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Data documentation Checklist (1-2)

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, identify needed documentations which will enable data preservation and reuse in the future. The different types of documentations include:

  • Laboratory notebooks & experimental protocols
  • Questionnaires, code books with full variable and value labels & data dictionaries
  • Information about equipment settings & instrument calibration
  • Software syntax & output files
  • Database schema
  • Methodology reports
  • Assumptions made during analysis
  • Provenance information about sources of derived data, different versions of the dataset

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

 

Read More: Dataset Metadata

Please refer to https://www.academia.edu/4664891/Dataset_Metadata for more information on dataset metadata and its related services at UCF Libraries.

 

Data documentation Checklist (3-5)

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. Create a readme text file to describe the list of files and documentations in a folder.

Please refer to Data Management Plan & Metadata, common 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

 

 

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