The principles behind the model have been evolving for many years, and the FAIR data model builds on earlier work in the areas of data sharing, data management, and data preservation. The FAIR data model has gained widespread adoption in the research community and is now considered a key framework for improving the management and sharing of data.

The model provides a framework for ensuring that data is well-described, easy to locate, and can be reused by other researchers (data users) in the future. It emphasizes the use of persistent identifiers, standardized metadata, open licenses, and other best practices.

Findable relates to the idea that data collected should be simple for both humans and machines to find and recognize. It is required to give the data and related metadata distinctive and durable identifiers, to make the data discoverable. Utilizing defined vocabularies and ontologies, the metadata should be fully and accurately described and made accessible through searchable registries and catalogs. Along with contact information for the data’s creators or curators, the metadata ought to contain details on the data’s provenance, versioning, and licensing. Researchers can more quickly find and access pertinent data by making it findable, which can save time and effort, encourage scientific collaboration, and increase reproducibility.

Accessible refers to well-documented, and easily usable interfaces that facilitate discovery, searching, and accessing data by both humans and applications. Data must also be stored in a well-defined, standardized format. To make it simpler to search for and assess the data or metadata. Accessibility also entails making sure that the data and the metadata that goes with it are simple to comprehend and use, which calls for detailed descriptions of the data’s structure, substance, and context. Data must be made accessible in a way that satisfies the requirements of different stakeholders, business users, and anyone who leverages data in their day-to-day activities.

Interoperability in the FAIR data model refers to a data set’s capacity for integration and analysis across various platforms, systems, and applications. Data sharing and reuse are made possible through interoperability, which is essential for fostering collaboration. Defining an interoperability framework can help facilitate the adoption of community practices for collaboration, data formats, and metadata standards. Data must be formatted in a consistent manner that is generally acknowledged and understood by all members within your internal data community. To enable the interchange of information between various systems and applications, this includes the usage of standard metadata and common data formats. That way, all data users and internal stakeholders can better communicate and exchange data, enabling the business strategy and adoption of a data culture.

Reusable within the FAIR model refers to the ability of data to be used and repurposed. The reusability of data is essential to ensure that data may be used to its fullest extent across an enterprise. Various stakeholders and lines of business can both leverage and “use” data for their specific functions. Data must be made available with usage rights that are explicit and allow for unlimited reuse and repurposing to be reusable under the FAIR model. Furthermore, the data must be made available in a machine-readable format that is well-known and understood by all relevant parties. Reusing data can take many different forms, such as reanalyzing previously acquired data, merging data from diverse sources to produce fresh insights, or mixing data from numerous use cases to answer new business questions. The term “Data is the New Oil” was coined by many but has been challenged as Oil cannot be reused where Data can and should be.

5 FAIR Steps:

• Unique IDs: Unique IDs for vocabulary are always referenced correctly, globally, unique, and never changing
• Describing Datasets: being generous, comprehensive, encompassing details about the context, quality and condition of the data, as well as its provenance and lineage
• Semantic Modeling: permits us to concentrate on knowledge and meaning rather than just describing data structures.
• Integrated Controlled Vocabularies: connect to common vocabularies. These vocabularies are used by our teams, groups, business, or external parties.
• Simple Knowledge Organization System: captures similarities, makes them clear, and allows for the exchange of data and technology across applications.

By leveraging the FAIR model, organizations can help accelerate data discovery, collaboration, and foster data usage and literacy within an organization. This approach can also support data stewardship by providing platforms and tools that support data acquisition, curation, storage, sharing, and reuse is essential. As many data governance programs are moving towards a collaborative model, many agree that the FAIR model serves as a great foundation for promoting cross-functional relationships and the adoption of a data-driven culture.