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Research Data Management: Documenting data

Introduction

Documentation is the contextual and explanatory information required to make sense of the dataset. It is a users' guide to your data making it understandable, verifiable and reusable.

Document your data so that...

  • You remember the details later
  • Others can understand your research
  • Your findings can be verified
  • Your results can be replicated
  •  Risk of misinterpretation is avoided
  • Your data can be archived for access and re-use

Research data should be documented at various levels:

Study level

  • Describes the research project, the data creation processes, rights and general contexts. Good study level data should include information about research design and context, data collection methods, structure of data files, secondary data sources used, data validation procedures, conditions of use.

Data level

  • Describes how all the files (or tables in a database) that make up the dataset relate to each other; what format are they are in; whether they supercede or are superceded by previous files. A readme.txt file is the classic way of accounting for all the files and folders in a project.

Examples of data documentation

  • Database schema
  • Information about equipment settings and instrument calibration
  • Laboratory notebooks and experimental protocols
  • Methodology reports
  • Provenance information about sources of derived or digitised data
  • Questionnaires, codebooks, data dictionaries
  • Software syntax and output files

Learn more...

Advice about good practice relating to documentation and metadata is available from the UK Data Service