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)
Core Parts of 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 https://doi.org/10.25490/a97f-egyk
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)
In a good Plan, components describe the entire Life Cycle image from: DataONE https://www.dataone.org/best-practices
image from: USGS https://doi.org/10.3133/ofr20131265
image from: https://dx.doi.org/10.3163%2F1536-5050.103.3.011
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.
Primer on Data Management from DataONE - good beginner resource
Data Management Training Clearinghouse - repository of educational resources on research data management
DataONE Education Modules - 10 Lessons
Subject/Domain specific Lesson Plans on Data Management
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.