Data sharing practices differ from field to field, and often project to project, but here are the main points to consider in a data sharing plan:
- What data outputs will your research generate and which data will have value to other researchers?
- e.g., it may be possible that you won't share raw data, but rather a subset (cleaned/analyzed) that supports research findings
- When will you share the data?
- e.g., a dataset may be shared at time of publication, embargoed for 6-12 months, etc.
- Where will you make the data available?
- e.g., in an institutional repository like PURR, a national repository like NCBI, or a disciplinary repository like NCAR
- How will other researchers be able to access the data?
- e.g., download from repository, connect API, request via email
- Are any limits to data sharing required - for example, to either safeguard research participants or to gain appropriate intellectual property protection?
- e.g., original files may be prohibited from sharing due to consent form, but de-identified datasets or transcripts may be available
- How will you ensure that key datasets are preserved to ensure their long-term value?
- e.g., will a repository maintain data after project funding ends?
- What resources will you require to deliver your plan?
- e.g., many funder allow storage, curation charges as part of data sharing