API Case Study on Crossref

Evolving the preprint evaluation world with Sciety

Luis Montilla — Tue, 17 Jun 2025 00:00:00 +0000

This post is based on an interview with Sciety team at eLife.

What is Sciety?

Sciety is a community-led initiative developed by a team within eLife, that brings together expert evaluations of papers in one place. It is focused on preprints, preprint review and curation.

Can you tell us more about how Sciety works?

Sciety aggregates preprints from different sources to facilitate the processes of discovery and evaluation. Groups can triage the content and offer preprint reviews and endorsements, and individual researchers can learn about and share preprints of interest and their evaluations. We see the value of increasing trust in preprints, and transparency around the process of peer review, and we are trying to highlight this value and encourage more people to take part.

There are two key angles to Sciety: first, as preprints proliferate, we’re helping to make people more productive in their research by only surfacing the content they might be interested in and that they know they can trust. Second, we are also trying to get more people involved in the public review and curation of preprints. Contributors on Sciety are part of ‘groups’, representing organisations and other communities that facilitate some form of preprint evaluation. We’re broadly talking about peer review, but we also see the highlighting and summarisation of research. eLife, Biophysics Colab, MetaROR and Gigabyte, for example, are all providing some kind of review summary which Sciety shows as a ‘curation statement’. There’s also this additional layer of individual curation on top of it: we have people creating their own highlights in lists which they curate by topic; for example, ‘preprints by authors in the Global South’ or ‘Papers we want to discuss in our lab’. There is also an update feed available to users to help them keep track of all the reviews and endorsements from the groups they follow. We post these assessments and reviews alongside the preprint, which others can then use as an indicator of trust: why should one care about this particular study? As a given group – let’s say GigaByte – and its reviewers highlight the specific strengths of a preprint or reference an updated version, this feedback offers essential context for readers.

By making this evaluation and curation activity visible, Sciety clarifies who has reviewed the work and which groups have added it to their lists. These signals are invaluable for readers seeking reliable, curated research. The activity feed, which at present shows you all the added value in the form of comments, reviews and curation we are bringing from diverse sources, could be expanded to show different forms of curation activity in the future. Furthermore, other providers ingest and surface this information on their own platforms, such as Europe PMC and bioRxiv.

What is your main use of Crossref resources?

We started using the Crossref API to pull in the front matter of articles. Originally, these were only bioRxiv preprints, and then we expanded to various other preprint servers. We would aggregate reviews and build on top of all the preprint servers that have put the authors’ content out there.

We were mostly after a representation of the papers that we could link to: titles, authors, abstracts, publication dates, and, to have a way to go from the DOI of a paper, a classic Crossref entry point. Initially, we used the public API, but the performance wasn’t high enough for what we needed and we switched to Metadata Plus. This immediately increased the speed at which we got data to the point where we could compose pages on the fly and talk to Crossref simultaneously. Even if we needed to pull 10 or 20 different paper titles at the same time to show a list of articles, it stayed that way for a long time. Next, we implemented caching – that is, we started storing temporal local copies to improve performance further. Eventually, we expanded the set of preprint servers we were interested in. It’s always been quite a good experience to be able to put in a DOI and use the same code, essentially, to pull out titles, author information and so on. Crossref does this great job of aggregating the world of content so that we don’t have to. The metadata standardisation via Crossref’s API saves us the need to write special code for every new preprint server.

By the end of 2023, we were interested in multiple revisions and versions of a single preprint. Because the scholarly world is moving on, we can now see cases where the updates to a manuscript produce multiple versions in bioRxiv, and these might eventually evolve into an article in eLife, Nature, or another journal. The publication history complexity of papers has been increasing and we started relying a lot on Crossref to trace the relationships and the different versions of a paper across time. There is some good support on the relationship metadata on Crossref APIs, where you can see that a preprint has a new version with a different DOI, or conversely, that a preprint has an older version. Or you can see that a preprint has become a journal article, or the journal article was originally a preprint – along with all the dates that accompany these different versions. And we can establish the time it took for a preprint to become a journal article. In some cases it can take years, which is not great, right? We don’t want science to be stuck and not relied upon for years. So it helps us to make our case that preprints are the evolution of publishing, that authors publish them and then the preprints evolve rather than being stuck between gates kept by journals.

What can you tell us about the use of preprints?

We have noticed an increase in the interest in how a paper evolves over time and the cross-links between different preprint expressions or journal articles. We’re now seeing enthusiasm from those who are trying alternative publishing models to bring reviewed scientific preprints to people faster, and there is also interest in the transparency of a journal. And I think that’s part of what the Crossref relationship metadata gives us.

For example, we collaborated on a paper aimed at enhancing the culture of preprint peer review. One of the things we observed was that it was published on an OSF preprint server, and then went on to be published in PLOS Biology. As we’d started this project to show the relationships between something that had originally been a preprint, we noticed that the connection between PLOS and OSF for that specific preprint was not explicit. So, we asked a colleague if this was something that could be done. And our contact at PLOS said, “yes, we’ll do this”. At the time, we were aware of Crossref’s intention to either make this more manageable or to do it in bulk. This also prompted another group on Sciety to explore whether they could do the same. Consequently, GigaByte and GigaScience, two other reviewing communities on Sciety, inquired with their publishing platform, Riverview, if they could do the same. Eventually, they realised there was a way to connect the dots through Crossref, and they also started doing it. So, there seems to be a lot of enthusiasm around this idea of making the relationships more explicit: we should show if something has been a preprint, because it’s important to the authors, and it’s important to show the transparency in the journey. That was a real-world example of something that we’re able to service through Sciety by using the Crossref metadata, and the community is responding in a very positive manner to that.

How has your experience been using Crossref services? What are you looking forward to seeing in the future?

The works endpoint is really the 99% of what we have been historically interested in. We generally experiment by putting DOIs in the public API or trying to discover content in the API itself. The amount of data is so big that there are always different examples of what we seek. And we don’t have many performance problems now because we have adopted some aggressive caching. So anything that comes from Crossref is typically cached for 24 hours.

For example, take a bioRxiv preprint that might have multiple versions available on bioRxiv itself, because it’s quite common for authors to update the preprint as they make new changes to it. With this context, an example of something we would like to see is supporting the preprint version number. So this is something that we could implement for bioRxiv over some specific preprint servers on Sciety. But in the end, as we expanded our set of preprint servers, we had to get rid of that, because there wasn’t a sustainable way to aggregate it across most servers, like we would do with Crossref. So there’s probably a space there for papers as living documents. And we certainly have an interest in preprint-specific metadata – that’s where we will place our bets.

Also, as part of the preprint review metadata group, which is something that formed out of the recent meeting with EMC Europe and ASAPbio, we’re trying to drive forward a recommendation and prototypes for more consistency in preprint review metadata. It’s quite exciting to be involved in this and, as you can see, Sciety is a place where we’re starting to pull all this stuff together. And like I say, it is a bit of a Wild West. There are so many things that are called a review, but in metadata, we know there are different terminologies. As people are saying, everyone should be commenting on preprints, everyone should be curating them, and we’re trying to make some sense of that.

Working on Sciety and exploring Crossref metadata to make preprint review more open and valuable has been a rewarding experience.

With thanks to Giorgio Sironi, former Tech Lead Manager, and Mark Williams, Product Manager, at eLife

Drawing on the Research Nexus with Policy documents: Overton’s use of Crossref API

Luis Montilla — Sat, 15 Jun 2024 00:00:00 +0000

Update 2024-07-01: This post is based on an interview with Euan Adie, founder and director of Overton._

What is Overton?

Overton is a big database of government policy documents, also including sources like intergovernmental organisations, think tanks, and big NGOs and in general anyone who’s trying to influence a government policy maker. What we’re interested in is basically, taking all the good parts of the scholarly record and applying some of that to the policy world. By this we mean finding all the documents, finding what’s out there, collecting metadata for them consistently, fitting to our schema, extracting references from all the policy documents we find, adding links between them, and then we also do citation analysis.

What do you mean by the good parts of the scholarly record?

What I mean by the good parts of the scholarly record is, from a data perspective, having persistent open metadata for items on different stable, interoperable platforms and being able to build up layers of data to suit specific use cases. That’s a better approach than trying to do everything in a silo here and a silo there and trying to do stuff bit by bit or in a hundred different ways.

There’s also a bad part, which is less to do with metadata and more around citation analysis and responsible metrics. With all this data… as the famous Spiderman quote goes… with great power comes a great responsibility: once you start systematically collecting this data, it’s very easy to fall into the trap of thinking that if we can put numbers on it, and then maybe we could start reading meaning into those numbers, and then it spirals out of control. So the idea for Overton was: can we take the system, some of the infrastructure and apply those ideas? But then come at it already knowing where the later pitfalls are and try to avoid them.

What is your main use of Crossref resources?

We rely heavily on Crossref to link policy documents to the scholarly record. The question we’re trying to answer is: does this government document cite academic work? We work a lot with universities, think tanks, and IGOs. They’re asking where is the research we produce ending up? Is it being used by the government? In some countries, like the UK, there’s a big impact agenda where it’s quite important to demonstrate that for government funding. In the US as well, state universities for example aim to impact the local policy environment. Right? Are we producing things that went on to change life for local residents for the better? And that’s really what we’re trying to support. And so that’s one of the main use cases of the database.

Can you tell us a little bit more about the story of Overton, how did this idea start?

It really came from two things. The first one is that I’d always been interested in this area and before Overton, I founded a company called Altmetric.com, which was looking at kind of broader impact metrics for papers. And we looked at Twitter, and news, and blogs, and other things, including policy. But policy wasn’t a primary focus.

When I left Altmetric two things were happening in the UK – not that everything is about Brexit, but Brexit was happening, and then COVID happened as well. And in both cases, I think it just drove home to me that other people seemed to be very interested in the evidence that the government has used to make decisions. Be they good decisions like some of the evidence based initatives in COVID or bad decisions like Brexit. So, how can you find out what it was? And it is actually very difficult to do. You can’t really track back how this decision was made. I thought that there is a growing need for that kind of impact analysis. So the second thing was, can we do something that helps make it easy to see what evidence goes into policy? The scholarly evidence but also the other kind of policy influence that goes into any document or discussion.

What are the main challenges that you face when you are trying to retrieve these policy documents?

Well, first is another thing that the scholarly record does well, which is persistence. We have CLOCKSS and all the dark archives¹. So the whole idea is that if you have a DOI, if something moves, we can track it and it maintains the ID, and even if the publisher goes bust it’ll never disappear. For citing it, then there’s always going to be a copy of it somewhere available even if it’s in a library or a dark archive.

One of the biggest challenges with policy documents is that kind of persistence doesn’t exist… There are a lot of statistics about link rot², and they hold true for policy documents as much as anywhere else. Every year a percentage of the links everywhere basically break because websites are redesigned or a government changes, it’s even worse because it can be by design. If you think about it, a new government comes into power, they change… let’s say the Department of Agriculture and they merge it with the Department of Fisheries. That would refer to a completely new third thing. And the other two departments disappear or they start linking off, like, redirecting or whatever.

One of the challenges is just keeping track of all the changes in the landscape and constantly trying to stay on top of the data. And that’s a big part of what we do. Another challenge for us, and I think about it compared to journals, when you cite something in a scholarly document, you cite it in a given style, but there are no standards for referencing styles in policy documents. So even in the same document, we can see, like, four or five different ways of referring to something, and sometimes they’re missing important data and sometimes they’re not. And it means when we’re using Crossref search, we usually have much more unparsable text.

How has your experience been so far using our Crossref API or our services in general?

It’s been great. I would happily say this anywhere, I always talk about the Crossref API as being one of the best examples of a well-done scholarly infrastructure API. It’s well-documented. It’s fast. It’s clear. The rate limits are clear. It’s up when it should be up. I like that you can trust it. So the technical aspect is great. From an organisational aspect, in contrast with a lot of infrastructure in the scholarly world that you don’t know if it’s even going to be there in a given time, Crossref is pretty stable.

What would you say are the main challenges or things that we can improve in the future? What other expectations or suggestions do you have?

It depends, if we’re talking about how the service could be improved versus how the data could be improved. Data-wise, and I appreciate this is a publisher problem, not a Crossref one, but, we still have to pull other data from OpenAlex, for example, for things like affiliations just because it’s missing from so many articles. And then equally things like ORCID for authors. And in fact also disambiguation in general. This is a huge problem that either the user doesn’t solve or you end up using a hundred different author disambiguation systems. I don’t know if there’s necessarily something Crossref wants to get into, but there’s definitely not something out there generally accepted already.

Another kind of improvement I see is to make sure that changes in one API are reflected in the other, and they don’t get out of sync. When somebody updates their ORCID record, I’d like it reflected in the Crossref record if we’re using that as the “canonical” metadata record for the DOI. Retrospectively enriching records.

I think it’s harder than I expected to just find preprints because you can’t simply use the item type but I understand that this is maybe a bigger issue. So maybe it’s not for a short time.

Finally, this is very specific, but we experienced friction when going from the snapshots to having something useful, either in Elasticsearch or in, like, Postgres. It might be nice to have some open-source scripts to download and process everything, convert it to relational tables, or send it to an Elasticsearch cluster or something.

Crossref metadata for bibliometrics

Ginny Hendricks — Fri, 21 Feb 2020 00:00:00 +0000

Our paper, Crossref: the sustainable source of community-owned scholarly metadata, was recently published in Quantitative Science Studies (MIT Press). The paper describes the scholarly metadata collected and made available by Crossref, as well as its importance in the scholarly research ecosystem.

Containing over 106 million records and expanding at an average rate of 11% a year, Crossref’s metadata has become one of the major sources of scholarly data for publishers, authors, librarians, funders, and researchers. The metadata set consists of 13 record types, including not only traditional types, such as journals and conference papers, but also data sets, reports, preprints, peer reviews, and grants. The metadata is not limited to basic publication metadata, but can also include abstracts and links to full text, funding and license information, citation links, and the information about corrections, updates, retractions, etc. This scale and breadth make Crossref a valuable source for research in scientometrics, including measuring the growth and impact of science and understanding new trends in scholarly communications. The metadata is available through a number of APIs, including REST API and OAI-PMH.

In the paper, we describe the kind of metadata that Crossref provides and how it is collected and curated. We also look at Crossref’s role in the research ecosystem and trends in metadata curation over the years, including the evolution of its citation data provision. We summarize the research that used Crossref’s metadata and describe plans that will improve metadata quality and retrieval in the future.

Using the Crossref REST API (with Open Ukrainian Citation Index)

Rachael Lammey — Wed, 05 Feb 2020 00:00:00 +0000

Over the past few years, I’ve been really interested in seeing the breadth of uses that the research community is finding for the Crossref REST API. When we ran Crossref LIVE Kyiv in March 2019, Serhii Nazarovets joined us to present his plans for the Open Ukrainian Citation Index, an initiative he explains below.

But first an introduction to Serhii and his colleague Tetiana Borysova.

Serhii Nazarovets is a Deputy Director for Research at the State Scientific and Technical Library of Ukraine. Serhii has a Ph.D. in Social Communication Science. His research interests lie in the area of scientometrics and library science. Serhii is the Associate Editor for DOAJ (www.doaj.org) and the Regional Editor for E-LIS (Eprints in Library and Information Science). Serhii has worked in different scientific libraries of Ukraine for more than 10 years. Tetiana Borysova is a Senior Researcher at the State Scientific and Technical Library of Ukraine. Her research interests are focused on topics such as research data management, journal management and scientometrics.

Introducing OUCI

OUCI (Open Ukrainian Citation Index) is a new search engine and a citation database based on publication metadata from Crossref members.

OUCI is intended to simplify the search of scientific publications, to attract the editors’ attention to the problem of completeness and quality of the metadata of Ukrainian scholarly publications, and will allow bibliometricians to freely study the relations between authors and documents from various disciplines, in particular in the field of social sciences and humanities. OUCI is open for every user in the world without any restrictions.

OUCI launched in November 2019. The project is being implemented by the State Scientific and Technical Library of Ukraine with the support of the Ministry of Education and Science of Ukraine.

In Ukraine, we do not have a national citation database, and this significantly impedes the search and analysis of information about Ukrainian publications. According to preliminary estimates, more than 3,000 titles of scientific journals are currently published in Ukraine. At the same time, only around 100 Ukrainian journal titles are indexed in authoritative citation databases, such as Scopus and Web of Science Core Collection. Thus, researchers and managers lack this citation data to understand the impact of Ukrainian journals and their demand in the scientific communication system. Our approach is that OUCI database contains metadata from all publishers that use the Crossref’s Cited-by service and who support the Initiative for Open Citations by making the reference metadata they publish with Crossref openly available.

How is Crossref metadata used in OUCI?

A publication can only be indexed in OUCI if there is a DOI. At first glance, the idea of creating an index of national publications based on this condition may seem too optimistic. However, in January 2018, a new requirement was adopted by the List of scientific publications of Ukraine (a list of Ukrainian journals recognized by experts as qualitative for publishing their research results for a scientific degree), which listed a DOI as one of the requirements for inclusion. After that, the number of publishers who received the DOI prefix from Crossref has tripled, to 352 in November 2019.

Another important feature of OUCI is that publishers have to use Crossref’s Cited-by service and support the Initiative for Open Citations. We are working to build a new fair infrastructure where everyone who is interested in the dissemination of scientific knowledge can present their publications to the community, develop expert judgment skills and access citations to explore the links between documents. The philosophy of the index is to use only open resources to fill it.

In addition to standard filters from Crossref metadata (such as publisher, publication, type, year), OUCI offers to refine search results by:

indexation in Web of Science and/or Scopus,
journal category (A or B according to the List of scientific publications of Ukraine),
the field of knowledge and scientific specialties (according to the Ukrainian legislation) and other aspects important to Ukrainian users characteristics.

Figure 1: OUCI search and filter options

Beyond the ability to search articles, OUCI displays profiles for Ukrainian journals (the titles of these journals will include hyperlinks in the search results). Administrators can manage them, add and edit information about their journals: web-site, aims and scope, scientific fields of the journal according to the Ukrainian classification. Also, you can see some quantitative characteristics of journals: number of publications, number of citations, h-index, i10-index etc.

Figure 2: Display of journal information in OUCI

In addition, we have implemented an analytics module. Using the data about the number of articles and citations from Crossref, it allows users to analyze Ukrainian journals by field.

Figure 3: Publication and citation information

What are the future plans for OUCI?

In the near future, we plan to add:

the ability to export search results for further analysis;
integration with Unpaywall;
alternative metrics from Crossref Event Data.

In the ideal future for our index, every Ukrainian article will be registered with Crossref and have open references. We plan to promote the importance of reach and quality metadata in Crossref among Ukrainian publishers. We also encourage all publishers to support the Initiative for Open Citations.

What else would OUCI like to see in Crossref metadata?

One of the main problems we encountered when creating OUCI was the metadata about the authors. Very few publications contain data about the author’s ORCID iD. Focusing publishers on the need to transmit full metadata to Crossref, as well as monitoring their quality is a must for the resources like this. Also we look forward to the growing usage of ROR (Research Organization Registry) - identifiers for research organisations, similar to the way that ORCID offers identifiers for researchers. We believe that the ROR will help to obtain reliable data for analyzing the scientific activity of Ukrainian institutions.

Another issue we’ve identified in some Ukrainian journals that some of the small publishers that register content via Crossref Sponsors did not take care getting their own prefix, so it can be difficult to see their publications - this is something that showing the metadata via an index can help them see and therefore fix.

Questions?

We’ve had lots of questions about OUCI in the run up to the launch and now that it’s live. Here is a selection of our FAQs, all available on our website. You can also get in touch directly if you have another question we haven’t answered yet.

How Crossref metadata is helping bring migration research in Europe under one roof

Christine Cormack Wood — Tue, 29 Jan 2019 00:00:00 +0000

Conflict, instability and economic conditions are just some of the factors driving new migration into Europe—and European policy makers are in dispute about how to manage and cope with the implications. Everyone agrees that in order to respond to the challenges and opportunities of migration, a better understanding is required of what drives migration towards Europe, what trajectories and infrastructures facilitate migration, and what the key characteristics of different migrant flows are, in order to inform and improve policy making.

The abstract above is taken from the successful Horizon 2020[1] project proposal called CrossMigration, an initiative of IMISCOE, Europe’s largest migration research network, in which a consortium of 15 universities, think tanks and international organisations, led by Erasmus University Rotterdam is currently designing a Migration Research Hub. The Hub is a web-based platform aimed at helping researchers and policymakers get a quick and comprehensive overview on research in the field of migration studies. This platform will also feature reports on specific fields, methodological briefing papers and other relevant content produced by the consortium.

The core of this Hub will consist of a database providing access to publications, research projects and datasets on migration drivers, and infrastructures, flows, and policies on current and future migration questions, indicators and scenarios. And that’s where our metadata story starts.

At the tail end of December I had the pleasure of speaking to the four researchers and developers working on this database; Vienna-based researchers Roland Hosner and Meike Palinkas from the International Centre for Migration Policy and Development (ICMPD), Bogdan Taut, CEO of YoungMinds, in Bucharest, Romania, and Nathan Levy, currently studying for his PhD at Erasmus University Rotterdam, Department of Public Administration and Sociology, Netherlands.

There are four of you, can each of you give me a very brief introduction to yourselves and how you fit into project?

Bogdan: I’m from YoungMinds, based in Bucharest in Romania. We were the last to join the consortium as the technical developer on the project. I am the project manager of the team, coordinating the technical development of the database.

Roland: I am a research officer with the International Centre for Migration Policy Development (ICMPD) in Vienna, and we are leading a part of this research project which deals with the population and implementation of the research database—which is core to the Migration Research Hub, and to the whole project.

Meike: I am also a research officer at ICMPD and work together with Roland. I joined the team in September this year.

Nathan: I’m part of the coordinating team of the overall project of CrossMigration. We are coordinating putting together the Migration Research Hub, the biggest part of which is the migration database. I am based at Erasmus University in Rotterdam and I work for Professor Peter Scholten who is the overall coordinator of the whole project along with Dr. Asya Pisarevskaya.

How long has the project been in progress?

Roland: It’s a two-year project than runs from March 2018 to the end of February 2020.

So it’s a two-year project and you are 10 months in—that makes it nearly at the halfway mark. Have you encountered any stumbling blocks that have held you back?

Bogdan: How to put this in a diplomatic way? We are all working around the clock to meet the deadline that we set ourselves and promised to deliver by. We have made the decision to produce the database in stages—very soon we will have the beta version out, so we have something to present. Then we are going to continue populating it with more items from every record type – journal articles, datasets, books, book chapters, reports etc.. At this point the other partners in the consortium can actually use it and work with it to map the fields and find the most recent and relevant literature on their respective subtopics such as migration drivers or migration infrastructures. In the summer when we are confident that it is a sound and attractive tool to be released, we will make it publicly available.

Nathan: In terms of specific deliverables for the project so far, our team has developed a taxonomy for migration research to give the fields a logical structure, and to structure this research database.

How has Crossref metadata contributed to your project?

Bogdan: We began by discussing all of the sources that need to be in the database and we put together an inventory of publishers, books and book chapters, etc., that would be relevant. Part of the scope of work for YoungMinds was to find ways of extracting information and relevant content from those sources. Once we started to dig into the content we found out that there are relevant aggregators, such as Scopus, Crossref, Web of Science and so on. We actually found Crossref through a recommendation from Scopus, someone there said ‘OK Crossref might be able to help you more’. Then Crossref became one of our main sources for metadata—in terms of basic metadata related to some types of content we gather for our database, such as journals and journal articles.

Roland: The more we moved forward, the more we saw how difficult it was to get in touch with each publisher individually, with each journal individually, to try and secure an agreement with them. So, it became very clear to us very quickly that we would not be able to create a properly inclusive database this way and we knew we had to look for partners and make use of existing resources. As we progressed from one conversation to the next we received a lot of advice, and that’s how we found out about Crossref. It soon became clear that Crossref was the ideal source for us because everything that has a DOI can be found in there. We knew if we had an agreement with Crossref then our project is half won, our database is halfway built, perhaps even more. And, then we just need to fill the gaps.

Nathan: Yes, this is one of Crossref’s key strengths—rather than having individual researchers or individual projects go to each publisher to try to find the appropriate people to talk to and negotiate—you use Crossref.

Which of the metadata values are important to you, what do you extract?

Roland: We thought about this a lot at the beginning, what we wanted to include. There are certain key things that are indisputably relevant—such as titles, names of the authors, editors, the year, DOI, dataset and so on, because we always link to the original source—the publisher’s website, or the journal article website. Ideally we would include keywords and abstracts (where they are available) because the richer the information the better. We also wanted to classify the items we have according to the taxonomy the CrossMigration project has established.

Nathan: In addition, abstracts and keywords have value for us. We want to apply a logical structure into the taxonomy on migration research, but we need content in order to do that. We need something for the algorithms that YoungMinds have developed to read to in order categorize research accordingly. The body of research on migration is so great and we cannot read through every abstract that’s ever been published on migration. That’s where the value of abstracts and keywords comes in for the Taxonomizer (as we fondly refer to it!).

What else would you like to see in the REST API that isn’t there?

Roland: More abstracts! We love abstracts!

Bogdan: Our data schema contains more fields, so we need more metadata than we can find from Crossref and other sources. Basically, the publisher’s website would produce the richest data, but it is the hardest to read. We are on a quest to find more sources because our algorithm works better if it has more information.

Once it’s complete, what are your plans to roll it out to the wider world?

Bogdan: IMISCOE is the leading organisation of this consortium and it is in touch with most of the migration experts in Europe, so we already have all the contacts of the relevant people in the field.

Meike: It’s a tool for helping the community, so once we have all the relevant content inside it, we believe that word will spread relatively easily.

Have you all actually met in person?

Roland: Yes! Myself and Nathan met at the project kick-off meeting in Rotterdam in March 2018, then we met at a conference in Florence in June that was partly for the consortium but also had other invited experts and scholars. That was where we met face-to-face for the first time—it was just after we signed with YoungMinds for the IT services. And we recently met at another joint conference of IMISCOE and CrossMigration called 'Towards the IMISCOE Research Infrastructure of the Future'.

[1] Horizon 2020, the biggest EU Research and Innovation programme ever with nearly €80 billion of funding available.

Great speaking to you all and learning a bit about this important project that will help policymakers manage and cope with the implications of migration—and may possibly even help them find ways to influence it.

If you’d like to share how you use our Metadata APIs please contact the Community team.

Using the Crossref REST API. Part 12 (with Europe PMC)

Christine Cormack Wood — Wed, 10 Oct 2018 00:00:00 +0000

As part of our blog series highlighting some of the tools and services that use our API, we asked Michael Parkin—Data Scientist at the European Bioinformatics Institute—a few questions about how Europe PMC uses our metadata where preprints are concerned.

Tell us a bit about Europe PMC

Europe PMC is a knowledgebase for life science research literature and a platform for innovation based on the content, such as text mining. It contains 34.6 million abstracts and 5 million full-text articles. At Europe PMC we support the research community by developing tools for knowledge discovery, linking publications with underlying research data, and building infrastructure to support text and data mining. Our goal is to create a supportive environment around open access content and data, to maximise its reuse.

What problem is your service trying to solve?

Recent years have seen a dramatic increase in the popularity of preprints within life sciences literature. Preprints have been supported by Crossref since November 2016. In response to the rise in popularity, we have started indexing preprints alongside traditional journal publishing within Europe PMC. We expect this will:

provide another means to access and discover this emergent form of scholarly content
help explore more transparently the role of preprints in the publishing ecosystem
support their inclusion in processes such as grant reporting and credit attribution systems

How do you use Crossref metadata?

Europe PMC operates an open citation network that uses reference lists from our full-text content, supplemented with metadata supplied by the Crossref OAI-PMH API. The number of citations we retrieve from Crossref increased significantly in 2017 thanks to the efforts of the Initiative for Open Citations (I4OC) in improving awareness about sharing citation data.

Our work to ingest preprints into Europe PMC, however, represents our first use of the Crossref REST API. We make a series of queries for each preprint provider, making use of the “posted-content”, “prefix” and (optionally) “has-abstract” filters. We intend to migrate to using the REST API for the majority of retrievals of Crossref content in due course.

What metadata values do you make use of?

Currently we make use of the following fields:

posted as a publication date
abstract
DOI
author for author given names and surnames
title as the preprint title
is-preprint-of to establish preprint –> article links

How often do you extract/query metadata?

We query the REST API daily making use of the from-index-date filter and cursor pagination to insert new or modify existing records. This means that preprints will be available in Europe PMC within 24 hours of the metadata being sent to Crossref. We store the full REST response in MongoDB, a document-based database. Here are some examples of Crossref API queries used to preprint provider PeerJ Preprints:

calling `https://api-crossref-org.pluma.sjfc.edu/works?filter=type:posted-content,has-abstract:true,from-index-date:2018-07-29,prefix:10.7287&sort=updated&rows=1000&cursor=*`
calling `https://api-crossref-org.pluma.sjfc.edu/works?filter=type:posted-content,has-abstract:true,from-index-date:2018-07-29,prefix:10.7287&sort=updated&rows=1000&cursor=AoN4ldf88uQCe6e1g%2FPkAj8SaHR0cDovL2R4LmRvaS5vcmcvMTAuNzI4Ny9wZWVyai5wcmVwcmludHMuMjcwNjJ2MQ%3D%3D`
Done importing PeerJ Preprints
modified: 2
inserted: 10

What do you do with the metadata?

From the database we parse out the relevant fields and pass them to our main relational database prior to indexing. This avails the preprint abstracts to all of the value-added services we offer for peer-reviewed abstracts, such as citations, grants, ORCID claiming, text mining, etc. We assign a unique persistent identifier comprising “PPR” followed by a number (1) to each preprint record.

This is displayed on the Europe PMC site as an abstract record, analogous to PubMed records, but with an obvious banner (2) indicating to readers the preprint designation; a tooltip provides further explanation of what a preprint is in comparison to a peer-reviewed article.

Once available on the Europe PMC platform, we then apply downstream processes including:

providing an Unpaywall link directly to the full-text (3);
adding a hyperlink to the final published version (if there is one that we can detect) (4);
incorporating the preprint into our citation network (5);
adding useful links to e.g. alternative metrics, scientific comments and peer reviews, underlying research data in life science databases (6);
providing text mined annotations via SciLite (7);
including funding information (8);
displaying ORCID claims in the author list (9).

What are the future plans for Europe PMC and preprints?

The inclusion of preprints within Europe PMC is of immediate benefit to researchers who want to explore the very latest research. Moreover we see this as an opportunity for both ourselves and the community to explore how preprints fit into the wider publishing ecosystem; for example to answer questions such as: How often will they be cited? How will they be linked to grant funding and other credit systems? How will they be reused?

What else would you like our API to do?

The REST API and rich metadata model provided by Crossref around preprints are both excellent, but the population of the metadata fields by preprint providers can be limited and/or heterogeneous. The key challenge we see is in encouraging providers to populate the Crossref metadata fields more fully and in a uniform manner.

Thanks to Michael.

If you’d like to share how you use our Metadata APIs please contact the Community team.

Using the Crossref REST API. Part 11 (with MDPI/Scilit)

Christine Cormack Wood — Tue, 18 Sep 2018 00:00:00 +0000

Continuing our blog series highlighting the uses of Crossref metadata, we talked to Martyn Rittman and Bastien Latard who tell us about themselves, MDPI and Scilit, and how they use Crossref metadata.

Can you give us a brief introduction yourselves, and to MDPI/Scilit

Martyn is Publishing Services Manager at MDPI. He joined five years ago as an editor and has worked on editorial, production, and software projects. Prior to joining MDPI, he completed a PhD and worked as a postdoc. His research covered physical chemistry, biochemistry and instrument development. Bastien Latard is the project leader of Scilit. He created Scilit as part of his Master’s degree in 2013. He is now completing a PhD on the subject of semantically linking research articles, using data from Scilit.

Scilit was developed in 2014 by open access (OA) publisher MDPI with the goal of having a backup of metadata for all OA articles. Soon, Scilit became more general and embraced all articles with a digital object identifier (DOI) from Crossref and those with a Pubmed ID (PMID). After seeing the potential of the database and how it could be used in a number of different contexts, we decided to make it public. Recently, other article types, including preprints have been integrated. Our main goal now is to provide useful services to the research and academic publishing communities.

What problem is your service trying to solve?

Other indexing databases offer paid access, are highly selective, or host documents apart from research articles. We want to offer a comprehensive database, but also one that clearly identifies open access material. The last part is still a work in progress, but we have made good progress recently.

To make the access as direct as possible, we have recently integrated several OA aggregators that pick up or host free versions of full-text articles, including CORE, Unpaywall, and PubMed Central.

Can you tell us how you are using the Crossref Metadata API at MDPI/Scilit?

Scilit queries Crossref’s API in order to index metadata for single articles. DOIs are a key part of the system; because they are standards, we can use them to merge new sources into Scilit while avoiding duplicates. We cross-check the data from Crossref against other sources and update it as necessary. Citation data is also really appreciated and opens doors to further developments.

As a publisher, MDPI makes daily deposits to Crossref, to register journal articles on mdpi.com, conference papers from sciforum.net, and preprints from Preprints.org. We also use the data collected at Scilit to find suitable reviewers and let authors know when their work has been cited.

What metadata values do you pull from the API?

As much as we can! Scilit crawls the latest indexed articles every few hours to ensure it is as up-to-date as possible. This is the most important function of our system because it provides metadata for the very latest published articles, including a link to the publisher version. Scilit parses Crossref metadata and saves them. They are then indexed into our solr search engine for fast, real-time usage.

Have you built your own interface to extract this data?

We wrote our own code to get the data, but the API interface made this very straightforward. Scilit has been developed completely in-house by MDPI and the lead developer, Bastien Latard, is currently completing a PhD looking at how to make the most of the data using semantic data extraction.

What are the future plans for MDPI/Scilit?

Scilit is and will be highly used in MDPI current and future projects. We have a few ideas about how to improve Scilit. We are, for example, implementing a scientific profile networking service, which will allow scholars to build their own (scientific) network with lots of functionalities. We think that it will be a really good place to search, comment, exchange around articles… maybe even more!

What else would you like to see the REST API offer?

Crossref is already doing a great job, especially with its integrated citation data. Maybe further analysis and mapping of data about organisations and institutions would be an improvement.

Thank you Martin and Bastien. If you’d like to share how you use the Crossref Metadata APIs please contact the Community team.

Using the Crossref REST API. Part 10 (with Kudos)

Christine Cormack Wood — Mon, 13 Aug 2018 00:00:00 +0000

Continuing our blog series highlighting the uses of Crossref metadata, we talked to David Sommer, co-founder and Product Director at the research dissemination management service, Kudos. David tells us how Kudos is collaborating with Crossref, and how they use the REST API as part of our Metadata Plus service.

Introducing Kudos

At Kudos we know that effective dissemination is the starting point for impact. Kudos is a platform that allows researchers and research groups to plan, manage, measure, and report on dissemination activities to help maximize the visibility and impact of their work.

We launched the service in 2015 and now work with almost 100 publishers and institutions around the world, and have nearly 250,000 researchers using the platform.

We provide guidance to researchers on writing a plain language summary about their work so it can be found and understood by a broad range of audiences, and then we support researchers in disseminating across multiple channels and measuring which dissemination activities are most effective for them.

As part of this, we developed the Sharable-PDF to allow researchers to legitimately share publication profiles across a range of sites and networks, and track the impact of their work centrally. This also allows publishers to prevent copyright infringement, and reclaim lost usage from sharing of research articles on scholarly collaboration networks.

An example of a Kudos publication page showing the plain language summary

How is Crossref metadata used in Kudos?

Since our launch, Crossref has been our metadata foundation. When we receive notification from our publishing partners that an article, book or book chapter has been published, we query using the Crossref REST API to retrieve the metadata for that publication. That data allows us to populate the Kudos publication page.

We also integrate earlier in the researcher workflow, interfacing with all of the major Manuscript Submission Systems to support authors who want to build impact from the point of submission.

More recently, we started using the Crossref REST API to retrieve citation counts for a DOI. This enables us to include the number of times content is cited as part of the ‘basket of metrics’ we provide to our researchers. They can then understand the performance of their publications in context, and see the correlation between actions and results.

A Kudos metrics page, showing the basket of metrics and the correlation between actions and results

What are the future plans for Kudos?

We have exciting plans for the future! We are developing Kudos for Research Groups to support the planning, managing, measuring and reporting of dissemination activities for research groups, labs and departments. We are adding a range of new features and dissemination channels to support this, and to help researchers to better understand how their research is being used, and by whom.

What else would Kudos like to see in Crossref metadata?

We have always found Crossref to be very responsive and open to new ideas, so we look forward to continuing to work together. We are keen to see an industry standard article-level subject classification system developed, and it would seem that Crossref is the natural home for this.

We are also continuing to monitor Crossref Event Data which has the potential to provide a rich source of events that could be used to help demonstrate dissemination and impact.

Finally, we are pleased to see the work Crossref are doing to help improve the quality of the metadata and supporting publishers in auditing their data. If we could have anything we wanted, our dream would be to prevent “funny characters” in DOIs that cause us all kinds of escape character headaches!

Thank you David. If you would like to contribute a case study on the uses of Crossref Metadata APIs please contact the Community team.

Using the Crossref REST API. Part 9 (with Dimensions)

Christine Cormack Wood — Wed, 27 Jun 2018 00:00:00 +0000

Continuing our blog series highlighting the uses of Crossref metadata, we talked to the team behind new search and discovery tool Dimensions: Daniel Hook, Digital Science CEO; Christian Herzog, ÜberResearch CEO; and Simon Porter, Director of Innovation. They talk about the work they’re doing, the collaborative approach, and how Dimensions uses the Crossref REST API as part of our Metadata Plus service, to augment other data and their workflow.

Introducing Dimensions

Dimensions is a next-generation approach to discovering, connecting with and contextualising research. Modern academics need data about the research ecosystem in which they exist as much as the administrators who develop institutional research strategies. All academics are now required to think long-range about their research projects, contextualise their research, and demonstrate the impact of their program. Additionally, they need to find funding, ensure that students go on to good positions, and hire talented colleagues whose skills fit well with ongoing projects. Dimensions gives the first fully-linked view of publications, grants, patents and clinical trials in an analytically-centred user experience.

How is Crossref data used within Dimensions?

For an article to appear in Dimensions it must have a Crossref DOI, so it would not be possible to create Dimensions’ Publication index without Crossref’s data. Dimensions is built on several principles that we’ve talked about before. Here the most relevant of those principles are:

unique identifiers should underlie everything that we do;
data should not be inclusive and the tool should allow the user to select what they want to see;
data should be more available to our community;
data should be presented with as much contextual information as possible;
the community should have enough data available to be able to create and experiment with their own metrics and indicators.

In the context of these principles, Crossref makes a perfect starting place to create a tool like Dimensions. We use the Crossref data to know about our possible “universe” of articles. We then enhance the Crossref core with data from several different places: open access publications in the DOAJ, PubMed, BioArXiv, and through relationships with publishers. In all, 60 million of the 95 million articles in the Dimensions index have a full text version that we can text and data mine for additional information.

In Dimensions’ enhancement stage we can extract address information (where not included in the original Crossref record) and map it to GRID funding information and the list of funders in Crossref’s Funder Registry as well as to our database of grants in Dimensions.

How have you incorporated citation data?

Access to citations has historically been a thorny issue for citations databases. However, I4OC celebrated its first anniversary in April this year and this project has been a key driver in helping us to build Dimensions with the level of citation coverage that we managed –– it is a fantastic enabling initiative and should be warmly welcomed by the sector. Crossref is not the only source we were able to use to gather citation data; some text mining was needed to get a full graph. Dimensions goes beyond inter-article citations and includes links between patents and publications, links between clinical trials and publications, and Altmetric mentions of publications.

Is Dimensions openly available?

Given that there is so much open data in Dimensions, it was always our intention to give a free version to the community. If you visit http://app.dimensions.ai.pluma.sjfc.edu then you’ll be able to play with the system and use it for your research. While only the publications index is fully open, when you see a link to a grant, patent or clinical trial in an article detail page, you’ll be able to navigate to that record so that you can see the full context of the data.

Beyond the ability to link the publications, Dimensions also displays the CV information which the researcher made visible publicly.

Most recently, we’ve integrated ORCID into Dimensions. This means that you can push data from Dimensions into ORCID if you connect your ORCID account to your Dimensions account.

What are the future plans for Dimensions?

Dimensions is still moving quickly and adding more functionality. Our aim is to release more data facets very soon. We plan to add a Policy Document archive and a Research Data archive. We’ve already found some fascinating insights from joining the existing data together and these two new archives should add even more interesting data.

What else would Dimensions like to see in Crossref metadata?

Open access information is something that we work with Unpaywall to source for Dimensions right now. It would be great if Crossref and Unpaywall could work together to make this data higher quality and more ubiquitous.

Thank you Daniel, Christian and Simon.

If you would like to contribute a case study on the uses of Crossref Metadata APIs please contact the Community team.

Using the Crossref REST API. Part 8 (with Researchfish)

Christine Cormack Wood — Mon, 23 Apr 2018 00:00:00 +0000

Continuing our blog series highlighting the uses of Crossref metadata, we talked to Gavin Reddick, Chief Analyst at Researchfish about the work they’re doing, and how they’re using our REST API as part of their workflow.

Introducing Researchfish

Researchfish is the world’s leading platform for the reporting of the outputs, outcomes and impacts of funded research. It is used by over 100 funding organisations in Europe, North America and Australasia and currently tracks around €50 billion of funding, across 125,000 grants. Researchers have reported around 2.5 million attributed outcomes in Researchfish and roughly half of these are publications with the other half being collaborations, further funding, data sets, policy influences, engagement activities etc.

Funders use Researchfish to ask grantees to report on the outcomes of their grant and Researchfish makes it easy for researchers to do this in a structured way. Researchfish seeks to improve the quality and robustness of the evidence base available for evaluation. It works with funders, research organisations and researchers to present, explain and evaluate the impact of research across all disciplines and a wide range of output types.

How is the Crossref REST API used in Researchfish?

Search
As publications are a major output of research it is important to make the reporting of those publications be as easy as possible and quality of the information on those publications as high as possible. Researchfish integrates with a number of publication APIs, including Crossref, which enables users to enter a number of DOIs or search by author, title, etc. to find their publication.
Direct Harvest
Researchfish uses funding acknowledgements in the Crossref metadata to add publications to researchers’ portfolios and report the publications as arising from the grant. If the acknowledgement exists it’s important to use it instead of asking researchers to report the same thing twice.
Interoperability
Research organisations can upload publications to Researchfish on behalf of researchers, re-using information from their local systems. We use the Crossref REST API to validate the data provided by universities before uploading.
Metadata Enrichment – Open Access
We use the license and embargo period information in the Crossref metadata to help understand the open access status of publications and whether they meet any policy requirements, without researchers having to take any steps to report in this complex area.
Metadata Enrichment – Normalisation/deduplication
As Researchfish allows users to add information from lots of different sources it is very important to normalise the data and prevent the same publication being reported multiple times in different ways. We use the Crossref REST API as part of this process.

What are the future plans for Researchfish?

We are looking to expand the range of integrations to support non-publication outputs and allow some of the same functionality that we have built for publications. We already have integrations to support the reporting of patents, collaborations, further funding and next destinations but are looking to enhance these, along with expanding links to data sets, clinical trials, software and spin out companies.

What else would Researchfish like to see in Crossref?

Crossref is an excellent resource and most of our wish list would be to see more uptake of existing fields e.g. retractions and the ability to use them more flexibly in the REST API. We would also like to see a little more consistency in some of the metadata – publication type is the area that seems to cause the most confusion, particularly around conference proceedings and clinical trials.

Thank you Researchfish! If you would like to contribute a case study on the uses of Crossref Metadata APIs please contact the Community team.

Using the Crossref REST API. Part 7 (with CHORUS)

Christine Cormack Wood — Mon, 27 Nov 2017 00:00:00 +0000

Continuing our blog series highlighting the uses of Crossref metadata, we talked to Sara Girard and Howard Ratner at CHORUS about the work they’re doing, and how they’re using our REST API as part of their workflow.

Introducing CHORUS

CHORUS (www.chorusaccess.org) is an innovative non-profit organisation that supports funders, publishers, authors and institutions to deliver public access to articles reporting on funded research. Our vision is to create a future where the output flowing from funded research is easily and permanently discoverable, accessible and verifiable by anyone in the world.

CHORUS currently monitors over 400,000 articles for more than 20 US federal and two international funding agencies, and has partnerships with Department of Defense, Department of Energy, National Science Foundation, National Institute of Standards and Technology, Office of the Director National of Intelligence: Intelligence Advanced Research Projects Activity, Smithsonian Institution, US Department of Agriculture, US Geological Survey, Japan Science and Technology Agency, and the Australian Research Council. CHORUS is supported by over 50 publisher and affiliate members who represent the majority of funded published research.

What problem is your service trying to solve?

CHORUS is the first service of CHOR Inc., founded in 2013 in response to the directive of the US Office of Science and Technology Policy (OSTP) for all US federal research agencies to develop and implement plans to widen public access to publications and data associated with federally funded research.

CHORUS aims to minimize public access compliance burdens and ensure the long-term preservation and accessibility of articles reporting on funded research. We provide the necessary metadata infrastructure and governance to enable a smooth, low-friction interface between funders, authors, institutions and publishers in a distributed network environment. CHORUS’ services track public accessibility of articles regardless of whether they are published Gold OA or made open by the publisher.

Can you tell us how you are using the Crossref REST API at CHORUS?

The Crossref REST API is a key source for the metadata database that powers the CHORUS Dashboard, Search and Reporting services for Funders, Institutions and Publishers.

What metadata values do you pull from the API?

We pull the basic bibliographic information such as publisher, journal title, article title, authors and publication date. Perhaps even more important to our area of focus are the funder, grant and license information.

How often do you extract/query data?

CHORUS uses the Crossref REST API every day.

Can you describe your workflow using Crossref metadata?

Every night we query the Crossref API to send us metadata for all article or conference proceeding records for our member publishers that have funder metadata matching the funders monitored by CHORUS.

CHORUS monitors these DOIs for public accessibility on publisher websites; inclusion in agency search tools; deposit in a growing list of funder repositories (e.g.,US DOE PAGES,NSF PAR, and USGS Publications Warehouse and NIH PubMed Central); and for associated ORCID researcher records. CHORUS also uses the reuse license metadata to identify when an article is expected to be made publicly accessible.

Finally, we check for ingestion in CLOCKSS and/or Portico to ensure long-term preservation and accessibility of research findings reported in journal and proceedings articles. Our preservation partners keep the full text in their dark archives, only making it available when the content may no longer be made publicly accessible by the publisher.

The collected and enhanced metadata is presented in our dashboard, search and reporting services all including links back to the publisher sites via the Crossref DOI.

What are the future plans for CHORUS?

Following the success of our Funder and Publisher Dashboards, CHORUS is expanding the services we provide to international funders, non-governmental funders, and institutions. Our first funder partnership outside of the United States is with the Japan Science and Technology Agency (JST). CHORUS announced its new Institution Dashboard service this Autumn after successfully concluding pilots with the University of Florida and University of Denver. CHORUS will also be adding links to relevant datasets and other metadata utilizing forthcoming identifiers and metadata standards.

What else would you like to see the REST API offer

It would be great to see more identification of funders from Crossref members. While we have seen great leaps since 2013, we all have a long way to go. We are also eager to see Crossref incorporate the organisation Identifiers that they have begun with ORCID, DataCite and others.

Thanks, CHORUS! If you would like to contribute a case study on the uses of Crossref Metadata APIs please contact the Community team.

Using the Crossref REST API. Part 6 (with NLS)

Christine Cormack Wood — Fri, 06 Oct 2017 00:00:00 +0000

Continuing our blog series highlighting the uses of Crossref metadata, we talked to Ulf Kronman, Bibliometric Analyst at the National Library of Sweden about the work they’re doing, and how they’re using our REST API as part of their workflow.

Introducing the National Library of Sweden (NLS)

The NLS is a state agency, has a staff of about 320, and its main offices in Stockholm. Its primary duty is to preserve the Swedish cultural heritage by collecting everything printed in Sweden, and has been doing so since 1661. Nowadays the library also collects Swedish TV and radio programs, movies, videos, music, and computer games.

The National Library coordinates services and programs for all publicly funded libraries in Sweden and runs the national library catalogue system Libris and the national database for Swedish scholarly output, SwePub. The library also runs the Bibsam consortium, negotiating national subscription licenses and open access publishing agreements with publishers.

Images left to right: External and internal view of the National Library of Sweden, and Ulf Kronman, Bibliometric Analyst at NLS.

What problem is your service trying to solve?

The metadata in the national scholarly publication database SwePub is harvested from the Swedish universities’ local publication systems, where data often is entered manually by librarians and researchers. This means that the metadata can contain a lot of omissions, synonyms, spelling variants and errors. Using Crossref, we can enhance and correct the metadata delivered to us, if we just have a correct DOI.

Can you tell us how you are using Crossref metadata at the National Library of Sweden?

The Crossref metadata is presently used in two projects; Open APC Sweden and in our local analysis database for publication statistics used in negotiations with publishers.

Open APC Sweden is a pilot project to gather data on open access publication costs (APC’s – Article Processing Charges) from Swedish universities. The project is modelled from the German Bielefeld University Open APC initiative, which is a part of the INTACT project. After APC data has been delivered to the APC system, scripts are run against the Crossref API to fetch information about publishers and journals. A description of Open APC Sweden can be found here.

When building our local analysis database for publisher statistics, we download data from the SwePub database, use the Crossref DOIs for API lookup against Crossref to add correct ISSN and publisher data to the records and then match the records against a list of publisher serials. In this way, we can get information about how much Swedish researchers have been publishing with a certain publisher and use this data when negotiating conditions for open access publishing with the publisher in question.

What metadata values do you pull from the API?

In Open APC Sweden, a Python script supplied by staff at the Bielefeld University is used to pull metadata about publisher and journal names and ISSN’s from the Crossref API. The result is entered into an enriched version of the APC data files delivered by the universities and then statistics can be calculated on the result using an R script. The result can be seen here.

In the local analysis database, a modified copy of the Bielefeld Python script is used to add the same metadata to the records before matching them against publisher serial ISSNs.

Have you built your own interface to extract this data?

In Open APC Sweden, the Python script is developed and maintained at the Bielefeld University and an exact copy is being run in the Swedish project.

In the local analysis system, the Python script is somewhat modified to suit the special demands of this system.

But sometimes it is very convenient just to use the main DOI lookup to do a manual check-up of problematic records.

How often do you extract/query data?

In Open APC Sweden, usually about two-three times a month, when new datasets are delivered from the universities. In the local analysis database, usually lookups are being done on a daily basis as development of the database continues.

What do you do with the metadata once it’s pulled from the API?

In Open APC Sweden, the metadata is going into the APC data files for processing of statistics. In the local analysis database, the metadata is used to match against publisher journal ISSN’s.

What plans do you have for the future?

For the Open APC Sweden I would like to build a database system to make the system more scalable than just working with flat data files.

With both the SwePub system and the local analysis system, we are now using the new service oaDOI and their API to look up metadata about the open access status of the publications to enrich our local systems.

What else would you like to see the REST API offer?

In the process of normalising the publishers’ names, the names returned are sometimes at a “too high” or on a too generic level to be used to generate good statistics. For instance, Springer Nature are sometimes returned as Springer Nature, sometimes as Springer Science + Business Media and sometimes as Nature Publishing Group. A similar thing is valid for Taylor & Francis, where the mother company Informa UK Limited is returned instead of the publishing subsidiary of the company. One thing to wish for here is that we could agree on some kind of normalisation of the publishers’ names and that Crossref could return this as a supplement to the present metadata.

Thanks Ulf! If you would like to contribute a case study on the uses of Crossref Metadata APIs please contact the Community team.

Using the Crossref REST API. Part 5 (with OpenCitations)

Christine Cormack Wood — Sun, 10 Sep 2017 00:00:00 +0000

As part of our blog post series on the Crossref REST API, we talked to Silvio Peroni and David Shotton of OpenCitations (OC) about the work they’re doing, and how they’re using the Crossref REST API as part of their workflow.

Introducing OpenCitations

OpenCitations employs Semantic Web technologies to create an open repository of the citation data that publishers have made available. This repository, called the OpenCitations Corpus (OCC), contains RDF-based scholarly citation data that are made freely available so that others may use and build upon them. All the resources published by OC – namely the data within the OCC, the ontologies describing the data, and the software developed to build the OCC – are available to the public with open licenses.

What problem is your service trying to solve?

OC was started to address the lack of RDF-based open citation data. To our knowledge, when the project formally started with Jisc funding in 2010 the prototype OCC was the first RDF-based dataset of open citation data.

We collect accurate scholarly citation data derived from bibliographic references harvested from the scholarly literature, so as to make them available under a Creative Commons public domain dedication (CC0) by means of Semantic Web technologies, thus making them findable, accessible, interoperable, and re-usable, as well as structured, separable, and open.

OCC citation data are described using standard and/or well-known vocabularies, including the SPAR Ontologies , PROV-O, the Data Catalog Vocabulary, and VoID. The use of such vocabulary is described in the OCC metadata document, and is implemented by means of the OpenCitations Ontology (OCO).

The OCC resources are made available and accessible in different ways, so as to facilitate their reuse in different contexts: as monthly dumps, via the SPARQL endpoint, and by accessing them directly by means of the HTTP URIs of the stored resources (via content negotiation; example)

Can you tell us how you are using the Crossref Metadata API at OpenCitations?

At present, basic citation information is retrieved from PubMed Central, and the Crossref API is then used to retrieve additional metadata describing the citing and cited articles, and to disambiguate bibliographic resources and agents by means of the identifiers retrieved (e.g., DOI, ISSN, ISBN, URL, and Crossref member URL). In future, we will retrieve full citation data direct from Crossref.

What metadata values do you pull from the API?

We pull the titles, subtitles, identifiers (e.g. DOI, ISSN, ISBN, URL, and Crossref member URL), author list, publisher, container resources (issue, volume, journal, book, etc.), publication year and pages.

Have you built your own interface to extract this data?

The SPAR Citation Indexer, a.k.a. SPACIN, is a script and a series of Python classes that allow one to process particular JSON files containing the bibliographic reference lists of papers, produced from the PubMed Central API by another script included in the OpenCitations GitHub repository.

SPACIN processes such JSON files and retrieves additional metadata information about all the citing and cited articles by querying the Crossref API, among others. Once SPACIN has retrieved all these metadata, RDF resources are created (or reused, if they have been already added in the past) and stored in the file system in JSON-LD format. In addition, they are also uploaded to the OCC triplestore (via the SPARQL UPDATE protocol).

How often do you extract/query data?

The entire OpenCitations ingestion workflow is running continuously, processing about half a million citations per month.

What do you do with the metadata once it’s pulled from the API?

All the metadata relevant to bibliographic entities are stored by using the OCC metadata model. The ontological terms of such metadata model are collected within an ontology called the OpenCitations Ontology (OCO), which includes several terms from the SPAR Ontologies and other vocabularies. In particular, the following six bibliographic entity types occur in the datasets created by SPACIN:

bibliographic resources (br), class fabio:Expression – resources that either cite or are cited by other bibliographic resources (e.g. journal articles), or that contain such citing/cited resources (e.g. journals);
resource embodiments (re), class fabio:Manifestation – details of the physical or digital forms in which the bibliographic resources are made available by their publishers;
bibliographic entries (be), class biro:BibliographicReference – literal textual bibliographic entries occurring in the reference lists of bibliographic resources;
responsible agents (ra), class foaf:Agent – names of agents having certain roles with respect to the bibliographic resources (i.e. names of authors, editors, publishers, etc.);
agent roles (ar), class pro:RoleInTime – roles held by agents with respect to the bibliographic resources (e.g. author, editor, publisher);
identifiers (id), class datacite:Identifier – external identifiers (e.g. DOI, ORCID, PubMedID) associated to bibliographic resources and agents.

Do you have plans to enhance your metadata input?

We already handle additional information, such as ORCIDs, that are extracted by means of the ORCID API applied to the citing and cited articles included in the OCC. In addition, we are developing scripts in order to use all the new citation data Crossref now makes available as consequence of the Initiative for Open Citations (I4OC).

What are the future plans for OpenCitations?

With funding received from the Alfred P. Sloan Foundation, we will shortly extend the current infrastructure and the rate of data ingest. Our immediate goal is to increment the daily ingestion of citation data from about half a million citations per month to about half a million citations per day. In addition, we plan to analyse the OCC so as to understand the quality of its current data, and to develop new user interfaces, including graph visualizations of citation networks, that will expand the means whereby users can interact with the OpenCitations data.

What else would you like to see our REST API offer?

Categorising articles/journals/any bibliographic resources according to their main discipline (Computer Science, Biology, etc.) and, eventually, by means of subject terms and/or keywords. Additionally, provision of authors’ institutional affiliations and funder information would be extremely valuable.

Thank you Silvio and David!

If you are keen to share what you’re doing with the our Metadata APIs, contact feedback@crossref.org and share your story.

Using the Crossref REST API. Part 4 (with CLA)

Rachael Lammey — Tue, 25 Jul 2017 00:00:00 +0000

As a follow-up to our blog posts on the Crossref REST API we talked to the Copyright Licensing Agency (CLA) about the work they’re doing, and how they’re using the Crossref REST API as part of their workflow.

Alex Cole, Senior Business Analyst at the Copyright Licensing Agency introduces the DCS

The Digital Content Store (DCS) is an innovative rights, technology and content platform for UK Higher Education Institutions (HEIs), which was developed collaboratively with HEIs, publishers and technology partners. The platform is included in the CLA annual licence fee and is an optional tool for licensees.

At its core, the system is a searchable repository of digital copies that have been created under the licence by HEIs (the CLA Digital Content Store), it also functions as a workflow management tool. When extracts are digitised by HEIs under the CLA Licence, they are uploaded directly to the DCS. Once an extract is uploaded and assigned to a course, students are able to access the extract via a secure link. Every year HEIs are obliged to report all of these digitised items to CLA as part of the terms of their copyright blanket licence. Prior to the DCS, HEIs were having to submit this data manually, a process that could take days, if not weeks. The system removes the need for annual census reporting to CLA, reducing the data collection burden on the HE sector and creating administrative efficiencies through streamlining the digital course pack creation process.

Can you talk about how you’re using the Crossref REST API within CLA Digital Content Store (DCS)?

When a DCS user adds a new extract to a course they need to include relevant metadata. This metadata is necessary, as it ultimately helps CLA in correctly identifying the copyright owner of the extract so that we can make sure they receive fair payment in our royalties distributions. The Crossref REST API supplies the DCS user with article and journal metadata so that they can provide the correct information about the content they are uploading. Using the API saves the user the time they would have otherwise spent searching for this data, streamlining their workflow and making the process more efficient.

Searching for and adding content in the DCS

What are your future development plans?

We’re continuing to develop the DCS in order to improve user experience for our customers. We’re currently looking into opening up access for our users by allowing academics to submit requests to the DCS via a web-form and our own DCS Course Content URL API. We are also looking into incorporating the Crossref REST API into some of our back office workflows to improve efficiency and simplify our workflow. The metadata that we can retrieve from Crossref can help us match customer usage to our rights database.

What else would you like to see in Crossref metadata?

Going forward we’d like to see:

More books included in the database.
Indicating if an ISSN is associated with the print or digital edition of a journal.

Thanks Alex!

Using the Crossref REST API. Part 3 (with SHARE)

Rachael Lammey — Thu, 01 Dec 2016 00:00:00 +0000

As a follow-up to our blog posts on the Crossref REST API we talked to SHARE about the work they’re doing, and how they’re employing the Crossref metadata as a piece of the puzzle. Cynthia Hudson-Vitale from SHARE explains in more detail…

Cynthia Hudson-Vitale, digital data librarian in Research Data and GIS Services at Washington University in St. Louis Libraries and visiting program office for SHARE

SHARE (http://share-research.org) is building a free, open, data set about research and scholarly activities across their life cycle. It is a higher education initiative whose mission is to maximize research impact by making research widely accessible, discoverable, and reusable. SHARE’s data set is free, openly licensed, and built with open source technology developed at the Center for Open Science (COS). Launched in beta in April 2015 the data set has grown to more than 6 million records from 100+ providers, including Crossref, Social Science Research Network (SSRN), DataONE, 50+ library institutional repositories, and more.

How is the Crossref REST API used within SHARE?

SHARE currently harvests metadata from Crossref using the Crossref application programming interface (API). We pull such metadata values as journal title, author, DOI, journal name, and publisher, to name just a few. This metadata is then fed into our data processing pipeline, normalized, and aggregated into the full data set.

What are the future plans for SHARE?

Phase II of SHARE, launched in late 2015, focuses on adding metadata providers, enhancing the metadata, and making connections and links between the metadata records. These links will show the entire life cycle of research and scholarship—connecting a data management plan, grant award information, data deposits, analytic/software code, pre-publications, final manuscripts, and more.

To move these plans forward, SHARE is applying machine-learning and automation techniques and working with the community to verify metadata enhancements and curate the metadata. Current technology work focuses on imputing subject domain keywords and object types into the SHARE data set using learning models and heuristics. Data models and schemas are in development to connect the research lifecycle, connect multiple instances of an object to a single entity, and capture metadata provenance.

What else would SHARE like to see in Crossref metadata?

We would love to see rights-declaration metadata elements and article references/citations included in the metadata about digital objects. The rights-declaration information is invaluable for individuals who want to know what category the object is in (public domain, copyrighted, etc.), what constraints or permission requirements exist, contact information, and more. Additionally, networks of research can be discovered and meta-scholarship facilitated by making article reference lists machine-readable and openly available.

What’s next?

Does this give you any ideas? Feel free to get in touch with questions or take the API for a spin yourself and let us know what you can do with it!

Using the Crossref Metadata API. Part 2 (with PaperHive)

Rachael Lammey — Thu, 08 Sep 2016 00:00:00 +0000

We first met the team from PaperHive at SSP in June, pointed them in the direction of the Crossref Metadata API and let things progress from there. That’s the nice thing about having an API - because it’s a common and easy way for developers to access and use metadata, it makes it possible to use with lots of diverse systems and services.

So how are things going? Alexander Naydenov, PaperHive’s Co-founder gives us an update on how they’re working with the Crossref metadata:

PaperHive

PaperHive is a web-platform for collaborative reading and a cross-publisher layer of interaction on top of research documents. It lets researchers communicate in published documents in a productive and time-saving way. PaperHive thus puts academic literature, which is integrated with the platform, in the limelight and increases content usage and reader engagement.

Transforming reading into a process of collaboration gives researchers a reason to return to the content and discover new enrichments they can benefit from. Functionality like hiving, deep linking, and the PaperHive browser extension embeds communication in the researcher’s workflow. PaperHive is free to use!

How is the Crossref API used within PaperHive?

PaperHive extends the concept of a living document and offers an innovative way of displaying content without hosting it. Instead, academic documents are dynamically pulled from the publisher’s servers thus ensuring compliance with content licensing. It enables readers to stay in touch with the articles of interest beyond just saving them in an offline folder.

Crossref is the common ground on which third party companies and initiatives can build valuable services for publishers and researchers. It facilitates the integration of content into PaperHive by providing the metadata of articles and books from numerous publishers independent of the technology behind their content platforms. Moreover, if the publishers provide ORCID identifiers of authors in the Crossref metadata, researchers can immediately interact with the readers of their works.

What are the future plans for PaperHive?

In addition to integrating further publishers’ content and extending PaperHive’s feature set for readers, we also plan to extend our partnerships with other technology providers.

As far as our cooperation with Crossref is concerned, we are looking forward to the implementation of the Crossref Event Data API.

What else would you like to see in Crossref metadata?

- The quality of the existing metadata should be improved significantly. We noticed that important fields such as author or title are missing in the metadata of many documents. PaperHive ignores articles and books with incomplete metadata because it impairs the user experience. Publishers, authors and readers can only benefit from the wider and more active usage of content, so we hope that more publishers will improve the data their provide Crossref with.

- Since researchers are working with full texts on PaperHive, it would be great if links to the full text are provided in the metadata of all articles and books. The metadata should also contain information about the format of the full text (e.g., PDF, EPUB, HTML).

Thanks Alex!

Just getting started with the API or what to know more? Get in touch via feedback@crossref.org and pass on your questions and comments.

Using the Crossref Metadata API. Part 1 (with Authorea)

Rachael Lammey — Thu, 18 Aug 2016 00:00:00 +0000

Did you know that we have a shiny, not so new, API kicking around? If you missed Geoffrey’s post in 2014 (or don’t want a Cyndi Lauper song stuck in your head all day), the short explanation is that the Crossref Metadata API exposes the information that publishers provide Crossref when they register their content with us. And it’s not just the bibliographic metadata either-funding and licensing information, full-text links (useful for text-mining), ORCID iDs and update information (via Crossmark)-are all available, if included in the publishers’ metadata.

Interested? This is the kickoff a series of case studies on the innovative and interesting things people are doing with the Metadata API. Welcome to Part 1.

What can you do with the Metadata API?

Build search interfaces. We’ve built some ourselves. Check out Crossref Metadata Search to search the metadata of over 80 million journal articles, books, standards, datasets & more. Or Crossref Funder Search to search nearly 15,000 funders and the 982,162 records we have that contain funding data.

Provide cross-publisher support for text and data mining applications.

Get really interesting top-level reports on the metadata Crossref holds - or look at subsets of the information you’re interested in.

Third parties are free to build their own products and tools that build off of the Metadata API (below are some of the many examples that we will highlight in this series).

Importantly, there’s no sign-up required to use the Metadata API - the data are facts from members, therefore not subject to copyright and free to use for whatever purpose anyone chooses.

To help, Scott Chamberlain of rOpenSci has built a set of robust libraries for accessing the Metadata API. These libraries are now available in the R, Python and Ruby languages. Scott’s blog post has some great information on those. For those using the libraries, there have been a few updates since Scott’s post - to serrano, and support for field queries has been added to habanero (coming to serrano and rCrossref soon). Any feedback/bug reports can be submitted via the GitHub repos serrano or habanero. There’s also a javascript library, authored by Robin Berjon.

Who’s using the Crossref Metadata API?

We get around 30 million requests a month. We’d like to share a few case studies to showcase what they’re doing and how they’re using it. Look out for a series of posts over the next few months where we’ll open the floor to those using the API and let them explain how and why.

We’ll let Authorea kick things off…

Alberto Pepe, co-founder of Authorea explains:

Authorea is a word processor for researchers and scholars. It is a collaboration platform to write, share and openly research in real-time: write manuscripts and include rich media, such as data sets, software, source code and videos. The media-rich, data-driven capabilities of Authorea make it the perfect platform to create and disseminate a new generation of research articles, which are natively web-based, open, and reproducible. Authorea is free to use.

How is the Crossref Metadata API used within Authorea?

Authorea is specifically made for scholarly documents such as research articles, conference papers, grey literature, class notes, student papers, and problem sets. What makes scholarly documents so peculiar are their citations and references, mathematical notation, tables, and data. For citations and references, we built a citation tool which allows authors to search and cite scholarly papers with ease, without having to leave the editor. While in the middle of writing a sentence, authors can click the “cite” button and a citation tool opens up:

We currently use two engines for searching scholarly literature via their APIs: Crossref and Pubmed. Our authors love being able to search (by author name, paper title, topic, etc) and add references to their papers on the fly, in one click.

What are the future plans for Authorea?

Among the many plans we have for the future, there is one which is also tied to Crossref: we are going to let authors assign DOIs to Authorea articles such as blog posts, preprints, “data papers”, “software papers” and other kinds of grey literature which does not fit in the traditional scholarly journals.

What else would you like to see in our metadata?

Well, since you ask: we would love to see unique BibTex IDs being served by the Metadata API (right now, you create the ID automatically using author name and year). Also, in some cases, some important metadata fields are missing (even author or title). I think it is actually more important to fix existing metadata rather than add new fields!

Keen to share what you’re doing with the Crossref Metadata API? Contact feedback@crossref.org and share your story.