Evidence map›Paper›PMID 39267798›Full record

ArticleExposome2023

A roadmap to advance exposomics through federation of data.

Charles P Schmitt, Jeanette A Stingone, Arcot Rajasekar, Yuxia Cui, Xiuxia Du, Chris Duncan, Michelle Heacock, Hui Hu, Juan R Gonzalez, Paul D Juarez and 1 more

Abstract read
In one paragraph

Article in Exposome, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Global research trends on the human exposome: a bibliometric analysis (2005-2024).Environmental science and pollution research international · 2025
    Review
  4. Article
  5. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Charles P SchmittOffice of Data Science, National Institute of Environmental Health Sciences, Durham, NC, USA.
Jeanette A StingoneDepartment of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York, USA.
Arcot RajasekarRenaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Yuxia CuiExposure, Response, and Technology Branch, National Institute of Environmental Health Sciences, Durham, NC, USA.
Xiuxia DuDepartment of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC, USA.
Chris DuncanGenes, Environment, and Health Branch, National Institute of Environmental Health Sciences, Durham, NC, USA.
Michelle HeacockHazardous Substances Research Branch, National Institute of Environmental Health Sciences, Durham, NC, USA.
Hui HuChanning Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Juan R GonzalezCenter for Research in Environmental Epidemiology, Universitat Pompeu Fabra (UPF), Barcelona, Catalonia, Spain.
Paul D JuarezDepartment of Family & Community Medicine, Meharry Medical College, Nashville, Tennessee, USA.
Alex I SmirnovDepartment of Chemistry, North Carolina State University, Raleigh, NC, USA.

Funding

CRCNS:NeuroBridge: Connecting Big Data for Reproducible Clinical NeuroscienceR01DA053028 · NIDA · OHIO STATE UNIVERSITY · PI AMBITE, JOSE LUIS, RAJASEKAR, ARCOT · 2020 to 2023
$1.7M
Development of Data Science MethodsZIAES103364 · NIEHS · NATIONAL INSTITUTE OF ENVIRONMENTAL HEALTH SCIENCES · PI SCHMITT, CHARLES · 2021 to 2025
$1.6M
Intramural NIH HHS ZIA ES103364NIDA NIH HHS R01 DA053028
6 · The paper itself

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.

Indexed as

data ecosystemdata sharingdata standardsexposomefederation

Identifiers

PMID39267798
PMCPMC11391905

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.