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Research Data Management

Learn how to manage your research data throughout the data lifecycle: including data management plans, data organization, file formats, as well as data sharing/re-use.

Research Data Management 101

Definitions of research data:

“Data are outputs of research and inputs to scholarly publications and inputs to subsequent sharing and learning” (Borgman, 2007)  

“…Recorded factual material commonly accepted in the research community as necessary to validate research findings.” (OMB Circular A-110 36.d.2.i.)

Research data comes in many different forms and definitions of research data can vary based on the research community.

Examples of data include:

  • Images and Video
  • Mapping/GIS Data/Geodatabases
  • Numerical measurements
  • Survey responses
  • Focus group or interview transcripts
  • Economic indicators
  • Polls
  • Computer modeling
  • Simulations
  • Observations and/or field studies
  • Code or Software
  • DNA or Blood Samples
  • Physical Collections

Federal funding agencies also define data in different ways. Below are just a few examples of the varying definitions.

NIH defines data as “recorded factual material commonly accepted in the scientific community as of sufficient quality to validate and replicate research findings.” Source: NIH Policy for Data Management and Sharing

NSF takes a flexible approach to defining research data, acknowledging that what constitutes data may vary by directorate, division, and program. Researchers should consult their program officers for specific guidance on data requirements for their field. Source: NSF Public Access Policy FAQ (NSF 18-041)

NEH defines data as “materials generated or collected during the course of conducting research.” Source: Data Management Plans for NEH Office of Digital Humanities Proposals and Awards.

Data management is the day-to-day management of data to support the collection, organization, sharing, preservation and reuse of project data. 

 

The Research Data Management Lifecycle

 

Image source: The Dataverse Project presentation at IDCC 2016 by Eleni Castro, Research Coordinator at IQSS, Harvard University. Research Life Cycle Workflow diagram adapted from UCI Libraries.

 

As data moves through your research lifecycle, consider:

  • How will you collect and organize your data?
  • How will you document your research data and methods?
  • What privacy and access requirements apply?
  • Where will you archive and share your data?

 

Funder Requirements

The Uniform Guidance (2 CFR Part 200) outlines the administrative requirements for federal grants awarded to institutions of higher education, hospitals, and other non-profit organizations. This guidance includes provisions under the Freedom of Information Act requiring grantees to provide timely access to federally funded research data. See Public Access Policies for more detailed information about agency requirements.

Managing research data throughout its lifecycle is important for three main reasons:

  • Data Accessibility: Your data and records must be accurate and complete, making them understandable and usable by both your research team and anyone outside the project. This supports research collaboration, validation, and future applications.
  • Funding Requirements: Major funding bodies, including federal agencies, require researchers to have data management plans and make their data publicly accessible. Meeting these requirements is essential for securing and maintaining research funding.
  • Publication Standards: Leading academic journals and publishers now require researchers to make their data publicly available, supporting research transparency and reproducibility in scholarly communication.

Quick Tip: Managing your data properly from the beginning of your research project will save valuable time and resources during analysis, sharing, and publication phases.

Also, you'll avoid scenarios like this one: