ACRL/EBSS Psychology Committee

Data management involves tracking, organizing, and documenting research data for the purposes of preservation and sharing. This page has two parts. The first part lists resources related to the data life cycle including data management plans, data collection and formats, data analysis, and sharing data/using shared data. The second part lists resources about how librarians can support patrons with data management needs. The goal is to provide general and psychology-specific resources so that librarians can feel more competent providing services in this area.

Principles of data management are directly tied to open research and reproducibility. See these linked pages for additional information.

Before a research project begins, researchers should prepare a data management plan and data documentation guidelines. Everyone on a research team should be trained to carry out the plan.

Data Management Plans

Researchers often write data management plans as part of grant proposals. Data management plans may include the following:

• Descriptions of the types of data that will be produced
• Information about how the data will be cleaned, processed, and analyzed
• Plans for formatting data and metadata
• Plans for naming conventions and version control
• Information about how the data will be stored
• Plans for data backup
• Plans for data security and protection
• Plans for future data access, data sharing, and data re-use

The following links highlight resources or tools that may help researchers develop data management plans. 

• DMP Tool
  • The DMP Tool helps researchers create a data management plan that meets institutional and/or funder requirements. If your university is not a member, you can still create a free account. (See https://dmptool.org/about_us.)
• American Psychological Association: Data Acquisition, Management, Sharing, and Ownership
• American Psychologist, 73(2). (2018). Data Sharing in Psychology.
• National Institutes of Health (NIH) Data Management & Sharing Activities
  • This includes a recent draft of an NIH policy for data management and sharing.
• National Science Foundation (NSF) on Dissemination and Sharing of Research Results
• Data Management for NSF Social, Behavioral, and Economic Sciences Directorate
• Digital Curation Center. (2013). Checklist for a Data Management Plan. 
• University of Michigan's Inter-University Consortium for Political and Social Research (ICPSR). Guidelines for Effective Data Management Plans. 

Data Documentation

Data documentation is the “memory” of a research project. It involves writing up how and where data was collected, how it was processed (e.g., cleaned) and analyzed, and how the data is organized in saved files. Another element of data documentation is consistent file naming, organization, and version control practices. While data documentation is an ongoing process during a research project, it is important to prepare for this early on. Good data documentation allows researchers to use, reuse, and share their data.

Data documentation should be implemented at the project level and the file level. It may include materials such as:

• Readme Files: text files that provide basic information about project or file. For example, at the project level, a read me file may describe the naming conventions and version control practices used for a project.
• Data dictionary or codebook: detailed description of each element or variable in a dataset 
• Lab notebook: timeline of the study and how decisions were made

 The following links highlight resources or tools that may help researchers develop their data documentation. 

• Data Document Initiative
• Social Science Research Council. Managing Qualitative Social Science Data 
• Managing Your Research Data and Documentation (book)
• Data Nudge: Data Documentation 
• Cornell University. Guide to Writing "Readme" Style Metadata
• Metadata Standards Related to Specific Disciplines
• NYU Libraries. File Organization
• James Cook University. Tips on Organizing Data and Version Control 

Data Analysis

See the Statistics page on this LibGuide for a list of programs used for quantitative and qualitative data analyses.

Data Sharing

Data sharing and data reuse is becoming an increasingly important part of the research process. The following links provide guidelines that may help researchers to develop a research data practice from the perspective of usability and reproducibility.

• What is data reuse? https://nnlm.gov/data/thesaurus/data-reuse
• FAIR principles that help optimize future reusability of your data: https://www.go-fair.org/fair-principles/
• Data sharing policies:
  • Office of Science & Technology. (2013). Increasing access to the results of federally funded scientific research.
  • National Institutes of Health. (2003 and upcoming 2023 policies available). Data management and sharing policy.
  • National Science Foundation. Dissemination and sharing of research results.
  • National Science Foundation. Data archiving policy.
  • Scholarly Publishing and Academic Resources Coalition (SPARC). Compare data sharing requirements from various federal agencies.
• How to share data:
  • Alter, G., & Gonzalez, R. (2018). Responsible practices for data sharing. American Psychologist, 73(2), 146-156.https://doi.org/10.1037/amp0000258 .
  • Gilmore, R. O., Kennedy, J. L., & Adolph, K. E. (2018). Practical solutions for sharing data and materials from psychological research. Advances in Methods and Practices in Psychological Science, 1(1), 121-130. https://doi.org/10.1177/2515245917746500 
• When to share data:
  • Recommendation from Research Data Services at University of Illinois at Urbana-Champaign: Publish data after final acceptance of a peer-reviewed paper. Include link to data in copyedited version of the paper.
• Where to share data:
  • University repository
  • Discipline-specific repository
    • Inter-University Consortium for Political and Social Research (ICPSR)
    • American Psychological Association. Links to data sets and repositories.
  • General repository
    • See a sample list here.

What Librarians Can Do To Support Data Management

• Participate in data management training in your library (e.g., ACRL Roadshow: Building Your Research Data Management Toolkit: Integrating RDM into Your Liaison Work).
• Develop an understanding of research services available across your campus.
• Build partnerships and collaboration with other groups on campus who support data management so you can make referrals and IT (for data storage infrastructure, computing resources), grant application, research data service, Library, campus open sciences activities. 
• Email faculty with tips/tools/resources about data management.
• Hold a workshop or webinar for new graduate students and/or faculty about best practices in data management (see Educopia ETD+ Toolkit). 
• Provide support for faculty writing data management plans for grant applications.