| Publish & Reuse | Prepare and disseminate research data, software, and materials for long-term access, reuse, and attribution. Outputs: Archived dataset, code repository, dataset DOI, data availability statement, preprints, and citation-ready metadata. | - Depending on data type and sensitivity, deposit datasets in designated or general-purpose repositories (e.g., Zenodo, Dryad, PRIDE, GEO, Harvard Dataverse).
- Assign persistent identifiers (DOIs for datasets/code; ORCID for researchers; ROR for institutions) to ensure citation and discoverability.
- Apply open licenses (e.g., CC-BY, CC0) that define reuse conditions clearly.
- Include a Data Availability Statement in all publications that explains how to access the underlying data and software.
- Comply with institutional and funder mandates for data sharing using approved repositories and timelines.
- Register your datasets in data catalogs or institutional registries to support internal and public discovery.
- Publish preprints to accelerate dissemination, promote transparency, and receive feedback before peer review (e.g., via bioRxiv or medRxiv).
- Explore new avenues of scholarly communication, including open peer review, protocol publications, and registered reports.
- Treat all research outputs—data, code, workflows, and protocols—as first-class scholarly products worthy of citation and credit.
- Share analysis scripts and computational workflows using platforms like GitHub, OSF, or institutional Git repositories, and archive them with DOIs (e.g., Zenodo integration).
- Provide rich metadata, README files, and documentation to ensure others can find, understand, and reuse your work.
- Consider publishing in data journals or creating supplemental data articles to increase dataset visibility, reproducibility, and impact.
- Promote and track data reuse through platforms that support metrics, citations, and author visibility.
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