A roadmap to advance exposomics through federation of data

Author:

Schmitt Charles P1ORCID,Stingone Jeanette A2,Rajasekar Arcot34ORCID,Cui Yuxia5,Du Xiuxia6,Duncan Chris7,Heacock Michelle8,Hu Hui9,Gonzalez Juan R10,Juarez Paul D11,Smirnov Alex I12

Affiliation:

1. Office of Data Science, National Institute of Environmental Health Sciences , Durham, NC, USA

2. Department of Epidemiology, Mailman School of Public Health, Columbia University , New York, NY, USA

3. Renaissance Computing Institute, University of North Carolina at Chapel Hill , Chapel Hill, NC, USA

4. School of Information and Library Science, University of North Carolina at Chapel Hill , Chapel Hill, NC, USA

5. Exposure, Response, and Technology Branch, National Institute of Environmental Health Sciences , Durham, NC, USA

6. Department of Bioinformatics and Genomics, University of North Carolina at Charlotte , Charlotte, NC, USA

7. Genes, Environment, and Health Branch, National Institute of Environmental Health Sciences , Durham, NC, USA

8. Hazardous Substances Research Branch, National Institute of Environmental Health Sciences , Durham, NC, USA

9. Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School , Boston, MA, USA

10. Center for Research in Environmental Epidemiology, Universitat Pompeu Fabra (UPF) , Barcelona, Catalonia, Spain

11. Department of Family & Community Medicine, Meharry Medical College , Nashville, TN, USA

12. Department of Chemistry, North Carolina State University , Raleigh, NC, USA

Abstract

Abstract The scale of the human exposome, which covers all environmental exposures encountered from conception to death, presents major challenges in managing, sharing, and integrating a myriad of relevant data types and available data sets for the benefit of exposomics research and public health. By addressing these challenges, the exposomics research community will be able to greatly expand on its ability to aggregate study data for new discoveries, construct and update novel exposomics data sets for building artificial intelligence and machine learning-based models, rapidly survey emerging issues, and advance the application of data-driven science. The diversity of the field, which spans multiple subfields of science disciplines and different environmental contexts, necessitates adopting data federation approaches to bridge between numerous geographically and administratively separated data resources that have varying usage, privacy, access, analysis, and discoverability capabilities and constraints. This paper presents use cases, challenges, opportunities, and recommendations for the exposomics community to establish and mature a federated exposomics data ecosystem.

Publisher

Oxford University Press (OUP)

Subject

General Economics, Econometrics and Finance

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