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FAIR Metroline steps

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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.

planning goals governance

data steward ELSI expert researcher semantic expert

Data holder Data user

Goals & expertise

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.

expertise support governance

data steward infrastructure professional project manager researcher

Data holder Data user

Goals & expertise

Identify and select training

This page brings forward the training and educational elements available to guide the team in their journey through the Metroline subsequent steps.

training skills

data steward researcher trainer

Data holder Data user

Goals & expertise

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.

assessment FAIR maturity

data steward research software engineer

Data holder Data user

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 implementation

data steward infrastructure professional project manager researcher

Data holder Data user

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.

data access data retrieval

data steward ELSI expert infrastructure professional researcher

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.

metadata availability discovery

data steward researcher

Data holder Data user

Select Identifier Scheme

In simple terms, selecting an identifier scheme is like deciding how books, authors and libraries should be identified in a catalogue. A book may have an ISBN, an author may have their own identifier and a library may use another identifier system. Using the right identifier scheme for each type of entity makes it easier to distinguish, find and connect them reliably. The same applies to research data: appropriate identifiers help data and related entities to be discovered, referenced and reused.

identifiers persistence

Researcher Data steward Technical specialist Infrastructure professional

Data holder Data user

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 publication discovery

data steward infrastructure professional researcher

Register & expose

Register structural metadata

Structural metadata can describe many aspects of how data is organised, represented and related within a dataset or resource. In this step, the focus is primarily on structural metadata describing data elements, their definitions and formats, such as codebooks or data dictionaries. Publishing structural metadata helps others understand, find and reuse your data more easily. This step also describes approaches for making structural metadata easier for computers to process automatically, which can support specific FAIRification objectives.

codebook metadata publication

data steward researcher

Register & expose

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.

codebook standardisation CDE

data steward researcher

Data holder Data user

Data definition

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).

codebook semantics

data steward researcher semantic expert

Data holder Data user

Data definition

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.

eCRF data collection codebook

data collection specialist data steward researcher

Data definition

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.

semantic model semantics

semantic expert

Data holder Data user

Semantic modeling

Use ontologies in the model

This process involves linking your semantic (meta)data model to ontologies, which are formal, shared vocabularies that provide precise, machine-readable definitions for concepts. This step is written for a broad FAIRification audience and may be used by researchers, data stewards, metadata specialists and ontology or semantic web technology experts, depending on the project context. Not all activities need to be carried out by the same person. researchers and project teams can use this step to understand the process and provide domain knowledge, while specialist support may be needed for ontology selection, concept mapping, formal representation, validation and maintenance. By assigning standard definitions to the elements in your model, you make their meanings explicit and unambiguous. This allows different computer systems to correctly understand, combine and compare data from various sources consistently, making the data more interoperable and easier to reuse across projects.

semantic model ontologies semantics

semantic expert

Data holder Data user

Semantic modeling

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 data sharing legal

Apply (meta)data model

Think of your data like a book in a library. A metadata model defines the kind of information included in the catalogue entry, such as the title, author, topic, and publication date, helping people find and understand the book. A data model defines how the contents of the book are organised and related, like the structure of chapters, sections, and references. Using both makes it easier for people and machines to find, understand, and reuse your data.

data transformation tools semantic model

data steward researcher semantic expert technical specialist

Semantic modeling

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.

data transformation RDF publication

data steward infrastructure professional researcher semantic expert

Register & expose

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.

data sharing legal governance

data curator data steward ELSI expert researcher

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.

data scientist researcher semantic expert

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.

assessment FAIR maturity

data steward research software engineer researcher

Data holder Data user

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.

standards interoperability governance

ELSI expert researcher semantic expert

Data holder Data user

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FAIR Metroline by Health-RI is licensed under a Creative Commons Attribution 4.0 International License