# Publish & Reuse Stage

## **Publish & Reuse — Detailed Summary**


Lifecycle Stage	Purpose & Key Outputs	Best Practices and Recommendations reNEW
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.

Lifecycle Stage

Purpose & Key Outputs

Best Practices and Recommendations reNEW

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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