There are a few conceptions of the research data lifecycle.
Managing Research Data has some thoughts on this.
- Plan
- Create
- Process
- Analyze
- Disseminate
- Preserve
- Reuse
Or Corti et al (table 2.1):
Corti, L., Eynden, V. van den, Bishop, L., Woollard, M., Haaker, M., & Summers, S. (2020). Managing and sharing research data: A guide to good practice (2nd edition.). SAGE.
- Discovery and planning
- Data collection
- Data processing and analysis (merged?)
- Publishing and sharing
- Preserving data
- Reusing data
Or Thompson, E. by K., Hill, E., Carlisle-Johnston, E., Dennie, D., & Fortin, É. (Eds.). (2023). Research Data Management in the Canadian Context. Western University, Western Libraries. https://doi.org/10.5206/ZRUV7849
- Plan
- Collect
- Process
- Analyze
- Preserve
- Share (merged?)
None covers discarding data. Processing feels like it should be a separate step, taking the raw data and cleaning it up/joining it/etc to get it to a point where it can be analyzed. Preservation doesn't necessarily imply reuse. Sharing kind of does. Reuse is dependent on consent among other things.
DDI Alliance has a 5-step model that feels too simplistic to bother considering.
A lot of Data Management Plans are just the application of the data research lifecycle model to a particular research project.
The Digital Curation Centre has another model https://www.dcc.ac.uk/sites/default/files/documents/publications/DCCLifecycle.pdf that is more complex, having lifecycle elements including community watch and participation, transformation, conceptualization and disposal. It's an interesting take.
Pages that link here:
Broken links on this page:
- ↛ Data Management Plan