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 (CC BY 4.0 / MIT), and a checksum manifest for integrity verification. Meant for a member’s IT or data-governance function to vet and redistribute internally. Complements, but is kept separate from, the framework documentation on the FAIR CoE hub.
Resource Tag: Delivering Data Driven Value
FAIR Business Value Calculator
Interactive, offline calculator that quantifies the financial return of FAIR data practices. Users select a “recipe” tied to a specific value driver, enter organisation-specific inputs (from 195 defined parameters), and get a savings/ROI estimate. Includes the framework visualisation embedded. Self-contained HTML file : opens directly in any browser, no install, server, or internet connection required. Runs entirely client-side from disk; nothing is uploaded, and saved scenarios stay in local browser storage only. CC BY 4.0 (content) / MIT (code).
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. Self-contained HTML file : opens directly in any browser, no install, server, or internet connection required. Built for presentations and first-look exploration of the framework’s structure ahead of using the calculator itself. Published by the Pistoia Alliance FAIR Community of Experts under CC BY 4.0.
Why Connected Research is the Next Frontier in Discovery
For decades, the standard response to a research bottleneck has been simple: “We need more data.” But in today’s landscape, the limiting factor is rarely a lack of information—it is the fragmentation of the data we already own.
Join info pro Mary Ellen Bates and Mark Hahnel (Digital Science) for a discussion on “The Data Integration Challenge.” In this session, we will move past the hype of AI-first solutions to explore the architectural shifts necessary to truly unlock scientific productivity. Drawing on insights from recent industry interviews, we will explore why the path to faster, more defensible decision-making lies in knowledge graphs that link biological entities, clinical records, and literature across siloed systems.
We will discuss how organizations can shift their strategy from mere collection to meaningful connection, ensuring that data is not just findable, but ready for the complex cross-disciplinary reasoning that drives drug discovery.
Key Takeaways
- The Invisible Tax: Understanding why data preparation consumes the majority of research time and how to reclaim it.
- The Architecture of Trust: How connected data and provenance chains allow teams to defend AI-derived insights to regulators and stakeholders.
- Connecting Domains: Practical approaches to bridging the gap between clinical, chemical, and biological datasets using knowledge graphs.
GSRS Software Roadmap (2026+)
The GSRS Software Roadmap sets out the planned development of the Global Substance Registration System (GSRS), the open-source, FDA/NCATS-maintained platform for exchanging information about substances in medicines. Developed collaboratively between our GSRS Community and regulators, it maps upcoming releases through 2029 across three tracks: modernizing the architecture, adding new capabilities, and improving data quality.
Please note that this roadmap is subject to change; timelines and deliverables may be adjusted as circumstances evolve.
IDMP Ontology Information Sheet
The IDMP Ontology is an expert-built, vendor-neutral framework that gives medicinal product data a shared and consistent digital language across systems, companies, and borders. It is the pharmaceutical industry’s directly deployable implementation layer for the five ISO IDMP standards, owned and governed by the Pistoia Alliance as a neutral not-for-profit.
Rather than each organization interpreting the ISO standards on its own, the IDMP Ontology provides a shared structure that keeps product data consistent, connected, and interoperable for internal data management, regulatory alignment, cross-system integration, and AI-ready data.
This information sheet is a plain-language introduction for regulators, industry teams, and anyone new to the ontology.
- What the IDMP Ontology is and what makes an ontology different from a spreadsheet or database schema.
- The problems it solves, like harmonizing multiple names and IDs for the same product, and unifying interpretations of the ISO standards.
- How it’s governed: Pistoia Alliance stewardship, a vendor-neutral Steering Committee, and quarterly maintenance releases.
- How it’s used today, from EMA PMS mapping and multi-market submissions to partner data exchange and AI readiness.
- How to get started and access it: three implementation levels and the public, member, and sponsor access tiers.
Download the information sheet to learn more.
Pharma General Ontology (PGO)-Terminology: Building a Semantic Backbone for FAIR and AI-Ready Knowledge Graphs
This talk was presented at Bio-IT World 2026 (Knowledge Graphs session, Boston) by Giovanni Nisato, PhD — Project Manager, Pistoia Alliance.
As life science innovation becomes increasingly data-driven, semantic interoperability is essential to scaling FAIR data and trustworthy AI. Yet the pharma ecosystem still lacks a shared reference vocabulary. Today, the same concept (compound, molecule, substance, drug) is used inconsistently across domains and organizations, deep domain ontologies proliferate in disconnected silos, and there is no cross-pharma lingua franca. Teams spend enormous effort on semantic reconciliation before any real work begins, and interoperability stays aspirational.
The Pharma General Ontology (PGO)-Terminology project is a Pistoia Alliance–led, industry-governed initiative that tackles this challenge head-on. It is building a community-governed reference set of core pharmaceutical concepts. Think if it as a shared “lingua franca” to serve as a semantic backbone for interoperable knowledge graphs, cross-domain data integration, and AI-ready foundations across the pharma ecosystem.
In this talk, Giovanni Nisato introduces PGO-Terminology’s scope, design principles, current stage, and long-term vision.
What you’ll learn:
- The problem: Why limited semantic interoperability creates costly “semantic silos” that slow the path to data-centricity across R&D, manufacturing, clinical, regulatory, and commercial domains.
- What is PGO-Terminology? A community-governed, cross-domain reference set of core concepts, published open access. It is not a replacement for MedDRA, SNOMED, or ChEBI, not a top-down mandate, and not (at this stage) a full ontology.
- How it’s built: An industry governance model with clear definition rules: reuse existing definitions from known sources, keep them human-readable across domains, and give every concept a publicly resolvable URL/IRI.
- Where the project stands: Phase 1 delivered 17 core Research & Early Development concepts at controlled-vocabulary level. Phase 2 is expanding beyond R&D, moving toward machine-readable output, and mappings to external ontologies.
- Why it matters for AI: Knowledge graphs are only as good as their underlying semantics. Features of the PGO-Terminology help to reduce hallucination risk in RAG and LLM pipelines.
- The long-term vision: To deliver a machine-readable core terminology that is discoverable on resources such as EMBL-EBI OLS and BioPortal, to promote data assets tagged with PGO URIs, and to facilitate cross-company, CRO, partner, and regulator data exchange without manual reconciliation.
PGO-Terminology is developed openly on GitHub under permissive licenses (CC BY 4.0 / MIT)
Learn more on the PGO-Terminology project webpage.
Pistoia Alliance Ontology Design Principles
The Pistoia Alliance Ontology Design Principles establishes a comprehensive governance framework for developing, maintaining, and releasing high-quality ontologies and semantic artefacts across the life sciences. It defines best practices covering ontology architecture, metadata standards, term reuse, modularity, quality control, versioning, documentation, and long-term governance, while promoting interoperability through the adoption of FAIR principles and established standards such as the Basic Formal Ontology (BFO). Designed for ontology developers, project teams, and governance bodies, the guidance provides a consistent foundation for creating sustainable, reusable semantic resources that support collaboration across the Pistoia Alliance ecosystem.
FAIR Forward in Life Sciences
What does it take to move FAIR data adoption forward across an entire ecosystem?
On April 2026, 36 pharma and life sciences professionals gathered at the Royal Society of Medicine in London for the FAIR Forward Workshop, held alongside the Pistoia Alliance Annual Conference. The participants spanned industry, academia, and service providers and came prepared: a pre-workshop survey mapped their views on FAIR maturity, actors in the ecosystems, and what they see as constraints and enablers.
On the day, four groups worked through a structured landscape-mapping exercise, placing thematic elements on a shared board across effort and time-horizon axes. The result: 28 clusters capturing what it would take the lifesceince ecosystem to move it forward in terms of FAIR data implementation.
In this webinar, Pistoia Alliance FAIR Steering Group members, who also participated on the day, will present key findings and what they mean for the field.
FAIR Forward 2026 Ecosystem Landscape Map
On 15 April 2026, 36 participants from across the pharmaceutical and life sciences ecosystem gathered at the Royal Society of Medicine in London for the FAIR Forward Workshop, held as part of the Pistoia Alliance Annual Conference. Working in four pre-assigned groups (BALDER, INANNA, OSIRIS, PERSEPHONE), participants independently identified the actors, constraints, and enabling assets shaping FAIR data adoption in pharma R&D, then collectively placed their outputs on a shared board. This interactive map is the primary output of that exercise, produced by the Pistoia Alliance FAIR Community of Experts.
The map organises 28 thematic clusters on two axes: time horizon (left to right, from near-term to long-term) and effort required (bottom to top, from low to high). Three zones emerged from the collective synthesis: a low-hanging fruit zone in the lower left, where resources and tools are available today at low effort; a collaborative potential zone in the centre, where progress requires coordinated community action; and a hard-to-do zone in the upper right, where change depends on regulatory, legislative, and systemic conditions that no single actor controls. Each hexagonal cluster is interactive: click to expand and explore the elements it contains. Use pinch-to-zoom or the on-screen controls to navigate.
This is not a roadmap. It is a landscape: a shared, multi-perspective snapshot of where the ecosystem stands and what it will take to move it forward. Different actors will read it differently. A pharma company assessing its FAIR programme may focus on the capability and culture clusters. A technology provider may see entry points in the low-hanging fruit zone. A standards body or regulator may find the hard-to-do zone most relevant. We invite you to explore it from your own vantage point, identify where your organisation sits today, and consider what role you might play in moving the landscape forward.
Building the Future of IDMP-O: Community, Adoption, and What’s Next
The IDMP Ontology has entered a new phase defined by long-term stewardship, governed quarterly releases, and a growing community of implementers. Join us for this Community of Interest webinar to hear directly from the team shaping IDMP-O’s future: where it stands today, how it’s being used in practice, and how you can get involved. As medicines data requirements tighten globally, the case for a shared digital language for IDMP has never been stronger.
Agenda
- Welcome and introduction
- The next phase of IDMP-O: vision and funding
An overview of the new stewardship model, governance structure, and what the long-term commitment means for adopters. - Building the IDMP-O community
How the Community of Interest works, how to participate, and why peer engagement is central to the ontology’s evolution. - Current use and refactoring strategy
Real-world applications of IDMP-O and the roadmap for simplifying implementation and streamlining adoption. - Demonstration: IDMP-O supporting EMA PMS use cases
A live look at how IDMP-O supports the EMA’s Product Management System. - Open discussion and Q&A
Bring your questions. Hear how your peers are navigating implementation.
Speakers
- Aditya Tyagi, Pistoia Alliance
- Heiner Oberkampf, CEO & Co-Founder, Accurids
- Sheila Elz, Senior RIM Manager, Boehringer Ingelheim
- Raphael Sergent, Head of QA & Pharma Solution Lead, Accurids
FAIR-Community of Experts Documentation Hub
The FAIR Community of Experts (FAIR CoE) Documentation Hub is a collaborative knowledge base created by the Pistoia Alliance to support the implementation of FAIR (Findable, Accessible, Interoperable, and Reusable) data principles across the life sciences industry. It brings together best practices, frameworks, tools, maturity models, case studies, and community-driven resources designed to help organizations improve data quality, interoperability, AI-readiness, and long-term research value. The hub showcases outputs from the FAIR for Pharma community, including the FAIR Toolkit, FAIR Maturity Matrix, implementation guidance, and working group initiatives, while providing an open platform for sharing standards, methodologies, and practical approaches that enable more effective data-driven research and collaboration in pharmaceutical and healthcare R&D.