I wanted to call your attention to a new report by California Digital Library (CDL) written with our partners at the Association of Research Libraries (ARL), the Association of American Universities (AAU), and the Association of Public and Land-grant Universities (APLU): Implementing Effective Data Practices: Stakeholder Recommendations for Collaborative Research Support .
This report came out of a December 2019 National Science Foundation-sponsored conference on implementing effective data practices with experts from library, research, and scientific communities across the US, focused on developing a set of recommendations for the broad adoption and implementation of NSF’s recommended data practices as described in its May 2019 Dear Colleague Letter.
The conference focused on designing guidelines for (1) using persistent identifiers for datasets, and (2) creating machine-readable data management plans, both of which have been recommended by NSF. Based on the information and insights shared during the conference, the project team developed a set of recommendations for the broad adoption and implementation of NSF’s preferred data practices.
The report focuses on recommendations for research institutions and also provides guidance for publishers, tool builders, and professional associations. It identifies five core PIDs that are fundamental and foundational to an open data ecosystem to ensure that basic metadata about research is standardized, networked, and discoverable in scholarly infrastructure:
- DOIs from DataCite to identify research data, as well as from Crossref to identify publications
- ORCID iDs to identify researchers
- Research Organization Registry (ROR) IDs to identify research organization affiliations
- Crossref Funder Registry IDs to identify research funders
- Crossref Grant IDs to identify grants and other types of research awards
The report is intended to encourage collaboration and conversation among a wide range of stakeholder groups in the research enterprise by showcasing how collaborative processes help with implementing PIDs and machine-actionable DMPs (maDMPs) in ways that can advance public access to research.