Good research data management is a series of activities that make it easier to locate, access, share, and use data, and it applies to all methodologies for creating or collecting data. Invest time in planning and take action throughout the research data lifecycle to ensure your data is well-managed.
This guide breaks down data management practices into five major areas activity.
Planning is essential. Write a data management plan (DMP) that covers the life of research data from creation to preservation.
At the project level, document and describe objectives, hypotheses, collection methods, instruments, data formats, metadata schemas, and quality assurance procedures. Data-level documentation should record names for variables, values, classification systems, etc.
Who will have access to your project data, how, and for how long? If you wish to share it--or are required to--how will that be achieved, and what limitations might apply? Access may be restricted by ethical, privacy, and ownership concerns.
How will you ensure the security and integrity of valuable research data? Data need to be backed-up and stored to provide safe current and future access in reliable formats.
Your funder or institution may have a data retention policy, or you may have other reasons to keep the data you've collected. How can you prepare your data to be preserved, and where might it be deposited for safe-keeping?
Good data management practices can help to:
Advice and best practice, for individuals and for institutions.