Last Updated on August 18, 2020, 3:08 pm ET
Note: The public comment period for the recommendations closed on August 7, 2020.
The planning committee for “Implementing Effective Data Practices: A Conference on Collaborative Research Support” is pleased to release for community review a set of recommendations to support the broad adoption of persistent identifiers (PIDs) and machine-actionable data management plans (DMPs).
The recommendations and guidelines are organized as a set of core principles and associated recommendations for research stakeholders on how to increase adoption and incorporate PIDs and machine-actionable DMPs into the scholarly communications ecosystem.
Please visit the draft of the full recommendations released for public review. (The final version of the recommendations is forthcoming in summer 2020.)
Background
In December 2019, the library community, represented by the Association of Research Libraries (ARL) and the California Digital Library (CDL), in partnership with the Association of American Universities (AAU) and the Association of Public and Land-grant Universities (APLU), and with funding provided by the National Science Foundation (NSF), convened a small conference to discuss the current state of PIDs and machine-readable DMPs.
The goal of the “Implementing Effective Data Practices” conference was to frame the suggested best practices of PIDs and DMPs within the larger stated commitment by AAU and APLU to expand public access to research data and to advance open science and scholarship within the framework of the National Academies of Sciences, Engineering, and Medicine vision for 21st-century research.
Attendees of the workshop-style conference included US federal agency representatives, private funding organization officers, IT professionals, vice chancellors for research, professional society representatives, domain repository managers, tool builders, data librarians, and several active researchers. The conference provided an opportunity for fresh thinking on how the scientific community and the library community might partner for better data management, better stewardship, and better compliance with funders’ requirements, without increasing researchers’ administrative burden. The attendees pushed beyond NSF’s recent “Dear Colleague Letter” in two key dimensions: (1) they promoted a core set of PIDs, not just for data sets, and (2) they defined the objective as machine-actionable DMPs (maDMPs), rather than machine-readable.
Conference Planning Committee
John Chodacki, California Digital Library
Cynthia Hudson-Vitale, Pennsylvania State University
Natalie Meyers, University of Notre Dame
Jennifer Muilenburg, University of Washington
Maria Praetzellis, California Digital Library
Kacy Redd, Association of Public and Land-grant Universities
Judy Ruttenberg, Association of Research Libraries
Katie Steen, Association of American Universities
Additional Report and Conference Contributors
Joel Cutcher-Gershenfeld, Brandeis University
Maria Gould, California Digital Library
