The term "research data" broadly encompasses any information collected or created that you use to validate research findings. All research, at its core, uses data.
Data can take many forms. Traditionally, people think of data as things like measurements, observations, survey responses, and interview recordings. Data can take other forms, too - photographs and art, archival records and accompanying research notes, and even pieces of literature can be research data depending on your context. Data management is just as important for less conventional forms of data as it is for more traditional forms.
RDM refers to the organization, collection, storage, documentation, preservation, and dissemination of research data. A good data management plan (DMP) will cover all sections of the research data lifecycle:
Source: Jisc. (n.d.). Research Data Lifecycle. Research Data Management Toolkit. Retrieved January 29, 2021, from https://rdmtoolkit.jisc.ac.uk/research-data-lifecycle/
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Researchers are already managing their data, whether or not they think of it formally as "RDM". At its base, RDM is documenting, formalizing, and making explicit all the steps taken to manage data as part of a research project. By formalizing these steps within the research process, researchers can ensure consistency across all members of the research team, identify any gaps in current practices, and ensure that data remains useable over the course of their research careers.
Why should you think of data management as an integral part of your research?
Proper data management will help you:
The overall benefits of RDM:
Source: Portage Network. (2019, January). Research Data Management (RDM) 101. https://portagenetwork.ca/research-data-management-rdm-101/
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