Research Data Metrics Landscape: An update from the NISO Altmetrics Working Group B: Output Types & Identifiers

Working Group Connections LIVE!

About the Event

In June 2013, the Alfred P. Sloan Foundation awarded NISO a grant to undertake a two-phase initiative to explore, identify, and advance standards and/or best practices related to a new suite of potential metrics in the community.The NISO Altmetrics Project has successfully moved to Phase Two, the formation of three working groups, A, B, & C. Working Group B, led by Kristi Holmes, PhD, Director, Galter Health Sciences Library at Northwestern University, and Mike Taylor, Senior Product Manager, Informetrics at Elsevier, is focused on the Output Types & Identifiers within the alternative metrics landscape. 

Speakers will highlight examples of the work being done in this area.

Event Sessions



11:00 a.m.

Brief background on Working Group B: Output Types & Identifiers


11:10 a.m.

Session I: Persistent Identifiers 

11:15 a.m. - 12:15 p.m.






Data-Level Metrics with DataCite


In the NSF-funded Making Data Count project we identified download stats and citations as the metrics that researchers are primarily interested in. In the project DataONE collected usage stats according to the COUNTER standard for about 150k datasets, and we collected data citations for these datasets from a number of sources. Together with CrossRef DataCite is now scaling this activity to track citation events for all datasets registered with DataCite, and we are collaborating with other organizations in the collection, distribution and display of these citation links. 

Presenter Q&A 

Moderated by Kristi Holmes, PhD, Director, Galter Health Sciences Library, Northwestern University and Mike Taylor, Senior Product Manager, Informetrics, Elsevier 

Attendees are invited to ask any questions related to persistent identifiers.

Session II: Metrics

12:15 p.m. - 1:00 p.m.

Bibliometrics for Data – what counts and what doesn’t?


Sarah Callaghan

Research Scientist and Project Manager
British Atmospheric Data Centre

The outputs of scholarly research are no longer limited to published outputs such as books and journal articles – data, software and workflows are increasingly acknowledged as part of the research process that should be disseminated along with the results and conclusions drawn from research. Obviously, the creators of these “non-traditional” outputs should be rewarded for their efforts in making them available, and there should be ways of measuring the impact of these outputs both in their field, and in the wider scientific and general communities. This talk will focus on data, and will outline the complications that data have with regards to traditional bibliometrics.

FORCE 11 Attribution: Getting (and giving) credit for all that we do


Melissa Haendel

Associate Professor
Ontology Development Group, OHSU Library, Department of Medical Informatics and Epidemiology, Oregon Health & Science University

Current metrics for scholarly output rely heavily on those things which are easy to count, such as papers, grant dollars, and patents. With altmetrics taking hold, new opportunities are emerging to understand broader societal impact of scholarship. However, many of these measures don’t track the specific products that someone creates, nor their specific participation in various scholarly activities. This limits an understanding of personal impact and value in the scholarly ecosystem and has negative consequences for funding, career progression, and program planning. Further, there is a need to understand downstream outcomes that leverage prior contributions. This presentation will discuss approaches for attribution of non-traditional scholarly products and their relationship to people, organizations, and more traditional scholarly works.

Presenter Q&A

Moderated by Kristi Holmes, PhD, Director, Galter Health Sciences Library, Northwestern University and Mike Taylor, Senior Product Manager, Informetrics, Elsevier 

Attendees are invited to ask any questions related to research data metrics.

Additional Information

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