What are best general practices for sharing research data?

The best practices for sharing research data are context-specific, but there are still core elements of best practices for data management and sharing that should be followed. They include:

• Developing a Data Management and Plan (DMP) that documents your obligations and plans. This is especially important for externally funded research, which may require a data management and sharing plan. You can find support for creating your this through the RCData Management and Data Methods ConsultationsConsulting on data management plans, methods, tool & processing RC Data Management and Data Methods Consultations and by using the RCDMPToolEases creation of machine-actionable data management & sharing plans RC DMPTool .
• Knowing your obligations for data management and sharing, including:
  • Legal requirements - international, federal, state, and local
  • Ethical obligations of your professional and/or research communities
  • Funder or sponsor requirements
  • Institutional requirements
  • Publisher or journal requirements
• Using data management and sharing plans to ensure consistency in practices such as file naming, storing data securely, data curation, data retention and disposal, etc.
• Adopting and implementing relevant data and metadata standards such as the RCFAIR Guiding PrinciplesGuiding principles for scientific data management and stewardship RC FAIR Guiding Principles and the RCCARE Principles for Indigenous Data GovernanceCARE Principles for Indigenous Data Governance RC CARE Principles for Indigenous Data Governance . 

In practice, this means: 

• Choosing a well-documented repository for sharing your data, assigning a persistent identifier (such as a DOI), and providing comprehensive metadata so that others can easily discover and interpret your dataset.
• Including clear README files, codebooks, or data dictionaries that explain the dataset’s structure, variables, units of measurement, and any relevant methodologies.
• Using standardized file formats (such as CSV for tabular data or TIFF for images) to ensure long-term accessibility, and data should be structured in a way that makes it easy for others to analyze without needing proprietary software.
• Using consistent metadata and controlled vocabularies also enhances the reusability of data. Many disciplines have established metadata standards, such as Dublin Core, DDI, or ISO 19115, which ensure that datasets can be properly indexed and understood by different users and systems.
• Clearly defining licensing and access conditions to inform others about how the data can be used. Applying an open license (such as CC-BY or Open Data Commons) makes it clear that others can reuse the data, provided they give proper attribution.
•  If restrictions apply due to ethical or legal considerations, providing guidance on how others can request access ensures that the data remains available while protecting sensitive information.

Each of these is a consideration for determining the best way to share your data. You can learn more about these and additional good research and data practices by visiting the RCCenter for Digital Scholarship's Good Data PracticesCore elements of good practices for research data management and sharing. RC Center for Digital Scholarship's Good Data Practices website.