FAIR² Data in Carbon Materials: Synthesis, Characterization, and Performance

Carbon science covers a diverse class of materials - carbon allotropes, heterostructures, graphene, diamond, and ultra-wide bandgap carbon, among others. What they share is a tendency to generate rich, multi-parameter data at every stage: synthesis and processing conditions, structural and spectroscopic characterization, and the electrochemical, functional or device-level measurements that determine whether a material is ready for real-world use.From Raman maps of graphene defects to performance curves for carbon-based sensors and devices, the data underlying carbon materials research carries enormous latent value - yet most of it never reaches the community. Studies consistently show that more than 90% of research data becomes inaccessible within a few years of publication, and carbon science, where progress depends on comparing synthesis routes and benchmarking performance across studies, feels this reproducibility gap acutely.This Research Topic responds to that problem. It invites contributions as FAIR² Data Articles - a peer-reviewed, citable article type in which a well-curated dataset is the primary scientific output, published alongside structured metadata and an interactive, AI-ready data portal designed to make the data genuinely reusable by others.We welcome data from all types of studies: observational and experimental measurements, simulation outputs, and datasets generated through the development and application of carbon-based materials and devices. Topics of interest include, but are not limited to: • Synthesis and processing datasets for graphene, carbon nanotubes, and porous or biomass-derived carbons • Structural and spectroscopic characterization data (Raman, XPS, XRD, SEM/TEM) with full acquisition metadata • Electrochemical and functional performance datasets (supercapacitors, batteries, catalysis, sensors and biosensors) • Diamond, ultra-wide bandgap, and carbon-based heterostructure or device datasets • Cross-cutting or multi-technique datasets, machine-learning benchmark datasets, and perspectives on FAIR data practices in carbon science Topic editors: Oomman K Varghese Source: https://www.frontiersin.org/research-topics/85606/fair2-data-in-carbon-materials-synthesis-characterization-and-performance