Go FAIR: Guiding principles for scientific data management and stewardship

The guide aims to provide guiding principles to improve the searchability, accessibility, interoperability and reusability of scientific data generated in research processes as data increases in volume, complexity and speed of creation.

The Go FAIR initiative was initially promoted by the ministries of science and education of the Netherlands, Germany, and France as a coordinated effort to implement the FAIR principles at the European and global levels. It is organized as a decentralized network of national offices, hosted by institutions such as the San Diego Supercomputer Center (USA), the Brazilian Institute of Information in Science and Technology (IBICT), and the Austrian consortium formed by TU Wien, TU Graz, and the University of Vienna. Overall coordination is carried out by the International Support and Coordination Office (GFISCO), with additional technical and strategic support from the GO FAIR Foundation, which drives FAIR sustainability and certification.

El proceso de “fairificación” contempla cuatro ejes para que los datos sean:

1. Buscables: tanto por humanos como por máquinas, permitiendo el descubrimiento automático de datasets y servicios.
   1. Los (meta)datos son identificados mediante un identificador único y persistente.
   2. Los datos se describen con metadatos ricos en información.
   3. Los metadatos incluyen clara y explícitamente el identificador de los datos.
   4. Los (meta)datos son registrados o indexados en fuentes buscables y recuperables.
2. Accesibles: una vez encontrados, las personas usuarias necesitan saber cómo acceder a los datos, incluyendo en su caso la autenticación y/o autorización.
   1. Los (meta)datos son recuperables mediante su identificador persistente utilizando protocolos de comunicación estándares.
      1. Dichos protocolos son abiertos, libres y universalmente implementables.
      2. Dichos protocolos permiten procedimientos de autenticación y autorización si es necesario.
   2. Los metadatos son accesibles, incluso cuando los datos ya no están disponibles.
3. Interoperables: generalmente los datos necesitan ser integrados con otros datos, interoperando con aplicaciones o flujos de trabajo para el análisis, el almacenamiento o el procesamiento.
   1. Los (meta)datos usan un lenguaje accesible, comprensible y ampliamente aplicable para la representación del conocimiento.
   2. Los (meta)datos utilizan vocabularios que siguen los principios FAIR.
   3. Los (meta)datos incluyen referencias a otros (meta)datos.
4. Reutilizables: para lograrlo, los metadatos y los datos deben estar bien descritos para que puedan ser replicados/combinados en diferentes conjuntos.
   1. Los (meta)datos son ricamente descritos con una pluralidad de atributos relevantes y precisos.
      1. Los (meta)datos se facilitan con una licencia de uso clara y accesible.
      2. Los (meta)datos son asociados con su procedencia detallada.
      3. Los (meta)datos se sirven mediante estándares relevantes para la comunidad de su ámbito de conocimiento.

The Go FAIR initiative primarily benefits researchers, scientific institutions, data stewards, and funders by facilitating more open, accessible, and reusable data management. Its main stakeholders include the Implementation Networks (INs), national offices in various countries, and technological infrastructures. Libraries, repositories, scientific publishers, and international organizations such as EOSC, RDA, CODATA, and WDS also participate, all of which promote open science and data interoperability.

As a direct result of the application of the FAIR principles, the Go FAIR community has developed tools, recommendations, and concrete procedures to support their implementation. Key outcomes include:

• The Metadata for Machines (M4M) working group, which creates machine-readable metadata from the outset.
• The FAIR Implementation Profile (FIP) working group, which defines common best practices.
• The FAIR Data working group, focused on general principles and guidelines.
• Guidelines for selecting and configuring infrastructures designed for FAIR digital objects.
• The GOing FAIR, DOing FAIR initiative, which gathers practical implementation experiences.
• The Convergence Matrix tool, used to align FAIR strategies across different communities.
• The FAIR Made Easy for Funders resource, aimed at research funders.

In addition, the International Convergence Symposium, co-organized by Go FAIR and CODATA, showcased successful cases of FAIRification of data, interoperable vocabularies, technical specifications, and governance models for open science.

One of the main challenges of the Go FAIR initiative has been harmonizing the implementation of the FAIR principles across highly diverse contexts, both in terms of scientific disciplines and geographic regions. Lack of interoperability between systems, vocabularies, and formats remains a key issue, especially in communities with less developed digital infrastructures.

Another significant challenge is the training of key stakeholders (researchers, data stewards, technical staff) and the promotion of a responsible and collaborative data management culture, which requires sustained efforts in capacity building and awareness.

Ensuring the long-term sustainability of FAIR infrastructures and services—covering funding, maintenance, and certification—is also a major concern. Finally, the need to align with national and international policies and regulations demands ongoing coordination among offices, institutions, and global organizations.

• The FAIR principles form the conceptual and technical foundation of the European Open Science Cloud (EOSC), driving a global infrastructure where data and digital services can be optimally found and reused.
• Go FAIR has established a solid governance model based on a bottom-up, stakeholder-driven approach, enabling its adoption across multiple disciplines and regions.
• Tools such as the Convergence Matrix, GOing FAIR DOing FAIR, Metadata4Machines (M4M), and FAIR Made Easy for Funders have been launched and implemented, along with key working groups (M4M, FIP, FAIR Data), reinforcing the practical applicability of the FAIR principles.
• The Convergence Symposium, co-organized with CODATA, showcases real cases of “FAIRification” and governance experience, enhancing knowledge sharing and strengthening Go FAIR’s credibility.
• The creation of a FAIR Certification Office, through the Pioneer Program, has laid the groundwork for assessing, certifying, and ensuring the quality of infrastructures, events, individuals, and technologies according to FAIR standards—building trust and accelerating adoption.
• Go FAIR has demonstrated its ability to expand the global network of FAIR Data & Services, coordinating national offices and Implementation Networks (INs) that promote convergence across communities and types of infrastructure.

• Go FAIR: https://www.go-fair.org/
• FAIR Principles: https://www.go-fair.org/fair-principles/
• FAIR Convergence Conference: https://conference.codata.org/conference/FAIRconvergence2020/plenary_sessions/
• FAIR implementation profile: https://www.go-fair.org/how-to-go-fair/fair-implementation-profile/
• GO-FAIR: A Member States-Up Strategy for the EOSC Implementation : https://www.zbw-mediatalk.eu/2017/01/go-fair-a-member-states-up-strategy-for-the-eosc-implementation
• GO FAIR initiative: https://www.dtls.nl/large-scale-research-infrastructures/go-fair/