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This guide has resources to help you effectively manage your data and to understand requirements from grant funding agencies for data management plans.
What is a Data Management Plan?
In order to promote open access to research data, many U.S. funding agencies such as the National Science Foundation (NSF) and the National Institutes of Health (NIH) require researchers to supply detailed plans called Data Management Plans for managing their research data. Data management is the systematic organization and planning for data throughout the research cycle. and a Data Management Plan (DMP) describes the data and how it will be made accessible throughout its lifetime.
Benefits of a Data Management Plan
- Maintain compliance with funding agencies.
- Ensure that your data will be accessible and usable in the future.
- Create and maintain a permanent archive of the data that supports your research findings.
- Provide enhanced access to your publications.
What is in a data management plan?
- Description of types of data, samples, and physical collections you will be creating.
- Standards you will use for your data and the metadata that will describe it.
- Policies for sharing, accessing, and re-using your data.
- Methods for archiving and preserving your data.
While there are many ways of looking at the data life cycle, this particular image emphasizes the repurposing and re-use of data, which is a driving force behind the success of data-intensive science and the reason why data management has been deemed so important.
Source: Humphrey, Charles. (2006). “e-Science and the life cycle of research.” Retrieved 23 January 2011 from http://datalib.library.ualberta.ca/~humphrey/lifecycle-science060308.doc
Available through Interlibrary Loan
The Fourth Paradigm: Data-Intensive Scientific Discovery by
Publication Date: October 16, 2009
In The Fourth Paradigm: Data-Intensive Scientific Discovery, the collection of essays expands on the vision of pioneering computer scientist Jim Gray for a new, fourth paradigm of discovery based on data-intensive science and offers insights into how it can be fully realized.