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At a broad level, data are items of recorded information considered collectively for reference or analysis.
Data can occur in a variety of formats that include, but are not limited to:
software and code
measurements from laboratory or field equipment (such as IR spectra or hygrothermograph charts)
images (such as photographs, films, scans, or autoradiograms)
Data can be defined in a variety of ways, depending on the discipline and the context. WHen it comes to making decisions about managing your research data, you may wish to consult the definitions used by your funder and by The Chicago School of Professional Psychology.
NIH defines data in its Data Sharing Policy and Implementation Guidance.
What is research data management?
Research data management (or RDM) is a term that describes the organization, storage, preservation, and sharing of data collected and used in a research project. It involves the everyday management of research data during the lifetime of a research project (for example, using consistent file naming conventions). It also involves decisions about how data will be preserved and shared after the project is completed (for example, depositing the data in a repository for long-term archiving and access).
There are a host of reasons why research data management is important:
Data, like journal articles and books, is a scholarly product.
Data (especially digital data) is fragile and easily lost.
There are growing research data requirements imposed by funders and publishers.
Research data management saves time and resources in the long run.
Good management helps to prevent errors and increases the quality of your analyses.
Well-managed and accessible data allows others to validate and replicate findings.
Research data management facilitates sharing of research data and, when shared, data can lead to valuable discoveries by others outside of the original research team
Data Management Planning
An important first step in managing your research data is planning. To get you started thinking about data management planning, here are some of the issues you need to consider:
Your institution's and funding agency's expectations and policies
Whether you collect new data or reuse existing data
The kind of data collected and its format
The quantity of data collected
Whether versions of the data need to be tracked
Storage of active data and backup policy and implementation
Storage and archiving options and requirements
Organizing and describing or labeling the data
Data access and sharing
Privacy, consent, intellectual property, and security issues
Roles and responsibilities for data management on your research team