Overview Metroline steps
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.
Build the Team
Coming soon.
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.
Get Training
Coming soon.
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.
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.
Data access and retrieval
Coming soon.
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.
Select Identifier Scheme
Coming soon.
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.
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.
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.
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).
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.
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.
Use ontologies in the data model
Coming soon.
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.
Enter data in eCRF (data collection)
On hold.
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.
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.
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.
Query (use) over resources
Coming soon.
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.
Did you reach your FAIRification objectives
On hold.
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.