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Data Management

Documenting data

Clear and detailed data documentation is vital for ensuring that your data be understood and interpreted for long-lasting usability. Effectively sharing your data would be impossible without documentation (also known as metadata) .

Documentation of your data should start at the very beginning of your research project. This will make data documentation easier and reduce the likelihood that you will forget aspects of your data later in the research project.

 Research Project Documentation

  • Context of data collection
  • Data collection methods
  • Structure, organization of data files
  • Data sources used
  • Data validation, quality assurance
  • Transformations of data from the raw data through analysis
  • Information on confidentiality, access & use conditions

Dataset documentation

  • Variable names, and descriptions
  • Explanation of codes and classification schemes used
  • Algorithms used to transform data
  • File format and software (including version) used

Describing data

Researchers can choose among various metadata standards, often tailored to a particular file format or discipline. Following are some general guidelines for aspects of your project and data that you should document, regardless of your discipline.  At minimum, store this documentation in a readme.txt file or the equivalent, together with the data. One can also reference a published article which may contain some of this information.

Title Name of the dataset or research project that produced it
Creator Names and addresses of the organization or people who created the data
Identifier Number used to identify the data, even if it is just an internal project reference number
Subject Keywords or phrases describing the subject or content of the data
Funders Organizations or agencies who funded the research
Rights Any known intellectual property rights held for the data
Access information Where and how your data can be accessed by other researchers
Language Language(s) of the intellectual content of the resource, when applicable
Dates Key dates associated with the data, including: project start and end date; release date; time period covered by the data; and other dates associated with the data lifespan, e.g., maintenance cycle, update schedule
Location Where the data relates to a physical location, record information about its spatial coverage
Methodology How the data was generated, including equipment or software used, experimental protocol, other things one might include in a lab notebook
Data processing Along the way, record any information on how the data has been altered or processed
Sources Citations to material for data derived from other sources, including details of where the source data is held and how it was accessed
List of file names List of all data files associated with the project, with their names and file extensions (e.g. 'NWPalaceTR.WRL', 'stone.mov')
File Formats Format(s) of the data, e.g. FITS, SPSS, HTML, JPEG, and any software required to read the data
File structure Organization of the data file(s) and the layout of the variables, when applicable
Variable list List of variables in the data files, when applicable
Code lists Explanation of codes or abbreviations used in either the file names or the variables in the data files (e.g. '999 indicates a missing value in the data')
Versions Date/time stamp for each file, and use a separate ID for each version (see organizing your files)
Checksums To test if your file has changed over time (see backups)