- Plan: Describe best practices to manage your data throughout the data life cycle
- Outline project and prepare a DMP.
- Outline nature, scope, and scale of project.
- Use a data life cycle planning template.
- Ensure legal/ethical/institutional compliance.
- Revise throughout the project as necessary.
- Create: Process of creating/collecting data
- Create data using transparent and meaningful naming conventions.
- Create project ID as file prefix.
- Develop relatable variable names.
- Create a document outlining details of data.
- Process: Act of organizing, cleaning, and securely storing your data
- Describe each variable.
- Document file naming and version control decisions.
- Clean dataset.
- Develop a secure protocol for transferring sensitive data.
- Anonymize data where necessary.
- Ensure variable names are clear.
- Preserve copy of raw data.
- Clean and validate data.
- Analyse: The process of detailing decisions made during analysis and the justification for them
- Keep notes regarding software used and reasons for analysis decisions.
- Document process.
- Document known data issues.
- Document transformations using coding/versioning.
- Share: The process of sharing findings at the end of the research project
- Identify suitable vehicles to share findings.
- Determine how to advise the research community of work/findings.
- Preserve: The process of ensuring measures are in place for long-term access
- Identify data with long-term value.
- Determine what iterations of data development should be preserved.
- Abide by obligations to finding bodies, and the law
- Ensure documentation and metadata are complete.
- Select a well-established reputable repository.
- Reuse: The process of creating a plan to prepare data for reuse
- Decide whether all or part of data will be made available.
- Identify legal, funding, and institutional restrictions or requirements that might influence data reuse.
- Gather all documentation necessary to reuse the data at a later date.
- Consider necessary restrictions to reuse.