Data Management

Some researchers will not need to submit a formal written data management plan, however, certain best practices and guidelines should be followed by all researchers. Following these best practices can increase research impact and save time.

• Plan for standardized file naming and cataloging conventions (this worksheet can help brainstorm and plan)
• Plan for your data storage during and after the study window - Where will you keep the data? Who will have access to it? How can you ensure data security, especially for sensitive data?
• Start with documentation before collecting data

• Manage your data - back your data up (3-2-1 rule - 3 copies, 2 types of media, 1 off-site copy)
• Track version history - save early, save often, don’t be afraid to “save as”
• Pick a data format appropriate for your field of study - what is the standard?
• Create metadata about your data - who, what, where, when, how, why, quality
• Create ReadMe files and change logs
• Annotate your code

• Use smart naming conventions (ie. dating files, v2, etc.) and organization to describe versions
• Include important components in file names - project acronym, study title, location, investigator, year(s) of study, data type, version number, and file type
• Avoid $ % ^ & # and spaces

• Deidentify datasets with security/privacy issues (ex. HIPAA/FERPA, any data with PII) before sharing
• Store data in a file format likely to be accessible long-term
• UIS offers a number of storage solutions for data

• Open Data should be FAIR - Findable, Accessible, Interoperable, and Reusable
• Consider sharing data in a repository