This thematic glossary explains key concepts used in FAIR data preparation and research data management.
It covers the research data lifecycle, Data Management Plans, documentation, storage, quality control, preservation, access, responsible sharing and the four FAIR dimensions: Findable, Accessible, Interoperable and Reusable.
Thematic research data management glossary
Researchers, research groups, data stewards, curators and repository users
While preparing Data Management Plans, metadata, FAIR packages and repository deposits
Preferred English terms with Ukrainian equivalents
The FAIR Principles concern the ability to find, access, interoperate with and reuse data under clearly defined conditions. A dataset may be restricted or embargoed and still follow FAIR practices when its metadata remain discoverable and the access procedure is clearly documented.
Open data are made available for access and reuse under stated conditions. FAIR data may be open, restricted, embargoed or, in justified cases, closed.
Select a letter to move to the corresponding glossary section.
Ukrainian: умови доступу
Rules and procedures that determine whether, when and how users may obtain a dataset or individual files.
Access conditions may include open access, an embargo, registration, authentication, approval by a data owner or a formal access request.
Ukrainian: доступні · FAIR dimension: A
A FAIR dimension requiring that data or metadata can be retrieved by their identifier using a clearly defined and standardised access procedure.
Authentication or authorisation may be required. Metadata should remain available where possible even when the data files are restricted or no longer available.
Ukrainian: активні дослідницькі дані
Data that are currently being collected, generated, analysed, transformed or used during an ongoing research project.
Active data normally require working storage, access controls, regular backup, version management and documentation of changes.
Ukrainian: резервне копіювання
The creation of additional copies of data so that working files can be recovered after deletion, corruption, hardware failure or another incident.
A backup is normally intended for recovery and should not be confused with long-term preservation or repository publication.
Ukrainian: кураторський супровід даних; курування даних
A set of activities that improve the organisation, documentation, quality, integrity, preservation and reuse readiness of research data.
Data curation may include reviewing files, correcting metadata, preparing a README and manifest, documenting provenance, checking formats and validating a publication package.
Ukrainian: дані
Recorded representations of observations, measurements, calculations, simulations, classifications, statements or other information that can be stored and processed.
Data may be quantitative or qualitative, digital or non-digital, structured or unstructured.
Ukrainian: документація даних
Information that explains the content, structure, origin, methods, variables, formats, processing and limitations of a dataset.
Documentation may be provided through a README, data dictionary, codebook, protocol, workflow description, provenance record or technical report.
Ukrainian: життєвий цикл дослідницьких даних
The sequence of stages through which research data pass, from planning and creation to processing, analysis, preservation, publication, reuse and eventual disposal.
The lifecycle is often iterative: reused data may become input for a new project and enter another cycle of management and documentation.
Ukrainian: план управління даними · Abbreviation: DMP
A structured document describing how research data will be collected or generated, organised, documented, stored, protected, preserved, shared and managed during and after a project.
A DMP should be reviewed and updated when the project, methodology, data types, access conditions or publication plans change.
Ukrainian: мінімізація даних
The practice of collecting, processing and retaining only the data necessary for a defined research purpose.
It is particularly important when working with personal, confidential or sensitive information.
Ukrainian: FAIR-пакет даних
An organised collection of research data, metadata, documentation and supporting files prepared for verification, preservation, publication and reuse.
A package may contain a README, manifest, dataset metadata, scripts, configuration files, software-environment information, provenance and quality-control evidence.
Ukrainian: збереження дослідницьких даних
Managed actions intended to maintain the integrity, authenticity, accessibility and usability of data for a defined period.
Preservation may include format management, integrity checks, redundant storage, preservation metadata and documented responsibility.
Ukrainian: якість даних
The degree to which data are accurate, complete, consistent, valid and suitable for their intended scientific purpose.
Data quality should be supported by documented methods, calibration, validation, uncertainty information, quality controls and known limitations.
Ukrainian: строк зберігання даних; утримання даних
The period for which data and related documentation must or should be retained before review, transfer, anonymisation or disposal.
Retention periods may be determined by scientific, legal, contractual, ethical, institutional or funder requirements.
Ukrainian: надання доступу до даних; поширення даних
The controlled provision of research data to other people, organisations or systems.
Sharing may be public or restricted and should include clear access, licence, citation, confidentiality and responsibility conditions.
Ukrainian: дані, підготовлені відповідно до принципів FAIR
Data and accompanying metadata managed so that they can be found, accessed under defined conditions, combined with other data and reused.
FAIR is primarily concerned with the conditions and infrastructure supporting discovery, access, interoperability and reuse. It is not a single file format or certificate.
Ukrainian: принципи FAIR
A set of guiding principles for improving the Findability, Accessibility, Interoperability and Reusability of digital research objects.
The principles apply to both human users and machines and emphasise identifiers, metadata, standard protocols, vocabularies, provenance and reuse conditions.
Ukrainian: FAIR-ізація даних
A planned process of improving data and metadata so that they better satisfy the FAIR Principles.
FAIRification may include assigning identifiers, improving metadata, applying controlled vocabularies, documenting provenance, defining licences and access procedures, and preparing machine-readable records.
Ukrainian: такі, що можуть бути знайдені · FAIR dimension: F
A FAIR dimension requiring that data and metadata can be reliably discovered by people and machines.
Findability is supported by persistent identifiers, sufficiently rich metadata, explicit inclusion of the data identifier in the metadata and registration or indexing in a searchable resource.
Ukrainian: сумісні · FAIR dimension: I
A FAIR dimension requiring that data and metadata can be combined, exchanged and interpreted across systems, organisations and scientific communities.
Interoperability is supported by shared formats, formal metadata structures, controlled vocabularies, stable identifiers and explicitly defined relationships.
Ukrainian: відкриті дані
Data made available for access and reuse under explicit terms that permit their use beyond the original purpose.
Open data should have clear documentation, a suitable licence or rights statement and a stable access location. Openness alone does not guarantee FAIRness or scientific quality.
Ukrainian: відтворюваність
The ability to obtain consistent results by using the available data, methods, code, parameters and computational or analytical procedures.
Reproducibility requires sufficient documentation and may also require software versions, execution environments, workflow information and provenance records.
Ukrainian: дослідницькі дані
Data collected, observed, measured, created, calculated or derived during research and used as evidence for scientific reasoning, validation or conclusions.
Research data may include measurements, observations, survey responses, images, recordings, simulation outputs, software inputs and outputs, models, laboratory records and derived tables.
Ukrainian: управління дослідницькими даними · Abbreviation: RDM
The organisation, documentation, storage, protection, preservation, sharing and oversight of research data throughout their lifecycle.
RDM includes technical activities, policies, responsibilities, legal and ethical decisions, metadata preparation, quality control and planning for preservation and reuse.
Ukrainian: відповідальне поширення даних
The provision of access to research data in a way that respects legal, ethical, contractual, security and scientific responsibilities.
Responsible sharing requires verification of rights, confidentiality, appropriate access controls, documentation, licences, attribution and possible risks to people, organisations or research infrastructure.
Ukrainian: придатні для повторного використання · FAIR dimension: R
A FAIR dimension requiring that data and metadata contain sufficient information for appropriate use beyond the original research context.
Reusability is supported by detailed descriptions, provenance, clear licences, quality information, community standards and documentation of methods and limitations.
Ukrainian: чутливі дані
Data whose unauthorised disclosure, alteration or loss could create legal, ethical, security, commercial, personal or social harm.
Sensitive data may require restricted access, encryption, controlled storage, anonymisation or pseudonymisation, formal agreements and documented approval procedures.
Ukrainian: зберігання даних
The technical provision and organisation of space in which research data are kept during or after a project.
Storage decisions should consider capacity, reliability, access control, backup, location, security, performance and the period for which the data must remain available.
Ukrainian: контроль версій
A method for recording, identifying and managing changes to data, code, documentation or metadata over time.
Version control helps distinguish approved, draft, corrected and superseded objects and supports traceability, collaboration and reproducibility.
Apply the preferred terminology consistently across Data Management Plans, metadata records, README files, repository deposits, institutional policies and training materials.
Use the terms when preparing a Data Management Plan and assigning responsibilities.
Apply consistent terminology in README files, methods, descriptions and local instructions.
Distinguish access, licence, preservation and reuse concepts in repository records.
Use the definitions as introductory explanations rather than as substitutes for specialised standards or legal definitions.
These definitions are intended for practical research data support. Where a term has a formal definition in legislation, institutional policy, a metadata standard, the FAIR Guiding Principles or a disciplinary specification, the authoritative source takes precedence.