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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: From 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 Office of Management and Budget (OMB) Circular A-110 describes the administrative requirements for grants and proposals awarded to institutions of higher education, hospitals and other non-profit organizations. In 1999, Circular A-110 was revised to include a data sharing component under the provision of the Freedom of Information Act which requires grantees to provide access to research data funded by the federal government in a timely manner. See Public Access Policies for more detailed information about agency requirements.

Managing your data throughout the data lifecycle is important for three main reasons:

  1. It ensures research data and records are accurate and complete making data understandable to people outside the project.
  2. Data management and public access to data is a requirement of federal grant and funding bodies.
  3. Journals and Publishers are also now requiring data to be publicly available.

Managing your data from the beginning of the research project will save you time and effort later on.

Also, you won't have to have this conversation: