![\"Cityscape](\"https://pkp.sfu.ca/wp-content/uploads/2026/05/PKP-Crossref-May-2026-WP-1024x576.jpg\")
Scholarly journals don\u2019t operate in a vacuum \u2014 they are part and parcel of disciplinary, community, practitioner, and professional networks exposed and connected via open scholarly infrastructure. Upgrading OJS is how journals connect to that network reliably.
\nScholarly publishing doesn\u2019t happen in isolation. Journals today, including those using Open Journal Systems (OJS), exist within a connected ecosystem of repositories, funders, institutions, indexing services, researcher profiles, and metadata registries \u2013 all linked together through open scholarly infrastructure.\u00a0
\nFor journals using OJS, staying connected to that ecosystem increasingly depends on one foundational step \u2013 keeping OJS up to date.\u00a0
\nAn OJS upgrade is not simply a software maintenance task. It is how journals maintain interoperability with the broader scholarly communications network, improve discoverability for authors, and support growing expectations around institutional reporting, metadata, quality, and trust.\u00a0
\nWhile there are variations in how \u201copen scholarly infrastructure\u201d is defined (and named; Goudarzi and Dunks, 2023), generally it refers to shared, open source, community-governed digital platforms, tools, and services that support the research lifecycle.\u00a0
\nFunctions of open scholarly infrastructure include:\u00a0
\nBut more than the physical and digital means, the concept addresses values. Organizations (like PKP) aligned with principles like the Principles of Open Scholarly Infrastructure (POSI), and supporting initiatives like the Barcelona Declaration on Open Research Information (Barcelona DORI) emphasize openness, stakeholder-governance, transparency, trust, and sustainability so that knowledge can be freely and openly created, shared, used, and preserved.\u00a0\u00a0\u00a0\u00a0\u00a0
\nIn scholarly publishing, infrastructure is valuable because of how well systems connect \u2013 not because one single tool or platform can do everything on its own.\u00a0\u00a0
\nThis is especially important in the context of diamond open access and emerging community frameworks such as Diamond Open Access Standards (DOAS), where sustainability depends on shared services and open interoperability rather than proprietary lock-in.\u00a0
\nA journal may use OJS to manage submissions and publishing workflows, but its articles can also connect to:
\nThere are many other open infrastructures and organizations that are interoperable with OJS. To explore more, please check out the post we published for International Love Data Week 2026, \u201cWhere\u2019s the Data? Finding it, Moving it, and Loving it through PKP Interoperability.\u201d\u00a0
\nThe quality of these connections directly affects the visibility, trustworthiness, and reusability of journal content.\u00a0
\nPoor metadata or broken integrations can limit journal discoverability, reduce citation tracking accuracy, and create compliance challenges for authors and institutions.\u00a0
\nOJS is now used by more than 58,000 journals around the world. For many journals, OJS serves as the operational foundation of publishing workflows. But increasingly, OJS also functions as the journal\u2019s infrastructure layer \u2013 the point where local publishing activity connects outward into the global scholarly ecosystem.\u00a0
\nNewer OJS versions support stronger integrations with key infrastructure providers, improved metadata handling, better API support, and evolving publishing standards. These capabilities are essential for journals seeking to fully participate in open scholarly publishing and communications.\u00a0
\nAmong the many organizations supporting open scholarly infrastructure, Crossref plays a particularly important role.\u00a0
\nCrossref is best known for DOI registration, but its broader function is metadata connectivity. They run open infrastructure to link research objects, entities, and actions, creating a lasting and reusable scholarly record that underpins open science. Together with 25,000 members in 167 countries, they drive metadata exchange, facilitating global research communication, for the benefit of society.
\nWhen journals deposit metadata with Crossref, they are contributing information that helps scholarly outputs become discoverable, trackable, and linked across the research ecosystem.\u00a0
\nThat metadata supports:\u00a0
\nThe more rich and accurate the metadata, the more effectively articles can circulate throughout the ecosystem.\u00a0
\nNewer OJS versions are better equipped to support these metadata workflows and evolving Crossref requirements, including better affiliations handling, contributor identifiers, and metadata schemas.
\nOpen scholarly infrastructure evolves continuously. Metadata standards mature, APIs change, integrations improve, and new expectations emerge from institutions, funders, and indexing services.\u00a0
\nOlder OJS versions were developed before many current interoperability practices became standard. When journals remain on older OJS versions, they can encounter interoperability limitations such as:\u00a0
\nOver time, these limitations make it harder for journals to remain connected to the systems that researchers and institutions increasingly rely on.\u00a0
\nA useful example comes from de Jonge and Kramer (2026), who identify technical infrastructure and system configuration as key factors affecting metadata completeness and transfer. While there are also policy and collection reasons for incomplete metadata records, this highlights the importance of maintaining up-to-date OJS installations, integrations, and plugins to ensure metadata is properly captured and deposited with services such as Crossref.\u00a0
\nUpgrading OJS is often framed as a technical project. But increasingly, it\u2019s an infrastructure strategy.\u00a0
\nAs open scholarly infrastructure becomes more interconnected, journals that invest in updated interoperable systems will be better positioned to support authors, institutions, and readers.
\nIf your journal relies on open infrastructure, upgrading OJS is essential!\u00a0
\nBe sure to join us! Upcoming events are:
\nUpgrading and learning resources include:\u00a0
\nThe call to action is to upgrade your OJS installation. OJS version 3.5.0-4 was released April 9th, 2026. OJS 3.5 has not yet received a Long-Term Support (LTS) designation but it is planned. If you are already on 3.5, keep up to date with patch releases and your system will be on LTS when the designation is applied.\u00a0
\nJoin in contributing fully to the global open scholarly publishing ecosystem by upgrading today, and be sure you are making the most out of all that OJS and interoperability partners have to offer!
\nThis post is part of a PKP-Crossref series. Please stay tuned for more.
\nThe post Why Upgrading OJS is Key to Participating in Open Scholarly Infrastructure appeared first on Public Knowledge Project.
","doi":"https://doi.org/10.59350/9prg7-yqe71","funding_references":null,"guid":"https://pkp.sfu.ca/?p=19070","id":"81e5cf11-2eef-4f3b-ad48-407edc56adc1","image":"https://pkp.sfu.ca/wp-content/uploads/2026/05/PKP-Crossref-May-2026-WP-1024x576.jpg","images":[{"alt":"Cityscape in the foreground represents connecting people and infrastructure and is similar to the image on Crossref's homepage. The archipelago in the background represents PKP's community newsletter called Archipelago because of the many independent yet interconnected aspects of the scholarly publishing ecosystem. PKP and Crossref logos are side by side at the bottom, representing our collaboration. The photo was taken at a PKP Sprint location.","height":"576","sizes":"(max-width: 1024px) 100vw, 1024px","src":"https://pkp.sfu.ca/wp-content/uploads/2026/05/PKP-Crossref-May-2026-WP-1024x576.jpg","srcset":"https://pkp.sfu.ca/wp-content/uploads/2026/05/PKP-Crossref-May-2026-WP-1024x576.jpg, https://pkp.sfu.ca/wp-content/uploads/2026/05/PKP-Crossref-May-2026-WP-300x169.jpg, https://pkp.sfu.ca/wp-content/uploads/2026/05/PKP-Crossref-May-2026-WP-768x432.jpg, https://pkp.sfu.ca/wp-content/uploads/2026/05/PKP-Crossref-May-2026-WP-1536x864.jpg, https://pkp.sfu.ca/wp-content/uploads/2026/05/PKP-Crossref-May-2026-WP.jpg","width":"1024"},{"src":"https://pkp.sfu.ca/wp-content/uploads/2026/05/PKP-Crossref-May-2026-WP-1024x576.jpg"}],"indexed":true,"indexed_at":1779308846,"language":"en","parent_doi":null,"published_at":1779307842,"reference":[],"registered_at":0,"relationships":[],"rid":"nh5k8-cqy30","status":"active","summary":"\n \n Scholarly journals don\u2019t operate in a vacuum \u2014 they are part and parcel of disciplinary, community, practitioner, and professional networks exposed and connected via open scholarly infrastructure. Upgrading OJS is how journals connect to that network reliably.\n \n\nScholarly publishing doesn\u2019t happen in isolation.","tags":["Community Newsletter","News","News For Developers","News For Hosted Clients","Collaboration Corner"],"title":"Why Upgrading OJS is Key to Participating in Open Scholarly Infrastructure","updated_at":1779307843,"url":"https://pkp.sfu.ca/2026/05/20/pkp-crossref-upgrading-participating-in-open-scholarly-infrastructure/","version":"v1"}},{"document":{"abstract":"Die Software, die das fr\u00fche Web zum Laufen brachte, entstand an einem Forschungsinstitut in der Schweiz. Statt sie zu kommerzialisieren, machte man sie frei verf\u00fcgbar und schlie\u00dflich sogar zu Public Domain. Ein Gl\u00fcck, denn wer wei\u00df, wie die Geschichte des Web sonst verlaufen w\u00e4re?","archive_url":null,"authors":[{"contributor_roles":[],"family":"Crueger","given":"Jens"}],"blog":{"archive_collection":22135,"archive_host":null,"archive_prefix":"https://wayback.archive-it.org/22135/20231101173016/","archive_timestamps":null,"authors":[{"name":"Redaktion iRights.info"}],"canonical_url":true,"category":"law","community_id":"30df0209-0965-4b95-afa1-70d6c8a7d086","created_at":1694736000,"current_feed_url":null,"description":"Urheberrecht und kreatives Schaffen in der digitalen Welt","doi":null,"doi_as_guid":false,"favicon":"https://rogue-scholar.org/api/communities/7d3b25fd-a4a8-4155-8e76-99d6be06706a/logo","feed_format":"application/atom+xml","feed_url":"https://irights.info/feed/atom","filter":null,"funding":null,"generator":"WordPress","generator_raw":"WordPress","home_page_url":"https://irights.info/","id":"26f4046a-7e6f-4c1c-8866-f4e055096c30","indexed":false,"issn":null,"language":"de","license":"https://creativecommons.org/licenses/by/4.0/legalcode","mastodon":null,"prefix":"10.59350","registered_at":1729753013,"relative_url":null,"ror":null,"secure":true,"slug":"irights","status":"active","subfield":"3308","subfield_validated":null,"title":"iRights.info","updated_at":1779267261.799606,"use_api":false,"use_mastodon":false,"user_id":"81a5b5f1-97c2-416b-8715-46e10f37018c"},"blog_name":"iRights.info","blog_slug":"irights","content_html":"Die Software, die das fr\u00fche Web zum Laufen brachte, entstand an einem Forschungsinstitut in der Schweiz. Statt sie zu kommerzialisieren, machte man sie frei verf\u00fcgbar und schlie\u00dflich sogar zu Public Domain. Ein Gl\u00fcck, denn wer wei\u00df, wie die Geschichte des Web sonst verlaufen w\u00e4re?
\nAls Ende der 1980er Jahre der Physiker und Informatiker Tim Berners-Lee f\u00fcr seine Vorgesetzten am CERN (der Europ\u00e4ischen Organisation f\u00fcr Kernforschung) einige Vorschl\u00e4ge f\u00fcr das Informationsmanagement formulierte, h\u00e4tte niemand erwartet, was daraus entstehen w\u00fcrde.
\nSeine Kernidee: Eine Alternative zu hierarchischen Informationssystemen durch eine netzartige Struktur aus miteinander verlinkten Notizen. Dieses Konzept wurde zur Blaupause f\u00fcr das World Wide Web.
\nAm 12. November 1990 publizierten Tim Berners-Lee und sein Kollege Robert Cailliau dann gemeinsam ein Memorandum unter der \u00dcberschrift: \u201eWorldWideWeb: Proposal for a HyperText Project\u201c. Dieses Memorandum war ein wichtiger Meilenstein auf dem Weg zur Entstehung des World Wide Web.
\nDer Gedanke der freien Zug\u00e4nglichkeit des \u201eWorldWideWeb\u201c war bereits zum diesem Zeitpunkt enthalten. Denn ein in dem Memorandum definiertes Ziel des Projekts lautete, \u201esoweit m\u00f6glich Public-Domain-Software zu verwenden oder Schnittstellen zu bereits vorhandenen propriet\u00e4ren Systemen zu schaffen.\u201c
\nBis zur Public Domain-Software sollte es aber eine Weile dauern, denn noch war das Web kaum mehr als eine sehr spannende Idee. Die f\u00fcr das Web ben\u00f6tigte Software wurde in der Folge von einem kleinen Team am CERN entwickelt.
\nDabei handelte es sich um die Webkernsoftware, die in einzelnen Programmkomponenten zu einer Softwarebibliothek zusammengestellt wurde. Mit diesen Softwarebausteinen sollte es externen Programmierern erm\u00f6glicht werden, eigene Projekte f\u00fcr das Web zu entwickeln, seien es Browser, Server oder andere Anwendungen.
\nDie Frage, die sich nun stellte, war die nach der Zug\u00e4nglichmachung dieser am CERN entwickelten Software. Sollte sie gegen Geb\u00fchr an interessierte Web-Pioniere verkauft werden, oder sollte sie kostenlos bereitgestellt werden?
\nEs kostete Tim Berners-Lee einige \u00dcberzeugungsarbeit gegen\u00fcber seinen Vorgesetzten am CERN, um diese von der freien Zug\u00e4nglichmachung der entwickelten Software zu \u00fcberzeugen. So beschrieb es sp\u00e4ter sein Kollege Robert Cailliau in dessen Buch \u201eDie Wiege des Web\u201c: Berners-Lee habe mit der \u201estolzen akademischen Tradition\u201c argumentiert, der das World Wide Web folge, da es den \u201ekostenlosen Austausch von Informationen f\u00f6rdere\u201c. Daher solle, so sein Pl\u00e4doyer, die f\u00fcr das Web notwendige Software ebenfalls kostenlos zur Verf\u00fcgung gestellt werden.
\nSeitens der CERN-Leitung wiederum kursierte eine Kalkulation, die als Preis f\u00fcr die Software angesetzt werden sollte. Sie belief sich auf eine Preisspanne zwischen 100 und 200 Schweizer Franken pro Softwarepaket.
\nAllerdings ern\u00fcchterte ein Blick in das damalige sehr fr\u00fche Web das Management hinsichtlich der Preisvorstellung. Die geringe Zahl der damaligen Webnutzer lie\u00df es dem CERN als schlichtweg nicht kostendeckend erscheinen, Geb\u00fchren f\u00fcr die Software zu erheben. Der Verwaltungsaufwand wurde schlicht als zu hoch erachtet.
\nSo kam es, dass Tim Berners-Lee die Erlaubnis des CERN erhielt, die dort entwickelte Web-Software zur kostenlosen Nutzung ins Internet zu stellen. Am 6. August 1991 gab er \u00fcber die Newsgroup alt.hypertext bekannt, dass die Software zum Download auf der CERN-Website bereitst\u00fcnde. Er verband dies mit dem Aufruf \u201eCollaborators welcome!\u201c
\nDie Website des CERN unter der Webadresse Info.cern.ch wurde ab diesem Zeitpunkt zu einer wichtigen Anlaufstelle im fr\u00fchen Web. Dort konnte von einem ftp-Server die Software heruntergeladen werden, die das Web nun allm\u00e4hlich wachsen lie\u00df. Zu dieser Software geh\u00f6rte eine Bibliothek von Programmcode (bekannt als CERNLIB), die Software f\u00fcr den Betrieb eines Servers, sowie f\u00fcr den Client. Kurze Zeit sp\u00e4ter ging dann auch schon der erste Webserver au\u00dferhalb Europas online. Er befand sich im US-amerikanischen Stanford, und der Rest ist Geschichte.
\nAls das World Wide Web 1993 Fahrt aufgenommen hatte, gelang es Tim Berners-Lee und Robert Cailiau schlie\u00dflich, ihre Vorgesetzten am CERN zu \u00fcberzeugen, die Software f\u00fcr das Web als Public Domain der \u00d6ffentlichkeit bereitzustellen. Mit einer Erkl\u00e4rung vom 30. April 1993 verzichtete das CERN auf seine s\u00e4mtlichen geistigen Eigentumsrechte an dem Web-Code, sowohl am Quellcode als auch an den Bin\u00e4rdateien. Es erteilte jedem die Erlaubnis, den Code zu nutzen, zu vervielf\u00e4ltigen, zu ver\u00e4ndern und zu verbreiten.
\nIn den \u201eWorld-Wide Web News\u201d, einem monatlichen Newsletter des CERN, wurde in der Mai-Ausgabe 1993 berichtet (eigene \u00dcbersetzung):
\n\u201eCERN-W3-Software wird gemeinfrei
\nNach langen Diskussionen hat das CERN nun einen bestimmten Teil der W3-Software als gemeinfreien Code ver\u00f6ffentlicht. Dies geschah, um die Verbreitung des Webs voranzutreiben und sicherzustellen, dass die Protokolle einheitlich verwendet werden. Es bestand die Gefahr, dass Entwickler den Protokollcode neu erfinden und dabei erneut inkompatible Fehler machen w\u00fcrden. Da die Code-Bibliothek \u201elibwww\u201c, die die Grundlage vieler Browser bildet, nun gemeinfrei ist, kann sie als gemeinsame Basis dienen. [\u2026]\u201c
\nUrspr\u00fcnglich hatte Tim Berners-Lee \u00fcbrigens erwogen, die Web-Software nicht als Public Domain, sondern unter einer GNU-Lizenz zu ver\u00f6ffentlichen. Die Kontakte zwischen dem CERN und der Free Software Foundation (FSF) mit ihrem GNU-Projekt waren gut.
\nJedoch war man am CERN besorgt, gro\u00dfe IT-Unternehmen k\u00f6nnten das Web m\u00f6glicherweise ignorieren, sollte dort das Risiko irgendwelcher Lizenzprobleme bestehen. So entschied man sich f\u00fcr die offene Bereitstellung als Public Domain. Den Programmierern rund um den Erdball gab das die maximale Freiheit, die Software des CERN f\u00fcr ihre Projekte zu nutzen.
\nR\u00fcckblickend \u00e4u\u00dferte Tim Berners-Lee im Jahr 2025 im The Guardian, dass seine Vision f\u00fcr das Web auf \u201eauf Teilen, nicht auf Ausbeutung\u201c beruht habe. Sein Ziel sei es gewesen, mit dem Web einen Raum f\u00fcr Kreativit\u00e4t und Zusammenarbeit zu schaffen.
\nDaf\u00fcr sei es n\u00f6tig, dass alle Interessierten einen niedrigschwelligen Zugang zum Web erhalten und bereit seien, sich dort mit ihren Ideen und ihrem K\u00f6nnen einzubringen. Dies habe er als zentrale Herausforderung gesehen. Man h\u00e4tte daher unm\u00f6glich verlangen k\u00f6nnen, interessierte Nutzer \u201ef\u00fcr jede Suche oder jeden Upload bezahlen\u201c zu lassen.
\nFreie Software als Schl\u00fcssel zu einer kollaborativen und kreativen Websph\u00e4re klingt vertraut. Wer in der heutigen Zeit nach Visionen f\u00fcr das Web k\u00fcnftiger Tage fragt, darf sich gerne solche Anregungen aus der Gr\u00fcndungsphase des Web entleihen.
\niRights.info\u00a0informiert und erkl\u00e4rt rund um das Thema \u201eUrheberrecht und Kreativit\u00e4t in der digitalen Welt\u201c. Alle Texte erscheinen kostenlos und offen lizenziert.
\nWenn Sie m\u00f6gen, k\u00f6nnen Sie uns \u00fcber die\u00a0gemeinn\u00fctzige\u00a0Spendenplattform Betterplace\u00a0unterst\u00fctzen und daf\u00fcr eine Spendenbescheinigung erhalten. Betterplace akzeptiert PayPal, Bankeinzug, Kreditkarte, paydirekt oder \u00dcberweisung.
\nBesonders freuen wir uns \u00fcber einen regelm\u00e4\u00dfigen Beitrag, beispielsweise als monatlicher Dauerauftrag.\u00a0F\u00fcr Ihre Unterst\u00fctzung dankt Ihnen herzlich der\u00a0gemeinn\u00fctzige iRights e.V.!
\nDOI f\u00fcr diesen Text:\u00a0\u00b7 automatische DOI-Vergabe f\u00fcr Blogs \u00fcber The Rogue Scholar
\nThe post Das World Wide Web \u2013 Erfolgsbeispiel f\u00fcr Open Software appeared first on iRights.info.
","doi":"https://doi.org/10.59350/gnsnh-61109","funding_references":null,"guid":"https://irights.info/?post_type=custom_artikel&p=32826","id":"dc5f6951-7092-4648-9ec0-7fb0a9093b77","image":null,"images":[],"indexed":true,"indexed_at":1779291703,"language":"de","parent_doi":null,"published_at":1779289129,"reference":[],"registered_at":0,"relationships":[],"rid":"dz9kt-bts41","status":"active","summary":"Die Software, die das fr\u00fche Web zum Laufen brachte, entstand an einem Forschungsinstitut in der Schweiz. Statt sie zu kommerzialisieren, machte man sie frei verf\u00fcgbar und schlie\u00dflich sogar zu Public Domain. Ein Gl\u00fcck, denn wer wei\u00df, wie die Geschichte des Web sonst verlaufen w\u00e4re?","tags":["Allgemein","Creative Commons + Lizenzen","Netzkulturen","Software + Open Source","Technologie"],"title":"Das World Wide Web \u2013 Erfolgsbeispiel f\u00fcr Open Software","updated_at":1779289129,"url":"https://irights.info/artikel/das-world-wide-web-erfolgsbeispiel-fuer-open-software/32826","version":"v1"}},{"document":{"abstract":null,"archive_url":null,"authors":[{"contributor_roles":[],"family":"Wehrhahn","given":"Dawn"}],"blog":{"archive_collection":null,"archive_host":null,"archive_prefix":null,"archive_timestamps":null,"authors":null,"canonical_url":null,"category":"engineeringAndTechnology","community_id":"db0d8909-9e37-46d0-b16c-0551f575e86b","created_at":1749798261.334959,"current_feed_url":null,"description":"Das Blog der TIB \u2013 Leibniz-Informationszentrum Technik und Naturwissenschaften und Universit\u00e4tsbibliothek","doi":null,"doi_as_guid":true,"favicon":"https://blog.tib.eu/wp-content/uploads/2015/12/TIB_fav_icon_24x24.png","feed_format":"application/atom+xml","feed_url":"https://blog.tib.eu/feed/atom/","filter":null,"funding":null,"generator":"WordPress","generator_raw":"WordPress 6.8.1","home_page_url":"https://blog.tib.eu/","id":"135a354f-2969-4852-9a7c-b6cda0a692a4","indexed":true,"issn":null,"language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","mastodon":null,"prefix":"10.65527","registered_at":0,"relative_url":null,"ror":null,"secure":true,"slug":"tib","status":"active","subfield":"1802","subfield_validated":null,"title":"TIB-Blog","updated_at":1779268236.390099,"use_api":true,"use_mastodon":false,"user_id":null},"blog_name":"TIB-Blog","blog_slug":"tib","content_html":"Am 20. Mai wird der Tag des Messens (auch Weltmetrologietag oder Tag der Metrologie) begangen. An diesem Tag wurde im Jahr 1875 die internationale Meterkonvention unterzeichnet.
\nDie Fertigkeiten eines Feldmessers waren schon in der Antike gefragt und haben sich seit damals immer weiterentwickelt. In der heutigen Zeit soll der Tag auf die Bedeutung des Messens f\u00fcr die globale Wirtschaft, f\u00fcr die Wissenschaft und f\u00fcr unseren Alltag und f\u00fcr die Gesellschaft hinweisen. Ohne weltweite einheitliche Standards w\u00e4re ein globaler Warenaustausch nicht denkbar. Exakte Messungen sind f\u00fcr Industrie, Forschung und Entwicklung von zentraler Bedeutung. Ob beim B\u00e4cker, an der Zapfs\u00e4ule oder auf der Waage, Messungen sind im Alltag nicht wegzudenken.
\nDer Tag wird vom Internationalen B\u00fcro f\u00fcr Ma\u00df und Gewicht (BIPM) und der Internationalen Organisation f\u00fcr das gesetzliche Me\u00dfwesen (OIML) ausgerichtet. Seit dem Jahr 2004 stehen die Tage jeweils unter einem Motto. F\u00fcr 2026 lautet es: Metrology: Building Trust in Policy Making.
\nInteressante Werke zu den Geowissenschaften, des Messwesens oder der Vermessungskunde befinden sich auch im Altbestand der TIB. Davon haben wir bereits einige digitalisiert, einige ausgew\u00e4hlte m\u00f6chten wir hier kurz vorstellen:
\n![\"\"](\"https://blog.tib.eu/wp-content/uploads/2026/05/00000152.jpg\")
Handbuch der gesammten Vermessungskunde, die neuesten Erfindungen und Entdeckungen in derselben zugleich enthaltend; oder vollst\u00e4ndige Anleitung zur Me\u00dfkunst, f\u00fcr Offiziere, Forstbediente, Bergleute und Feldmesser (Abtheilung 1+2) 1820
\u201eDie Vermessungskunde lehret sowohl die Ausdehnung eines in Vergleichung mit der ganzen Oberflache der Erde nur kleinen St\u00fcck Landes, als auch die Gro\u00dfe und Figur der Erde durch Anwendung der Lehren der reinen und angewandten Mathematik zu bestimmen. Sie zerf\u00e4llt, r\u00fccksichtlich des Endzwecks, zu welchem man die Ausdehnung eines Theils der Erdoberfl\u00e4che zu wissen verlangt, in folgende zwei Haupttheile:
\nLehrbuch der praktischen Geometrie, nebst einem Anhange \u00fcber die Elemente der Markscheidekunst (Theil 1+2) 1857 2. Theil S. 139
\n
\n
Die Landes-Vermessung und die in ihrem Gefolge befindlichen Arbeiten, erl\u00e4utert durch die im K\u00f6nigreich W\u00fcrttemberg zur Ausf\u00fchrung kommende Vermessung (Heft 1-4) 1841-1843
\n![\"\"](\"https://blog.tib.eu/wp-content/uploads/2026/05/000000061-598x1024.jpg\")
Die Praxis des Vermessungsingenieurs (Band 1+2) 1923 Band 1, Bild 443
\n![\"\"](\"https://blog.tib.eu/wp-content/uploads/2026/05/00000443-727x1024.jpg\")
Die K\u00f6niglich Preussische Landes-Triangulation (Theil 1-11) 1870-1901
\n![\"\"](\"https://blog.tib.eu/wp-content/uploads/2026/05/00000020.jpg\")
Die h\u00f6here Geod\u00e4sie oder die Wissenschaft, die Reiche der Erde, und diese selbst, geographisch aufzunehmen und zu chartieren (Abtheilung 1) 1816 Bild 460
\n![\"\"](\"https://blog.tib.eu/wp-content/uploads/2026/05/00000460-1007x1024.jpg\")
Systematisches Handbuch der gesammten Land- und Erd-Messung mit ebener und sph\u00e4rischer Trigonometrie auch Beschreibung der neuern brauchbaren Me\u00dfinstrumente (Band 1+2) 1819, Bild 330
\n![\"\"](\"https://blog.tib.eu/wp-content/uploads/2026/05/00000330-1024x982.jpg\")
Anleitung f\u00fcr die Herstellung und Justierung geod\u00e4tischer Instrumente (Teil 1+2) 1907-1911 Seite 75 und 276
\n![\"\"](\"https://blog.tib.eu/wp-content/uploads/2026/05/000000843-901x1024.jpg\"){kind=spacer}
![\"\"](\"https://blog.tib.eu/wp-content/uploads/2026/05/00000285-666x1024.jpg\")
Viele wissenschaftliche Sch\u00e4tze schlummern in den analogen Best\u00e4nden der TIB \u2013 meist schwer zug\u00e4nglich und oft nur vor Ort nutzbar. Dank Retrodigitalisierung \u00e4ndert sich das: Durch die Digitalisierung werden gedruckte B\u00fccher, Karten und weitere analoge Materialien gesichert und historisches Wissen sichtbar, durchsuchbar und nachhaltig verf\u00fcgbar gemacht.
\nIn der Blogreihe \u201eRetrodigitalisierung \u2013 vom Papier zum Pixel\u201c zeigt die TIB Sch\u00e4tze, die nun nicht mehr nur in den Regalen der Bibliothek stehen, sondern online von \u00fcberall auf der Welt angesehen werden k\u00f6nnen. Auch einen Blick hinter die Kulissen der Retrodigitalisierung wird es geben: Wie werden aus analogen Best\u00e4nden digitale Ressourcen? Welche technischen und rechtlichen Herausforderungen gibt es dabei? Von der Auswahl der Materialien \u00fcber Scanprozesse bis zur Langzeitarchivierung \u2013 die Reihe beleuchtet Retrodigitalisierung als eine wichtige Aufgabe moderner Bibliotheken.
\n
During the\u00a0Syrian Civil War, over 5 million Syrians lived as refugees in first countries of asylum, of whom more than 40% were children.\u00a0Many of these children experienced social exclusion, leading to a lack of future prospects. To secure their children\u2019s future, some parents came to see taking them on boats to Europe across the Mediterranean Sea as the only viable option, despite the grave risks involved. The blog is built on narratives of the parents who journeyed over the sea with children, the decision that landed them in a profound dilemma: the very act of fulfilling parental responsibility entailed exposing their children to significant danger. This moral tension continued to haunt parents long after migration.
\n\u00a0
\nIn previous research among Syrian refugees living under temporary protection in Turkey, evidence showed that the refugees\u2018 assessments of risks linked to onward migration to Europe across the Mediterranean differ depending on a person\u2019s stage in the life course. For example, single Syrians in Turkey were more inclined to accept the risks of the onward migration than those with families. Although many Syrian parents interviewed for that study wished to migrate to Europe, they often considered it too dangerous to bring the family along on the journey or leave it behind.
\nThis blog draws on a study that observed the opposite pattern through the narrative accounts of seven Syrian refugee parents who travelled with young children after spending varying periods in first countries of asylum \u2014 Turkey, Egypt, and Lebanon. They are three mothers and four fathers in their mid-thirties and early forties who settled in Sweden, Denmark, and Germany. Their narratives unfolded chronologically, beginning with migration motivations embedded in the lived realities of these first countries of asylum. Accordingly, their risky migration decisions were grounded in a sense of parental responsibility and framed as a moral obligation to protect their children. This initial framing was later unsettled by a reflective turn at the end of the interviews, where participants revisited the rationality of the risks they had taken from a present-day standpoint, revealing an ongoing process of reinterpretation.
\n\u00a0
\nThe Syrian conflict displaced millions of Syrian families, leaving those with young children in neighbouring countries such as Turkey, Jordan, and Lebanon in situations of heightened vulnerability. Economic pressures often forced refugee children to forgo schooling and enter the labour market, with estimates suggesting that around one in ten were engaged in child labour. At the same time, early marriage\u2014often functioning as a coping mechanism for families facing acute economic and social insecurity\u2014affected up to one in three girls in some refugee communities.
\nOne of the parents in this study remembered her time in Turkey with her young family: \u201eMy daughter reached the age of eight without a single day in school. At her age, I was already reading and writing. She couldn\u2019t even hold a pen.\u201c These circumstances confronted parents with an impossible dilemma: to stay or to leave, with both options carrying serious risks. Whether staying or leaving, each decision undermined core parental commitments by exposing their children to different, yet equally unacceptable, forms of risk. The decision to embark on a perilous sea journey with children emerged only after a long and agonizing period of reflection, marked by profound moral and emotional struggle.
\nFor some fathers in this study, the idea of braving the sea alone while leaving their spouses and children behind in neighbouring countries was perceived as too risky and therefore unacceptable. Although having their families join later through reunification might have spared them the traumas associated with the journey, they ultimately chose to travel together. For them, leaving their families behind without a male head of household would have imposed a profound emotional and moral burden.
\nOn the other hand, for fathers who could tolerate the risks of leaving the family behind (spouse with children), the increasingly restrictive\u2014and in some cases nearly inaccessible\u2014reunification policies in Europe fuelled fears that the process would take too long or ultimately fail, resulting in prolonged family separation. These concerns often contributed to the decision to embark on the perilous journey together as a family.
\nIn the same vein, the mothers reported that their husbands had departed earlier under the agreement that they and their children would later join the husbands through a safer and legal family reunification pathway. However, growing fears that this possibility might be withdrawn raised the risk of permanent family separation. Consequently, the mothers decided to undertake the same perilous boat journey with their young children, retracing the route their husbands had taken before them.
\n\u201eI was afraid that reunification could become a long process. So, I decided not to wait, even though my family was against me travelling with a three\u2011month\u2011old baby and other two\u2011year\u2011old child.\u00a0 But it was the future of my children that was at stake, and I had to do it\u201c, Esme opened her story by affirming the perceived rightness of her decision not to wait for legal reunification with her husband in Sweden, who had migrated earlier alone while she had to remain in Turkey to recover from childbirth.
\n\u00a0
\nBy taking their children on dangerous migration journeys, parents knowingly exposed them to serious risks. The photographs of the three-year-old Syrian boy Alan Kurdi, whose lifeless body washed ashore on a beach in Greece in 2015, became a stark illustration of the tragic outcomes that parents feared. A mother, Zehra, who travelled with two toddlers recounted that moment: \u201eI was packing to leave Istanbul for Bodrum to embark on a boat to Greece, when I turned on the TV and saw a boy\u2019s body lying on the shore. It was Alan Kurdi. He was the same age as my son. I was petrified.\u201c
\nThe paradox of risking their children\u2019s lives to save them was something parents tried to comprehend through a sense of their normative and ethical role as caregivers. They emphasised that the risk of the sea journey\u2014one that could have deadly consequences for their children and for which they themselves would bear the moral weight\u2014was ultimately overridden by what they perceived as the supreme moral imperative of parenthood. This reflects a form of virtuous risking, in which the magnitude and consequences of risk become secondary to the deeper question: What kind of person am I (or do I want to be) and what kind of life should I live if I accept or reject a given risk? Therefore, at the centre of virtue ethics is the moral character of a person. This was confirmed by Zehra\u2019s final thought on the decision to eventually take her two young children on the boat, despite the fright she experienced knowing of young Alan Kurdi\u2019s death: \u201eIt paralyzed me for a day or two. But eventually, I gathered the courage again. I felt that I had to do it for the sake of my children.\u201c Her decision was motivated by a deep determination to show, both to herself and to her children, the strength of her commitment as a parent.
\nAmina is a mother who, like Zehra, travelled alone with her two young children to follow her husband\u2019s path and reunite in Germany. As she laid down her story, she found herself in disbelief at what she perceived as her own carelessness in exposing her children to such danger: \u201eWhere was my mind when I put my children on that boat?\u201c, she asked herself. Yet moments later, she provided the answer. She reminded herself that, as a mother, she had no real choice. She feared that if they stayed in Turkey, she would one day see her children as teenagers roaming the streets, selling drugs\u2014the only future she felt awaited them. \u201eIt was a difficult decision, but there is nothing I would not do to save my children\u201c, Amina affirmed. This statement again illustrates the alignment between her moral character and the risky migration she chose to pursue.
\n\u00a0
\nThe moral value placed on courageous risk-taking shaped the parents\u2018 narratives about their decision to migrate at the beginning of their stories. Yet over the storytelling\u2014when revisiting the decision from a \u201ehere and now\u201c perspective\u2014they found it difficult to rely on virtue alone to justify the risks they had accepted. Their closing reflections revealed a persistent tension between guilt for exposing their children to danger and the perceived inevitability of a moral imperative to secure their children\u2019s future.
\nEnes travelled with his young family, a decision rooted in his sense of responsibility as the male head of household, ensuring he would not leave his family behind. However, by the end of the interview, he questioned whether this conviction had ultimately failed him. This doubt became particularly troubling when, years after the migration, his son, remembering the tumultuous journey, asked: \u201eDad, we could have died at sea, right?\u201c Enes described the pain of being unable to provide a convincing answer, as he himself was in profound perplexity.
\nAnother extremely poignant story was narrated by Esme. At the beginning of her story, she embodied the same moral conviction that had animated the accounts of other parents, a sense of righteousness that likewise informed her decision to migrate. However, upon reaching the point of the sea crossing, her narrative shifted. The ending revealed fear and confusion\u2014not only about what the right course of action was, but also about which risks she was responsible for avoiding.
\n\n\u201eAfter 30 minutes of sailing towards Greece, the boat encountered a problem. With too many people on board and not enough gasoline, the boat gradually came to a halt. As the waves grew stronger, water began splashing onto the children, including my four-month-old son. I saw water entering his mouth. Tears streamed down my face as I whispered, \u2018I have killed my child\u2019. I wanted to die first\u2026 Even after so many years have passed, the memory still haunts my dreams\u2014the moment the boat came to a halt in the vast expanse of the sea, and those same words: \u201aI have just killed my child!'\u201c
In virtuous risking, a person is not primarily judged by their capacity for logical calculation, but by their state of mind, in which multiple faculties\u2014perception, desire, and importantly, emotions\u2014interact as integral to virtue. While these elements provided a solid grounding for the parents to make the migration decisions, this same logic faltered when the journeys were assessed in hindsight. Years after the migration, they remain haunted by the anguish that, in protecting their children\u2019s lives, they ultimately put those same lives at risk.
\n\u00a0
\nTo be both a parent and a refugee is to carry a profound responsibility: beyond ensuring one\u2019s own survival, it entails protecting the lives and futures of one\u2019s children. This study showcases stories of parents who were confronted with an excruciating moral dilemma: to fulfil their parental responsibility to protect their children from the war, parents exposed them to deadly danger of sea migration to Europe.
\nThe parents\u2018 experiences interviewed for this study are shaped directly by migration policies. For Syrian refugees, the European response has taken the form of increasingly fortified borders. These structural constraints, which left families with few viable options, shifted the burden of decision-making to individuals, effectively transferring responsibility to parents. The migration stories in this text underscore the urgent need for safe and legal pathways for refugees, particularly young families with children, as these can prevent family separation and reduce reliance on dangerous routes.
\nUNHCR promotes complementary pathways as safe and regular migration options for refugees, including family reunification, education opportunities, and labour mobility schemes. However, it acknowledges that these avenues are not fully inclusive. Its broader vision therefore emphasizes more diversified pathways to ensure protection for those who fall outside categories such as students or skilled workers, providing them with viable routes to safety. Additional measures, including humanitarian visas and private sponsorship schemes, are also needed. These mechanisms are particularly important for young families, as the traumas associated with irregular migration can affect all family members and may persist across generations.
\nDer Beitrag Risking Children to Save Them: Moral Dilemmas Faced by Parents in Forced Migration erschien zuerst auf Netzwerk Fluchtforschung.
","doi":"https://doi.org/10.59350/a38hb-maf05","funding_references":null,"guid":"https://fluchtforschung.net/?p=15805","id":"41b0e2c5-dc03-4200-a858-35ff380490be","image":null,"images":[],"indexed":true,"indexed_at":1779258602,"language":"en","parent_doi":null,"published_at":1779258488,"reference":[],"registered_at":0,"relationships":[],"rid":"ck1b2-9s103","status":"active","summary":"\n During the\u00a0Syrian Civil War, over 5 million Syrians lived as refugees in first countries of asylum, of whom more than 40% were children.\u00a0Many of these children experienced social exclusion, leading to a lack of future prospects. To secure their children\u2019s future, some parents came to see taking them on boats to Europe across the Mediterranean Sea as the only viable option, despite the grave risks involved.\n","tags":["Englisch","Forschung","Children's Rights","Forced Migration","Moral Dilemma"],"title":"Risking Children to Save Them: Moral Dilemmas Faced by Parents in Forced Migration","updated_at":1779258488,"url":"https://fluchtforschung.net/risking-children-to-save-them-moral-dilemmas-faced-by-parents-in-forced-migration/","version":"v1"}},{"document":{"abstract":"Die TIB erm\u00f6glicht ihren Nutzerinnen und Nutzern einen einfachen und direkten Fernzugriff auf die Inhalte der bundesweiten DEAL-Vertr\u00e4ge.","archive_url":null,"authors":[{"contributor_roles":[],"family":"Vosberg","given":"Dana"}],"blog":{"archive_collection":null,"archive_host":null,"archive_prefix":null,"archive_timestamps":null,"authors":null,"canonical_url":null,"category":"engineeringAndTechnology","community_id":"db0d8909-9e37-46d0-b16c-0551f575e86b","created_at":1749798261.334959,"current_feed_url":null,"description":"Das Blog der TIB \u2013 Leibniz-Informationszentrum Technik und Naturwissenschaften und Universit\u00e4tsbibliothek","doi":null,"doi_as_guid":true,"favicon":"https://blog.tib.eu/wp-content/uploads/2015/12/TIB_fav_icon_24x24.png","feed_format":"application/atom+xml","feed_url":"https://blog.tib.eu/feed/atom/","filter":null,"funding":null,"generator":"WordPress","generator_raw":"WordPress 6.8.1","home_page_url":"https://blog.tib.eu/","id":"135a354f-2969-4852-9a7c-b6cda0a692a4","indexed":true,"issn":null,"language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","mastodon":null,"prefix":"10.65527","registered_at":0,"relative_url":null,"ror":null,"secure":true,"slug":"tib","status":"active","subfield":"1802","subfield_validated":null,"title":"TIB-Blog","updated_at":1779268236.390099,"use_api":true,"use_mastodon":false,"user_id":null},"blog_name":"TIB-Blog","blog_slug":"tib","content_html":"Die TIB erm\u00f6glicht ihren Nutzerinnen und Nutzern einen einfachen und direkten Fernzugriff auf die Inhalte der bundesweiten DEAL-Vertr\u00e4ge. \u00dcber diese Vereinbarungen erhalten wissenschaftliche Einrichtungen in Deutschland Zugang zu den Publikationen der gro\u00dfen Wissenschaftsverlage Wiley, Springer Nature und Elsevier.\u00a0Besonders hilfreich ist das Angebot f\u00fcr Angeh\u00f6rige akademischer Einrichtungen sowie Privatpersonen, deren eigene Institution nicht an einem der DEAL-Vertr\u00e4ge teilnimmt.
\nIn dieser Schritt-f\u00fcr-Schritt-Anleitung zeigen wir, wie der Zugang eingerichtet wird und wie die Nutzung der Inhalte funktioniert:
\nSie haben im TIB-Portal recherchiert und einen Artikel gefunden, der bei Wiley oder Springer erschienen ist?
\n![\"\"](\"https://blog.tib.eu/wp-content/uploads/2026/05/fernzugriff-1-1024x761.jpg\")
In der Detailanzeige des Treffers klicken Sie auf \u201eAnmelden\u201c und werden dann entweder direkt zum Login oder \u2013 wenn Sie noch kein registrierter Nutzer der TIB sind – auf die Registrierungsseite weitergeleitet:
\n![\"\"](\"https://blog.tib.eu/wp-content/uploads/2026/05/Fernzugriff-DEAL-2.jpg\")
Melden Sie sich im TIB-Portal \u00fcber den Login an. Wenn Sie noch nicht als Nutzerin oder Nutzer imTIB-Portal registriert sind, f\u00fchren Sie zun\u00e4chst Ihre Registrierung durch, damit Sie das TIB-Portal nutzen k\u00f6nnen:
\n![\"\"](\"https://blog.tib.eu/wp-content/uploads/2026/05/Fernzugriff-DEAL-3.jpg\")
Falls Sie noch kein registrierter Nutzer der TIB sind, m\u00fcssen Sie im Rahmen des Registrierungsprozesses
\neinige Angaben zu Ihrer Person machen und Ihre E-Mail-Adresse angeben:
![\"\"](\"https://blog.tib.eu/wp-content/uploads/2026/05/Fernzugriff-DEAL-4.jpg\")
Melden Sie sich nach Ihrer erfolgreichen Registrierung im TIB-Portal an:
\n![\"\"](\"https://blog.tib.eu/wp-content/uploads/2026/05/Fernzugriff-DEAL-5.jpg\")
Die Registrierung und Anmeldung im TIB-Portal wird in einem separaten Blog-Beitrag noch einmal ausf\u00fchrlich beschrieben: Registrierung und Anmeldung im TIB-Portal – TIB-Blog
\nNach erfolgreicher Registrierung bzw. erfolgreichem Login k\u00f6nnen Sie das gew\u00fcnschte
\nDokument herunterladen:
![\"\"](\"https://blog.tib.eu/wp-content/uploads/2026/05/Fernzugriff-DEAL-6.jpg\")
![\"\"](\"https://blog.tib.eu/wp-content/uploads/2026/05/Fernzugriff-DEAL-7.jpg\")
![\"\"](\"https://blog.tib.eu/wp-content/uploads/2026/05/Fernzugriff-DEAL-8.jpg\")
Diese direkte Zugriffsm\u00f6glichkeit erstreckt sich auf alle Zeitschriften von Wiley und Springer, die Bestandteil der DEAL-Vertr\u00e4ge sind. Eine Auflistung der Zeitschriften ist hier zu finden: Wiley (XLSX) und Springer (XLSX).
\nFalls Sie gezielt nach den Inhalten f\u00fcr den kostenlosen, direkten Zugriff suchen m\u00f6chten, k\u00f6nnen Sie folgenden Filter setzen: \u201eGgf. freier Zugriff\u201c. Danach klicken Sie auf \u201eFilter anwenden\u201c und erhalten eine Trefferliste mit den entsprechenden \u00fcber das TIB-Portal frei zug\u00e4nglichen Inhalten.
\n![\"\"](\"https://blog.tib.eu/wp-content/uploads/2026/05/Fernzugriff-DEAL-9.jpg\")
Falls Sie Zugriffsprobleme oder weitere Fragen haben, wir helfen Ihnen gern weiter unter\u00a0kundenservice@tib.eu
\nMany scholarly infrastructure business models fit institutions poorly. They tie cost to transactions, outputs, or rising use in ways that make adoption harder to sustain over time. That is a problem for libraries and research institutions, which are trying to normalize and support good practice, not disincentivize against it by tying payments to countable units. DataCite\u2019s recent move away from per-DOI pricing offers a useful example of a different approach. It shows what it can look like when an infrastructure organization designs its funding model in a way that better matches institutional values and institutional realities.
The issue is not whether institutions understand that infrastructure costs money. Of course they do. In some cases, they will even choose to build and maintain systems themselves, not because doing so is necessarily cheaper or more efficient, but because predictable responsibility can feel easier to manage than open-ended financial exposure. The real question is whether infrastructure can be supported in ways that are stable and aligned with how institutions actually work, while providing sustainable funding to those providing the infrastructure. Transactional infrastructure models are not only competing against one another. They are also competing against institutional instincts toward local control, internal ownership, and operational predictability.
Too often, the answer is no. Many infrastructure models still assume that use should be counted and billed one unit at a time. That logic may work in settings where value is primarily individual and immediate. However, scholarly infrastructure is usually not one of those settings. Repositories, PID systems, metadata services, curation platforms, and related infrastructure create value that institutions sustain collectively so those capabilities remain broadly and equitably available over time. They help preserve the scholarly record, improve practice, support compliance, and make core capacities available to the communities institutions serve.
This is why collective models matter. A transactional model ties cost to individual acts of use. A collective model ties support to sustaining shared capacity. That distinction is not just financial. It shapes whether institutions can confidently encourage adoption, whether the entire community is rewarded for broad uptake or punished by it, and whether infrastructure actually behaves like and is treated like a shared investment rather than just another vendor.
Institutions around the world understand that scholarly infrastructure costs money. The harder question is whether those costs can be structured in ways institutions can support responsibly over time. By \u2018transactional model\u2019, I mean a model in which institutional cost rises with discrete acts of use, outputs, or participation, rather than through a stable collective commitment to sustain the infrastructure as a whole.
For most institutions, the central problem is not cost itself but unpredictability. Libraries and research organizations can plan for recurring commitments when those commitments are understandable, stable, and tied to a known scope of support. What becomes difficult is a model in which ordinary adoption, routine use, or successful growth creates open-ended financial exposure. At that point, infrastructure stops feeling like an investment and starts feeling like a moving target.
That changes the internal conversation institutions have about infrastructure. Instead of asking how to support participation, improve practice, or build durable capacity, institutions begin asking what happens to the budget if those efforts succeed. Once that happens, the pricing model is no longer neutral. It begins shaping behavior. This is one reason transactional models can create friction in institutional settings. Research institutions operate through annual budgets, recurring commitments, and long-term operational responsibilities. They can usually absorb predictable commitments much more easily than uncertain growth in costs tied to usage. When ordinary success becomes financially difficult to forecast, it becomes harder to normalize infrastructure, harder to advocate for adoption locally, and harder to sustain support over time. In some cases, institutions may also become more cautious about dependence itself, particularly when local ownership appears easier to predict than external variable costs.
The problem with transactional models is not only that it can be harder to budget for. It is that it can create incentives that run directly against the purpose of the infrastructure itself. When cost rises with each deposit, identifier, publication, or workflow, institutions can find themselves paying more precisely when the infrastructure is being used in the ways they have been encouraging all along.
That is the core perverse incentive. Libraries and research institutions are often trying to move communities toward better practice: more data sharing, stronger metadata, broader use of PIDs, better preservation, and more consistent compliance with policy and funder expectations. They are often the very outcomes that infrastructure providers, libraries, and research offices all say they want. But if each incremental success also increases cost, then business models start to punish adoption rather than support it.
The tension may not always be stated explicitly, but it shows up in institutional decision-making all the time. A library that wants to normalize good practice across the research lifecycle has to consider whether each additional use carries a budget consequence. Once that happens, pricing models are no longer neutral. It is shaping behavior. And it is doing so in a way that makes institutions more cautious about advocating for the very practices they are supposed to enable.
This is especially problematic in infrastructure, where the goal is not to reduce demand but to build uptake and shared norms. If adoption increases cost, then the pricing models are working against the purpose of the infrastructure. That is not a minor design flaw. It is a sign that the models are poorly matched to the role the infrastructure is meant to play.
A transactional model assumes a fairly direct relationship between user, benefit, and payment. That logic may work in settings where the value is primarily individual and immediate but scholarly infrastructure is usually not one of those settings. The immediate user may be a researcher depositing a dataset, registering an identifier, or using a metadata or curation service, but the value created does not stop with that act or with that person.
The benefits of scholarly infrastructure are often distributed across time. They include stewardship of the scholarly record, support for better practice across the institution, the existence of shared capacity that can be relied on when needed, and value that extends well beyond the immediate user to librarians, curators, educators, compliance staff, future researchers, and the entire research ecosystem. In other words, the person using the service is not always the only one who benefits, and often not the only one the institution is trying to support.
Pricing models do not just recover cost. They also express an assumption about where value resides. When infrastructure is priced transaction by transaction, it privileges the most visible unit of activity and can understate the wider institutional and communal benefit being sustained. It treats infrastructure as if institutions were merely reimbursing individual use rather than sustaining institutional research capacity. This is one reason the fit can feel so poor from an institutional perspective. Institutions are often not paying simply to cover a series of isolated acts. They are paying to sustain continuity, readiness, and the conditions for good practice over time. That is a different kind of value proposition. And it is one that is often misdescribed when the funding model is built around one-to-one transactions.
Collective models often fit institutional operating realities better than highly transactional ones. Research institutions do not support repositories, PID systems, metadata services, and preservation infrastructure simply as narrow transactional services. They support them as part of the broader research environment: shared capacities that enable stewardship, compliance, discoverability, and continuity over time.
That distinction matters operationally as much as philosophically. Institutions generally manage infrastructure more effectively when support can be framed as a stable commitment with a clear communal rationale, rather than as a variable bill tied to successful engagement. A collective model fits that reality because institutions are usually trying to sustain shared research capacity, not support one researcher transaction at a time.
This does not eliminate the need for accountability or careful governance. It does mean, however, that the financial model begins to match the way institutions actually justify, budget for, and sustain infrastructure over time.
Predictability benefits infrastructure organizations as much as institutions. A collective funding model does not eliminate risk, but it changes how risk is managed. Instead of pushing financial variability back onto institutions one transaction at a time, infrastructure providers must think more deliberately about scope, staffing, reserves, governance, and sustainable growth. That can be a strength rather than a weakness. A predictable model encourages a clearer relationship between mission, service levels, and community support. It pushes organizations to be more transparent about what baseline funding supports, where additional investment is needed, and how expansion should occur responsibly over time.
Transparency is essential. Communities should be able to understand how fees are determined, what the baseline covers, what reserves exist, and how future changes will be evaluated. A sustainable collective model should also plan for continuity, including governance transitions and responsible wind-down where necessary. In that sense, predictability is not simply a concession to institutional budgeting concerns. It is part of a healthier governance model for infrastructure itself. For institutions, predictability can make support easier to justify and sustain. For providers, it creates a more intentional and legible operational model grounded in long-term stewardship rather than continual transactional expansion.
A collective model does not require pretending that all institutions are the same, and it does not require a single identical fee for everyone. The more important distinction is whether the model is structured around sustaining shared capacity. Infrastructure is itself inherently collective: its value emerges not only from individual acts of use, but from the existence of shared systems that institutions and communities can rely on over time. A model can still be collective while using formulas, tiers, or contribution levels that reflect institutional size, capacity, or role. Not every differentiated pricing model is transactional in the problematic sense; a model can still be tiered or formula-based and remain collective if it is designed to sustain infrastructure. This does not have to remain abstract. DataCite recently announced a new membership model that moves away from per-DOI pricing and toward a collective approach, with fees adjusted using country-level economic indicators. It offers a useful example of how an infrastructure organization can differentiate contributions without reverting to transactional logic.
One of the easiest objections to collective funding is the claim that it is somehow simplistic or unfair. In reality, the fairness of a model can recognize institutional differences without reducing infrastructure support to a running tally of routine use. In practice, some of the strongest collective arrangements are precisely the ones that are transparent about how costs are distributed and why. Formula-based approaches can make it possible for larger institutions to contribute more, for smaller organizations to participate meaningfully, and for the whole community to see how growth, accountability, and shared support fit together. That is a very different logic from a model where each additional act of participation simply adds to the bill. So the choice is not between a crude flat fee and finely tuned fairness. The better contrast is between models that fund infrastructure predictably and models that meter normal use as though the infrastructure were simply a series of individual transactions.
There are already plenty of vendors in the scholarly infrastructure space. The point of infrastructure is not simply to add more providers to that landscape. It is to create a different kind of relationship, one grounded in stewardship, accountability, transparency, and a clearer connection between what is being funded and the broader benefit it supports. When that distinction fades, something important is lost.
This is why the funding model matters beyond pricing mechanics. If a membership organization or mission-driven infrastructure relies too heavily on transactional logic, it can start to look and feel like an ordinary vendor relationship. The institution is no longer supporting shared capacity with a communal rationale. It is managing exposure, counting usage, and evaluating the arrangement as one more procurement decision, often against the perceived predictability of local control. At that point, the infrastructure may still be valuable, but its business model is no longer doing much to distinguish it from the market logic it was supposed to counterbalance.
That does not mean infrastructures should ignore sustainability, discipline, or cost recovery. It means that their distinctiveness should show up in the model itself. If a community is being asked to support infrastructure because it is mission-driven or built for long-term collective benefit, then the way it is funded should reflect those same qualities. Otherwise, the rhetoric starts to drift away from the reality of the relationship.
The issue is not simply whether institutions can afford one more service. It is whether the scholarly community is building genuine infrastructure or just reproducing the vendor landscape in a slightly different form. If infrastructures adopt vendor logic, it risks becoming just another vendor in a space that already has too many. We do not need more infrastructure providers acting like vendors. We need infrastructure that actually operates like a investment.
Research infrastructure and open access publishing are not the same thing, but there is still a useful parallel. Institutions have already spent years grappling with business models that tie costs to discrete units of activity. APC-based publishing models are one familiar example. The issue is not simply that institutions pay for publishing. It is that costs can become difficult to predict and difficult to sustain as adoption grows.
That tension is one reason institutions have often been drawn toward models that create more stable and legible forms of support, whether through negotiated agreements, collective funding arrangements, or Diamond OA approaches that move away from per-article transactions. In some cases, these efforts have also produced collective funding experiments, including models such as the Open Library of Humanities and SciPost, that attempt to sustain publishing infrastructure through broader community support rather than article-level billing. These approaches are not interchangeable, but they reflect a familiar institutional preference: supporting systems through models that are easier to normalize and sustain over time.
The same tension appears in scholarly infrastructure. Institutions are not only evaluating whether a service is useful. They are evaluating whether the funding model fits the operational realities of long-term institutional support. In that sense, the OA conversation is useful not because it offers a perfect analogy, but because it demonstrates a broader and recurring challenge across scholarly communication: how to sustain these systems without making adoption financially destabilizing.
Collective models are not perfect, and the argument for them is weaker if that is ignored. As communities grow, governance can become more complicated. Not every participant wants deep engagement, shared decision-making, or a strong sense of communal identity. Some institutions may still evaluate a collective arrangement through a narrow transactional lens, asking whether local usage \"justifies\" the cost even when the benefit is broader and less easily measured. Those are real challenges.
There are also legitimate questions about scope and edge cases. Some services or institutions may place unusually large demands on an infrastructure. Some kinds of support may be bespoke enough that they do not fit neatly within a shared baseline model. A collective approach does not eliminate those questions. It simply means that they should be handled as contextual decision points within a framework, not used to define the logic of the whole system.
It is also true that collective models require explanation. Institutions need to understand what they are supporting, how the model works, and why the benefit is greater than a count of immediate uses. Providers need to be transparent about what the baseline covers, how contributions are determined, and where additional needs may arise. Without that clarity, a collective model can start to feel vague or unconvincing, especially in institutional settings where every recurring cost is under scrutiny.
But none of those tradeoffs change the larger point. The existence of complexity does not mean transactional pricing is better. It means collective models have to be designed carefully, governed well, and explained in a way that makes the communal value visible.
When it comes to infrastructure, the real choice is not between paying for it and getting it for free. Institutions have never sustained research support that way. The choice is between models that sustain shared institutional capacity and models that tie cost to participation. That distinction is important because it shapes not only what institutions pay, but how they understand the relationship, how they justify it internally, and how confidently they can encourage broader adoption.
If institutions are going to sustain repositories, PID systems, metadata services, curation platforms, and other scholarly infrastructure as part of their remit, then the business models behind those services need to reflect the kind of value they create. In most cases, that value is not only singular and immediate. It is institutional, communal, and long-term. It shows up in stewardship of the scholarly record, in support for better practice across the institution and in benefits that extend well beyond the immediate user.
That is why this conversation is ultimately about more than pricing. It is about what kind of infrastructure ecosystem the scholarly community wants to build. For institutions, that means moving away from models that punish ordinary success and toward models that support capacity building, predictable commitment, and long-term stewardship. DataCite\u2019s recent move away from per-DOI pricing offers one important example of what that transition can look like in practice. We need more infrastructures to also operate like investments in the future.
Acknowledgements: Thank you to Kathleen Gregory, Dorothea Strecker, Maria Gould, and my colleagues at California Digital Library (G\u00fcnter Waibel, Catherine Mitchell, Miranda Bennett, Kurt Ewoldsen) for their help with this article.
","doi":"https://doi.org/10.54900/9xya5-agx80","funding_references":null,"guid":"https://doi.org/10.54900/9xya5-agx80","id":"dcda1294-dfcd-4363-bd3f-0d360aaa4ea0","image":"https://images.unsplash.com/photo-1564998115952-368e7d4969ea?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=M3wxMTc3M3wwfDF8c2VhcmNofDI0fHx2ZW5kaW5nJTIwbWFjaGluZXxlbnwwfHx8fDE3Nzg1MjM3MzB8MA&ixlib=rb-4.1.0&q=80&w=2000","images":[],"indexed":true,"indexed_at":1779295472,"language":"en","parent_doi":null,"published_at":1779229439,"reference":[],"registered_at":0,"relationships":[],"rid":"hkcsy-hf385","status":"active","summary":"Many scholarly infrastructure business models fit institutions poorly. They tie cost to transactions, outputs, or rising use in ways that make adoption harder to sustain over time. That is a problem for libraries and research institutions, which are trying to normalize and support good practice, not disincentivize against it by tying payments to countable units.","tags":["Thought Pieces","Insights"],"title":"Not the Right Fit: How Transactional Infrastructure Models Clash with Institutional Realities","updated_at":1779292001,"url":"https://upstream.force11.org/why-institutions-struggle-with-transactional-infrastructure-models/","version":"v1"}},{"document":{"abstract":"Many scholarly infrastructure business models fit institutions poorly. They tie cost to transactions, outputs, or rising use in ways that make adoption harder to sustain over time. That is a problem for libraries and research institutions, which are trying to normalize and support good practice, not disincentivize against it by tying payments to countable units.","archive_url":null,"authors":[{"affiliation":[{"id":"https://ror.org/00dmfq477","name":"University of California Office of the President"}],"contributor_roles":[],"family":"Chodacki","given":"John","url":"https://orcid.org/0000-0002-7378-2408"}],"blog":{"archive_collection":22124,"archive_host":null,"archive_prefix":"https://wayback.archive-it.org/22124/20231105103706/","archive_timestamps":[20231105103706,20240505132151,20241105103657,20250505103918],"authors":null,"canonical_url":null,"category":"humanities","community_id":"aeaafcbb-94b5-477a-a89f-8fba5925e926","created_at":1673568000,"current_feed_url":"https://upstream.force11.org/atom/","description":"The community blog for all things Open Research.","doi":null,"doi_as_guid":false,"favicon":"https://rogue-scholar.org/api/communities/b56ef314-34f7-4c7f-b0e2-d0bf13bfe83b/logo","feed_format":"application/atom+xml","feed_url":"https://upstream.force11.org/atom-complete/","filter":null,"funding":null,"generator":"Ghost","generator_raw":"Ghost 5.25","home_page_url":"https://upstream.force11.org","id":"e3952730-ffb7-4ef9-b4a5-6433d86b2819","indexed":false,"issn":null,"language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","mastodon":"https://scicomm.xyz/@upstream","prefix":"10.54900","registered_at":1729244339,"relative_url":null,"ror":null,"secure":true,"slug":"upstream","status":"active","subfield":"1802","subfield_validated":null,"title":"Upstream","updated_at":1779268293.824907,"use_api":true,"use_mastodon":false,"user_id":"08014cf6-3335-4588-96f4-c77ac1e535b2"},"blog_name":"Upstream","blog_slug":"upstream","content_html":"Many scholarly infrastructure business models fit institutions poorly. They tie cost to transactions, outputs, or rising use in ways that make adoption harder to sustain over time. That is a problem for libraries and research institutions, which are trying to normalize and support good practice, not disincentivize against it by tying payments to countable units. DataCite\u2019s recent move away from per-DOI pricing offers a useful example of a different approach. It shows what it can look like when an infrastructure organization designs its funding model in a way that better matches institutional values and institutional realities.
The issue is not whether institutions understand that infrastructure costs money. Of course they do. In some cases, they will even choose to build and maintain systems themselves, not because doing so is necessarily cheaper or more efficient, but because predictable responsibility can feel easier to manage than open-ended financial exposure. The real question is whether infrastructure can be supported in ways that are stable and aligned with how institutions actually work, while providing sustainable funding to those providing the infrastructure. Transactional infrastructure models are not only competing against one another. They are also competing against institutional instincts toward local control, internal ownership, and operational predictability.
Too often, the answer is no. Many infrastructure models still assume that use should be counted and billed one unit at a time. That logic may work in settings where value is primarily individual and immediate. However, scholarly infrastructure is usually not one of those settings. Repositories, PID systems, metadata services, curation platforms, and related infrastructure create value that institutions sustain collectively so those capabilities remain broadly and equitably available over time. They help preserve the scholarly record, improve practice, support compliance, and make core capacities available to the communities institutions serve.
This is why collective models matter. A transactional model ties cost to individual acts of use. A collective model ties support to sustaining shared capacity. That distinction is not just financial. It shapes whether institutions can confidently encourage adoption, whether the entire community is rewarded for broad uptake or punished by it, and whether infrastructure actually behaves like and is treated like a shared investment rather than just another vendor.
Institutions around the world understand that scholarly infrastructure costs money. The harder question is whether those costs can be structured in ways institutions can support responsibly over time. By \u2018transactional model\u2019, I mean a model in which institutional cost rises with discrete acts of use, outputs, or participation, rather than through a stable collective commitment to sustain the infrastructure as a whole.
For most institutions, the central problem is not cost itself but unpredictability. Libraries and research organizations can plan for recurring commitments when those commitments are understandable, stable, and tied to a known scope of support. What becomes difficult is a model in which ordinary adoption, routine use, or successful growth creates open-ended financial exposure. At that point, infrastructure stops feeling like an investment and starts feeling like a moving target.
That changes the internal conversation institutions have about infrastructure. Instead of asking how to support participation, improve practice, or build durable capacity, institutions begin asking what happens to the budget if those efforts succeed. Once that happens, the pricing model is no longer neutral. It begins shaping behavior. This is one reason transactional models can create friction in institutional settings. Research institutions operate through annual budgets, recurring commitments, and long-term operational responsibilities. They can usually absorb predictable commitments much more easily than uncertain growth in costs tied to usage. When ordinary success becomes financially difficult to forecast, it becomes harder to normalize infrastructure, harder to advocate for adoption locally, and harder to sustain support over time. In some cases, institutions may also become more cautious about dependence itself, particularly when local ownership appears easier to predict than external variable costs.
The problem with transactional models is not only that it can be harder to budget for. It is that it can create incentives that run directly against the purpose of the infrastructure itself. When cost rises with each deposit, identifier, publication, or workflow, institutions can find themselves paying more precisely when the infrastructure is being used in the ways they have been encouraging all along.
That is the core perverse incentive. Libraries and research institutions are often trying to move communities toward better practice: more data sharing, stronger metadata, broader use of PIDs, better preservation, and more consistent compliance with policy and funder expectations. They are often the very outcomes that infrastructure providers, libraries, and research offices all say they want. But if each incremental success also increases cost, then business models start to punish adoption rather than support it.
The tension may not always be stated explicitly, but it shows up in institutional decision-making all the time. A library that wants to normalize good practice across the research lifecycle has to consider whether each additional use carries a budget consequence. Once that happens, pricing models are no longer neutral. It is shaping behavior. And it is doing so in a way that makes institutions more cautious about advocating for the very practices they are supposed to enable.
This is especially problematic in infrastructure, where the goal is not to reduce demand but to build uptake and shared norms. If adoption increases cost, then the pricing models are working against the purpose of the infrastructure. That is not a minor design flaw. It is a sign that the models are poorly matched to the role the infrastructure is meant to play.
A transactional model assumes a fairly direct relationship between user, benefit, and payment. That logic may work in settings where the value is primarily individual and immediate but scholarly infrastructure is usually not one of those settings. The immediate user may be a researcher depositing a dataset, registering an identifier, or using a metadata or curation service, but the value created does not stop with that act or with that person.
The benefits of scholarly infrastructure are often distributed across time. They include stewardship of the scholarly record, support for better practice across the institution, the existence of shared capacity that can be relied on when needed, and value that extends well beyond the immediate user to librarians, curators, educators, compliance staff, future researchers, and the entire research ecosystem. In other words, the person using the service is not always the only one who benefits, and often not the only one the institution is trying to support.
Pricing models do not just recover cost. They also express an assumption about where value resides. When infrastructure is priced transaction by transaction, it privileges the most visible unit of activity and can understate the wider institutional and communal benefit being sustained. It treats infrastructure as if institutions were merely reimbursing individual use rather than sustaining institutional research capacity. This is one reason the fit can feel so poor from an institutional perspective. Institutions are often not paying simply to cover a series of isolated acts. They are paying to sustain continuity, readiness, and the conditions for good practice over time. That is a different kind of value proposition. And it is one that is often misdescribed when the funding model is built around one-to-one transactions.
Collective models often fit institutional operating realities better than highly transactional ones. Research institutions do not support repositories, PID systems, metadata services, and preservation infrastructure simply as narrow transactional services. They support them as part of the broader research environment: shared capacities that enable stewardship, compliance, discoverability, and continuity over time.
That distinction matters operationally as much as philosophically. Institutions generally manage infrastructure more effectively when support can be framed as a stable commitment with a clear communal rationale, rather than as a variable bill tied to successful engagement. A collective model fits that reality because institutions are usually trying to sustain shared research capacity, not support one researcher transaction at a time.
This does not eliminate the need for accountability or careful governance. It does mean, however, that the financial model begins to match the way institutions actually justify, budget for, and sustain infrastructure over time.
Predictability benefits infrastructure organizations as much as institutions. A collective funding model does not eliminate risk, but it changes how risk is managed. Instead of pushing financial variability back onto institutions one transaction at a time, infrastructure providers must think more deliberately about scope, staffing, reserves, governance, and sustainable growth. That can be a strength rather than a weakness. A predictable model encourages a clearer relationship between mission, service levels, and community support. It pushes organizations to be more transparent about what baseline funding supports, where additional investment is needed, and how expansion should occur responsibly over time.
Transparency is essential. Communities should be able to understand how fees are determined, what the baseline covers, what reserves exist, and how future changes will be evaluated. A sustainable collective model should also plan for continuity, including governance transitions and responsible wind-down where necessary. In that sense, predictability is not simply a concession to institutional budgeting concerns. It is part of a healthier governance model for infrastructure itself. For institutions, predictability can make support easier to justify and sustain. For providers, it creates a more intentional and legible operational model grounded in long-term stewardship rather than continual transactional expansion.
A collective model does not require pretending that all institutions are the same, and it does not require a single identical fee for everyone. The more important distinction is whether the model is structured around sustaining shared capacity. Infrastructure is itself inherently collective: its value emerges not only from individual acts of use, but from the existence of shared systems that institutions and communities can rely on over time. A model can still be collective while using formulas, tiers, or contribution levels that reflect institutional size, capacity, or role. Not every differentiated pricing model is transactional in the problematic sense; a model can still be tiered or formula-based and remain collective if it is designed to sustain infrastructure. This does not have to remain abstract. DataCite recently announced a new membership model that moves away from per-DOI pricing and toward a collective approach, with fees adjusted using country-level economic indicators. It offers a useful example of how an infrastructure organization can differentiate contributions without reverting to transactional logic.
One of the easiest objections to collective funding is the claim that it is somehow simplistic or unfair. In reality, the fairness of a model can recognize institutional differences without reducing infrastructure support to a running tally of routine use. In practice, some of the strongest collective arrangements are precisely the ones that are transparent about how costs are distributed and why. Formula-based approaches can make it possible for larger institutions to contribute more, for smaller organizations to participate meaningfully, and for the whole community to see how growth, accountability, and shared support fit together. That is a very different logic from a model where each additional act of participation simply adds to the bill. So the choice is not between a crude flat fee and finely tuned fairness. The better contrast is between models that fund infrastructure predictably and models that meter normal use as though the infrastructure were simply a series of individual transactions.
There are already plenty of vendors in the scholarly infrastructure space. The point of infrastructure is not simply to add more providers to that landscape. It is to create a different kind of relationship, one grounded in stewardship, accountability, transparency, and a clearer connection between what is being funded and the broader benefit it supports. When that distinction fades, something important is lost.
This is why the funding model matters beyond pricing mechanics. If a membership organization or mission-driven infrastructure relies too heavily on transactional logic, it can start to look and feel like an ordinary vendor relationship. The institution is no longer supporting shared capacity with a communal rationale. It is managing exposure, counting usage, and evaluating the arrangement as one more procurement decision, often against the perceived predictability of local control. At that point, the infrastructure may still be valuable, but its business model is no longer doing much to distinguish it from the market logic it was supposed to counterbalance.
That does not mean infrastructures should ignore sustainability, discipline, or cost recovery. It means that their distinctiveness should show up in the model itself. If a community is being asked to support infrastructure because it is mission-driven or built for long-term collective benefit, then the way it is funded should reflect those same qualities. Otherwise, the rhetoric starts to drift away from the reality of the relationship.
The issue is not simply whether institutions can afford one more service. It is whether the scholarly community is building genuine infrastructure or just reproducing the vendor landscape in a slightly different form. If infrastructures adopt vendor logic, it risks becoming just another vendor in a space that already has too many. We do not need more infrastructure providers acting like vendors. We need infrastructure that actually operates like a investment.
Research infrastructure and open access publishing are not the same thing, but there is still a useful parallel. Institutions have already spent years grappling with business models that tie costs to discrete units of activity. APC-based publishing models are one familiar example. The issue is not simply that institutions pay for publishing. It is that costs can become difficult to predict and difficult to sustain as adoption grows.
That tension is one reason institutions have often been drawn toward models that create more stable and legible forms of support, whether through negotiated agreements, collective funding arrangements, or Diamond OA approaches that move away from per-article transactions. In some cases, these efforts have also produced collective funding experiments, including models such as the Open Library of Humanities and SciPost, that attempt to sustain publishing infrastructure through broader community support rather than article-level billing. These approaches are not interchangeable, but they reflect a familiar institutional preference: supporting systems through models that are easier to normalize and sustain over time.
The same tension appears in scholarly infrastructure. Institutions are not only evaluating whether a service is useful. They are evaluating whether the funding model fits the operational realities of long-term institutional support. In that sense, the OA conversation is useful not because it offers a perfect analogy, but because it demonstrates a broader and recurring challenge across scholarly communication: how to sustain these systems without making adoption financially destabilizing.
Collective models are not perfect, and the argument for them is weaker if that is ignored. As communities grow, governance can become more complicated. Not every participant wants deep engagement, shared decision-making, or a strong sense of communal identity. Some institutions may still evaluate a collective arrangement through a narrow transactional lens, asking whether local usage \"justifies\" the cost even when the benefit is broader and less easily measured. Those are real challenges.
There are also legitimate questions about scope and edge cases. Some services or institutions may place unusually large demands on an infrastructure. Some kinds of support may be bespoke enough that they do not fit neatly within a shared baseline model. A collective approach does not eliminate those questions. It simply means that they should be handled as contextual decision points within a framework, not used to define the logic of the whole system.
It is also true that collective models require explanation. Institutions need to understand what they are supporting, how the model works, and why the benefit is greater than a count of immediate uses. Providers need to be transparent about what the baseline covers, how contributions are determined, and where additional needs may arise. Without that clarity, a collective model can start to feel vague or unconvincing, especially in institutional settings where every recurring cost is under scrutiny.
But none of those tradeoffs change the larger point. The existence of complexity does not mean transactional pricing is better. It means collective models have to be designed carefully, governed well, and explained in a way that makes the communal value visible.
When it comes to infrastructure, the real choice is not between paying for it and getting it for free. Institutions have never sustained research support that way. The choice is between models that sustain shared institutional capacity and models that tie cost to participation. That distinction is important because it shapes not only what institutions pay, but how they understand the relationship, how they justify it internally, and how confidently they can encourage broader adoption.
If institutions are going to sustain repositories, PID systems, metadata services, curation platforms, and other scholarly infrastructure as part of their remit, then the business models behind those services need to reflect the kind of value they create. In most cases, that value is not only singular and immediate. It is institutional, communal, and long-term. It shows up in stewardship of the scholarly record, in support for better practice across the institution and in benefits that extend well beyond the immediate user.
That is why this conversation is ultimately about more than pricing. It is about what kind of infrastructure ecosystem the scholarly community wants to build. For institutions, that means moving away from models that punish ordinary success and toward models that support capacity building, predictable commitment, and long-term stewardship. DataCite\u2019s recent move away from per-DOI pricing offers one important example of what that transition can look like in practice. We need more infrastructures to also operate like investments in the future.
Acknowledgements: Thank you to Kathleen Gregory, Dorothea Strecker, Maria Gould, and my colleagues at California Digital Library (G\u00fcnter Waibel, Catherine Mitchell, Miranda Bennett, Kurt Ewoldsen) for their help with this article.
","doi":"https://doi.org/10.54900/9xya5-agx80","funding_references":null,"guid":"6a021cf4b1b32c6d4a88824b","id":"8e935153-7a89-4533-921d-164dc690a690","image":"https://images.unsplash.com/photo-1564998115952-368e7d4969ea?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=M3wxMTc3M3wwfDF8c2VhcmNofDI0fHx2ZW5kaW5nJTIwbWFjaGluZXxlbnwwfHx8fDE3Nzg1MjM3MzB8MA&ixlib=rb-4.1.0&q=80&w=2000","images":[],"indexed":true,"indexed_at":1779235800,"language":"en","parent_doi":null,"published_at":1779229439,"reference":[],"registered_at":0,"relationships":[],"rid":"hkcsy-hf385","status":"active","summary":"Many scholarly infrastructure business models fit institutions poorly. They tie cost to transactions, outputs, or rising use in ways that make adoption harder to sustain over time. That is a problem for libraries and research institutions, which are trying to normalize and support good practice, not disincentivize against it by tying payments to countable units.","tags":["Thought Pieces","Insights"],"title":"Not the Right Fit: How Transactional Infrastructure Models Clash with Institutional Realities","updated_at":1779234873,"url":"https://upstream.force11.org/why-institutions-struggle-with-transactional-infrastructure-models/","version":"v1"}},{"document":{"abstract":null,"archive_url":null,"authors":[{"contributor_roles":[],"family":"Niemeyer","given":"Sandra"}],"blog":{"archive_collection":null,"archive_host":null,"archive_prefix":null,"archive_timestamps":null,"authors":null,"canonical_url":null,"category":"engineeringAndTechnology","community_id":"db0d8909-9e37-46d0-b16c-0551f575e86b","created_at":1749798261.334959,"current_feed_url":null,"description":"Das Blog der TIB \u2013 Leibniz-Informationszentrum Technik und Naturwissenschaften und Universit\u00e4tsbibliothek","doi":null,"doi_as_guid":true,"favicon":"https://blog.tib.eu/wp-content/uploads/2015/12/TIB_fav_icon_24x24.png","feed_format":"application/atom+xml","feed_url":"https://blog.tib.eu/feed/atom/","filter":null,"funding":null,"generator":"WordPress","generator_raw":"WordPress 6.8.1","home_page_url":"https://blog.tib.eu/","id":"135a354f-2969-4852-9a7c-b6cda0a692a4","indexed":true,"issn":null,"language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","mastodon":null,"prefix":"10.65527","registered_at":0,"relative_url":null,"ror":null,"secure":true,"slug":"tib","status":"active","subfield":"1802","subfield_validated":null,"title":"TIB-Blog","updated_at":1779268236.390099,"use_api":true,"use_mastodon":false,"user_id":null},"blog_name":"TIB-Blog","blog_slug":"tib","content_html":" read this article in English\nDie Blogreihe \u201eFrauen in der Wissenschaft\u201c stellt Frauen aus der TIB vor, die Einblicke in ihre Wege und ihre pers\u00f6nlichen Erfahrungen in der Wissenschaft geben. Dr. Lauren Snyder studierte \u201eEcology and Conservation Biology\u201d (\u00d6kologie und Naturschutz) an der Boston University und promovierte in \u201eEcology & Evolutionary Biology\u201c (\u00d6kologie und Evolutionsbiologie) an der Cornell University.
\nHeute ist sie Mitbegr\u00fcnderin von \u201eTIB Knowledge Loom\u201c, einer digitalen Open-Science-Bibliothek. Dort nutzt sie ihren interdisziplin\u00e4ren Forschungshintergrund, um Tools zu entwickeln, die die Transparenz und Wiederverwendbarkeit wissenschaftlicher Erkenntnisse verbessern. Im Interview spricht sie dar\u00fcber, warum klare Zielvorstellungen genauso wichtig sind wie Offenheit f\u00fcr Unerwartetes und warum die Menschen, mit denen man arbeitet, eine besondere Rolle spielen.
\n![\"Portr\u00e4tfoto](\"https://blog.tib.eu/wp-content/uploads/2026/03/snyder_lauren-1024x1021.jpg\")
Was fasziniert dich an der Arbeit in der Wissenschaft?
\nIch liebe die Natur, arbeite gern mit Menschen und m\u00f6chte einen Beitrag zur Gesellschaft leisten. Die Wissenschaft erm\u00f6glicht es mir, all das miteinander zu verbinden. Sie gibt mir die Chance, die Umwelt zu verstehen, in der ich lebe und von der ich abh\u00e4ngig bin, sowie die Art und Weise, wie Menschen sie gestalten und welche komplexen Wechselwirkungen dabei entstehen. Das erf\u00fcllt mich mit einem tiefen Gef\u00fchl von Sinn und Verbundenheit mit der Welt um mich herum.
\nWas h\u00e4ttest du als Frau in der Wissenschaft gerne fr\u00fcher gewusst?
\nIch w\u00fcnschte, ich h\u00e4tte fr\u00fcher erkannt, dass eine klare Zielvorstellung ein starker Motivationsfaktor ist, dass aber das starre Festhalten an einem bestimmten Ziel zu Frustration und verpassten Chancen f\u00fchren kann.
\nOffen f\u00fcr das Unerwartete zu sein, bedeutet nicht, seine Ziele aufzugeben. Es bedeutet, darauf zu vertrauen, dass man Chancen in wertvolle Erfahrungen verwandeln kann, die mit den eigenen Werten und Zielen im Einklang stehen. Ich habe gelernt, dass es bei der Entscheidung, welche Chancen man wahrnehmen sollte, genauso wichtig ist, auf die Menschen zu achten wie auf die Position oder die Stellenbeschreibung selbst.
\nDie Zusammenarbeit mit Menschen, die sich f\u00fcr dich einsetzen und dir helfen, dich weiterzuentwickeln, kann eine auf den ersten Blick unerwartete Gelegenheit zu einer der besten beruflichen Entscheidungen machen, die du treffen kannst. Mit einer klaren Vision und den richtigen Menschen an deiner Seite wirst du vielversprechende Chancen erkennen und auch wissen, wann eine Erfahrung ihren H\u00f6hepunkt erreicht hat. Es braucht Zeit, dieses Selbstvertrauen und diese Intuition zu entwickeln, und das ist in Ordnung.
\nWelchen Rat w\u00fcrdest du M\u00e4dchen und jungen Frauen geben, die eine wissenschaftliche Laufbahn anstreben?
\nAls Frau hat man manchmal das Gef\u00fchl, dass es an Kritik nie mangelt. Wenn ich M\u00fche habe, meine eigene Meinung unter all den Meinungen anderer zu h\u00f6ren, denke ich an einen Satz, der mir einmal begegnet ist: Nimm keine Kritik von jemandem an, von dem du auch keinen Rat annehmen w\u00fcrdest.
\nViele Menschen werden deine Entscheidungen kommentieren oder kritisieren. Eine wichtige F\u00e4higkeit besteht darin, die Stimmen, die es wert sind, angeh\u00f6rt zu werden, vom Hintergrundrauschen zu unterscheiden. Indem du dir ein Umfeld aus Menschen aufbaust, zu denen du wirklich aufschaust \u2013 Menschen, die deine Werte, Leidenschaften und deine Vision eines erf\u00fcllten Lebens teilen \u2013, wird es dir leichter fallen, konstruktive Kritik zu erkennen und dich auf das zu konzentrieren, was dir hilft, zu wachsen, und den Rest loszulassen. Wende dich an dieses Umfeld, wenn das Leben schwer wird. W\u00e4hle Mentoren und Mitstreiter danach aus, wer sie als Menschen sind, und nicht nur aufgrund ihres Rufs oder ihrer Produktivit\u00e4t.
\n![\"\"](\"https://blog.tib.eu/wp-content/uploads/2026/04/FidW-LaurenSnyder-zitat-de.jpg\")
\n
\n
\n
\n
Ein Wunsch f\u00fcr die Zukunft von Frauen und M\u00e4dchen in der Wissenschaft …
\nIch hoffe, dass k\u00fcnftige Generationen von M\u00e4dchen eine Welt vorfinden, in der ihre Sichtweisen als unverzichtbar angesehen werden und in der sie nicht mehr um ihren Platz in der Wissenschaft oder der Gesellschaft k\u00e4mpfen m\u00fcssen.
\nIn der Blogreihe \u201eFrauen in der Wissenschaft\u201c werden Frauen an der TIB vorgestellt, die Einblicke in ihre wissenschaftlichen Wege, Rollenbilder und ihre Erfahrungen aus dem Arbeitsalltag geben. Sie alle teilen ihre Perspektive und ihre W\u00fcnsche f\u00fcr die Zukunft der Wissenschaft und ermutigen andere Frauen, ihren Platz selbstbewusst einzunehmen.
\n
The advent of AI agents based on large language models (LLMs) has put the idea of automating the intellectual and cognitive work of researchers on the table. A lively, sometimes even heated discussion is already going on. A frequently missing piece in this debate is the question why we, individually and as a society, actually do science. I will examine this question first, and then consider what it implies for introducing automation into science.
\n\nFirst of all, what is science? Any short definition is necessarily a caricature, but I hope that the following caricature is at least a useful one: science is a collective process that aims at accumulating reliable knowledge about the world we live in, emphasizing doubt and epistemic humility in order to counterbalance human cognitive biases. In other words: science proceeds with the assumption that anything can turn out to be wrong, and that the default answer to any question is \"we don't know\". Everything we think we know can be questioned and revised in the light of new evidence or new critical examination.
\nNext, why do we do science? This depends very much on who is that \"we\". Science started in the 16th century as a leisure activity by wealthy or sponsored people to satisfy their curiosity. Nowadays, science is funded by governments in order to support economic growth and policy decisions, which is a much more utilitarian stance. And yet, indivdual researchers are still largely motivated by curiosity. But curiosity and utilitarianism are not as distinct as they may seem. From an evolutionary perspective, it makes sense for organisms living in a complex world to use spare resources for acquiring knowledge that may be useful in an uncertain future. Curiosity is thus a trait that helps making people and societies more robust.
\nScientific institutions have the role of maintaining and extending the collective knowledge base, which is a complex network of interconnected pieces of information. There is knowledge about the world we live in, of course, but also knowledge about how to make observations and how to interpret their results, plus theories and knowledge about these theories, and a lot more. And all that knowledge comes with a judgement of its reliability attached to it, which is important because the ultimate goal of science is obtaining reliable knowledge.
\nIf you imagine the collective knowledge base as a huge library full of books and journals, or as a collection of Web sites that look like Wikipedia, you are missing an important piece. Information archives are important for science, but they cannot capture everything required to interpret this information. The archives contain marks on paper, or bit patterns for the digital ones. The procedural knowledge required to make sense of these information snippets, and to relate them to the world we live in, is embodied in practicing scientists. You cannot learn chemistry from reading chemistry books alone. At some point, you have to manipulate chemicals, touch them, smell them, mix them, and see what happens. More generally, if you want to learn everything required to understand and contribute meaningfully to the chemistry literature, you need to work as an apprentice to an experienced chemist. If for some reason all chemists die, the books and Web sites will become unintellegible. This is not abstract theory. We have written documents from the past that nobody can read any more because nobody living today knows the language and writing system used by the authors.
\nMost popular narratives about science concentrate on the task of extending the collective knowledge base, by making new observations about the world or new theories and models explaining such observations. Maintenance gets a lot less attention. It consists essentially of two processes: training the next generation of scientists, and re-examining existing knowledge, in the light of new information or new ideas for representing this knowledge. A modern textbook on classical mechanics looks very different from Isaac Newton's \"Principia Mathematica\", but it describes the same theoretical framework. What has changed is the notation and the presentation, making the material easier to understand and apply, and easier to integrate with other theories in physics but also in other fields using the same mathematical notation. And the more a theory is applied, integrated, and tested, the more reliable it becomes. The best evidence for the reliability of Newton's mechanics (when applied within its limits of applicability) is the fact that it underlies a huge part of the technology we use every day. Centuries of refinement have turned Newton's intellectual exercise into knowledge that we can rely on. Maintenance matters!
\nNot all scientific knowledge has been revised and applied for centuries. How then do we judge its reliability? That's an important question that is not examined often enough, in particular in the ongoing AI-for-science debate. An early and still relevant technique is double checking. If multiple researchers do similar work and obtain similar results, their results strengthen each other's reliability. And if the results disagree, the causes of the differences can be explored systematically. The simple version of double-checking that I have described here works only for studies of simple systems, where \"similar work\" and \"similar results\" are well-defined concepts. But the idea can be extended to more complex systems, where one would examine the coherence of findings from a large number of individual studies.
\nBut there remains an important condition: a judgement of reliability requires a detailed understanding of all the studies involved. Nobody can have that level of competence in more than one or two narrow domains. And yet, everybody doing research needs to rely on results from other domains and disciplines. A biologist performing data analysis is rarely a trained statistician, for example. And a physicist performing numerical simulations is rarely a trained numericist. All researchers nowadays need to trust the reliability judgements of experts in other domains. And that's also what decision makers in politics and industry do in order to figure out which scientific findings they should turn into plans for action.
\nHuman societies rely on webs of trust, because trust is the foundation for cooperation. In today's industrial societies, this web links together individuals, institutions, ideas, technologies, and physical objects, via numerous mechanisms such as reputation, certification, accountability, or punishment by law. Consider why you trust the train that you take to work every morning to transport you safely. Your trust builds on a trust in the engineers who designed the train, scientific findings that the engineers relied on, the workers that built the train, laws that define safety-related obligations, government agencies that oversee the respect of these obligations, and a lot more.
\nA large part of the boring grunt work of parlaments and government agencies is maintaining this web of trust, in contact with the scientific web of trust, among others. The web of trust behind train safety has grown over centuries, since long before the first railways were built. A society's web of trust is a big part of its social capital.
\nDigital technology remains a challenge for the web of trust, because it evolves much faster than traditional trust mechanisms can adapt. The one technology that is almost completely exempt from legal and contractual obligations concerning safety and reliability is software. Major perturbations such as the CrowdStrike incident have contributed to a growing awareness about this problem, but so far nothing much has changed at the legal level. Software vendors are not sanctioned for negligence, nor even for intentional malice (such as Grok producing deepfake porn).
\nIn science, digital technologies have likewise been adopted enthusiastically and uncritically. The publication and quality control process, which has been based on journal publications and peer review since about the 1960s, is no longer adequate for today's research work, which due to the support by digital technology now features large collaborations, big datasets, and complex computational analyses. The replication crisis is to a large part the result of this mismatch between the imagined value of peer review as a quality control mechanism and its real value as a rough credibility check. As with the safety issues I mentioned above, we are only starting to understand and correct for this evolution. And while we are grappling with these issues, LLMs are causing another earthquake in the foundations of the scientific web of trust.
\nTo what degree can science possibly be automated? Let's start with the highest imaginable level: fully automated science. That would be a machine that supplies supposedly reliable knowledge via some sort of interface, perhaps a supercharged chatbot. You could ask the machine a question, and it would enter into a dialog to request additional input from you, before in the end giving you an answer. This answer could well be \"I don't know yet, ask again a month from now while I do some more research\". Obviously this machine would have to be more than a bunch of computers. It would have to interact with the real world, making observations, setting up experiments, etc. Think of a network of computers and robots if you want a concrete image.
\nWould you trust such a machine to provide reliable answers?
\nWould you agree on having the machine do experiments on you? Would you trust its affirmation that these experiments are in your best interest?
\nFor most of us, answering such questions comes down to trusting others who we perceive as experts or authorities, or who are involved in designing or operating the machine. What would be the profile of an expert whose affirmations about the machine's reliability you would trust? Which institution would you trust to issue a certification for the science machine?
\nIf you have some expertise in science or engineering yourself, you might want to start by inspecting the processes that led to the creation of the machine. That's a good start. You might end up becoming one of those experts that the rest of us rely on. But if the machine will do all future science, then there won't be human scientists left a few decades later. And maybe no engineers either. So... who will take over your job as an expert? Why would your grandchildren trust the machine? And who will keep the machine running? It can't look after itself, as a living organism does.
\nThe good news is that nobody talking about automating science actually proposes this extreme level of automation. The bad news is the obvious conclusion that many people who propose automating science are unaware of many of the aspects of the process they wish to automate. My proposal: when discussing automation, always say explicitly where you see the interface between machines and humans. It's always there, somewhere. As long as there are humans interested in accumulating reliable knowledge, there will be a science process run by humans, who delegate specific tasks to machines. As we have been doing for quite a while already, e.g. when using DNA sequencers, or when deploying software on a computer. Automation, in science and elsewhere, has been with us for a few centuries, since the beginning of industrialization.
\nThere are three main motivations for automating a task, as compared to have humans perform it:
\nEconomy. Machines make many things cheaper than humans do, at least in our current economic model that ignores externalities such as resource depletion and environmental pollution. Often the machines produce less useful or less versatile products, but at a so much lower price that the trade-off looks favorable. As an example, consider buying an industrially produced chair as compared to making a chair yourself, or having one tailor-made by a craftsperson.
Quality. Machines do a better job at producing certain items. Staying in the carpentry theme, consider nails. Humans have made and used nails since pre-historical times, but with the arrival of industrial-made nails, human-made nails have disappeared. Machines do a better job at tasks that require high precision. They make nails that are both better and cheaper.
Complexity. Some artefacts are so complex that industrial production is the only viable option. Consider a modern car with its mechanical and electronic complexity. I doubt that anyone has ever even tried to make such a car using nothing but human labor.
In the current debate on automating science, the only motivation I see cited is economy: LLMs would allow us to do more science given the same number of people and the same resources. Most proponents of LLMs for science (e.g. this one, to give a concrete example) conveniently gloss over what \"more science\" actually means. They use the same bibliometric proxies whose inadequacy for research assessment is finally being recognized: more science means more papers. Some largely LLM-written papers have already been accepted in scientific journals, so the claim that LLMs can write papers that can pass peer review is credible. However, \"passing peer review\" is not the same as \"useful contributions to science\". In other words, the problem is not so much LLMs as an outdated quality assessment process from the 1950s that has not kept up with the enormous changes in research over the last 70 years.
\nIf we want to update our quality assessment, the question we should focus on is: how can we assess the reliability of knowledge that we obtain with the help of LLMs? Again this is not a new question. It's a question that we have asked about every single scientific instrument or experimental setup since the dawn of science. The goal is not to eliminate unreliable information sources. They often contribute useful information, and in some cases, such as in the beginnings of a new field of research, all available information may be of low reliability. That's fine, as lack of reliability can be compensated by diversity and coherence. The sum of many information sources is often more reliable than any single one on its own. But it does matter that we can estimate the reliability of each information source. Which, for experimental setups, we usually can.
\nIt is much harder to estimate the reliability of computed information, due to the complexity of software. And so... like society at large (see the last section), when it comes to software, scientists have mostly suspended the doubt that used to be their trademark. In parallel to developing computational methods, we should have developed processes for establishing trust in them, but we didn't. Only with the arrival of LLMs we realized that establishing trust is an important and difficult problem. Well, better late than never. Let's start. My contributions so far are this opinion piece about reviewing research software, and this preprint that analyzes the reviewability of software and AI tools. Unfortunately, in the meantime we will have to deal with the ongoing massive assault of our journals by LLM-generated submissions, most of which are likely to be of bad quality.
\nMy prediction is that, once the excitement about \"automating science\" has died off, we will forget this idea and concentrate on using LLMs under human supervision for well-defined tasks in which they have proven to be useful, reliable, and cost effective. The last part is rarely discussed, but it's important to keep in mind that today's AI operators run at a huge loss in order to encourage massive adoption of their product. They won't do this forever, so prices will increase, while research budgets for non-AI topics are diminishing in many countries. Nevertheless, LLMs could turn out to be a good trade-off for specific tasks in software development, in data analysis, or in the presentation of results.
\nHowever, establishing responsible LLM use in research is possible only if researchers can try out and evaluate these tools before committing to their use and making themselves dependent on them. This cannot happen in a few weeks. It cannot happen either in the current strongly polarized atmosphere where people are divided into two opposing camps, one crying \"Only AI can save us!\" and the other one replying \"AI is the devil!\" More than anything else, we need to remember the self-doubting attitude inherent to science, and admit that anyone's views on LLMs may need a revision.
\nIt is also dubious if responsible use is possible at all with today's generation of LLMs. In addition to the ethical issues, which I will address in the next section, there is a contradiction between the complete opacity of these models and the transparency requirements of science. This means you can use them only if you can audit the results in some way, as you can in some software development settings. It doesn't help that the companies that control these models openly support a government that is actively destroying scientific institutions. There is absolutely no reason to trust these companies to support science; at best, we can hope that they completely ignore research applications in developing their tools. The minimum condition for LLMs that are safe for science would be a disclosure of the training data set and the tweaks that happen after the ingestion of the training data. There are various projects to construct LLMs under such conditions, but they don't seem to be ready for practical applications.
\nIn the last section, I have looked at automation in science by LLMs with a narrow focus on the topic. That means I have taken into account the properties of science as far as automation is concerned, and the properties of LLMs as far as they relate to scientific research. I have not taken into account other characteristics of science and LLMs. This narrow-focus view is our culture's default way of analyzing things. It keeps complexity to a minimum, which is helpful. But it also hides potentially relevant aspects from view. Which is why I will now adopt a wider focus: taking into account more aspects, though necessarily in less detail.
\nScience doesn't happen in a monastery located on a remote island. It is embedded in industrial societies, where it has ties to philosophy, politics, education, industry, and a lot more. LLMs didn't fall from the sky. They were developed by people inside organizations, tied to philosophy, politics, education, industry, and a lot more. LLMs are deployed on physical machines that need to be designed, built, and operated. All that means that adoption of LLMs in science implies also bringing some of the LLM context into science. And if science one day becomes a major user of LLMs, in terms of quantity or prestige, the reverse will happen as well.
\nThere are two major criticisms of today's LLMs that derive from this wide-focus view:
\nThe impact of LLM use on the natural environment, via their enormous resource requirements.
The process by which LLMs were created: much of the training material was used without permission from its authors, and the unpleasant human labor involved (screening for atrocities) was outsourced to people who are too poor to be able to refuse the job.
Many scientists reject LLMs for these reasons, much like they reject experiments on animals or excessive air travel to conferences: not because LLMs are bad for science, but because they are bad for entities outside of science, be they humans, non-human organisms, or the entire biosphere. Expressed in the jargon of economics, LLM use has significant negative externalities.
\nNegative externalities are of course not specific to LLMs. Much of what we do in daily life (and even more so in scientific research) comes with negative externalities that we prefer not to think about because we cannot really do much about them. Climate change is the most visible one: the metabolism of our societies runs on fossil fuels, without which we couldn't even feed the human population of the planet, let alone provide the material security and comfort that we are used to. The ubiquity of negative externalities in our lives is probably the main reason why so many people, including scientists, do not see a particular issue with the negative externalities of LLMs. It's just one more item on the long list of negative externalities that we accept in order to go on with life. In this light, LLM rejection is comparable to veganism or flight shame: a conscious rejection of social norms in order to make at least a first step away from industrial societies' path towards ever increasing resource consumption and exploitation of other living beings.
\nCan these ethical issues with LLMs be overcome? In theory, yes. There are ideas for eliminating every single one. Resource consumption can be reduced by designing more efficient hardware. Less generalist LLMs could be trained with less training material, which could then be gathered with permission of its authors. Screening for atrocities becomes a non-issue when training domain-specific LLMs for science. The whole training process can be made transparent. Unfortunately, in the current economic context, none of this is likely to happen. And even in the best imaginable scenario, it would take years to decades to develop ethical LLMs for science.
\nSuch a delay, however, is inacceptable to those putting forward ethical arguments for LLM use: an acceleration of knowledge acquisition in ethically relevant domains such as health research. What if LLMs can help us cure cancer more rapidly? Is it ethically defendable not to do this? Most of these arguments are fallacious. Whereas the ethical arguments against LLMs are based on real observed negative externalities, the ethical arguments for LLM use that I have seen so far are based on speculation about hypothetical benefits. I have not seen anyone outline a credible path to an accelerated development of cancer treatments with the help of LLMs. The best you can say is that it is not logically impossible. My suspicion is that proponents of such arguments severely underestimate what it takes to develop cancer treatments. Experiments and clinical trials take a lot of time, which is not compressible by computation of any kind. And never forget the trust issue: in the end, a practicing oncologist must trust new treatments before they can actually make a difference.
\nThere are, however, quite probably some less sensational contexts in which LLM use does speed up research that has credible societal benefits. And therefore the argument \"my LLM use is ethically justifiable because the benefits outweigh the negative externalities\" cannot be rejected in general. However, so far I haven't seen any attempt to estimate such a trade-off, let alone the combination of net ethical benefit and reliable outcomes.
\nLet me end this post with my personal conclusion: I do not use LLMs for any aspect of my scientific research. Not for writing articles (nor blog posts), not for writing software, not for information retrieval, not for anything else. My research has always been more methodological than applied, and over the years it has moved more and more towards foundational questions such as reproducibility, in the topic space of metascience and philosophy of science. I consider these topics important, but not urgent. They don't justify contributing to massive harm elsewhere, nor putting quantity above quality - quite on the contrary!
\nWhat I haven't made up my mind about yet is the use of LLM-written software. LLM use in software developments comes in many shades, from code cleanup to 100% vibe coding. The latter is incompatible with the transparency requirements of science anyway, except for code snippets small enough to be audited by humans. My provisional policy is to take a critical look at LLM-supported software before adopting it. Yes, that's vague, but the only way I see to refine my policy is through practice, and that takes time! What I will not do, however, is completely reject LLM use by others. That would imply no longer collaborating with many of my colleagues, and that's a bad idea: nobody has anything to gain from the scientific community splitting into pro-AI and anti-AI camps.
\nAI slop and the destruction of knowledge by cognitive scientist Iris van Rooij. She illustrates with a concrete example what happens when LLM-generated erroneous information is incorporated unchecked into formerly trusted scientific knowledge repositories. This is happening in many places right now, and it is not clear how we will ever manage to clean up these knowledge repositories again, assuming we even decide to do it.
Scientists invented a fake disease. AI told people it was real by science journalist Chris Stokel-Walker illustrates the other side of the knowledge destruction process: fake information clearly marked as such is nevertheless absorbed by LLMs and contributes to their output.
How much of the scientific literature is generated by AI? by science journalist Miryam Naddaf. A report on the magnitude of LLM use in article preparation, and why it is quite difficult to estimate.
AI agents may be skilled researchers\u2014but not always honest ones by science journalist Nicola Jones. Another story about AI agents that are intended to automate aspects of research but do so unreliably.
Context Widows by Kevin Baker. He explains in much more detail than I did above how the goal displacement from \"quality contribution to science\" to \"citation metrics\" in the 1960s and 1970s prepared the ground for an exploitation of the new goals via LLMs, while the initial goal of quality contributions to science is silently abandoned.
Academics Need to Wake Up on AI, Part III by sociologist Alexander Kustov. His key point is that today's LLMs can produce research papers that are no worse than many human-written ones that pass peer review. He concludes that in the context of today's incentives and funding criteria, academics cannot afford not to use LLMs without losing out to their competitors who do. This confirms Kevin Baker's point about goal displacement.
A BlueSky thread by cognitive scientist Molly Crocket, pointing out the disequilibrium in science funding that prioritizes AI development while defunding everything else. Quote: \"We risk all of science if we rush to build \u201cAI Scientists\u201d, before we understand the value of human science.\"
Why do we do astrophysics? by astrophysicist David W. Hogg. He argues that for non-utilitarian fields such as his own, automating research work makes no sense because the main value of the research is not the findings but the maintenance of the research community. Many of his arguments are of interest to utilitarian perspectives as well, so this is worth reading even if you care only about \"useful\" science.
The fall of the theorem economy by mathematician David Bessis. His main point is that the value of theorem proving in mathematics is not the catalog of proven theorems but the insight gained from coming up with the proof. Theorem proving by LLMs doesn't provide this value. He predicts that the increasing use of LLMs will lead to a shift of evaluation criteria, away from valuing proofs as a proxy for the work that went into constructing them. Similar arguments can be made in other disciplines, e.g. theoretical physics.
The machines are fine. I'm worried about us. by physicist and mathematician Minas Karamanis. He worries about the consequences of students using LLMs to speed up their PhD work. The students don't learn much about research, and the supervisors could well be tempted to use LLMs directly and stop bothering with students. In either case, we lose the next generation of scientists able to do research, with or without LLMs.
Against the uncritical adoption of \u2018AI\u2019 technologies in academia by a multidisciplinary team of researchers. A very detailed and well-documented analysis of the numerous issues that makes many past and present AI technologies problematic in research and education.
A report by Inside Higher Education presents promising trials of journals that paid reviewers for writing a report. Turnaround times dropped and the quality of reports was high. It is certainly good that journals explore payment of reviewers, but I am skeptical that this can ease the “peer review crisis”, meaning to improve low turnaround times and reduce difficulties in recruiting reviewers in the first place.
As the report states, this is plausible because the compensation likely induces a sense of commitment and responsibility that accelerates the review process. I think the key question is: Does a payment model scale? When I get one invitation for a paid review report, I give it priority, but may also put other reports on hold and may decline other invitations for non-paid reports. What would happen when I get paid for all reports and all create the same sense of responsibility? Would one attach priority to all of them, putting research and teaching commitments second? I find this unlikely, so paid reports at scale may leave us in the same place as we are right now. Or researchers become more selective in accepting invitations. This is probably superior to accepting invites and not delivering a report, but it may also mean that fewer manuscript get reviewed at all, which is not desirable, in my opinion.
\n\n\n\nIn relation with this, I don’t follow this statement in the article: “There could be some percentage of papers that never get reviewed because it\u2019s not worth a journal\u2019s limited resources to peer review, which puts a quality stamp on the papers that do get reviewed,\u201d he said. This seems circular. I think the quality stamp derives from the review reports, so how can one say that a manuscript is of low quality if it is not sent to reviewers in the first place? There may be clear cases for desk rejections, but also many grey cases for which low quality is not obvious and papers that seem promising at first and do not stand closer scrutiny.
\n\n\n\nAnother point is the following: I believe that for papers on certain topics or using certain methods it is more difficult to find reviewers. The “field”, generally speaking, does not value them highly for reasons unrelated to quality. Maybe one would have to pay more for getting such papers reviewed, but even this may not help and may create wrong incentives for accepting peer review invitations in the first place.
\n\n\n\nThere may be other arguments for or against paying reviewers. While the goal of easing current issues with the peer review system is laudable, I think payment of reviewers is not clearly an effective instrument for achieving this goal.
\n\n\n\n\n","doi":"https://doi.org/10.59350/3c8m1-rcw29","funding_references":null,"guid":"https://ingorohlfing.wordpress.com/?p=1785","id":"5fb50669-a5a1-4448-b6a7-47f888e1e5d8","image":"","images":[],"indexed":true,"indexed_at":1779135059,"language":"en","parent_doi":null,"published_at":1779133238,"reference":[],"registered_at":0,"relationships":[],"rid":"43m8q-aa608","status":"active","summary":"A report by Inside Higher Education presents promising trials of journals that paid reviewers for writing a report. Turnaround times dropped and the quality of reports was high. It is certainly good that journals explore payment of reviewers, but I am skeptical that this can ease the \u201cpeer review crisis\u201d, meaning to improve low turnaround times and reduce difficulties in recruiting reviewers in the first place.","tags":["Publishing"],"title":"Will Paying Reviewers Ease the Peer Review Crisis? I am skeptical","updated_at":1779133238,"url":"https://ingorohlfing.wordpress.com/2026/05/18/will-paying-reviewers-ease-the-peer-review-crisis-i-am-skeptical/","version":"v1"}}],"items":[{"abstract":"Scholarly journals don\u2019t operate in a vacuum \u2014 they are part and parcel of disciplinary, community, practitioner, and professional networks exposed and connected via open scholarly infrastructure. Upgrading OJS is how journals connect to that network reliably. Scholarly publishing doesn\u2019t happen in isolation.","archive_url":null,"authors":[{"contributor_roles":[],"family":"Racy","given":"Famira"}],"blog":{"archive_collection":null,"archive_host":null,"archive_prefix":null,"archive_timestamps":null,"authors":null,"canonical_url":null,"category":"socialScience","community_id":"77c8c2e4-ebda-4e7c-9458-6c06b604344b","created_at":1752226126.418889,"current_feed_url":null,"description":null,"doi":null,"doi_as_guid":false,"favicon":"https://rogue-scholar.org/api/communities/77c8c2e4-ebda-4e7c-9458-6c06b604344b/logo","feed_format":"application/atom+xml","feed_url":"https://pkp.sfu.ca/feed/atom","filter":null,"funding":null,"generator":"WordPress","generator_raw":"WordPress","home_page_url":"https://pkp.sfu.ca/news/","id":"1fc8db8d-6943-4efd-8a78-7723c41ab59f","indexed":true,"issn":null,"language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","mastodon":null,"prefix":"10.59350","registered_at":0,"relative_url":null,"ror":null,"secure":true,"slug":"pkp","status":"active","subfield":"1710","subfield_validated":null,"title":"Public Knowledge Project","updated_at":1779267780.738995,"use_api":null,"use_mastodon":false,"user_id":null},"blog_name":"Public Knowledge Project","blog_slug":"pkp","content_html":"Scholarly journals don\u2019t operate in a vacuum \u2014 they are part and parcel of disciplinary, community, practitioner, and professional networks exposed and connected via open scholarly infrastructure. Upgrading OJS is how journals connect to that network reliably.
\nScholarly publishing doesn\u2019t happen in isolation. Journals today, including those using Open Journal Systems (OJS), exist within a connected ecosystem of repositories, funders, institutions, indexing services, researcher profiles, and metadata registries \u2013 all linked together through open scholarly infrastructure.\u00a0
\nFor journals using OJS, staying connected to that ecosystem increasingly depends on one foundational step \u2013 keeping OJS up to date.\u00a0
\nAn OJS upgrade is not simply a software maintenance task. It is how journals maintain interoperability with the broader scholarly communications network, improve discoverability for authors, and support growing expectations around institutional reporting, metadata, quality, and trust.\u00a0
\nWhile there are variations in how \u201copen scholarly infrastructure\u201d is defined (and named; Goudarzi and Dunks, 2023), generally it refers to shared, open source, community-governed digital platforms, tools, and services that support the research lifecycle.\u00a0
\nFunctions of open scholarly infrastructure include:\u00a0
\nBut more than the physical and digital means, the concept addresses values. Organizations (like PKP) aligned with principles like the Principles of Open Scholarly Infrastructure (POSI), and supporting initiatives like the Barcelona Declaration on Open Research Information (Barcelona DORI) emphasize openness, stakeholder-governance, transparency, trust, and sustainability so that knowledge can be freely and openly created, shared, used, and preserved.\u00a0\u00a0\u00a0\u00a0\u00a0
\nIn scholarly publishing, infrastructure is valuable because of how well systems connect \u2013 not because one single tool or platform can do everything on its own.\u00a0\u00a0
\nThis is especially important in the context of diamond open access and emerging community frameworks such as Diamond Open Access Standards (DOAS), where sustainability depends on shared services and open interoperability rather than proprietary lock-in.\u00a0
\nA journal may use OJS to manage submissions and publishing workflows, but its articles can also connect to:
\nThere are many other open infrastructures and organizations that are interoperable with OJS. To explore more, please check out the post we published for International Love Data Week 2026, \u201cWhere\u2019s the Data? Finding it, Moving it, and Loving it through PKP Interoperability.\u201d\u00a0
\nThe quality of these connections directly affects the visibility, trustworthiness, and reusability of journal content.\u00a0
\nPoor metadata or broken integrations can limit journal discoverability, reduce citation tracking accuracy, and create compliance challenges for authors and institutions.\u00a0
\nOJS is now used by more than 58,000 journals around the world. For many journals, OJS serves as the operational foundation of publishing workflows. But increasingly, OJS also functions as the journal\u2019s infrastructure layer \u2013 the point where local publishing activity connects outward into the global scholarly ecosystem.\u00a0
\nNewer OJS versions support stronger integrations with key infrastructure providers, improved metadata handling, better API support, and evolving publishing standards. These capabilities are essential for journals seeking to fully participate in open scholarly publishing and communications.\u00a0
\nAmong the many organizations supporting open scholarly infrastructure, Crossref plays a particularly important role.\u00a0
\nCrossref is best known for DOI registration, but its broader function is metadata connectivity. They run open infrastructure to link research objects, entities, and actions, creating a lasting and reusable scholarly record that underpins open science. Together with 25,000 members in 167 countries, they drive metadata exchange, facilitating global research communication, for the benefit of society.
\nWhen journals deposit metadata with Crossref, they are contributing information that helps scholarly outputs become discoverable, trackable, and linked across the research ecosystem.\u00a0
\nThat metadata supports:\u00a0
\nThe more rich and accurate the metadata, the more effectively articles can circulate throughout the ecosystem.\u00a0
\nNewer OJS versions are better equipped to support these metadata workflows and evolving Crossref requirements, including better affiliations handling, contributor identifiers, and metadata schemas.
\nOpen scholarly infrastructure evolves continuously. Metadata standards mature, APIs change, integrations improve, and new expectations emerge from institutions, funders, and indexing services.\u00a0
\nOlder OJS versions were developed before many current interoperability practices became standard. When journals remain on older OJS versions, they can encounter interoperability limitations such as:\u00a0
\nOver time, these limitations make it harder for journals to remain connected to the systems that researchers and institutions increasingly rely on.\u00a0
\nA useful example comes from de Jonge and Kramer (2026), who identify technical infrastructure and system configuration as key factors affecting metadata completeness and transfer. While there are also policy and collection reasons for incomplete metadata records, this highlights the importance of maintaining up-to-date OJS installations, integrations, and plugins to ensure metadata is properly captured and deposited with services such as Crossref.\u00a0
\nUpgrading OJS is often framed as a technical project. But increasingly, it\u2019s an infrastructure strategy.\u00a0
\nAs open scholarly infrastructure becomes more interconnected, journals that invest in updated interoperable systems will be better positioned to support authors, institutions, and readers.
\nIf your journal relies on open infrastructure, upgrading OJS is essential!\u00a0
\nBe sure to join us! Upcoming events are:
\nUpgrading and learning resources include:\u00a0
\nThe call to action is to upgrade your OJS installation. OJS version 3.5.0-4 was released April 9th, 2026. OJS 3.5 has not yet received a Long-Term Support (LTS) designation but it is planned. If you are already on 3.5, keep up to date with patch releases and your system will be on LTS when the designation is applied.\u00a0
\nJoin in contributing fully to the global open scholarly publishing ecosystem by upgrading today, and be sure you are making the most out of all that OJS and interoperability partners have to offer!
\nThis post is part of a PKP-Crossref series. Please stay tuned for more.
\nThe post Why Upgrading OJS is Key to Participating in Open Scholarly Infrastructure appeared first on Public Knowledge Project.
","doi":"https://doi.org/10.59350/9prg7-yqe71","funding_references":null,"guid":"https://pkp.sfu.ca/?p=19070","id":"81e5cf11-2eef-4f3b-ad48-407edc56adc1","image":"https://pkp.sfu.ca/wp-content/uploads/2026/05/PKP-Crossref-May-2026-WP-1024x576.jpg","images":[{"alt":"Cityscape in the foreground represents connecting people and infrastructure and is similar to the image on Crossref's homepage. The archipelago in the background represents PKP's community newsletter called Archipelago because of the many independent yet interconnected aspects of the scholarly publishing ecosystem. PKP and Crossref logos are side by side at the bottom, representing our collaboration. The photo was taken at a PKP Sprint location.","height":"576","sizes":"(max-width: 1024px) 100vw, 1024px","src":"https://pkp.sfu.ca/wp-content/uploads/2026/05/PKP-Crossref-May-2026-WP-1024x576.jpg","srcset":"https://pkp.sfu.ca/wp-content/uploads/2026/05/PKP-Crossref-May-2026-WP-1024x576.jpg, https://pkp.sfu.ca/wp-content/uploads/2026/05/PKP-Crossref-May-2026-WP-300x169.jpg, https://pkp.sfu.ca/wp-content/uploads/2026/05/PKP-Crossref-May-2026-WP-768x432.jpg, https://pkp.sfu.ca/wp-content/uploads/2026/05/PKP-Crossref-May-2026-WP-1536x864.jpg, https://pkp.sfu.ca/wp-content/uploads/2026/05/PKP-Crossref-May-2026-WP.jpg","width":"1024"},{"src":"https://pkp.sfu.ca/wp-content/uploads/2026/05/PKP-Crossref-May-2026-WP-1024x576.jpg"}],"indexed":true,"indexed_at":1779308846,"language":"en","parent_doi":null,"published_at":1779307842,"reference":[],"registered_at":0,"relationships":[],"rid":"nh5k8-cqy30","status":"active","summary":"\n \n Scholarly journals don\u2019t operate in a vacuum \u2014 they are part and parcel of disciplinary, community, practitioner, and professional networks exposed and connected via open scholarly infrastructure. Upgrading OJS is how journals connect to that network reliably.\n \n\nScholarly publishing doesn\u2019t happen in isolation.","tags":["Community Newsletter","News","News For Developers","News For Hosted Clients","Collaboration Corner"],"title":"Why Upgrading OJS is Key to Participating in Open Scholarly Infrastructure","updated_at":1779307843,"url":"https://pkp.sfu.ca/2026/05/20/pkp-crossref-upgrading-participating-in-open-scholarly-infrastructure/","version":"v1"},{"abstract":"Die Software, die das fr\u00fche Web zum Laufen brachte, entstand an einem Forschungsinstitut in der Schweiz. Statt sie zu kommerzialisieren, machte man sie frei verf\u00fcgbar und schlie\u00dflich sogar zu Public Domain. Ein Gl\u00fcck, denn wer wei\u00df, wie die Geschichte des Web sonst verlaufen w\u00e4re?","archive_url":null,"authors":[{"contributor_roles":[],"family":"Crueger","given":"Jens"}],"blog":{"archive_collection":22135,"archive_host":null,"archive_prefix":"https://wayback.archive-it.org/22135/20231101173016/","archive_timestamps":null,"authors":[{"name":"Redaktion iRights.info"}],"canonical_url":true,"category":"law","community_id":"30df0209-0965-4b95-afa1-70d6c8a7d086","created_at":1694736000,"current_feed_url":null,"description":"Urheberrecht und kreatives Schaffen in der digitalen Welt","doi":null,"doi_as_guid":false,"favicon":"https://rogue-scholar.org/api/communities/7d3b25fd-a4a8-4155-8e76-99d6be06706a/logo","feed_format":"application/atom+xml","feed_url":"https://irights.info/feed/atom","filter":null,"funding":null,"generator":"WordPress","generator_raw":"WordPress","home_page_url":"https://irights.info/","id":"26f4046a-7e6f-4c1c-8866-f4e055096c30","indexed":false,"issn":null,"language":"de","license":"https://creativecommons.org/licenses/by/4.0/legalcode","mastodon":null,"prefix":"10.59350","registered_at":1729753013,"relative_url":null,"ror":null,"secure":true,"slug":"irights","status":"active","subfield":"3308","subfield_validated":null,"title":"iRights.info","updated_at":1779267261.799606,"use_api":false,"use_mastodon":false,"user_id":"81a5b5f1-97c2-416b-8715-46e10f37018c"},"blog_name":"iRights.info","blog_slug":"irights","content_html":"Die Software, die das fr\u00fche Web zum Laufen brachte, entstand an einem Forschungsinstitut in der Schweiz. Statt sie zu kommerzialisieren, machte man sie frei verf\u00fcgbar und schlie\u00dflich sogar zu Public Domain. Ein Gl\u00fcck, denn wer wei\u00df, wie die Geschichte des Web sonst verlaufen w\u00e4re?
\nAls Ende der 1980er Jahre der Physiker und Informatiker Tim Berners-Lee f\u00fcr seine Vorgesetzten am CERN (der Europ\u00e4ischen Organisation f\u00fcr Kernforschung) einige Vorschl\u00e4ge f\u00fcr das Informationsmanagement formulierte, h\u00e4tte niemand erwartet, was daraus entstehen w\u00fcrde.
\nSeine Kernidee: Eine Alternative zu hierarchischen Informationssystemen durch eine netzartige Struktur aus miteinander verlinkten Notizen. Dieses Konzept wurde zur Blaupause f\u00fcr das World Wide Web.
\nAm 12. November 1990 publizierten Tim Berners-Lee und sein Kollege Robert Cailliau dann gemeinsam ein Memorandum unter der \u00dcberschrift: \u201eWorldWideWeb: Proposal for a HyperText Project\u201c. Dieses Memorandum war ein wichtiger Meilenstein auf dem Weg zur Entstehung des World Wide Web.
\nDer Gedanke der freien Zug\u00e4nglichkeit des \u201eWorldWideWeb\u201c war bereits zum diesem Zeitpunkt enthalten. Denn ein in dem Memorandum definiertes Ziel des Projekts lautete, \u201esoweit m\u00f6glich Public-Domain-Software zu verwenden oder Schnittstellen zu bereits vorhandenen propriet\u00e4ren Systemen zu schaffen.\u201c
\nBis zur Public Domain-Software sollte es aber eine Weile dauern, denn noch war das Web kaum mehr als eine sehr spannende Idee. Die f\u00fcr das Web ben\u00f6tigte Software wurde in der Folge von einem kleinen Team am CERN entwickelt.
\nDabei handelte es sich um die Webkernsoftware, die in einzelnen Programmkomponenten zu einer Softwarebibliothek zusammengestellt wurde. Mit diesen Softwarebausteinen sollte es externen Programmierern erm\u00f6glicht werden, eigene Projekte f\u00fcr das Web zu entwickeln, seien es Browser, Server oder andere Anwendungen.
\nDie Frage, die sich nun stellte, war die nach der Zug\u00e4nglichmachung dieser am CERN entwickelten Software. Sollte sie gegen Geb\u00fchr an interessierte Web-Pioniere verkauft werden, oder sollte sie kostenlos bereitgestellt werden?
\nEs kostete Tim Berners-Lee einige \u00dcberzeugungsarbeit gegen\u00fcber seinen Vorgesetzten am CERN, um diese von der freien Zug\u00e4nglichmachung der entwickelten Software zu \u00fcberzeugen. So beschrieb es sp\u00e4ter sein Kollege Robert Cailliau in dessen Buch \u201eDie Wiege des Web\u201c: Berners-Lee habe mit der \u201estolzen akademischen Tradition\u201c argumentiert, der das World Wide Web folge, da es den \u201ekostenlosen Austausch von Informationen f\u00f6rdere\u201c. Daher solle, so sein Pl\u00e4doyer, die f\u00fcr das Web notwendige Software ebenfalls kostenlos zur Verf\u00fcgung gestellt werden.
\nSeitens der CERN-Leitung wiederum kursierte eine Kalkulation, die als Preis f\u00fcr die Software angesetzt werden sollte. Sie belief sich auf eine Preisspanne zwischen 100 und 200 Schweizer Franken pro Softwarepaket.
\nAllerdings ern\u00fcchterte ein Blick in das damalige sehr fr\u00fche Web das Management hinsichtlich der Preisvorstellung. Die geringe Zahl der damaligen Webnutzer lie\u00df es dem CERN als schlichtweg nicht kostendeckend erscheinen, Geb\u00fchren f\u00fcr die Software zu erheben. Der Verwaltungsaufwand wurde schlicht als zu hoch erachtet.
\nSo kam es, dass Tim Berners-Lee die Erlaubnis des CERN erhielt, die dort entwickelte Web-Software zur kostenlosen Nutzung ins Internet zu stellen. Am 6. August 1991 gab er \u00fcber die Newsgroup alt.hypertext bekannt, dass die Software zum Download auf der CERN-Website bereitst\u00fcnde. Er verband dies mit dem Aufruf \u201eCollaborators welcome!\u201c
\nDie Website des CERN unter der Webadresse Info.cern.ch wurde ab diesem Zeitpunkt zu einer wichtigen Anlaufstelle im fr\u00fchen Web. Dort konnte von einem ftp-Server die Software heruntergeladen werden, die das Web nun allm\u00e4hlich wachsen lie\u00df. Zu dieser Software geh\u00f6rte eine Bibliothek von Programmcode (bekannt als CERNLIB), die Software f\u00fcr den Betrieb eines Servers, sowie f\u00fcr den Client. Kurze Zeit sp\u00e4ter ging dann auch schon der erste Webserver au\u00dferhalb Europas online. Er befand sich im US-amerikanischen Stanford, und der Rest ist Geschichte.
\nAls das World Wide Web 1993 Fahrt aufgenommen hatte, gelang es Tim Berners-Lee und Robert Cailiau schlie\u00dflich, ihre Vorgesetzten am CERN zu \u00fcberzeugen, die Software f\u00fcr das Web als Public Domain der \u00d6ffentlichkeit bereitzustellen. Mit einer Erkl\u00e4rung vom 30. April 1993 verzichtete das CERN auf seine s\u00e4mtlichen geistigen Eigentumsrechte an dem Web-Code, sowohl am Quellcode als auch an den Bin\u00e4rdateien. Es erteilte jedem die Erlaubnis, den Code zu nutzen, zu vervielf\u00e4ltigen, zu ver\u00e4ndern und zu verbreiten.
\nIn den \u201eWorld-Wide Web News\u201d, einem monatlichen Newsletter des CERN, wurde in der Mai-Ausgabe 1993 berichtet (eigene \u00dcbersetzung):
\n\u201eCERN-W3-Software wird gemeinfrei
\nNach langen Diskussionen hat das CERN nun einen bestimmten Teil der W3-Software als gemeinfreien Code ver\u00f6ffentlicht. Dies geschah, um die Verbreitung des Webs voranzutreiben und sicherzustellen, dass die Protokolle einheitlich verwendet werden. Es bestand die Gefahr, dass Entwickler den Protokollcode neu erfinden und dabei erneut inkompatible Fehler machen w\u00fcrden. Da die Code-Bibliothek \u201elibwww\u201c, die die Grundlage vieler Browser bildet, nun gemeinfrei ist, kann sie als gemeinsame Basis dienen. [\u2026]\u201c
\nUrspr\u00fcnglich hatte Tim Berners-Lee \u00fcbrigens erwogen, die Web-Software nicht als Public Domain, sondern unter einer GNU-Lizenz zu ver\u00f6ffentlichen. Die Kontakte zwischen dem CERN und der Free Software Foundation (FSF) mit ihrem GNU-Projekt waren gut.
\nJedoch war man am CERN besorgt, gro\u00dfe IT-Unternehmen k\u00f6nnten das Web m\u00f6glicherweise ignorieren, sollte dort das Risiko irgendwelcher Lizenzprobleme bestehen. So entschied man sich f\u00fcr die offene Bereitstellung als Public Domain. Den Programmierern rund um den Erdball gab das die maximale Freiheit, die Software des CERN f\u00fcr ihre Projekte zu nutzen.
\nR\u00fcckblickend \u00e4u\u00dferte Tim Berners-Lee im Jahr 2025 im The Guardian, dass seine Vision f\u00fcr das Web auf \u201eauf Teilen, nicht auf Ausbeutung\u201c beruht habe. Sein Ziel sei es gewesen, mit dem Web einen Raum f\u00fcr Kreativit\u00e4t und Zusammenarbeit zu schaffen.
\nDaf\u00fcr sei es n\u00f6tig, dass alle Interessierten einen niedrigschwelligen Zugang zum Web erhalten und bereit seien, sich dort mit ihren Ideen und ihrem K\u00f6nnen einzubringen. Dies habe er als zentrale Herausforderung gesehen. Man h\u00e4tte daher unm\u00f6glich verlangen k\u00f6nnen, interessierte Nutzer \u201ef\u00fcr jede Suche oder jeden Upload bezahlen\u201c zu lassen.
\nFreie Software als Schl\u00fcssel zu einer kollaborativen und kreativen Websph\u00e4re klingt vertraut. Wer in der heutigen Zeit nach Visionen f\u00fcr das Web k\u00fcnftiger Tage fragt, darf sich gerne solche Anregungen aus der Gr\u00fcndungsphase des Web entleihen.
\niRights.info\u00a0informiert und erkl\u00e4rt rund um das Thema \u201eUrheberrecht und Kreativit\u00e4t in der digitalen Welt\u201c. Alle Texte erscheinen kostenlos und offen lizenziert.
\nWenn Sie m\u00f6gen, k\u00f6nnen Sie uns \u00fcber die\u00a0gemeinn\u00fctzige\u00a0Spendenplattform Betterplace\u00a0unterst\u00fctzen und daf\u00fcr eine Spendenbescheinigung erhalten. Betterplace akzeptiert PayPal, Bankeinzug, Kreditkarte, paydirekt oder \u00dcberweisung.
\nBesonders freuen wir uns \u00fcber einen regelm\u00e4\u00dfigen Beitrag, beispielsweise als monatlicher Dauerauftrag.\u00a0F\u00fcr Ihre Unterst\u00fctzung dankt Ihnen herzlich der\u00a0gemeinn\u00fctzige iRights e.V.!
\nDOI f\u00fcr diesen Text:\u00a0\u00b7 automatische DOI-Vergabe f\u00fcr Blogs \u00fcber The Rogue Scholar
\nThe post Das World Wide Web \u2013 Erfolgsbeispiel f\u00fcr Open Software appeared first on iRights.info.
","doi":"https://doi.org/10.59350/gnsnh-61109","funding_references":null,"guid":"https://irights.info/?post_type=custom_artikel&p=32826","id":"dc5f6951-7092-4648-9ec0-7fb0a9093b77","image":null,"images":[],"indexed":true,"indexed_at":1779291703,"language":"de","parent_doi":null,"published_at":1779289129,"reference":[],"registered_at":0,"relationships":[],"rid":"dz9kt-bts41","status":"active","summary":"Die Software, die das fr\u00fche Web zum Laufen brachte, entstand an einem Forschungsinstitut in der Schweiz. Statt sie zu kommerzialisieren, machte man sie frei verf\u00fcgbar und schlie\u00dflich sogar zu Public Domain. Ein Gl\u00fcck, denn wer wei\u00df, wie die Geschichte des Web sonst verlaufen w\u00e4re?","tags":["Allgemein","Creative Commons + Lizenzen","Netzkulturen","Software + Open Source","Technologie"],"title":"Das World Wide Web \u2013 Erfolgsbeispiel f\u00fcr Open Software","updated_at":1779289129,"url":"https://irights.info/artikel/das-world-wide-web-erfolgsbeispiel-fuer-open-software/32826","version":"v1"},{"abstract":null,"archive_url":null,"authors":[{"contributor_roles":[],"family":"Wehrhahn","given":"Dawn"}],"blog":{"archive_collection":null,"archive_host":null,"archive_prefix":null,"archive_timestamps":null,"authors":null,"canonical_url":null,"category":"engineeringAndTechnology","community_id":"db0d8909-9e37-46d0-b16c-0551f575e86b","created_at":1749798261.334959,"current_feed_url":null,"description":"Das Blog der TIB \u2013 Leibniz-Informationszentrum Technik und Naturwissenschaften und Universit\u00e4tsbibliothek","doi":null,"doi_as_guid":true,"favicon":"https://blog.tib.eu/wp-content/uploads/2015/12/TIB_fav_icon_24x24.png","feed_format":"application/atom+xml","feed_url":"https://blog.tib.eu/feed/atom/","filter":null,"funding":null,"generator":"WordPress","generator_raw":"WordPress 6.8.1","home_page_url":"https://blog.tib.eu/","id":"135a354f-2969-4852-9a7c-b6cda0a692a4","indexed":true,"issn":null,"language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","mastodon":null,"prefix":"10.65527","registered_at":0,"relative_url":null,"ror":null,"secure":true,"slug":"tib","status":"active","subfield":"1802","subfield_validated":null,"title":"TIB-Blog","updated_at":1779268236.390099,"use_api":true,"use_mastodon":false,"user_id":null},"blog_name":"TIB-Blog","blog_slug":"tib","content_html":"Am 20. Mai wird der Tag des Messens (auch Weltmetrologietag oder Tag der Metrologie) begangen. An diesem Tag wurde im Jahr 1875 die internationale Meterkonvention unterzeichnet.
\nDie Fertigkeiten eines Feldmessers waren schon in der Antike gefragt und haben sich seit damals immer weiterentwickelt. In der heutigen Zeit soll der Tag auf die Bedeutung des Messens f\u00fcr die globale Wirtschaft, f\u00fcr die Wissenschaft und f\u00fcr unseren Alltag und f\u00fcr die Gesellschaft hinweisen. Ohne weltweite einheitliche Standards w\u00e4re ein globaler Warenaustausch nicht denkbar. Exakte Messungen sind f\u00fcr Industrie, Forschung und Entwicklung von zentraler Bedeutung. Ob beim B\u00e4cker, an der Zapfs\u00e4ule oder auf der Waage, Messungen sind im Alltag nicht wegzudenken.
\nDer Tag wird vom Internationalen B\u00fcro f\u00fcr Ma\u00df und Gewicht (BIPM) und der Internationalen Organisation f\u00fcr das gesetzliche Me\u00dfwesen (OIML) ausgerichtet. Seit dem Jahr 2004 stehen die Tage jeweils unter einem Motto. F\u00fcr 2026 lautet es: Metrology: Building Trust in Policy Making.
\nInteressante Werke zu den Geowissenschaften, des Messwesens oder der Vermessungskunde befinden sich auch im Altbestand der TIB. Davon haben wir bereits einige digitalisiert, einige ausgew\u00e4hlte m\u00f6chten wir hier kurz vorstellen:
\nHandbuch der gesammten Vermessungskunde, die neuesten Erfindungen und Entdeckungen in derselben zugleich enthaltend; oder vollst\u00e4ndige Anleitung zur Me\u00dfkunst, f\u00fcr Offiziere, Forstbediente, Bergleute und Feldmesser (Abtheilung 1+2) 1820
\u201eDie Vermessungskunde lehret sowohl die Ausdehnung eines in Vergleichung mit der ganzen Oberflache der Erde nur kleinen St\u00fcck Landes, als auch die Gro\u00dfe und Figur der Erde durch Anwendung der Lehren der reinen und angewandten Mathematik zu bestimmen. Sie zerf\u00e4llt, r\u00fccksichtlich des Endzwecks, zu welchem man die Ausdehnung eines Theils der Erdoberfl\u00e4che zu wissen verlangt, in folgende zwei Haupttheile:
\nLehrbuch der praktischen Geometrie, nebst einem Anhange \u00fcber die Elemente der Markscheidekunst (Theil 1+2) 1857 2. Theil S. 139
\n
\n
Die Landes-Vermessung und die in ihrem Gefolge befindlichen Arbeiten, erl\u00e4utert durch die im K\u00f6nigreich W\u00fcrttemberg zur Ausf\u00fchrung kommende Vermessung (Heft 1-4) 1841-1843
\n
Die Praxis des Vermessungsingenieurs (Band 1+2) 1923 Band 1, Bild 443
\n
Die K\u00f6niglich Preussische Landes-Triangulation (Theil 1-11) 1870-1901
\n
Die h\u00f6here Geod\u00e4sie oder die Wissenschaft, die Reiche der Erde, und diese selbst, geographisch aufzunehmen und zu chartieren (Abtheilung 1) 1816 Bild 460
\n
Systematisches Handbuch der gesammten Land- und Erd-Messung mit ebener und sph\u00e4rischer Trigonometrie auch Beschreibung der neuern brauchbaren Me\u00dfinstrumente (Band 1+2) 1819, Bild 330
\n
Anleitung f\u00fcr die Herstellung und Justierung geod\u00e4tischer Instrumente (Teil 1+2) 1907-1911 Seite 75 und 276
\n
Viele wissenschaftliche Sch\u00e4tze schlummern in den analogen Best\u00e4nden der TIB \u2013 meist schwer zug\u00e4nglich und oft nur vor Ort nutzbar. Dank Retrodigitalisierung \u00e4ndert sich das: Durch die Digitalisierung werden gedruckte B\u00fccher, Karten und weitere analoge Materialien gesichert und historisches Wissen sichtbar, durchsuchbar und nachhaltig verf\u00fcgbar gemacht.
\nIn der Blogreihe \u201eRetrodigitalisierung \u2013 vom Papier zum Pixel\u201c zeigt die TIB Sch\u00e4tze, die nun nicht mehr nur in den Regalen der Bibliothek stehen, sondern online von \u00fcberall auf der Welt angesehen werden k\u00f6nnen. Auch einen Blick hinter die Kulissen der Retrodigitalisierung wird es geben: Wie werden aus analogen Best\u00e4nden digitale Ressourcen? Welche technischen und rechtlichen Herausforderungen gibt es dabei? Von der Auswahl der Materialien \u00fcber Scanprozesse bis zur Langzeitarchivierung \u2013 die Reihe beleuchtet Retrodigitalisierung als eine wichtige Aufgabe moderner Bibliotheken.
\n
During the\u00a0Syrian Civil War, over 5 million Syrians lived as refugees in first countries of asylum, of whom more than 40% were children.\u00a0Many of these children experienced social exclusion, leading to a lack of future prospects. To secure their children\u2019s future, some parents came to see taking them on boats to Europe across the Mediterranean Sea as the only viable option, despite the grave risks involved. The blog is built on narratives of the parents who journeyed over the sea with children, the decision that landed them in a profound dilemma: the very act of fulfilling parental responsibility entailed exposing their children to significant danger. This moral tension continued to haunt parents long after migration.
\n\u00a0
\nIn previous research among Syrian refugees living under temporary protection in Turkey, evidence showed that the refugees\u2018 assessments of risks linked to onward migration to Europe across the Mediterranean differ depending on a person\u2019s stage in the life course. For example, single Syrians in Turkey were more inclined to accept the risks of the onward migration than those with families. Although many Syrian parents interviewed for that study wished to migrate to Europe, they often considered it too dangerous to bring the family along on the journey or leave it behind.
\nThis blog draws on a study that observed the opposite pattern through the narrative accounts of seven Syrian refugee parents who travelled with young children after spending varying periods in first countries of asylum \u2014 Turkey, Egypt, and Lebanon. They are three mothers and four fathers in their mid-thirties and early forties who settled in Sweden, Denmark, and Germany. Their narratives unfolded chronologically, beginning with migration motivations embedded in the lived realities of these first countries of asylum. Accordingly, their risky migration decisions were grounded in a sense of parental responsibility and framed as a moral obligation to protect their children. This initial framing was later unsettled by a reflective turn at the end of the interviews, where participants revisited the rationality of the risks they had taken from a present-day standpoint, revealing an ongoing process of reinterpretation.
\n\u00a0
\nThe Syrian conflict displaced millions of Syrian families, leaving those with young children in neighbouring countries such as Turkey, Jordan, and Lebanon in situations of heightened vulnerability. Economic pressures often forced refugee children to forgo schooling and enter the labour market, with estimates suggesting that around one in ten were engaged in child labour. At the same time, early marriage\u2014often functioning as a coping mechanism for families facing acute economic and social insecurity\u2014affected up to one in three girls in some refugee communities.
\nOne of the parents in this study remembered her time in Turkey with her young family: \u201eMy daughter reached the age of eight without a single day in school. At her age, I was already reading and writing. She couldn\u2019t even hold a pen.\u201c These circumstances confronted parents with an impossible dilemma: to stay or to leave, with both options carrying serious risks. Whether staying or leaving, each decision undermined core parental commitments by exposing their children to different, yet equally unacceptable, forms of risk. The decision to embark on a perilous sea journey with children emerged only after a long and agonizing period of reflection, marked by profound moral and emotional struggle.
\nFor some fathers in this study, the idea of braving the sea alone while leaving their spouses and children behind in neighbouring countries was perceived as too risky and therefore unacceptable. Although having their families join later through reunification might have spared them the traumas associated with the journey, they ultimately chose to travel together. For them, leaving their families behind without a male head of household would have imposed a profound emotional and moral burden.
\nOn the other hand, for fathers who could tolerate the risks of leaving the family behind (spouse with children), the increasingly restrictive\u2014and in some cases nearly inaccessible\u2014reunification policies in Europe fuelled fears that the process would take too long or ultimately fail, resulting in prolonged family separation. These concerns often contributed to the decision to embark on the perilous journey together as a family.
\nIn the same vein, the mothers reported that their husbands had departed earlier under the agreement that they and their children would later join the husbands through a safer and legal family reunification pathway. However, growing fears that this possibility might be withdrawn raised the risk of permanent family separation. Consequently, the mothers decided to undertake the same perilous boat journey with their young children, retracing the route their husbands had taken before them.
\n\u201eI was afraid that reunification could become a long process. So, I decided not to wait, even though my family was against me travelling with a three\u2011month\u2011old baby and other two\u2011year\u2011old child.\u00a0 But it was the future of my children that was at stake, and I had to do it\u201c, Esme opened her story by affirming the perceived rightness of her decision not to wait for legal reunification with her husband in Sweden, who had migrated earlier alone while she had to remain in Turkey to recover from childbirth.
\n\u00a0
\nBy taking their children on dangerous migration journeys, parents knowingly exposed them to serious risks. The photographs of the three-year-old Syrian boy Alan Kurdi, whose lifeless body washed ashore on a beach in Greece in 2015, became a stark illustration of the tragic outcomes that parents feared. A mother, Zehra, who travelled with two toddlers recounted that moment: \u201eI was packing to leave Istanbul for Bodrum to embark on a boat to Greece, when I turned on the TV and saw a boy\u2019s body lying on the shore. It was Alan Kurdi. He was the same age as my son. I was petrified.\u201c
\nThe paradox of risking their children\u2019s lives to save them was something parents tried to comprehend through a sense of their normative and ethical role as caregivers. They emphasised that the risk of the sea journey\u2014one that could have deadly consequences for their children and for which they themselves would bear the moral weight\u2014was ultimately overridden by what they perceived as the supreme moral imperative of parenthood. This reflects a form of virtuous risking, in which the magnitude and consequences of risk become secondary to the deeper question: What kind of person am I (or do I want to be) and what kind of life should I live if I accept or reject a given risk? Therefore, at the centre of virtue ethics is the moral character of a person. This was confirmed by Zehra\u2019s final thought on the decision to eventually take her two young children on the boat, despite the fright she experienced knowing of young Alan Kurdi\u2019s death: \u201eIt paralyzed me for a day or two. But eventually, I gathered the courage again. I felt that I had to do it for the sake of my children.\u201c Her decision was motivated by a deep determination to show, both to herself and to her children, the strength of her commitment as a parent.
\nAmina is a mother who, like Zehra, travelled alone with her two young children to follow her husband\u2019s path and reunite in Germany. As she laid down her story, she found herself in disbelief at what she perceived as her own carelessness in exposing her children to such danger: \u201eWhere was my mind when I put my children on that boat?\u201c, she asked herself. Yet moments later, she provided the answer. She reminded herself that, as a mother, she had no real choice. She feared that if they stayed in Turkey, she would one day see her children as teenagers roaming the streets, selling drugs\u2014the only future she felt awaited them. \u201eIt was a difficult decision, but there is nothing I would not do to save my children\u201c, Amina affirmed. This statement again illustrates the alignment between her moral character and the risky migration she chose to pursue.
\n\u00a0
\nThe moral value placed on courageous risk-taking shaped the parents\u2018 narratives about their decision to migrate at the beginning of their stories. Yet over the storytelling\u2014when revisiting the decision from a \u201ehere and now\u201c perspective\u2014they found it difficult to rely on virtue alone to justify the risks they had accepted. Their closing reflections revealed a persistent tension between guilt for exposing their children to danger and the perceived inevitability of a moral imperative to secure their children\u2019s future.
\nEnes travelled with his young family, a decision rooted in his sense of responsibility as the male head of household, ensuring he would not leave his family behind. However, by the end of the interview, he questioned whether this conviction had ultimately failed him. This doubt became particularly troubling when, years after the migration, his son, remembering the tumultuous journey, asked: \u201eDad, we could have died at sea, right?\u201c Enes described the pain of being unable to provide a convincing answer, as he himself was in profound perplexity.
\nAnother extremely poignant story was narrated by Esme. At the beginning of her story, she embodied the same moral conviction that had animated the accounts of other parents, a sense of righteousness that likewise informed her decision to migrate. However, upon reaching the point of the sea crossing, her narrative shifted. The ending revealed fear and confusion\u2014not only about what the right course of action was, but also about which risks she was responsible for avoiding.
\n\n\u201eAfter 30 minutes of sailing towards Greece, the boat encountered a problem. With too many people on board and not enough gasoline, the boat gradually came to a halt. As the waves grew stronger, water began splashing onto the children, including my four-month-old son. I saw water entering his mouth. Tears streamed down my face as I whispered, \u2018I have killed my child\u2019. I wanted to die first\u2026 Even after so many years have passed, the memory still haunts my dreams\u2014the moment the boat came to a halt in the vast expanse of the sea, and those same words: \u201aI have just killed my child!'\u201c
In virtuous risking, a person is not primarily judged by their capacity for logical calculation, but by their state of mind, in which multiple faculties\u2014perception, desire, and importantly, emotions\u2014interact as integral to virtue. While these elements provided a solid grounding for the parents to make the migration decisions, this same logic faltered when the journeys were assessed in hindsight. Years after the migration, they remain haunted by the anguish that, in protecting their children\u2019s lives, they ultimately put those same lives at risk.
\n\u00a0
\nTo be both a parent and a refugee is to carry a profound responsibility: beyond ensuring one\u2019s own survival, it entails protecting the lives and futures of one\u2019s children. This study showcases stories of parents who were confronted with an excruciating moral dilemma: to fulfil their parental responsibility to protect their children from the war, parents exposed them to deadly danger of sea migration to Europe.
\nThe parents\u2018 experiences interviewed for this study are shaped directly by migration policies. For Syrian refugees, the European response has taken the form of increasingly fortified borders. These structural constraints, which left families with few viable options, shifted the burden of decision-making to individuals, effectively transferring responsibility to parents. The migration stories in this text underscore the urgent need for safe and legal pathways for refugees, particularly young families with children, as these can prevent family separation and reduce reliance on dangerous routes.
\nUNHCR promotes complementary pathways as safe and regular migration options for refugees, including family reunification, education opportunities, and labour mobility schemes. However, it acknowledges that these avenues are not fully inclusive. Its broader vision therefore emphasizes more diversified pathways to ensure protection for those who fall outside categories such as students or skilled workers, providing them with viable routes to safety. Additional measures, including humanitarian visas and private sponsorship schemes, are also needed. These mechanisms are particularly important for young families, as the traumas associated with irregular migration can affect all family members and may persist across generations.
\nDer Beitrag Risking Children to Save Them: Moral Dilemmas Faced by Parents in Forced Migration erschien zuerst auf Netzwerk Fluchtforschung.
","doi":"https://doi.org/10.59350/a38hb-maf05","funding_references":null,"guid":"https://fluchtforschung.net/?p=15805","id":"41b0e2c5-dc03-4200-a858-35ff380490be","image":null,"images":[],"indexed":true,"indexed_at":1779258602,"language":"en","parent_doi":null,"published_at":1779258488,"reference":[],"registered_at":0,"relationships":[],"rid":"ck1b2-9s103","status":"active","summary":"\n During the\u00a0Syrian Civil War, over 5 million Syrians lived as refugees in first countries of asylum, of whom more than 40% were children.\u00a0Many of these children experienced social exclusion, leading to a lack of future prospects. To secure their children\u2019s future, some parents came to see taking them on boats to Europe across the Mediterranean Sea as the only viable option, despite the grave risks involved.\n","tags":["Englisch","Forschung","Children's Rights","Forced Migration","Moral Dilemma"],"title":"Risking Children to Save Them: Moral Dilemmas Faced by Parents in Forced Migration","updated_at":1779258488,"url":"https://fluchtforschung.net/risking-children-to-save-them-moral-dilemmas-faced-by-parents-in-forced-migration/","version":"v1"},{"abstract":"Die TIB erm\u00f6glicht ihren Nutzerinnen und Nutzern einen einfachen und direkten Fernzugriff auf die Inhalte der bundesweiten DEAL-Vertr\u00e4ge.","archive_url":null,"authors":[{"contributor_roles":[],"family":"Vosberg","given":"Dana"}],"blog":{"archive_collection":null,"archive_host":null,"archive_prefix":null,"archive_timestamps":null,"authors":null,"canonical_url":null,"category":"engineeringAndTechnology","community_id":"db0d8909-9e37-46d0-b16c-0551f575e86b","created_at":1749798261.334959,"current_feed_url":null,"description":"Das Blog der TIB \u2013 Leibniz-Informationszentrum Technik und Naturwissenschaften und Universit\u00e4tsbibliothek","doi":null,"doi_as_guid":true,"favicon":"https://blog.tib.eu/wp-content/uploads/2015/12/TIB_fav_icon_24x24.png","feed_format":"application/atom+xml","feed_url":"https://blog.tib.eu/feed/atom/","filter":null,"funding":null,"generator":"WordPress","generator_raw":"WordPress 6.8.1","home_page_url":"https://blog.tib.eu/","id":"135a354f-2969-4852-9a7c-b6cda0a692a4","indexed":true,"issn":null,"language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","mastodon":null,"prefix":"10.65527","registered_at":0,"relative_url":null,"ror":null,"secure":true,"slug":"tib","status":"active","subfield":"1802","subfield_validated":null,"title":"TIB-Blog","updated_at":1779268236.390099,"use_api":true,"use_mastodon":false,"user_id":null},"blog_name":"TIB-Blog","blog_slug":"tib","content_html":"Die TIB erm\u00f6glicht ihren Nutzerinnen und Nutzern einen einfachen und direkten Fernzugriff auf die Inhalte der bundesweiten DEAL-Vertr\u00e4ge. \u00dcber diese Vereinbarungen erhalten wissenschaftliche Einrichtungen in Deutschland Zugang zu den Publikationen der gro\u00dfen Wissenschaftsverlage Wiley, Springer Nature und Elsevier.\u00a0Besonders hilfreich ist das Angebot f\u00fcr Angeh\u00f6rige akademischer Einrichtungen sowie Privatpersonen, deren eigene Institution nicht an einem der DEAL-Vertr\u00e4ge teilnimmt.
\nIn dieser Schritt-f\u00fcr-Schritt-Anleitung zeigen wir, wie der Zugang eingerichtet wird und wie die Nutzung der Inhalte funktioniert:
\nSie haben im TIB-Portal recherchiert und einen Artikel gefunden, der bei Wiley oder Springer erschienen ist?
\n
In der Detailanzeige des Treffers klicken Sie auf \u201eAnmelden\u201c und werden dann entweder direkt zum Login oder \u2013 wenn Sie noch kein registrierter Nutzer der TIB sind – auf die Registrierungsseite weitergeleitet:
\n
Melden Sie sich im TIB-Portal \u00fcber den Login an. Wenn Sie noch nicht als Nutzerin oder Nutzer imTIB-Portal registriert sind, f\u00fchren Sie zun\u00e4chst Ihre Registrierung durch, damit Sie das TIB-Portal nutzen k\u00f6nnen:
\nFalls Sie noch kein registrierter Nutzer der TIB sind, m\u00fcssen Sie im Rahmen des Registrierungsprozesses
\neinige Angaben zu Ihrer Person machen und Ihre E-Mail-Adresse angeben:
Melden Sie sich nach Ihrer erfolgreichen Registrierung im TIB-Portal an:
\n
Die Registrierung und Anmeldung im TIB-Portal wird in einem separaten Blog-Beitrag noch einmal ausf\u00fchrlich beschrieben: Registrierung und Anmeldung im TIB-Portal – TIB-Blog
\nNach erfolgreicher Registrierung bzw. erfolgreichem Login k\u00f6nnen Sie das gew\u00fcnschte
\nDokument herunterladen:
Diese direkte Zugriffsm\u00f6glichkeit erstreckt sich auf alle Zeitschriften von Wiley und Springer, die Bestandteil der DEAL-Vertr\u00e4ge sind. Eine Auflistung der Zeitschriften ist hier zu finden: Wiley (XLSX) und Springer (XLSX).
\nFalls Sie gezielt nach den Inhalten f\u00fcr den kostenlosen, direkten Zugriff suchen m\u00f6chten, k\u00f6nnen Sie folgenden Filter setzen: \u201eGgf. freier Zugriff\u201c. Danach klicken Sie auf \u201eFilter anwenden\u201c und erhalten eine Trefferliste mit den entsprechenden \u00fcber das TIB-Portal frei zug\u00e4nglichen Inhalten.
\n
Falls Sie Zugriffsprobleme oder weitere Fragen haben, wir helfen Ihnen gern weiter unter\u00a0kundenservice@tib.eu
\nMany scholarly infrastructure business models fit institutions poorly. They tie cost to transactions, outputs, or rising use in ways that make adoption harder to sustain over time. That is a problem for libraries and research institutions, which are trying to normalize and support good practice, not disincentivize against it by tying payments to countable units. DataCite\u2019s recent move away from per-DOI pricing offers a useful example of a different approach. It shows what it can look like when an infrastructure organization designs its funding model in a way that better matches institutional values and institutional realities.
The issue is not whether institutions understand that infrastructure costs money. Of course they do. In some cases, they will even choose to build and maintain systems themselves, not because doing so is necessarily cheaper or more efficient, but because predictable responsibility can feel easier to manage than open-ended financial exposure. The real question is whether infrastructure can be supported in ways that are stable and aligned with how institutions actually work, while providing sustainable funding to those providing the infrastructure. Transactional infrastructure models are not only competing against one another. They are also competing against institutional instincts toward local control, internal ownership, and operational predictability.
Too often, the answer is no. Many infrastructure models still assume that use should be counted and billed one unit at a time. That logic may work in settings where value is primarily individual and immediate. However, scholarly infrastructure is usually not one of those settings. Repositories, PID systems, metadata services, curation platforms, and related infrastructure create value that institutions sustain collectively so those capabilities remain broadly and equitably available over time. They help preserve the scholarly record, improve practice, support compliance, and make core capacities available to the communities institutions serve.
This is why collective models matter. A transactional model ties cost to individual acts of use. A collective model ties support to sustaining shared capacity. That distinction is not just financial. It shapes whether institutions can confidently encourage adoption, whether the entire community is rewarded for broad uptake or punished by it, and whether infrastructure actually behaves like and is treated like a shared investment rather than just another vendor.
Institutions around the world understand that scholarly infrastructure costs money. The harder question is whether those costs can be structured in ways institutions can support responsibly over time. By \u2018transactional model\u2019, I mean a model in which institutional cost rises with discrete acts of use, outputs, or participation, rather than through a stable collective commitment to sustain the infrastructure as a whole.
For most institutions, the central problem is not cost itself but unpredictability. Libraries and research organizations can plan for recurring commitments when those commitments are understandable, stable, and tied to a known scope of support. What becomes difficult is a model in which ordinary adoption, routine use, or successful growth creates open-ended financial exposure. At that point, infrastructure stops feeling like an investment and starts feeling like a moving target.
That changes the internal conversation institutions have about infrastructure. Instead of asking how to support participation, improve practice, or build durable capacity, institutions begin asking what happens to the budget if those efforts succeed. Once that happens, the pricing model is no longer neutral. It begins shaping behavior. This is one reason transactional models can create friction in institutional settings. Research institutions operate through annual budgets, recurring commitments, and long-term operational responsibilities. They can usually absorb predictable commitments much more easily than uncertain growth in costs tied to usage. When ordinary success becomes financially difficult to forecast, it becomes harder to normalize infrastructure, harder to advocate for adoption locally, and harder to sustain support over time. In some cases, institutions may also become more cautious about dependence itself, particularly when local ownership appears easier to predict than external variable costs.
The problem with transactional models is not only that it can be harder to budget for. It is that it can create incentives that run directly against the purpose of the infrastructure itself. When cost rises with each deposit, identifier, publication, or workflow, institutions can find themselves paying more precisely when the infrastructure is being used in the ways they have been encouraging all along.
That is the core perverse incentive. Libraries and research institutions are often trying to move communities toward better practice: more data sharing, stronger metadata, broader use of PIDs, better preservation, and more consistent compliance with policy and funder expectations. They are often the very outcomes that infrastructure providers, libraries, and research offices all say they want. But if each incremental success also increases cost, then business models start to punish adoption rather than support it.
The tension may not always be stated explicitly, but it shows up in institutional decision-making all the time. A library that wants to normalize good practice across the research lifecycle has to consider whether each additional use carries a budget consequence. Once that happens, pricing models are no longer neutral. It is shaping behavior. And it is doing so in a way that makes institutions more cautious about advocating for the very practices they are supposed to enable.
This is especially problematic in infrastructure, where the goal is not to reduce demand but to build uptake and shared norms. If adoption increases cost, then the pricing models are working against the purpose of the infrastructure. That is not a minor design flaw. It is a sign that the models are poorly matched to the role the infrastructure is meant to play.
A transactional model assumes a fairly direct relationship between user, benefit, and payment. That logic may work in settings where the value is primarily individual and immediate but scholarly infrastructure is usually not one of those settings. The immediate user may be a researcher depositing a dataset, registering an identifier, or using a metadata or curation service, but the value created does not stop with that act or with that person.
The benefits of scholarly infrastructure are often distributed across time. They include stewardship of the scholarly record, support for better practice across the institution, the existence of shared capacity that can be relied on when needed, and value that extends well beyond the immediate user to librarians, curators, educators, compliance staff, future researchers, and the entire research ecosystem. In other words, the person using the service is not always the only one who benefits, and often not the only one the institution is trying to support.
Pricing models do not just recover cost. They also express an assumption about where value resides. When infrastructure is priced transaction by transaction, it privileges the most visible unit of activity and can understate the wider institutional and communal benefit being sustained. It treats infrastructure as if institutions were merely reimbursing individual use rather than sustaining institutional research capacity. This is one reason the fit can feel so poor from an institutional perspective. Institutions are often not paying simply to cover a series of isolated acts. They are paying to sustain continuity, readiness, and the conditions for good practice over time. That is a different kind of value proposition. And it is one that is often misdescribed when the funding model is built around one-to-one transactions.
Collective models often fit institutional operating realities better than highly transactional ones. Research institutions do not support repositories, PID systems, metadata services, and preservation infrastructure simply as narrow transactional services. They support them as part of the broader research environment: shared capacities that enable stewardship, compliance, discoverability, and continuity over time.
That distinction matters operationally as much as philosophically. Institutions generally manage infrastructure more effectively when support can be framed as a stable commitment with a clear communal rationale, rather than as a variable bill tied to successful engagement. A collective model fits that reality because institutions are usually trying to sustain shared research capacity, not support one researcher transaction at a time.
This does not eliminate the need for accountability or careful governance. It does mean, however, that the financial model begins to match the way institutions actually justify, budget for, and sustain infrastructure over time.
Predictability benefits infrastructure organizations as much as institutions. A collective funding model does not eliminate risk, but it changes how risk is managed. Instead of pushing financial variability back onto institutions one transaction at a time, infrastructure providers must think more deliberately about scope, staffing, reserves, governance, and sustainable growth. That can be a strength rather than a weakness. A predictable model encourages a clearer relationship between mission, service levels, and community support. It pushes organizations to be more transparent about what baseline funding supports, where additional investment is needed, and how expansion should occur responsibly over time.
Transparency is essential. Communities should be able to understand how fees are determined, what the baseline covers, what reserves exist, and how future changes will be evaluated. A sustainable collective model should also plan for continuity, including governance transitions and responsible wind-down where necessary. In that sense, predictability is not simply a concession to institutional budgeting concerns. It is part of a healthier governance model for infrastructure itself. For institutions, predictability can make support easier to justify and sustain. For providers, it creates a more intentional and legible operational model grounded in long-term stewardship rather than continual transactional expansion.
A collective model does not require pretending that all institutions are the same, and it does not require a single identical fee for everyone. The more important distinction is whether the model is structured around sustaining shared capacity. Infrastructure is itself inherently collective: its value emerges not only from individual acts of use, but from the existence of shared systems that institutions and communities can rely on over time. A model can still be collective while using formulas, tiers, or contribution levels that reflect institutional size, capacity, or role. Not every differentiated pricing model is transactional in the problematic sense; a model can still be tiered or formula-based and remain collective if it is designed to sustain infrastructure. This does not have to remain abstract. DataCite recently announced a new membership model that moves away from per-DOI pricing and toward a collective approach, with fees adjusted using country-level economic indicators. It offers a useful example of how an infrastructure organization can differentiate contributions without reverting to transactional logic.
One of the easiest objections to collective funding is the claim that it is somehow simplistic or unfair. In reality, the fairness of a model can recognize institutional differences without reducing infrastructure support to a running tally of routine use. In practice, some of the strongest collective arrangements are precisely the ones that are transparent about how costs are distributed and why. Formula-based approaches can make it possible for larger institutions to contribute more, for smaller organizations to participate meaningfully, and for the whole community to see how growth, accountability, and shared support fit together. That is a very different logic from a model where each additional act of participation simply adds to the bill. So the choice is not between a crude flat fee and finely tuned fairness. The better contrast is between models that fund infrastructure predictably and models that meter normal use as though the infrastructure were simply a series of individual transactions.
There are already plenty of vendors in the scholarly infrastructure space. The point of infrastructure is not simply to add more providers to that landscape. It is to create a different kind of relationship, one grounded in stewardship, accountability, transparency, and a clearer connection between what is being funded and the broader benefit it supports. When that distinction fades, something important is lost.
This is why the funding model matters beyond pricing mechanics. If a membership organization or mission-driven infrastructure relies too heavily on transactional logic, it can start to look and feel like an ordinary vendor relationship. The institution is no longer supporting shared capacity with a communal rationale. It is managing exposure, counting usage, and evaluating the arrangement as one more procurement decision, often against the perceived predictability of local control. At that point, the infrastructure may still be valuable, but its business model is no longer doing much to distinguish it from the market logic it was supposed to counterbalance.
That does not mean infrastructures should ignore sustainability, discipline, or cost recovery. It means that their distinctiveness should show up in the model itself. If a community is being asked to support infrastructure because it is mission-driven or built for long-term collective benefit, then the way it is funded should reflect those same qualities. Otherwise, the rhetoric starts to drift away from the reality of the relationship.
The issue is not simply whether institutions can afford one more service. It is whether the scholarly community is building genuine infrastructure or just reproducing the vendor landscape in a slightly different form. If infrastructures adopt vendor logic, it risks becoming just another vendor in a space that already has too many. We do not need more infrastructure providers acting like vendors. We need infrastructure that actually operates like a investment.
Research infrastructure and open access publishing are not the same thing, but there is still a useful parallel. Institutions have already spent years grappling with business models that tie costs to discrete units of activity. APC-based publishing models are one familiar example. The issue is not simply that institutions pay for publishing. It is that costs can become difficult to predict and difficult to sustain as adoption grows.
That tension is one reason institutions have often been drawn toward models that create more stable and legible forms of support, whether through negotiated agreements, collective funding arrangements, or Diamond OA approaches that move away from per-article transactions. In some cases, these efforts have also produced collective funding experiments, including models such as the Open Library of Humanities and SciPost, that attempt to sustain publishing infrastructure through broader community support rather than article-level billing. These approaches are not interchangeable, but they reflect a familiar institutional preference: supporting systems through models that are easier to normalize and sustain over time.
The same tension appears in scholarly infrastructure. Institutions are not only evaluating whether a service is useful. They are evaluating whether the funding model fits the operational realities of long-term institutional support. In that sense, the OA conversation is useful not because it offers a perfect analogy, but because it demonstrates a broader and recurring challenge across scholarly communication: how to sustain these systems without making adoption financially destabilizing.
Collective models are not perfect, and the argument for them is weaker if that is ignored. As communities grow, governance can become more complicated. Not every participant wants deep engagement, shared decision-making, or a strong sense of communal identity. Some institutions may still evaluate a collective arrangement through a narrow transactional lens, asking whether local usage \"justifies\" the cost even when the benefit is broader and less easily measured. Those are real challenges.
There are also legitimate questions about scope and edge cases. Some services or institutions may place unusually large demands on an infrastructure. Some kinds of support may be bespoke enough that they do not fit neatly within a shared baseline model. A collective approach does not eliminate those questions. It simply means that they should be handled as contextual decision points within a framework, not used to define the logic of the whole system.
It is also true that collective models require explanation. Institutions need to understand what they are supporting, how the model works, and why the benefit is greater than a count of immediate uses. Providers need to be transparent about what the baseline covers, how contributions are determined, and where additional needs may arise. Without that clarity, a collective model can start to feel vague or unconvincing, especially in institutional settings where every recurring cost is under scrutiny.
But none of those tradeoffs change the larger point. The existence of complexity does not mean transactional pricing is better. It means collective models have to be designed carefully, governed well, and explained in a way that makes the communal value visible.
When it comes to infrastructure, the real choice is not between paying for it and getting it for free. Institutions have never sustained research support that way. The choice is between models that sustain shared institutional capacity and models that tie cost to participation. That distinction is important because it shapes not only what institutions pay, but how they understand the relationship, how they justify it internally, and how confidently they can encourage broader adoption.
If institutions are going to sustain repositories, PID systems, metadata services, curation platforms, and other scholarly infrastructure as part of their remit, then the business models behind those services need to reflect the kind of value they create. In most cases, that value is not only singular and immediate. It is institutional, communal, and long-term. It shows up in stewardship of the scholarly record, in support for better practice across the institution and in benefits that extend well beyond the immediate user.
That is why this conversation is ultimately about more than pricing. It is about what kind of infrastructure ecosystem the scholarly community wants to build. For institutions, that means moving away from models that punish ordinary success and toward models that support capacity building, predictable commitment, and long-term stewardship. DataCite\u2019s recent move away from per-DOI pricing offers one important example of what that transition can look like in practice. We need more infrastructures to also operate like investments in the future.
Acknowledgements: Thank you to Kathleen Gregory, Dorothea Strecker, Maria Gould, and my colleagues at California Digital Library (G\u00fcnter Waibel, Catherine Mitchell, Miranda Bennett, Kurt Ewoldsen) for their help with this article.
","doi":"https://doi.org/10.54900/9xya5-agx80","funding_references":null,"guid":"https://doi.org/10.54900/9xya5-agx80","id":"dcda1294-dfcd-4363-bd3f-0d360aaa4ea0","image":"https://images.unsplash.com/photo-1564998115952-368e7d4969ea?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=M3wxMTc3M3wwfDF8c2VhcmNofDI0fHx2ZW5kaW5nJTIwbWFjaGluZXxlbnwwfHx8fDE3Nzg1MjM3MzB8MA&ixlib=rb-4.1.0&q=80&w=2000","images":[],"indexed":true,"indexed_at":1779295472,"language":"en","parent_doi":null,"published_at":1779229439,"reference":[],"registered_at":0,"relationships":[],"rid":"hkcsy-hf385","status":"active","summary":"Many scholarly infrastructure business models fit institutions poorly. They tie cost to transactions, outputs, or rising use in ways that make adoption harder to sustain over time. That is a problem for libraries and research institutions, which are trying to normalize and support good practice, not disincentivize against it by tying payments to countable units.","tags":["Thought Pieces","Insights"],"title":"Not the Right Fit: How Transactional Infrastructure Models Clash with Institutional Realities","updated_at":1779292001,"url":"https://upstream.force11.org/why-institutions-struggle-with-transactional-infrastructure-models/","version":"v1"},{"abstract":"Many scholarly infrastructure business models fit institutions poorly. They tie cost to transactions, outputs, or rising use in ways that make adoption harder to sustain over time. That is a problem for libraries and research institutions, which are trying to normalize and support good practice, not disincentivize against it by tying payments to countable units.","archive_url":null,"authors":[{"affiliation":[{"id":"https://ror.org/00dmfq477","name":"University of California Office of the President"}],"contributor_roles":[],"family":"Chodacki","given":"John","url":"https://orcid.org/0000-0002-7378-2408"}],"blog":{"archive_collection":22124,"archive_host":null,"archive_prefix":"https://wayback.archive-it.org/22124/20231105103706/","archive_timestamps":[20231105103706,20240505132151,20241105103657,20250505103918],"authors":null,"canonical_url":null,"category":"humanities","community_id":"aeaafcbb-94b5-477a-a89f-8fba5925e926","created_at":1673568000,"current_feed_url":"https://upstream.force11.org/atom/","description":"The community blog for all things Open Research.","doi":null,"doi_as_guid":false,"favicon":"https://rogue-scholar.org/api/communities/b56ef314-34f7-4c7f-b0e2-d0bf13bfe83b/logo","feed_format":"application/atom+xml","feed_url":"https://upstream.force11.org/atom-complete/","filter":null,"funding":null,"generator":"Ghost","generator_raw":"Ghost 5.25","home_page_url":"https://upstream.force11.org","id":"e3952730-ffb7-4ef9-b4a5-6433d86b2819","indexed":false,"issn":null,"language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","mastodon":"https://scicomm.xyz/@upstream","prefix":"10.54900","registered_at":1729244339,"relative_url":null,"ror":null,"secure":true,"slug":"upstream","status":"active","subfield":"1802","subfield_validated":null,"title":"Upstream","updated_at":1779268293.824907,"use_api":true,"use_mastodon":false,"user_id":"08014cf6-3335-4588-96f4-c77ac1e535b2"},"blog_name":"Upstream","blog_slug":"upstream","content_html":"Many scholarly infrastructure business models fit institutions poorly. They tie cost to transactions, outputs, or rising use in ways that make adoption harder to sustain over time. That is a problem for libraries and research institutions, which are trying to normalize and support good practice, not disincentivize against it by tying payments to countable units. DataCite\u2019s recent move away from per-DOI pricing offers a useful example of a different approach. It shows what it can look like when an infrastructure organization designs its funding model in a way that better matches institutional values and institutional realities.
The issue is not whether institutions understand that infrastructure costs money. Of course they do. In some cases, they will even choose to build and maintain systems themselves, not because doing so is necessarily cheaper or more efficient, but because predictable responsibility can feel easier to manage than open-ended financial exposure. The real question is whether infrastructure can be supported in ways that are stable and aligned with how institutions actually work, while providing sustainable funding to those providing the infrastructure. Transactional infrastructure models are not only competing against one another. They are also competing against institutional instincts toward local control, internal ownership, and operational predictability.
Too often, the answer is no. Many infrastructure models still assume that use should be counted and billed one unit at a time. That logic may work in settings where value is primarily individual and immediate. However, scholarly infrastructure is usually not one of those settings. Repositories, PID systems, metadata services, curation platforms, and related infrastructure create value that institutions sustain collectively so those capabilities remain broadly and equitably available over time. They help preserve the scholarly record, improve practice, support compliance, and make core capacities available to the communities institutions serve.
This is why collective models matter. A transactional model ties cost to individual acts of use. A collective model ties support to sustaining shared capacity. That distinction is not just financial. It shapes whether institutions can confidently encourage adoption, whether the entire community is rewarded for broad uptake or punished by it, and whether infrastructure actually behaves like and is treated like a shared investment rather than just another vendor.
Institutions around the world understand that scholarly infrastructure costs money. The harder question is whether those costs can be structured in ways institutions can support responsibly over time. By \u2018transactional model\u2019, I mean a model in which institutional cost rises with discrete acts of use, outputs, or participation, rather than through a stable collective commitment to sustain the infrastructure as a whole.
For most institutions, the central problem is not cost itself but unpredictability. Libraries and research organizations can plan for recurring commitments when those commitments are understandable, stable, and tied to a known scope of support. What becomes difficult is a model in which ordinary adoption, routine use, or successful growth creates open-ended financial exposure. At that point, infrastructure stops feeling like an investment and starts feeling like a moving target.
That changes the internal conversation institutions have about infrastructure. Instead of asking how to support participation, improve practice, or build durable capacity, institutions begin asking what happens to the budget if those efforts succeed. Once that happens, the pricing model is no longer neutral. It begins shaping behavior. This is one reason transactional models can create friction in institutional settings. Research institutions operate through annual budgets, recurring commitments, and long-term operational responsibilities. They can usually absorb predictable commitments much more easily than uncertain growth in costs tied to usage. When ordinary success becomes financially difficult to forecast, it becomes harder to normalize infrastructure, harder to advocate for adoption locally, and harder to sustain support over time. In some cases, institutions may also become more cautious about dependence itself, particularly when local ownership appears easier to predict than external variable costs.
The problem with transactional models is not only that it can be harder to budget for. It is that it can create incentives that run directly against the purpose of the infrastructure itself. When cost rises with each deposit, identifier, publication, or workflow, institutions can find themselves paying more precisely when the infrastructure is being used in the ways they have been encouraging all along.
That is the core perverse incentive. Libraries and research institutions are often trying to move communities toward better practice: more data sharing, stronger metadata, broader use of PIDs, better preservation, and more consistent compliance with policy and funder expectations. They are often the very outcomes that infrastructure providers, libraries, and research offices all say they want. But if each incremental success also increases cost, then business models start to punish adoption rather than support it.
The tension may not always be stated explicitly, but it shows up in institutional decision-making all the time. A library that wants to normalize good practice across the research lifecycle has to consider whether each additional use carries a budget consequence. Once that happens, pricing models are no longer neutral. It is shaping behavior. And it is doing so in a way that makes institutions more cautious about advocating for the very practices they are supposed to enable.
This is especially problematic in infrastructure, where the goal is not to reduce demand but to build uptake and shared norms. If adoption increases cost, then the pricing models are working against the purpose of the infrastructure. That is not a minor design flaw. It is a sign that the models are poorly matched to the role the infrastructure is meant to play.
A transactional model assumes a fairly direct relationship between user, benefit, and payment. That logic may work in settings where the value is primarily individual and immediate but scholarly infrastructure is usually not one of those settings. The immediate user may be a researcher depositing a dataset, registering an identifier, or using a metadata or curation service, but the value created does not stop with that act or with that person.
The benefits of scholarly infrastructure are often distributed across time. They include stewardship of the scholarly record, support for better practice across the institution, the existence of shared capacity that can be relied on when needed, and value that extends well beyond the immediate user to librarians, curators, educators, compliance staff, future researchers, and the entire research ecosystem. In other words, the person using the service is not always the only one who benefits, and often not the only one the institution is trying to support.
Pricing models do not just recover cost. They also express an assumption about where value resides. When infrastructure is priced transaction by transaction, it privileges the most visible unit of activity and can understate the wider institutional and communal benefit being sustained. It treats infrastructure as if institutions were merely reimbursing individual use rather than sustaining institutional research capacity. This is one reason the fit can feel so poor from an institutional perspective. Institutions are often not paying simply to cover a series of isolated acts. They are paying to sustain continuity, readiness, and the conditions for good practice over time. That is a different kind of value proposition. And it is one that is often misdescribed when the funding model is built around one-to-one transactions.
Collective models often fit institutional operating realities better than highly transactional ones. Research institutions do not support repositories, PID systems, metadata services, and preservation infrastructure simply as narrow transactional services. They support them as part of the broader research environment: shared capacities that enable stewardship, compliance, discoverability, and continuity over time.
That distinction matters operationally as much as philosophically. Institutions generally manage infrastructure more effectively when support can be framed as a stable commitment with a clear communal rationale, rather than as a variable bill tied to successful engagement. A collective model fits that reality because institutions are usually trying to sustain shared research capacity, not support one researcher transaction at a time.
This does not eliminate the need for accountability or careful governance. It does mean, however, that the financial model begins to match the way institutions actually justify, budget for, and sustain infrastructure over time.
Predictability benefits infrastructure organizations as much as institutions. A collective funding model does not eliminate risk, but it changes how risk is managed. Instead of pushing financial variability back onto institutions one transaction at a time, infrastructure providers must think more deliberately about scope, staffing, reserves, governance, and sustainable growth. That can be a strength rather than a weakness. A predictable model encourages a clearer relationship between mission, service levels, and community support. It pushes organizations to be more transparent about what baseline funding supports, where additional investment is needed, and how expansion should occur responsibly over time.
Transparency is essential. Communities should be able to understand how fees are determined, what the baseline covers, what reserves exist, and how future changes will be evaluated. A sustainable collective model should also plan for continuity, including governance transitions and responsible wind-down where necessary. In that sense, predictability is not simply a concession to institutional budgeting concerns. It is part of a healthier governance model for infrastructure itself. For institutions, predictability can make support easier to justify and sustain. For providers, it creates a more intentional and legible operational model grounded in long-term stewardship rather than continual transactional expansion.
A collective model does not require pretending that all institutions are the same, and it does not require a single identical fee for everyone. The more important distinction is whether the model is structured around sustaining shared capacity. Infrastructure is itself inherently collective: its value emerges not only from individual acts of use, but from the existence of shared systems that institutions and communities can rely on over time. A model can still be collective while using formulas, tiers, or contribution levels that reflect institutional size, capacity, or role. Not every differentiated pricing model is transactional in the problematic sense; a model can still be tiered or formula-based and remain collective if it is designed to sustain infrastructure. This does not have to remain abstract. DataCite recently announced a new membership model that moves away from per-DOI pricing and toward a collective approach, with fees adjusted using country-level economic indicators. It offers a useful example of how an infrastructure organization can differentiate contributions without reverting to transactional logic.
One of the easiest objections to collective funding is the claim that it is somehow simplistic or unfair. In reality, the fairness of a model can recognize institutional differences without reducing infrastructure support to a running tally of routine use. In practice, some of the strongest collective arrangements are precisely the ones that are transparent about how costs are distributed and why. Formula-based approaches can make it possible for larger institutions to contribute more, for smaller organizations to participate meaningfully, and for the whole community to see how growth, accountability, and shared support fit together. That is a very different logic from a model where each additional act of participation simply adds to the bill. So the choice is not between a crude flat fee and finely tuned fairness. The better contrast is between models that fund infrastructure predictably and models that meter normal use as though the infrastructure were simply a series of individual transactions.
There are already plenty of vendors in the scholarly infrastructure space. The point of infrastructure is not simply to add more providers to that landscape. It is to create a different kind of relationship, one grounded in stewardship, accountability, transparency, and a clearer connection between what is being funded and the broader benefit it supports. When that distinction fades, something important is lost.
This is why the funding model matters beyond pricing mechanics. If a membership organization or mission-driven infrastructure relies too heavily on transactional logic, it can start to look and feel like an ordinary vendor relationship. The institution is no longer supporting shared capacity with a communal rationale. It is managing exposure, counting usage, and evaluating the arrangement as one more procurement decision, often against the perceived predictability of local control. At that point, the infrastructure may still be valuable, but its business model is no longer doing much to distinguish it from the market logic it was supposed to counterbalance.
That does not mean infrastructures should ignore sustainability, discipline, or cost recovery. It means that their distinctiveness should show up in the model itself. If a community is being asked to support infrastructure because it is mission-driven or built for long-term collective benefit, then the way it is funded should reflect those same qualities. Otherwise, the rhetoric starts to drift away from the reality of the relationship.
The issue is not simply whether institutions can afford one more service. It is whether the scholarly community is building genuine infrastructure or just reproducing the vendor landscape in a slightly different form. If infrastructures adopt vendor logic, it risks becoming just another vendor in a space that already has too many. We do not need more infrastructure providers acting like vendors. We need infrastructure that actually operates like a investment.
Research infrastructure and open access publishing are not the same thing, but there is still a useful parallel. Institutions have already spent years grappling with business models that tie costs to discrete units of activity. APC-based publishing models are one familiar example. The issue is not simply that institutions pay for publishing. It is that costs can become difficult to predict and difficult to sustain as adoption grows.
That tension is one reason institutions have often been drawn toward models that create more stable and legible forms of support, whether through negotiated agreements, collective funding arrangements, or Diamond OA approaches that move away from per-article transactions. In some cases, these efforts have also produced collective funding experiments, including models such as the Open Library of Humanities and SciPost, that attempt to sustain publishing infrastructure through broader community support rather than article-level billing. These approaches are not interchangeable, but they reflect a familiar institutional preference: supporting systems through models that are easier to normalize and sustain over time.
The same tension appears in scholarly infrastructure. Institutions are not only evaluating whether a service is useful. They are evaluating whether the funding model fits the operational realities of long-term institutional support. In that sense, the OA conversation is useful not because it offers a perfect analogy, but because it demonstrates a broader and recurring challenge across scholarly communication: how to sustain these systems without making adoption financially destabilizing.
Collective models are not perfect, and the argument for them is weaker if that is ignored. As communities grow, governance can become more complicated. Not every participant wants deep engagement, shared decision-making, or a strong sense of communal identity. Some institutions may still evaluate a collective arrangement through a narrow transactional lens, asking whether local usage \"justifies\" the cost even when the benefit is broader and less easily measured. Those are real challenges.
There are also legitimate questions about scope and edge cases. Some services or institutions may place unusually large demands on an infrastructure. Some kinds of support may be bespoke enough that they do not fit neatly within a shared baseline model. A collective approach does not eliminate those questions. It simply means that they should be handled as contextual decision points within a framework, not used to define the logic of the whole system.
It is also true that collective models require explanation. Institutions need to understand what they are supporting, how the model works, and why the benefit is greater than a count of immediate uses. Providers need to be transparent about what the baseline covers, how contributions are determined, and where additional needs may arise. Without that clarity, a collective model can start to feel vague or unconvincing, especially in institutional settings where every recurring cost is under scrutiny.
But none of those tradeoffs change the larger point. The existence of complexity does not mean transactional pricing is better. It means collective models have to be designed carefully, governed well, and explained in a way that makes the communal value visible.
When it comes to infrastructure, the real choice is not between paying for it and getting it for free. Institutions have never sustained research support that way. The choice is between models that sustain shared institutional capacity and models that tie cost to participation. That distinction is important because it shapes not only what institutions pay, but how they understand the relationship, how they justify it internally, and how confidently they can encourage broader adoption.
If institutions are going to sustain repositories, PID systems, metadata services, curation platforms, and other scholarly infrastructure as part of their remit, then the business models behind those services need to reflect the kind of value they create. In most cases, that value is not only singular and immediate. It is institutional, communal, and long-term. It shows up in stewardship of the scholarly record, in support for better practice across the institution and in benefits that extend well beyond the immediate user.
That is why this conversation is ultimately about more than pricing. It is about what kind of infrastructure ecosystem the scholarly community wants to build. For institutions, that means moving away from models that punish ordinary success and toward models that support capacity building, predictable commitment, and long-term stewardship. DataCite\u2019s recent move away from per-DOI pricing offers one important example of what that transition can look like in practice. We need more infrastructures to also operate like investments in the future.
Acknowledgements: Thank you to Kathleen Gregory, Dorothea Strecker, Maria Gould, and my colleagues at California Digital Library (G\u00fcnter Waibel, Catherine Mitchell, Miranda Bennett, Kurt Ewoldsen) for their help with this article.
","doi":"https://doi.org/10.54900/9xya5-agx80","funding_references":null,"guid":"6a021cf4b1b32c6d4a88824b","id":"8e935153-7a89-4533-921d-164dc690a690","image":"https://images.unsplash.com/photo-1564998115952-368e7d4969ea?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=M3wxMTc3M3wwfDF8c2VhcmNofDI0fHx2ZW5kaW5nJTIwbWFjaGluZXxlbnwwfHx8fDE3Nzg1MjM3MzB8MA&ixlib=rb-4.1.0&q=80&w=2000","images":[],"indexed":true,"indexed_at":1779235800,"language":"en","parent_doi":null,"published_at":1779229439,"reference":[],"registered_at":0,"relationships":[],"rid":"hkcsy-hf385","status":"active","summary":"Many scholarly infrastructure business models fit institutions poorly. They tie cost to transactions, outputs, or rising use in ways that make adoption harder to sustain over time. That is a problem for libraries and research institutions, which are trying to normalize and support good practice, not disincentivize against it by tying payments to countable units.","tags":["Thought Pieces","Insights"],"title":"Not the Right Fit: How Transactional Infrastructure Models Clash with Institutional Realities","updated_at":1779234873,"url":"https://upstream.force11.org/why-institutions-struggle-with-transactional-infrastructure-models/","version":"v1"},{"abstract":null,"archive_url":null,"authors":[{"contributor_roles":[],"family":"Niemeyer","given":"Sandra"}],"blog":{"archive_collection":null,"archive_host":null,"archive_prefix":null,"archive_timestamps":null,"authors":null,"canonical_url":null,"category":"engineeringAndTechnology","community_id":"db0d8909-9e37-46d0-b16c-0551f575e86b","created_at":1749798261.334959,"current_feed_url":null,"description":"Das Blog der TIB \u2013 Leibniz-Informationszentrum Technik und Naturwissenschaften und Universit\u00e4tsbibliothek","doi":null,"doi_as_guid":true,"favicon":"https://blog.tib.eu/wp-content/uploads/2015/12/TIB_fav_icon_24x24.png","feed_format":"application/atom+xml","feed_url":"https://blog.tib.eu/feed/atom/","filter":null,"funding":null,"generator":"WordPress","generator_raw":"WordPress 6.8.1","home_page_url":"https://blog.tib.eu/","id":"135a354f-2969-4852-9a7c-b6cda0a692a4","indexed":true,"issn":null,"language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","mastodon":null,"prefix":"10.65527","registered_at":0,"relative_url":null,"ror":null,"secure":true,"slug":"tib","status":"active","subfield":"1802","subfield_validated":null,"title":"TIB-Blog","updated_at":1779268236.390099,"use_api":true,"use_mastodon":false,"user_id":null},"blog_name":"TIB-Blog","blog_slug":"tib","content_html":" read this article in English\nDie Blogreihe \u201eFrauen in der Wissenschaft\u201c stellt Frauen aus der TIB vor, die Einblicke in ihre Wege und ihre pers\u00f6nlichen Erfahrungen in der Wissenschaft geben. Dr. Lauren Snyder studierte \u201eEcology and Conservation Biology\u201d (\u00d6kologie und Naturschutz) an der Boston University und promovierte in \u201eEcology & Evolutionary Biology\u201c (\u00d6kologie und Evolutionsbiologie) an der Cornell University.
\nHeute ist sie Mitbegr\u00fcnderin von \u201eTIB Knowledge Loom\u201c, einer digitalen Open-Science-Bibliothek. Dort nutzt sie ihren interdisziplin\u00e4ren Forschungshintergrund, um Tools zu entwickeln, die die Transparenz und Wiederverwendbarkeit wissenschaftlicher Erkenntnisse verbessern. Im Interview spricht sie dar\u00fcber, warum klare Zielvorstellungen genauso wichtig sind wie Offenheit f\u00fcr Unerwartetes und warum die Menschen, mit denen man arbeitet, eine besondere Rolle spielen.
\nWas fasziniert dich an der Arbeit in der Wissenschaft?
\nIch liebe die Natur, arbeite gern mit Menschen und m\u00f6chte einen Beitrag zur Gesellschaft leisten. Die Wissenschaft erm\u00f6glicht es mir, all das miteinander zu verbinden. Sie gibt mir die Chance, die Umwelt zu verstehen, in der ich lebe und von der ich abh\u00e4ngig bin, sowie die Art und Weise, wie Menschen sie gestalten und welche komplexen Wechselwirkungen dabei entstehen. Das erf\u00fcllt mich mit einem tiefen Gef\u00fchl von Sinn und Verbundenheit mit der Welt um mich herum.
\nWas h\u00e4ttest du als Frau in der Wissenschaft gerne fr\u00fcher gewusst?
\nIch w\u00fcnschte, ich h\u00e4tte fr\u00fcher erkannt, dass eine klare Zielvorstellung ein starker Motivationsfaktor ist, dass aber das starre Festhalten an einem bestimmten Ziel zu Frustration und verpassten Chancen f\u00fchren kann.
\nOffen f\u00fcr das Unerwartete zu sein, bedeutet nicht, seine Ziele aufzugeben. Es bedeutet, darauf zu vertrauen, dass man Chancen in wertvolle Erfahrungen verwandeln kann, die mit den eigenen Werten und Zielen im Einklang stehen. Ich habe gelernt, dass es bei der Entscheidung, welche Chancen man wahrnehmen sollte, genauso wichtig ist, auf die Menschen zu achten wie auf die Position oder die Stellenbeschreibung selbst.
\nDie Zusammenarbeit mit Menschen, die sich f\u00fcr dich einsetzen und dir helfen, dich weiterzuentwickeln, kann eine auf den ersten Blick unerwartete Gelegenheit zu einer der besten beruflichen Entscheidungen machen, die du treffen kannst. Mit einer klaren Vision und den richtigen Menschen an deiner Seite wirst du vielversprechende Chancen erkennen und auch wissen, wann eine Erfahrung ihren H\u00f6hepunkt erreicht hat. Es braucht Zeit, dieses Selbstvertrauen und diese Intuition zu entwickeln, und das ist in Ordnung.
\nWelchen Rat w\u00fcrdest du M\u00e4dchen und jungen Frauen geben, die eine wissenschaftliche Laufbahn anstreben?
\nAls Frau hat man manchmal das Gef\u00fchl, dass es an Kritik nie mangelt. Wenn ich M\u00fche habe, meine eigene Meinung unter all den Meinungen anderer zu h\u00f6ren, denke ich an einen Satz, der mir einmal begegnet ist: Nimm keine Kritik von jemandem an, von dem du auch keinen Rat annehmen w\u00fcrdest.
\nViele Menschen werden deine Entscheidungen kommentieren oder kritisieren. Eine wichtige F\u00e4higkeit besteht darin, die Stimmen, die es wert sind, angeh\u00f6rt zu werden, vom Hintergrundrauschen zu unterscheiden. Indem du dir ein Umfeld aus Menschen aufbaust, zu denen du wirklich aufschaust \u2013 Menschen, die deine Werte, Leidenschaften und deine Vision eines erf\u00fcllten Lebens teilen \u2013, wird es dir leichter fallen, konstruktive Kritik zu erkennen und dich auf das zu konzentrieren, was dir hilft, zu wachsen, und den Rest loszulassen. Wende dich an dieses Umfeld, wenn das Leben schwer wird. W\u00e4hle Mentoren und Mitstreiter danach aus, wer sie als Menschen sind, und nicht nur aufgrund ihres Rufs oder ihrer Produktivit\u00e4t.
\n
\n
\n
\n
\n
Ein Wunsch f\u00fcr die Zukunft von Frauen und M\u00e4dchen in der Wissenschaft …
\nIch hoffe, dass k\u00fcnftige Generationen von M\u00e4dchen eine Welt vorfinden, in der ihre Sichtweisen als unverzichtbar angesehen werden und in der sie nicht mehr um ihren Platz in der Wissenschaft oder der Gesellschaft k\u00e4mpfen m\u00fcssen.
\nIn der Blogreihe \u201eFrauen in der Wissenschaft\u201c werden Frauen an der TIB vorgestellt, die Einblicke in ihre wissenschaftlichen Wege, Rollenbilder und ihre Erfahrungen aus dem Arbeitsalltag geben. Sie alle teilen ihre Perspektive und ihre W\u00fcnsche f\u00fcr die Zukunft der Wissenschaft und ermutigen andere Frauen, ihren Platz selbstbewusst einzunehmen.
\n
The advent of AI agents based on large language models (LLMs) has put the idea of automating the intellectual and cognitive work of researchers on the table. A lively, sometimes even heated discussion is already going on. A frequently missing piece in this debate is the question why we, individually and as a society, actually do science. I will examine this question first, and then consider what it implies for introducing automation into science.
\n\nFirst of all, what is science? Any short definition is necessarily a caricature, but I hope that the following caricature is at least a useful one: science is a collective process that aims at accumulating reliable knowledge about the world we live in, emphasizing doubt and epistemic humility in order to counterbalance human cognitive biases. In other words: science proceeds with the assumption that anything can turn out to be wrong, and that the default answer to any question is \"we don't know\". Everything we think we know can be questioned and revised in the light of new evidence or new critical examination.
\nNext, why do we do science? This depends very much on who is that \"we\". Science started in the 16th century as a leisure activity by wealthy or sponsored people to satisfy their curiosity. Nowadays, science is funded by governments in order to support economic growth and policy decisions, which is a much more utilitarian stance. And yet, indivdual researchers are still largely motivated by curiosity. But curiosity and utilitarianism are not as distinct as they may seem. From an evolutionary perspective, it makes sense for organisms living in a complex world to use spare resources for acquiring knowledge that may be useful in an uncertain future. Curiosity is thus a trait that helps making people and societies more robust.
\nScientific institutions have the role of maintaining and extending the collective knowledge base, which is a complex network of interconnected pieces of information. There is knowledge about the world we live in, of course, but also knowledge about how to make observations and how to interpret their results, plus theories and knowledge about these theories, and a lot more. And all that knowledge comes with a judgement of its reliability attached to it, which is important because the ultimate goal of science is obtaining reliable knowledge.
\nIf you imagine the collective knowledge base as a huge library full of books and journals, or as a collection of Web sites that look like Wikipedia, you are missing an important piece. Information archives are important for science, but they cannot capture everything required to interpret this information. The archives contain marks on paper, or bit patterns for the digital ones. The procedural knowledge required to make sense of these information snippets, and to relate them to the world we live in, is embodied in practicing scientists. You cannot learn chemistry from reading chemistry books alone. At some point, you have to manipulate chemicals, touch them, smell them, mix them, and see what happens. More generally, if you want to learn everything required to understand and contribute meaningfully to the chemistry literature, you need to work as an apprentice to an experienced chemist. If for some reason all chemists die, the books and Web sites will become unintellegible. This is not abstract theory. We have written documents from the past that nobody can read any more because nobody living today knows the language and writing system used by the authors.
\nMost popular narratives about science concentrate on the task of extending the collective knowledge base, by making new observations about the world or new theories and models explaining such observations. Maintenance gets a lot less attention. It consists essentially of two processes: training the next generation of scientists, and re-examining existing knowledge, in the light of new information or new ideas for representing this knowledge. A modern textbook on classical mechanics looks very different from Isaac Newton's \"Principia Mathematica\", but it describes the same theoretical framework. What has changed is the notation and the presentation, making the material easier to understand and apply, and easier to integrate with other theories in physics but also in other fields using the same mathematical notation. And the more a theory is applied, integrated, and tested, the more reliable it becomes. The best evidence for the reliability of Newton's mechanics (when applied within its limits of applicability) is the fact that it underlies a huge part of the technology we use every day. Centuries of refinement have turned Newton's intellectual exercise into knowledge that we can rely on. Maintenance matters!
\nNot all scientific knowledge has been revised and applied for centuries. How then do we judge its reliability? That's an important question that is not examined often enough, in particular in the ongoing AI-for-science debate. An early and still relevant technique is double checking. If multiple researchers do similar work and obtain similar results, their results strengthen each other's reliability. And if the results disagree, the causes of the differences can be explored systematically. The simple version of double-checking that I have described here works only for studies of simple systems, where \"similar work\" and \"similar results\" are well-defined concepts. But the idea can be extended to more complex systems, where one would examine the coherence of findings from a large number of individual studies.
\nBut there remains an important condition: a judgement of reliability requires a detailed understanding of all the studies involved. Nobody can have that level of competence in more than one or two narrow domains. And yet, everybody doing research needs to rely on results from other domains and disciplines. A biologist performing data analysis is rarely a trained statistician, for example. And a physicist performing numerical simulations is rarely a trained numericist. All researchers nowadays need to trust the reliability judgements of experts in other domains. And that's also what decision makers in politics and industry do in order to figure out which scientific findings they should turn into plans for action.
\nHuman societies rely on webs of trust, because trust is the foundation for cooperation. In today's industrial societies, this web links together individuals, institutions, ideas, technologies, and physical objects, via numerous mechanisms such as reputation, certification, accountability, or punishment by law. Consider why you trust the train that you take to work every morning to transport you safely. Your trust builds on a trust in the engineers who designed the train, scientific findings that the engineers relied on, the workers that built the train, laws that define safety-related obligations, government agencies that oversee the respect of these obligations, and a lot more.
\nA large part of the boring grunt work of parlaments and government agencies is maintaining this web of trust, in contact with the scientific web of trust, among others. The web of trust behind train safety has grown over centuries, since long before the first railways were built. A society's web of trust is a big part of its social capital.
\nDigital technology remains a challenge for the web of trust, because it evolves much faster than traditional trust mechanisms can adapt. The one technology that is almost completely exempt from legal and contractual obligations concerning safety and reliability is software. Major perturbations such as the CrowdStrike incident have contributed to a growing awareness about this problem, but so far nothing much has changed at the legal level. Software vendors are not sanctioned for negligence, nor even for intentional malice (such as Grok producing deepfake porn).
\nIn science, digital technologies have likewise been adopted enthusiastically and uncritically. The publication and quality control process, which has been based on journal publications and peer review since about the 1960s, is no longer adequate for today's research work, which due to the support by digital technology now features large collaborations, big datasets, and complex computational analyses. The replication crisis is to a large part the result of this mismatch between the imagined value of peer review as a quality control mechanism and its real value as a rough credibility check. As with the safety issues I mentioned above, we are only starting to understand and correct for this evolution. And while we are grappling with these issues, LLMs are causing another earthquake in the foundations of the scientific web of trust.
\nTo what degree can science possibly be automated? Let's start with the highest imaginable level: fully automated science. That would be a machine that supplies supposedly reliable knowledge via some sort of interface, perhaps a supercharged chatbot. You could ask the machine a question, and it would enter into a dialog to request additional input from you, before in the end giving you an answer. This answer could well be \"I don't know yet, ask again a month from now while I do some more research\". Obviously this machine would have to be more than a bunch of computers. It would have to interact with the real world, making observations, setting up experiments, etc. Think of a network of computers and robots if you want a concrete image.
\nWould you trust such a machine to provide reliable answers?
\nWould you agree on having the machine do experiments on you? Would you trust its affirmation that these experiments are in your best interest?
\nFor most of us, answering such questions comes down to trusting others who we perceive as experts or authorities, or who are involved in designing or operating the machine. What would be the profile of an expert whose affirmations about the machine's reliability you would trust? Which institution would you trust to issue a certification for the science machine?
\nIf you have some expertise in science or engineering yourself, you might want to start by inspecting the processes that led to the creation of the machine. That's a good start. You might end up becoming one of those experts that the rest of us rely on. But if the machine will do all future science, then there won't be human scientists left a few decades later. And maybe no engineers either. So... who will take over your job as an expert? Why would your grandchildren trust the machine? And who will keep the machine running? It can't look after itself, as a living organism does.
\nThe good news is that nobody talking about automating science actually proposes this extreme level of automation. The bad news is the obvious conclusion that many people who propose automating science are unaware of many of the aspects of the process they wish to automate. My proposal: when discussing automation, always say explicitly where you see the interface between machines and humans. It's always there, somewhere. As long as there are humans interested in accumulating reliable knowledge, there will be a science process run by humans, who delegate specific tasks to machines. As we have been doing for quite a while already, e.g. when using DNA sequencers, or when deploying software on a computer. Automation, in science and elsewhere, has been with us for a few centuries, since the beginning of industrialization.
\nThere are three main motivations for automating a task, as compared to have humans perform it:
\nEconomy. Machines make many things cheaper than humans do, at least in our current economic model that ignores externalities such as resource depletion and environmental pollution. Often the machines produce less useful or less versatile products, but at a so much lower price that the trade-off looks favorable. As an example, consider buying an industrially produced chair as compared to making a chair yourself, or having one tailor-made by a craftsperson.
Quality. Machines do a better job at producing certain items. Staying in the carpentry theme, consider nails. Humans have made and used nails since pre-historical times, but with the arrival of industrial-made nails, human-made nails have disappeared. Machines do a better job at tasks that require high precision. They make nails that are both better and cheaper.
Complexity. Some artefacts are so complex that industrial production is the only viable option. Consider a modern car with its mechanical and electronic complexity. I doubt that anyone has ever even tried to make such a car using nothing but human labor.
In the current debate on automating science, the only motivation I see cited is economy: LLMs would allow us to do more science given the same number of people and the same resources. Most proponents of LLMs for science (e.g. this one, to give a concrete example) conveniently gloss over what \"more science\" actually means. They use the same bibliometric proxies whose inadequacy for research assessment is finally being recognized: more science means more papers. Some largely LLM-written papers have already been accepted in scientific journals, so the claim that LLMs can write papers that can pass peer review is credible. However, \"passing peer review\" is not the same as \"useful contributions to science\". In other words, the problem is not so much LLMs as an outdated quality assessment process from the 1950s that has not kept up with the enormous changes in research over the last 70 years.
\nIf we want to update our quality assessment, the question we should focus on is: how can we assess the reliability of knowledge that we obtain with the help of LLMs? Again this is not a new question. It's a question that we have asked about every single scientific instrument or experimental setup since the dawn of science. The goal is not to eliminate unreliable information sources. They often contribute useful information, and in some cases, such as in the beginnings of a new field of research, all available information may be of low reliability. That's fine, as lack of reliability can be compensated by diversity and coherence. The sum of many information sources is often more reliable than any single one on its own. But it does matter that we can estimate the reliability of each information source. Which, for experimental setups, we usually can.
\nIt is much harder to estimate the reliability of computed information, due to the complexity of software. And so... like society at large (see the last section), when it comes to software, scientists have mostly suspended the doubt that used to be their trademark. In parallel to developing computational methods, we should have developed processes for establishing trust in them, but we didn't. Only with the arrival of LLMs we realized that establishing trust is an important and difficult problem. Well, better late than never. Let's start. My contributions so far are this opinion piece about reviewing research software, and this preprint that analyzes the reviewability of software and AI tools. Unfortunately, in the meantime we will have to deal with the ongoing massive assault of our journals by LLM-generated submissions, most of which are likely to be of bad quality.
\nMy prediction is that, once the excitement about \"automating science\" has died off, we will forget this idea and concentrate on using LLMs under human supervision for well-defined tasks in which they have proven to be useful, reliable, and cost effective. The last part is rarely discussed, but it's important to keep in mind that today's AI operators run at a huge loss in order to encourage massive adoption of their product. They won't do this forever, so prices will increase, while research budgets for non-AI topics are diminishing in many countries. Nevertheless, LLMs could turn out to be a good trade-off for specific tasks in software development, in data analysis, or in the presentation of results.
\nHowever, establishing responsible LLM use in research is possible only if researchers can try out and evaluate these tools before committing to their use and making themselves dependent on them. This cannot happen in a few weeks. It cannot happen either in the current strongly polarized atmosphere where people are divided into two opposing camps, one crying \"Only AI can save us!\" and the other one replying \"AI is the devil!\" More than anything else, we need to remember the self-doubting attitude inherent to science, and admit that anyone's views on LLMs may need a revision.
\nIt is also dubious if responsible use is possible at all with today's generation of LLMs. In addition to the ethical issues, which I will address in the next section, there is a contradiction between the complete opacity of these models and the transparency requirements of science. This means you can use them only if you can audit the results in some way, as you can in some software development settings. It doesn't help that the companies that control these models openly support a government that is actively destroying scientific institutions. There is absolutely no reason to trust these companies to support science; at best, we can hope that they completely ignore research applications in developing their tools. The minimum condition for LLMs that are safe for science would be a disclosure of the training data set and the tweaks that happen after the ingestion of the training data. There are various projects to construct LLMs under such conditions, but they don't seem to be ready for practical applications.
\nIn the last section, I have looked at automation in science by LLMs with a narrow focus on the topic. That means I have taken into account the properties of science as far as automation is concerned, and the properties of LLMs as far as they relate to scientific research. I have not taken into account other characteristics of science and LLMs. This narrow-focus view is our culture's default way of analyzing things. It keeps complexity to a minimum, which is helpful. But it also hides potentially relevant aspects from view. Which is why I will now adopt a wider focus: taking into account more aspects, though necessarily in less detail.
\nScience doesn't happen in a monastery located on a remote island. It is embedded in industrial societies, where it has ties to philosophy, politics, education, industry, and a lot more. LLMs didn't fall from the sky. They were developed by people inside organizations, tied to philosophy, politics, education, industry, and a lot more. LLMs are deployed on physical machines that need to be designed, built, and operated. All that means that adoption of LLMs in science implies also bringing some of the LLM context into science. And if science one day becomes a major user of LLMs, in terms of quantity or prestige, the reverse will happen as well.
\nThere are two major criticisms of today's LLMs that derive from this wide-focus view:
\nThe impact of LLM use on the natural environment, via their enormous resource requirements.
The process by which LLMs were created: much of the training material was used without permission from its authors, and the unpleasant human labor involved (screening for atrocities) was outsourced to people who are too poor to be able to refuse the job.
Many scientists reject LLMs for these reasons, much like they reject experiments on animals or excessive air travel to conferences: not because LLMs are bad for science, but because they are bad for entities outside of science, be they humans, non-human organisms, or the entire biosphere. Expressed in the jargon of economics, LLM use has significant negative externalities.
\nNegative externalities are of course not specific to LLMs. Much of what we do in daily life (and even more so in scientific research) comes with negative externalities that we prefer not to think about because we cannot really do much about them. Climate change is the most visible one: the metabolism of our societies runs on fossil fuels, without which we couldn't even feed the human population of the planet, let alone provide the material security and comfort that we are used to. The ubiquity of negative externalities in our lives is probably the main reason why so many people, including scientists, do not see a particular issue with the negative externalities of LLMs. It's just one more item on the long list of negative externalities that we accept in order to go on with life. In this light, LLM rejection is comparable to veganism or flight shame: a conscious rejection of social norms in order to make at least a first step away from industrial societies' path towards ever increasing resource consumption and exploitation of other living beings.
\nCan these ethical issues with LLMs be overcome? In theory, yes. There are ideas for eliminating every single one. Resource consumption can be reduced by designing more efficient hardware. Less generalist LLMs could be trained with less training material, which could then be gathered with permission of its authors. Screening for atrocities becomes a non-issue when training domain-specific LLMs for science. The whole training process can be made transparent. Unfortunately, in the current economic context, none of this is likely to happen. And even in the best imaginable scenario, it would take years to decades to develop ethical LLMs for science.
\nSuch a delay, however, is inacceptable to those putting forward ethical arguments for LLM use: an acceleration of knowledge acquisition in ethically relevant domains such as health research. What if LLMs can help us cure cancer more rapidly? Is it ethically defendable not to do this? Most of these arguments are fallacious. Whereas the ethical arguments against LLMs are based on real observed negative externalities, the ethical arguments for LLM use that I have seen so far are based on speculation about hypothetical benefits. I have not seen anyone outline a credible path to an accelerated development of cancer treatments with the help of LLMs. The best you can say is that it is not logically impossible. My suspicion is that proponents of such arguments severely underestimate what it takes to develop cancer treatments. Experiments and clinical trials take a lot of time, which is not compressible by computation of any kind. And never forget the trust issue: in the end, a practicing oncologist must trust new treatments before they can actually make a difference.
\nThere are, however, quite probably some less sensational contexts in which LLM use does speed up research that has credible societal benefits. And therefore the argument \"my LLM use is ethically justifiable because the benefits outweigh the negative externalities\" cannot be rejected in general. However, so far I haven't seen any attempt to estimate such a trade-off, let alone the combination of net ethical benefit and reliable outcomes.
\nLet me end this post with my personal conclusion: I do not use LLMs for any aspect of my scientific research. Not for writing articles (nor blog posts), not for writing software, not for information retrieval, not for anything else. My research has always been more methodological than applied, and over the years it has moved more and more towards foundational questions such as reproducibility, in the topic space of metascience and philosophy of science. I consider these topics important, but not urgent. They don't justify contributing to massive harm elsewhere, nor putting quantity above quality - quite on the contrary!
\nWhat I haven't made up my mind about yet is the use of LLM-written software. LLM use in software developments comes in many shades, from code cleanup to 100% vibe coding. The latter is incompatible with the transparency requirements of science anyway, except for code snippets small enough to be audited by humans. My provisional policy is to take a critical look at LLM-supported software before adopting it. Yes, that's vague, but the only way I see to refine my policy is through practice, and that takes time! What I will not do, however, is completely reject LLM use by others. That would imply no longer collaborating with many of my colleagues, and that's a bad idea: nobody has anything to gain from the scientific community splitting into pro-AI and anti-AI camps.
\nAI slop and the destruction of knowledge by cognitive scientist Iris van Rooij. She illustrates with a concrete example what happens when LLM-generated erroneous information is incorporated unchecked into formerly trusted scientific knowledge repositories. This is happening in many places right now, and it is not clear how we will ever manage to clean up these knowledge repositories again, assuming we even decide to do it.
Scientists invented a fake disease. AI told people it was real by science journalist Chris Stokel-Walker illustrates the other side of the knowledge destruction process: fake information clearly marked as such is nevertheless absorbed by LLMs and contributes to their output.
How much of the scientific literature is generated by AI? by science journalist Miryam Naddaf. A report on the magnitude of LLM use in article preparation, and why it is quite difficult to estimate.
AI agents may be skilled researchers\u2014but not always honest ones by science journalist Nicola Jones. Another story about AI agents that are intended to automate aspects of research but do so unreliably.
Context Widows by Kevin Baker. He explains in much more detail than I did above how the goal displacement from \"quality contribution to science\" to \"citation metrics\" in the 1960s and 1970s prepared the ground for an exploitation of the new goals via LLMs, while the initial goal of quality contributions to science is silently abandoned.
Academics Need to Wake Up on AI, Part III by sociologist Alexander Kustov. His key point is that today's LLMs can produce research papers that are no worse than many human-written ones that pass peer review. He concludes that in the context of today's incentives and funding criteria, academics cannot afford not to use LLMs without losing out to their competitors who do. This confirms Kevin Baker's point about goal displacement.
A BlueSky thread by cognitive scientist Molly Crocket, pointing out the disequilibrium in science funding that prioritizes AI development while defunding everything else. Quote: \"We risk all of science if we rush to build \u201cAI Scientists\u201d, before we understand the value of human science.\"
Why do we do astrophysics? by astrophysicist David W. Hogg. He argues that for non-utilitarian fields such as his own, automating research work makes no sense because the main value of the research is not the findings but the maintenance of the research community. Many of his arguments are of interest to utilitarian perspectives as well, so this is worth reading even if you care only about \"useful\" science.
The fall of the theorem economy by mathematician David Bessis. His main point is that the value of theorem proving in mathematics is not the catalog of proven theorems but the insight gained from coming up with the proof. Theorem proving by LLMs doesn't provide this value. He predicts that the increasing use of LLMs will lead to a shift of evaluation criteria, away from valuing proofs as a proxy for the work that went into constructing them. Similar arguments can be made in other disciplines, e.g. theoretical physics.
The machines are fine. I'm worried about us. by physicist and mathematician Minas Karamanis. He worries about the consequences of students using LLMs to speed up their PhD work. The students don't learn much about research, and the supervisors could well be tempted to use LLMs directly and stop bothering with students. In either case, we lose the next generation of scientists able to do research, with or without LLMs.
Against the uncritical adoption of \u2018AI\u2019 technologies in academia by a multidisciplinary team of researchers. A very detailed and well-documented analysis of the numerous issues that makes many past and present AI technologies problematic in research and education.
A report by Inside Higher Education presents promising trials of journals that paid reviewers for writing a report. Turnaround times dropped and the quality of reports was high. It is certainly good that journals explore payment of reviewers, but I am skeptical that this can ease the “peer review crisis”, meaning to improve low turnaround times and reduce difficulties in recruiting reviewers in the first place.
As the report states, this is plausible because the compensation likely induces a sense of commitment and responsibility that accelerates the review process. I think the key question is: Does a payment model scale? When I get one invitation for a paid review report, I give it priority, but may also put other reports on hold and may decline other invitations for non-paid reports. What would happen when I get paid for all reports and all create the same sense of responsibility? Would one attach priority to all of them, putting research and teaching commitments second? I find this unlikely, so paid reports at scale may leave us in the same place as we are right now. Or researchers become more selective in accepting invitations. This is probably superior to accepting invites and not delivering a report, but it may also mean that fewer manuscript get reviewed at all, which is not desirable, in my opinion.
\n\n\n\nIn relation with this, I don’t follow this statement in the article: “There could be some percentage of papers that never get reviewed because it\u2019s not worth a journal\u2019s limited resources to peer review, which puts a quality stamp on the papers that do get reviewed,\u201d he said. This seems circular. I think the quality stamp derives from the review reports, so how can one say that a manuscript is of low quality if it is not sent to reviewers in the first place? There may be clear cases for desk rejections, but also many grey cases for which low quality is not obvious and papers that seem promising at first and do not stand closer scrutiny.
\n\n\n\nAnother point is the following: I believe that for papers on certain topics or using certain methods it is more difficult to find reviewers. The “field”, generally speaking, does not value them highly for reasons unrelated to quality. Maybe one would have to pay more for getting such papers reviewed, but even this may not help and may create wrong incentives for accepting peer review invitations in the first place.
\n\n\n\nThere may be other arguments for or against paying reviewers. While the goal of easing current issues with the peer review system is laudable, I think payment of reviewers is not clearly an effective instrument for achieving this goal.
\n\n\n\n\n","doi":"https://doi.org/10.59350/3c8m1-rcw29","funding_references":null,"guid":"https://ingorohlfing.wordpress.com/?p=1785","id":"5fb50669-a5a1-4448-b6a7-47f888e1e5d8","image":"","images":[],"indexed":true,"indexed_at":1779135059,"language":"en","parent_doi":null,"published_at":1779133238,"reference":[],"registered_at":0,"relationships":[],"rid":"43m8q-aa608","status":"active","summary":"A report by Inside Higher Education presents promising trials of journals that paid reviewers for writing a report. Turnaround times dropped and the quality of reports was high. It is certainly good that journals explore payment of reviewers, but I am skeptical that this can ease the \u201cpeer review crisis\u201d, meaning to improve low turnaround times and reduce difficulties in recruiting reviewers in the first place.","tags":["Publishing"],"title":"Will Paying Reviewers Ease the Peer Review Crisis? I am skeptical","updated_at":1779133238,"url":"https://ingorohlfing.wordpress.com/2026/05/18/will-paying-reviewers-ease-the-peer-review-crisis-i-am-skeptical/","version":"v1"}],"out_of":50219,"page":1,"per_page":10,"total-results":50219}