Data Management

What types of data, samples, physical collections, code, software, curriculum materials and other materials will be produced in the course of the project?

Consider: 

• Including a brief description of each type of data to be generated (e.g., experimental, qualitative, raw, processed).
• How much data you anticipate will be generated over the course of the project.
• Which data you will share and at what stage (raw, processed, reduced, or analyzed).
• Why the data you will share will be of interest to a broader community and how your plan will maximize potential for reuse.
• Whether you are using data from other sources; if so, provide a brief description, including content, source, and any conditions required for obtaining and using that data. If you will combine existing data with your own, describe the relationship between the data sets.
   

What standards will be used for your files and metadata?

Consider:

• Formats of data files and approximate volume of data.
  • Select nonproprietary file formats for sharing and archiving to maximize the potential for reuse and longevity, and describe the plans for conversion to those formats if necessary.
• Metadata that will be created or captured; when it will be created; and who will create it.
  • Identify community metadata standards. Indicate if no applicable standards exist and describe what additional documentation you will provide to make the data understandable and usable by others (e.g., readme file).
• Data organization, such as how data will be distributed among files; file naming conventions; directory organization; and version management.

Learn More:

• File management.
• File formats.
• Metadata and describing data.
• Readme metadata.
• Tabular data.
   

What are the roles and who has responsibilities for managing data?

Consider:

• Primary responsibility for implementing the data management plan.
  • If multiple institutions are involved, funding agencies typically assign the execution of the DMP to the lead PI.
• Plans for transfer of responsibility if key personnel depart from the project.
   

What methods will you use for sharing data and metadata during and after the award period?

Consider:

• How will the data sets will be stored (if secure storage and/or restricted access are required) and backed up during the project?
  • Describe hardware, storage environment, and local or external services to be used.
  • Include the costs for these services in the proposal budget, if applicable.
• Who will have access to working data, and how will access be managed before and after the grant period?
• How will the data be transferred and shared among collaborators?
• When will you share the data? 
• How will the data will be made available (e.g., disciplinary data center or repository; an institutional repository; as supplementary material supporting a publication)?
• How will users discover the data (e.g., a specific repository; data citation; project website; Internet search engines)?
  • Some funders and publishers require an identifier (e.g., DOI, handle) for persistent access to the dataset.
• How will users obtain the data (e.g., direct download, registration and download, upon request)?
• What tools and software will be needed to work with data and metadata? 

Learn more:

• Sharing and archiving data
• Data citation
• Services: Storage, backup and recovery
   

How will you meet funder requirements to provide public access to your data while protecting privacy, confidentiality, security and intellectual property rights?

Consider: 

• Conditions for reuse of the data by others, including any licenses that will be applied.
• Whether data acquired from another source will be shared and under what conditions.

• Privacy and security:

  • How the data will be managed to protect privacy (e.g., measures taken to anonymize data; disposition of data including personally identifiable information).
  • Legal and ethical requirements that may preclude sharing of any of your data. If so, explain the circumstances that prevent you from sharing data.
  • Whether your research is subject to oversight by the institutional review board. Refer to applicable requirements and describe how your data management practices will ensure compliance.

• Intellectual property:

  • Whether copyright protection extends to your data. Some standard licensing options (Creative Commons, Open Data Commons) exist. Many metadata standards accommodate rights or usage statements where conditions for reuse may be expressed. 
  • Funding agencies (including the National Science Foundation) often recognize that commercialization potential may delay or preclude data sharing and may exempt trade secrets and commercial information from the data sharing requirement.

Learn More:

• Sharing and archiving data
• Intellectual property
• Data citation
• Services: Privacy and confidentiality 
   

How will you preserve the integrity of your data over time? Note: some of these issues may already by addressed in the section on public access, sharing and reuse.

Consider:

• Departmental, institutional, or programmatic policies on data retention, how they influence your plan and how you will adhere to the policies.
• How long data will be retained or preserved and why.  
  • Some data may only be retained for the lifetime of the project; some may be retained for the project plus a specified number of years; some may be worth the effort of long-term preservation (several years to decades). 
  • Consider what data are needed to validate the research; what data directly support publications based on the research; and what data have the greatest potential for reuse.
• Hardware or campus or commercial services to be used to assure data preservation.
• Costs for any of these activities or services; these may be included in your proposal budget.
   

Some funders and/or RFPs require that additional data management areas be addressed in a DMP.

These may include: 

• Rationale.
• Monitoring and reporting.
• Specific assurance of having resources to carry out the plans.
• Data processing workflow (e.g., how you plan to get data from point of collection to point of access).
• Data quality assurance or quality control measures.
• For sensitive data, the security measures and any formal standards that will be used (e.g., biological agent permitting).