Documentation
Whether you are sharing your data publicly or not, carefully documenting decisions you’ve made with your data collection and data analysis are crucial steps in the research data lifecycle process. Appropriate documentation will minimize the time required to revisit your own data down the line and will improve others’ understanding of your data for reuse, if sharing.
Document the basics
- Who contributed to the project (authors, research assistants, etc.)?
- What kinds of data and analysis were used?
- When and how was the data collected? When was analysis performed? Any other pertinent dates?
- Where does the project take place? Does it involve a particular geographic area, such as the state of Minnesota, or the Twin Cities, or Antarctica?
- Why is the work important? What is the impetus for the project? What questions are you trying to answer?
Detailed documentation
Imagine that you are taking over a project in the middle of the grant, but you have no way to contact the former project manager. What type of information would you need to continue successfully?
- File handling (file format, naming convention, folder structure)
- Processing steps (how to get from point A to B)
- Protocols (what decisions were made and why)
- Field abbreviations/name glossary (what does ABC3130 stand for)
Keep documentation in one place
Download an Example Readme.txt (plain text file) template that can be adapted for your data. Update the template frequently throughout the project. The file will contain all the metadata related to your research.
Metadata
Metadata is data about your data. It is searchable, descriptive information about your research data. If your metadata is incorrect or contains errors, there is the likelihood that it will be less discoverable by a search engine when you share publicly. There are certain characteristics that you should focus on when creating your metadata:
- Be specific
- Be consistent
- Be timely
- Check your spelling
- Consider concise (variable labels) versus descriptive (data collection methods) metadata fields
- Some disciplines have specific metadata standards, consult discipline standards