Every sector is racing to make its data AI-ready. Senscience wants to make it easier. It has built a human-in-the-loop AI Data Steward which aims to turn raw data into intelligent, reusable products that empower experts and accelerate insight.
“Every business today is a data business, but most spend an enormous effort cleaning, reconciling, and documenting data before they can even start to use it,” says Dr. Sean Hill, co-founder and CEO of Senscience, a venture backed by Frontiers, an open-science publisher. “Our human-in-the-loop AI Data Steward automates the hardest parts of data organization and validation while empowering domain experts to manage their own data effortlessly and ensure it is richly documented and meaningful for reuse.”
The result is a new class of AI-ready data products complete with context, provenance, and interactive AI tools that let users ‘talk to the data,” he says. Senscience’s FAIR² Data Management was officially launched at the International Data Week event in Brisbane, Australia, which took place 13-16 October.
“Whether in finance ensuring data lineage, pharma accelerating discovery, or sustainability teams linking economic and environmental data, Senscience’s FAIR² Data Management provides a trusted foundation for responsible AI — turning data from an operational burden into a catalyst for innovation,” Hill said in response to written questions from The Innovator.
A key advantage of this approach is how it empowers domain experts, Hill says. “By automating the most time-consuming and technical aspects of data management — such as schema alignment, metadata generation, and compliance checks — the AI Data Steward frees experts to focus on what matters most: ensuring that datasets are richly documented and meaningful for reuse. This also means that analytics and modeling can begin immediately, with AI assistance available from the start. “
The initial goal is to enable open-source scientific research. Most scientific data never fuels the discoveries they should, says Hill. For every 100 datasets created around 80 remain in the lab, 20 are shared by rarely reused and fewer than two meet FAIR (Findable, Accessible, Interoperable, Reusable) standards and only one typically drives new findings. The result, says Senscience, is delayed cancer treatments, climate models short on evidence and research that can’t be reproduced.
“ With Frontiers FAIR² Data Management, no dataset and no discovery need ever be lost again — every contribution can now fuel progress, earn the credit it deserves, and unleash science,” Dr. Kamila Markram, CEO of Frontiers, said in a statement. She and her husband Henry Markram, who is also a neuroscientist, founded Frontiers with the stated goal of accelerating collaboration and increasing the quality of science across all academia through open science.
Beyond science, there is now a strong and growing interest from other sectors that rely on high-quality data to power models and make decisions, Hill says. He listed the following emerging opportunities:
- Pharma and life sciences: structuring experimental and clinical data for AI-assisted discovery and validation.
- Finance and insurance: ensuring data lineage, auditability, and explainability in risk modeling and predictive analytics.
- Manufacturing and materials: converting experimental and operational data into standardized, machine-actionable knowledge assets.
- Sustainability and climate: integrating environmental, economic, and policy data for forecasting and planning.
- Government and policy: creating transparent, interoperable data foundations for evidence-based decision-making.
FAIR² Data Management has already launched a number of pilot projects, The SARS-CoV-2 Variant Properties dataset unites experimental findings and predictive results in a traceable format that supports comparison and further study. In neuroscience, the Preclinical Brain Injury MRI dataset provides a reproducible foundation that allows imaging data from multiple centers to be understood and applied consistently. In sustainability research, the Environmental Pressure Indicators dataset brings together decades of observed and projected data in a transparent framework for examining long-term trends. And the Indo-Pacific Atoll Biodiversity dataset links ecological, climatic, and human-use information across hundreds of atolls, offering a coherent basis for conservation and environmental planning.
By embedding methods, provenance, and context directly within each dataset, FAIR² Data Management ensures that data retain their context, integrity, and usability — enabling confident, responsible reuse across disciplines, says Hill. The same principles are relevant beyond research: organizations in fields such as pharmaceuticals, energy, and materials face similar challenges in preparing complex data so they can be understood, verified, and confidently applied to new problems, he says.
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