A scientist operating an autoclave. Image by Tim Sandle.
Huge quantities of valuable research data remain unused, trapped in laboratories or lost to time. It is estimated that out of every 100 datasets produced, about 80 stay within the lab, 20 are shared but seldom reused, fewer than two meet the standards of shared research, and only one typically leads to new findings.
Researchers are seeking to change this with artificial intelligence. This is with a new algorithm called FAIR² Data Management. This AIdriven system makes datasets reusable, verifiable, and citable.
By uniting curation, compliance, peer review, and interactive visualization in one platform, FAIR² empowers scientists to share their work responsibly and gain recognition.
What’s FAIR?
The openscience publisher Frontiers has introduced Frontiers FAIR² Data Management, described as a comprehensive, AIpowered research data service. It is designed to make data both reusable and properly credited by combining all essential steps — curation, compliance checks, AIready formatting, peer review, an interactive portal, certification, and permanent hosting — into one seamless process. The goal is to ensure that today’s research investments translate into faster advances in health, sustainability, and technology.
FAIR² builds on the FAIR principles (Findable, Accessible, Interoperable and Reusable) with an expanded open framework that guarantees every dataset is AIcompatible and ethically reusable by both humans and machines. The FAIR acronym and principles were defined in a March 2016 paper in the journal Scientific Data by a consortium of scientists and organisations. At the 2016 G20 Hangzhou summit, the G20 leaders issued a statement endorsing the application of FAIR principles to research.
The FAIR² Data Management system is the first working implementation of this model, arriving at a moment when research output is growing rapidly and artificial intelligence is reshaping how discoveries are made. It turns highlevel principles into real, scalable infrastructure with measurable impact.
Democratic science?
It is hoped that many obstacles within the current science trajectory will be overcome, including slower progress in cancer treatment, climate models that lack sufficient evidence, and studies that cannot be replicated.
To achieve these aims, work that once required months of manual effort — from organising and verifying datasets to generating metadata and publishable outputs — is now completed in minutes by the AI Data Steward, powered by Senscience, the Frontiers venture behind FAIR².
Researchers who submit their data receive four integrated outputs: a certified Data Package, a peerreviewed and citable Data Article, an Interactive Data Portal featuring visualisations and AI chat, and a FAIR² Certificate. Each element includes quality controls and clear summaries that make the data easier to understand for general users and more compatible across research disciplines.
These outputs should ensure that every dataset is preserved, validated, citable, and reusable, helping accelerate discovery while giving researchers proper recognition.
Example: Environmental Pressure Indicators (19902050)
Combining observed data and modeled forecasts across 43 countries over six decades, this dataset tracks emissions, waste, population, and GDP. It underpins sustainability benchmarking and evidencebased climate policy planning.
Frontiers FAIR² also seeks to enhance visibility and accessibility, supporting responsible reuse by scientists, policymakers, practitioners, communities, and even AI systems, allowing society to extract greater value from its investment in science.
In terms of the reception from the global science community, according to Dr. Ángel Borja, Principal Researcher, AZTI, Marine Research, Basque Research and Technology Alliance (BRTA): “I highly [recommend using] this kind of data curation and publication of articles, because you can generate information very quickly and it’s useful formatting for any end users.”
