http://www.chinalibs.net 2020/9/25
[作者] Jessica Aiwuyor
[摘要] Today a group of research library and higher education leadership associations released Implementing Effective Data Practices: Stakeholder Recommendations for Collaborative Research Support. In this new report, experts from library, research, and scientific communities provide key recommendations for effective data practices to support a more open research ecosystem. In December 2019, an invitational conference was convened by the Association of Research Libraries (ARL), the California Digital Library (CDL), the Association of American Universities (AAU), and the Association of Public and Land-grant Universities (APLU). The conference was sponsored by the US National Science Foundation (NSF).
[关键词] New Report Recommendations Effective Data Practices National Science Foundation Research Enterprise
Today a group of research library and higher education leadership associations released Implementing Effective Data Practices: Stakeholder Recommendations for Collaborative Research Support. In this new report, experts from library, research, and scientific communities provide key recommendations for effective data practices to support a more open research ecosystem. In December 2019, an invitational conference was convened by the Association of Research Libraries (ARL), the California Digital Library (CDL), the Association of American Universities (AAU), and the Association of Public and Land-grant Universities (APLU). The conference was sponsored by the US National Science Foundation (NSF).
The conference focused on designing guidelines for (1) using persistent identifiers (PIDs) for data sets, and (2) creating machine-readable data management plans (DMPs), two data practices that were recommended by NSF. Professor Joel Cutcher-Gershenfeld, of Heller School for Social Policy and Management at Brandeis University, designed and facilitated the convening with the project team.
The project team includes: John Chodacki from California Digital Library, Cynthia Hudson-Vitale from Pennsylvania State University, Natalie Meyers from University of Notre Dame, Jennifer Muilenburg from University of Washington, Maria Praetzellis from California Digital Library, Kacy Redd from Association of Public and Land-grant Universities, Judy Ruttenberg from Association of Research Libraries, and Katie Steen from the Association of American Universities. There were additional contributions from Joel Cutcher-Gershenfeld from Brandeis University and Maria Gould from California Digital Library.
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 recommended data practices. The report focuses on recommendations for research institutions and also provides guidance for publishers, tool builders, and professional associations. The AAU-APLU Institutional Guide to Accelerating Public Access to Research Data, forthcoming in spring 2021, will include the recommendations.
Five key takeaways from the report are:
Center the researcher by providing tools, education, and services that are built around data management practices that accommodate the scholarly workflow.
Create closer integration of library and scientific communities, including researchers, institutional offices of research, research computing, and disciplinary repositories.
Provide sustaining support for the open PID infrastructure that is a core community asset and essential piece of scholarly infrastructure. Beyond adoption and use of PIDs, organizations that sustain identifier registries need the support of the research community.
Unbundle the DMP, because the DMP as currently understood may be overloaded with too many expectations (for example, simultaneously a tool within the lab, among campus resource units, and with repositories and funding agencies). Unbundling may allow for different parts of a DMP to serve distinct and specific purposes.
Unlock discovery by connecting PIDs across repositories to assemble diverse data to answer new questions, advance scholarship, and accelerate adoption by researchers.
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.
The full report is now available online.
引用本文:
Jessica Aiwuyor．New Report Provides Recommendations for Effective Data Practices Based on National Science Foundation Research Enterprise Convening[DB/OL]．[2025-04-30]．http://www.chinalibs.net/ArticleInfo.aspx?id=490184．
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