Data-PASS Alliance supports NSF “Dear Colleague Letter: Effective Practices for Data"

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The National Science Foundation (NSF) recently issued a Dear Colleague Letter: Effective Practices for Data which described and encouraged research data management to enable efficient, transparent, and reproducible science.  The Data Preservation Alliance for the Social Sciences (Data-PASS), a voluntary partnership of organizations that archive, catalog, and disseminate data for social science research and evidence-based policymaking, strongly supports these recommendations and applauds NSF’s leadership in this area.

The NSF Dear Colleague Letter recommends the use of two key practices:

  • Persistent identifiers, such as Digital Object Identifiers (DOIs), that make data more findable and accessible, enable citation and permit linking to data from within publications; and 
  • Machine readable data management plans (DMPs), which describe how researchers will share research results, data, and associated materials, and which are interpretable by a computer program.   

The NSF Dear Colleague Letter also noted that NSF allows researchers to budget for data curation, which the foundation recognizes as a distinct undertaking that goes above and beyond a stated research activity. In addition, the letter reminded readers that for biographical sketches the NSF considers scientific data sets to have equal standing to traditional publications.

The repositories making up the Data-PASS Alliance provide a number of services that add value to data, and to the publications that rely on their analysis, such as curation, preservation, arrangements for sharing data with different levels of risk (e.g. controlled access), pre-publication verification, and tracking related publications. Collectively, these organizations represent the most important and most accessed social science data repositories in the world.

This collaborative statement by the undersigned members of the Data-PASS Alliance expresses the repositories’ strong agreement with the goals and methods laid out in the NSF Dear Colleague Letter. We fully support researchers generating DMPs, including identifying a repository with a commitment to rendering those data Findable, Accessible, Interoperable, and Reusable (FAIR), and hence which would assign a persistent identifier.  We are also enthusiastic about the promise of machine-readable DMPs and welcome opportunities to link DMP tools with data repositories, in order to increase the social and scientific return from investments in data collection as well as increase the transparency and reproducibility of the resulting scientific research.

Statement endorsed by:

Karen E. Adolph, Co-Director, Databrary.org, and Professor of Psychology, Neural Science, and Applied Psychology, New York University

Micah Altman, Director of Research, Center for Research on Equitable and Open Scholarship, MIT

William C. Block, Director, Cornell Institute for Social and Economic Research, Cornell University.

Thu-Mai Christian, Assistant Director for Archives, H. W. Odum Institute for Research in Social Science, University of North Carolina at Chapel Hill

Mercè Crosas, Harvard University’s Research Data Officer and Chief Data Science and Technology Officer, Institute for Quantitative Social Science, Harvard University

Tim Dennis, Director, Social Science Data Archive, University of California, Los Angeles

Colin Elman, Director, Qualitative Data Repository, and Professor of Political Science, Syracuse University

Jeff Gill, Distinguished Professor of Government and Director of the Center for Data Science, American University

Rick O. Gilmore, Co-Director, Databrary.org, and Professor of Psychology, The Pennsylvania State University

Margaret Levenstein, Director, Inter-university Consortium for Political and Social Research, and Research Professor, Institute for Social Research and School of Information, University of Michigan

Steven Ruggles, Director of IPUMS and Regent Professor of History and Population Studies, University of Minnesota


 

Sep 17, 2019

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