In Vivo Efficacy Data Standards Initiative
Current oncology efficacy data is fragmented and hard to reuse. By defining practical, pre-competitive standards aligned with CDISC SEND, the life sciences community can unlock powerful AI insights, streamline regulatory submissions, and accelerate innovation for patient impact.
Benchmarks for Natural Language Data Mining with LLMs
We are proposing 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.
Allotropy Open-Source Project
Proprietary lab instrument data formats hinder FAIR data and interoperability. We propose an open-source, community-driven effort to standardize data conversion, enabling scalable reuse, reducing redundancy, and maximizing scientific ROI.
Chemical Exchange Format Committee
Inconsistent chemical exchange formats hinder interoperability and innovation. A vendor-neutral committee will clarify standards, document ambiguities, and guide improvements—boosting data quality, reducing inefficiencies, and enabling accurate, scalable chemical data exchange.
The Safety and PV AI Community of Experts
The Safety and PV AI Community of Expert aims to act as a forum to address the challenge of effectively integrating artificial intelligence (AI) into pharmacovigilance (PV) practices for the ultimate benefits of patients worldwide.
Secure Benchmarking of AI Solutions
A secure benchmarking system for AI solutions enables performance assessment on confidential datasets. By protecting sensitive information, this project supports objective evaluation, fosters trust in AI tools, and accelerates innovation in pharmaceutical research and development.
Agent-Agent Communication Protocol and AI Agent Standard Specs
This initiative aims to establish a standardized framework for AI agents to communicate and collaborate effectively across diverse platforms. By defining clear protocols and roles, it seeks to enhance interoperability, streamline workflows, and accelerate innovation in pharmaceutical research and development.
BAO Update
The current ontologies available to support annotation of experiments and assays (BAO,AFO) don't suffice yet to enable experiment data interoperability and their reusability.
Standard API Protocol For Ontology
Organisations are increasingly working to standardised ontologies and terminologies to increase the interoperability of data as part of their FAIR data strategy.
Synthetic Data in Medical Imaging
Artificial Intelligence (AI) models have been shown to have impressive capabilities in the medical image analysis domain with potential to assist clinicians in their everyday tasks, from the diagnosis to disease monitorisation and surgical planning procedure, thereby enhancing patient care.
Instruments and Equipment Inventory Ontology
The problem to be solved is the lack of an ontology and/or standard vocabularies in instruments and equipment inventories, which makes the categorizing and querying of these systems difficult.