Develop and maintain a community-governed, open-source library of converters that transform laboratory instrument outputs into recognized open data standards, eliminating duplicated effort across the industry, advancing FAIR data, and accelerating AI/ML-enabled R&D
A multi‑stakeholder initiative to create a SEND‑aligned, FAIR‑ready framework that harmonizes in vivo efficacy data across pharma, CROs, and technology providers. The project will deliver interoperable, analysis‑ready, AI/ML‑enabled datasets that address a critical standards gap in non-clinical R&D.
Develop comprehensive benchmarks for Natural Language data mining and Scientific Chat applications to facilitate the rigorous evaluation of Large Language Model (LLM) performance across key stages of the data-to-insight pipeline in pharmaceutical R&D, ultimately supporting more effective deployment of AI in drug discovery.
The Chemical Exchange Format Committee provides a neutral forum where pharma companies, software vendors, and standards owners can collaboratively tackle chemical data formats and drive standardization.
The Minimal MetaData Set (MNMS) project brings together biopharma, academia, vendors, and regulators to demonstrate how sharing harmonised metadata alongside raw locomotion data enables fully reproducible analyses. Focusing on untreated C57BL/6J mice in standard conditions, the project highlights the value of rich, FAIR-compliant metadata in advancing data reuse and reproducibility.
PS2 is an open, vendor-neutral standards initiative for life sciences safety management spanning pharmacovigilance, vaccine safety, and medical device vigilance, designed to cut implementation costs, ease regulatory compliance, and drive innovation through modular, interoperable technology ecosystems.
We propose to develop harmonized standards for describing animal alternative methods, their characterization, and develop best practices for management and analysis of data.
The goal of the system is to make it easier for regulators and other stakeholders to exchange information about substances in medicines, supporting scientific research on the use and safety of the ingredients in medicinal products.
The goal of the In Vitro Pharmacology project is to develop a shared data template that standardizes description vitro methodologies.
This collaborative project was launched to create a bottom-up qualitative database of Natural Language Processing (NLP) use cases, enabling practitioners in pharmaceutical companies to share successes and failures with peers. In 2024, the database was expanded to include use cases relying on Large Language Models (LLMs), with the explicit aim of identifying factors that drive success or failure of NLP and LLM projects.
The use of Large Language Models such as GPT-4, presents a transformative opportunity for pharmaceutical R&D, particularly in target discovery and validation.
This project aims to convert unstructured assay protocol descriptions into a high-quality FAIR data set, and create standards for this information.
