Writing a Data Management Plan
- Know the guidelines for your grant program.
- Assign responsibilities for managing data.
- Understand what types of data you will be producing and the approximate volume.
- Budget for research data management; the NSF, for example, allows reasonable costs related to implementing the data management plan to be included in the budget, if justified.
Grant Agency Guidelines
- Department of Defense
- Department of Energy
- National Institute of Standards and Technology (NIST)
- National Institutes of Health
- National Science Foundation
- U.S. Department of Transportation
- DMPTool offers agency-specific templates to generate ready-to-use data management plans. Rice is a member, so you can log in using your netid and password.
- Sample Data Management Plan for Depositing Data with ICPSR
- Sample NSF Data Management Plans (UCSD)
- Public Data Management Plans (DMPTool)
Writing a proposal? Need help writing a data management plan? Contact the Research Data Services Team at firstname.lastname@example.org. We will review draft plans.
- Plan. Think in advance about key issues that will affect your research data.
- What types of data will be generated?
- How much data will be collected?
- What data do you need to retain long term?
- Consider creating a data inventory to understand and track your data.
- Choose appropriate file formats. File formats for long term access have these characteristics:
- Open, documented standard
- In common usage by research community
- Use standard character encoding (ASCII, UTF-8)
- Name your files well.
- Be consistent (always use same information and order of information)
- Use unique identifiers (e.g. acronym for project)
- Do not use spaces or special characters (\ / : * ? ” < > |)
- When using dates follow the Date and Time Formats (W3C-DTF) standard (YYYYMMDD[hh][mm][ss])
- To keep track of updated versions, use sequential numbering (v1, v2, etc.) rather than words, such as “Final.”
- Separate ongoing and completed work.
- Before you amass lots of folders and files, it may be useful to separate your original data from that you are currently working on.
- Differentiate between ongoing and completed work.
- Create a copy of your original data and put in a folder named something like “Original.”
- Make multiple back ups in multiple locations.
- Be selective.
- Decide whether/when it is appropriate to delete digital materials and data, based upon standards of your discipline and guidelines of your funding agency.
- Plan this with your colleagues.
- Describe your data.
- Create a data dictionary with a detailed description of your data set or data model.
- Use community based standards when possible; here is a short guide to metadata.
- Include the data collection methods, variable names, codes, algorithms, file formats and software versions, structure of the data files, sources, quality control or related issues, transformations and any issues regarding privacy or confidentiality and use/re-use.
Tools and Methods
The Research Data Services Team can recommend best practices for organizing and naming files, help you develop and implement a plan for managing data, and assist with developing a framework for data documentation.
Options for Storing Your Data
Rice offers researchers several options for storing data, including:
- Box: “enterprise cloud-based storage & collaboration service."
- Research Data Facility: learn more about options through the Center for Research Computing.
As you select an appropriate storage option, consider:
- Ability to share data with collaborators
Best Practices for Data Preservation
“Data often have a longer lifespan than the research project that creates them. Researchers may continue to work on data after funding has ceased, follow-up projects may analyse or add to the data, and data may be re-used by other researchers.” [UK Data Archive]
Preservation is a key part of the data life cycle model. Active management will help ensure the data remains accessible for the long term and support reuse for continuing or future research. Note the “3-2-1” rule: make three copies, store two on different types of media and one in a different location.
- Guidance on digital preservation (UK National Archives)
- National Digital Stewardship Alliance (Library of Congress) NDSA Levels of Digital Preservation
Rice University’s Digital Scholarship Archive (RDSA) provides support for many of these recommended practices, such as storage in diverse geographic locations, multiple redundant copies, and automatic backup and synchronization of files, all of which help reduce the risk of data loss and ensure long term data integrity. You can deposit small, publicly-accessible datasets to this archive. You can also deposit your papers, presentations, conference papers, reports, white papers and other scholarly works, provided that they can be made publicly accessible. Rice Digital Scholarship Archive provides a stable URL for citation purposes and manages scholarly information for the long-term.
Why share data?
- Meet funding agency requirements
- Support transparency and replication
- Increase visibility of research
Where can I deposit and share my data?
- Disciplinary data repositories. By sharing your data through an established disciplinary repository, you can associate it with related content, make it easier to discover, and use standards associated with your discipline.
- Rice Digital Scholarship Archive. You may share small to medium, final datasets through Rice’s digital repository, which provides a stable URL and preservation services. Contact email@example.com for more information.
How can others use my data (licensing and intellectual property)?
- See Rice’s Research Data Management Policy for information about intellectual property rights and research data.
- For more information on data licensing and open data licenses, see:
How can I get credit for my data?
- Proper citation of data sets ensures that your work is credited and facilitates linking publications with data.
- Alyssa Goodman, et al. “Ten Simple Rules for the Care and Feeding of Scientific Data.” PLoS Comput Biol 10, no. 4 (April 24, 2014). https://doi.org/10.1371/journal.pcbi.1003542