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Research Data Management at UF: DMP Step-by-Step

This is a guide on resources available at the University of Florida and beyond on research data management. It includes information about tools for data management planning, data and file sharing, metadata and data standards, and data storage.

DMP Step-by-Step

A data management plan (DMP) will help you manage your data, meet funder requirements, and help others use your data if shared.

The DMPTool is a web-based tool that helps you construct data management plans using templates that address specific funder requirements. From within this tool, you can save your plans and access funder templates. 

Or use the steps, questions on this page and any other tabs in this Library Guide along with the specific data management requirements from the relevant  funding agency to write your data management plan. 

Step One: Describe Research Project and Identify Your Data

Make a data inventory:

  • What the data describes and what type it is.
  • How the data is collected, generated, etc.
  • How much of it there will be (i.e. volume estimates).
  • How it will be saved (file formats)
  • What software will be used (file types).

Creating a complete inventory is an important step even though you may only be able to provide a summary in a Federal DMP due to length ( most less than two pages some only one paragraph).

Step One Questions

What is your research purpose?

What type of data is it?

Examples:Spatial, temporal, observational, experimental, survey responses, etc.

How will the data be collected?

Examples:recorded interviews, surveys, models, sensors, image analysis, DNA sequencing, word counts, camera traps, etc.

How will the data be saved and opened?

(i.e. file types and file formats)

Example file types:text, spreadsheets, databases, digital images, sound files, digital video, computer code, algorithms, etc.
Example file formats:PDF (portable document format), .csv (comma separated values), .docx (Microsoft Word), .txt (plain text), etc.

How much data will be generated for this research?

How long will the data be collected and how often will it change?

Are you using data that someone else produced? If so, where is it from?>

Who is responsible for managing the data? Who will ensure that the data management plan is carried out?

Step Two: Documentation, Organization and Storage

Various methods can be used to keep data organized. Some of these are simple things we do every day such as saving a new version of a draft using "Save As..." instead of "Save" while others require commitment and planning to implement. File management systems are a trade-off: while they take time to set up, they will save you time later by reducing errors.

While data organization and quality control methods are very important, they should not take up a significant portion of your federal DMP. As with all sections of the Federal DMP you will need to summarize.

Step Two Questions

Will you be using version control?

Version control, also known as "file versioning", is when you save an updated file as a new file instead of overwriting the old file. This lets you "revert" back to an earlier version if needed. While this process can be done by hand it is much easier (and safer) to have a machine assist you. ( e.g. Git)

What documentation will you be creating in order to make the data understandable by other researchers?

Are you using metadata that is standard to your field? How will the metadata be managed and stored?

What file formats will be used? Do these formats conform to an open standard and/or are they proprietary?

Are you using a file format that is standard to your field? If not, how will you document the alternative you are using?

What directory and file naming convention will be used?

What are your local storage and backup procedures? Will this data require secure storage?

Step Three: Access, Sharing, and Re-Use

It is important to understand that data sharing is an expected part of every data management plan requirement.

There are a few cases when data sharing may not be appropriate. In general data which may compromise research subjects or be tied to intellectual property such as patents, are acceptable exceptions.

Data Sharing

Data sharing is expected by both federal funders and an increasing number of journals. Requirements vary widely but there are some universal best practices to use as a starting point.

Data Sharing Best Practices

  1. Share as much data as possible. 
  2. Provide comprehensive documentation and metadata.
  3. Share data in simple, nonproprietary formats.
  4. Share data in its rawest state.
  5. Share data in a controlled and monitored environment (i.e. a data repository). 

Step Three Questions

  • Who has the right to manage this data? Is it the responsibility of the PI, student, lab, UF or funding agency?
  • What data will be shared, when, and how?
  • Does sharing the data raise privacy, ethical, or confidentiality concerns?  Do you have a plan to protect or anonymize data, if needed?
  • Who holds intellectual property rights for the data and other information created by the project? Will any copyrighted or licensed material be used? Do you have permission to use/disseminate this material?
  • Are there any patent- or technology-licensing-related restrictions on data sharing associated with this grant?  UF Innovate can provide this information.
  • Will this research be published in a journal that requires the underlying data to accompany articles?
  • Will there be any embargoes on the data?
  • Will you permit re-use, redistribution, or the creation of new tools, services, data sets, or products (derivatives)? Will commercial use be allowed?

Step Four: Archive

Data repositories

Data repositories are the best choice for research data sharing, distribution, and preservation.

The Data sharing tab on this guide provides a basic overview of the topic, links to some of the largest and well known data repositories, and tools to help you select a repository.

Data Papers or Data in Brief

Data papers are a new type of scholarly publication that provide a citable manuscript and data documentation in one package. The tab on data sharing provides an overview of topic and a selective list of journals that publish these manuscripts.

Step Four Questions

  • How will you be archiving the data? Will you be storing it in an archive or repository for long-term access? If not, how will you preserve access to the data?
  • Is a discipline-specific repository available?
  • How will you prepare data for preservation or data sharing? Will the data need to be anonymized or converted to more stable file formats?
  • Are software or tools needed to use the data? Will these be archived?
  • How long should the data be retained? 3-5 years, 10 years, or forever?


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