Research Data Management (RDM) is the process of planning, organizing, storing, protecting, and sharing all research data generated during the research process. This includes understanding the data lifecycle, selecting appropriate storage solutions, implementing data protection measures, ensuring data accessibility and reproducibility, and ultimately preserving or disposing of the data. Good research data management improves research efficiency, increases data integrity and reuse value, and meets the requirements of research funders and journal providers.
Research data is generated by the researcher in the course of research through various means, such as observing, collecting, or creating. The primary purpose of this data is to support or validate the researcher's observations, findings, or results.
Discipline-specific: Because each discipline has different research methods and topics, each discipline generates and analyzes its own unique research data, and each discipline has its own unique way of obtaining and describing data.
Multiple formats: Research data is presented in a variety of formats, including numbers, raw data, text, documents, images, sounds, pictures, models, analytical codes, algorithms, or databases. For example, a researcher in the social sciences may generate a large amount of statistical data; a researcher in the humanities may generate a large amount of textual records; a researcher conducting chemical experiments may generate a large amount of experimental data, and so on.
Uncertainty of definition: Because the definition of research data varies from discipline to discipline and school to school, there are different views on whether, for example, a lab notebook is research data. Therefore, the scope of research data must be defined according to the organization and discipline of the scholar.
Data sharing and managment short video (from NYU Health Sciences Library)