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This guide provides information on processes, procedures and policies with Regards to Research Data Management (RDM) and lists some tools and resources that support RDM.
“Data”, or “Research data” are regarded as the primary unit of any research enquiry. Data are collected to identify patterns in the field that support a particular theory or hypothesis. Data can be collected using a variety of methods and can take a wide variety of formats. Quantitative data may be numerical such as temperature taking over a specified period of time. Quantitative data can be a series of field notes, anecdotes or photographs concerning particular events or experiments.
While data may have been collected to address a particular research question, it may also answer different questions or prompt the new questions for further research, either in the same field or a completely different one.
While research projects have their own lifespan, research data can have a life beyond that of the project it was collected for. Managing your research data during collection, storage, analysis, publication and making provision for access to it after the conclusion of your project is crucial to the scientific community.
NRF Statement on Open Access (2015)
Sharing data ensures that other researchers can access and use your data for further study. The FAIR data principles address the sharing of data by providing the following guidelines:
Findable: The research data record need to be discoverable by other researchers. Applying the appropriate description using general or subject specific metadata allows researchers to discover your data.
Accessible: Your data needs to be stored for the long term in an recognised storage facility such as a data repository. The data needs to be freely accessible and downloadable and useable.
Interoperable: Data and metadata needs to be written in a format that is accessible and can be interpreted by researchers and integrated with other data for analysis and processing.
Re-useable: The goal is the optimum re-use of data. Data needs to be fully described in as much detail as possible including its provenance and using community or subject specific standards.
These principles apply to three aspects, the data (digital object), the metadata (a description of that object) and the infrastructure where the data is stored and from which it is shared.
Data sharing has become a mandatory requirement by institutions, funders and publishers.
Benefits of data sharing with research colleagues and communities include:
Zenodo – UWC Community
Data Management Planning
Institutions and funders increasingly require a detailed description of how funded research data is going to be managed.
Data Management Plans include (but not limited to) the following:
Data description: what will be collected, how and for whom?
Access and sharing: How will the data be stored and accessed? Are there any restrictions?
Metadata: which metadata standards will be used?
Intellectual Property Rights: who owns the data? Are the any copyright or funder restrictions?
Ethics and Privacy: How will consent be obtained? How will the subjects be protected?
Format: Which format will be used?
Archiving and preservation: What are the long-term storage plans? What are the backup plans?
Retention period: How long will the data be stored?
Resources and Responsibilities: Which resources are required and who is responsible for what?
Data Management Plans are unique to each project and can be tailored to include more or less information.