FAIR² Data Articles in Future Transportation: Certifying, Sharing, and Reusing Transportation Datasets

Transportation research generates vast and heterogeneous datasets — from mobility surveys and traffic sensors to autonomous vehicle telemetry, emissions logs, and urban infrastructure records. Yet much of this data remains siloed, inconsistently formatted, or locked within individual projects, limiting its potential for reuse, cross-disciplinary analysis, and policy application.The FAIR principles — Findability, Accessibility, Interoperability, and Reusability — were established to address exactly this challenge. Frontiers has taken these principles further with FAIR² Data Management, an AI-powered service that certifies, structures, and publishes research data as a formally recognized scientific output. FAIR² extends traditional FAIR compliance by adding AI-readiness, ethical governance, and a structured, open specification — ensuring datasets are not only accessible but validated and optimized for modern computational and policy-driven research.In the field of future transportation, the quality and reusability of underlying datasets is critical. A dataset describing real-world autonomous vehicle behavior, multimodal travel patterns, or road safety incidents has value far beyond any single paper. FAIR² Data Articles give that value a permanent, peer-reviewed, and citable form.Why It Matters“Modern Transportation research is heavily data-driven. Although a vast amount of data is potentially available, there is a lack of sufficiently validated data for credible scientific use. FAIR² Data Articles change this. When a dataset is peer-reviewed, structured, and openly published, it stops being a supporting file and becomes a scientific contribution in its own right. The future of transportation depends not only on the insights we generate, but on the data we leave behind for others to build on.”— Prof. Mehrdad Dianati, Field Chief Editor, Frontiers in Future Transportation, Queen’s University BelfastThis Research Topic welcomes FAIR² Data Article submissions from researchers working across all areas covered by Frontiers in Future Transportation, including but not limited to:o Autonomous and connected vehicleso Electric mobility and charging infrastructureo Shared and on-demand transport systemso Active travel and pedestrian mobilityo Urban freight and last-mile logisticso Traffic management and intelligent transport systems (ITS)o Transportation emissions and environmental impacto Passenger behavior and travel demand modelingo Transport safety and road risk datao Infrastructure performance and resilienceA FAIR² Data Article is a dedicated, dataset-centric publication — focused on the structure, provenance, and reuse potential of a single dataset, rather than hypothesis-driven analysis. Each submission is peer-reviewed, published open access, and accompanied by a custom interactive data portal with AI-assisted exploration, visualizations, and computation notebooks. Once published, the dataset becomes permanently citable and discoverable under the ODC-BY 1.0 licence.Call for SubmissionsWe invite researchers, institutions, and transport agencies to submit their datasets as FAIR² Data Articles in Frontiers in Future Transportation. Whether your data underpins a published study, represents a standalone longitudinal dataset, or captures field observations not yet tied to a specific research question — if it is valuable to the transportation community, it deserves to be findable, reusable, and credited.Submissions are open now. To get started, select FAIR² Data as the article type during submission and upload your dataset directly to the Frontiers FAIR² platform. Our AI data steward Clara will guide you through curation, metadata enrichment, and article preparation.For full details on the service, pricing, and what is included — including AI-assisted curation, peer review, interactive portal design, and lifetime data hosting — visit the Frontiers FAIR² Data Management fees page. Topic editors: Frontiers in Future Transportation Source: https://www.frontiersin.org/research-topics/81583/fair2-data-articles-in-future-transportation-certifying-sharing-and-reusing-transportation-datasets