6.0 Activating FAIR implementation

To explore, analyze and assess technical processes and choices that enhance FAIR implementation

Why should I do this?

To develop technological foundations for your project that will help support your investment’s FAIR implementation.

In this step you will:

In this step, you will be introduced to six technical themes and illustrative scenarios to help you navigate these themes:

6.1. Illustrative scenarios: Two scenarios illustrating how six technology themes can be navigated to help implement FAIR principles. This is a good starting point in this step, as these can be compared to your own projects before you work through the themes, each of which is independent but mutually supportive. You can work through this in any order, but covering all content will give you a good level of knowledge and confidence of the main technical aspects that can affect FAIR implementation in any project. The content includes introduction to these concepts, as well as approaches on how to get started for your investment.
6.2. Application Programming Interfaces (APIs): Establishing a set of rules and protocols that allows different software applications to communicate with each other, enabling the exchange of data.
6.3. Common Data Models (CDMs): Establishing a standardized, consistent and shared data structure designed to organize and describe data in a way that facilitates interoperability and data integration across various applications and domains.
6.4. Data standards: Establishing conventions or rules that specify how data is structured, represented and exchanged within a particular context or domain.
6.5. Data catalogs: Choosing an approach to structure the inventory of data assets within your investment.
6.6. Data licenses: Identifying the legal framework for granting permission to access, use, share and monetize data, and understanding the implication of those choices on the reusability of data.
6.7. Data privacy and security: Establishing practices that ensure personal and sensitive data is handled, stored and processed securely to prevent unauthorized access or misuse.
6.8. FAIR enabling resources (FERs): Exploring options that aid in achieving some aspect of FAIR and that are explicitly linked to one or more FAIR principles. They include a whole range of proven technology options (including repositories) to help with data findability, accessibility, interoperability and reusability to help you investigate your options. They also contain further information and comparison tables to aid evaluation. Unlike in the previous steps, you will not have a specific template or worksheet to complete, as the approach followed on each project may differ significantly. Please select your technical team’s preferred choice to ensure process documentation is created.

- Findable
  
  To use data, it must first be found. Metadata and data should be easy to find for both humans and machines. Machine-readable metadata is essential for automatic discovery of datasets and services.
  - F1 – Data is assigned a globally unique and persistent identifier.
  - F2 – Data is described with rich metadata.
  - F3 – Metadata clearly and explicitly includes the identifier of the data described.
  - F4 – Data is registered or indexed in a searchable resource.
  Accessible
  
  Once the user finds the required data, they need to know how it can be accessed.
  - A1 – Data is retrievable by its identifier using a standardized communications protocol.
  - A1.1 – The protocol is open, free, and universally implementable.
  - A1.2 – The protocol allows for an authentication and authorization procedure, where necessary.
  - A2 – Metadata is accessible, even when the data is no longer available.
  Interoperable
  
  Data is more valuable when combined with other data, and it needs to interoperate with applications or workflows for analysis, storage and processing.
  - I1 – Data uses a formal, accessible, shared, and broadly applicable language for knowledge representation.
  - I2 – Data uses vocabularies that follow FAIR principles.
  - I3 – Data includes qualified references to other data.
  Reusable
  The ultimate goal of FAIR is to optimise the reuse of data. To achieve this, data and metadata should be well-described so that they can be replicated and/or combined in different settings.
  - R1 – Data is richly described, with a plurality of accurate and relevant attributes.
  - R1.1 – Data is released with a clear and accessible data usage license.
  - R1.2 – Data is associated with detailed provenance.
  - R1.3 – Data meets domain-relevant community standards.

FAIR data enables the automation of data management processes, thereby streamlining the utilization of data for innovative purposes.

Defining at a practical level what FAIR means for AI models

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Step 1: Discovery

Step 2: Understanding

Step 3: Planning

Step 4: Co-developing

Step 5: Strategy

Step 6: Implementing

Why should I do this?

In this step you will:

When should I do this?

Key concepts

FAIR principles

Application Programming Interfaces (APIs)

Common Data Models (CDMs)

Data standards

Data catalogs

Data licenses

Privacy and security

FAIR Enabling Resource (FER)

FAIR implementation profile (FIP)