The news comes in the context of “a wider pushback against the current model in academic publishing, where researchers publish and review papers for free but have to buy expensive subscriptions to the journals in which they are published to analyse data relating to their work.” It includes a quote from OurResearch/OpenAlex co-founder and CEO Jason Priem: “We felt there’s a mismatch between the values of the academy and the shareholder boardroom. Research is fundamentally about sharing, while for-profits are fundamentally about capturing and enclosing. We aim to create and sustain research infrastructure that’s truly aligned with . . . the values of the research community.”
We at OpenAlex are thrilled at Sorbonne University’s recent announcement that they will be switching to OpenAlex for their publication database and bibliometric analytics, abandoning the use of proprietary products! The Sorbonne, a leading French university, made their announcement in a recent post (click here for the English version; click here for the French version). Starting in 2024, they will be ending their subscription to Web of Science and Clarivate’s bibliometric tools. They will instead be adopting “open, free and participatory tools, and [they are] now working on the consolidation of a sustainable and international alternative, relying in particular on the OpenAlex tool.”
OpenAlex has been working closely with the Sorbonne to make this switch possible, and as they note, “A partnership agreement will shortly be established between Sorbonne University and OpenAlex to formalize their contributions and mutual commitments … and to bring about developments that will meet the needs of its community.” This is an extremely exciting milestone for us and for open science! We invite you all to celebrate with us 🎉🎉🎉!
The New England Journal of Medicine uses a non-standard format when presenting authors and their institutional affiliations, which is a problem when we want to keep track of these links in our data. We developed a custom algorithm to solve this problem, preserving more than a hundred thousand author-institution links.
Linking works, authors, and institutions
OpenAlex data has links between works, authors, and institutions.
Works, authors, and institutions are three of the basic entities in the OpenAlex data. Keeping track of the relationships between these entities is one of the core things we do. It’s important that we identify these links correctly, so they can be used for downstream tasks like university research intelligence, ranking, etc. Often, this information comes to us via structured data which is not difficult to ingest. Many times, however, the data is messy, and using it is not so straightforward.
Affiliation data in the New England Journal of Medicine
Publications from the New England Journal of Medicine (NEJM) are an example of this messiness. Author affiliations in these papers are presented in a format that is human-readable, but not straightforward for a computer to parse automatically. In most other journals, authors are listed alongside their affiliated institutions, and so it is relatively easy for a program to link them together. NEJM does it a different way—as shown in the screenshot of a paper from the journal’s website, institutions are listed together with the initials of the authors, which in turn correspond to the full author names at the top of the paper.
Author affiliations in NEJM come in a nonstandard format that is not easy for a computer to parse.
We might hope that the structured metadata we get from Crossref would have the data in a more standard format. But alas, this isn’t the case, as shown in the screenshot of data from the Crossref API.
Data about the paper from the Crossref API is also in the nonstandard format.
There are around 170,000 works from this journal. This is a relatively tiny proportion of the total number of works in OpenAlex. However, NEJM is a highly influential journal in medicine, so it’s a priority that we get this right.
Custom OpenAlex solution to assign institutions to NEJM authors
OpenAlex team member Nolan created a bespoke algorithm specifically for NEJM papers to parse the affiliation strings and assign authors to institutions. This rule-based algorithm identifies the author initials that might correspond to the full names, and uses those as a mapping to get the link from institution to author, as shown in the screenshot from the OpenAlex API of the example paper from above. The full data for this work can be found at https://api.openalex.org/works/W4386208393.
We have been able to apply this to around 35,000 articles, amounting to 158,000 institutional affiliations. Additionally, we identified about ten thousand raw affiliation strings that we couldn’t match to an institution, but can still prove useful to our users.
The NEJM case is an example of the attention to data and extra effort that is part of the value that OpenAlex hopes to provide. The data can be messy sometimes. It’s our mission to help make sense of it, so the world can have access to high-quality, free and open data.
OpenAlex data has institutional affiliations as structured, fully linked data.
New research from the Max Planck Institute for Demographic Research analyzes global migration of scholars, using bibliometric data. They do a side-by-side comparison of this analysis between Scopus and OpenAlex data.
Counts of scholars by country are highly correlated between Scopus and OpenAlex.
Migration events are less correlated between the two, but trends in migration between top pairs of countries are consistent between them. There is higher correlation with Western countries, and OpenAlex has more coverage of non-Western countries.
OpenAlex is open. Scopus is not. This puts limits on how researchers can perform and share this type of analysis.
A new working paper[1] from researchers at the Max Planck Institute for Demographic Research (MPIDR) uses bibliometric data to study the migration patterns of scholars between countries. Within the field of demography, there is a lack of high-quality data about human migration; so this use of scholarly publication data to infer global-scale migration of scholars is a welcome contribution. They compare the use of two sources of large-scale bibliometric data: “Elsevier’s proprietary Scopus and the openly available OpenAlex.”
The findings of the paper suggest that OpenAlex is a source of open data that shows promise as a replacement for the more established—but more restricted—Scopus data. Overall counts of scholars between countries over time have a high correlation between Scopus and OpenAlex, “with a median correlation close to 1.” The analysis of migration events between the two databases shows less correlation overall, but among the top pairs of countries, “the bilateral flows … are consistent in the two databases.” The authors go on to discuss the reason for the differences, noting that “[this] could signal a large difference in coverage of individual migration trajectories between these two databases and can also stem from the small net migration rates which fluctuate with small differences in measurement rather than population counts which are larger and small changes do not cause them to fluctuate.” In other words, while smaller scale trends may present differently between different data sources due to the nuances and idiosyncrasies of each one, the larger-scale trends are consistent.
The results also suggest that, in some cases, OpenAlex may be an even better resource than Scopus for this analysis. The authors note that the magnitude of migration flows is much larger in OpenAlex compared to Scopus, and that “this could indicate that the higher coverage of publications in OpenAlex might help discover some under-explored scholarly migration corridors worldwide.”
The paper does note some limitations of using OpenAlex as opposed to Scopus for their purposes, specifically, “the quality of the author name disambiguation and identifiers in OpenAlex needs further evaluation in future research.” Evaluating the job that OpenAlex has done assigning authors to all of their papers was outside the scope of this research, but they are able to refer to established research validating the Scopus data. We look forward to this validation on the OpenAlex data both from us and from other independent researchers. We’re also happy to say that we are continually making improvements in our author name disambiguation, so our data will be getting better and better!
Finally, there is the big difference between the two services: OpenAlex is open, while Scopus is not. The authors touch on this several times throughout the paper, both directly and indirectly. They mention that they must limit the years of their analysis, due to “our license terms for Scopus data”. In their Methods section, they describe the multiple steps they had to take to gain access to and acquire the Scopus data, while for OpenAlex, the process was much simpler: “we obtain the publicly available data and process it ourselves”. And in the Acknowledgements section, they explain that the Scopus license terms only permit sharing aggregated results, and no individual data is shared.
Overall, we are very proud that OpenAlex is being recognized as an emerging high-quality, completely open source of bibliometric data that can be used for demographic research. The lack of restrictions on our data is extremely important as it eliminates barriers that researchers face in doing their work. Please check out their paper to learn more about their work!
Hi, I’m Jason Portenoy, and I’m very happy to be joining OurResearch as the newest full-time team member! As a data engineer, I will be focusing my efforts on user engagement and outreach for OpenAlex. It is my responsibility to understand the OpenAlex dataset—its strengths and limitations—and work with the user community to improve it and make it easier to use.
I completed my PhD in Information Science at the University of Washington, studying the use of the scholarly literature as data to curate, explore, and evaluate scientific research. This field—known by various terms including scientometrics, science of science, metascience, and Big Scholarly Data—captivated me from the moment I learned about it. As the scale of scientific output continues to increase well beyond the capacity of any individual to make sense of it, the need for new tools and techniques to help becomes more and more pronounced. Working with Dr. Jevin West at the UW Datalab, I developed these tools and techniques—analyzing and visualizing scholarly data, and building recommendersystems to connect scientists to new research and ideas. I extended this work through projects with Semantic Scholar, the Chan-Zuckerberg Initiative, and JSTOR.
While working on these tools and analyses, I came to rely on several scholarly data sets, such as Web of Science and Microsoft Academic Graph. Through my experience, I became an advocate for having high-quality, open, and accessible data for researchers and builders to use. A solid foundation of quality data will strengthen all downstream applications, from simple counts and bibliometric statistics, to advanced natural language processing and complex systems approaches.
Joining the OpenAlex team is a fantastic opportunity for me to contribute to the future of scholarly data. When Microsoft decided to end its academic service, myself and many others in the community wondered what would come next. It has become clear that OpenAlex will play a key role in the future of this field. I come to this position with technical training as a data engineer and data scientist, as well as experience with scholarly data. My goal is to work with the community of users to continually improve the OpenAlex data and experience. If there’s anything you think I might be able to help with, please let us know!
