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Bethel Libraries

Data Management: Data Management

Learn about best practices for research data management and find links to external resources of help.

About this guide

The purpose of this guide is to provide information and links to resources about the topic of research data management, seeking to:

  • Define the key concepts surrounding data management
  • Link to resources on Data Management Plans associated with prominent grant programs
  • Identify discipline-specific services like data repositories and opportunities for data sharing

Input and further resources

Make a suggestion

Data Management is practiced differently among researchers in different disciplines, so if this guide fails to address your area of research, let me know via email.

Links for further reading on Data Management

Please consult Michael's bibliography in Google Docs or as a RefWorks folder. For a more comprehensive bibliography, please consult Charles W. Bailey's Research Data Curation Bibliography.

What is data management?

"Data management" is a term that covers an array of practies related to the storage, organization, description, and long-term preservation of "data," or research materials.  A researcher's "data" could be the observations, notes, simulations, facts, or other material that informs and underpins his or her scholarly work.  

Goals of Data Management

  • Ensure that researchers get credit for the  data that support their publications
  • Facilitate the easy understanding of one's research by putting results in context
  • Make data more high profile or discoverable for potential re-use by others

Basic Data Management

  • Collecting numeric data in a spreadsheet
  • Using a departmental drive to organize several files into a folder
  • Ensuring that collaborators have  appropriate access to shared files

More Robust Data Management Practices

  • Providing descriptions ("metadata") of various pieces of data
  • Tracking transformations or changes the data undergoes ("provenance") in the course of a project
  • Writing a "codebook" to explain one's variables or abbreviations used
  • Naming files in a way that is consistent and understandable to others
  • Compiling a "dataset" that encompassed all the materials used in a given project or publication
  • Archiving or posting data to a repository for others to discover

Why invest time in data management?

Several stakeholders benefit from good data management, starting with the data producer.

Benefits as a data producer:

  • Up-front investment in good data practices frees you to focus on research throughout a project 
  • Receive credit and citations for data that you make available
  • A demonstrated "citation benefit" associated with open data practices. (Piowar & Vision, 2013)

Benefit to Research Community

  • Well-documented data can be made available for re-use by others
  • The pace of discovery can be accelerated
  • Your work's visibility is raised, and you may discover new research partners.

Funding agencies, journals, and publishers

  • The second tab of this guide, Data Management Plans, explores this in more depth.

 

Subject Guide

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Michael Mitchell
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