Data Management

This guides introduces the concept of data management, data management plans, funding agency requirements for data management, and best practices for data management.

What is Data?

There is no single definition for the term "research data." The definition can vary by discipline or by regulatory agency. Generally, however, the term refers to data in any format or medium that relate to or support research, scholarship, or artistic activity.

Data can be classified as:

  • Raw or primary data: Information recorded as notes, images, video footage, paper surveys, computer files, etc., pertaining to a specific research project
  • Processed data: Analyses, descriptions, and conclusions prepared as reports or papers
  • Published data: Information distributed to people beyond those involved in data acquisition and administration*

What is Data? (University of Minnesota Libraries): Types of data and Glossary of Data Related Terms

*Source: University of Minnesota Qualitative Research Data Management Workshop

Why Manage Data?

Good data management benefits your own project as well as the various discipline-specific communities you are a part of. As a result, many funding agencies now require data management plans.

By managing your data you will:

  • Meet grant requirements, or have a more competitive grant application.
  • Better guarantee your research data and records are accurate, complete, authentic and reliable.
  • Enhance data security and reduce risk of data loss.
  • More easily preserve your research data for the long-term.
  • Increase the impact and visibility of your research having a more readily findable research data citation.

*Source: University of Notre Dame Libraries Data Management

What is a data management plan (DMP)?

A data management plan will help you to properly manage your data for your own use, meet funder requirements, and enable data sharing in the future. A DMP describes the structure and nature of the data as well as the activities and technical requirements to gather, merge, transfer, organize, document, analyze, and preserve research data.

Several funding agencies including the National Science Foundation (NSF) have specific requirements for a data management plan. However, there are fundamental data management issues that apply to most disciplines, formats, and projects. While data management plans will be tailored to the specific problem and subject domain, they all have common elements.

Elements of a Data Management Plan

There are five main elements of a data management plan:

  1. Types of data to be produced in the course of the project.
  2. Metadata standards that will be applied for documentation and format
  3. Any protection or security measures taken to protect participant confidentiality or intellectual property.
  4. Policies for access, sharing, and re-use.
  5. Plans for archiving and preservation (if applicable).

Writing a Plan

  1. Review your funding agency requirements.
  2. Familiarize yourself with the elements of a data management plan by reading the information on this website.
  3. Use the DMPTool online template to draft your data management plan (select UTSA from institutional dropdown list and login with your abc123 and passphrase).

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Material adapted from University of Texas Libraries, MIT Libraries, California Digital Library/UC3, and University of Oregon Libraries, used under a Creative Commons Attribution-Share Alike license: http://creativecommons.org/licenses/by-sa/3.0/