Skip to main content
It looks like you're using Internet Explorer 11 or older. This website works best with modern browsers such as the latest versions of Chrome, Firefox, Safari, and Edge. If you continue with this browser, you may see unexpected results.

Data Management & Sharing

Surveying Existing & Potential Data

Surveying Existing & Potential Data:

The first step in writing a strong DMP is to inventory all the data—data you’re using, producing, and sharing.

  • Are you building upon or reusing any existing data? If so, how are you accessing that data? Are there any restrictions due to copyright or privacy concerns? What documentation of that data is available? Which file formats are available?
  • What will the output of your research be? You may not need to account for every kind of data in your final DMP, but it’s a good idea to start as broadly as possible from project planning through implementation and publication. This can include quantitative/qualitative datasets, software code, reports, articles, etc.

Organization & Management

Organization & Management:

Funders want to see that you have a plan for responsibly managing data as it is being created and accessed by project team members. This is particularly important for research that involves human subjects or personally identifiable information. A good rule of thumb is to keep three copies of your data in geographically dispersed locations. At Ball State, some units maintain servers for such active data management, though a tool like Box may also be suitable for some research. With funding from the NIH, DARPA, and others, the Center for Open Science has also developed a popular, free collaborative research environment, the Open Science Framework.

  • Where will your data be stored day-to-day prior to its submission in a public repository?
  • Who will be responsible for managing data? Does the size of the team require the ability for multiple people to collaborate in data management? Are these people based at one or multiple institutions?
  • Are there any gaps in expertise or training for individuals responsible for data management?
  • Are there any privacy or confidentiality concerns regarding the data? How will your team protect the security of this data? How will it be anonymized/de-identified prior to submission in a public repository?

Formats, Standards, & Documentation

Formats, Standards, & Documentation:

Different funders will look for different levels of detail in a data management plan, but it’s important to understand the details of how you will describe your data so that others may access, understand, and reuse it. The answers to the questions below may vary widely depending on the nature of your data, your plans for data sharing, disciplinary best practices, publisher/funder requirements, etc.

  • What kind of file naming convention will be used, either manually or automatically generated?
  • What file formats will you be generating over the course of the project?
  • What kinds of metadata or other documentation will need to be associated with my data in order for other researchers to understand and/or replicate your research? Are there any specific standards or templates common to research in your field? Note that this may be guided in part by your choice of public repository for long-term archiving.

Long-Term Preservation & Data Sharing

Long-Term Preservation & Data Sharing:

Funders are increasingly concerned with how data is saved and disseminated over time, in part to ensure long-term impact of grant dollars. Whether or not your funder requires it, it is a good idea to deposit your digital files in a system managed by an organization—Ball State, a scholarly society, a funder, etc.—rather than trying to care for data on your own in perpetuity after your research has concluded. Ball State University Libraries can offer support in this area, though depending on your discipline/needs there are many other options such as Dryad, ICPSR, Figshare, Open Context, Humanities Commons, etc. The Registry of Research Data Repositories offers a large directory of options. Many funders and repositories require that you allow public access to the data generated by your project, though there is typically an option to embargo access for a limited period of time.

  • Are there any specific data repositories recommended by your funder or publisher? If so, what information do these repositories offer about preparing and submitting data and documentation? Do they offer any boilerplate text to insert into data management plans? What about a timeline for submission?
  • If your funder/publisher does not offer guidance on existing repositories, have you considered discipline-specific options listed in the Registry of Research Data Repositories (re3data.org) or elsewhere?
  • Are you interested in submitting data for long-term preservation in Ball State’s institutional repository, Cardinal Scholar (one way to meet funder public access requirements)? Have you requested a letter of commitment from the University Libraries?
  • What is the copyright status of the data? To encourage wide access and reuse, will my collaborators and I consider open licenses such as those recommended by Creative Commons (https://creativecommons.org/licenses/), Open Data Commons (https://opendatacommons.org/licenses/), or Github (https://choosealicense.com/)?