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FAIR

Toolkit

Delivering Data Driven Value

FAIR Business Value Framework v1.0

Full offline release package of the FAIR Business Value Framework v1.0: the calculator, the standalone visualisation, definitions for all 195 input parameters, six worked reference cases with expected results for validation, a README, dual licence, and a checksum manifest for integrity verification.

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Toolkit

Delivering Data Driven Value

FAIR Business Value Calculator

Interactive, offline calculator that quantifies the financial return of FAIR data practices.

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Toolkit

Delivering Data Driven Value

FAIR Business Value Visualisation

Interactive sunburst visualisation of the FAIR Business Value Framework, mapping its four strategic value drivers, twelve business value areas, and thirty-eight underlying value items as a single explorable diagram.

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Webinar

Delivering Data Driven Value

FAIR Forward in Life Sciences

Discover the key findings from the FAIR Forward Workshop, where experts from across the life sciences ecosystem identified the priorities, challenges, and opportunities for advancing FAIR data adoption.

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Presentations

Delivering Data Driven Value

FAIR Forward 2026 Ecosystem Landscape Map

The FAIR Forward 2026 Ecosystem Landscape Map is a visualization that captures the outcomes of the Pistoia Alliance FAIR CoE workshop, mapping the key technical, organisational and regulatory factors influencing FAIR data adoption across the life sciences ecosystem, from immediate opportunities to long-term challenges.

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Github Repository

Delivering Data Driven Value

FAIR-Community of Experts Documentation Hub

The FAIR Community of Experts Documentation Hub is a Pistoia Alliance knowledge platform that provides tools, guidance, frameworks, and community-driven best practices to help life sciences organizations implement FAIR data principles and improve data interoperability, reuse, and AI-readiness.

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Whitepaper

Harnessing AI To Expedite R&D

Advancing AI & ML In Life Sciences R&D Whitepaper 2025

The Next Phase of AI in Life Sciences The findings presented in this report reflect the state of thinking across the life sciences sector during the second half of 2025. In a field where technological cycles increasingly compress into months rather than years, it is important to acknowledge the temporal context. The pace of AI advancement continues to accelerate. Organizational capability evolves more gradually. The widening distance between these two curves is central to the conclusions of this report.

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Video

Delivering Data Driven Value

FAIR Maturity Matrix Introduction – Japanese Subtitles

This video, with Japanese subtitles, introduces the FAIR principles and explains how the Pistoia Alliance Community of Experts developed an organizational FAIR maturity model to help life sciences organizations benchmark and assess their FAIR implementation progress.

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Video

Delivering Data Driven Value

Unlocking the ROI of FAIR Data in Pharma and Life Sciences – Japanese Subtitles

This video, with Japanes subtitles, explains how applying the FAIR data principles helps pharmaceutical and life sciences organizations unlock ROI by making data easier to find, integrate, and reuse, accelerating research and innovation.

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Webinar

Delivering Data Driven Value

FAIR Business Value Framework: An Introduction

This webinar shares new insights from the Pistoia Alliance’s FAIR working group—including the FAIR Business Value Report and forthcoming FAIR Value Calculator—to show how FAIR data maturity delivers measurable business value and provides a structured framework for modelling the ROI of FAIR investments.

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Presentations

Delivering Data Driven Value

Interoperability in Cheminformatics – Susan Leung

Susan Leung outlines the significant interoperability and standardization challenges in exchanging cheminformatics data across complex, multi-vendor environments, calling for improved communication, education, and harmonization of formats.

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Presentations

Delivering Data Driven Value

Challenges in Cheminformatics: The View of An Independent Consultant

Thomas Doerner highlights persistent chemical and technical challenges in cheminformatics—from unFAIR data to complex compound representation and non-classic environments—and urges the community to prioritize and collaboratively address them.

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