Research data

Research data is "information, other than scientific publications, in electronic form that is collected or created in the course of research or development, and is used as evidence in the research or development process, or that is generally accepted by the research community as necessary to validate findings and results of research or development.”

Research data management

Research data management describes the organization, storing and sharing of research data collected and used in a research project. It also includes decision on how the research data will be stored and shared after the project ends.

Data Life Cycle

It is useful to look at research data from the perspective of the data life cycle: different steps are needed in the planning phase than in the data analysis or data sharing phase.

TIP: Take a look at the RDMkit, page, where the individual steps of research data management are described.

vyzkumna-data
https://rdmkit.elixir-europe.org/RDMkit by ELIXIR-CONVERGE is licensed under the Creative Commons Attribution 4.0 International License.
FAIR data
vyzkumna-data

Principy FAIR = good research data management.

For the reusability of research data, it is important to use FAIR principles so that the data is:

  • Findable
    • Data identifier (DOI, Handle).
    • Data are described with metadata, so that the data will be accessible and understandable.
    • Metadata are registered in searchable resources (e.g., OpenAIRE).
    • Cite research data according to the citation rules of your field.
    • Use author identifiers (ResearcherID, ORCID) in metadata.
  • Accessible
    • Use a trustworthy repository.
    • Clearly define data accessibility.
    • If the data cannot be open, at least the metadata should be accessible so that the information can be searched.
    • Use time embargo, if needed: state clearly why and for how long you consider the restriction of access to the data necessary. Make the (meta)data available as soon as possible.
    • (meta)data are available by their identifiers (DOI, Handle).
  • Interoperable
    • Use relevant field keywords and structure the description of your data (metadata) according to them from the beginning of the research project.
    • Find out what standards the selected repository uses. Structure your data according to these standards, preferably from the beginning of the research project.
    • Improve the quality of your data: implement procedures that minimize the risk of data collection errors.
    • Use long-term sustainable file formats (open, standardized and internationally recognized).
  • Reusable
    • Systematically document the data (make available descriptive codes and abbreviations used, information on missing data, how the database was created, methods used, etc.).
    • Follow the file naming rules.
    • Use common file formats (in addition to formats for archiving, use common file formats for data sharing, such as XLSX next to CSV or ESRI SHP next to MID/MIF).
    • Maintain data integrity (data used for research must be identical to data, that were made open).
    • License the data for reuse.
    • Metadata is according to community standards.

The context is as important as the data itself. Check how FAIR your data is.

FAIR data does not mean open data:
As Open as Possible, as Closed as Necessary

fair-principles
The Turing Way project illustration by Scriberia. Original version on Zenodo. http://doi.org/10.5281/zenodo.3695300
Additional Materials