Research Data Management

Research data can be characterised as any information that has been collected, observed, generated or produced for the purpose of verifying or reproducing research results. Research data can have different forms and they can be both digital and non-digital.

Some examples of research data:

• tables;
• documents,
• audio and video recordings;
• pictures, photos;
• questionnaires, answers to test questions, interview transcripts;
• software, scripts;
• laboratory journals, field notes, diaries;
• samples, specimens;
• artifacts.

Research data lifecycle

Research Data Management (RDM) refers primarily to the organisation, storage and long-term preservation of data generated during a research project.

Description of the different parts of the research data life cycle:

1. Experiment planning: designing the experiment, planning the data management as well as the conditions that will then enable data sharing, planning the data collection.
2. Data generation: generation of research data or acquisition of data from a third party.
3. Primary and secondary data processing: primary data processing involves the processing of raw data, cleaning them from noise, etc., in connection with this, data documentation is also created and the data are converted into appropriate formats; secondary data processing means specific processing for the needs of analysis, possible anonymisation or pseudonymisation of data, interpretation. The data must also be processed and prepared for prospective publication.
4. Data storage: data must be processed in a way that is suitable for long-term storage.
5. Open disclosure of data: making scientific data available according to principles of open research data (ensuring legal, technical and managerial openness and interoperability of data).
6. Data reuse: use of data for further research or other purposes, the level of data reuse is determined by the level of data disclosure in point 5.