Generalist repository "coopetition" to enhance data sharing and discovery
Creators: name: Van Gulick, Ana
name: Chodacki, John
name: Federer, Lisa
name: Belletti, Luca
name: Sara, Gonzales
name: Matt, Buys
Publisher: name: Zenodo
Year: 2023
Subjects: Generalist Repository Ecosystem Initiative (GREI)
Data Management and Sharing
Metrics
Metadata
Open Data
SciDataCon 2023

description: 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