A generalist institutional data repository offering both open and restricted access to support NIH data sharing compliance
DOI:
https://doi.org/10.5195/jmla.2026.2339Keywords:
Data Repository, Data Sharing, Data Curation, NIH Data Management and Sharing Policy, Institutional Repositories, FAIR data principles, Restricted Access, Open AccessAbstract
In response to the 2023 NIH Data Management and Sharing (DMS) Policy, Washington University School of Medicine in St. Louis launched Digital Commons Data@Becker, a generalist institutional data repository supporting both open and restricted access to research data. Managed by Bernard Becker Medical Library’s DMS Team, the repository offers a fully mediated curation workflow that guides researchers through consultation, metadata capture, documentation, and quality control. Draft Digital Object Identifiers (DOIs) can be issued once access type is determined, with final DOI publication following curation and QC. Restricted datasets require Human Research Protection Office (HRPO) review and Data Transfer and Use Agreements (DTUAs), while open access datasets are freely downloadable.
The repository leverages persistent identifiers such as Open Researcher and Contributor ID (ORCID iDs), Research Organization Registry (ROR) IDs, and DOIs, along with the DataCite metadata schema and custom metadata fields. Since its launch in 2023, Digital Commons Data@Becker has published 30 datasets spanning biomedical imaging, sequencing, quantitative assays, flow cytometry, and qualitative survey data. Across all datasets, there have been 4,409 views and 4,120 files downloaded, with restricted datasets generating 13 access requests, three of which were granted through DTUAs. Researchers emphasize the value of free institutional curation, flexible access models, and rapid DOI assignment.
Digital Commons Data@Becker demonstrates how a generalist institutional data repository can balance accessibility and security to support NIH compliance, while advancing FAIR (Findable, Accessible, Interoperable, Reusable) data sharing and long-term stewardship.
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Copyright (c) 2026 Seonyoung Kim, Xing Jian, Marcy Vana

This work is licensed under a Creative Commons Attribution 4.0 International License.
