
Presentation for Panel Session at SciDataCon 2023 in Salzburg, Austria on October 26, 2023 https://www.scidatacon.org/IDW-2023-Salzburg/sessions/492/NIH Data Management and Sharing Policy went into effect, a policy which will likely be a significant turning point that advances the sharing of NIH-funded research data. It will hopefully drive an ongoing shift in the culture of data management and sharing towards more open and transparent research and increase the frequency of both FAIR data sharing and data reuse. In parallel, in February 2022, the NIH Office of Data Science Strategy launched the Generalist Repository Ecosystem Initiative (GREI), which brings together seven generalist repositories (Dataverse, Dryad, Figshare, Mendeley Data, Open Science Framework, Vivli, and Zenodo) to enhance support for NIH data sharing and discovery in generalist repositories. This program recognizes that generalist repositories play a key role in the NIH data sharing landscape to support the FAIR sharing of data and other research outputs in a trusted repository when they cannot be deposited in a discipline-specific repository. A key component of GREI is "coopetition" (cooperation + competition), a term coined to describe the collaboration among the generalist repositories to advance repository functionality. The GREI program has a mission to "establish a common set of cohesive and consistent capabilities, services, metrics, and social infrastructure across various generalist repositories and to raise general awareness and help researchers to adopt FAIR principles to better share and reuse data." While focused on NIH-funded data, GREI is a global collaboration that seeks to leverage existing and emerging community standards, partner with other initiatives in the research data community, and inspire greater interoperability across the data repository landscape beyond generalist repositories. In the first year of the program, the GREI coopetition has focused on objectives that reduce the barriers to FAIR data sharing and improve interoperability and discovery of data across repositories. Emerging recommendations include a catalog of generalist repository use cases, a common metadata schema built on DataCite metadata fields along with recommended controlled vocabularies, common metrics for reporting on data use, and training and outreach events and materials for research communities and other stakeholders at NIH and academic institutions. Future GREI collaborative work will be influenced by community feedback and partnership with global audiences, such as SciDataCon, to better support the data sharing needs of our research communities. This panel session included short presentations from a variety of speakers involved in GREI followed by a panel discussion and audience Q&A on the challenges and opportunities for coopetition among repositories to enhance the data landscape. Talks highlighted GREI recommendations and ongoing GREI work including: The "coopetition" model of collaboration - Benefits and opportunities in collaborating across generalist repositories and in partnership with community initiatives as part of the data sharing landscape NIH perspective on the role of generalist repositories in the data sharing landscape and the role of coopetition in fostering data interoperability and tracking Generalist repository use cases and community needs for data sharing, discovery, and tracking including examples from specific GREI repositories The GREI common metadata schema including DataCite fields and controlled vocabularies as well as examples of how GREI repositories have implemented these Common metrics to report on the reuse of NIH-funded research data
Metadata, SciDataCon 2023, Open Data, Metrics, Data Management and Sharing, Generalist Repository Ecosystem Initiative (GREI)
Metadata, SciDataCon 2023, Open Data, Metrics, Data Management and Sharing, Generalist Repository Ecosystem Initiative (GREI)
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
