FAIRification resource: FAIR Metroline

The FAIR Metroline provides step-by-step guidance, practical scenarios, and tooling recommendations to help you make your health and life sciences data FAIR (Findable, Accessible, Interoperable and Reusable).

Common FAIRification Process

Define goals

Determine the purpose and expected outcome of FAIRification, in terms of desired usability of the research asset that isn’t currently possible.

Resource	Relevance
Define FAIRification objectives FAIRification objectives aim to make data more Findable, Accessible, Interoperable, and Reusable (FAIR). They focus on improving how data is organised and shared, ensuring that it can be easily accessed, understood and used by both humans and machines. Achieving such objectives maximises the value of data, enhancing its usefulness and long-term usability.	Not provided

Examine requirements

Assess the current state of the activity against the FAIRification goal, including available tools, software, expertise, budget, and time constraints.

Resource	Relevance
Have a FAIR data steward on board A FAIR data steward guides teams in organising, storing, and describing data to meet the FAIR principles, ensuring research data can be understood and reused, making science more efficient and transparent.	Not provided
Organise training This page brings forward the training and educational elements available to guide the team in their journey through the Metroline subsequent steps.	Not provided
Pre-FAIR assessment A pre-FAIR assessment evaluates the current state of your data and its alignment with the FAIR principles. Performing this assessment early makes you aware of possibilities for increasing the FAIRness of your data, which in turn increases its impact and ensures its long-term usability.	Not provided
Design solution plan This step is about turning the findings from the pre-FAIR assessment into a clear, actionable plan. It means choosing the right tools, deciding who does what, and making sure the process is simple and effective so data can actually become FAIR.	Not provided
Creating a FAIR Implementation Profile (FIP) A FAIR Implementation Profile (FIP) describes the practical choices a community makes to apply the FAIR data principles using shared standards and tools. It lists the specific resources, such as metadata schemas, ontologies and licences, that make data findable, accessible, interoperable and reusable. Other communities can reuse these profiles to align their practices, fill gaps and improve collaboration.	Not provided

Design & implement

Define and deliver on practical, achievable objectives across one or more release cycles to realise the overall FAIRification goal.

Resource	Relevance
Assess FAIRness Now that you’ve FAIRified your data, it’s time to check the resulting FAIRness and decide if you’ve reached your goals. Use tools and other methods to assess if your data is truly Findable, Accessible, Interoperable, and Reusable. If needed, adjust or improve things so your data stays FAIR in the long run.	Not provided

Review & report

Review outcomes and assess success against original goals, also disseminating lessons learned to support future FAIRification activities.

Resource	Relevance
Assess FAIRness Now that you’ve FAIRified your data, it’s time to check the resulting FAIRness and decide if you’ve reached your goals. Use tools and other methods to assess if your data is truly Findable, Accessible, Interoperable, and Reusable. If needed, adjust or improve things so your data stays FAIR in the long run.	Not provided

FAIRification template

Outlines a general course of action in eight steps with links to related capabilities across the dimensions Hosting, Format and Content

Obtain the research object

This step establishes how the project team will obtain the research object and the metadata, documentation, dependencies and provenance required to be able to analyse and work on it.

Research object access

Ensure that the FAIRification team is permitted and technically able to reach the research object and its associated metadata.

Examples of concerns:

• Open, registered or controlled access
• Authentication and authorisation
• Data-access applications and approvals
• Secure-environment requirements
• API or repository availability
• Applicable conditions affecting acquisition and project use

Resource	Relevance
Data access and retrieval Before data can be made FAIR, we first need to get it the right way. That means finding trustworthy sources, making sure we’re allowed to use the data, and keeping it safe and private. When we do this properly, it ensures that the data can be trusted, shared, and reused to support new research and innovation.	Focuses on how data is accessed and retrieved from the source, which directly underpins data access.
Define access conditions When making health care data FAIR, data holders have to keep the balance between making data accessible for reuse and protecting personal information of subjects present in the data. This Metroline step helps you pick the appropriate access level to your data and publish the necessary information in the metadata.	Clarifies who can access the data and under which restrictions, making data access operational.
Obtain informed consent To ensure your data and materials can be reused in the future, your subject information sheet (SIS) and informed consent form (ICF) must address reuse. This Metroline step provides consideration and resources for preparing your SIS and ICF.	Links data access to the conditions set by consent, especially where future use and reuse are concerned.

Research object retrieval

Ensure that the required research object, version, components and metadata can be selected, obtained/downloaded and verified by the FAIRification team.

Considerations relating to research object retrieval, eg query language, results representation and exporting capabilities

Resource	Relevance
Data access and retrieval Before data can be made FAIR, we first need to get it the right way. That means finding trustworthy sources, making sure we’re allowed to use the data, and keeping it safe and private. When we do this properly, it ensures that the data can be trusted, shared, and reused to support new research and innovation.	Addresses how data can be obtained from its source, which is central to data retrieval.
Query (use) over resources Querying FAIR resources is the process of asking structured questions to retrieve data that is easy to find, access, and reuse. When resources follow FAIR principles, queries become more efficient because the data is well-organized and clearly described. In this step, we will explore the tools and platforms that enable effective querying of FAIR-compliant resources.	Enables retrieval through searching and querying resources in a structured and reusable way.

Adopt a domain model

This step establishes a shared understanding of the types of research objects, components, domain concepts and relationships involved in the FAIRification activity, and selects or defines a domain model to guide the work.

Identify research object types

Identify and describe the types of research objects and components included in the FAIRification activity.

Research object type identification informs the selection of appropriate domain models, metadata profiles, identifier schemes, standards, vocabularies and target hosting environments. It also helps determine which relationships and dependencies must be preserved.

Distinguish between:

• Research object types, such as datasets, software, workflows, models, notebooks, protocols, and compound research objects.
• Components, such as files, records, modules, workflow steps, metadata documents and referenced resources.
• Domain entity types, such as samples, organisms, participants, observations, assays, images, sequences and variables.
• Representation types, such as schemas, file formats and serialisations, which are examined and implemented in later steps.

A research object may have more than one type or contain several different types of components. Classification should therefore describe the composition of the research object set rather than force each object into a single category.

Resource	Relevance
Analyse data semantics In this step, the aim is to gain more insight into the existing data, or the data that you aim to collect. Clearly defining the meaning (semantics) of the data is an important step for creating the semantic model, as well as for data collection via, for example, electronic case report forms (eCRFs).	Clarifying the meaning of the data helps determine which data types are present and relevant.
Create or reuse a semantic (meta)data model Creating a semantic (meta)data model often requires considerable effort as it is a complex task. Reusing an existing model, where possible, can save time and increase interoperability. Start by checking if a suitable model already exists. If not, follow a structured approach to create one. Once the model is in place, share it using common platforms and provide clear documentation to support reuse by others.	Defining concepts and structure provides a basis for identifying which data types need to be represented.
Register structural metadata This step focuses on how to share your resource’s structural metadata - an explanation of what each piece of your data means and how it’s organised. Publishing it helps others understand, find and reuse your data more easily. This step also shows how to make the metadata readable by computers, which can support specific FAIR objectives you may already have.	Describing the structure of a resource helps recognise and distinguish the data types involved.
Assess availability of your metadata Metadata describes a resource, like a book’s title and author or a photo’s date and location, which help with organization and discovery of that resource. This Metroline step describes the types of metadata, where you can find them for your resource, and how to improve their quality. Filling metadata gaps enhances a resource’s visibility and reusability.	Showing which metadata is already available helps identify how data types are currently described.

Select identifier schemes

This step defines what needs to be assigned an identifier and selects appropriate identifier schemes. Established identifiers should be reused where possible, with new unique, persistent and resolvable identifiers introduced only where required.

Identifier minting

Create, assign and register new identifiers—unique, persistent and resolvable.

This capability is required when no suitable established identifier exists or when a new research object, version or independently identifiable component is created. It includes selecting an appropriate identifier scheme and issuing authority; preventing duplicate assignment; associating identifiers with the correct subjects and metadata; and supporting the required resolution, persistence, versioning and lifecycle arrangements.

Identifier minting would normally be provided by a repository, registry, institutional service or infrastructure provider rather than brought in by the FAIRification team itself.

Resource	Relevance
Design solution plan This step is about turning the findings from the pre-FAIR assessment into a clear, actionable plan. It means choosing the right tools, deciding who does what, and making sure the process is simple and effective so data can actually become FAIR.	Planning how unique, persistent and resolvable identifiers will be created gives direction to identifier minting.

Identifier discovery and reuse

Recognise, assess, retain and apply established identifiers for research objects, components and domain entities.

This capability enables consistent identification across systems and communities. It relies on and preserves existing links, avoids duplicate identities and enables information about the same subject to be connected across research objects, repositories, services and workflows. Identifier reuse may depend on external authorities, registries, repositories, lookup services or community expertise. It includes determining what existing identifiers represent, whether they are suitable for reuse and how they should be used and referenced in the FAIRification activity.

Resource	Relevance
Use ontologies in the model This process involves linking your data model to ontologies, which are essentially formal, shared dictionaries that provide precise, computer-readable definitions for concepts. By assigning these standard definitions to the elements in your data model, you make their meanings explicit and clear. This allows different computer systems to correctly understand, combine, and compare data from various sources without confusion, making the data more interoperable and easier to reuse across different projects.	Linking model elements to established semantic resources supports reuse of community identifiers.
Creating a FAIR Implementation Profile (FIP) A FAIR Implementation Profile (FIP) describes the practical choices a community makes to apply the FAIR data principles using shared standards and tools. It lists the specific resources, such as metadata schemas, ontologies and licences, that make data findable, accessible, interoperable and reusable. Other communities can reuse these profiles to align their practices, fill gaps and improve collaboration.	Documenting community choices on identifier systems makes reuse of those identifiers explicit and consistent.

Convert to standard formats

This step establishes which community or domain standards, profiles, formats and serialisations will be used to represent the research object and applies and validates them in ways that support the intended uses.

Standards discovery and reuse

Find, assess and choose existing standards, profiles, formats and serialisations that can represent the research object in ways that support the FAIRification goal and intended uses.

This capability includes identifying standards used by relevant communities, determining what each standard covers and assessing whether it can represent the selected domain model, identifiers, metadata, components and relationships.

The capability can be provided through project expertise but also through authoritative guidelines from communities, standards registries, target repositories/hosting environments, standards organisations or other infrastructure providers.

Resource	Relevance
Apply common data elements Common data elements (CDEs) are standardised data elements, such as variables and measurements, paired with defined rules for how values should be recorded. They are developed to promote consistency and reuse in data collection across different settings, enabling seamless integration and comparison. This step encourages you to search for relevant CDEs and offers guidance on what to do if no suitable CDE is available.	Using agreed data elements turns reuse of existing standards into something practical and concrete.
Create or reuse a semantic (meta)data model Creating a semantic (meta)data model often requires considerable effort as it is a complex task. Reusing an existing model, where possible, can save time and increase interoperability. Start by checking if a suitable model already exists. If not, follow a structured approach to create one. Once the model is in place, share it using common platforms and provide clear documentation to support reuse by others.	Embedding existing standards in a shared semantic model supports their reuse without redefining them.
Creating a FAIR Implementation Profile (FIP) A FAIR Implementation Profile (FIP) describes the practical choices a community makes to apply the FAIR data principles using shared standards and tools. It lists the specific resources, such as metadata schemas, ontologies and licences, that make data findable, accessible, interoperable and reusable. Other communities can reuse these profiles to align their practices, fill gaps and improve collaboration.	Recording which standards a community adopts makes reuse choices transparent and reusable.

Developing standards

Create, profile, extend and maintain a specification where existing standards cannot adequately represent the research object or support the intended uses.

This can include creating a new standard, but it also includes less extensive approaches such as defining a community profile, adding constraints, developing an extension or proposing changes to an established standard. Standards development usually requires coordination beyond the immediate FAIRification team. Domain communities, intended users, standards maintainers, repositories, software developers and other implementers may need to participate. An important question to ask is whether the required development capability can be established through collaboration.

This capability should be considered only after relevant existing standards and profiles have been examined.

Resource	Relevance
Create or reuse a semantic (meta)data model Creating a semantic (meta)data model often requires considerable effort as it is a complex task. Reusing an existing model, where possible, can save time and increase interoperability. Start by checking if a suitable model already exists. If not, follow a structured approach to create one. Once the model is in place, share it using common platforms and provide clear documentation to support reuse by others.	Defining concepts, relations and structure supports the development of new standards where these do not yet exist.
Apply common data elements Common data elements (CDEs) are standardised data elements, such as variables and measurements, paired with defined rules for how values should be recorded. They are developed to promote consistency and reuse in data collection across different settings, enabling seamless integration and comparison. This step encourages you to search for relevant CDEs and offers guidance on what to do if no suitable CDE is available.	Formalising shared data elements can be part of building a new standard.

Applying standards

Create or transform research objects so that they conform to selected standards, profiles, formats and serialisations.

This includes applying standards when a research object is first created and applying them retrospectively to existing research objects. It may involve restructuring content, converting formats, generating metadata, packaging components, preserving identifiers and provenance, and documenting transformation decisions.

The capability can be provided through data-processing tools, export services, repository submission workflows, conversion libraries, schemas, templates, and specialists in the implementation team.

Resource	Relevance
Apply common data elements Common data elements (CDEs) are standardised data elements, such as variables and measurements, paired with defined rules for how values should be recorded. They are developed to promote consistency and reuse in data collection across different settings, enabling seamless integration and comparison. This step encourages you to search for relevant CDEs and offers guidance on what to do if no suitable CDE is available.	Consistent use of agreed data elements is one of the main ways data standards are applied in practice.
Apply (meta)data model Think of your data like a book in a library. A metadata model is like the card in the catalogue that tells people what the book is about and who wrote it. A data model is like the book’s table of contents - it helps everyone understand what’s inside and how to read it. Using both makes it easier for people and machines to find, understand, and reuse your data.	Implementing the agreed model in data and metadata makes data standards actionable.
Transform and expose FAIR (meta)data Think of your data like a well-written book that’s hidden away on a shelf that no one knows about. By transforming it into a standardised, machine-readable format and publishing it through trusted channels, you place it in a public library where everyone, people and machines, can easily find, open, and read it.	Turning data and metadata into a more structured FAIR form helps put standards into practice.
Design eCRF (data collection) A Case Report Form (CRF) is a structured document used to capture information about study participants. When this process is conducted electronically, the form is referred to as an electronic Case Report Form (eCRF). The use of eCRFs offers several advantages: it facilitates efficient data storage, reduces the risk of human error and saves time throughout the study lifecycle. In this step, we will focus on how to design and develop an eCRF in preparation for data collection.	Building standards directly into eCRF design connects application of standards to data collection.

Validating against standards

Assess whether a research object representation conforms to the requirements of a specified standard, version, profile or extension.

This can include syntactic, structural, schema-based, semantic, rules-based, completeness and cross-component validation. Different validation mechanisms may be required for different parts of a compound research object.

Validation establishes conformance with defined requirements. It does not by itself establish that the research object is FAIR, scientifically correct or suitable for every intended use.

The capability can be provided through official validators, schemas, repository submission checks, testing frameworks, quality-control workflows, community services or manual expert review.

Resource	Relevance
Pre-FAIR assessment A pre-FAIR assessment evaluates the current state of your data and its alignment with the FAIR principles. Performing this assessment early makes you aware of possibilities for increasing the FAIRness of your data, which in turn increases its impact and ensures its long-term usability.	An early assessment helps reveal whether data already aligns with relevant standards and where gaps remain.
Assess FAIRness Now that you’ve FAIRified your data, it’s time to check the resulting FAIRness and decide if you’ve reached your goals. Use tools and other methods to assess if your data is truly Findable, Accessible, Interoperable, and Reusable. If needed, adjust or improve things so your data stays FAIR in the long run.	Checking whether data and metadata meet FAIR requirements complements validation against standards.
Design eCRF (data collection) A Case Report Form (CRF) is a structured document used to capture information about study participants. When this process is conducted electronically, the form is referred to as an electronic Case Report Form (eCRF). The use of eCRFs offers several advantages: it facilitates efficient data storage, reduces the risk of human error and saves time throughout the study lifecycle. In this step, we will focus on how to design and develop an eCRF in preparation for data collection.	Structured forms can support validation by enforcing standard compliant input at the moment of collection.

Harmonise content elements

This step establishes which controlled vocabularies, terminologies and ontologies will be used to describe the research object and its associated metadata, and applies their terms in ways that support consistent interpretation and reuse.

Vocabulary discovery and selection

Find, assess and choose semantic resources that provide appropriate identifiers and descriptions for the concepts represented in the research object and its associated metadata.

This capability includes determining which concepts need controlled terms, identifying relevant community resources and evaluating whether those resources provide sufficient coverage, granularity, semantic precision and operational support.

The capability may be provided through project expertise, domain communities, vocabulary registries, lookup services, ontology portals, standards organisations, repositories or other FAIR-enabling resources.

Resource	Relevance
Use ontologies in the model This process involves linking your data model to ontologies, which are essentially formal, shared dictionaries that provide precise, computer-readable definitions for concepts. By assigning these standard definitions to the elements in your data model, you make their meanings explicit and clear. This allows different computer systems to correctly understand, combine, and compare data from various sources without confusion, making the data more interoperable and easier to reuse across different projects.	Choosing ontology terms that make concepts explicit and machine readable is a key part of selecting vocabularies.
Create or reuse a semantic (meta)data model Creating a semantic (meta)data model often requires considerable effort as it is a complex task. Reusing an existing model, where possible, can save time and increase interoperability. Start by checking if a suitable model already exists. If not, follow a structured approach to create one. Once the model is in place, share it using common platforms and provide clear documentation to support reuse by others.	The concepts and relations defined in a semantic model guide which vocabularies should be selected.
Creating a FAIR Implementation Profile (FIP) A FAIR Implementation Profile (FIP) describes the practical choices a community makes to apply the FAIR data principles using shared standards and tools. It lists the specific resources, such as metadata schemas, ontologies and licences, that make data findable, accessible, interoperable and reusable. Other communities can reuse these profiles to align their practices, fill gaps and improve collaboration.	Recording chosen semantic resources in a FIP makes vocabulary selection explicit at community level.

Vocabulary extension and development

Add or develop new concepts when established semantic resources do not adequately cover the requirements of the FAIRification activity.

This capability may involve requesting a new term from an existing authority, contributing corrections or relationships, creating a governed local extension, defining an application ontology or developing a new semantic resource.

The preferred sequence is to:

1. Reuse a suitable existing term.
2. Request a new term, definition or correction from the maintaining authority.
3. Use an established extension mechanism.
4. Create a governed local extension that reuses existing identifiers where possible.
5. Combine or extract modules from compatible semantic resources.
6. Develop a new vocabulary or ontology only where no suitable alternative exists.

Vocabulary development usually requires coordination with domain communities, intended users, ontology or terminology specialists, repositories and software implementers. The required capability may therefore be provided outside the immediate FAIRification team.

Resource	Relevance
Create or reuse a semantic (meta)data model Creating a semantic (meta)data model often requires considerable effort as it is a complex task. Reusing an existing model, where possible, can save time and increase interoperability. Start by checking if a suitable model already exists. If not, follow a structured approach to create one. Once the model is in place, share it using common platforms and provide clear documentation to support reuse by others.	Defining the concepts needed in the model creates the foundation for developing vocabularies.
Use ontologies in the model This process involves linking your data model to ontologies, which are essentially formal, shared dictionaries that provide precise, computer-readable definitions for concepts. By assigning these standard definitions to the elements in your data model, you make their meanings explicit and clear. This allows different computer systems to correctly understand, combine, and compare data from various sources without confusion, making the data more interoperable and easier to reuse across different projects.	Newly developed terms can become part of the ontology driven model.

Semantic annotation

Associate research objects, components, metadata elements and values with identifiable concepts from selected semantic resources.

This capability includes selecting the correct concept, representing its identifier in the appropriate context and recording sufficient provenance to understand how and why the annotation was made.

In addition to a term from a vocabulary, an annotation usually also includes the subject being described and the relationship between the subject and the concept that the term represents. For example, stating that a research object “is about” a disease, “uses” a method or “has specimen type” a biological material expresses different meanings.

The capability may be provided through manual curation, data-entry systems, transformation workflows, text-mining or annotation tools, repository services or combinations of automated and expert processes.

Resource	Relevance
Use ontologies in the model This process involves linking your data model to ontologies, which are essentially formal, shared dictionaries that provide precise, computer-readable definitions for concepts. By assigning these standard definitions to the elements in your data model, you make their meanings explicit and clear. This allows different computer systems to correctly understand, combine, and compare data from various sources without confusion, making the data more interoperable and easier to reuse across different projects.	Linking data and metadata to formal ontology or vocabulary terms gives annotation its meaning.
Apply (meta)data model Think of your data like a book in a library. A metadata model is like the card in the catalogue that tells people what the book is about and who wrote it. A data model is like the book’s table of contents - it helps everyone understand what’s inside and how to read it. Using both makes it easier for people and machines to find, understand, and reuse your data.	Putting the chosen semantic model into practice includes annotating data and metadata with the right terms.

Vocabulary management

Maintain reliable and reproducible use of semantic resources over time.

For most FAIRification activities, this means managing the project’s use of externally maintained vocabularies: recording versions, monitoring changes, updating annotations and preserving reproducibility.

Where the project or community maintains a vocabulary, ontology, value set or extension, the capability additionally includes editorial governance, identifiers, releases, publication, support and long-term maintenance.

Resource	Relevance
Creating a FAIR Implementation Profile (FIP) A FAIR Implementation Profile (FIP) describes the practical choices a community makes to apply the FAIR data principles using shared standards and tools. It lists the specific resources, such as metadata schemas, ontologies and licences, that make data findable, accessible, interoperable and reusable. Other communities can reuse these profiles to align their practices, fill gaps and improve collaboration.	Documenting and maintaining community choices over time supports vocabulary management.
Use ontologies in the model This process involves linking your data model to ontologies, which are essentially formal, shared dictionaries that provide precise, computer-readable definitions for concepts. By assigning these standard definitions to the elements in your data model, you make their meanings explicit and clear. This allows different computer systems to correctly understand, combine, and compare data from various sources without confusion, making the data more interoperable and easier to reuse across different projects.	Sustained and consistent use of ontologies depends on active vocabulary management.

Transform to match use cases

This step creates mappings, translations and alternative representations that allow the research object to meet integration and interoperability requirements of the primary use cases.

Identifier mapping

Establish and maintain documented relationships between identifiers used by different schemes, authorities, repositories or systems.

This capability allows integrations to recognise when identifiers refer to the same subject or to subjects connected through version, component, derivation, replacement or other defined relationships. Since mapping are not always assertions of an exact identity match, the relations should be stated explicitly:

• Exactly the same subject.
• Different records describing the same subject.
• Different versions or releases.
• A collection and one of its components.
• An original and a derived research object.
• A deprecated and replacement identifier.
• Closely related but distinct subjects.

The capability may be provided through authoritative registries, repository cross-references, identifier-resolution services, community mapping resources, lookup services or project-maintained mappings (that themselves can be shared).

Resource	Relevance
Use ontologies in the model This process involves linking your data model to ontologies, which are essentially formal, shared dictionaries that provide precise, computer-readable definitions for concepts. By assigning these standard definitions to the elements in your data model, you make their meanings explicit and clear. This allows different computer systems to correctly understand, combine, and compare data from various sources without confusion, making the data more interoperable and easier to reuse across different projects.	Semantic relations can help connect equivalent identifiers across systems and contexts.
Apply (meta)data model Think of your data like a book in a library. A metadata model is like the card in the catalogue that tells people what the book is about and who wrote it. A data model is like the book’s table of contents - it helps everyone understand what’s inside and how to read it. Using both makes it easier for people and machines to find, understand, and reuse your data.	Representing mapped identifiers consistently in the applied model makes identifier mapping usable in practice.

Vocabulary alignment

Establish and maintain documented semantic relationships between concepts from different controlled vocabularies, terminologies, taxonomies or ontologies.

This capability allows the concepts used in the canonical representation to be interpreted or translated for communities and systems that use other semantic resources. Possible alignment relationships include:

• Exact match.
• Close match.
• Broader match.
• Narrower match.
• Related match.
• Context-dependent correspondence.
• Composite or one-to-many correspondence.
• No suitable match.

The capability may be provided through mappings published by vocabulary authorities, community alignment projects, ontology services, mapping registries, specialist tools or project-generated mapping sets.

Resource	Relevance
Use ontologies in the model This process involves linking your data model to ontologies, which are essentially formal, shared dictionaries that provide precise, computer-readable definitions for concepts. By assigning these standard definitions to the elements in your data model, you make their meanings explicit and clear. This allows different computer systems to correctly understand, combine, and compare data from various sources without confusion, making the data more interoperable and easier to reuse across different projects.	Alignment depends on relating equivalent or closely related terms through semantic links.
Creating a FAIR Implementation Profile (FIP) A FAIR Implementation Profile (FIP) describes the practical choices a community makes to apply the FAIR data principles using shared standards and tools. It lists the specific resources, such as metadata schemas, ontologies and licences, that make data findable, accessible, interoperable and reusable. Other communities can reuse these profiles to align their practices, fill gaps and improve collaboration.	Shared documentation of semantic choices can support alignment across communities.

Model mapping

Relate entities, properties, relationships, structures and constraints across domain, data, metadata or structural models.

This capability connects the canonical model and representation to the models expected by target communities, repositories, services, tools and workflows. Model mapping may describe conceptual correspondences, structural crosswalks or executable transformation rules.

Mappings are not limited to one-to-one equivalence. A source element may correspond to several target elements, several source elements may be combined, or a target value may need to be derived.

The capability may be provided through standards crosswalks, repository mappings, schemas, transformation specifications, ETL workflows, query languages or project-developed mapping artefacts.

Resource	Relevance
Apply (meta)data model Think of your data like a book in a library. A metadata model is like the card in the catalogue that tells people what the book is about and who wrote it. A data model is like the book’s table of contents - it helps everyone understand what’s inside and how to read it. Using both makes it easier for people and machines to find, understand, and reuse your data.	Mapping between data models depends on expressing those mappings in a model that can be implemented consistently.
Create or reuse a semantic (meta)data model Creating a semantic (meta)data model often requires considerable effort as it is a complex task. Reusing an existing model, where possible, can save time and increase interoperability. Start by checking if a suitable model already exists. If not, follow a structured approach to create one. Once the model is in place, share it using common platforms and provide clear documentation to support reuse by others.	Clearly defined semantic models make equivalent concepts across sources easier to compare and map.

Deploy to hosting environments

This step deploys the research object, its alternative representations and any supporting resources to one or more hosting environments that meet the functional, operational, security and preservation requirements of the FAIRification activity.

Research object deposition

Persist, manage, expose and operate research objects, metadata, mappings, alternative representations and supporting services in an environment suitable for their intended uses.

This capability includes selecting or confirming a hosting environment, deploying the required objects and services, and establishing the storage, preservation, discovery, security, operational and sustainability arrangements needed to support them.

The capability may be provided by an external repository, institutional infrastructure, community platform, secure environment, commercial provider or project-operated service. The FAIRification team does not necessarily have to operate the environment itself.

Resource	Relevance
Register resource level metadata To make your resource (e.g. data), available for reuse, its metadata can be published in a catalogue. This step helps you find a catalogue where you can register these resource metadata and explains why adding your resource to such a catalogue is important.	Describing hosted resources with sufficient metadata makes them easier to find, understand and reuse.
Transform and expose FAIR (meta)data Think of your data like a well-written book that’s hidden away on a shelf that no one knows about. By transforming it into a standardised, machine-readable format and publishing it through trusted channels, you place it in a public library where everyone, people and machines, can easily find, open, and read it.	Publishing data and metadata in FAIR forms connects hosting to access and reuse.
Query (use) over resources Querying FAIR resources is the process of asking structured questions to retrieve data that is easy to find, access, and reuse. When resources follow FAIR principles, queries become more efficient because the data is well-organized and clearly described. In this step, we will explore the tools and platforms that enable effective querying of FAIR-compliant resources.	Search and query functions make hosted resources more usable once they are made available.
Assess availability of your metadata Metadata describes a resource, like a book’s title and author or a photo’s date and location, which help with organization and discovery of that resource. This Metroline step describes the types of metadata, where you can find them for your resource, and how to improve their quality. Filling metadata gaps enhances a resource’s visibility and reusability.	Checking whether enough metadata is available supports effective hosting and discovery.

Research object versioning

Identify, relate, preserve and manage changes to deployed research objects and their associated metadata, mappings, representations and services.

This capability allows users and machines to distinguish a changing research object from a particular reproducible version and to understand how versions, releases, components and derived representations relate.

The capability may be provided by a repository, version-control system, package registry, storage service, workflow platform or project-operated versioning process.

Resource	Relevance
Register resource level metadata To make your resource (e.g. data), available for reuse, its metadata can be published in a catalogue. This step helps you find a catalogue where you can register these resource metadata and explains why adding your resource to such a catalogue is important.	Metadata that distinguishes and describes different versions is essential for proper versioning.
Transform and expose FAIR (meta)data Think of your data like a well-written book that’s hidden away on a shelf that no one knows about. By transforming it into a standardised, machine-readable format and publishing it through trusted channels, you place it in a public library where everyone, people and machines, can easily find, open, and read it.	Making different FAIR versions of data and metadata visible supports transparent versioning.

Research object transfer

Move, ingest, synchronise or register research objects and associated information between environments securely, completely and verifiably.

This capability may involve transferring files or packages, migrating databases or object stores, depositing through a repository interface, synchronising environments or registering an object in place without copying it.

Transfer is distinct from retrieval. Retrieval concerns what a user or service can select and receive. Transfer concerns the operational movement or ingestion of research objects between source, staging and target environments.

Transfer is not required where the research object remains in place and the existing environment is upgraded to fulfil the target role.

Resource	Relevance
Data access and retrieval Before data can be made FAIR, we first need to get it the right way. That means finding trustworthy sources, making sure we’re allowed to use the data, and keeping it safe and private. When we do this properly, it ensures that the data can be trusted, shared, and reused to support new research and innovation.	Accessing, moving and obtaining data across systems is part of what data transfer needs to support.
Transform and expose FAIR (meta)data Think of your data like a well-written book that’s hidden away on a shelf that no one knows about. By transforming it into a standardised, machine-readable format and publishing it through trusted channels, you place it in a public library where everyone, people and machines, can easily find, open, and read it.	Structuring data and metadata for exchange helps make transfer more FAIR and reusable.

Activate sharing and reuse

This step authorises and activates the release of the validated research object deployment under appropriate rights, access and privacy conditions, and provides the documentation, support and monitoring needed for its intended reuse.

Rights and reuse conditions

Authorise and communicate the terms under which a research object and its components may be accessed, used, modified, combined and redistributed.

This capability includes declaring rights holders and release authorities; evaluating licences, contracts and third-party terms; selecting suitable licences or rights statements; defining permitted uses; and communicating the resulting conditions to people and machines.

The capability may be provided by rights holders, legal advisers, research organisations, repositories, data-access committees, ethics or information-governance functions, community authorities or other authorised decision-makers.

Resource	Relevance
Define access conditions When making health care data FAIR, data holders have to keep the balance between making data accessible for reuse and protecting personal information of subjects present in the data. This Metroline step helps you pick the appropriate access level to your data and publish the necessary information in the metadata.	Setting the terms for access, sharing and reuse makes define access conditions directly relevant to licensing.
Creating a FAIR Implementation Profile (FIP) A FAIR Implementation Profile (FIP) describes the practical choices a community makes to apply the FAIR data principles using shared standards and tools. It lists the specific resources, such as metadata schemas, ontologies and licences, that make data findable, accessible, interoperable and reusable. Other communities can reuse these profiles to align their practices, fill gaps and improve collaboration.	Community documentation of licence choices helps make licensing decisions more transparent and consistent.

Privacy and disclosure control

Identify, assess and manage the risk that releasing a research object, metadata or associated information could expose personal, confidential, sensitive or otherwise restricted information.

This capability is broad and complex and its nature will vary greatly depending on the research object and intended use. For example, anonymisation may be inappropriate or insufficient where the information is highly distinctive, where external information creates linkage risks or where removing sufficient detail would undermine the intended use.

The capability may be provided by information-governance, data-protection, statistical-disclosure, ethics, security or domain specialists and by authorised data-access or release bodies.

Resource	Relevance
Define access conditions When making health care data FAIR, data holders have to keep the balance between making data accessible for reuse and protecting personal information of subjects present in the data. This Metroline step helps you pick the appropriate access level to your data and publish the necessary information in the metadata.	Access conditions depend in part on how privacy risks are handled through anonymisation.
Data access and retrieval Before data can be made FAIR, we first need to get it the right way. That means finding trustworthy sources, making sure we’re allowed to use the data, and keeping it safe and private. When we do this properly, it ensures that the data can be trusted, shared, and reused to support new research and innovation.	Protecting privacy through anonymisation can enable safer access to and retrieval of data.
Obtain informed consent To ensure your data and materials can be reused in the future, your subject information sheet (SIS) and informed consent form (ICF) must address reuse. This Metroline step provides consideration and resources for preparing your SIS and ICF.	Consent conditions shape how anonymised data may later be used and reused.

Release management

Define, approve, activate, document and manage a specific release of the research object.

This capability controls when and what is being released through which channels and interfaces, which version and representations are included, where they are available, who may access them and which conditions apply.

The capability may be provided through repository deposition and approval workflows, institutional release procedures, community governance, project release management or combinations of these.

Resource	Relevance
Register resource level metadata To make your resource (e.g. data), available for reuse, its metadata can be published in a catalogue. This step helps you find a catalogue where you can register these resource metadata and explains why adding your resource to such a catalogue is important.	Releasing data requires metadata that makes the resource discoverable and understandable to others.
Transform and expose FAIR (meta)data Think of your data like a well-written book that’s hidden away on a shelf that no one knows about. By transforming it into a standardised, machine-readable format and publishing it through trusted channels, you place it in a public library where everyone, people and machines, can easily find, open, and read it.	Publishing data and metadata in FAIR ways is a core part of data release.
Define access conditions When making health care data FAIR, data holders have to keep the balance between making data accessible for reuse and protecting personal information of subjects present in the data. This Metroline step helps you pick the appropriate access level to your data and publish the necessary information in the metadata.	Clear decisions on access rights and reuse conditions are necessary before data can be released.
Assess availability of your metadata Metadata describes a resource, like a book’s title and author or a photo’s date and location, which help with organization and discovery of that resource. This Metroline step describes the types of metadata, where you can find them for your resource, and how to improve their quality. Filling metadata gaps enhances a resource’s visibility and reusability.	Availability of sufficient metadata helps determine whether data is ready for release.

Hosting

Capabilities of the environment(s) where the research objects are hosted and made available for access and distributions. Exchange formats for the research objects are covered under Formats & representation, while APIs, indexing and query interfaces are covered here.

Formats

What is reported in the Dataset (data) & the Dataset Descriptor (metadata)

Content

What is reported in the research object set (research object) & the research object set Descriptor (metaresearch object).