This 4 session training course "Training on FAIR Principles in Data Management" was tailored for Kyiv School of Economics, the Ukrainian partner in the BRIDGE twinning project that is being implemented together with University of Tartu. Covered basic data management best practices and trickiest aspects of handling data with a focus on social sciences. Session descriptions: Organise your data Topics covered: folder structures, file naming, metadata standards Learning outcomes: Design consistent file naming conventions and hierarchical folder structures Locate discipline-specific metadata standards Critically evaluate existing data structures to identify gaps in documentation Privacy fundamentals Topics covered: GDPR, data minimisation, resources for your field, anonymisation vs pseudonymisation, anonymisation tools Learning outcomes: Describe the primary daily responsibilities researchers have under GDPR Explain the difference between pseudonymization and anonymization Choose the most appropriate anonymization method (and software tool) Managing Data Over Time Topics covered: versioning, open formats, storage, backup, data catalogues, provenance, lineage, tools Learning outcomes: Describe data storage best practices Compare different methods of version control Explain what data catalogs and provenance are and how they are related to FAIR FAIR in practice Topics covered: FAIR principles, PID, data access statements, licenses, finding repositories, FAIR tools, README, data dictionary, Codebook Learning outcomes: Explain FAIR principles Metadata relevance Documentation Apply FAIR principles in practice