Data Science in Environmental Health Research.
Christine Choirat1, Danielle Braun2,3, Marianthi-Anna Kioumourtzoglou4
1Swiss Data Science Center, ETH Zurich and EPFL, Switzerland.
Current Epidemiology Reports
|November 15, 2019
Summary
Data science offers powerful tools to advance environmental health research by addressing complex data challenges. Collaboration between environmental health researchers and data scientists is crucial for robust and interpretable findings.
Area of Science:
- Environmental Health Sciences
- Data Science
- Computational Biology
Background:
- Environmental health research generates vast amounts of complex data.
- Traditional methods may struggle with high-dimensional outcomes and exposures.
- Data science offers novel approaches to analyze environmental health data.
Purpose of the Study:
- To review the application of data science in environmental health research.
- To highlight opportunities and potential pitfalls for researchers.
- To emphasize the need for interdisciplinary collaboration.
Main Methods:
- Review of data science concepts relevant to environmental health.
- Discussion of computationally scalable Big Data handling.
- Exploration of efficient research data platform design.
Main Results:
- Data science provides solutions for methodological challenges like high-dimensional data and prediction modeling.
- Tools for reproducible research are discussed.
- Opportunities exist to enhance environmental research capabilities through data science.
Conclusions:
- Embracing data science can significantly improve environmental research.
- Researchers must be aware of potential pitfalls when applying data science.
- Close collaboration with biostatisticians and data scientists is essential for robust results.
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