Data Management Plans

For any proposal, the data management plan is a formal document that outlines what you will do with your data during and after you complete your research. Most researchers have at least an informal data management plan, but often don't consider the full scope of issues. Some may plan to figure it out later. It is not only wise to identify your data management plan and processes at the outset of your project, it is now essential. Doing so will also make it easier to track your progress throughout the project and beyond; the data management plan can be directly linked to your project's evaluation plan. For the purposes of the NSF, your short data plan proposal supplement should address the following issues:

  1. the types of data, samples, physical collections, software, curriculum materials, and other materials to be produced in the course of the project;
  2. the standards to be used for data and metadata format and content (where existing standards are absent or deemed inadequate, this should be documented along with any proposed solutions or remedies);
  3. policies for access and sharing including provisions for appropriate protection of privacy, confidentiality, security, intellectual property, or other rights or requirements, including the right to embargo data for a specified time period to allow first publication and thorough use of the data;
  4. policies and provisions for fair re-use, re-distribution, and the production of derivatives; and
  5. plans for archiving data, samples, and other research products, and for preservation of access to them.

Note: There may be some projects for which a Data Management Plan is not meaningful. Even in those situations, an NSF proposal must include an explanation of why the Data Management Plan is unnecessary.

Planning for Data Management as Part of your Research Proposal

Preparation is key to developing your plan, and should be part of the development of your project from the outset. Consider these issues, to help you be prepared to write your Data Management Plan:

  1. Review any specific guidelines from the funding agency and the funding opportunity announcement that would apply to your research project.
  2. Determine what software tools you will use.
  3. Identify data storage resources for all stages of your project. Consider:
    1. Where will your study's raw data be stored? How will it be backed up? How will it be preserved intact?
    2. Where will your analyzed (derived) data be stored? How will it be backed up?
    3. Where will your derived data be stored for eventual access and possible reuse?
  4. Consider any subject identification / privacy issues that might affect your proposal.
    1. Do you need to keep the data embargoed from public or peer access due to licensing, privacy, time for exclusive use, or other restrictions?
    2. Will your data require de-identification before it can be archived for citation and possible re-use? Consult Rutgers IRB office if you have questions.
    3. Is it advisable to include details about possible re-use and data sharing in your subject consent forms?
  5. It is important to identify research group, departmental, disciplinary or institutional data storage resources that are available to you at the various stages of your research project. There should always be three copies (live plus two backups) of all institutional data, including research data.

Data Archiving

Many research communities have established data archives. An excellent example at Rutgers is the Protein Data Bank . If your research community has such an archive, it should be an important component of your data archiving plans. Some journals allow or require data to be archived with publications. NIH's PubMed Central allows uploading of supplemental material such as data to accompany publications.

Rutgers also provides two mechanisms for permanently archiving your data that can be used in addition to external repositories or in cases where there is no community archive. These Rutgers resources allow all the data of a researcher or research group to be collected in one place.

  • Rutgers University Libraries Community Repository (RUCORE) offers a repository of digital research and educational materials created and used by the University community and its strategic collaborators. The goal of the Rutgers University Community Repository is to advance research and learning at Rutgers, to foster interdisciplinary collaboration, and to contribute to the development of new knowledge through the archiving, preservation, and presentation of digital resources. Original research products including data and publications will be permanently preserved and made accessible with tools developed to facilitate and encourage their continued use. The RUCORE system is being enhanced to improve the ease of archiving and searching for research generated data sets. Importantly, RUCORE staff can assist researchers with the creation of appropriate metadata. The RUCORE Staff can be contacted at
  • This Rutgers University Research Data Repository has been developed to be a streamlined resource to allow researchers to upload their archival research data sets along with textual descriptive information such as the software used, file attributes and information about data variables. This service is available now for data archival. The basic service it provides is a way to upload a file of any type (e.g., a compressed archive) and receive a permanent URL that may be included in a publication or given to others such as The data stored is here is centrally maintained and backed up.

The relationship between these two resources is that the Rutgers Research Data Repository is a quick simple way to archive any file. RUCORE is a more sophisticated system than Rutgers University Research Data Repository with metadata, semantic interoperability and access control, but does not yet does not yet support direct deposit or upload from a faculty computer or server.

Note: The best way to convince reviewers that you will archive new data is to have already archived existing data. Consider depositing your data in an archive prior to proposal submission and using that as an example in your plan.

Additional Resources