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If you are looking for useful tools to address your data management challenges, start with these:
Think of what information would be needed to understand and analyze your data, and/or replicate your results, 20 years from now. That’s what needs to be included in your metadata. People are fond of saying that metadata is “data about data.” NISO has a nice guide called Understanding Metadata.
For a given research project, metadata are generally created at two levels: project- and data-level. Project-level metadata describes the “who, what, where, when, how and why” of the dataset, which provides context for understanding why the data were collected and how they were used.
Examples of project-level metadata are:
- Name of the project
- Dataset title
- Project description
- Dataset abstract
- Principal investigator and collaborators
- Contact information
- Dataset handle (DOI or URL)
- Dataset citation
- Data publication date
- Geographic description
- Time period of data collection
- Subject/keywords
- Project sponsor
- Dataset usage rights
Dataset level metadata are more granular. They explain, in much better detail, the data and dataset. (perhaps not surprisingly).
Data-level metadata might include:
- Data origin: experimental, observational, raw or derived, physical collections, models, images, etc.
- Data type: integer, Boolean, character, floating point, etc.
- Instrument(s) used
- Data acquisition details: sensor deployment methods, experimental design, sensor calibration methods, etc.
- File type: CSV, mat, xlsx, tiff, HDF, NetCDF, etc.
- Data processing methods, software used
- Data processing scripts or codes
- Dataset parameter list, including
- Variable names
- Description of each variable
- Units
More background on metadata...
