Data Science in Environmental Health Research.

IF 3 Q2 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH Current epidemiology reports Pub Date : 2019-09-01 Epub Date: 2019-07-15 DOI:10.1007/s40471-019-00205-5

Christine Choirat, Danielle Braun, Marianthi-Anna Kioumourtzoglou

{"title":"Data Science in Environmental Health Research.","authors":"Christine Choirat, Danielle Braun, Marianthi-Anna Kioumourtzoglou","doi":"10.1007/s40471-019-00205-5","DOIUrl":null,"url":null,"abstract":"Purpose of review: Data science is an exploding trans-disciplinary field that aims to harness the power of data to gain information or insights on researcher-defined topics of interest. In this paper we review how data science can help advance environmental health research.Recent findings: We discuss the concepts computationally scalable handling of Big Data and the design of efficient research data platforms, and how data science can provide solutions for methodological challenges in environmental health research, such as high-dimensional outcomes and exposures, and prediction models. Finally, we discuss tools for reproducible research.Summary: In this paper we present opportunities to improve environmental research capabilities by embracing data science, and the pitfalls that environmental health researchers should avoid when employing data scientific approaches. Throughout the paper, we emphasize the need for environmental health researchers to collaborate more closely with biostatisticians and data scientists to ensure robust and interpretable results.","PeriodicalId":94310,"journal":{"name":"Current epidemiology reports","volume":null,"pages":null},"PeriodicalIF":3.0000,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s40471-019-00205-5","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current epidemiology reports","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s40471-019-00205-5","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2019/7/15 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH","Score":null,"Total":0}

引用次数: 7

Abstract

Purpose of review: Data science is an exploding trans-disciplinary field that aims to harness the power of data to gain information or insights on researcher-defined topics of interest. In this paper we review how data science can help advance environmental health research.

Recent findings: We discuss the concepts computationally scalable handling of Big Data and the design of efficient research data platforms, and how data science can provide solutions for methodological challenges in environmental health research, such as high-dimensional outcomes and exposures, and prediction models. Finally, we discuss tools for reproducible research.

Summary: In this paper we present opportunities to improve environmental research capabilities by embracing data science, and the pitfalls that environmental health researchers should avoid when employing data scientific approaches. Throughout the paper, we emphasize the need for environmental health researchers to collaborate more closely with biostatisticians and data scientists to ensure robust and interpretable results.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

环境健康研究中的数据科学。

综述目的：数据科学是一个爆炸性的跨学科领域，旨在利用数据的力量获得研究人员定义的感兴趣主题的信息或见解。在这篇论文中，我们回顾了数据科学如何帮助推进环境健康研究。最近的发现：我们讨论了大数据的计算可扩展处理和高效研究数据平台的设计概念，以及数据科学如何为环境健康研究中的方法挑战提供解决方案，如高维结果和暴露，以及预测模型。最后，我们讨论了可复制研究的工具。摘要：在本文中，我们介绍了通过拥抱数据科学来提高环境研究能力的机会，以及环境卫生研究人员在使用数据科学方法时应避免的陷阱。在整个论文中，我们强调环境卫生研究人员需要与生物统计学和数据科学家更密切地合作，以确保稳健和可解释的结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

Current epidemiology reports

自引率

0.00%

发文量