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Research Data Management

“When we have all data online it will be great for humanity. It is a prerequisite to solving many problems that humankind faces.” – Robert Cailliau, Belgian informatics engineer and computer scientist

Documenting Data

Documenting your data is simply providing sufficient descriptive information about your data so that it can be used properly by you, your colleagues, and other researchers in the future. Well documented data is identifiable, understandable, and usable in the future. You should document your data at each stage of the research process, rather than attempting to recreate information at a later stage.

 

How to Document Data

The term metadata is used to refer to your documentation since you are providing data about data. Researchers can choose among various metadata standards, often tailored to a particular file format or discipline.  One such standard is DDI , designed to document numeric data files. Additional standards are listed on the left of this page.

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.

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
Checksums To test if your file has changed over time

 

 

Ensuring Future Usability

An equally important part of documentation is providing the information necessary to fully understand and interpret the data.  At a minimum this should include:

  • a file manifest
  • a short text describing the dataset including any information that is not adequately represented in the structured metadata
  • codebooks
  • variable descriptions
  • documentation of experimental methods
  • provision of software code used in analysis
  • discussion of the file structure and relationships.

Remember, it is easier to collect this as the data is created rather than after the fact.

Most data repositories and archives allow the submission of supporting documentation.  And even if you have no plans to publish or distribute your data, keeping good records of the data as it evolves will pay dividends by helping you and your research team work easily with the data over time.

Metadata Standards/Schema

Selecting a standard or schema does not obligate you to use it to its fullest extent. You can use as much (or as little) as you need.

General Purpose Schemas

Science Schemas

Social Science Schemas

Humanities Schemas