AR² - Autism Replication, Validation, and Reproducibility Center - Abstract Autism Spectrum Disorder (ASD) research, particularly in light of its increasing prevalence and societal impact, requires rigorous replication and validation (R&V) to ensure that scientific findings are reproducible, generalizable, and applicable across diverse populations and settings. We propose an independent Autism Replication, Validation, and Reproducibility (AR²) Center in response to the NIH Autism Data Science Initiative (ADSI) Task IV, “Model Validation or Method Replication”. The primary goal of AR² is to ensure that every ADSI-generated resource is paired with an AR²-certified, complete, standalone package—fully aligned with FAIR (Findable, Accessible, Interoperable, and Reusable) principles—that enables independent reproduction of data generation, aggregation, or modeling processes in external environments and supports transparent, verifiable downstream analyses by the broader autism research community. AR² builds upon the nationally recognized Cornell Center for Social Sciences (CCSS) Data and Reproduction Archive, a CoreTrustSeal-certified infrastructure with a data archive and results replication (R2) pipeline that has archived over 2,150 studies and replicated more than 130 published datasets since 1982. Expanding on this proven infrastructure, AR² will establish a standardized, co-ownership pipeline with ADSI teams to collaboratively define R&V scope, success criteria, and deliverables; execute internal and external validation where applicable; and deliver certified R&V packages, including annotated code, test datasets, metrics, and compliance documentation (Aim 1). AR² will draw upon large-scale, racially, and geographically diverse autism-relevant data sources—including the INSIGHT Clinical Research Network, PEDSnet, PCORnet, Inovalon, and Medicaid claims datasets—to support robust validation and generalizability evaluation. Throughout the ADSI funding period, AR² will promote R&V best practices across the autism research community through targeted training and workshops and will coordinate closely with ADSI program staff, project teams, and a Community Advisory Board to ensure alignment with evolving scientific and community priorities. Final deliverables will be disseminated through accessible, trusted repositories to maximize transparency and impact (Aim 2). By applying standardized, FAIR-aligned workflows, leveraging a nationally recognized R&V infrastructure, and utilizing diverse datasets spanning racial/ethnic, geographic, and socioeconomic groups, AR² will rigorously replicate, validate, and document the generalizability of individual ADSI projects. Through these efforts, AR² will foster a culture of open, reproducible autism research and accelerate the translation of autism data science into clinical practice and policy.
