Unlocking Chemical Biology’s Hidden Data: A FAIR² Data Collection

Chemical biology is one of the most data-intensive disciplines in the life sciences, routinely producing omics datasets, structural coordinates, spectroscopic libraries, bioactivity screening results, and computational outputs that far exceed what any single publication can fully report. Much of this data ends up buried in supplementary files or fragmented repositories with inconsistent metadata, and the researchers who generate it receive no formal scholarly credit for the data itself. Meanwhile, the NIH, Horizon Europe, and UKRI now mandate FAIR-aligned data management, placing researchers under compliance pressure with no clear publishing pathway that also delivers academic recognition. Frontiers’ FAIR² Data Management closes this gap. Building on the FAIR principles - Findable, Accessible, Interoperable, and Reusable - FAIR² adds AI-readiness, ethical governance, and structured validation, ensuring datasets are not only accessible but optimized for computational reuse. Developed by the FAIR² Alliance, the format is built to an open specification that ensures datasets are structured for seamless reuse across disciplines. Each submission is a peer-reviewed, citable publication focused on a single dataset, paired with an interactive data portal featuring live notebooks, AI-assisted data exploration, and direct visualization tools. Full details are available at  frontiersin.org/about/fair2. This Research Topic invites submissions from researchers across the full breadth of chemical biology - computational, structural, analytical, synthetic, and beyond. We particularly welcome dataset types currently underserved by existing publishing venues: MD trajectories, docking campaigns, cryo-EM images, X-ray diffraction data collections, and structural ensembles rarely find a formal publication home despite their significant reuse potential. Submissions may include, but are not limited to: • Omics datasets (proteomics, genomics, metabolomics, lipidomics, metallomics) • Spectral and structural libraries (NMR, mass spectrometry, cryo-EM, X-ray crystallography) • Molecular dynamics trajectories, conformational ensembles, and simulation benchmarks • Bioactivity screening results and drug-target interaction datasets • Machine learning training sets and docking benchmark collections • Enzyme kinetics databases and mechanistic data collections • Reaction condition, synthesis yield, and compound property datasets • Scholarly perspectives on the development, implementation, and impact of FAIR data in healthcare and the role of the new FAIR² Data ArticlePeer review focuses on data quality, metadata completeness, and reuse potential - not hypothesis validation. Reviewers assess whether the dataset is systematically collected, consistently documented with source, experimental details and methodology, and accompanied by sufficient context for independent reuse. Authors are encouraged to surface data previously confined to supplementary files. Topic editors: Antonello Merlino, Andrea Ilari Source: https://www.frontiersin.org/research-topics/81579/unlocking-chemical-biologys-hidden-data-a-fair2-data-collection