Data Management Lifecycle


Data often have a longer lifespan than the research project that creates them. Researchers may continue to work on data after funding has ceased, follow-up projects may analyze or add to the data, and data may be re-used by other researchers. Good data management considers all parts of the research data lifecycle and all aspects of handling, organizing, documenting and enhancing research data, and enabling their sustainability and sharing.

Creating data
  • design research
  • plan data management (formats, storage etc)
  • plan consent for sharing
  • locate existing data
  • collect data (experiment, observe, measure, simulate)
  • capture and create metadata
Processing data
  • enter data, digitize, transcribe, translate
  • check, validate, clean data
  • anonymize data where necessary
  • describe data
  • manage and store data
Analyzing data
  • interpret data
  • derive data
  • produce research outputs
  • author publications
  • prepare data for preservation
Preserving data
  • migrate data to best format
  • migrate data to suitable medium
  • back-up and store data
  • create metadata and documentation
  • archive data
Giving access to data
  • distribute data
  • share data
  • control access
  • establish copyright
  • promote data
Re-using data
  • follow-up research
  • new research
  • undertake research reviews
  • scrutinize findings
  • teach and learn

© UK Data Archive Research Data Lifecycle