Every work in OpenAlex now gets its topics from a new classifier. The topics themselves stay the same: same 4,516 topics, same names, same IDs, same 252 subfields, 26 fields and 4 domains. Your filters, dashboards and code keep working. What changes is which works are assigned to each topic, and the new assignments match what most readers expect: the topic fits what the title and abstract say.

It’s an incremental change. Institutions’ field profiles barely move, and university rankings by citation impact hardly change. Here’s what’s different, why, and what to do if you report on topics.

What’s different

Topics now match the title and abstract more often. Most people read a topic like a subject heading: they look at a paper’s title and abstract and expect the topic to describe them. The old topics often didn’t, and that confused people. One user wrote to us about a paper on universities: “it’s in ‘Diagnosis and Management of Kawasaki Disease’, though it’s about higher education in Australia.”

The mismatch came from how the old classifier worked. CWTS built the topics by clustering the citation network (our 2024 announcement), and the old classifier learned to predict which cluster a work belongs to, from its title, abstract, journal and references. For well-cited articles that works well, and a paper’s cluster usually matches its subject. Not always, and not always wrongly: a 2005 paper showing that hair follicle stem cells can repair severed nerves sat in “Hair Growth and Disorders”, because hair researchers cite it; the new classifier puts it in “Nerve injury and regeneration”. Both make sense. But for works with little to go on, the old classifier had to guess, and a few bugs made its guesses worse (below).

The new classifier reads each work’s title and abstract, in any language, and picks the topics whose subject matches. If the work has a venue, like a journal or a repository, it reads that name too, but it doesn’t need one, and it doesn’t use citations. So a dataset or a paper published yesterday gets topics as good as a well-cited article’s.

The gains are biggest where the old classifier had least to go on. We tested both classifiers on works that played no part in building the new one (how we tested):

It keeps what the clusters mean. We also ran the test the old classifier was built for: publications CWTS clustered, held out from the old classifier’s training, each scored against the cluster CWTS put it in. The new classifier never saw those labels, yet it matches CWTS as often as the old one does, 55% each, and has CWTS’s topic in its top three more often (73% against 70%) (details).

The bugs it fixes

From biggest to smallest:

1. One topic held 20 million unrelated works. The old classifier gave most papers in Japanese, Chinese, Russian and Korean with no abstract the same three topics with the same scores, as if it couldn’t see their titles at all. The first was “Military Technology and Strategies“: 20.6 million works, 1 in 18 works with a topic. Now it holds 169,000 works, and they’re about the military.
2. Works with no abstract got weak guesses. Four in ten works have no abstract. The old classifier leaned on abstracts and references; with only a title, its main topic matched the work 20% of the time. The new one: 75%.
3. Datasets and other records that aren’t papers got no topic, or a stray one. Three in ten works are datasets, specimen records, proposals and the like. The old pipeline scored only papers, so most of these got no topic, and a classifier trained on journal articles misread the rest: 6% matched. The new one: 86%.
4. Papers not in English got weaker topics. A quarter of works aren’t in English. The old classifier learned mostly from English papers (83% of its training set); 9% matched, partly because of the first bug. The new one: 72%.
5. Records with nothing to classify got a topic anyway. A table of contents or a bare file name now gets no topic instead of a guess.

Bug	Work	Old topic	New topic
One topic for 20 million works	Veselago’s 1967 paper on materials with negative ε and μ, in Russian, no abstract	Military Technology and Strategies	Metamaterials and Metasurfaces Applications
No abstract	A paper on hand-gesture recognition with millimeter-wave radar	Plant pathogens and resistance mechanisms	Hand Gesture Recognition Systems
Not a paper	A telescope proposal to map the tidal streams of globular clusters	Methane Hydrates	Galaxies: Formation, Evolution, Phenomena
Not in English	A Spanish-language history of Yiddish-speaking communists in Buenos Aires	Virology and Viral Diseases	Argentine historical studies

Where the topics come from

CWTS at Leiden University built the topics in 2024 by clustering the citation network of OpenAlex works (our announcement). We built the classifier that assigns works to them. The classifier is what’s new; the clusters, their names and the hierarchy above them are untouched.

Your reports helped. Support tickets about topics showed us specific failure modes, like the 20-million-work bin, and the works in them became test cases. New AI models made the rest practical: a large model labeled two million works carefully, and we trained a small, fixed classifier on those labels. Its weights are open, so anyone can run it and get the same answer we do. The code, training labels and every test set are on GitHub.

If you report on topics

If you count your institution’s output by topic or field, or compare it with peers, here’s what changes and what to do.

Your field-level numbers barely move. We made before-and-after profiles for the 109 institutions that support OpenAlex. For the median one with at least 20,000 papers, its mix of fields is 97% the same and its mix of subfields 92% the same. Topics move more: 78% the same, and 7 of its top 10 topics stay in its top 10. Universities that publish a lot in languages other than English move most.

Rankings barely change. FWCI and citation percentiles compare a work with others in its subfield, so they shift when subfields shift. To see how much, we computed each university’s mean FWCI for its 2023–2025 papers both ways:

Across 3,621 universities, mean FWCI with the new topics tracks the old closely: the ranking barely changes (rank correlation 0.99), and the median university moves 4.5%. Universities that publish mostly in other languages stay on the line too, moving about as much as English-language universities with similar citation impact. Why anyone moves: the old catch-all topics sat in a few subfields, like Aerospace Engineering, and their millions of rarely cited works dragged those subfields’ baselines down. With fairer baselines, universities strong in those subfields slip a little, and universities strong in languages and the humanities rise a little.

Topics now cover every kind of work, not just papers. The old pipeline gave topics to papers; the new classifier gives them to datasets, specimen records and every other kind of work too. That’s part of our commitment to open science. For nearly 20 years, studies have shown that sharing data pays off (2007, 2013), and reformers of research assessment, from DORA to CoARA, ask that datasets and software count as research outputs. When datasets have topics, they show up next to the papers in their field. A few of these collections are huge: about 14 million fusion-experiment shot records now sit, correctly, in “Magnetic confinement fusion research” (39 million counting the expansion corpus, which API queries leave out by default), and millions of specimen records in botany and fungi topics. If your report should count papers only, filter by type (articles, reviews, books and chapters), and those topics look normal again.

Check your own numbers. The 109 profiles are on GitHub. For any other institution, group its works by field on the website or the API (MIT since 2015, by field) and compare with your last report.

See what changed. The change-set files list every topic, subfield and field with its works before and after, where each old topic’s works went, and, for each topic, how often an independent judge agreed with its works before and after.

Need the old topics? They’re kept. Every work’s old topics and scores, frozen on 5 October, are attached to the v2.0.0 release (12 files, 9 GB). The old classifier stays public too: its code is on GitHub and its weights and training data are on Zenodo, so you can keep running it. Our text-tagging endpoint also keeps it, at /text/topics?version=1, until 13 January 2027, so you have three months to compare the two side by side. Snapshot users: this change doesn’t touch updated_date, so reload fully from the 14 October snapshot.

Also new: topics and keywords are connected

Each keyword now shows the topics it’s most used in, with the share of its works in each (“Related topics”), and each topic shows its characteristic keywords. A keyword can belong to several topics, and we show how much. Explore topics, their keywords and the whole hierarchy in the aboutness viewer.

A month of aboutness improvements

Topics are the last of a run of changes to how OpenAlex describes what works are about. In each, your reports showed us what to fix. The middle column is our accuracy on the old system, then on the new one; the notes below the table say how we measured each.

Change	Old → new	Links
Topics1	25% → 82%	code
SDGs2	29% → 70%	post, code
Keywords3	44% → 91%	post, code
Study designs4	didn’t exist → 99.7%	post, code
Semantic search5	43% → 55%	list

Thanks to everyone who sends us examples when something looks off. Keep them coming.

OpenAlex has a new classifier for the UN Sustainable Development Goals, and it’s much more accurate than the old one. On 2,000 works drawn at random from OpenAlex, its SDG tags are right 68% of the time; the old model’s were right 29% of the time. It replaces the old tags on every work today.

Benchmark	New classifier	Old classifier (Aurora SDG-BERT)
2,000 random OpenAlex works, judged by AI judges (F1)	0.70	0.29
…precision (share of tags that are right)	0.68	0.29
…recall (share of right answers found)	0.72	0.31
Goals where it does better (of 17)	17	0
8,767 researcher votes from the Aurora survey (F1)	75.5	63.6

What was wrong with the old tags. Our SDG tags came from Aurora’s SDG-BERT model, which we’ve served for years. On a random sample of OpenAlex, about 7 of every 10 of its tags were wrong. It was worst on energy: SDG 7 was its single biggest label, and only about 1 in 20 of those tags held up. Several of you told us so, and you were right.

How we measured it. We drew 2,000 works at random from all of OpenAlex, including the 37% that have only a title. Two AI judges from different labs (Claude Opus 5.5 and GPT-6.1 Sol) read each work against the UN’s own text for each goal and its targets, independently; where they disagreed, a third model (Claude Fable 5.1) decided. The judges agreed with each other on 98.9% of decisions. The ranking held under every judge on its own. We also scored both models on the 8,767 paper-and-goal questions from the Aurora survey, where 244 researchers voted on whether papers belong to a goal; these papers came from Aurora’s own search queries, so it’s the old model’s home turf. The new classifier wins there too.

How it works. A large language model read titles and abstracts for 200,000 works and decided, goal by goal, whether each work addresses that goal. We trained a small classifier on those decisions, using the vector OpenAlex already stores for every work. That makes it cheap to run on all 475 million works, and new works get tagged within a day or two of being added. The training labels, the 2,000 judged works, the judging rubric and the code are on GitHub: ourresearch/openalex-sdgs.

What counts as an SDG. A work counts when its main subject addresses one of the goal’s UN targets. Being about the topic isn’t enough: a paper on atomic energy levels isn’t SDG 7, and a species description or a museum specimen record isn’t SDG 15 (life on land) unless it’s aimed at conservation or land use. Researchers who answered the Aurora survey were split on pure taxonomy papers; we went with the stricter reading, because the people who use this field most are counting research that contributes to the goals.

What you’ll see. About a third of works now carry at least one goal. Counts per goal move a lot:

Goal	Old tags	New tags
3 Good health and well-being	26.8M	75.4M
16 Peace, justice, and strong institutions	13.0M	18.8M
4 Quality education	18.0M	18.5M
2 Zero hunger	19.0M	15.9M
9 Industry, innovation and infrastructure	6.5M	12.4M
8 Decent work and economic growth	11.4M	7.8M
10 Reduced inequalities	14.2M	6.1M
11 Sustainable cities and communities	11.4M	6.1M
7 Affordable and clean energy	25.1M	5.9M
13 Climate action	6.4M	5.4M
12 Responsible consumption and production	1.5M	4.7M
15 Life on land	8.1M	4.6M
6 Clean water and sanitation	7.7M	4.1M
5 Gender equality	9.6M	2.8M
14 Life below water	9.7M	2.3M
1 No poverty	2.1M	1.6M
17 Partnerships for the goals	3.7M	0.9M

If you report SDG counts for your institution, your numbers will change, probably by a lot; the new ones are much closer to right. We’d love to hear where they still look off: support@openalex.org.

For API and snapshot users:

• sustainable_development_goals keeps the same shape: id, display_name, score.
• score is the classifier’s confidence from 0 to 1, and a goal appears at 0.4 or above. Higher means surer, but it’s not a probability: in our checks, tags scored just over 0.4 were right about a quarter of the time, 0.5 to 0.8 about half the time, and above 0.9 about 8 times in 10.
• updated_date did not change for this switch. If you sync incrementally, reload this field from the snapshot.
• The old tags stay available for about a month in a deprecated field, sustainable_development_goals_aurora (read-only), then go away in early November.
• The experimental x_sdgs filter is gone.

Details, per-goal numbers and how to read the scores: help.openalex.org/data/sdgs.

Jason

OpenAlex keywords used to be bad. Now they’re good. 419 million works in OpenAlex have new keywords, drawn from a vocabulary of 1.88 million. They’re 91% accurate, up from 44% for the old keywords.

The old keywords were guessed from each work’s topics, so they were often off. The paper announcing the first gravitational waves got “Black hole (networking)” and “Binary number.” The new tagger reads each work’s title and abstract and says what it’s about:

Paper	Old keywords	New keywords
Observation of gravitational waves from a binary black hole merger (2016, here)	Black hole (networking), Binary number, Computer science	binary black hole mergers, LIGO, ringdown, tests of general relativity
Deep learning (2015, here)	Representation (politics), Layer (electronics)	backpropagation, convolutional neural networks, recurrent neural networks
Highly accurate protein structure prediction with AlphaFold (2021, here)	Computer science, Function (biology)	AlphaFold, protein structure prediction, protein folding problem, multi-sequence alignment

How much better

Our judge is an AI model from another lab: OpenAI’s GPT-6 Astra, since our keywords were built with Anthropic’s Claude. It read 400 random works and rated each keyword on its own. You can see the results above. Shown both lists side by side, the judge preferred the new keywords for 395 of the 400 works.

The new keywords also match the keywords authors chose for their own papers twice as often as the old ones did, and they beat our previous best models on two standard keyphrase benchmarks. Every number is here.

The new keywords cover 89% of works, down from 95%. That’s on purpose: when a record doesn’t say what it’s about (a bare dataset entry, a table of contents), the tagger now returns nothing rather than a guess.

What they’re good for

• Finding what text search misses. A keyword finds the paper even when authors used different words for the same concepts. 133 million works with no abstract still get keywords.
• Mapping a field. Group any set of works by keyword to see what it’s made of, and by year to see what’s rising.
• Finding experts. Filter to a keyword and group by author; in our tests the top ten were real specialists on 24 of 24 queries.
• Searching across languages. Keywords are in English, but papers in any language get them: a Chinese paper on microplastics in soil carries the same microplastics keyword as an English one. About half of what the keywords add to a search on microplastics or remote work isn’t in English.

How to: find the papers your text search misses

Say you’re studying antimicrobial resistance. Just search: OpenAlex search now reads keywords along with titles and abstracts. When a phrase you type names a keyword, a paper matches if the phrase is in its title or abstract or the paper carries that keyword. A title and abstract search for the phrase finds 118,920 works; this one finds 241,167:

https://api.openalex.org/works?search.title_abstract_keywords="antimicrobial resistance"

The antimicrobial-resistance keyword brings in about 122,000 of them, and our judge rates 81% of those on topic: about 100,000 relevant works the phrase search missed, so you find about twice as many. Some say “antibiotic resistance” or “drug-resistant bacteria” instead. 47,269 have no abstract at all, and 74% of those are on topic, like “Increased resistance to antibiotics among microorganisms isolated in a general hospital” (1958, here) and “Lack of development of new antimicrobial drugs” (2005, here); no text search could find those.

On openalex.org there’s nothing to set: “Title, abstract, & keywords” is now the search box’s default, and the top results are reranked so the papers most clearly about your topic come first. Every other word you type still has to be in the text, so “remote work productivity” finds papers tagged remote work that mention productivity, never a paper tagged only productivity. In OQL it’s works where title-abstract-keywords has ("antimicrobial resistance").

Let your AI agent do a thorough search

The search catches the keywords your own words name. An AI agent can go further: find the keywords that mean your topic in other words, add them, and read the results to drop the strays. The /text/keywords endpoint takes any text, including a title or abstract, and returns the keywords that fit it, and an agent like Claude Code or Codex can use it for you. Tell it something like this:

Check help.openalex.org first, then use the OpenAlex API to find papers on <your topic>. Use the title, abstract and keywords search, and add OpenAlex keywords that describe my topic in other words, all in one query. Each paper should cover every part of my topic. Read the results as you go and keep only the papers that are really about my topic. Save the 200 most-cited of those as a CSV and show me the query and how many papers each part found.
My API key: <your key>

Keep the “check help.openalex.org” part: without it, agents work from what they remember about the OpenAlex API, which is out of date. We tested this prompt on fresh installs of Claude Code and Codex: every run found the title, abstract and keywords search on the help site and built one query with it, and on remote work and employee wellbeing, 99% of the papers in their final lists were at least partly on topic (about seven in ten clearly so). In the Claude app, set the chat to Auto (next to the model name, under the message box), or it will ask your permission before every search. You’ll need a free API key; the full recipe is here.

Or, if you use Claude, just ask. Add the free OpenAlex connector, now in Claude’s connector directory, and ask in one sentence: “Do a thorough search for open-access papers on how remote work affects employee wellbeing.” The connector searches titles, abstracts and keywords, adds keywords that mean your topic in other words, reranks the results so the most relevant come first, tells you how many works each part found, and hands back the query so you can rerun it.

The vocabulary keeps changing

This isn’t a fixed list. New fields show up every year, and we’ll add keywords for them as they do. We already merge keywords that mean the same thing (“Neanderthals” and “Neandertals”), and we’ll keep merging, splitting and adding as you tell us what’s wrong. Send us feedback on any keyword that misses.

Keywords are already linked to Wikidata: about 22% of keywords have a Wikidata id, and those cover about 56% of keyword uses. That makes it easy to join OpenAlex keywords to other data. Keyword search also knows 5 million synonyms, so searching keywords for “heart attack” finds myocardial infarction. Next, we’ll look at joining keywords to other outside vocabularies like MeSH, and at vector search, so that searching the keywords for “antibacterial resistance” finds antimicrobial resistance. We also want to assign each keyword to one or more of our topics, so keywords become part of the whole OpenAlex aboutness taxonomy.

Check our work

Everything is open: the code, prompts, benchmarks, vocabulary and model weights, with a release that lets you rerun the tagger yourself. The keywords help page has the details.

Edit, 3 October 2026: An earlier version of this post showed how to run a title and abstract search and a keyword search side by side and combine them by hand. A few hours after it went out, we shipped a search that does both at once (title, abstract and keywords, now the default on openalex.org), so we rewrote the how-to section and the AI prompt to use it.

Edit, 3 October 2026, later the same day: The OQL field in this post is now spelled title-abstract-keywords (it was title/abstract/keywords), because slashes already separate the parts of OpenAlex IDs. The old spelling still works.

You can now filter all of OpenAlex by study design: randomized controlled trials, systematic reviews, meta-analyses and more. 21 million works, in every field and language, carry one. Our randomized-trial tags are 99.7% precise, and on the same papers they’re more precise than PubMed’s, long the gold standard.

When you’re searching, you can focus on just the highest-quality evidence. And if you build evidence tools, you get study design for the whole literature, free, through the OpenAlex API.

Seven designs, every field

The designs come from PubMed’s own vocabulary, and we add nothing to it. What’s new is reach. PubMed tags the journals it indexes; we tag every work with an abstract. Half of the randomized trials we tag, 306,000, are in journals PubMed’s MEDLINE doesn’t index.

Here’s a graph how many works are tagged with each study design, in both OpenAlex and PubMed:

Every randomized trial is also a clinical trial, and every meta-analysis is also a systematic review, so a work can count twice. PubMed’s Observational Study tag dates only from 2014. Works without an abstract get no design.

Find the trials, see the evidence

Filter to one design. Of 34,395 papers that mention semaglutide, 533 are randomized trials; one filter returns just those, on openalex.org or in the API with filter=study_designs.id:randomized-controlled-trial. AI agents that search OpenAlex can do the same.

Group by design to see what kind of evidence a field has. Long COVID research is mostly observational: 31 observational studies for every randomized trial. Semaglutide already has more meta-analyses (1,341) than randomized trials.

Tagged papers on each topic, by design. Papers an OpenAlex search for the topic returns.

Share of each topic’s papers that carry a design; protocols left out. We tag conservatively, so real counts run higher, trials most of all.

Built on PubMed, tuned for precision

PubMed’s publication types are carefully indexed and trusted across medicine. But its tags sometimes describe the study a paper came from rather than the paper itself, so a methods paper or a later analysis of trial data can carry the trial’s tag. We tag only what the paper itself reports, and only when we’re confident.

How often each tag is right

Share of tags an independent judge confirms, on the same PubMed-indexed papers.

Where the tags differ

Here are some examples of papers that PubMed tags as RCTs, but we (correctly) don’t apply the RCT.

Paper	What it is
Bland and Altman, Lancet 1986: measuring agreement	A statistics methods paper
TOAST stroke classification, 1993	Definitions written for a trial
The Implicit Association Test, 1998	Lab experiments that built a test
Oncotype DX gene test, NEJM 2004	A gene test on tumors from one arm of an earlier trial
SAPS II severity score, JAMA 1993	A risk score built from a cohort

Our model is tuned for precision, not recall–when we say it’s an RCT, it’s an RCT, with 99.9% accuracy. PubMed captures some RCTs that our stingy approach misses, but at the cost of some accuracy.

Judge: Claude Opus 5.5, under written definitions, on 8,308 papers sampled so that every estimate is weighted to the whole index; nothing was tuned on that sample. Re-reading 436 open-access papers in full barely moved the verdicts.

Check our work

Everything behind these numbers is public: the code, the models, the samples and every judge’s verdict. One command reruns every table. github.com/ourresearch/openalex-study-designs

When people want to know whether a piece of research is any good, they usually have two options: ask an expert, or count citations. Both have well-known problems. Expert review is slow, expensive, subjective, and does not scale to portfolios of thousands of papers. Citations take years to accumulate, reward the already well-cited, and say more about whether a paper was used than whether it said anything new.

That second gap is the interesting one. Creative work, including scientific discovery, is generally understood to be both new and useful. Citations are a reasonable proxy for usefulness. We have had no widely trusted, scalable measure for new. Funders say they want novel research, and then evaluate it with tools that cannot see novelty.

Earlier this year an international challenge set out to change that, and the winning approach now exists as a working prototype. We are considering adding a novelty score to OpenAlex. Before we write a spec or scope resources to implement it, we want to know whether you would use it, what for, and what would make you trust it.

The challenge

The Metascience Novelty Indicators Challenge was launched in September 2025 by the UK Metascience Unit at UK Research and Innovation; designed by the Science Policy Research Unit (SPRU) at the University of Sussex and RAND Europe; and operated by Challenge Works. Teams were asked to build a tool that could assess the novelty of a research paper at the time of publication.

To evaluate the entries, SPRU built a ground-truth dataset the hard way: a random sample of recent publications across all disciplines (drawn from OpenAlex), each rated for novelty by human experts in the relevant field who were provided with the full text. Challenge entrants never saw those ratings. Their indicators were scored on how closely they matched the experts’, and the evaluators were blinded to who had submitted what.

The winner, announced just a few months ago in June 2026, was a team from Forschungszentrum Jülich in Germany: Jan Göpfert, Samuel Kieling, Jann Weinand, Titan Hartono, and Patrick Kuckertz. Their approach came top on most evaluation criteria (in a “winner of winners approach”), and they received a £300,000 prize, provided by Coefficient Giving, to develop it further.

Coverage: https://www.nature.com/articles/d41586-025-01882-7 and https://www.science.org/content/article/how-novel-research-paper-competition-quantify-concept-crowns-winner.

How the Jülich indicator works

Most existing novelty indicators work from metadata: unusual combinations of references, keywords, or journals. The Jülich indicator reads the paper instead.

It uses a large language model to analyse the study and a selection of the works it cites, and from those, it reconstructs the state of knowledge at the time of publication, including the open questions in the field. It then asks what the focal study contributes anew: an original method, a surprising result, or a solution to a problem that was previously unsolved. It deliberately collects arguments for and against the paper’s novelty and weighs them. The output is a score from 0 to 100, an interval showing how confident the model is, and a written justification that a reader can check.

Jülich press release: https://www.fz-juelich.de/en/news/archive/press-release/2026/ai-identifies-scientific-novelty-julich-team-wins-international-challenge

The team is clear that this is a support for human judgement rather than a replacement for it, and that a score which could be gamed, or which quietly disadvantaged some fields or languages, would be worse than no score at all. Those are our concerns too.

Why OpenAlex is interested, and what it would take

OpenAlex already carries citation counts, topics, and a growing set of work-level signals (e.g., field-weighted citation impact). A novelty score that is transparent, reproducible, and available for every work, free, in the same place, would be a genuinely new thing for funders, institutions, journals, and researchers to work with. The UK Metascience Unit has told us that it would be of interest to a large proportion of the roughly 100 research teams it funds, as well as to UKRI’s own analysts. And we would rather that such a novelty indicator be built in the open than watch it appear behind a paywall.

It is not a small undertaking, and the open questions are the reason for this post:

• How far back? Scoring every work in OpenAlex is very different from scoring everything published since, say, 2019.
• Which works? Journal articles only, or preprints, conference papers, book chapters, and dissertations as well? The indicator was validated so far only on journal articles. Maybe that’s enough (after all, FWCI only applies to a few work types), but maybe it’s worth the effort to evaluate the indicator in a broader set of work types first. 
• One-off or continuous? A static file scored once is much cheaper than a live score that updates as new works arrive and as records change. A live production database like OpenAlex really wants the latter, but maybe the community only needs static scores, updated yearly.
• Open-weight or commercial models? Open-weight LLMs would make the scores reproducible over the long term and could run on public research computing. Jülich is testing whether they perform as well.
• Cost and compute. Running a language model over hundreds of millions of works is a significant compute bill, however it is done. That’s why answers to the questions above are especially important before we fully spec this out.
• Transparency versus gaming. Publishing the full method makes the score checkable and makes it easier to write to. Where should the line sit and is that an argument for static historic application vs. on-going scoring of the indicator against new works?
• Use in evaluation. A score built to describe a literature can be misused to judge individuals. What safeguards would you want before it existed at all?

What we are asking

This is a vibe check with a light-touch questionnaire, not a full consultation. We want enough signal on demand and scope to write a spec and a resourcing case, and we will go deeper later if the response warrants it.

Fill in the form. About six questions, five minutes, any language. It stays open until Friday 13 November 2026.

https://docs.google.com/forms/d/e/1FAIpQLScOlmbDN7AdJic8jHO1dnQWMhppFIrMK96J0s6TmcU1oME3ZQ/viewform?usp=publish-editor

Join the webinar. Wednesday 7 October 2026, 8:00 to 9:00am Pacific (11am Eastern, 4pm UK, 5pm Central Europe). Ben Steyn (UK Metascience Unit) on why the challenge was run, Jan Göpfert (Jülich) on how the indicator works, and Kyle Demes (OpenAlex) on what integrating it into OpenAlex would look like, followed by half an hour of questions. Sarah Otner (SPRU) will be on hand for questions about the evaluation. Recording and slides posted afterwards.

https://zoom.us/webinar/register/WN_EetlA-l3QgK2FvQVxE0GLg

Pass it on. Funders, research offices, metascience groups, tool builders, and anyone who has ever wished that citations told them something they do not.

What happens next

We will share a synthesis of the responses with the UK Metascience Unit and the Jülich team in late November, and publish a summary here. If there is clear demand and a workable scope, the next step is a spec and a scoped first run. If there is not, we will say so and keep the door open.

Thanks in advance for any input you share. If you have any other questions about this initiative, let me know at: kyle@openalex.org 

Since 2018, Unpaywall and then OpenAlex have described the open access status of a work with a colour: gold, green, hybrid, bronze, or closed. The labels were a pragmatic answer to a practical question (“can I read this, and where?”) and they caught on. Today they are embedded in major bibliometric databases, funder compliance dashboards, institutional reports, and national open access monitors around the world.

They have also started to crack. Earlier this year the OpenAlex Community Advisory Board convened a working group to look at the problems and recommend a way forward. The group, chaired by Juan Pablo Alperin, published its report last month:

Alperin, J.P. et al. (2026). Recommendations for Revising the Open Access Classification System in OpenAlex. Zenodo. https://doi.org/10.5281/zenodo.21934115

Before we further consider such significant changes to the open access classification in OpenAlex (and Unpaywall), we want to know whether the problems the working group identified match your experience, and whether the direction it proposes makes sense for the way you use open access data. This post explains both, and asks for ten minutes of your time.

What has gone wrong with the colours

The working group’s diagnosis comes down to four things.

• The colours were built for journal articles. Preprints get called green, which implies they are a copy of a publisher version that may never exist. Dissertations live in university repositories and fit nowhere. Datasets and software are rarely “published” by a publisher at all. Books can have open chapters and closed chapters in the same volume. None of this maps onto a scheme that assumes every work has a publisher version and possibly a repository copy.
• They lean on journal-level policy instead of what is true of the work. Gold is assigned because a journal is listed in DOAJ or is empirically mostly open, not because the article in front of you is open. Journals flip, get acquired, adopt subscribe-to-open, or publishers change their mind. A journal can be gold today while all of its back catalogue is closed. This also cuts against the direction the research assessment community has taken with DORA, which asks us to look at the work rather than the venue.
• They mix up access with business model. Gold tells you something about how a journal is funded and something about whether you can read the article, and it is not always clear which. Green does not say whether the repository copy carries a licence you can reuse. Bronze is a catch-all for “free to read, no idea why”, and a work can move in and out of bronze several times in a year.
• They have no sense of time. An embargoed article that opens after twelve months, or a free article that a publisher later closes, gets a single snapshot label. Anyone tracking compliance or studying how access changes over time has to work around that.

What the working group proposes

Instead of one label that tries to say everything, the report recommends describing each work along three separate dimensions:

• Access: is the work free to read, or closed?
• Location: is the free copy at the publisher, or on another platform such as a repository or preprint server?
• Licence: does the work carry an open licence, a restrictive one, or none that can be identified?

Combined, these give plain-language, work-level labels such as Publisher Open, Platform Public, or Closed that work the same way for an article, a preprint, a dataset, or a book chapter. Venue-level questions (is this an open access journal?) are left to be derived from the works themselves, or handled by a separate classification.

Just as important is what the proposal does not try to do. It says nothing about business models: APCs, diamond, hybrid agreements, subscribe-to-open, transformative agreements. The working group’s view is that business model is a property of a venue, access is a property of a work, and squashing them into one label is where much of the confusion came from. They recommend a separate effort on a venue business model taxonomy, and we agree.

The report includes a mapping from the proposed labels back to the current colours, use cases from funders, institutions, publishers, and database providers, and a discussion of transition. It is 15 pages, summarizing deliberations from thought-leaders in the space and worth reading in full.

What we are asking

This is a vibe check, not a line-by-line review. We want to know three things: does the problem resonate, does the three-dimension direction make sense, and what would you need from us to adopt it?

Fill in the form. About six questions, five minutes, any language. It stays open until Friday 13 November 2026.

https://docs.google.com/forms/d/e/1FAIpQLSew0id7AefKryr-SGeb8DChSQkxoMUM6WP2ebmV7U3hVjuo3A/viewform?usp=publish-editor

Join the webinar. Tuesday 6 October 2026, 7:00 to 8:00am Pacific (10am Eastern, 3pm UK, 4pm Central Europe). Juan Pablo Alperin will present the report, and Ashley Farley (Gates Foundation, working group member) and I will join for Q&A. The recording and slides will be posted afterwards.

https://zoom.us/webinar/register/WN_NbIKwVu6Q4SI83Fv5qwXxw

Pass it on. If you know people whose reporting, policies, or reimbursement workflows depend on the colours today, please send this to them. Those are exactly the voices we most need to hear before anything changes.

What happens next

Nothing changes in the OpenAlex API or website as a result of this post. We will synthesise the responses and take them to the Community Advisory Board on 10 December 2026. If the community response supports the direction, the next phase would be implementation planning: a transition period with old and new labels available side by side, direct engagement with the databases and tools that reuse our open access data, and a separate conversation about business models. If the community does not support the proposal, we will have learned that cheaply, and we will say so.

Thanks to the working group for the report: Juan Pablo Alperin, Amélie Church, Nees Jan van Eck, Ashley Farley, Rupert Gatti, Melissa Harrison, Bianca Kramer, Christopher Marcum, Dom Mitchell, and Roberta Takenaka. 

If you have any questions about this initiative more generally, send me an email at kyle@openalex.org 

Researchfish is closing on 31 July 2027. For the more than 90 funders that relied on it, including UKRI, NIHR, and many of the UK’s largest research charities, the question of how to track what their funding produced is suddenly urgent. When UKRI last went to tender, it concluded there was no viable alternative product (Johnson 2026). We think that was the wrong question. The alternative to Researchfish is not another product. It is open infrastructure that is already carrying this data at scale, plus a policy decision about where researchers assert the link between their grants and their outputs.

In this post we (Melissa Harrison, Europe PMC and Kyle Demes, OpenAlex) want to show what already exists, what our two organisations are connecting next, and what funders can do today.

Two different jobs, only one of which belongs in the open

Progress reporting bundles together two very different kinds of information, only one of which belongs in the open, so it is important to be precise about which one we are talking about. Narrative reporting, such as progress against objectives, team composition, and unpublished or sensitive results, belongs in funders’ own systems, and nothing in this post suggests moving it.

Output reporting is different. Which publications, preprints, datasets, software, and clinical trials resulted from which award is factual information that already lives in the scholarly record. Yet for years, researchers have been asked to retype it annually into a proprietary platform, where it was locked away from the very community that produced it. That is the part of Researchfish’s job that open infrastructure can take over, and the part this post is about.

This is not hypothetical. It is running today.

The infrastructure exists

Europe PMC and OpenAlex play distinct but complementary roles, and the pipeline between them is already operating at scale.

Europe PMC is a world-leading open biomedical literature resource that combines a discovery platform with a full-text repository. Through its submission system, Europe PMC plus, researchers funded by 35 Europe PMC funders already link their publications directly to the grants that supported them, at the moment they deposit. Those grant–publication links live in Europe PMC’s open database connected to other core resources like the life science datasets underpinning these publications.

OpenAlex is the open scholarly graph: hundreds of millions of works connected to authors, institutions, sources, and funders, all openly licensed with open APIs. OpenAlex now holds over 16 million award records from roughly 30,000 funders worldwide, drawn from funders’ own open databases (Gateway to Research, NIH ExPORTER, Wellcome’s open grants data, and dozens more) and from assertions in the scholarly record. More than half of those awards are already linked to at least one research output, and those outputs are not just journal articles: they include hundreds of thousands of datasets, preprints, reports and dissertations, as well as software and more.

This is the part that makes this a report on progress rather than a proposal: Europe PMC is already one of the largest sources of grant–output linkages in OpenAlex, with millions of researcher- and funder-connected assertions flowing from Europe PMC into the open graph. A researcher who links a paper to a grant at deposit in Europe PMC is already contributing to an open, reusable record of what that funding produced.

Publisher-supplied funding metadata (the kind that flows through Crossref and DataCite) has incredible value,  however, researchers have no easy way to correct or extend it after publication and the process relies on publishers delivering this metadata when registering DOIs. Europe PMC and OpenAlex are the places in the open ecosystem where researchers themselves can assert and confirm grant-output links. That researcher-confirmed layer is precisely the human-curated data Researchfish was built to collect. It already exists in the open, it just needs to become the default place this reporting happens.

Open means many front doors

A proprietary platform gives every funder the same front door and a single supplier. Open infrastructure works differently, and this flexibility is its core advantage. A funder deciding what to do before July 2027 has several routes, all available now:

Use Europe PMC as the reporting platform. For existing Europe PMC funders, grant linking at deposit is in production today. Point grantees to a workflow they already use for open access compliance, and the linkages accumulate continuously in the open instead of arriving in an annual burst. For other life sciences funders, becoming a Funder of Europe PMC is the closest thing to a like-for-like replacement for the outputs portion of Researchfish, and it exists now.

Work with OpenAlex directly. Funders across all disciplines can have their awards represented in OpenAlex and build reporting and analysis straight on the open graph, covering output types and fields well beyond biomedicine. Many funders are already doing this.

Integrate open infrastructure into systems you already run. A funder can keep its grants management system or portal and pull candidate outputs from open APIs, asking researchers only to confirm a funder or grant ID rather than to type it. One funder OpenAlex works with already compares open grant-output linkages against its own compliance records and automatically reminds exactly the researchers whose assertions are missing or their requirement. Reporting becomes exception handling instead of data entry.

Both Europe PMC and OpenAlex provide open APIs and openly licensed data, so research information system vendors and other open infrastructures can build on the same foundation. With a proprietary platform, you choose a supplier. With open infrastructure, you choose a workflow and you can change your mind later without losing the data, because the data was never trapped inside a platform.

What OpenAlex and Europe PMC are working on together

The foundations exist; our collaboration is about tightening the loop between them. The directions we are exploring include making the flow of researcher assertions from Europe PMC into OpenAlex fully systematic and timely, so a link confirmed at deposit is visible to funders on OpenAlex within days; presenting researchers with candidate linkages from text mining and the open graph to confirm rather than enter from scratch; and capturing links to datasets, software, and other outputs at the same natural moment in the Europe PMC workflow. We will share more as this work develops, and we would rather shape it with the community than announce it finished.

What funders can do now

Two decisions, both available today, would do more than any new platform:

Publish your award list openly. Even a minimal feed of award IDs and titles, without personal data, gives researchers something to select instead of retype, lets open infrastructure link outputs to your grants, and means your reporting data accrues in the open from day one. Funders like Wellcome and UKRI (through Gateway to Research) already show this is routine.

Require funding metadata at the point of deposit, and accept researchers’ assertions in open infrastructures as reporting. When linking the funder and grant is part of the process of depositing the output, the information is captured once, at the natural moment, and can be reused everywhere. The annual reporting burst simply stops being necessary.

Join the conversation

Researchfish’s closure sets a deadline, but it is also the first real opening in a decade to move output reporting onto infrastructure the community owns. If you are a funder weighing your options, an institution with reporting requirements, or an open infrastructure interested in connecting, we would love to hear from you: 

Melissa Harrison mharrison@ebi.ac.uk 

Kyle Demes kyle@openalex.org

Research relies on software. Software written by scientists, for science, runs through the entire modern research stack: NumPy and SciPy, R and ggplot2, Jupyter, BLAST, ImageJ, AlphaFold. Yet in the scholarly record, that software is nearly invisible. Software is not usually cited formally in publications and is usually just mentioned in the text, which means the software (and the people who build and maintain it) never shows up in the databases the research world uses to find, measure, and reward scholarship.

We’re going to fix that. Thanks to a 2-year grant from Schmidt Sciences, OpenAlex is joining forces with a team at University of Texas at Austin led by James Howison to make research software a first-class part of the open scholarly graph. It’s a problem we’ve been chipping away at for a long time: over a decade ago we built Depsy as an early proof of concept for measuring the impact of research software. Now we get to do it properly, at the scale of the full scholarly graph. Work kicks off in mid-August 2026 (you can read the full proposal here).

Why software, and why now

The scholarly graph records the relationships among papers, authors, institutions, funders, and topics. It’s how researchers find work, how contributors get credit, and how funders and institutions make decisions. But because software so rarely appears in reference lists, it can’t be found or measured in any of today’s scholarly databases, open or proprietary.

That invisibility has real costs. Research software engineers whose tools underpin entire disciplines struggle in promotion and tenure processes because their contributions don’t “count.” Funders can’t see where software investments would make the biggest difference, or what impact past investments have had. And as AI coding assistants become part of everyday research practice, the missing link between papers and the code behind them is exactly the context those systems need most.

What you can expect in OpenAlex

This grant funds a set of major additions to OpenAlex that users have been asking about for years:

Software as a research entity. We’ll seed OpenAlex with an initial registry of software projects and add software as a work type (in fact, we just launched this), connected to the rest of the graph: authors, institutions, topics, funders, and the papers that use it. Each software entity will link out to external identifiers (package registries, repository URLs, DOIs, Software Heritage IDs) with full provenance.

Software mentions, extracted from full text. Most software never makes it into reference lists, so we’ll find it where it actually lives: in the text of papers. We’re building a mention-extraction and disambiguation pipeline that links papers to the specific software they use, and we’ll expose the results through a new citation object, complete with the surrounding text context, the citation type, and confidence scores, all searchable through its own API endpoint.

True versioning, for software and for all works. Software changes constantly, and “which version did they use?” matters for everything from reproducibility to impact measurement. We’re adding a generalized versioning model to the OpenAlex schema. Software is the first beneficiary, but versioning will extend to other work types where it’s increasingly important, like datasets and preprints.

Credit for the people who build research software. Author profiles will gain software-specific contribution and adoption metrics, so the maintainers of foundational tools can finally show where their work is used, much as in  systems that already track publications.

Along the way, we’ll be upgrading the machinery underneath: broader full-text coverage, faster PDF parsing, and improved author disambiguation, all of which will improve OpenAlex well beyond software.

All of it will be open, as always: CC0 data, open source code, and open documentation, available through the OpenAlex API, UI, and snapshot.

We’re hiring

OpenAlex will build and operate the production systems, while a team at the University of Texas at Austin, led by James Howison, brings deep expertise in research software scholarship and software mention extraction. A project this size needs more hands. OpenAlex will be opening several new positions in the next few weeks, spanning data and software engineering and community management roles. Keep an eye on openalex.org/jobs, and if this is the kind of problem you want to work on, we’d love to hear from you.

Thank you

We’re grateful to Schmidt Sciences for backing this work. Making research software visible has been a goal of the research software community for more than a decade, and we’re honored to help build the open infrastructure that finally makes it routine.

As always, we’d love your feedback: if you have use cases for software metadata in OpenAlex, ideas, or concerns, get in touch at support@openalex.org.

—The OpenAlex Team

*This post was written with Claude. Every number and example was verified by a human against the live data.

Unlike most scholarly databases, OpenAlex doesn’t start from journals. Other databases pick a journal, index it, and attach its articles — so their journal metadata is clean by construction. OpenAlex works the other way: we index scholarly works first, then connect each one to the rest of the research graph — authors, institutions, and publication venues. Venue information arrives from many different sources, unstandardized and often without persistent identifiers. That’s how one journal can quietly become two or three records. And because source metadata is critical for many of our users — especially librarians — it was time for a comprehensive cleanup.

Over the past two weeks we merged 27,728 duplicate source records — the same journal listed under a translated name, an old name, or a small spelling difference, its papers split among the copies — correcting the venue records of more than 6 million works. After the cleanup, those papers live under one journal: one complete works count, one citation profile, one page. The active catalog went from 282,924 source records to 255,434; every removed record was a duplicate, and no real venue lost its page.

Three examples

Chemischer Informationsdienst → ChemInform. Wiley renamed this chemistry alerting service decades ago — but the catalog carried both names as separate journals, splitting fifty years of chemistry down the middle: 307,000 works under the German name, the rest under the English one. It’s one record now, 794,000 works, with the German identity preserved as an alternate title and searchable in both languages.

Journal of the American Medical Association → JAMA. Even the world’s best-known journals weren’t immune: 85,611 papers were filed under the spelled-out name as if it were a separate journal from JAMA. Roughly a sixth of JAMA’s output was invisible from its own page. One record now — 354,735 works — and the spelled-out name remains searchable as an alternate title.

镇江医学院学报 → Journal of Zhenjiang Medical College. This journal existed twice in the catalog — once under its Chinese name, once under its English one. The telling detail: the duplicate’s publication activity stops in 2001 — the exact year Zhenjiang Medical College merged into Jiangsu University. The bibliographic data echoed institutional history; the two records are now one.

How it works

The hard part of this work isn’t finding lookalike records, it’s that academic publishing is full of journals with similar names that must not be merged. Every merge decision ran through four independent layers:

• Full-evidence judgment, never name matching. Each candidate pair was decided on its complete records: ISSNs, publishers, countries, and year-by-year publication activity. Two journals publishing independently in the same years is evidence of two real journals, no matter how similar the names.
• An adversarial second pass. Every approved merge went to a second model with the opposite job: find concrete evidence the two records are different venues. Only merges that survived their own prosecution proceeded.
• An independent re-check of every consequential pair. All 12,660 merges involving journals with 50+ papers were re-reviewed by a separate model with fresh evidence. This layer caught things the first two missed — for example, a proposed merge of 터널과 지하공간 (the Korean Society for Rock Mechanics’ journal) with a similarly named but distinct tunneling journal carrying different ISSNs. That layer alone caught 570 proposed merges that were wrong.
• Human audits, on random samples. Merges were hand-checked throughout: a stratified 50-pair review sheet was graded before each execution wave, plus a purely random sample drawn from the merges as they shipped — zero were rejected. And the largest merges were never automated at all: every pair whose retiring record held 10,000+ works was individually reviewed and approved by a human, with the four disputed cases held out entirely.

More than 158,000 examined pairs were confirmed distinct — disjoint ISSN families, different publishers, independent publication histories — and recorded, so future passes build on settled ground instead of starting over. This was a first comprehensive pass; as metadata improves, more merges will follow.

What you’ll notice

• Journal pages with more complete works counts and consolidated ISSNs.
• Search that works in every language a journal has ever been named in — merged records keep every historical and cross-language title. One Czech agriculture journal now carries all five names it has published under since the 1950s.
• Retired duplicate IDs now return 404. If you have stored source IDs that stopped resolving, the surviving record has everything — papers, ISSNs, all names — and searching by ISSN or by any of the journal’s names will find it.

What’s next

New records arrive every day, and the pipeline that did this — find candidates, judge on full evidence, verify independently — is now a repeatable tool for keeping the catalog clean. Next up for source metadata: cleaning up the publisher and host-organization lists, and improving how works get matched to their journals in the first place.

TL;DR. Research security screening checks author affiliations against lists of organizations of concern, using bibliometric data. Affiliation matching is imperfect, though, especially for companies and small institutes with short names or no ROR record, so a researcher can end up linked to an organization they have no connection to. As a first pass we cross-referenced the major watch lists to ROR and cleaned up 6,191 such over-merges across 192 listed organizations. We invite the research security community to collaborate with us to improve OpenAlex and use bibliometric data responsibly for this use case.

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Research security screening is now routine. Funders, universities, and commercial compliance tools check author affiliations against government lists of organizations of concern (e.g., entities flagged for military, defence, or sanctions reasons). The lists are public and the screening is increasingly required.

One part of that screening process often relies on affiliation data from research publications. The general idea is to ask: has the research team been associated with organizations of concern directly (i.e., they worked for or were funded by them) or indirectly (through their networks of collaborators). A positive match in a bibliometric database then triggers follow-up scrutiny to determine if the connection is real and the risks associated with the connection.

Anyone who has worked with affiliation data knows that it can get messy. To demonstrate the problem, the University of British Columbia has almost 200,000 distinct text strings that have been used in publications to refer to the University, covering departments, addresses, translations, typos, text encoding issues from publishers, and more. Here are a few examples:

• “The University of British Columbia”
• “UBC”
• “Univ of British Columbia, Vancouver, BC, Canada”
• “University of Brtish Columbia, Vancouver, Canada”
• “University of British Colombia#TAB#”

And so algorithmic approaches are used to associate these text strings with institutions in the Research Organization Registry. Research information databases each have their own processes for doing this. We make our source code openly available, and you can find our methods detailed on github. But long story short, we have linked over 150M unique text strings to 130k+ unique institutions. These algorithms generally work well, with >98% precision and >90% recall for most academic institutions. But across the full corpus of all scholarly works in OpenAlex (500M+), that’s still millions of errors.

These errors can be especially acute for companies or small research institutes because they may have very few publications to train the matching model and they often have short names that are common words. Consider the example of the company “Optical Sciences”. They have a ROR record because they publish research, but they get erroneously associated with the Optical Sciences Center of the University of Arizona which does not have its own ROR record.

Most bibliometricians are accustomed to navigating these errors in their analyses, and some even know how to fix them upstream (e.g., curating ROR records and string matching in OpenAlex). On the other hand, research security professionals come from diverse backgrounds and only sometimes have a nuanced technical understanding of bibliometric databases. That means an affiliation error that used to be a minor data-quality annoyance can now hold up a grant, trigger a foreign-influence review, or put a researcher into a compliance inquiry over a relationship that never existed.

A first pass at minimizing false positives

We took the published watch lists that screening actually runs against (Canada’s Named Research Organizations, the US Department of Defense 1260H list, the US Consolidated Screening List including the BIS Entity List, US OFAC, the US UFLPA list, the UK FCDO sanctions list, and Australia’s DFAT list) and cross-referenced each entity to ROR with a strict country-consistency check. That produced a verified crosswalk of 192 organizations covering roughly 4.24 million works.

Then, for each organization, we screened every affiliation string linked to it, about 2.5 million in all, looking for this specific failure: an unrelated company or institution absorbed into a listed entity because their names collide (often with an organization lacking a ROR record). We found 6,191 such over-merges, roughly a quarter of one percent of those strings, and we submitted manual curations to remove the linkages. The scale of the collision problem is easier to see in a few real examples than to describe.

• A small US biotechnology firm, identified on its papers by a short name only that also refers to a broad class of medical interventions, was linked to a Russian research institute whose registered acronym is nearly identical. The two have nothing to do with each other. The firm simply had no ROR record, so the closest-looking acronym won.
• System Equipments Div., Asahi Engineering Co., Ltd, a Japanese engineering company, was linked to a Chinese entity on the BIS Entity List whose registered name is essentially “System Equipment.”
• ENGINEERING-PHYSICS, INC. was linked to a Russian Institute of Engineering Physics that appears on these lists, on the strength of the shared words alone.

None of these organizations has any connection to the listed entity it was linked to. In each case the two names are close enough that you can see why an algorithm reached for the wrong one because no record existed for the real organization. This work helps reduce these errors, but they still exist.

Advice to analysts doing the screening

If you’re an analyst in charge of screening research applications for potential connections with a list of organizations of concern, never assume that a bibliometric match is a definitive signal of a connection. A match can help you shorten your list of connections to confirm manually, but there are errors and you need to confirm any potential connections. You can do that easily by inspecting the raw_affiliation_string field in the OpenAlex API. For instance: https://api.openalex.org/works/w3134510327 -> raw_affiliation_string : “Optical Sciences, University of Arizona, Tucson, Arizona, USA”. You can also visit the publication’s landing page or PDF for more information directly from the source. All of that information is linked conveniently in the OpenAlex record.

You should also take efforts to identify false negatives. For instance, if the organization of concern does not have a ROR record (or doesn’t have enough information on how the organization represents itself in publications), then we won’t be able to make any matches to that institution. More broadly, I highly recommend that you connect with experts in this field on how to successfully leverage bibliometric tools in your work while respecting their limitations.

Advice to researchers submitting to screenings

Take a look in OpenAlex in advance to see if there might be issues in your author profile (my profile below). Sometimes another authors’ works might have snuck into your author profile which you can edit that there directly. And sometimes there are errors in affiliation attribution even when the work is yours (like in the examples above). When that happens, the quickest way to get it sorted out is to send details in an email to support@openalex.org. But keep in mind that funders and analysts use different databases and tools for research security compliance workflows and many of them use closed, proprietary databases you won’t be able to access.

Let’s work on this together

If you work in research security, or at a funder or institution that screens affiliations, we want to hear from you.

Screening decisions carry real weight, so the data behind them should be open to inspection. In OpenAlex every affiliation link traces back to the source publication, every correction is public and permanent, and the code that makes the matches is on GitHub (github.com/ourresearch). Nothing sits in a proprietary index that no one outside the vendor can check. When a match is wrong you can see why, contest it, and get it fixed, and that fix then helps everyone who screens after you. For a decision that can affect a researcher’s funding or reputation, that is not a small difference. Open access to the data also means scrutiny is not gated by budget: a well-resourced agency and an under-resourced one work from the same public record.

We do not think we have this solved. We know some of the limits and we are certain there are more we have not found. What we want is to work with people who do this screening every day to map those limits honestly, so nobody treats a match as more certain than it is, and to build the documentation and tooling that would make an open, auditable corpus genuinely good infrastructure for this work. A few experts collaborating with us in the open could improve it for the whole community, globally.

Get in touch at kyle@openalex.org.