dashes and the \"not x, not Y, but Z\" and the like a good use of my time? Is this the thing I worked so hard for for the past few decades, all the training, all the effort crafting my voice, all the writing I do here to try to become a better writer? Do I want to spend my time doing this at all? No. Not at all. Maybe (surely?) the AIs will get better at not using these bland and tired constructions, but I imagine something else will take the place of the emdashes and tricolon abuse and whatnot.
These past few weeks I was much happier spending more time thinking of how to phrase something or fleshing out an idea, while spending zero time managing AI and correcting all the tells. Was the writing I did myself unassisted better than writing with an AI assist followed by extensive editing? I don't know. I'd be willing to wager it might not be (test your own judgment, you'll be surprised). But I certainly enjoyed the experience of writing a lot more, and stayed in the flow for longer, without the sidebar of all my other recent Claude chats begging to distract me (see below on bugs, bikes, engines, plumbing).
Earlier this year I wrote about token anxiety and how session limits can really upset my work life balance. And I'm not in the camp of using the \"good enough\" faster/cheaper models when a better one exists. The cost of a hallucination or mistake that Kimi makes that Opus doesn't is far more expensive than the tokens I burn on Opus. I'm maxing Opus 4.8 with high or extra high thinking enabled most of the time.
When not using AI, I didn't have this session limit anxiety, and that freed up so much mental bandwidth to focus on a problem on my terms, on my timeline, rather than when my session limit restarted.
The past few weeks I had a better handle on my attention.
With no chat window open there was no sidebar of half-finished conversations begging for my attention, no checking whether the thing I asked for thirty seconds ago had finished. I wrote in longer stretches with deeper flow (being summer and not being as meeting-packed around here helps). A morning that used to splinter into a bunch of prompt-and-wait cycles, these past few weeks became a more cohesive single deep flow session.
The dopamine hit from one-shotting a working prototype is real, and I defend it below. I think that same hit fragments a writing session. Every prompt is a small slot machine, and the pull to keep feeding it competes with the slower payoff of working a on paragraph or whatever until it feels right. A couple weeks of abstinence (or at least moderation) made me notice how often I'd reach for AI without ever deciding to.
I still like writing code. I don't get to do it much in my job any more, but there's still something fun about solving a little puzzle, like figuring out a video game with very cryptic goals. It's not as valuable of a skill as it used to be, but I still enjoy it.
Much ink has been spilled on the risks and benefits of writing code with AI. I'll just note that I was able to discover or rediscover newer features of tools and libraries that the LLMs don't really know about yet. Take dplyr 1.2.0, for instance, released in February. There are some really nice expansions of the filter() function, as well as some nice additions like recode_values(). Same thing with Quarto. Quarto 1.9 has some nice goodies like PDF accessibility features and support for Typst books (which I used for the PDF version of my DSTT book).
I've been building course materials for a new genomics foundation course I'll be teaching this fall to our data science students. As I mentioned in the original post, I'm going to ask my students to struggle through some of the readings and assignments, so it's only fair if I struggle through creating those assignments and course materials.
However, one of the things I've found AI really helpful for is for building one-off web apps to demonstrate a concept. I've found vibe coding a throwaway interactive web app can take <5 minutes and can be far superior to janky diagrams I can build in PowerPoint that take much more time. For example, back in May I vibe coded this little interactive genetic drift simulator in pure HTML+JavaScript deployed to a GitHub pages site. I suppose I could have made an exception for this kind of thing, but I wanted to avoid that dopamine hit you get from one-shotting something like this, afraid of starting a slippery slope to abandon the whole AI Dry July plan (which, I eventually did). I'll probably go back to some of my materials I created over the last few weeks and vibe up a few more tools like this one.
Another thing I missed was the kind of research that you can do with specialized tools like Consensus (like I talked about here) or even with the generic \"Deep Research\" tools in Claude/ChatGPT. On a few literature searches I ended up spending more time on this and surfaced far fewer papers than if I had used some AI help here. I suppose I could have made an exception for this one too, but it's nearly impossible to separate the \"find me papers around this topic\" from \"summarize all the papers you found on this topic.\" Most of these tools do both at once.
I don't get to write a lot of code these days, but still I had a few small projects in mind that I wanted to do this month. I ended up just not starting some of these projects at all. One involved tinkering around with an nf-core workflow for CRISPR screen analysis, and another involved getting some data from the IUCN Red List API and doing something with it (I started doing this with Claude Science right at the end of June, but put the project down these last few weeks). In both cases, I had a pretty clear picture of what I want to do, but the activation energy was just high enough in both to make me put them off. I haven't written Nextflow code in a while, and I didn't feel like reading the API docs for the IUCN project. I could have spent time on both, but (1) since I don't need to write much Nextflow I don't care that I'm deskilling there, and (2) the LLMs are great at reading API docs and helping me construct the correct call, such that doing this myself would have been more time-consuming with little/no benefit to me.
More generally, the bigger cost to me over the experiment was time, and the things I didn't do. There were a few RFPs with short turnaround times that slipped. I worked a few evenings and a weekend on a side hustle project that probably would have taken me an hour if I wasn't doing this to myself.
Writing/coding/doing/etc without AI is fine when my calendar is open, like it is over the summer with no courses, far fewer meetings, fewer demands on my time. It's a different call when I have every 15 minutes of every day scheduled this week and it's Thursday night and I have a midnight deadline.
I don't want to dress up doing things without AI as something that's always virtuous. Struggling through a complicated topic in a proposal or manuscript or code base is an investment. Struggling through administrative chores or boilerplate I'll never think about again is just slower and an inefficient waste of my time (and my time requires more water and energy usage than a Claude query). Without doubt I lost hours to friction that taught me nothing, nor sharpened any skill I care to retain.
While working up some examples for a Bioconductor lab I want to incorporate I ran into some trouble. When I hit a wall this month I noticed where I used to turn to for help. I previously turned to SeqAnswers or Biostars. Stack Overflow was the reflex for a decade, and it's a shell of what it once was. The good answers are old and the people who wrote them have scattered. It's not 2018 any more. The fallbacks have eroded, and AI is part of why. It handed me a faster tool but it drained the places I used to go when I needed help working something out. The cost of abstaining is higher now.
I had to make a few exceptions along the way. AI meeting notes I can't live without. With some of the other items, could I have done these without genAI? Sure, but it would have taken me much longer, and at great expense. The things here aren't things I care about deskilling in, because I don't really have much skill in anyway. If anything, I've learned an incredible amount from asking follow-up questions, and attempting to DIY things I never would have without AI help.
But one exception leads to another, leads to another, and so on.
I have Zoom AI companion automatically enabled on all of my meetings, even when they're in person. Unapologetically. It's a godsend. I can better engage in a conversation if I'm not scribbling down notes. And I can't write by hand that fast (and typing notes on a computer is distracting to me and my counterparts - I could be writing an essay like the one you're reading now, for all you'd know). And I rely on these notes all the time.
So I didn't turn this feature off, and I'm glad I didn't. Writing legible notes fast by hand with a pen isn't a skill I'm worried about deskilling in. Could I have asked my EA to come to all my meetings and take notes by hand? Sure, but man what an expensive waste of their time this would have been, to absolutely no one's benefit.
I'm working on slides for a genomics course I'll be teaching in the fall. We'll need to meet the federal digital accessibility compliance deadline next Spring, so I'm going ahead and taking care of this now. A big component here is adding alt-text for screen readers to the images. I don't know what PowerPoint is using behind the scenes to do this, but it's pretty good. I manually examine everyone before approving, and I haven't caught one yet that isn't perfect, or at least much better than what I could write with limited time and competing priorities.
I wrote here a while back about how to do this in Quarto with Claude Code if you're using Quarto for slides or other materials.
I used AI liberally for mechanical/maintenance issues. I needed to troubleshoot my son's mountain bike. I was chasing gears for hours trying to troubleshoot the derailleur indexing when the culprit was actually a bent hanger. My lawnmower self-propulsion stopped working, and I used ChatGPT to help me narrow down whether it was a transmission or worn gears. I also used Chat/Claude to help me with a plumbing issue, and an issue with the rod bearings on my Kia's engine causing excessive oil consumption. Sure I guess I could have spent $100 at a bike shop, $200 at some small engine repair shop, $500 for a plumber, and $1,000+ at the chop shop, but some AI assistance and DIY elbow grease saved me thousands of dollars and many hours of hauling broken gear around to all these places.
I recently had a conversation with a fellow Software Carpentry instructor about AI in the SWC curriculum. The discussion touched on the possibility of using AI as a learning assistant. I'm torn on this one. Using AI for these things really helped me learn more about bike components, small engine repair, etc. But I think the difference here is that there was a physical barrier. Claude could tell me to how to adjust limit screws and cable tension until things lined up, but couldn't do it for me. I physically had to put my phone down and do something with my hands in the real world. Contrast that to coding - it'll just do it for you, or at minimum be the helpful assistant, asking you, \"would you like me to implement and test this for you?\"
The next class of exception fell into insect, plant, and plant pathogen identification. I found a few really interesting moths in my garden. I wanted to confirm that indeed my hedges are covered in poison ivy. And I figured out that a palm plant in my office is infested with spider mites (and asked for treatment options). I asked what could be done about these damn lanternflies (nothing). Sure, there are other apps out there that let you do this, but don't they all or mostly use genAI behind the scenes? And there are probably local Slack or Discord servers with channels dedicated to this kind of thing, but I bet most real people on those servers are using AI before responding.
I have a mud room / foyer between my back door and the entrance to the rest of the house. The floors needed stripping, sanding, and painting. Badly. We did a two-color diamond pattern, but before committing I tried a few color combinations by taking a photo of the room (top left) and telling Nano Banana what I had in mind, supplying candidate colors from the Behr paint catalog. This helped me visualize what the room might look like before starting the project (bottom row). We ended up going with black and white (top right).
It helped me outdoors also. Last year we ripped out a section of the lawn and planted a bunch of natives. Long term we'd love to rip out the entire lawn and make the entire backyard a meadow filled with natives, maybe even get a few honeybee hives. I asked chat for some ideas about what this might look like after going over my preferences, and while the plants shown aren't necessarily central Virginia native species, it helped me visualize what a no-grass meadow in place of a lawn might look like.
More than anything, this exercise convinced me that I need to pay a real landscape architect to come over, in real life, and help me with design. Which, I'm doing. This will be a labor of love for years to come, and I know enough to know when I need a professional. Also, there's something not quite right about using an algorithm to design a more inviting backyard for me to touch grass, literally.
This exception that really started the slippery slope at work.
I have a very tight deadline for a major proposal that we're leading here at UVA in partnership with several companies and university centers/departments. While I tried to (and ultimately was happy that I did) write most of what I contributed to the pre-proposal without relying on AI, using AI as a reviewer was absolutely necessary.
The solicitation itself suggested to applicants that the sponsor would be reviewing proposals with AI. At least that's what the Proposal Evaluation \u2192 Proposal Review Process \u2192 Handling sensitive information section seems to suggest. I have very reliable intelligence to suggest that other federal funders are also doing this.
As such, not using AI during prep for continuous review as the role of an evaluator in the sponsor's office wouldn't be some honorable thing to brag about. It would just be stubborn and foolish. And disrespectful to my colleagues - we're all spending hundreds of ours collectively on this proposal, and we should do whatever it takes to maximize the chances this gets funded. This isn't a typical NIH/NSF proposal. It's a behemoth with tons of compliance checks, any one of which could get your proposal that cost thousands of hours collectively, dismissed without review as being nonconforming.
Back in the GPT-4o / Opus 3 days, or even now with most open-weight models, it was the human's job to fact check the AI. Now I'm getting AI to fact check me.
For a few weeks now I've been writing a mini review paper on AI and Biosecurity, summarizing a lot of the truly great work coming out of RAND, NTI, SecureBio, Active Site, CLTR, UK AISI, US CAISI, IGSC, Microsoft, NIST, IBBIS, MIT, Scale AI, Berkeley, National Academies, NSCEB, Johns Hopkins, several of the frontier labs, and many others. The paper is expanding on many of the topics I've covered here.
It started off as another newsletter post here, but became so long and extensive that decided to turn this into a paper I'll try to publish soon. And I did this without using AI beyond some citation network analysis and literature discovery.
I read all these papers I'm citing in the review. Every one of them. And I wrote the review without using AI. However, with a few hundred papers, preprints, whitepapers, model cards, benchmarking websites, conference talks, and so on, and because I'm citing numbers and such in tables, I have a bit of anxiety about publishing this, even in preprint form, for fear that I've misrepresented a benchmarking claim or study design (at best), or misstated a result or numeric value (at worst). This is a very small community, and many authors are colleagues.
So I set up a simple but effective fact-checking agent with Claude Cowork. I exported the Zotero library along with a BiBTeX file of all the references I used in my manuscript. The BiBTeX file pointed to the location of each citation's PDF file on disk. I then got Claude to read every sentence, and where there's a number or claim backed by a citation, to read the actual PDF of the thing I'm citing and ensure that I didn't misrepresent the claim, and to return a table of claims and numbers cited, together with a judgment (verified, questionable, misrepresentation, inaccurate).
This thing burned hundreds of dollars in tokens reading hundreds of PDFs, making notes, and fact-checking my own writing, all using Opus 4.8-extra. But worth it to avoid even a single, simple human slip.
If you've made it this far, thanks for reading. And if you're an expert in the AIxBio / Biosecurity world (especially if you're at one of the organizations and likely one of the authors I've cited in this manuscript), and you want to review it and offer comments on this manuscript even before I publish it as a preprint, please email me (address here). Also, if you're interested in being a peer-reviewer if/when I eventually try to get it published in a journal, let me know that also.
As noted in the Negatives section, the opportunity costs were extraordinarily high, too high for me to take a month of complete abstinence. A few examples:
Anthropic redeployed Fable in the beginning of July, giving everyone on a subscription plan a week to use it before it dropped back to API rates only. I had a sort of work project, sort of side project thing in mind \u2014 something that would comb through the grants.gov and sam.gov postings using their respective APIs, then customize alerts to me based on my interest with some AI thing reading through the solicitation details. Fable one-shotted this thing, perfectly, installable with uv, unit tests with pytest. I still have work to do to add some features I'd like to add before releasing, but it's a great start, one that I wouldn't have started at all without AI.
The UVA AI Upskilling series is still going strong over the summer. I'm attending several of these. Hard to avoid using AI at an AI upskilling workshop.
In designing my upcoming fall course, I just wanted to see how well Claude could complete the assignments.
Answer: perfectly, instantly, and cheaply even with Sonnet 5. For both code and paper discussion assignments.
See also my previous lamentation on this topic, and toward the bottom of this post on why code isn't going to be front and center in this course.
So where does this leave me? Not as an abstainer.
There used to be a time when intelligent people could make a defensible argument that these tools aren't useful, they make too many mistakes, or that they're just worthless next-token predictors. That point has passed, and these arguments are no longer opinions. They're just factually wrong, incorrect statements. Driven by identity more than evidence.
The rule I'm keeping from my AI (semi-) Dry July fortnight is about which productive struggles are worth keeping. Some amount of friction is useful. At least looking at my code while debugging helps me consider how the system fits together, the way fixing one broken derailleur means I can fix the next. Other frictions teach me nothing, and I usually don't feel diminished by handing it off. The category error I'd drifted into was treating every cognitive task like something I could offload, outsourcing the parts that were actually mine to learn and defend.
So the policy, for me (which will evolve, no doubt). Prose I'll have to defend, proposals I'll write, papers in my own field, lectures I'll deliver, and some aspects of software architecture: these are all are mine to do (or at least mine to start off with). One-off demos, first-pass literature scans, editing, and genuinely disposable work I don't care about can and will go to the machine. And as I've mentioned in previous posts, I have never and will never use AI speak for me: if I'm expressing an opinion, or a sentence ever has an \"I\" in it, it's written by me, with zero LLMs speaking for me.
Teaching materials will probably land on a 90/10 split in favor of not using AI. Not because AI can't make good teaching materials, but because I'll be asking students to struggle through the material so it's only fair that I struggle through design, and also for the reasons outlined in Positive #1 (mental maps and recall).
I don't know what higher education will look like in 5 years, but abstinence-only education has never worked for anything, and I don't think that you should only engage in data science if you're in a long-term committed relationship with a card-carrying Data Scientist\u2122. When, how, and to what degree to thoughtfully incorporate AI into my classroom is something I don't have a perfect answer to yet.
These few weeks of self-experimentation didn't change my mind so much as sharpen what I already knew. AI does result in deskilling. Some things I'm okay deskilling in, and others I'm not. I'm going to be more intentional with what I hand off to the LLMs, and I'm no longer handing over wholesale the work I'd miss if I forgot how to do it, or anything I'll ever need to understand, defend, revise, and own.
\u2042
Over 15 years ago I made a terrible life decision. I tried giving up caffeine. I had developed a deep physiological dependency, getting severe headaches without my morning megadose. I traveled and I went out camping and backpacking more than I do now, and carrying coffee making supplies wasn't worth the weight and space and hassle it required. I thought I could rid myself of the addition and live more freely without the drug. It took me months to completely cut caffeine, and even months after having zero caffeine the craving never went away. I always felt like a part of my brain wasn't firing on all cylinders, and the craving for caffeine was still preoccupying.
I started adding caffeine back into my life on a very limited basis, once per week or less, and found the drug absolutely supercharged my productivity, mood, and focus (unsurprisingly). Once a week turned into twice per week, every day, and very soon back to multiple cups daily. The point is, even long after the drug had metabolized out of my system I always felt like I was missing a limb, like some part of my cognition had the brakes engaged, a governor module I couldn't remove. A physical addiction in the literal sense.
Michael Pollan describes addiction in his 2021 book, This Is Your Mind on Plants. In an earlier interview with NPR he notes:
\"I think the word 'addiction' has a lot of moral baggage attached to it,\" he says. \"As [Johns Hopkins researcher] Roland Griffiths told me, if you have a steady supply of something, you can afford it and it's not interfering with your life, there's nothing wrong with being addicted.\"
Is access to generative AI like access to my morning coffee? I have a steady supply. I can afford it (at least for now, as long as my employer keeps footing the bill). Is it interfering with my life? This experiment didn't give me a straight answer to that one.
","doi":"https://doi.org/10.59350/fk81y-9sh42","guid":"200128706","image":"https://substackcdn.com/image/fetch/$s_!PRie!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1e9537f0-ba39-4528-b4b5-9f5f6d6fe0c6_3128x1167.png","language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","published_at":1784332800,"rid":"3wmd0-rb806","summary":"Reflections on trying to go a month without AI. I made it two weeks (sort of). 5.2k words, 23 minutes reading time.","tags":["AI"],"title":"AI Dry July: Aborted","updated_at":1784370435,"url":"https://blog.stephenturner.us/p/ai-dry-july-aborted","version":"v1"},{"authors":[{"affiliation":[{"id":"https://ror.org/041kmwe10","name":"Imperial College London"}],"contributor_roles":[],"family":"Rzepa","given":"Henry","url":"https://orcid.org/0000-0002-8635-8390"}],"blog":{"authors":[{"name":"Henry Rzepa","url":"https://orcid.org/0000-0002-8635-8390"}],"community_id":"8fb94c86-e95f-41cf-aac2-a2877ffc1b5f","created":1693094400,"current_feed_url":null,"description":"Chemistry with a twist","favicon":"https://rogue-scholar.org/api/communities/8fb94c86-e95f-41cf-aac2-a2877ffc1b5f/logo","feed_format":"application/atom+xml","feed_url":"https://www.ch.ic.ac.uk/rzepa/blog/?feed=atom","filter":null,"generator":"WordPress","home_page_url":"https://www.ch.ic.ac.uk/rzepa/blog","issn":null,"language":"eng","license":"https://creativecommons.org/licenses/by/4.0/legalcode","prefix":"10.59350","relative_url":null,"secure":true,"slug":"rzepa","status":"active","subfield":"1606","title":"Henry Rzepa's Blog","updated":1784360740,"use_api":true},"blog_name":"Henry Rzepa's Blog","blog_slug":"rzepa","content_html":"(Some) chemists have a strange fascination with bonds between two specified atoms – more exactly how short or how long can such a bond get? I asked a slight different question[1] of a molecule known as nitrosobenzene dimer, noting that both nitrogens were both connected to each other and carried a (formal) positive charge; one might naively imagine that the coulomb effects between two positive atoms might result in a repulsion which would greatly lengthen the bond between them (it does not, but it does weaken it). I moved from this to asking how many examples of such molecules there might be, and whether any exhibited unusual bond lengths. After a search of\u00a0the\u00a0CSD, one (“JEGRAS”) caught my interest, shown in blue below[2],[3] and exhibiting a crystallographic N-N distance of 1.695\u00c5 (to answer the question posed in the title above).
\n
Now this is a long N-N bond, probably the longest such bond ever found! And the Lewis valence bond structure has a remarkable up to three centres containing groups with ionic charge separation.\u00a0The one shown in blue is how it is represented in the literature[2], but many others can be devised. The list above is by no means comprehensive, some with a double bond between the two nitrogens and others with no bond there! Two are aromatic (red) and one has little charge separation (green).\u00a0Some have three centres of charge separation, others with only one. This might be an interesting problem to set students in a tutorial – how many Lewis structures can they devise in a set time?
\nThe first task is to see if this effect might be replicated using DFT theory. Here are some models (again by no means comprehensive). It seems that adding a DCM solvation field\u00a0(to mimic the polar environment of a crystal)\u00a0does make a difference.
\nExpt
\n| Method | \nN-N length | \nDOI | \n
|---|---|---|
| Expt | \n1.695 | \n[3] | \n
| MN15L/Def2-TZVPP | \n1.732 | \n[4] | \n
| MN15L/Def2-TZVPP + water solvation | \n1.678 | \n[5] | \n
| MN15L/Def2-TZVPP + DCM solvation | \n1.686 | \n[6] | \n
| MN15L/Def2-QZVPP | \n1.728 | \n[7] | \n
| r2-SCAN-3c/Def2-mTZVPP + DCM | \n1.787 | \n– | \n
The calculated (MN15L/Def2-TZVPP + DCM solvation[8]) Wiberg bond index of the N-N bond is 0.5594, an unusually low value for a \u03c3-bond. The corresponding NBO localised orbital however has a normal electron occupancy (1.9507e).
\n
So this unusual N-N bond can be added to the list of “longest ever” bonds. However, unlike e.g. ultra long C-C single bonds, which tend to be dominated by steric repulsions, this one achieves its status purely electronically.
\n\"Diamond Open Access\" is not new. Latin America was publishing free to read and publish scholarship long before it had a name, and before it was emphasized internationally. Learn how Latin America has always been a leader in open access publishing, and how the region has been a major influence on how PKP has developed OJS.\u00a0
Long before the term \"Diamond Open Access\" existed, or became a term international policy bodies reached for, it was simply how most of Latin America published research. Public universities ran their own journals and provided hosting either internally or by partnering with fellow institutions. Editors worked without author fees because the idea of charging researchers to publish, or readers to read, never took hold the way it did elsewhere.\u00a0
\nLatin Americans built a scholar-led publishing culture on their own terms. The adoption of PKP's Open Journal Systems (OJS) emerged naturally as a result of values alignment: institutional ownership, non-commercial governance, and open access.\u00a0
\nPKP did not bring this ecosystem into being, but it found a home in one and has spent two decades being shaped by it\u2014in our software priorities, documentation, training programs, and research.
\nFor thousands of university-run and society-run journals, OJS supplied something the region's publishing culture had not previously had at scale: a standardized, low-cost technical backbone that could handle submission tracking, peer review, layout, and indexing without requiring each journal to build its own system or pay for a commercial platform.\u00a0
\nThis end-to-end platform let small editorial teams at regional public universities run peer-review processes as rigorous and procedurally as those at much larger institutions, and it made journals interoperable with the metadata standards that indexes like DOAJ, Latindex, and Redalyc required for inclusion.
\nThe scale that followed is now one of the most cited facts about OJS worldwide: a large share of the platform's global installations are concentrated in Latin America, with 8,413 journals in the region as of 2025. Much of this growth occurred through institutional adoption rather than individual journals seeking out software independently.\u00a0
\nNational networks such as Redalyc and SciELO encouraged member journals toward common technical standards; university library systems rolled out shared OJS instances that dozens of journals could use without each needing their own server or systems administrators; and national science agencies, in several countries, made a degree of technical standardization a condition of funding or indexing support.
\nThat growth reshaped OJS itself. Multilingual interface support, workflow features tailored to volunteer-run editorial teams, and integrations with regional indexing services all stemmed from demands that surfaced from the community, including Latin American users.\u00a0
\nPKP's own research has documented this dynamic directly, treating the region not as a market to study from the outside but as the source of much of what the organization now understands about sustaining non-commercial publishing.
\nFor those involved with software sustainability and contribution, software is a visible part of this infrastructure, but it is not the largest part. Sustaining thousands of non-commercial journals requires documentation, translation, metadata expertise, and governance\u2014work that rarely appears in adoption statistics but without which the software is unusable.\u00a0
\nIn Latin America, this work has largely been done by the community itself: editors training the editors who come after them, librarians running metadata workshops for smaller institutions, and university presses documenting migrations in Spanish.
\nThis is also where the region's dependence on partner organizations becomes visible, and vice versa. For example, DOAJ's indexing criteria have pushed journals toward more transparent editorial policies, often ahead of national requirements. Crossref and DataCite have made persistent identifiers accessible to journals that could never have afforded commercial DOI registration on their own, frequently through agreements negotiated by regional consortia rather than individual publishers.\u00a0
\nNo single organization runs this infrastructure. It holds together through overlapping contributions from regional networks, national library systems, service providers, and volunteer contributors, with PKP occupying one part of a much larger structure.
\nA significant share of this knowledge work happens in Spanish, and it is a condition for the infrastructure functioning at all. An editor working through partially translated documentation is, in practice, operating with degraded infrastructure regardless of what the software can do.\u00a0
\nPKP brings together a volunteer community from Latin America and Spain to translate the software, adapt, and often originate documentation that reflects how OJS is actually used in Spanish-language contexts. Further, PKP School develops Spanish-language courses, extending training to editors who would otherwise be excluded from professional development available to English-speaking colleagues, and the offers the spanish-speaking community a space to share experiences and solving issues with PKP Forum's Spanish Regional Network. Partner institutions across the region have independently produced guides and workshops that circulate well beyond their institutions of origin, forming a distributed, multilingual knowledge base that no single organization owns (e.g., Seminario Permanente de Editores).
\nAlongside this infrastructure work, the conversation about what counts as a rigorous journal is shifting. Mexico's science and technology authority, SECIHTI, has formally established the Sistema Nacional de Publicaciones Cient\u00edficas y Human\u00edsticas (SNPCyH), a policy instrument aimed at strengthening the country's ecosystem of non-commercial, open-access academic journals. Notably, the system evaluates journals on editorial presentation, editorial policy and management, and content accessibility explicitly without relying on citation metrics or impact-factor-style indicators.\u00a0
\nEarly discussion has centered less on indexing thresholds than on editorial quality, research integrity, and bibliodiversity, acknowledging that a healthy ecosystem needs journals publishing in multiple languages and on regional topics, not only those competing for international citation counts.
\nMexico's process should not be read as a regional template; assessment frameworks vary considerably by country. What appears more broadly is a shift in emphasis, visible in Mexico and in policy conversations elsewhere, away from indexing compliance as an end in itself and toward the editorial practices indexing was always meant to signal.
\nThat conversation will continue this August 2026, when the Second Latin American Open Science Forum (Foro Latinoamericano de Ciencia Abierta) will convene academics, researchers, editors, and PKP software users from across the region together in Heredia, Costa Rica. The forum's second edition follows an inaugural gathering in Quito.\u00a0
\nPKP will contribute workshops and panel sessions, and will spend at least as much time listening: these forums work best as spaces where attendees compare notes across national contexts, and where PKP staff hear directly what is and is not working in the software and documentation it maintains.
\nNone of the above suggests the region's publishing ecosystem is without strain. Editorial teams remain under-resourced, and the pressure to align with international indexing criteria has not disappeared even as its terms are renegotiated.\u00a0
\nBut Latin America's central contribution to global scholarly communication has never been a promise of frictionless success. Rather, Latin America has shown through the decades that non-commercial, scholar-led publishing can work at scale, sustained by a layered infrastructure \u2013 technical, linguistic, editorial, and institutional \u2013 that no single organization builds alone.\u00a0
\nPKP's software plays a critical role in this infrastructure, but it is one among many, including DOAJ, Crossref, DataCite, regional networks, university libraries, and the countless volunteer editors and translators whose names rarely appear in any organization's annual report.\u00a0
\nPKP's engagement with Latin America has brought us a clearer sense of what is needed for scholarly infrastructure to be embedded in the community and to be truly sustainable. PKP's work in the region is a standing reminder that the people who understand this best are usually the ones already doing the work.
\nThank you to Famira Racy, Juan Pablo Alperin, Marc Bria, and Pedro L\u00f3pez Casique for their helpful reviews of this post
\nThe post What Latin America Has Taught Us About Building Scholarly Infrastructure appeared first on Public Knowledge Project.
","doi":"https://doi.org/10.59350/9y1hp-rn072","guid":"https://pkp.sfu.ca/?p=19404","image":"https://pkp.sfu.ca/wp-content/uploads/2026/07/Latin-America-Regional-feature-V2-1024x576.png","language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","published_at":1784332800,"rid":"sj989-9y243","summary":"\"Diamond Open Access\" is not new. Latin America was publishing free to read and publish scholarship long before it had a name, and before it was emphasized internationally. Learn how [\u2026] The post What Latin America Has Taught Us About Building Scholarly Infrastructure appeared first on Public Knowledge Project.","tags":["Community Newsletter","News","Diamond OA","Latin America","Regional Feature"],"title":"What Latin America Has Taught Us About Building Scholarly Infrastructure","updated_at":1784335912,"url":"https://pkp.sfu.ca/2026/07/17/regional-feature-latin-america/","version":"v1"},{"authors":[{"contributor_roles":[],"family":"Marcum","given":"Christopher Steven","url":"https://orcid.org/0000-0002-0899-6143"}],"blog":{"authors":null,"community_id":"8bdb1ae7-4621-4fa5-ad1a-3a639417dfd5","created":1768694400,"current_feed_url":null,"description":"Perspectives on science, data, and technology that don't fit anywhere else.","favicon":"https://rogue-scholar.org/api/communities/8bdb1ae7-4621-4fa5-ad1a-3a639417dfd5/logo","feed_format":"application/atom+xml","feed_url":"http://chrismarcum.com/marcum-blog/feed.atom","filter":null,"generator":"Jekyll","home_page_url":"https://www.chrismarcum.com/marcum-blog/","issn":null,"language":"eng","license":"https://creativecommons.org/licenses/by/4.0/legalcode","prefix":"10.59350","relative_url":null,"secure":true,"slug":"chrismarcum","status":"active","subfield":"3312","title":"Open Evidence","updated":1784315682,"use_api":null},"blog_name":"Open Evidence","blog_slug":"chrismarcum","content_html":"\n\nThis post was co-authored with Abigail Haddad and a version of it is cross-posted at Present of Coding.
\n
We analyzed over 50,000 comments posted on the Office of Management and Budget's Proposed Regulation for Federal Financial Assistance (i.e., the Uniform Guidance codified at 2 CFR 200). Many news outlets, including the New York Times, have reported that there were 500,000 comments received and, thus, the posted comments represent only 10% of the true count.
\nThat's probably not true - the count is likely closer to what was actually posted by OMB.
\nWhy? Because there is a major flaw in the way that regulations.gov counts comments received that can be easily gamed to increase that denominator \u2013 and based on the patterns in the data in terms of when OMB has been receiving vs. posting comments, there's no 450,000 backlog of comments they have yet to post.
\n\nA qualification of a viral cartoon graph of the growth in public comments on this topic.
Arbitrarily increasing the comment count is easy for users. As we'll show, we were able to increase the comment count for a different proposed regulation by almost a million comments with just one actual comment submission. This is because the General Services Administration, which runs the site, has built-in a feature that allows gaming by design.
\nHere's how to game the system to increase the count:
\nWhen you add an attachment instead of only using the provided comment field, you're asked: Did you attach files that contain comments from more than one person/entity?
\nIf you check \"Yes\", you're asked to provide a value in a numeric field for the Number of persons/entities represented.
\nThis is where the game can happen. Whatever value you enter in that field is added to the total number of comments received.
\nAccording to the documentation, it takes until 11:59 PM ET of that day for this change to be reflected on the website, which adds to the lack of transparency about what is actually happening because it's not instantaneously reflected in the count.
\nTo illustrate this, we conducted a few repeatable experiments on notices that are currently open for comment. Here we share the most dramatic of those experiments where we incremented the count by 999,999 comments \u2013 this is the biggest number you can enter in that field.
\nWe picked \"Considering Lands with Wilderness Characteristics in the BLM Land Use Planning Process\". As of around 8:00 AM ET on 7/16/2026, the total number of comments received was just 310.
\nWe submitted a simple comment with an attachment and indicated that it represented 999,999 entities:
\nThe next day, the total count went up by roughly that amount, now showing a total of over 1 million comments received, and they had posted 301 of them:
\nWhat does this mean for the 500,000 reported comments on 2 CFR 200? They're likely not real. But it was reasonable for media outlets to assume they were real. The only way to figure out how this number is calculated via the website is to click on \"more details,\" then \"FAQ page,\" then \"How are Comments counted and posted to Regulations.gov?\". There, you'll find an explanation, more or less. But almost no one does that. Neither of us learned it that way; we only know this because it was explained to us.
\nComments have been closed for several days now, and based on the patterns in terms of when OMB is receiving vs. posting comments, we just don't think it's plausible that they have yet to post 450,000 \u2013 we can see when comments were submitted vs. posted, and they've clearly posted the vast majority of them already.
\nThis method is misleading and GSA should fix it. It should be replaced with a count that actually represents what everyone currently assumes it does\u2014the total number of comments that have been submitted. That way, it will be possible for those following public interest in regulations to track submitted comments, even if there's a lag in agency posting.
\nWhen this many people are getting the meaning of a published statistic wrong, the problem is with the statistic, not the people.
","doi":"https://doi.org/10.59350/wgyxn-jne09","guid":"https://www.chrismarcum.com/marcum-blog/2026/07/17/The%20Comment%20Count%20is%20Too%20Damn%20High","image":"https://github.com/user-attachments/assets/072f1800-e85f-439c-9fd5-5e1e034c52f2","language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","published_at":1784246400,"rid":"qrkyn-g0d50","summary":"We analyzed over 50,000 comments posted on the Office of Management and Budget's Proposed Regulation for Federal Financial Assistance (i.e., the Uniform Guidance codified at 2 CFR 200). Many news outlets, including the New York Times, have reported that there were 500,000 comments received and, thus, the posted comments represent only 10% of the true count.","tags":["General","Government"],"title":"It's (probably) Not 500,000 Comments on OMB's Proposed Regulation","updated_at":1784315742,"url":"https://www.chrismarcum.com/marcum-blog/2026/07/17/The-Comment-Count-is-Too-Damn-High.html","version":"v1"},{"authors":[{"contributor_roles":[],"family":"Royle","given":"Stephen","url":"https://orcid.org/0000-0001-8927-6967"}],"blog":{"authors":[{"name":"Stephen Royle"}],"community_id":"d1d0a116-fe9c-4f5a-b8c5-c3b69edb8327","created":1673740800,"current_feed_url":null,"description":"x == (s || z). You say it kwontized","favicon":"https://rogue-scholar.org/api/communities/d1d0a116-fe9c-4f5a-b8c5-c3b69edb8327/logo","feed_format":"application/atom+xml","feed_url":"https://quantixed.org/feed/atom/","filter":null,"generator":"WordPress","home_page_url":"https://quantixed.org","issn":null,"language":"eng","license":"https://creativecommons.org/licenses/by/4.0/legalcode","prefix":"10.59350","relative_url":null,"secure":true,"slug":"quantixed","status":"active","subfield":"1307","title":"quantixed","updated":1784299506,"use_api":true},"blog_name":"quantixed","blog_slug":"quantixed","content_html":"Niche tips are the bedrock of quantixed. Here is another. Posted purely because we reran into this problem and had to refigure out the answer.
\n\n\n\nWe have a segmented image. Each object has a unique integer assigned as a pixel value. How do we get a list of the integers corresponding to the objects?
\n\n\n\nLet’s assume the segmented objects are not labelled contiguously, or that we only want the objects in one particular slice/frame or within a certain ROI. We’d like to analyse only a subset of the total objects so we need to know which ones they are.
\nWorking with an image (matrix) in most languages, it is trivial to extract the unique integers in the image directly. But sticking with ImageJ, what built-in options do we have?
\n\n\n\nWe can use getRawStatistics(nPixels, mean, min, max, std, histogram); to get an array (histogram) which contains the number of pixels of each value. Excluding 0, we can find any pixel values that have non-zero counts and then we would have our list of objects.
\n// open file, optionally select ROI clear outside etc.\nname = getTitle;\nf = File.open("path/to/particles.txt");\nprint(f, "filename" + "\\t" + "particle_id" + "\\n");\n// get a list of all unique pixel values\ngetRawStatistics(nPixels, mean, min, max, std, histogram);\n// start at 1 because 0 is background\nfor (i = 1; i < histogram.length; j++) {\n\tif (histogram[i] > 0) {\n\t\tprint(f, name + "\\t" + i + "\\n");\n\t}\n}\nFile.close(f);\nGreat! But there’s a problem:
\n\n\n\nIn our case the segmented objects are in a 32-bit image stack that came from a CLIJ routine ext.CLIJx_voronoiOtsuLabeling()
This strategy relies on the histogram generated by getRawStatistics() which generates arrays as follows:
While this is annoying, it makes sense because 32 bit images are floating point and not integer based. No problem, surely we can just convert to 16-bit and we should be fine… well, yes but here is the other gotcha…
\n\n\n\nIf we convert from 32-bit to 16-bit we can see the following behaviour:
\n\n\n\nIn the 32-bit test image, we have a background of 0, and two squares: one with a value of 182 and the other with 221. A Glasbey LUT is used to show the objects. If we convert to 16-bit we see that the first square is now 1462 and the other is 65535!
\n\n\n\nThe downsampling to 16-bit results in a scaling effect. This is the default behaviour and can cause some confusion. There are two ways around this issue:
\n\n\n\nIn Edit > Options > Conversions, there is a checkbox to switch on/off “scaling”Scale when converting”. The default is for it to be on. In a script you can turn it off and then turn it back on again at the end of the script using:
\n\n\n\nsetOption("ScaleConversions", false);\n// do all the steps\nsetOption("ScaleConversions", true);\nIf we set the scale of the image to the 16-bit range and do the conversion we will get the desired result regardless of whether the user has the option checked or not.
\n\n\n\nsetMinAndMax(0, 65535);\nrun("16-bit");\nNow, using getRawStatistics() we get a 222 length histogram with pixel values of 182 and 221 as our objects of interest, as intended (the right image above).
I promised it was a niche topic, but the moral of the story is be careful when downsampling images as it may not result in the intended behaviour.
\n\n\n\n—
\n\n\n\nThis post is part of a\u00a0series of tips.
","doi":"https://doi.org/10.59350/y3tww-1gn97","guid":"https://quantixed.org/?p=3810","image":"https://quantixed.org/wp-content/uploads/2026/07/segment1.png","language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","published_at":1784246400,"rid":"8rs3k-msy71","summary":"Niche tips are the bedrock of quantixed. Here is another. Posted purely because we reran into this problem and had to refigure out the answer. Problem We have a segmented image. Each object has a unique integer assigned as a pixel value. How do we get a list of the integers corresponding to the objects?","tags":["Computing","Cell Biology","Code","FIJI","ImageJ"],"title":"Tips from the Blog XVIII: extracting segmented objects in ImageJ","updated_at":1784301221,"url":"https://quantixed.org/2026/07/17/tips-from-the-blog-xviii-extracting-segmented-objects-in-imagej/","version":"v1"},{"authors":[{"contributor_roles":[],"family":"Willighagen","given":"Lars","url":"https://orcid.org/0000-0002-4751-4637"}],"blog":{"authors":null,"community_id":"e0509c2b-3c92-4e55-a306-bb03ddf5f7c8","created":1673136000,"current_feed_url":null,"description":"Thoughts about bibliographic metadata, programming, statistics, taxonomy, and biology.","favicon":"https://rogue-scholar.org/api/communities/e0509c2b-3c92-4e55-a306-bb03ddf5f7c8/logo","feed_format":"application/atom+xml","feed_url":"https://larsgw.blogspot.com/feeds/posts/default","filter":null,"generator":"Blogger","home_page_url":"https://larsgw.blogspot.com/","issn":null,"language":"eng","license":"https://creativecommons.org/licenses/by/4.0/legalcode","prefix":"10.59350","relative_url":null,"secure":true,"slug":"syntaxus_baccata","status":"active","subfield":"1110","title":"Syntaxus baccata","updated":1784296096,"use_api":null},"blog_name":"Syntaxus baccata","blog_slug":"syntaxus_baccata","content_html":"The Library of Identification Resources (LoIR) is a database of identification keys and other publications and resources that assist in the taxonomic identification of animals, plants, and fungi. As a result, the database contains a lot of taxonomic info, which can be roughly divided into two parts. Firstly, the taxonomic scope of every identification key or other type of resource is recorded, like \"hoverflies\" or \"Syrphidae\" in a key to the hoverflies of Europe. Secondly, if feasible, checklists are made for all of the individual species (or genera, etc.) included in each of the identification keys. Both sets of scientific names of taxa are mapped to external databases to allow easier comparisons and, importantly, to power the search engine. Until two days ago, this external database was the GBIF Backbone Taxonomy, maintained by the Global Biodiversity Information Facility (GBIF).
\nHowever, in June of 2026, GBIF replaced the GBIF Backbone Taxonomy with the Catalogue of Life (COL). This comes with many improvements, as the Catalogue of Life gets frequent updates, includes taxa with intermediate ranks, and provides an Extended Release (COL XR). The COL XR integrates additional checklists leading to better coverage of less common taxa, obscure synonyms, and recent taxonomic changes, directly affecting the data quality of GBIF. As opposed to the GBIF Backbone Taxonomy, which used to integrate various datasets as well, there also seems to be a clear path for small data publishers like LoIR to contribute to the COL XR.
\nThis change also means that LoIR needed to move from the GBIF Backbone Taxonomy to COL (XR) before GBIF fully deprecates the old system. This transition came with its own challenges, for both parts of the taxonomic data in LoIR. Back in 2025, we found that the base release of COL missed 14.3% of the 'endpoint' taxa in checklists of identification keys in LoIR, compared to just 3.5% by the GBIF Backbone Taxonomy (Willighagen & Jongejans, 2025). The pressing question for me was whether COL XR could improve on this enough to use it as the main taxonomy in LoIR.
\nNow, after updating the tools used to develop LoIR (see Willighagen, 2026), all identification checklists have been mapped to COL XR instead of COL. This lead to a marked improvement, leaving just 3.5% of endpoint taxa unmapped compared to 5.0% with the GBIF Backbone Taxonomy. Only 1,029 scientific names (0.9%) were mapped to the GBIF Backbone Taxonomy but could not be mapped to COL XR (Fig. 1). This is a good sign!
\n\n\nNote that since 2025, the overall mapping coverage of both COL and the GBIF Backbone Taxonomy decreased, due to the addition of many publications about Heteroptera published between 1880 and 1963. Looking only at checklists added before August 4th, 2025 (see Willighagen, 2025), COL XR misses only 2.4% of endpoint taxa, compared to 3.5% with the GBIF Backbone Taxonomy at the time.
\n
\nFigure 1: Alluvial diagram of changes in the coverage of mappings of endpoint taxa in checklists between the GBIF Backbone Taxonomy and COL XR.
The other part of the taxonomic info in LoIR, the taxonomic scopes, was more difficult to convert. However, here COL (XR) also offers immense improvements by including taxa with intermediate ranks. For example, say an identification key is intended for all weevils, i.e. the superfamily Curculionoidea. Before, that taxonomic scope had to be mapped to all constituent families, because the GBIF Backbone Taxonomy did not include superfamilies or any other intermediate taxa. Now, it can simply be mapped directly to COL's entry for Curculionoidea: KV7G2.
\nThis was possible for most, but not all taxa. For example, the subfamilies of shield bugs (Pentatomidae) have not been added to COL (XR) yet, and therefore still had to be mapped to all genera separately. The list of taxonomic scopes also contain a lot of taxa that are now considered obsolete, and have to be mapped to multiple current taxa. However, whereas with the GBIF Backbone Taxonomy only 72.5% of taxonomic scopes could be mapped directly, with COL XR this raises to 90.2% (Fig. 2). Additionally, 23 taxa that were not mapped at all in the GBIF Backbone Taxonomy are now mapped to COL (XR). Unfortunately, there were still 5 taxa that could not be mapped to COL (XR) yet, mostly heteropteran genera.
\nCOL XR as a whole does still require some additional curation. An issue with COL's name index meant there are a lot of duplicate entries for the same taxa in the current release of COL XR (26.6). Additionally, some genera have been given an incorrect classification (see the previous link), and as aforementioned, several species, genera, and higher taxa are still missing. This should all improve as COL XR continues to develop though, and now hopefully the data in LoIR could contribute to this!
\n
\nFigure 2: Alluvial diagram of changes in the coverage of mappings of taxonomic scopes between the GBIF Backbone Taxonomy and COL XR.
After both sets of taxonomic info in LoIR were adapted to use COL XR, the search engine had to be updated to use the new mappings. This process was relatively simple, as GBIF has API endpoints for both the GBIF Backbone Taxonomy and COL XR. The API endpoint for COL XR is technically still experimental, but an equivalent, stable version will likely be added in the future.
\nAll in all, I think this is a great succes, and I am excited for future developments.
\nWissenschaftsblogs stellen einen wichtigen Bestandteil der wissenschaftlichen Kommunikationskultur dar (Ochsner and Pampel 2026). Als interaktive Publikationsformen (K\u00f6nig 2015) unterst\u00fctzen Blogs den Austausch unter Forschenden und tragen gleichzeitig zur Dialogf\u00f6rderung zwischen Wissenschaft und Gesellschaft bei. Blogs regen Diskussionen an und erm\u00f6glichen eine direkte, schnelle sowie offene Kommunikation von Forschungsergebnissen (K\u00f6nig 2015; Ochsner and Pampel 2026). In Bezug auf ihre Langzeitverf\u00fcgbarkeit und Zitierf\u00e4higkeit stehen Wissenschaftsblogs jedoch vor besonderen Herausforderungen (Ochsner and Pampel 2026). Um die Sicherung und Auffindbarkeit von Inhalten dauerhaft gew\u00e4hrleisten zu k\u00f6nnen, wurde im Rahmen des von der Deutschen Forschungsgemeinschaft (DFG) gef\u00f6rderten Projekts Infra Wiss Blogs ein Anforderungskatalog entwickelt, der anhand einer kooperativen Informationsinfrastruktur Ma\u00dfnahmen zur langfristigen Verf\u00fcgbarkeit und eindeutigen Zitierbarkeit von Wissenschaftsblogs darstellt (Ochsner and Pampel 2026). Als Teil der [Forschungsgruppe Information Management] (https://www.ibi.hu-berlin.de/de/forschung/infomanagement) am Institut f\u00fcr Bibliotheks- und Informationswissenschaft (IBI) habe ich zur bildlichen Vermittlung der Kataloginhalte ein wissenschaftliches Poster gestaltet.
\n
F\u00fcr die visuelle Gestaltung des Posters hatte ich zun\u00e4chst mehrere Ideen, die ich anhand von Mind-Maps und kleinen Skizzen auf Papier weiterentwickelt oder ver\u00e4ndert habe. Ich mag Fantasy und Science-Fiction und hatte deshalb den Einfall, die Inhalte des Anforderungskatalogs in ein 'magisches Bild-Setting' zu \u00fcbertragen, das gleichzeitig Spa\u00df beim Anschauen macht und die wissenschaftlichen Inhalte gut transportieren kann. Bisher habe ich vor allem Illustrationen f\u00fcr Veranstaltungs- und Konzertposter angefertigt und fand es spannend, mir die Gestaltung f\u00fcr ein wissenschaftliches Poster \u00fcberlegen zu k\u00f6nnen.
\nIm Prozess der Bildentwicklung habe ich alle Motive per Hand gezeichnet und anschlie\u00dfend am Computer eingef\u00e4rbt. Hierbei entschied ich mich dazu, die Zeichnungen in der Gr\u00f6\u00dfe der final geplanten Druckversion (DIN-A0) anzufertigen. Ich finde, dass man dadurch einen besonders guten Eindruck zur Wirkung einzelner Bildelemente und deren Beziehung zueinander erhalten kann. Weil ich kein Papier in der passenden Gr\u00f6\u00dfe zur Hand hatte, habe ich daf\u00fcr einfach mehrere A3-gro\u00dfe Bl\u00e4tter zusammengeklebt. Darauf konnte ich nun eine vergr\u00f6\u00dferte Bleistiftzeichnung meiner kleinen Skizze \u00fcbertragen. Nach einer erneuten \u00dcberarbeitung dieser Vorzeichnung trennte ich alle Papiere wieder voneinander, um im Anschluss einzelne Bildteile am Schreibtisch mit schwarzer Tusche und Pinsel nachzuzeichnen.
\n
Im n\u00e4chsten Schritt habe ich die insgesamt zehn fertig gezeichneten A3-Bl\u00e4tter eingescannt und am Computer mit verschiedenen Bildbearbeitungsprogrammen zu einer einzelnen Datei zusammengef\u00fcgt. Der Einf\u00e4rbungsprozess der bisherigen schwarz-wei\u00dfen Version fand ebenfalls digital statt. Nachdem der fertiggestellte Bildteil des Posters exportiert war, konnte ich mithilfe eines Textbearbeitungsprogramms den schriftlichen Inhalt zum Anforderungskatalog einf\u00fcgen und entsprechend formatieren.
\nDas Wissenschaftsposter war Teil der Poster-Ausstellung auf der 114. BiblioCon (2026) in Berlin (Schielke, Ochsner, and Pampel 2026) und ist derzeit am Institut f\u00fcr Bibliotheks- und Informationswissenschaft (IBI) der Humboldt-Universit\u00e4t zu Berlin ausgestellt.
\nWeitere Informationen zum Projekt InfraWissBlogs und zu der Forschungsgruppe Information Management sind auf unserer offiziellen Webseite zu finden.
\nDieser Text \u2013 mit Ausnahme von Zitaten und anderweitig gekennzeichneten Teilen \u2013 steht unter der CC BY 4.0 DEED.
\n@online{schielke2026,\n author = {Schielke, Johanna},\n title = {Entwicklung Eines {Posters} Zu {Anforderungen} an Die\n {Langzeitverfuegbarkeit} von {Wissenschaftsblogs}},\n date = {2026-07-16},\n url = {https://infomgnt.org/posts/2026-07-16-entwicklung-eines-posters-zu-anforderungen-an-die-langzeitverfuegbarkeit-von-wissenschaftsblogs},\n langid = {en}\n}\n