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February 25, 2016 by Gary Price

NISO Releases Draft of Recommended Practice on Altmetrics Data Quality

February 25, 2016 by Gary Price

From the National Information Standards Organization:

The National Information Standards Organization (NISO) seeks comments on the draft Altmetrics Data Quality Code of Conduct, NISO RP-25-201X-3, one output of a multi-pronged, multi-phase project that aims to build trust in and adoption of new methods of assessing impact. Further draft outputs from two other working groups, addressing altmetrics definitions, use cases, specific output types, and use of persistent identifiers, are expected to be released for public comment in the coming weeks.
Altmetrics are increasingly being used and discussed as an expansion of the tools available for measuring the impact of research in the knowledge environment. The NISO Alternative Assessment Metrics (Altmetrics) Initiative was begun in July 2013 with funding from the Alfred P. Sloan Foundation to address several areas of limitations and gaps that hinder the broader adoption of altmetrics. This document is one output from this initiative’s Phase 2, and is intended to help organizations that wish to use altmetrics to ensure the consistent generation and aggregation of altmetric data across the community. This Altmetrics Data Quality Working Group studied and discussed issues of data quality in the altmetrics realm, an essential aspect of heterogeneous data before metrics can be used for research and practical purposes.
“The Code of Conduct aims to improve the quality of altmetric data by increasing the transparency of data provision and aggregation as well as ensuring replicability and accuracy of online events used to generate altmetrics,” explains Stefanie Haustein, post-doctoral researcher at the Canada Research Chair on the Transformations of Scholarly Communication, Université de Montréal, and co-chair of Working Group C (Data Quality), the group that developed these recommendations.
“The recommendations themselves are rather brief in length,” comments Greg Tananbaum, Consultant at SPARC and co-chair of Working Group C (Data Quality). “However, appendixes to the document include a standard form of report that data providers and aggregators can use to publicly detail their adherence to the recommendations, and several sample reports that are intended to help an entity understand how they might demonstrate their approach to transparency, as recommended by the Code of Conduct.”
“NISO is soliciting feedback on this draft Recommended Practice from any organization that creates, uses, or is affected by altmetric data,” states Nettie Lagace, NISO Associate Director for Programs. “This feedback will be used to make any revisions to the document before final publication of all recommendations from the Altmetrics Initiative later this spring. We also plan to make additional documents from other working groups participating in this initiative available for public comment and invite additional parties to keep in touch for these announcements.”

Direct to DRAFT For Public Comment: Altmetrics Data Quality Code of Conduct
NISO RP-25-201X-3
41 pages; PDF.
Submit Comments (February 25-March 31, 2016)
Direct to Altmetrics Metrics Initiative Website
Including project timeline, original proposal, etc.
See Also: NISO Launches New Development Projects in New Forms of Assessing Impact & Altmetrics (December 15, 2014)

Filed under: Associations and Organizations, Data Files, Funding, News, Reports

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About Gary Price

Gary Price (gprice@gmail.com) is a librarian, writer, consultant, and frequent conference speaker based in the Washington D.C. metro area. He earned his MLIS degree from Wayne State University in Detroit. Price has won several awards including the SLA Innovations in Technology Award and Alumnus of the Year from the Wayne St. University Library and Information Science Program. From 2006-2009 he was Director of Online Information Services at Ask.com. Gary is also the co-founder of infoDJ an innovation research consultancy supporting corporate product and business model teams with just-in-time fact and insight finding.

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