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Data Management: Home

Help making Data Management Plan


Research Data Management (RDM) is a broad concept that includes processes undertaken to create organized, documented, accessible, and reusable quality research data. (American Library Association)

Why is Proper Data Management Important?

Core Parts of Data Management

  • ALWAYS keep original data
  • Documentation - description of work and data through good metadata
  • Secure storage - prevent loss of data through regular backup
  • File organization so that others can understand
  • Reproducibility of data for reliable verification
  • Long-term preservation in data repository
  • Ability to build upon existing information


Articles on the Importance of Good Data Management

McNutt, M., Lehnert, K., Hanson, B., Nosek, B. A., Ellison, A. M., & King, J. L. (2016). Liberating field science samples and data. Science, 351(6277), 1024. doi:10.1126/science.aad7048

Dickersin, K., & Mayo-Wilson, E. (2018). Standards for design and measurement would make clinical research reproducible and usable. Proc Natl Acad Sci U S A, 115(11), 2590-2594. doi:10.1073/pnas.1708273114

Pasquier, T., Lau, M. K., Trisovic, A., Boose, E. R., Couturier, B., Crosas, M., . . . Seltzer, M. (2017). If these data could talk. Scientific data, 4, 170114-170114. doi:10.1038/sdata.2017.114

Data Citation Synthesis Group: Joint Declaration of Data Citation Principles. Martone M. (ed.) San Diego CA: FORCE11; 2014

Data Science

The newly emerging discipline of Data Science, the study of extracting value from data, has emerged from both the development of Internet access and the advancing computer processing power. It unifies statistics, data analysis, machine learning, data mining, and related methodologies in order to analyze and understand how data informs new knowledge. (adapted from Wikipedia, Cassie Kozyrkov, Simon Elias Bibri)


author: @quaesita

Research Data Life Cycle

In a good Plan, components describe the entire Life Cycle   image from: DataONE

                   image from:

image from:


The cycle of data management can be shown linearly or circularly. As it is purely an artificial construct, it can have a varying number of steps, as few as five to up to sixteen. The importance of the graphics is as a reminder that it is a continuous process. Even if you do not re-use your data, data should be available for others who may need it for future research.

Lessons on Data Management


Primer on Data Management from DataONE - good beginner resource

Data Management Training Clearinghouse - repository of educational resources on research data management

Chapter on Data Management from Information Systems and Computer Applications online course

DataONE Education Modules - 10 Lessons

Subject/Domain specific Lesson Plans on Data Management  



Wesleyan Resources

Quantitative Analysis Center - through its programs it facilitates the integration of quantitative teaching and research activities.

WesScholar institutional repository for Wesleyan University, and was established with the goal of capturing, permanently preserving, and distributing materials of institutional and historical value to the University. It is a suite of services and platforms that enable Wesleyan students, faculty, and staff to store and share their research data and other scholarly and creative outputs in digital format.

Wesleyan Office of Corporate, Foundation & Government Grants - Data Management Plan page has links to NIH, NSF, NASA, NEH, and NEA sites for grant proposal help.

ITS Data Storage - Wesleyan offers a variety of data storage options. Here are found the options offered to the Wesleyan community.

Maps and GIS - links to GIS software and Wesleyan courses to help with developing spatial data analysis tools.