Blog

Make Data Count (MDC) is convening a two-day summit dedicated to the evaluation of open data usage, reach, and impact. 

Our inaugural Make Data Count Summit, taking place in Washington, DC, on September 12 and 13, will bring together representatives of the research community, government data administrators, funders, policymakers, publishers, and infrastructure providers to discuss and solve the diverse and complex challenges of implementing open data assessment metrics and the infrastructures that support them.

For years the MDC initiative has been focused on bringing together the research ecosystem to prioritize open data metrics through the development of social and technical infrastructure for data citation and data usage. By bringing together groups from across the research and policy landscape, the Summit’s goal is to evaluate and highlight key success stories and use cases from the last decades of investments made into open data, and establish  a collective vision for evidence-based data metrics. 

Incorporating facilitated discussions, panels, and brainstorming sessions, the event will be focused on identifying concrete next steps to drive adoption and recognition of data metrics. The meeting will be focused especially on how to draw on existing data metrics initiatives to develop solutions for improving academic and governmental infrastructures that support data impact evaluation globally. 

For more details and to register for the Summit: summit.makedatacount.org

Join us for a 3-part series on key issues and opportunities for the community to prioritize on our journey to open data metrics.

Watch the recordings of FORAGE, EXPLORE, and BEGIN!

By Kathleen Gregory and Anton Ninkov

Data citations hold great promise for a variety of stakeholders. Unfortunately, due in part to a lack of metadata, i.e. about disciplinary domains, many of those promises remain out of reach. Metadata providers – repositories, publishers and researchers – play a key role in improving the current situation.  

The potentials of data citations are many. From the research perspective, citations to data can help researchers discover existing datasets and understand or verify claims made in the academic literature. Citations are also seen as a way to give credit for producing, managing and sharing data, as well as to provide legal attribution. Researchers, funders and repository managers also hope that data citations can provide a mechanism for tracking and understanding the use and ‘impact’ of research data [1]. Bibliometricians, who study patterns in scholarly communication by tracing publications, citations and related metadata, are also interested in using data citations to understand engagements and relationships between data and other forms of research output. 

Figure 1. Perspectives about the potentials of data citation [2]

Realizing the potential of data citations relies on having complete, detailed and standardized metadata describing the who, what, when, where and how of data and their associated work. As we are discovering in the Meaningful Data Counts project, which brings together bibliometricians and members of the research data community as part of the broader Make Data Count initiative, the metadata needed to provide context for both data and data citations are often not provided in standardized ways…if they are provided at all. 

As a first step in this project, we have been mapping the current state of metadata, shared data, and data citations available in the DataCite corpus. Our openly available jupyter notebook pulls realtime metadata about data in DataCite [3] and demonstrates both the evolving nature of the corpus and the lack of available metadata. In particular, our work highlights the current lack of information about a critical metadata element for providing context about data citations – the disciplinary domain where data were created. 

For example, we find that the amount of data available in DataCite has increased by more than 1.5 million individual datasets over a 7 month period from January to July 2021, when the corpus increased from 8,243,204 to 9,930,000 datasets. In January, as few as 5.7% of the available datasets had metadata describing their disciplinary domain according to the most commonly used subject classification system (see the treemap in Figure 2). In July, despite the increased number of datasets overall, the percentage with a disciplinary domain dropped slightly to 5.63%.

Figure 2. Data with metadata describing disciplinary domain, according to the OECD Fields of Science classification, retrieved on July 9th, 2021. For an interactive version of this tree map, with the most current data, please see our Jupyter Notebook [3]

These low percentages reflect the fact that providing information about the subject or disciplinary domain of data is not a required field in the DataCite metadata schema. For the nearly 6% of data that do have subject information, the corpus contains multiple classification schemes of differing granularity levels, ranging from the more general to the more specific. DataCite currently works to automatically map these classifications to each other in order to improve disciplinary metadata. Organizations which submit their data to DataCite also have a role to play in improving these disciplinary descriptions, as this information underlies many of these mapping efforts.  

Subject or disciplinary classifications for data are typically created using three methods:

• Intellectually, where researchers, data creators or data curators use their expertise to assign a relevant subject classification. 

• Automatically, where automated techniques are used to extract subject information from other data descriptions, e.g. the title or abstract (if available)

• By proxy, where data are assigned the same subject classification as a related entity, e.g. when data are given the same subject classification as the repository where they are stored. This can be done either automatically or manually. 

Of these three methods, the intellectual method tends to be the most common, and also the most accurate and time-consuming approach. This method is often carried out by those closest to the data, i.e. researchers/data creators or data curators, who have expert knowledge about the data’s subject or disciplinary context which may be difficult to determine either automatically or by proxy.

While our work also exposes other examples of missing or incomplete metadata [4], we highlight here the current lack of information about disciplinary domains, as disciplinary information is important across all the perspectives shown in Figure 1. For example, disciplinary norms influence how data are shared, how they are made available, how they are understood and how they are reused. Information about disciplines is important for discovering data and is typically used by funders and research evaluators to place academic work in context. Disciplinary analyses are also a critical step in contextualizing citation practices in bibliometric studies, as citation behaviours have repeatedly been shown to follow discipline-specific patterns. Without disciplinary metadata, placing data citations into context will remain elusive and meaningful data metrics cannot be developed. 

In order to move forward with understanding data citations in context, we need better metadata – metadata about disciplinary domains, but also metadata describing other aspects of data creation and use. Metadata providers, from publishers to researchers to data repositories, can help to improve the current situation by working to create complete metadata records describing their data. Only with such metadata can the potentials of data citation be achieved. 

References

[1] These perspectives are visible, e.g. in the Joint Declaration of Data Citation Principles:

Data Citation Synthesis Group: Joint Declaration of Data Citation Principles. Martone M. (ed.) San Diego CA: FORCE11; 2014 https://doi.org/10.25490/a97f-egyk

[2] Gregory, K. (2021, July). Bringing data in sight: Data citations in research. Presentation. Presented at Forum Bibliometrie 2021, Technical University of Munich, online. https://www.ub.tum.de/forum-bibliometrie-2021 

[3] Ninkov, A. (2021). antonninkov/ISSI2021: Datasets on DataCite – an Initial Bibliometric Investigation (1.0) [Computer software]. Zenodo. https://doi.org/10.5281/ZENODO.5092301

[4] Ninkov, A., Gregory, K.; Peters, I., Haustein, S. (2021). Datasets on DataCite – An initial bibliometric investigation. Proceedings of the 18^th International Conference of the International Society for Scientometrics and Informetrics, Leuven, Belgium (virtual). Preprint: https://doi.org/10.5281/zenodo.4730857

Daniella Lowenberg, Rachael Lammey, Matthew B. Jones, John Chodacki, Martin Fenner

DOI: 10.5281/zenodo.4701079 

In the last decade, attitudes towards open data publishing have continued to shift, including a rising interest in data citation as well as incorporating open data in research assessment (see Parsons et al. for an overview). This growing emphasis on data citation is driving incentives and evaluation systems for researchers publishing their data. While increased efforts and interest in data citation are a move in the right direction for understanding research data impact and assessment, there are clear difficulties and roadblocks in having universal and accessible data citation across all research disciplines. But these roadblocks can be mitigated and do not need to keep us in a consistent limbo.

The unique properties of data as a citable object have attracted much needed attention, although it has also created an unhelpful perception that data citation is a challenge and requires uniquely burdensome processes to implement. This perception of difficulty begins with defining a ‘citation’ for data. The reality is that all citations are relationships between scholarly objects.  A ‘data citation’ can be as simple as a journal article or other dataset declaring that a dataset was important to the creation of that work.  This is not a unique challenge.  However, many publishers and funders have elevated the relationship of data that “underlies the research” into a Data Availability Statement (DAS).  This has helped address some issues publishers have found with typesetting or production techniques that stripped non-articles from citations.  However, because of this segmentation of data from typical citation lists, and the exclusion of data citations in article metadata, many communities have felt they are in a stalemate about how to move forward. 

Data citations are targeted as an area to explore in terms of research assessment. However, we do not have a clear understanding of how many data citations exist or how often data are reused. In the last few years, the majority of data citation conversations have been facilitated through groups at Research Data Alliance (via Scholix), Earth Science Information Partners (ESIP), EMBL- European Bioinformatics Institute (EMBL-EBI), American Geophysical Union (AGU), and FORCE11. These conversations have focused primarily on datasets and articles that have DOIs from DataCite and Crossref, respectively, emphasizing the relationship between datasets and published articles. While those relationships are areas that need broad uptake from repositories and publishers alike, they do not illustrate the full picture. Many citations are not being accounted for, namely biomedical datasets with accession numbers and compact identifiers that are not registered through DataCite but readily accessible through resolvers like identifiers.org. There is also a lack of understanding around the citations of datasets in other scholarly and non-scholarly (e.g., government documents, policy papers) outputs. 

For these reasons, we have tried to ensure that conversations about data citation are not framed solely around the notion of assigning “credit” or around assigning any specific meaning to citations, for that matter. Without a full picture of how many citations exist, how datasets are composed across disciplines, how citation behavior varies across disciplines, and what context the citations are used in, it is impossible and inappropriate to use citations as a shorthand for credit. The community is working towards a better understanding of citation behavior—and we believe we will get there—but we need to be careful and considered in doing so to avoid repeating previous mistakes (e.g., creating another impact factor).  

Why data citation is perceived as difficult

Data are complex. As mentioned in our 2019 book, data are nuanced. This means data citations are complex and understanding these nuances are essential for understanding true measures of data reuse. For instance, there is work to be done to understand the role of provenance, dataset to dataset re-usage, data aggregation and derivation, and other ways for measuring usage of datasets without a formal “citation”.

Data citations are complex. There is a well-established concept of scholarly citations, using reference lists of citations formatted in a defined citation style. The main challenges with the current approach center on making citations machine-readable using standardized metadata instead of citation styles meant for human readers, as well as on making citations machine-accessible using open APIs and aggregators. These are general challenges to be addressed with citation, but there are additional questions specific to handling data citations: is there a DOI or other persistent identifier and basic citation metadata for the dataset, is there tooling to bring this information into the citation style used by the publisher, should data citations go into the article reference list, what to do when the number of datasets cited in a publication goes into the 1000s or more, should datasets in reference lists be called out as data, etc.

There’s a lack of consistency in guidance. Despite the growing interest among various stakeholders (researchers, journals, repositories, preprint servers, and others)  in supporting data citation, there is no consistency in guidance for and across these groups. These inconsistencies are in respect to how citations should be formatted, how citations should be marked up and indexed, and what the role for each stakeholder should be (especially repositories). Some of this can be attributed to a constant reinvent-the-wheel approach as well as to the wide variety of stakeholder groups and hubs for this information—understandably, people are confused between how Scholix fits with OpenAire, Crossref, and DataCite, nevermind the profusion of other overlapping initiatives and projects in this space that can make it even more difficult to navigate. It is clear that our best way forward is to not consistently reinvent the wheel, spawning new groups and initiatives, but rather to build on the existing work, leveraging the successes of the last decade of investment in data citations, and finding solutions for the more advanced issues at hand. In short: let’s focus on developing the most basic, clear guidance, and work upwards from there.

• 

There’s a tension between data availability statements and data citations. In the last decade, publishers and funders have heavily focused on requiring data availability statements and ensuring that is the way to designate when articles have an associated dataset published. These data availability statements are rarely marked up as a relation in the article metadata or a note of re-use (outside of self citation). If we continue to focus solely on data availability statements as a required first step, which have yet to solve the “machine readability problem”, we will lose slim resources that would be better used to think about how each journal publisher can designate data reuse and citations.

Guidance and decision points

Understanding the many intricacies of data, citations, and data citation, we propose the following path forward for our communities to work effectively towards achieving widespread implementation of data citation and data reuse. This path forward begins with making decisions around clear guidance that needs to be provided, shifting focus away from “decision-pending” attitude and moving forward with clear recommendations on the following:

• Best practices for citing datasets in articles, preprints, and books. We have multiple sets of best practices. We don’t need more guidance documents, we need consolidation and rationalization of the guidance that already exists.  
• Simplifying relationship type complexity. The complexity of ontologies for relationships is causing unnecessary churn and delays in implementation. Providers should simplify this; however, the community shouldn’t wait. We can and should implement viable solutions now. We should be promoting datasets in reference lists as a first viable solution. 
• How non-DOIs are cited. We have too many conversations happening about DOIs and not enough happening about citation in other identifier communities. These communities need to reach some simple conventions around putting data citations into reference lists with globally unique PIDs and citation metadata, in order to avoid requiring massive text mining efforts looking for string matches to, for example, “PDB:6VXX”, the identifier for the spike protein for COVID-19.  
• Publisher support for those who are not working with Crossref. Not all publishers use Crossref services or have the ability to implement Crossref’s approaches to data citations. We need to focus attention on accessible methods for reference extraction (e.g., from PDFs) and larger support for smaller publishers that do not have the resources to retool to fit current guidance. 
• The role for data repositories. Publishers are key to implementing data citation but data repositories must also focus on declaring relationships to articles and other outputs in their metadata. Data repositories should focus on making their datasets citable through PIDs and declaring robust metadata as well as reporting all known citations and linkages publicly so they can be used for aggregation.
• Researchers should cite data despite these infrastructure hold-ups. Regardless of the hurdles to implementing all of the established best practices, the basic fact remains that researchers can currently cite data and they should, using approaches available today.  

Choosing adoption over perfection

Perfection is the enemy of good and finding solutions for every complexity of data citation does not need to be a roadblock to get started. We can use a phased approach to begin implementing best practices for data citations right now:

Phase I: basic implementation

Align as much as possible with existing community practices and workflows (e.g., using reference lists)

Phase II: advanced implementation

Address special use cases (e.g., relation types, machine-readable data availability statements, dynamic data, dataset-dataset provenance)

Phase III: beyond data citation

Build infrastructure for other indicators assessing data reuse

While we have dabbled in all three of these phrases already, we are still largely stuck in Phase I, constantly reinventing the same basic wheel that keeps spinning around the same place. 

Our focus should be on how to scale these best practices across all publishers and repositories, supporting the diverse research landscape. This includes advancing the conversation beyond the DOI-based focus. Once that happens we can really move forward with building mechanisms for credit and understanding data re-use for research assessment.

Despite the agenda ahead, there are many steps that can be taken right now to continue towards the dreamstate. The community should not wait for infrastructure to be perfect before engaging in data citation support. 

This is important, so let’s say it again! The community should not wait for infrastructure to be perfect before engaging in data citation support. 

Data citations are harder when we act like the adoption hurdles are insurmountable, so let’s simplify. Our infrastructure for data citations will continue to improve, use cases will continue to be defined and evolve, and we need as many broad stakeholders as possible to hop on board now and work with us towards comprehensive support for data citation.

Since 2014, the Make Data Count (MDC) initiative has focused on building the social and technical infrastructure for the development of research data metrics. With funding from the National Science Foundation, Gordon and Betty Moore Foundation, and Alfred P. Sloan Foundation, the initiative has transformed from a research project with an aim to understand what researchers value about their data, to an infrastructure development project, and now into a full-fledged adoption initiative.  The team is proud to announce additional funding from the Sloan Foundation to focus on widespread adoption of standardized data usage and data citation practices, the building blocks for open research data metrics.

Expanded team & expanded scope

In broadening our scope and refining our adoption efforts, we are thrilled to announce new MDC team members. By including key community players in the adoption and research landscapes, we can look beyond infrastructure development and more effectively reach our publisher and repository stakeholders. 

• Crossref: We welcome Crossref, who will help guide our data citation work at publishers in conjunction with existing data citation initiatives (e.g., Scholix). By having an increased presence at publisher meetings and building up support in the Crossref member community, we aim to see many more journals properly contributing to the data citation landscape. 
• Bibliometricians: With an increased pressure by research stakeholders to have data metrics at the ready, we are pleased to be working with a group of expert bibliometricians who will begin studies into researcher behavior around data re-use. It is essential that our driving motives for the development of data metrics are evidence based and we welcome Dr. Stefanie Haustein (University of Ottawa, Co-Director ScholCommLab) and Dr. Isabella Peters (ZBW – Leibniz Information Centre for Economics) and their labs to our team. 

“I am excited to join and work closely together with the MDC team on the development of data metrics. Our team at the ScholCommLab in Canada and Isabella’s research group in Germany will use a mixed-methods approach and apply bibliometric as well as qualitative methods to analyze discipline-specific data citation and reuse patterns. We hope to provide much-needed evidence to develop meaningful data metrics that can help researchers showcase the importance of data sharing.”
– Dr. Stefanie Haustein

Our goals for the MDC initiative going forward are three-fold:

1. Increased adoption of standardized data usage across repositories through enhanced processing and reporting services
2. Increased implementations of proper data citation practices at publishers by working in conjunction with publisher advocacy groups and societies
3. Promotion of bibliometrics qualitative and quantitative studies around data usage and citation behaviors

“The responsible use and application of data metrics and data citation must become a community norm across all disciplines if data creation, curation, stewardship, reuse and discovery are to be properly valued. By partnering with key infrastructure providers and researchers, Make Data Count is ensuring that the adoption of data metrics and data citation are researcher led, discipline specific and evidence based. This is crucial if we are to avoid the perverse consequences created by the misuse of article citations and metrics, such as those based on journal rank and impact factor.”
– Dr. Catriona MacCallum, Director of Open Science, Hindawi

“MDC has put data metrics at the center of the debate on data sharing. Now, it is time to make data metrics a reality. The development of an ambitious infrastructure for data metrics, supported by the research of Stefanie Haustein, Isabella Peters and colleagues, creates the unique environment to turn data metrics into a tangible reality; expanding the analytical toolset for scientometric research and science policy making. Such transformation is meant to contribute not only to increase the importance of data sharing in scientific practice, but also to radically transform how science is being currently developed, measured and evaluated.”
– Dr. Rodrigo Costas, Senior Researcher, CWTS, Leiden University

Driven by two separate grant funds, one focused on the deployment of data usage services, a bibliometrics dashboard, and publisher data citation campaigns (PI Lowenberg) and the other on understanding what is meaningful for data metrics (PI Haustein), the MDC team is moving full steam ahead on these adoption goals. The MDC initiative can only be effective with broad and diverse community participation. Follow along for announcements of webinars and events for community involvement and check out our announcement at the ScholCommLab blog for more details on the bibliometrics work ahead.

The Make Data Count team has been working on various infrastructure and outreach projects focused on how to measure the reach and impact of research data. While busy driving adoption of these frameworks and services, we have yet to discuss where we’re at in terms of high-level challenges and where we believe we need to go to.

To clarify to the community what our opinions and approaches are in terms of open data metrics, members from the Make Data Count team (Daniella Lowenberg, John Chodacki, Martin Fenner, Matt Jones) sat down and wrote a book that we hope will jump start a community conversation. We would love to hear your feedback and look forward to engaging with you on the topic.

http://opendatametrics.org 

Research data is at the center of science, and to date it has been difficult to understand its impact. To assess the reach of open data, and to advance data-driven discovery, the research and research supporting communities need open, trusted data metrics.

In Open Data Metrics: Lighting the Fire, the authors propose a path forward for the development of data metrics. They acknowledge historic players and milestones in the process and demonstrate the need for standardized, transparent, community-led approaches to establish open data metrics as the new normal.

Following advice from our workshop attendees at RDA13, we invite you to join us for our spring webinar.

Join us on May 8th at 8am PST/3pm GMT as we demo our new aggregation services at DataCite and DataONE. This webinar is intended to spotlight the features and services we can build off of our central infrastructure such as aggregated usage and citations. This webinar will be recorded and posted on our website.

Webinar Registration: bit.ly/MDCSpringWebinar 

 

 

 

 

Join us on March 26th at 8:00am PST/4:00pm GMT for a webinar on repository implementation of our COUNTER Code of Practice for Research Data and Make Data Count recommendations. This webinar will feature a panel of developers from repositories that have implemented or about to release standardized data metrics: Dataverse, Dryad, and Zenodo. We will interview each repository on their implementation process. This recorded discussion between our technical team and repositories, providing various perspectives of implementation, should be a helpful guidance for any repository interested in implementing!

To register for our webinar, please fill out this form.

For those who cannot make it, a recording will be made available on our website. Please tweet to us any questions that you may want asked.