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Data documentation

To make your data easy to understand and analyze through your research lifecycle and in the long term, it is considered good practice to document your data.  Data documentation is part of the data curation process.

1. Ensure that all data collected and generated through your research lifecycle is documented.

2. At the beginning of your research, check what kind of documentation is available or necessary, and identify needed documentations which will enable data preservation and reuse in the future. 

The various kinds of documentation may include:

  • Embedded documentation (included within the data, e.g., code, field and label descriptions, syntax, descriptive headers or summaries, transcripts)
  • Supporting documentation (in separate files, e.g., readme, project information, methodology report, working papers, lab books, questionnaires or interview guides, project reports, publications)
  • Catalog Metadata (for data archiving, identification and locating)

Please also note that research data can be documented at various levels: Project level, File or database level and Variable or item level.

3. During your research, document all research data formats utilized by your project.

    Research data comes in many varied formats, such as:

  • Text - flat text files, Word, Portable Document Format (PDF), Rich Text Format (RTF), Extensible Markup Languague (XML).
  • Numerical - Statistical Package for the Social Sciences (SPSS), Stata, Excel.
  • Multimedia - jpeg, tiff, dicom, mpeg, quicktime.
  • Models - 3D, statistical.
  • Software - Java, C.
  • Discipline specific - Flexible Image Transport System (FITS) in astronomy, Crystallographic Information File (CIF) in chemistry.
  • Instrument specific - Olympus Confocal Microscope Data Format, Carl Zeiss Digital Microscopic Image Format (ZVI).

4. Keep the wide variety of materials that are generated or collected in your research.

        Research data (traditional and electronic research) may include all of the following:

  • Documents (text, Word), spreadsheets
  • Laboratory notebooks, field notebooks, diaries
  • Questionnaires, transcripts, codebooks
  • Audiotapes, videotapes
  • Photographs, films
  • Test responses
  • Slides, artifacts, specimens, samples
  • Collection of digital objects acquired and generated during the process of research
  • Data files
  • Database contents (video, audio, text, images)
  • Models, algorithms, scripts
  • Contents of an application (input, output, logfiles for analysis software, simulation software, schemas)
  • Methodologies and workflows
  • Standard operating procedures and protocols

The following research records may also be important to manage during and beyond the life of a project:

  • Correspondence (electronic mail and paper-based correspondence)
  • Project files
  • Grant applications
  • Ethics applications
  • Technical reports
  • Research reports
  • Master lists
  • Signed consent forms

5. Utilize software to create embedded documentation for the data (if applicable), and make separate supporting documentation (e.g. readme text files) to describe the list of files and documentations in a folder. Further, discuss with relevant personnel on how data can be archived and shared in a data center, or a library digital repository for others to search, locate and reuse.

Please refer to Data Management Plan & Metadatacommon metadata standards and domain specific metadata standards for more metadata information.

Source: The University of Edinburgh: Information Services: How to manage research data: Defining research data.

Read More: Data Documentation & Metadata

Please also refer to Data documentation & metadata and Data Documentation, Analysis & Statistical Software for more information on dataset metadata and its related services at UCF Libraries.