Data life cycle
What is the data life cycle?
Research data often have a longer life span than the research project that creates them.
- Researchers may continue to work with some or all of their data even after project funding has ceased.
- Follow-up projects may re-analyse existing data or add new data to an existing dataset.
- Data may be re-used or re-purposed by other researchers.
1. Collection or creation
- design the research
- plan data management
- plan the consent for sharing arrangements
- find existing data and collect new data
- capture and create metadata
- describe data
2. Processing and analysis
- enter data, digitise, transcribe, translate
- check, validate, clean data
- anonymise data where necessary
- select data formats
- manage and store data
- back up and store data securely
- interpret data
- derive data
- produce research outputs
- author publications
3. Preservation
- prepare data for preservation
- migrate data to best format
- migrate data to suitable medium
- create metadata & document data
- archive data in long-term storage
4. Discovery
- control access to the data
- establish copyright licensing arrangements
- promote the data
- distribute and share data with others
5. Re-use
- conduct follow-up research
- conduct new research
- undertake research reviews
- scrutinise findings
'For science to effectively function, and for society to reap the full benefits from scientific endeavours, it is crucial that science data be made open.'
Panton Principles for Open Data in Science
Well-organised, well-documented, preserved and shared data are invaluable to
advance scientific enquiry and to increase opportunities for innovation
...
UK Data Archive
Loading