Data on Crossref

Strengthening support for data citations and saying goodbye to Event Data

Martyn Rittman — Tue, 24 Mar 2026 00:00:00 +0000

We’re excited to announce a new data citation API endpoint and are seeking your feedback. The new service makes existing data citation relationships in our metadata available, thereby surfacing this part of the research nexus. At the same time, we’ve decided that it’s time to move on from Event Data.

Time to say goodbye

Metadata about published scholarly research has evolved, and continues to evolve in exciting ways. A published article, book, or conference paper is only a single piece of the puzzle. A host of digital identifiers and items can be put together to form a more complete picture of a research project. This is what forms the basis of the research nexus—a rich and reusable open network of relationships connecting research organisations, people, things, and actions; a scholarly record that the global community can build on forever, for the benefit of society.

Ten years ago, we launched Event Data to surface mentions of research around the Internet. What were people saying about published research? Could this discourse contribute to post-publication review and validation? We set up Event Data to capture use of Crossref DOIs in the online world from a variety of sources, including blog posts, social media, websites, and annotations. The idea was that diverse mentions (or “events”) could supplement traditional citation counts as a way to capture the value of research.

Today, the focus is increasingly on transparency, research integrity, and the completeness of outputs. Trust in research is shaped by knowing who the funder was, being able to reanalyse the original data, or checking for bugs in the analysis code. There are also more identifiers for objects within the research space and they are used more widely. This shift is evident in the relatively low usage of Event Data. We can no longer justify the resources and cost that goes into maintaining it as a service. Instead, we will focus on enabling and surfacing relationships between different types of research outputs, starting with links to datasets.

For these reasons, we have decided to sunset the Event Data API and from 23 April 2026 it will no longer be available (although historical data will still be available). In its place, we’re making available an API endpoint for data citations.

Visibility for data citations

The new API endpoint focuses solely on data citations and uses references and relationships deposited by Crossref members, including Crossref articles referencing datasets with either Crossref or DataCite DOIs. While our metadata contains many data citations, and some of them are labelled as data citations, it is often difficult to find them because they are swamped in number by other citations. If you are trying to get data citations directly from our REST API, it’s like looking for a needle in a haystack. This new endpoint makes connections easier to find, enabling organisations to track when research is reused, cited, and built upon. By putting this metadata into a dedicated service, we are making it easier for interested organisations to track and find data citations. Our goal is to make existing sets of connections easier to access, giving clarity to how scholarly works link to the data that supports them.

This beta version will allow us the opportunity to incorporate feedback from the community and make changes to improve delivery. We received early positive feedback from a number of interested organisations. Later this year we will assess whether it is ready for production, needs more work, or if insufficient interest from the community suggests we should pursue a different solution.

Anyone interested in data citations is invited to try the new endpoint. Please let us know your feedback via the community forum. You can find supporting documentation on our website and Swagger documentation, including the opportunity to try out features.

Edited 15 June 2026: link to historical Event Data added.

Service Provider perspectives: A few minutes with our publisher hosting platforms

Jennifer Kemp — Mon, 24 May 2021 00:00:00 +0000

Service Providers work on behalf of our members by creating, registering, querying and/or displaying metadata. We rely on this group to support our schema as it evolves, to roll out new and updated services to members and to work closely with us on a variety of matters of mutual interest. Many of our Service Providers have been with us since the early days of Crossref. Others have joined as scholarly communications has grown and services have evolved. Though fewer than 20 in number, their impact far outweighs the size of the group.

They, like us, work with a great variety of members and have a broad view into publishing trends. In this post, we focus on views from some of the publishing hosting platform Service Providers, who’ve taken the time to share their thoughts on a few questions:

What is the biggest change you’ve experienced working with publisher metadata over the last few years and how have you adapted to it?

It has become more and more important that not only the DOIs are registered with the minimum of necessary metadata to get the DOIs registered, but that a most complete set of metadata is being sent along – including author identifiers, funding information, abstracts, licenses, to support other Crossref services and improve discoverability.

– de Gruyter

Our clients are increasingly aware of the key role metadata plays in the effective dissemination of research. With an increasing number of published articles and a clear domination of “search engines” and aggregation of content, metadata is the primary means of making sure that publications reach the right audience. Publishers’ value-add includes not just copy editing, formatting, and packaging, but also now creating journal articles for the digital age that are discoverable and well linked to the research corpus. Furthermore, we sense a clear move toward standardization, which goes beyond the structure to introduce standardized semantics: adopting common taxonomies for classifying content in different dimensions. Our response is to introduce effective, automated and consistent services that capture, and surface metadata throughout the value chain from authoring to publication and search.

– Atypon

Highwire’s publishers are always looking to use the latest DTD (Document Type Definition) for the content to stay up to current standards. Currently this would be JATS 1.2. They are choosing to remain current so that they can stay on top of all or new metadata that can enrich their deposits. We have handled this well and offer support for the latest version of DTD when they are released, but some publishers are not always familiar with what can/should be deposited with their content and this can be a learning process for them.

– MPS Limited

How do you explain to clients (and others!) why correct, quality metadata is important?

In the digital age, metadata is the key to enabling effective content consumption. Publications that cannot be effectively discovered are of little value. We can only increase the impact of research with “discoverable” and “machine readable” publications. So ensuring correct and quality metadata is the key to optimizing not only the processing (finding the right journal, editor, reviewers) but also to positioning each publication properly. As the volume of published scientific research increases, article metadata is the way forward — it brings “order” and enables our community to manage this volume.

– Atypon

Highwire always positions itself as “good content in” means “good content out”. This is true for our own content stores. Strong and valid metadata will result in valid and strong deposits. We explain this to all new clients on-boarded with Highwire and the use of current standards and for current client projects where content should/can be enriched through re-load.

– MPS Limited

Getting our journals to care about metadata is a two step process: First, make sure they understand how metadata will help their journal succeed (i.e. why it matters to them). Second, make it easy for them to produce metadata while minimizing the cost, time, or complexity of their workflow. The first step – making a case for why metadata matters – is often easier than you’d think. At the very least, most journal editors understand that metadata, e.g., JATS or DOI registration, is an important signifier of professionalism / prestige. In other words, they see that top journals publish metadata and want the same for their journal. From a more technical standpoint, metadata is important because that’s the format computers understand and, like it or not, the publishing ecosystem relies on computers to deliver all sorts of critical services – such as indexing, archiving, and discoverability. So, if you’re not publishing metadata, you’re likely missing the benefit of these services. The second step – making it easy to produce metadata – is more difficult. Journal editors generally understand metadata matters but often lack the technical skills or resources necessary to create metadata. This is where a platform, such as Scholastica, can be very helpful. Because platforms work with many journals, they can invest in tools to automate the creation of metadata, reducing costs for all their clients. For example, most platforms offer integrations to support automatic DOI registration. At Scholastica, we’re pushing this idea even further with automatic integration to more complicated services such as PubMed Central. By reducing cost and complexity, we can help new or small-budget journals have the same quality metadata normally reserved for large, established journals.

– Scholastica

We are sending other publishers’ metadata to academic libraries and distribution channels. Erroneous metadata will have a direct impact on how discoverable a title may be. The more uniform and correct the metadata, the better it will be indexed in other places.

– de Gruyter

What is the one industry development or trend you’re most excited about for the near future and why?

Open Science and the ability to deliver research with the tools for reproducing it is the most exciting and game changing trend. Technology has enabled the output of science to transition from two-dimensional printed text delivery into globally accessible and responsive web-based delivery. We are now taking the next steps to further leverage web technology to enhance research output with rich assets ranging from audio and video, datasets, executable code, high-resolution imagery, interactive applications and more. As more assets accompany research publications, viewing these assets as modular, individually citable, and reusable becomes a requirement. We are reviewing the whole research output flow from authoring to publishing, and most importantly to its dissemination through the myriad of discovery tools now available.

– Atypon

The move of everything to the cloud – this is changing and improving our infrastructure, our possibility to scale and to stay on top of technological development.

– de Gruyter

Thanks very much to the interviewees for their time and thoughts. We look forward to working with our entire Service Provider group on questions like these and many more. If you’d like more details, you can read about our Service Provider program or contact me for more information.

Event Data: A Plan of Action

Martyn Rittman — Mon, 01 Feb 2021 00:00:00 +0000

Event Data uncovers links between Crossref-registered DOIs and diverse places where they are mentioned across the internet. Whereas a citation links one research article to another, events are a way to create links to locations such as news articles, data sets, Wikipedia entries, and social media mentions. We’ve collected events for several years and make them openly available via an API for anyone to access, as well as creating open logs of how we found each event. Some organisations are already using Event Data and we are keen for more to come on board.

Last year we gave an update on Event Data with apologies for being so quiet and a promise of more information at a later date. It’s been some time, so here goes…

I joined Crossref in the middle of last year as a Product Manager and was tasked with looking into Event Data. The first thing I found was a large amount of enthusiasm for Event Data, both within Crossref and further afield. The idea of gathering information beyond the metadata deposited by our members is popular, and creates valuable connections between DOIs and a range of other sources. Interest spans the spectrum of academic research, publishing, bibliometrics, and beyond.

At the same time, I found a project with a very solid, well-built code base but unstable performance. After being put into production in 2018, we didn’t provide sufficient support. Coupled with staff changes and other competing priorities, Event Data hasn’t had the opportunity to live up to early expectations.

To address these issues, we have embarked on a plan to make the server infrastructure more robust, improve monitoring, and make sure that the future of Event Data makes the best use of the resources we have without over-stretching. It means working with the community to determine the most essential aspects of Event Data, and providing support where it’s needed.

The steps below are not necessarily sequential and some depend on the completion of work in other parts of Crossref, but they outline the priorities we have for Event Data in 2021.

The Plan

Stability

Since we put in place our original Event Data infrastructure, the amount of incoming data has grown, and at an ever-increasing rate. In 2017 we were creating 2 million new events per month, that number is now over 20 million. We have known for some time that we need to refresh the infrastructure, but didn’t have the resources to move forward: now we do.

In the first part of the plan we will renew the server infrastructure that underpins Event Data. Maybe not a headline-grabbing move, but the aim is to reduce downtime and pull in missing data. Through improving our monitoring and shortening the response time when things go wrong, we will be able to ensure that events are added on a regular basis and the API can reliably handle requests.

We’ve made the first steps in this direction by upgrading our API infrastructure and making some other tweaks to improve performance. There is still work to do, but we’ve already seen a significant improvement in performance with nearly >99.99% uptime in December.

Consolidation

The second component of the plan is to review performance and data quality. We will evaluate the event sources, update artefacts (such as the lists of publisher landing pages and news websites, and review performance reporting. This will help us to have a better understanding of Event Data in its current form: if the stability component is about improving what comes in and goes and out, this part will give us increased confidence in what Event Data already contains.

Future roadmap

While the two steps above are being carried out, we will revisit the applications of Event Data and talk to organisations that currently use it or have expressed an interest. These conversations will feed into future development in which we will evaluate new sources and other ways to optimize the service.

Central to the roadmap will be continued support of the data citation endpoint in Scholix format, which we run in close collaboration with DataCite. Additionally, we will add new data from relationships between Crossref works, for example a preprint is matched to a journal article, or where there are corrections, retractions, or translations of works.

We expect to continue supporting the current sources of events and where there are organisations with either a strong interest in a particular source or a database of events that they can send directly, we are keen to build collaborations. Event Data, like everything that Crossref does, is a community-based effort.

Staying in touch

To join the conversation about Event Data and keep informed, head over to our Community pages. You can also check out our Gitlab pages. At the end of last year we updated the Education pages where you can learn more about Event Data.

Events got the better of us

Bryan Vickery — Fri, 27 Mar 2020 00:00:00 +0000

Publisher metadata is one side of the story surrounding research outputs, but conversations, connections and activities that build further around scholarly research, takes place all over the web. We built Event Data to capture, record and make available these ‘Events’ –– providing open, transparent, and traceable information about the provenance and context of every Event. Events are comments, links, shares, bookmarks, references, etc.

In September 2018 we said Event Data was ‘production ready.’ What we meant was development of the service had reached a point where we expected no further major changes to the code, and we encouraged you to use it. What normally would have followed was a detailed handover to our operations team, for monitoring and performance management, and for Product Management to expand Event Data by adding new Crossref member domains and evaluating additional event sources.

Why so quiet?

But many things changed on the staff front, meaning 2019 was a year of reinvention for the Technical and Product teams and of critical knowledge sharing and learning –– Event Data had to take a back seat as we focused resources on other key projects (more on that later). From a technical perspective, we’ve found the Elasticsearch index is not performing well and the approach taken to specifically support data citations through Scholix has not really scaled.

When things go wrong, whether in ways you can or can’t anticipate, the most important thing is communication –– in dealing with the challenges we forgot to do that. We understand how frustrating that can be and we’re extremely sorry to have gone so quiet.

So, where are we today?

Event Data is important to us and clearly important to you too as you’ve contacted us about your use-cases and the reliability of the service. Event Data remains available and you’re welcome to use it, but you should expect instability to continue and be aware that it does not find events for DOIs/domains of our newer members (who joined Crossref since 2019) –– so we’re conscious it might be hard to say whether it’s a good fit for your project at this point.

What are we doing?

We have brought in additional expert Elasticsearch resources to assist with a separate project to migrate our REST API from SOLR to Elasticsearch. We’re making fantastic progress on this. As soon as we’re confident we can make this switch, we will move those same Elasticsearch resources to shoring up Event Data. The REST API takes priority over Event Data because we need to add support for important new record types (like research grants) that aren’t yet available via the API.

We’re also concluding the process of hiring two new Product Managers which means we’ll be in a position to assign someone to head up the product management of Event Data. When we do return to Event Data in the coming months, our initial priority will be increased support for data citation and Scholix. If that means radical changes to the rest of the service, we’ll let you know.

Opening up the discussion

We will have more news on Event Data in mid-2020. We’d love you to join the Crossref Community Forum; we’ve created a new Category for Event Data where you can post details of how you are using, or plan to use Event Data; post questions to the group; suggestions for future development and provide general feedback on the Event Data service.

Data Citation: what and how for publishers

Rachael Lammey — Fri, 23 Nov 2018 00:00:00 +0000

We’ve mentioned why data citation is important to the research community. Now it’s time to roll up our sleeves and get into the ‘how’. This part is important, as citing data in a standard way helps those citations be recognised, tracked, and used in a host of different services.

This week A Data Citation Roadmap for Scientific Publishers was published in Scientific Data. This roadmap is the outcome of a collaboration between different publishers that worked on identifying all steps you need to take as a publisher to implement data citation. If you want to know more about establishing a data policy, capturing data citations at the point of submission, or tagging data citations in your XML, we recommend you take a look at this article!

In this blog post, we’ll discuss the steps you need to take after you’ve implemented this roadmap. The steps in the roadmap describe how you can track & tag data citation yourself. Here we describe how Crossref can help you make these available to the rest of the community.

The ‘what’

Here’s the recap! From the Crossref perspective, there are two ways to add data citation links into the metadata that you register:

1. Metadata deposits using the references section of the schema

This is where ‘citations’ are normally recorded. Publishers include the data citation into the deposit of bibliographic references for each publication.

Publishers can deposit the full data or software citation as a unstructured reference. For guidance here, we recommend that authors cite the dataset or software based on community best practice (Joint Declaration of Data Citation Principles, FORCE11 citation placement, FORCE11 Software Citation Principles).

<citation key="ref=3">
<unstructured_citation>Morinha F, Dávila JA, Estela B, Cabral JA, Frías Ó, González JL, Travassos P, Carvalho D, Milá B, Blanco G (2017) Data from: Extreme genetic structure in a social bird species despite high dispersal capacity. Dryad Digital Repository. http://dx.doi.org.pluma.sjfc.edu/10.5061/dryad.684v0</unstructured_citation\>
</citation>
</citation_list>

Or they can employ any number of reference tags currently accepted by Crossref.

<citation key="ref2">
<doi>10.5061/dryad.684v0</doi>
<cYear>2017</cYear>
<author>Morinha F, Dávila JA, Estela B, Cabral JA, Frías Ó, González JL, Travassos P, Carvalho D, Milá B, Blanco G</author>
</citation>

We are exploring JATS4R recommendations to expand the current collection and better support these citations - more on this soon. We also encourage additional suggestions from the community.

2. Metadata deposits using the relations section of the schema

This is where other relationships can be recorded. Publishers assert the data link in the relationship section of the metadata deposit. Here, publishers can identify data which are direct outputs of the research results if this is known. This level of specificity is optional, but we’d recommend it as it can support scientific validation and research funding management.

Data and software citations via relation type enables precise tagging of the dataset and its specific relationship to the research results published. To tag the data & software citation in the metadata deposit, we ask for the description of the dataset & software (optional), dataset & software identifier and identifier type (DOI, PMID, PMCID, PURL, ARK, Handle, UUID, ECLI, and URI), and relationship type.

<program xmlns="http://www.crossref.org.pluma.sjfc.edu/relations.xsd">
<related_item>
<description>Data from: Extreme genetic structure in a social bird species despite high dispersal capacity</description>
<inter_work_relation relationship-type="references" identifier-type="doi">10.5061/dryad.684v0</inter_work_relation>
</related_item>
</program>
</doi_relations>

In general, use the relation type references for data and software resources.

Publishers who wish to specify that the data or software resource was generated as part of the research results can use the isSupplementedBy relation type.

The ‘how’

I create my own XML and register it with Crossref

Add links to datasets into your reference lists, including their DOIs if available as shown above and deposit them with Crossref. We’ll do the rest. If you want to add references to existing metadata records, you don’t need to redeposit the full article metadata, you can send us a resource-only deposit that just contains the reference metadata to append that to the existing metadata for the article. You can also use this method if you prefer to deposit references in a separate workflow to registering your content (we know some members prefer to work this way).

I’ve started using Metadata Manager for journal article deposits

Article<->Data relationships in Crossref

You can deposit data citations using either method using our new Metadata Manager tool. When entering journal article metadata, you can use the ‘Related Items’ section to enter the DOI (or other identifier) for the dataset, the type of identifier, a description of the relation type e.g. ‘Data from: Extreme genetic structure in a social bird species despite high dispersal capacity’, and the relation type - ‘references’ or ‘is supplemented by’ depending on the relationship between the data and the article as described above. When you make the deposit, this relationship information will be registered in Crossref along with the rest of the article metadata.

Metadata Manager also has a section where you can enter and match your references, and then deposit these with Crossref. If you choose this method, enter any data citations into the references section before depositing the article metadata with Crossref.

If you want to add this information to deposits you have already made using Metadata Manager, you can search for the journals and articles in the interface, bring up the existing metadata and add in the additional information before redepositing.

I use “simple text query” to search for and deposit references

Make sure you include any citations to data in the references you add into Simple Text Query. When you use simple text query to deposit these references, they will then be added into the article metadata in the Crossref database.

If you use OJS, they’re working on functionality (due for release soon) that will make it easier to deposit reference metadata with Crossref, so you can include citations to data in that.

All of this metadata—registered with Crossref—make it possible to build up pictures of data citations, linking, and relationships. Whether the citations come from the authors in the reference list or they are extracted by the publisher and then deposited, Crossref collects them across publishers. We then make the aggregate set freely available via Crossref’s APIs in multiple interfaces (REST, OAI-PMH, OpenURL) and formats (XML and JSON). DataCite does the same for data repositories and so this provides an easy way for publishers and data repositories to exchange information about data citations. As mentioned previously, this all feeds in Event Data. Data is made openly available to a wide host of parties across the extended research ecosystem including funders, research organisations, technology and service providers, indexers, research data frameworks such as Scholix, etc.

Do you have questions about how to add these links to your Crossref or DataCite metadata? We’ll be running a series of webinars in early 2019 to give you a chance to join us live and ask any questions you have. Eager to get started in the meantime? Let us know and we’ll start to coordinate.

Why Data Citation matters to publishers and data repositories

Helena Cousijn — Thu, 08 Nov 2018 00:00:00 +0000

A couple of weeks ago we shared with you that data citation is here, and that you can start doing data citation today. But why would you want to? There are always so many priorities, why should this be at the top of the list?

I’m sure you heard this before—data sharing and data citation are important for scientific progress. The three key reasons for this are:

1) Transparency and reproducibility

Most scientific results that are shared today are just a summary of what researchers did and found. The underlying data are not available, making it difficult to verify and replicate results. If data would always be made available with publications, transparency of research would be greatly improved.

2) Reuse

The availability of raw data allows other researchers to reuse the data. Not just for replication purposes, but to answer new research questions.

3) Credit

When researchers cite the data they used, this forms the basis for a data credit system. Right now researchers are not really incentivized to share their data, because nobody is looking at data metrics and measuring their impact. Data citation is a first step towards changing that.

The benefits described above are all quite long-term, so why, as a publisher or data repository, should you put your resources towards implementing data citation workflows now? During our pre-conference workshop at FORCE2018 we asked repositories and publishers this question. Below you’ll find some of the answers.

Data repositories

For data repositories, data citation leads to increased visibility of both the repository and the datasets. The workshop revealed that many repositories do a lot of work to establish links between articles and datasets, thereby significantly contributing to transparency in research. Some of the repositories explained that they hire curators that text mine articles to find associations and manually curate datasets to ensure information about links is part of the metadata. This is reflected in Event Data, where 99% of links between articles and datasets comes from data repository metadata. This downstream enrichment of metadata is useful, but it would be more effective if all stakeholders strive to establish these links at a much earlier stage in the research communication process.

ICPSR, the Inter-university Consortium for Political and Social Research, shared:

ICPSR views data citation as vital. As a large social science data archive, ICPSR curates, preserves, and distributes data for the research community to re-use over time. Data citation makes data visible to the research community. Without it, data cannot be accessed for re-use or reproduced for transparency. Its use cannot be tracked and counted to reveal its impact and potential for new uses by investigators in new fields or in combination with new types of data. Data creators cannot receive adequate credit for their intellectual output. And the original investment by funders and scientists to create those data stops producing dividends. Therefore, data citation plays an essential role in the data sharing lifecycle.

Proper data citation, with a unique identifier, makes it much easier to measure impact. When data use is not cited or cited obliquely, it is rendered virtually invisible. Hence, much data use is still not easily detected. The ICPSR Bibliography of Data-related Literature represents ICPSR’s efforts to identify publications that analyze data distributed at ICPSR and link them directly to the data in the ICPSR catalog. As of 2018, ICPSR has a searchable database that contains nearly 80,000 citations of published and unpublished works resulting from analyses of data held in the archive. ICPSR also makes the case for data citation in its brief new video, “ICPSR 101: Why Should I Cite Data?”

GBIF, the Global Biodiversity Information Facility, explained:

The work required to collect, clean, compile and publish biodiversity datasets is significant and deserves recognition. Researchers publish studies based on data made available through GBIF.org at a rate of about 2 papers every single day. It is crucial for GBIF to link these scientific uses to the underlying data as one measure of demonstrating the value and impact of sharing free and open biodiversity data. At the moment, however, only about 10 percent of authors cite or acknowledge the datasets used in research papers properly. As a result, data publishers efforts often risk going unnoticed, and the true impact of sharing data remains invisible. GBIF will continue to work with publishers and researchers to provide guidance and input for how to best cite the use of GBIF-mediated data in scientific journals to ensure proper attribution and reproducible research and to demonstrate the true value of free and open access to biodiversity data.

Publishers

By ensuring data is cited in a consistent way, publishers help provide transparency and context for the content they publish. Depositing that information as part of the Crossref metadata helps that work go further by uncovering how data is being used across multiple publications and publishers This means patterns can be explored and researchers can gain more comprehensive recognition and credit for the work they have done.

Melissa Harrison, Head of Production Operations at eLife says:

eLife is committed to ensuring researchers get credit for all their outputs, and data is a major component of this. We’re working with Crossref and JATS4R to enable publishers to tag their JATS data content consistently and thus create an easy crosswalk to their Crossref deposits. The JATS4R guidance on Data Availability Statements, linked to and incorporating data citations, will be updated soon, please watch that space!

It will be really interesting to see how much re-use of previously published data is happening, look for patterns in re-use, and see links and hopefully building up of data by different research groups. Ultimately, this will incentivize researchers and publishers to ensure it is correctly accredited at source and in publications, improving the cycle further.’

Anita de Waard, VP of Research Collaborations at Elsevier, says:

One of the key recommendations of the Force11 Manifesto was to “3.3 Add data, software, and workflows into the publication as first-class research objects”, which will allow greater reproducibility and rigor to experimental research, and allow the reuse of all digital artefacts in the scholarly lifecycle. By following the data citation principles, we achieve two things: the author presents a richer representation of their work, and the data producer receives credit for the hard work of curating and publishing citable datasets.

Mendeley Data and Elsevier are active contributors to the Scholix framework that as a collaborative and open standard, allows the open mining of relationships between articles and datasets. We are also active participants in the new Enabling FAIR Data Project, and next to supporting the TOP Guidelines in all domains, require all authors in the earth and space sciences to deposit their data before publication.

Next week at Crossref LIVE18, Patricia Cruse from DataCite will talk about Data Citations and why they matter. If you’re in Toronto next week, do not hesitate to ask her or anyone from Crossref anything you want to know about data citation!

Data citation: let’s do this

Rachael Lammey — Thu, 04 Oct 2018 00:00:00 +0000

Data citation is seen as one of the most important ways to establish data as a first-class scientific output. At Crossref and DataCite we are seeing growth in journal articles and other record types citing data, and datasets making the link the other way. Our organisations are committed to working together to help realize the data citation community’s ambition, so we’re embarking on a dedicated effort to get things moving.

Efforts regarding data citation are not a new thing. One of the first large-scale initiatives to establish data citation as a standard academic practice was the FORCE11 Joint Declaration of Data Citation Principles (JDDCP) in 2014. This declaration was endorsed by over 100 organisations in the scholarly community as well as many individuals.

Following this agreement on how data citation should be done, many projects followed. Within FORCE11, the Data Citation Implementation Pilot brought together publishers and repositories to put data citation into practice and work on the implementation of the JDDCP. Within the context of the Research Data Alliance, a data-literature linking group started under the name of Scholix to establish a framework for exchanging information about the relationships between articles and datasets. The infrastructure building blocks now feed into projects such as Make Data Count and Enabling FAIR Data.

Projects aside, if datasets are cited consistently and in a standard way, it will make it much easier for the research community to see links between different research outputs and work with these outputs. It also makes it much easier to count these citations, so that researchers can get credit for their data and the sharing of that data.

The underlying work has been done to create an infrastructure that will effectively support and disseminate information on data citation. Data citation is here today!

Different organisations know how to handle data citations, and are starting to count these and make that information available in turn. This means that the only thing that’s needed is for people to actually cite data, and this information be captured and passed on. Some Crossref and DataCite members have already made great progress on this already (see Melissa Harrison’s blog on what eLife is doing).

The goals of all the data citation projects can only be realized if you start doing data citation, and we know you’ll have questions about it…

In the coming months, we’ll be posting several blogs and organizing sessions to tell you how you can start doing data citation - if you’re attending FORCE2018 you can catch our joint workshop there. So stay tuned and please get in touch if you can’t wait, we’d love to help you get started!

Event Data is production ready

Christine Buske — Wed, 12 Sep 2018 00:00:00 +0000

We’ve been working on Event Data for some time now, and in the spirit of openness, much of that story has already been shared with the community. In fact, when I recently joined as Crossref’s Product Manager for Event Data, I jumped onto an already fast moving train—headed for a bright horizon.

What’s on the horizon? Well, the reality is you never really reach the horizon. Good product development—in my opinion—is like that train. You keep aiming for the horizon and passing all the stations (milestones) along the way, but the horizon keeps moving as you add features, improve the service, and maybe even review where you are headed. However, for Event Data we are pleased to say we have now arrived at a rather important station.

Technical readiness

Thank you to all the beta testers who have journeyed with us this far—we’ve listened and learned, refined and rebuilt with the help of your feedback. We are now thrilled to say that we are service production ready. We’ve reached the station called ‘technical readiness’, and are eager to see more users board our train!

During this time of building and refining, Event Data has grown to include at least 66,7 million events from sources like (in order of magnitude): Wikipedia, Cambia Lens, Twitter, Datacite, F1000, Newfeeds, Reddit links, Wordpress.com, Crossref, Reddit, Hypothesis, and Stackexchange. Wikipedia alone accounts for 50 million events (and counting).

What does this mean?

Event Data is production ready.

Being production ready means we are not going to make any breaking changes to the code, and we are excited to see more people jump on board to explore where you can go with Event Data, and what product or service you might want to build with it.

Getting started

Having a look at Event Data, and using it, is easy. While the user guide outlines everything you need to know to get fully engrossed, you can get your feet wet with a few sample queries:

Above I mentioned Event Data has about 50 million Wikipedia events, you can check if that has grown by looking at a query that lists all distinct events by source (your browser will need a JSON viewer extension):

https://api-eventdata-crossref-org.pluma.sjfc.edu/v1/events/distinct?facet=source/:*&rows=0

You can also see a live stream of events going through Event Data.

For all events registered for a specific content item, you simply query http://api.eventdata.crossref.org.pluma.sjfc.edu/v1/events?obj-id=https://doi-org.pluma.sjfc.edu/XXX, where XXX is replaced with the DOI.

What next?

We are now focusing on the final stretch towards the official roll-out. Beyond this, we will continue to add sources and features and have a healthy roadmap to keep us on track. We value any feedback you have for us about your own journey with Event Data. Your feedback may help shape the direction we take in the future. Most of all, we are all excited to see what people build with it!

We look forward to continuing on our Event Data journey and we welcome you all aboard the train! Please contact me with your ideas.

Hello, meet Event Data Version 1, and new Product Manager

Christine Buske — Thu, 29 Mar 2018 00:00:00 +0000

I joined Crossref only a few weeks ago, and have happily thrown myself into the world of Event Data as the service’s new product manager. In my first week, a lot of time was spent discussing the ins and outs of Event Data. This learning process made me very much feel like you might when you’ve just bought a house, and you’re studying the blueprints while also planning the house-warming party.

If Event Data is like a house, it’s been built and we’ve recently been putting on a last coat of paint. We’re very happy to announce version 1 of the API today. This is bringing us closer to the launch (house warming party), which will officially present Event Data to the world. Further to that analogy, while I bought into the house, I wasn’t around to see it being built. That’s both incredibly exciting and a little daunting.

Version 1 contains fixes for some challenges we came up against. Like scalability, data modeling for Wikipedia, and polishing. Version 1 is a new release of the data, but it is the same data set you already know and love. It should solve some of the recent stability issues, for which we apologize.

Moving forward, we expect the data model in V1 to persist and are not planning to make further large scale, fundamental changes to the Event Data API. As such, the version 1 release of the API is exceptional and a big step forward. It is important that we address these fixes before we go into production as it affects everyone who uses the service.

Same Event Data, new address

In setting up for the upcoming production service rollout, we have updated the Event Data API domain so that it is in line with Crossref’s suite of APIs. The Query API can now be found at a new URL. Here is an example query: https://api-eventdata-crossref-org.pluma.sjfc.edu/v1/events?rows=1

We have also simplified the standard query parameters in favor of a cleaner filter syntax.

Lastly, we have added a new “Mailto” parameter, just like in our REST API. It is encouraged but optional, so you are not obliged to supply it. We’ll only use it to contact you if there’s a problem.

Changes to the Wikipedia data structure

We’ve done a lot of work to use the canonical URLs for web pages to represent content as consistently as possible. This has entailed updating previously collected Events across data sources. As such, we’ve updated our Wikipedia data model to align with this. Because this update has impacted every Wikipedia Event in the system, we recommend those who have used or saved existing data from the deprecated Query API version to pull a new copy of the data. Read more about the rationale for changing the Wikipedia data model.

Updated data

This then brings me to how we now handle updated data. Sometimes we edit Events to add new features, or we may edit Events if there is an issue processing and/or representing the data when we provision it to the community. And sometimes we must remove Events to comply with a particular data source’s terms and conditions (ex: deleted Tweets). You can read about how updates work in the user guide.

To make life easier moving forward, we’ve split updated Events into two API endpoints. If you are already using Event Data, you will need to make some small updates to your client(s) to align with this. The new endpoints are further described in the documentation.

Event Data beta group

With the version 1 release we are making solid progress towards an official launch (the house-warming party!), we are quite excited to hear how you are using Event Data. Please consider [joining our beta group] (https://groups.google.com/forum/#!forum/crossref-event-data-beta-testers), if you are using the Event Data API or want to hear about updates.

This is also where you can read about these updates in more detail.

For more information and to get started with Crossref Event Data, please refer to the user guide.

I am looking forward to seeing how Event Data is being used, and working with the community to continuously improve what we can offer through this service. Feedback is always welcome, feel free to get in touch with me at eventdata@crossref.org.

Publishers, help us capture Events for your content

Madeleine Watson — Mon, 02 Oct 2017 00:00:00 +0000

The day I received my learner driver permit, I remember being handed three things: a plastic thermosealed reminder that age sixteen was not a good look on me; a yellow L-plate sign as flimsy as my driving ability; and a weighty ‘how to drive’ guide listing all the things that I absolutely must not, under any circumstances, even-if-it-seems-like-a-really-swell-idea-at-the-time, never, ever do.

The margin space dedicated to finger-wagging left little room for championing any driving-do’s. And as each page delivered a fresh new warning, my enthusiasm for hitting the road sunk to levels usually reserved for activities like trigonometry and visits to my orthodontist.

Many years (and an excellent driving record) later, I’m reminded of this again now when thinking about our own Event Data User Guide. Because it contains a chapter with some really important don’ts for our members. Really good, we’d-love-you-to-consider-not-doing-these-things type of advice. But despite our intent to encourage, I feel the ghost of finger-waggers past. So in the spirit of championing enthusiasm over ennui, I thought I’d attempt to contextualise our Event Data Best Practices Guide for Publishers and show you why there’s a lot of good reasons for publishers to be enthusiastic about these rules.

So if you’re a publisher, I encourage you to read on to learn more about how you can help us have the best chance possible of capturing Events for your content.

What’s in it for you? Well, collecting this data helps to give everyone (Crossref, yourself, and others) a better picture of how your content is being used, including for altmetrics.

1. Please let us in

Please do open the door when we come knocking, we promise not to stay long. You can do this by allowing the User Agent CrossrefEventDataBot to visit your site, and whitelisting it if necessary. The bot is how we visit URLs to confirm if they are for an item of content registered with us. The reason why we’re visiting your site could include:

someone tweeted an article landing page
someone discussed it on Reddit
it was linked to from a blog post

The Bot has only one job: to work out the DOI. No information beyond this is stored. Whenever we become aware of a link that we think points to a DOI or an Article Landing Page, we follow it so we can collect the required metadata. Everything in Crossref Event Data is linked via its DOI, so it’s important that we can collect this information.

The bot will identify itself using the standard method. It sets two headers:

Referer: https://eventdata-crossref-org.pluma.sjfc.edu
User-Agent: CrossrefEventDataBot (eventdata@crossref.org)

Once we confirm that a link points to registered content, we then log an Event for the DOI. You should expect our bot to visit no more than once or twice per second, although if there is a period of activity around your articles, you may see higher rates. The bot also takes a sample of DOIs and visits them to work out which domain names belong to our members, so it can maintain a list. This can happen every few weeks. You may see a small number of requests from the bot, but limited to one per second.

If we can’t enter your site to look for metadata though, then we won’t be able to collect Events for your DOIs. So by allowing our bot, you will be helping us to collect Event Data for your registered content.

If you’re worried about traffic on your site, consider sending us your mapping of article landing pages to DOIs. Because Resource URLs aren’t the same as article landing pages, we need more information than the DOI Resource URLs that you already send us.

If you’re running a blog or website (and you’re not a member of Crossref), you may also see our bot visiting, to look for links that comprise Events. Please allow us to visit, so we can record in our Event Data service the fact that your website links to registered content.

2. We ❤️ robots.txt

Robots.txt files are important and we ensure our Event Data Bot respects yours. If we are instructed not to visit a site, we won’t. So if you want us to visit your site in order to check the metadata of your article landing page, please ensure you provide an exception for our Bot, or make sure that you’re not blocking it. Check the restrictions in your file to see if we’re allowed to visit. This is just another way you can help us work for you.

3. Include the DC Identifier

Including good metadata is general best practice for scholarly publishing. When we visit a publisher’s site, we look for metadata embedded in the HTML document (such as DC.Identifier tags that, amongst other things, enable Crossmark to work).

By ensuring you include a Dublin Core identifier meta tag in each of your articles pages, our system can match your landing pages back to DOIs.

Here’s an example:

4. Let us in, even if we don’t bring cookies

We’re like that friend who turns up for dinner without bringing a bottle of wine. And we hope that you’ll be ok with that. Some Publisher sites don’t allow browsers to visit unless cookies are enabled and they block visitors that don’t accept them. If your site does this, we will be unable to collect Events for your DOIs. Allowing your site to be accessed without cookies will help give us the best chance of successfully reading your metadata.

5. We may not speak your language

Sometimes we come across a publisher’s site that won’t render unless JavaScript is enabled. This means that the site won’t show any content to browsers that don’t execute JavaScript. The Event Data Bot does not execute JavaScript when looking for a DOI. This means that if your site requires JavaScript, then we will be unable to collect DOIs for your Events. Consider allowing your site to be accessed without JavaScript. And if this is not possible, then if you ensure you include the tag in the HTML header, then we’ll do our best to collect Events for your registered content.

If you want to pass this on to your friendly system administrator, the best practice is documented in full here: https://www-eventdata-crossref-org.pluma.sjfc.edu/guide/best-practice/publishers-best-practice/. And sorry about all the don’ts you’ll find on that page…. don’t let them curb your enthusiasm for taking Event Data out for a spin!

Data citations and the eLife story so far

Melissa Harrison — Thu, 18 May 2017 00:00:00 +0000

When we set up the eLife journal in 2012, we knew datasets were an important component of research content and decided to give them prominence in a section entitled ‘Major datasets’ (see images below). Within this section, major previously published and generated datasets are listed. We also strongly encourage data citations in the reference list.

Major Datasets for “Structural basis of protein translocation by the Vps4-Vta1 AAA ATPase” by N. Monroe, H. Han, P. Shen, et. al.

Almost five years on and I feel we have still not cracked it! We have signed up to the Force11 data citation principles, which were published three years back; we have been actively involved in working groups of Force11 and others, for example the Data Citation Roadmap for Scientific Publishers and the JATS XML data citation recommendation of JATS4R. I am also currently working with other publishers to come up with recommended JATS XML tagging for data availability statements, which is easier said than done considering the nuances of dataset uses and also how different publishers approach this.

Added to this, there is still significant push-back from authors about putting all dataset citations in the reference list (for example, authors are concerned about self-citing by citing a dataset created as part of the research article; “dataset citations” that are in effect a link to a search results page on a database; and the necessitation of hundreds of reference entries if an author has used a large base for the research).

While eLife is very active in this space, and aims to arrange and mark up the datasets and citations produced by our authors in line with recommendations, the recommendations still have some gaps and the complete picture is not yet clear.

In late 2014, we brought in-house the process of depositing Crossref metadata (previously our online host did this for us). It gave us control of our processes and, at the time, we sent all the information we could to Crossref and have ensured our references are open and available in the Crossref public API. The code for this conversion process is all open-source and available for reuse. It can be found on GitHub. Since then, besides small improvements to the code and troubleshooting problems, we’ve not updated the code. I have been keeping a list of Crossref features and new deposit metadata we can add to our deposits, and now is the time for us to start working on this again.

One of the items we’ll be addressing is data citations.

The Crossref reference schema does not cater well for non-book or -journal content, and if an item does not have a DOI, the “reference” is not very useful because of the few tags available in the Crossref schema.

However, Crossref have introduced the relationship type to their schema, so data references can be well linked and mineable. As I see Crossref as a potential broker between publishers and data repositories in the future, using the relationship-type deposit for all datasets will assist this and also allow these data points to more easily be seen within the article Nexus framework (see the recent blog post, How do you deposit data citations?).

At eLife, we already distinguish between Dataset generated as part of research results (relationship type in the Crossref schema: “isSupplementedBy”) and Dataset produced by a different set of researchers or previously published (relationship type: “references”). Therefore, it will not be hard for us to convert all the information about data referencing that is within the dataset section into a relationship-type deposit in the conversion to Crossref XML.

We have also recently gone through an exercise of defining a set of rules for all our references and, of the 12 allowed types, one is data. The rules for Schematron (a rule-based validation language for making assertions about the presence or absence of patterns in XML trees; see also this useful article about Schematron on the JATS4R learning centre) have been written for the eLife ‘business’ rules. Subject to final testing, these will be integrated into our workflow (the Schematron is open source and available for reuse on GitHub, and we will also build an API for people to use the Schematron direct). This will allow us to easily identify all data references and convert them into relationship types in the XML delivered to Crossref. This way, they will not be lost in the references section of our deposits, but properly identified.

However, we do appreciate this will become harder for us as authors become more familiar with datasets as references, because we will not be able to identify the difference between generated and analysed datasets so easily.

The code developed and used to complete these conversions will, again, be on Github and open source, and we actively encourage the reuse of this.

While the industry is still working on the best way to deal with data and ensuring it is given the prominence it requires, we feel this is the best approach we can take. Nothing is forever and we can still change what we do in the future. The beauty of open-source code also means that if there is an alternative approach now or in the future, the code we wrote at eLife can be developed by someone else in the future and we can all benefit.

If you have any questions, please do not hesitate to contact us.

How do you deposit data citations?

Jennifer Lin — Thu, 02 Mar 2017 00:00:00 +0000

Please visit Crossref’s official Data & Software Citations Deposit Guide for deposit details.

Very carefully, one at a time? However you wish.

Last year, we introduced linking publication metadata to associated data and software when registering publisher content with Crossref Linking Publications to Data and Software. This blog post follows the “whats” and “whys” with the all-important “how(s)” for depositing data and software citations. We have made the process simple and fairly straightforward: publishers deposit data & software links by adding them directly into the standard metadata deposit via relation type and/or references. This is part of the **existing Content Registration ** process and requires no new workflows.

Relationships

Data & software citations are a valuable part of the “research article nexus”, comprised of the publication linked to a variety of associated research objects, including data and software, supporting information, protocols, videos, published peer reviews, a preprint, conference papers, etc. For all of these resources, we use relation types in the metadata deposit to “anchor” the article in the article nexus and link to it.

For data & software, we ask for:

identifier of the dataset/software
identifier type: “DOI”, “Accession”, “PURL”, “ARK”, “URI”, “Other” *
relationship type: “isSupplementedBy” or “references”
description of dataset or software.
*Additional identifier types beyond those used for data or software are also accepted, including ARXIV, ECLI, Handle, ISSN, ISBN, PMID, PMCID, and UUID.

Crossref maintains an expansive set of relationship types to support the various resources linked in the research article nexus. For data and software, we recommend “isSupplementedBy” and “references” as relationship types in the metadata. Use the former if it was generated de novo as part of the research results. For those generated by another project and then reused, we recommend applying “references” in the relationship type. These were selected in consultation with DataCite and data working groups. They will provide the level of specificity requested by the community.

To illustrate how to represent the link within the metadata deposit, we offer two examples from two popular dataset identifiers, one for each of the relationship types.

Dataset	Snippet of deposit XML containing link
Dataset with DOI: Data from: Extreme genetic structure in a social bird species despite high dispersal capacity. Database: Dryad Digital Repository DOI: https://doi-org.pluma.sjfc.edu/10.5061/dryad.684v0	`<program xmlns="http://www.crossref.org.pluma.sjfc.edu/relations.xsd">` `<related_item>` `<description>Data from: Extreme genetic structure in a social bird species despite high dispersal capacity</description>` `<inter_work_relation relationship-type="isSupplementedBy" identifier-type="doi">10.5061/dryad.684v0</inter_work_relation>` `</related_item>` `</program>`
Dataset with accession number: NKX2-5 mutations causative for congenital heart disease retain functionality and are directed to hundreds of targets Database: Gene Expression Omnibus (GEO) Accession number: GSE44902 URL: https://www-ncbi-nlm-nih-gov.pluma.sjfc.edu/geo/query/acc.cgi?acc=GSE44902	`<program xmlns="http://www.crossref.org.pluma.sjfc.edu/relations.xsd">` `<related_item>` `<description>NKX2-5 mutations causative for congenital heart disease retain and are directed to hundreds of targets</description>` `<inter_work_relation relationship-type="references" identifier-type="Accession">GSE44902</inter_work_relation>` `</related_item>` `</program>`

In the examples above, the Dryad dataset was generated as part of the research published in an article. Hence, it contains the “isSupplementedBy” relationship type. The GEO dataset was reused by and referenced in a scholarly article published separate from the project that generated this dataset. Hence, it contains the “references” relationship type.

Both Crossref and DataCite employ this method of linking. Data repositories who register their content with DataCite follow the same process and apply the same metadata tags. This means that we achieve direct data interoperability with links in the reverse direction (data and software repositories to journal articles).

References

Another mechanism for depositing data and software citations is to insert it into the manuscript’s references. Publishers then deposit it as part of the article’s references. To do so, publishers follow the general process for depositing references. (Visit Crossref’s Support page for step-by-step instructions.)

Publishers can deposit the full data or software citation as a unstructured reference.
<citation key="ref=3">
<unstructured_citation>Morinha F, Dávila JA, Estela B, Cabral JA, Frías Ó, González JL, Travassos P, Carvalho D, Milá B, Blanco G (2017) Data from: Extreme genetic structure in a social bird species despite high dispersal capacity. Dryad Digital Repository. http://dx.doi.org.pluma.sjfc.edu/10.5061/dryad.684v0</unstructured_citation\>
</citation>
</citation_list>

Or they can employ any number of reference tags currently accepted by Crossref. Most do not readily suit datasets and software as the suite was originally established to match article and book references. This leaves out substantial metadata needed to identify and describe the dataset, however, if the resource does not have a DOI.
<citation key="ref2">
<doi>10.5061/dryad.684v0</doi>
<cYear>2017</cYear>
<author>Morinha F, Dávila JA, Estela B, Cabral JA, Frías Ó, González JL, Travassos P, Carvalho D, Milá B, Blanco G</author>
</citation>
We are exploring the JATS4R recommendations while we consider expanding the current collection. We welcome additional suggestions from the community.

Precise, accessible links

Crossref’s infrastructure is setup to facilitate the flow of information about scholarly works across the research network. We maintain a fair degree of flexibility both in the structure and completeness of metadata deposited. The aim, though, is to make the links rich in metadata, accurate in associating literature to corresponding resource, and available to both human and machine consumers as per Principle #5 and #7 in the Joint Declaration of Data Citation Principles.

As with the other associated resources in the article nexus, we recommend depositing data/software links in the publication metadata via relationships. Publishers are free to do this on top of or independent of references. Relationship metadata offer a high degree of precision. References are a hodgepodge of various resources cited by the publication, including articles, books, media, blogs, reference materials, etc. and data citations are hard to isolate. Furthermore, the unstructured, “spaghetti string” text is difficult for systems to parse and extract specific information.

With relationship metadata, data and software resources are expressly designated. We obtain a more accurate link that specifies identifier type and explicitly identifies data generated as part of the research shared in the paper or as reuse of existing data). The richer metadata contained here enables consumers to conduct powerful queries based on different attributes (identifier type, description, relationship), taking data discovery and mining to the next level.

Furthermore, relationships are important for achieving full accessibility of data and software citations. Access to references is based on publisher permission so not all data citations can be shared (excluding DataCite DOIs). In contrast, all links deposited via relationships are publicly available.

Publishers play an important role in supporting research validation and reproducibility. Data & software citation is a basic part of of this practice, and instrumental in enabling the reuse and verification of these research outputs, tracking their impact, and creating a scholarly structure that recognizes and rewards those involved in producing them. For the full scoop of how to deposit (i.e., technical details and more), we encourage you to reference the Crossref Data & Software Citations Deposit Guide and contact us (support@crossref.org) with questions or feedback.

Linking Publications to Data and Software

Jennifer Lin — Wed, 07 Sep 2016 00:00:00 +0000

TL;DR

Crossref and Datacite provide a service to link publications and data. The easiest way for Crossref members to participate in this is to cite data using DataCite DOIs and to include them in the references within the metadata deposit. These data citations are automatically detected. Alternatively and/or additionally, Crossref members can deposit data citations (regardless of identifier) as a relation type in the metadata. Data & software citations from both methods are freely propagated. This blog post also describes how to retrieve the links collected between publication and data & software.

Data & software citation is good research practice (DataCite-STM Joint Statement and FORCE11 Joint Declaration of Data Citation Principles) and is part of the scholarly ecosystem supporting research validation and reproducibility. Data & software citation is also instrumental in enabling the reuse and verification of these research outputs, tracking their impact, and creating a scholarly structure that recognises and rewards those involved in producing them.

Crossref supports the propagation of data & software citations alongside a publisher’s standard bibliographic metadata. members deposit the data citation link as part of the overall publication metadata when registering their content. Crossref partners with DataCite and together, we jointly provide a clearinghouse for the citations collected. These are all made freely available to the community as open data.

Citation practices are evolving across different communities of practice. Crossref’s offering is flexible and easily accommodates variations and changes, since it does not rely on a specific set of citation metadata elements, citation format, nor manner of credit and attribution. Publishers deposit data & software citations in their metadata deposit via a) references and/or b) relation type.

Method A: Bibliographic references

Crossref and DataCite have partnered to provide automatic linking between publications registered with Crossref and datasets bearing DataCite DOIs. This is the most efficient and effective way to ensure that data citations are fully integrated into the scholarly research information network with full and accurate metadata.

All data & software citations that include datasets bearing a DataCite DOI are eligible for auto-update linking with Crossref. In this method: authors cite the dataset or software containing the DataCite DOI per journal article submission guidelines and add it to the article citation list (c.f. FORCE11 citation placement, FORCE11 Software Citation Principles). Publishers then deposit references as part of their standard practice when registering content. Crossref checks every reference deposited for a DOI. If the DOI is identified as DataCite’s, we automatically link it to the article. With this method, no additional action is needed when publishers register their content with Crossref.

Data citation links to non-DataCite DOIs can only be exposed in the references if the publisher makes references openly available. Even in the event that the data citation is shared, it remains undifferentiated from other references. Method B described below offers another approach.

Method B: Relation type

Publishers can link their publication to a variety of associated research objects as part of the article metadata directly in the metadata deposited to Crossref, including data & software, protocols, videos, published peer reviews, preprints, conference papers, etc. Doing so not only groups digital objects together, but formally associates them with the publication. Each link is a relationship and the sum of all these relationships constitutes a ‘research article nexus.’ Data & software citations are a valuable part of this.

To tag the citation in the metadata deposit, we ask for:

description of dataset or software (optional)

dataset or software identifier

identifier type

relationship type.

Crossref can accommodate research outputs with any identifier, though we currently only validate DOI relationships during metadata processing. Technical details are documented in the [Data & Software Citations Deposit Guide][4].

Combining methods increases total available citations

The two methods are independent and can be used exclusively or jointly. Each caters to a different set of conditions and their practical considerations. See the comparison of benefits and limitations for each method in the deposit guide. We recommend that publishers use both methods where possible at this time for optimum specificity and coverage.

How to access data & software citations

Crossref and DataCite make the data & software citations deposited by Crossref members and DataCite data repositories openly available to a wide host of parties, including both Crossref and DataCite communities as well as the extended research ecosystem (funders, research organisations, technology and service providers, research data frameworks such as Scholix, etc.).

Data & software citations from references can be accessed via the Crossref Event Data API Citations included directly into the metadata by relation type can be accessed via Crossref’s APIs in a number of formats (REST, OAI-PMH, OpenURL). (A single channel containing data & software citations across interfaces is in development and will be released next year.)

Publishers, visit our detailed guide on how to deposit data and software citations. We welcome your questions and concerns at feedback@crossref.org.

Special thanks to the following who provided valuable feedback in developing the guide: Martin Fenner (DataCite), Amye Kenall (Springer Nature), Brooks Hanson (AGU), Shelley Stall (AGU), and the FORCE11 Data Citation Implementation Pilot publisher’s subgroup.

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.

HTTPS and Wikipedia

Joe Wass — Tue, 31 May 2016 00:00:00 +0000

This is a joint blog post with Dario Taraborelli, coming from WikiCite 2016.

In 2014 we were taking our first steps along the path that would lead us to Crossref Event Data. At this time I started looking into the DOI resolution logs to see if we could get any interesting information out of them. This project, which became Chronograph, showed which domains were driving traffic to Crossref DOIs.

You can read about the latest results from this analysis in the “Where do DOI Clicks Come From” blog post.

Having this data tells us, amongst other things:

where people are using DOIs in unexpected places
where people are using DOIs in unexpected ways
where we knew people were using DOIs but the links are more popular than we realised

By the time the ALM Workshop 2014 rolled around there was some preliminary data and we realised that Wikipedia came into the third category. There are lots of DOIs in Wikipedia and people click them!

I met with Dario Taraborelli, head of research at the Wikimedia Foundation, and shared the data. Dario — who co-authored in 2010 the Altmetrics Manifesto — has been interested in understanding how scholarly citations are used in Wikipedia. Over the years, Wikipedia contributors have made extensive use of references to the scientific literature using DOIs, and by doing so they have created a resource that represents today in many ways the “front matter to all research”. There is growing interest in the community in understanding how DOIs are being used in Wikipedia and in non traditional scholarship.

During our discussions the subject of Wikipedia’s gradual transition to HTTPS was raised: we anticipated that this change would affect our data gathering.

Changes

When you’re reading webpage and click on a link to another page, your web browser will usually tell the server of that second page the last page you were on. This forms the basis of trackers like Google Analytics.

In the days before HTTPS, the next site would know the full URL that you were previously on. With the change to HTTPS, this was reduced to just sending the domain name and not the full URL, or no data at all if you click from an HTTPS page to HTTP.

DOI hyperlinks are just like any other hyperlink, and are mostly HTTP not HTTPS.

Up until 2015, Wikipedia was served over HTTP, only switching to HTTPS when users were logged in or if they requested it. The Wikimedia Foundation started planning to move to HTTPS and we knew that if they did that, and continued to use HTTP DOIs then we would lose valuable research data.

A Plan

We decided that the best course of action was to try and change the DOIs in Wikipedia to use HTTPS. Simple, right?

After some further research, Dario posted a proposal on how to mitigate the impact of the HTTPS rollout, to make sure that Wikipedia can still signal its importance as a traffic source, while preserving the privacy of its users. Discussion followed and the conclusion was to change the format of every single DOI on Wikipedia, which fortunately could be done without having to edit millions of pages. You can read the full story in this post from a year ago.

The result of this effort was that well in advance of the HTTPS switchover, the DOI links were ready to continue reporting referral data.

The Switch

In June 2015 the Wikimedia foundation made the announcement that they were finalising the switch, and that within a few weeks all traffic would be HTTPS.

We held our breath. Would it work? Would we lose all referral data from Wikipedia sites? In February 2016 the last piece of the puzzle fell into place as Wikipedia gained a ‘meta referrer’ tag to explicitly specify how they would like referrers to be sent: a detailed report on the effect of this change is coming up on the Wikimedia Foundation’s blog.

The results

As detailed in the last blog post the traffic that we measured coming from Wikipedia doesn’t seem to have slowed down during 2015:

I’d call that a success! Over the period covered in the graph, Wikipedia remained prominent as a non-publisher referral of traffic to DOIs.

Looking at the balance of HTTP vs HTTPS traffic coming from wikipedia.org, the switchover was dramatic:

Thank you to Dario Taraborelli, Nemo (Federico Leva), Aaron Halfaker, Alex Stinson and everyone who put in this effort.

I’ll leave the last word to Dario:

It’s great to see this data. It shows that the switchover happened successfully, which better protects the privacy of our users whilst still reporting the fact that Wikipedia is a prominent source of traffic. This is important validation of the increasing role that Wikipedia plays in the education and scientific community.

Crossref Event Data: early preview now available

Madeleine Watson — Mon, 18 Apr 2016 00:00:00 +0000

Test out the early preview of Event Data while we continue to develop it. Share your thoughts. And be warned: we may break a few eggs from time to time!

Chicken by anbileru adaleru from the The Noun Project

Want to discover which research works are being shared, liked and commented on? What about the number of times a scholarly item is referenced? Starting today, you can whet your appetite with an early preview of the forthcoming Crossref Event Data service. We invite you to start exploring the activity of DOIs as they permeate and interact with the world after publication.

But first, a bit of background

Discussion around scholarly research increasingly occurs online after publication, for example on blogs, sharing services, social media, and wikis. These ‘events’ occur across the web on numerous platforms and are a critical part of the scholarly enterprise. We are developing an infrastructure service (Crossref Event Data) that collects, stores, and delivers raw data of the events occurring with Crossref DOIs. We will store the data in an open, auditable and portable form for the community to access. Publishers, platforms, funders, bibliometricians and service providers may benefit from access to this raw data, and it can be used to feed into research records or proprietary tools and services that offer aggregation and analysis.

For more information, see our pilot blog post and description of potential use cases.

Collaborative, transparent development

Developers Martin Fenner (DataCite) and Joe Wass (Crossref) enjoy a tofu break

Lagotto, the software originally developed at PLOS, has been extended and improved in a joint effort between DataCite and Crossref. The two DOI Registration Agencies have partnered to envision, build and release the service. On the 13th of April, after a year of collaboration, we jointly released Lagotto 5.0. You can read about the collaboration on the DataCite blog post.

Crossref and DataCite will continue to work closely together to develop Lagotto and the Event Data service. Although Crossref Event Data has mostly Crossref DOIs at launch, you will be able to find DataCite DOIs if they are cited in Crossref or Wikipedia.

All of the software that runs Event Data, including Lagotto, is developed in the open and is open source. Please refer to the Crossref Event Data Technical User Guide for full details.

Preview the data

This service is currently under development with a full launch expected the second half of 2016. Before it is launched however, we invite you to take a look around and preview a subset of the data sources we plan to include. You may experience occasional hiccups while we continue building the service.

At this stage, we are working with data from three sources although we will greatly expand the variety of platforms from which we collect data as development progresses. At this stage, you can view Mendeley bookmarks, Wikipedia references, and Crossref to DataCite links.

Mendeley

Mendeley is a reference manager and academic social network for scholars. View the number of social bookmarks from scholars or groups on Mendeley.

For example, doi.org/10.1016/J.JIP.2016.03.007 currently has 8 readers on Mendeley to date.

Wikipedia

Wikipedia is an online encyclopaedia, the Internet’s largest and most popular general reference work. View references in Wikipedia of Crossref publications in Wikipedia articles in all languages.

For example, doi.org/10.3897/ZOOKEYS.565.7185 was referenced in the Russian Wikipedia page on Oxyscelio

Crossref to DataCite links

DataCite is a global consortium that assigns DOIs to research data. This enables people to find, share, use, and cite data. You can view all the data citations to DataCite research outputs found in Crossref publications (work is underway to make the links found in DataCite metadata available in Event Data).

For example, Global, Regional, and National Fossil-Fuel CO2 Emissions (doi.org/10.3334/CDIAC/00001) dataset has been referenced by six Crossref publications to date. Software links are also included. Another example is PGOPHER (doi.org/10.5523/bris.huflggvpcuc1zvliqed497r2), a general purpose software for simulating and fitting rotational, vibrational and electronic spectra, which has been referenced by seven Crossref publications to date.

Ready to take a spin?

You can explore the Crossref Event Data early preview by visiting http://eventdata.crossref.org.pluma.sjfc.edu and following the links to featured examples within our interim application for inspecting the data, technical documentation, and our Quick Start guide.

This service is currently under development and as such we welcome your thoughts and feedback on the data we are collecting currently from our three active sources. As a reminder, we expect to include the following sources as part of our full service launch later this year (pending confirmation):

[table id=1 /]

We’re also on the lookout for new data sources to investigate for future inclusion in the Event Data service so please do get in touch with requests and recommendations. As we continue to build the service throughout 2016, we will be committing to a model of continuous development so that we can make new sources available as they are completed.

Watch this blog for regular updates on our progress, or subscribe to receive new blog posts by email (just add your details to the upper right side of this page).

Event Data: open for your interpretation

Madeleine Watson — Thu, 25 Feb 2016 00:00:00 +0000

What happens to a research work outside of the formal literature? That’s what Event Data will aim to answer when the service launches later this year.

Following the successful DOI Event Tracker pilot in Spring 2014, development has been underway to build our new service, newly re-named Crossref Event Data. It’s an open data service that registers online activity (specifically, events) associated with Crossref metadata. Event Data will collect and store a record of any activity surrounding a research work from a defined set of web sources. The data will be made available as part of our metadata search service or via our Metadata API and normalised across a diverse set of sources. Data will be open, audit-able and replicable.

We expect to include the following sources at the launch of the clearinghouse in Q3 (pending final confirmation):

[table id=1 /]

What could you achieve?

Anyone interested in metrics and analytics will have direct and open access to a single collection of DOI activity data of events occurring outside of the formal literature. As Event Data records are time-stamped, you can be assured that the data you receive is both auditable and replicable. Collected and stored by Crossref in the one location, we invite researchers, publishers, funders and altmetrics providers to consider the possibilities Event Data offers to enrich and expand your work.

With such a corpus of open, transferable and auditable raw data at your fingertips, what could you achieve?

General and altmetrics service providers

Crossref Event Data is a centrally-managed resource, therefore as a third party vendor you will have the ability to collect real-time data from a central location to enrich, analyze, interpret and report via your own tools. Using our API, you will gain regular access to our collection of raw, auditable data to feed into your own tools and services ready for aggregation and analysis. Additionally, the optional benefit of an SLA with Crossref will ensure that your clients have access to a reliable and flexible source of event data.

Journal editors

Using the data collected in our service, as an editor you can attract authors by offering data on the audience’s research interest, track the full-scope of article dissemination and gain a better understanding of how the publications you manage compare to each other. By analysing the Event Data records, you can quickly find reviewers based on publication network analysis, identify new areas to grow author submissions and track the reach of submissions selected for publication.

Funders

As a Funder, you can use Event Data to isolate and track the dissemination and usage of the research you funded outside of the scholarly literature. As the data is portable, you can be assured that should a journal move, your ability to track its dissemination moves with it. Using the Event Data records collection, you can:

Efficiently track progress of the research impact of grant awardees in an automated fashion, with the signals most relevant to your organisation

Develop measurements of research engagement at the article level which reflect your mission and current funding priorities

Gain visibility into the potent success stories highlighting the impact of your work for your development campaigns

Analyze trends of past and future funding programs

More effectively pursue your funding strategy and manage your portfolio based on data-driven decision making.

Publishers and publishing platforms

By analyzing and interpreting the Event Data collection, as a publisher or content distributor you can use the records to undertake the following metric-lead analysis to help drive your business needs:

Conduct more robust publication growth analysis across titles, subject areas, or all published literature

Gain a balanced understanding of the engagement on your publications across subject areas, titles, or managing editors

Enhance author services (personalization, content discovery, profile management, etc.)

Focused and data-driven product development of tools and services to drive audience engagement

Provide content distributors data on downstream reach of publications.

Bibliometricians

Event Data heavily supports Bibliometric research by facilitating the tracking of DOI-related research activity across different platforms and channels. As a Bibliometrician, use trusted raw data as the underlying data for your research, which you can easily obtain from Crossref in a single, normalized format across a variety of sources. Additionally, as Event Data data is replicable, portable and auditable, you will be assured of high quality results in your research projects.

Research institutions

All of the stakeholders in your institution, from the research, development and marketing offices to the researchers themselves, will benefit from access to data about where and how your research is being discussed in mainstream and social media. As a research institution, Event Data can help you:

Track dissemination of publications (types of channels, rate of growth, etc.) by members of the institution

Access up-to-date information on the research progress of faculty members, useful for tenure and promotion decisions

View data on downstream impact of publications

Roll up data for custom reporting of department’s research activities

Stay tuned, testing begins soon!

With development work on the MVP (Minimum Viable Product) scheduled to complete shortly, we will soon be releasing a small subset of data sources that are collecting event data as well as a testing environment for interested parties to explore a very preliminary version of the software as we continue to work towards implementation of the full Event Data clearinghouse release in Q3. Look out for our MVP announcement, with full technical specifications and confirmation of the selected initial pull and push sources, over the coming weeks.

Crossref & the Art of Cartography: an Open Map for Scholarly Communications

Jennifer Lin — Fri, 08 Jan 2016 00:00:00 +0000

In the 2015 Crossref Annual Meeting, I introduced a metaphor for the work that we do at Crossref. I re-present it here for broader discussion as this narrative continues to play a guiding role in the development of products and services this year.

Metadata enable connections

At Crossref, we make research outputs easy to find, cite, link, and assess through DOIs. Publishers register their publications and deposit metadata through a variety of channels (XML, CSV, PDF, manual entry), which we process and transform into Crossref XML for inclusion into our corpus. This data infrastructure which makes possible scholarly communications without restrictions on publisher, subject area, geography, etc. is far more than a reference list, index or directory.

If research builds on what came before, one could claim that the process of knowledge production is partly the story of the very relationships between results disseminated (i.e., publications). So let’s consider each publication as a node in a graph where each has a coordinate and is connected by its citations to other publications (as well those that cite it). Additionally, each is associated with a set of people and places, along with a whole host of elements involved in the research and dissemination process.

But take a wider berth, and we begin to capture relationships between all such contributing agents and objects involved in the research process. Here we find an array of entities belonging to the scholarly graph, including different types of research artifacts, publisher and journal, funders, ORCIDs, peer reviews, publication status updates (corrections, retractions, etc.), citations, license information, additional URLs (machine destinations, hosting platforms, etc.), underlying data, software and protocols, materials, discussions and blog posts, recommendations, reference work mentions, etc. The entities on the graph multiply at an even higher rate as researchers share more outputs across more channels. And over time, the graph expands exponentially, producing a webbing that is far more dense and far more vast than we can currently imagine. Perhaps even to the point we realize Borges’ story where a cartographer builds a map so large it replicates the territory itself (On Exactitude in Science)!

From graph to cartography

At the heart of Borges’s poignant story is the map. Crossref’s graph of scholarly communications could be seen in the same light. It has a representational aspect, which is not purely abstract and can be visualized. Here, a map becomes an incredibly potent metaphor. Each link enabled by publisher-deposited metadata is a new street, bridge, or highway that takes us to a particular place (i.e., entity) of interest. These roads lead to articles, researchers, funders, institutions, etc., and in doing so, make them discoverable. They tell a story about the roles of each in the broader research in the landscape dotted with a plethora of places.

The scholarly web has a growing corpus of more than 78 million publications at this very moment registered with Crossref. On average ten to fifteen thousand new objects appear every day. Maps are all the more essential for getting around in a bewildering environment of new and unfamiliar places, even for known ones in areas of exploding growth. They are critical for orienteering, discovering relationships, identifying sets of associated objects, naming new neighborhoods that emerge (i.e., new research specialties), etc. And if each connection on the map is seen as an event, maps can also represent micro-narratives about the research process and the agents involved. A multi-dimensional map containing all these entities, which serves as an evolving representation of spacetime that is constantly updated and always available, would finally begin to depict the process of scholarly activity as a dynamic, evolving, almost living system.

An open map for scholarly communication

Crossref builds such a scholarly map of the research enterprise and makes it openly available for the entire research ecosystem. Call this a meta map or, more recently, call it metastructure. No matter what name it goes by we call it infrastructure at Crossref.

Crossref’s open map for scholarly communications is a core part of the open information infrastructure for scholarly research. Crossref map data are open, portable, as well as licensed and provisioned for maximum reuse to serve the whole community. This open resource has two entrances: one for humans, another for machines. The Crossref REST API enables machines to traverse this environment and mine it in equal measure to the humans behind them. It is configured so that a robot can learn, a phone can access, and platforms can be built.

OpenStreetMap and Google Maps, both widely used and mature infrastructure maps, are instructive examples when we consider a map of this kind for scholarly communications. Map data can be represented in unlimited ways, depending on any variety of needs and users. Third parties can add content via interactive layers that tell different stories such as health expenditure by country based on GDP and coral reefs at risk. They have a broad base of users across business models from philanthropic services aimed at disaster relief (Refugeemaps.eu) to commercial entities providing drivers with locations on open parking spaces (AppyParking on Google Map, PocketParker on OpenStreetMap). They power platforms and services that build maps for others (MapQuest, MapBox). They have applications far beyond the business of maps. For example, Place picker is a Google Maps widget that supports easy auto-complete the entry of any place or location on a mobile app where typing is a chore. And as far use cases close to home, the two have served as raw data for academic research (ex: workflow for generating multi-agent traffic simulation scenarios, automatic classification of GPS trajectories for transportation modes, etc.).

In kind, the Crossref infrastructure map also supports: the development of any variety of new maps which re-present the data, the makers of map platforms that power the research enterprise, tools that use map data, as well as academic research (bibliometrics). We extract slices of data of common interest from the map and add them as additional layers by which anyone can access and create applications on or across these bands of data:

Contributors (authors, editors, reviewers)
Funding information (funding body, grant number)
Trial & study information (clinical trials registry number, registered report, replication study)
Publication history (versions, updates, revisions, corrections, retractions, dates received/accepted/published)
Peer review (status, type, reviews)
Access indicators (publication license for text & data mining, machine mining URLs)
Resources & associated research artifacts (preprints, figures & tables, datasets, software, protocols, research resource IDs)
Activity surrounding the publication (peer reviews, comments & discussions, bookmarks, social shares, recommendations).

Today, the map powers a host of public and commercial organisations alike for a wide range of scholarly and non-scholarly purposes:

<td style="border: 1px solid #ffffff;">
<ul>
<li>
<span >Indexing services</span>
</li>
<li>
<span >Publishing vendors</span>
</li>
<li>
<span >Peer review systems</span>
</li>
<li>
<span >Reference manager systems</span>
</li>
<li>
<span >Lab & diagnostics suppliers</span>
</li>
<li>
<span >Info management systems</span>
</li>
<li>
<span >Educational tools</span>
</li>
<li>
<span >Data analytics systems</span>
</li>
<li>
<span >Literature discovery services</span>
</li>
</ul>
</td>

Publishers
Funders
Research institutions
Archives & repositories
Research councils
Data centres
Professional networks
Patent offices
Registration Agencies

We will follow up this post to highlight a cross-section of these consumers in the Crossref map ecosystem and elaborate on what & how they have built from our data. An infrastructure map offers endless potential to third parties across publishers, funders, research institutions, and vendors working to serve the scholarly research enterprise.

The art of cartography

In the Crossref Product Management team, we have ambitious plans for map enhancements this year. They focus on expanding information density and ease of access to the data. In the former case, we will introduce a new class of locations where activity surrounding the publications are occurring when we launch the DOI Event Tracker. We will also initiate an extensive publisher campaign to achieve full metadata deposit completeness across our membership. No one can keep pace with the sheer volume of research activity happening online nor wander the Lonely Web of research alone. The more metadata publishers provide for a publication, the more roads lead to its map location. After all, discoverability is closely associated with connectedness on a map. And finally, in the latter case, we will refresh and enhance the user interface to make it more powerful for humans to traverse the ever-changing landscape (as easily as the REST API enables machines!).

I gratefully acknowledge the feedback received from the following who served as generous and insightful sounding boards: Virginia Barbour, Theo Bloom, Martin Eve, Daniel S. Katz, Amye Kenall, Catriona MacCullum, Cameron Neylon, Mark Patterson, Kristen Ratan, Carly Strasser, and Kaitlin Thaney.

Distributed Usage Logging: A private channel for private data

Jennifer Lin — Fri, 04 Dec 2015 00:00:00 +0000

Forty wire telephone switchboard, 1907, Author unknown, Popular Science Monthly Vol 70, Wikimedia Commons.

A few months ago Crossref announced that we will be launching a new service for the community in 2016 that tracks activities around DOIs recording user content interactions. These “events” cover a broad spectrum of online activities including publication usage, links to datasets, social bookmarks, blog mentions, social shares, comments, recommendations, etc. The Event Data service collects the data and make it available to all in an open clearinghouse so that data are open, comparable, audit-able, and portable. These data are all publicly available from external platform partners, and they meet the terms of distribution from each partner.

But Crossref and its members are also concerned about privacy. We recognise that not all data can be made open and public. Particularly if it is sensitive, personally identifiable data about usage. With this in mind, we are also launching an affiliated service, Distributed Usage Logging (DUL), for external parties to transmit sensitive data on user content interactions directly to authorized end points. As researchers are increasingly using “alternative” (non-publisher) platforms to store, access and share literature, publishers are correspondingly interested in incorporating the activity on their publications into their COUNTER reports.

Interested third-party sites might include the following:

Institutional and subject repositories
Aggregator platforms (EBSCOhost, IngentaConnect)
Researcher-oriented social-networking sites (e.g. Academia.edu, ResearchGate, Mendeley)
Reading environments and tools (e.g. ReadCube, Utopia Documents)

For publishers to process such events via their COUNTER-compliant usage reporting streams, they need private usage information and a secure channel by which to receive the data from the external platforms. Crossref will provide a switchboard that will enable these non-publisher platforms can safely transmit private data directly to the publisher.

The work ahead entails close collaboration between Crossref, COUNTER, and the partners who will be sending and receiving the private data. The cross-organisational team will be working towards the following before launch: technical infrastructure development for production service, semantic definition of the usage logging message, assignment and validation of credentials to participants in the scheme, participant integration of the DUL API, and incorporation of this data type into the COUNTER Code of Practice. We will also continue to consult with data privacy and security authorities to ensure that the scheme respects all governmental obligations and community best practice regarding the processing of personal data.

We will share more about the launch of the service as we make progress along the way. Please contact Jennifer Lin for more information.

DOI Event Tracker (DET): Pilot progresses and is poised for launch

Jennifer Lin — Tue, 15 Sep 2015 00:00:00 +0000

Publishers, researchers, funders, institutions and technology providers are all interested in better understanding how scholarly research is used. Scholarly content has always been discussed by scholars outside the formal literature and by others beyond the academic community. We need a way to monitor and distribute this valuable information.

The Crossref DOI Event Tracker (DET)

To meet this need, Crossref will be introducing a new service that tracks activity surrounding a research work from potentially any web source where an event is associated with a DOI. Following a successful pilot run started Spring 2014, the service has been approved to move toward production and is expected to launch in 2016. Any party wishing to join this phase is welcome to contact Jennifer Lin. The DOI Event Tracker (DET) registers a wide variety of events such as bookmarks, comments, social shares, citations, and links to other research entities, from a growing list of online sources. DET aggregates them, and stores and delivers the data in many ways.

Open, portable, and licensed for maximum reuse

Crossref has long served as the citation linking and metadata infrastructure provider for scholarly communication; the new DOI Event Tracker is a natural next step, providing a practical solution as a resource for the whole community. The tracker offers the following features:

Data on event activity across a common pool of online channels.
Near real-time alerting for select sources with push notifications to the system.
Cross-publisher monitoring to enable benchmarking and provide context to the data.
Common format for normalizing data results across the diverse set of sources via modern REST API.
Secure and regularly refreshed backups of critical data for long term data preservation.
Transparency of data collection so as to ensure auditable, replicable, and trustworthy results.
Query-initiated retrieval or real-time alerts when an event of interest occurs.
CC-0 license for open and flexible propagation of data.

A number of platforms are already confirmed and more parties are welcomed at any stage. So far we have confirmation to track DOI events on the following platforms:

[table id=1 /]

This set of sources reflects our initial focus on parties willing to allow their data to be redistributed in the common pool. Efforts are underway to expand the source list to include Twitter and MyScienceWork, among others. Publishers can also act as sources by publishing and distributing DOI event data via the DET when an event occurs on its platform (for example, when a PDF is downloaded, or when a comment mentions a DOI in a locally hosted discussion forum, etc.). This would make local DOI activity globally available to funders, researchers, institutions, etc.

DET provides benefits of scale and ease of access as a central point for collecting and propagating data to the community. As a single point of access, it overcomes the business and technical hurdles that are a part of managing multiple online sources where scholarly activity occurs, in a rapidly changing landscape of online channels. This resource covers content across publishers and serves as a strong foundation to support the development of tools and services by any party. DET users will always be able to combine the DET data with those individually collected via negotiated or paid access. DET remains a utility separate from any value-added amenities, such as analytics, presentation, and reporting.

DET Service-Level Agreement

For those who seek the highest level of service and a more flexible range of access options, Crossref will provide a Service-Level Agreement (SLA) service for the DOI Event Tracker. The DET SLA includes the following additional features on top of the common data offering:

Access to the complete suite of sources, which includes restricted and/or paid sources in addition to common data, providing the fullest picture of DOI usage activity possible.
Guaranteed uptime and response time to the latest raw data on the aggregate activity surrounding a DOI.
Guaranteed support response time to questions and issues surrounding data and data delivery.
Flexible data access options: on-demand real time data access and scheduled bulk downloads for processing batch analytics.
Optimum retrieval rates and accelerated delivery speeds with the dedicated SLA API.
Access to a webhook API for events of interest as an alternative to polling DET.
Standardized and enhanced linkback service for the difficult-to-track, grey literature.

The DET SLA service has a simple, value-based pricing model based on subscriber size. Register your interest in Crossref’s DOI Event Tracker and the DET SLA service if you would like stay informed of the upcoming launch. Please contact Jennifer Lin for more information.

Image modified from “Radar” icon by Karsten Barnett from the Noun Project.

DataCite supporting content negotiation

Geoffrey Bilder — Mon, 10 Oct 2011 00:00:00 +0000

In April In April for its DOIs. At the time I cheekily called-out DataCite to start supporting content negotiation as well.

Edward Zukowski (DataCite’s resident propellor-head) took up the challenge with gusto and, as of September 22nd DataCite has also been supporting content negotiation for its DOIs. This means that one million more DOIs are now linked-data friendly. Congratulations to Ed and the rest of the team at DataCite.

We hope this is a trend. Back in June Knowledge Exchange organized a seminar on Persistent Object Identifiers. One of the outcomes of the meeting was “Den Haag Manifesto” a document outlining five relatively simple steps that different persistent identifier systems could take in order to increase interoperability. Most of these steps involved adopting linked data principles including support for content negotiation. We look forward to hearing about other persistent identifiers adopting these principles over the next year.

Having said that, this time I will refrain from calling-out anybody specifically…

Citation Typing Ontology

Geoffrey Bilder — Fri, 20 Mar 2009 00:00:00 +0000

I was happy to read David Shotton’s recent Learned Publishing article, Semantic Publishing: The Coming Revolution in scientific journal publishing, and see that he and his team have drafted a Citation Typing Ontology.^*

Anybody who has seen me speak at conferences knows that I often like to proselytize about the concept of the “typed link”, a notion that hypertext pioneer, Randy Trigg, discussed extensively in his 1983 Ph.D. thesis.. Basically, Trigg points out something that should be fairly obvious- a citation (i.e. “a link”) is not always a “vote” in favor of the thing being cited.
In fact, there are all sorts of reasons that an author might want to cite something. They might be elaborating on the item cited, they might be critiquing the item cited, they might even be trying to refute the item cited (For an exhaustive and entertaining survey of the use and abuse of citations in the humanities, Anthony Grafton‘s, The Footnote: A Curious History, is a rich source of examples)
Unfortunately, the naive assumption that a citation is tantamount to a vote of confidence has become inshrined in everything from the way in which we measure scholarly reputation, to the way in which we fund universities and the way in which search engines rank their results. The distorting affect of this assumption is profound. If nothing else, it leads to a perverse situation in which people will often discuss books, articles, and blog postings that they disagree with without actually citing the relevant content, just so that they can avoid inadvertently conferring “wuffie” on the item being discussed. This can’t be right.
Having said that, there has been a half-hearted attempt to introduce a gross level of link typology with the introduction of the “nofollow” link attribute- an initiative started by Google in order to try to address the increasing problem of “Spamdexing”. But this is a pretty ham-fisted form of link typing- particularly in the way it is implemented by the Wikipedia where Crossref DOI links to formally published scholarly literature have a “nofollow” attribute attached to them but, inexplicably, items with a PMID are not so hobbled (view the HTML source of this page, for example). Essentially, this means that, the Wikipedia is a black-hole of reputation. That is, it absorbs reputation (through links too the Wikipedia), but it doesn’t let reputation back out again. Hell, I feel dirty for even linking to it here ;-).
Anyway, scholarly publishers should certainly read Shotton’s article because it is full of good, and practical ideas about what can can be done with today’s technology in order to help us move beyond the “digital incunabula” that the industry is currently churning out. The sample semantic article that Shotton’s team created is inspirational and I particularly encourage people to look at the source file for the ontology-enhanced bibliography which reveals just how much more useful metadata can be associated with the humble citation.
And now I wonder whether CiteULike, Connotea, 2Collab or Zotero will consider adding support for the CItation Typing Ontology into their respective services?
* Disclosure:
a) I am on the editorial board of Learned Publishing
b) Crossref has consulted with David Shotton on the subject of semantically enhancing journal articles

Word Add-in for Scholarly Authoring and Publishing

Crossref — Wed, 26 Mar 2008 00:00:00 +0000

Last week Pablo Fernicola sent me email announcing that Microsoft have finally released a beta of their Word plugin for marking-up manuscripts with the NLM DTD. I say “finally” because we’ve know this was on the way and have been pretty excited to see it. We once even hoped that MS might be able to show the plug-in at the ALPSP session on the NLM DTD, but we couldn’t quite manage it.

The plugin is targeted at production/editorial staff, but, of course, it will be interesting to see if any of this work can be pushed back to the author. I won’t hold my breath on the latter score, but it will be fun to watch.

One thing I would note is that the NLM DTD can also be used in the humanities and social sciences, so, frankly, I think they should market it more broadly.

Anyway- the plugin can be downloaded from the Microsoft site.

And Pablo has setup a blog where testers can discuss the add-in.

And there is also an entry for the project on the Microsoft Research site (an interesting place to peruse, if you have a moment).

Congatulations to Pablo and his team.

DataNet

Tony Hammond — Fri, 12 Oct 2007 00:00:00 +0000

Last week, my colleague Ian Mulvany posted on Nascent an entry about NSF’s recent call for proposals on DataNet (aka “A Sustainable Digital Data Preservation and Access Network”). Peter Brantley, of DLF, has set up a public group DataNet on Nature Network where all are welcome to join in the discussion on what NSF effectively are viewing as the challenge of dealing with “big data”. As Ian notes in a mail to me:

“It seems that for a fully integrated flow of data then publisher involvement is going to be required, and it is clear from the proposal that the NSF are also interested in rights management or at negotiating that issue.”

Citing Data Sets

Tony Hammond — Fri, 30 Mar 2007 00:00:00 +0000

This D-Lib paper by Altman and King looks interesting: “A Proposed Standard for the Scholarly Citation of Quantitative Data”. (And thanks to Herbert Van de Sompel for drawing attention to the paper.) Gist of it (Sect. 3) is

_“We propose that citations to numerical data include, at a minimum, six required components. The first three components are traditional, directly paralleling print documents. … Thus, we add three components using modern technology, each of which is designed to persist even when the technology changes: a unique global identifier, a universal numeric fingerprint, and a bridge service. They are also designed to take advantage of the digital form of quantitative data.

An example of a complete citation, using this minimal version of the proposed standards, is as follows:

**Micah Altman; Karin MacDonald; Michael P. McDonald, 2005, “Computer Use in Redistricting”,

hdl:1902.1/AMXGCNKCLU UNF:3:J0PkMygLPfIyT1E/8xO/EA==

http://id.thedata.org/hdl%3A1902.1%2FAMXGCNKCLU

“_

So the abbreviated citation (author, date, title, unique ID) is supplemented by a UNF which fingerprints the data. UNFs would appear to be a sort of super MD5 in providing a signature of the data content independent of the data serialization to a filestore.

_“Thus, we add as the fifth component a Universal Numeric Fingerprint or UNF. The UNF is a short, fixed-length string of numbers and characters that summarize all the content in the data set, such that a change in any part of the data would produce a completely different UNF. A UNF works by first translating the data into a canonical form with fixed degrees of numerical precision and then applies a cryptographic hash function to produce the short string. The advantage of canonicalization is that UNFs (but not raw hash functions) are format-independent: they keep the same value even if the data set is moved between software programs, file storage systems, compression schemes, operating systems, or hardware platforms.

…

Finally, since most web browsers do not currently recognize global unique identifiers directly (i.e., without typing them into a web form), we add as the sixth and final component of the citation standard a bridge service, which is designed to make this task easier in the medium term.”_

Certainly looks promising. I’m not sure if there’s any other contestants in this arena.