Managing Research Data
Research data is material or information "on which an argument, theory, test or hypothesis, or another research output is based." 1
It can be in the form of images, textual, numerical, multimedia, code, software products, database, etc.
Research data can be collected through a variety of methods such as: observation, experimentation, simulation (model), survey, interview, derived/processed data, reference data, etc.
Research Data – Why manage it?
Carefully planning for data management can help you fulfill your objectives and your research sponsor's requirements, by increasing the accessibility, usability, and impact of your work.
Also, you never want to be either of these two!
Data Description & You
Wondering how to describe that lovely hard-won data so you (and maybe others) can find and use it in the future?
Check out some of the description schemas (aka 'metadata') listed here: Data Description Standards by the DCC
Tools and Resources
Funder-specific templates to help you create a data management plan.
Columbia's online research repository, Academic Commons, is a great place to store your final data sets.
Answers to common questions about data management best practices and policies at Columbia.
Information on the NSF's policies on data management and sharing.
A list of some of the repositories available to Columbia researchers.
A list of some of the training materials and products available on the Web.
Questions? Please contact us at CU-RDM@columbia.edu
Introduction to Data Management
1 Queensland University of Technology Management of research data policy