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Why Data Management?
If you are generating and/or using data in your research, a well-thought out approach to data management will save you time and frustration, maximize the impact of your work, and is an important component of the responsible conduct of scholarship. A data management plan can help you:
- Conduct research efficiently by analysing your data practices
- Simplify the use and reuse of your data through proper documentation and application of standards
- Increase your research visibility by publishing your datasets and documentation in repositories
- Meet funding agency, legal and ethical requirements for dissemination and documentation of your research
- Preserve and provide access to your data in the long term, allowing future scholars to build on your work
The Data Management Presentation (Powerpoint) summarizes the important factors in data management.
FALL workshop series on Data Management topics on October 9, 23, 30. Click here for details.
What is Data?
Research data can be any systematic collection of information that is used by the researcher for their analysis. Typical examples of data include sensor readings, experimental result recordings, survey results, or simulation output. Numeric data in various formats abounds, but research data can also include video, sound, or text data, if it is used for systematic analysis. For example, while a feature film would usually not be considered research data, a corpus of video interviews that had been tagged to identify gesture and facial expression would be considered research data.
Research data must be appropriately structured and documented in order for it to be used effectively for analysis. Any unique programs or models needed to analyse the data stream should be preserved as well.
Data at Rutgers
In addition, there are many Centers at Rutgers with major data collections, such as:
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