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Enhancing multi-site autism research through the development of a collaborative data platform.

Jeffrey T AndersonJeffrey D RothKashia A RosenauPatrick S DwyerAlice A KuoJulian A Martinez
Published in: Autism research : official journal of the International Society for Autism Research (2024)
Data repositories, particularly those storing data on vulnerable populations, increasingly need to carefully consider not only what data is being collected, but how it will be used. As such, the Autism Intervention Research Network on Physical Health (AIR-P) has created the Infrastructure for Collaborative Research (ICR) to establish standards on data collection practices in Autism repositories. The ICR will strive to encourage inter-site collaboration, amplify autistic voices, and widen accessibility to data. The ICR is staged as a three-tiered framework consisting of (1) a request for proposals system, (2) a REDCap-based data repository, and (3) public data dashboards to display aggregate de-identified data. Coupled with a review process including autistic and non-autistic researchers, this framework aims to propel the implementation of equitable autism research, enhance standardization within and between studies, and boost transparency and dissemination of findings. In addition, the inclusion of a contact registry that study participants can opt into creates the base for a robust participant pool. As such, researchers can leverage the platform to identify, reach, and distribute electronic materials to a greater proportion of potential participants who likely fall within their eligibility criteria. By incorporating practices that promote effective communication between researchers and participants, the ICR can facilitate research that is both considerate of and a benefit to autistic people.
Keyphrases
  • electronic health record
  • healthcare
  • autism spectrum disorder
  • primary care
  • randomized controlled trial
  • intellectual disability
  • mental health
  • data analysis
  • machine learning
  • single cell
  • deep learning