| Data Production |
- What type(s) of data and datasets will be produced? Will it be video data, traditional numerical data, electronic lab notebooks, software, other kinds of datasets?
- Will the data include human data? Will deidentification and/or anonymization be required?
- What file format(s) will the data be saved as? Are those file formats proprietary? Will they degrade?
- Will the data be reproducible?
- Do you need tools or software to create/process/visualize the data?
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| Data Size |
- How much data will be gathered, and at what growth rate?
- How often will the data change?
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| Data Transfer |
- How will the datasets be moved from local storage to long-term storage or from lab servers to other types of storage?
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| Data Usage |
- Who will potentially be using your data: both now and later?
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| Data Retention |
- How long should it be retained? (e.g., 3-5 years, 10-20 years, permanently).
- Does your institution have a data retention policy?
- What is your long-term plan for your data, especially once the research is concluded?
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| Privacy and Security |
- Does you data have any special privacy or security requirements? (e.g., human data, personal data, high-security data are all restricted types of data).
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| Data Sharing |
- Any sharing requirements? (e.g., funder data sharing policy, federal requirements such as the NIH guidelines).
- Have you chosen a repository in which to archive your data?
- If your data is sensitive (e.g. human data, personal data), can the repository properly handle that data?
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| Data Management Plan |
- Does your funding agency require a data management plan in the grant proposal?
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| Costs |
- Does you need to include the following costs in your Data Mangement Plan:
- Library Data Mangement assistance, up to including an embedded Data Mangement Librarian in your research team
- Repository fees (e.g. uploading your data, long term curation)
- Anonymization and deidentification fees
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| Data Documentation |
- How will you be documenting your data and project?
- What directory and file naming convention will be used?
- What project and data identifiers will be assigned?
- Is there a schema, ontological, or other metadata standard in your field for sharing data with others?
- Do you have a proper README file to explain all of your datasets, codes, codebooks, and other files?
- Do you have a file that documents all of the repositories and other places where your datasets and associated files are stored, including any needed software to access the datasets and files?
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| Storage and Backup |
- What are the strategies for storage and backup of the data?
- Are you aware of support backups?
- Which repositories will you use for your data? Can they handle the type of datasets that you need stored?
- Are you using one repository or several (e.g., Dryad, Github, Vivli, etc.)
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| Training |
- Will the team need training in data management best practices, working with metadata, making the datasets sharable and reproducible, or other data management topics?
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| Publication |
- When and where will the work be published?
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| Responsibility |
- Who in the research group will be responsible for data management?
- Who controls the data (PI, student, lab, institution, funder)?
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