Research data management involves the active organization and maintenance of data throughout the research process, and suitable archiving of the data at the project’s completion. It is an on-going activity throughout the data lifecycle.
Plan: Planning can include reviewing existing data sources, addressing informed consent, considering costs, and preparing a plan.
Create: Researchers produce data (experiment, observation, measurement, simulation) and/or collect and organize third-party data and materials. Metadata and related materials are captured and created.
Process: Data is converted to digital format (transcribed, converted, digitized, curated) according to quality assurance standards. Data is checked, validated, cleaned, recoded, versioned, and as needed, anonymized. All these processes are documented, and the data is described using the appropriate discovery metadata standard.
Analyze: Data is interpreted and analyzed to produce research findings, publications, and intellectual outputs. Data sources are cited.
Preserve: Data is saved to formats that conform to curation best practices, user documents and discovery metadata are created, a digital identifier (i.e. DOI) is added and data is linked to any published products, consideration is given to security and IP.
Share: Access rights are confirmed (ethics and intellectual property considerations). The data, along with user documentation and metadata, are made accessible, e.g. on a public domain server, or in a controlled repository.
Reuse: Potentially useful data, user documentation and metadata are located and obtained. Secondary analysis is conducted after any necessary data transformations are complete. Transformation are documented and data sources are cited.
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