Network Meeting - 12 May 2026
- Arie Baak
- Jun 8
- 2 min read
TOPICS
The networking meeting covered the following topics:
Welcome & Introductions
Updates
Starter Kit - 42Hills swarm learning
Pistoia Alliance - FAIR Maturity Matrix and FAIR Business Value Framework
Parking lot & close
SUMMARY
Starter Kit – 42Hills Swarmlearning
42Hills presented the Swarm Learning Hub, a federated data platform built on the principle of not sharing data directly, instead, algorithms and queries travel to where the data resides, and only insights return. The hub provides a self-service environment where organisations join a network, install a local node, and retain full control over who can access their data and under what conditions.
An interactive geospatial catalog makes datasets discoverable while keeping the underlying data local, with access levels ranging from read-only browsing through to federated machine learning training across multiple nodes. Identity management, for both users and machines, underpins every interaction, with short-lived tokens and a permissioned blockchain providing verifiable, auditable trust without a central custodian.
A live demo showed real-time analytics streaming across nodes in two countries simultaneously, and a gene therapy monitoring use case illustrated how dashboards can surface only clinically relevant signals, without exposing raw patient data. Within the LIFES ecosystem, 42Hills positions the hub as a FAIR-compatible data station capable of integrating with existing metadata catalogs and operating across jurisdictions.
Pistoia Alliance - FAIR Maturity Matrix and FAIR Business Value Framework
The Pistoia Alliance presented the FAIR Maturity Matrix, a publicly available framework for assessing and guiding organizational FAIRness, developed through pre-competitive collaboration among leading life science companies. The matrix covers seven interconnected dimensions, FAIR data, leadership, strategy, people, processes, knowledge, and tools and infrastructure, across six maturity levels, from initial awareness through to full ecosystem-level interoperability. A key insight is that the matrix is an organisational maturity model, not just a data maturity model: making data FAIR requires changing culture, leadership, and processes, not just applying technical standards. Each level describes what FAIR looks like in practice for different roles and personas, including a dedicated autonomous agent persona; reflecting the principle that trustworthy AI depends on FAIR data. The matrix is sector-agnostic, free to use under CC BY 4.0, and designed as a conversation and orientation tool rather than a prescriptive checklist.
The FAIR Business Value Framework, currently in development, was previewed which sets out to answer the question every business leader eventually asks: what is the return on investment for going FAIR? Based on a survey of Pistoia Alliance members and a review of the literature on the topic, four primary business drivers were identified: cost, speed, effectiveness, and trust. Each breaks down into specific, quantifiable metrics, thirty-eight in total, covering areas such as avoided experiment repetition, faster data discovery, reduced mapping overhead between datasets, and increased trustworthiness of AI outputs. Metrics come with calculation recipes and a disclosed methodology, allowing organisations to model expected benefits over time relative to project costs, and to track what value is actually being realized once a FAIRification journey is underway.
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