Data curation should be considered from the very beginning of a research project and carried out throughout the entire research data lifecycle. Establishing good curation practices early on can make day-to-day research more efficient: well-organized, consistently documented and clearly described data are easier to find, understand, manage and work with, both for individual researchers and when collaborating with others. This can save time, facilitate collaboration, reduce duplication and minimize the risk of errors or data loss.

Data curation therefore provides practical benefits throughout the research process by making workflows more efficient. It also becomes particularly important when preparing data for publication, reuse and long-term preservation.

The following measures are particularly important:

• Appraise data (check for completeness of the dataset, delete unnecessary, unused data, duplicates, etc.)
• Ensure metadata quality (metadata formatted according to standards, use of controlled vocabulary for keywords)
• Complete documentation (readme files, codebooks, etc.)
• Choose recommended formats for your Data and metadata (if in proprietary or unsupported formats, they must be transformed)
• Add persistent identifiers (ORCID IDs of the authors, grant numbers, DOI)