Mitchell Manware , Insang Song , Eva S. Marques , Mariana Alifa Kassien , Lara P. Clark , Kyle P. Messier
{"title":"Amadeus: Accessing and analyzing large scale environmental data in R","authors":"Mitchell Manware , Insang Song , Eva S. Marques , Mariana Alifa Kassien , Lara P. Clark , Kyle P. Messier","doi":"10.1016/j.envsoft.2025.106352","DOIUrl":null,"url":null,"abstract":"<div><div>Environmental health research increasingly uses large scale spatial data to understand relationships between environmental factors and health outcomes. Data access and analysis tools which improve the timeliness and reproducibility of environmental health research are crucial for advancing the field. We present the <em>amadeus</em> package for R, a tool to improve access to and utility with large scale environmental data, primarily covering the United States. <em>amadeus</em> aims to reduce the learning curve for conducting spatial data analyses in R by providing functions which download, process, and calculate covariates from various publicly available environmental data sources. The functions promote interoperability with popular spatial data R packages and integration across other programming languages. Created and maintained with test-driven development, <em>amadeus</em> supports the reproducibility of environmental data acquisition and preparation. The <em>amadeus</em> package has diverse data access and integration applications, ranging from health-oriented studies in environmental epidemiology to ecology and climatology.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"186 ","pages":"Article 106352"},"PeriodicalIF":4.8000,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environmental Modelling & Software","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1364815225000362","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
Environmental health research increasingly uses large scale spatial data to understand relationships between environmental factors and health outcomes. Data access and analysis tools which improve the timeliness and reproducibility of environmental health research are crucial for advancing the field. We present the amadeus package for R, a tool to improve access to and utility with large scale environmental data, primarily covering the United States. amadeus aims to reduce the learning curve for conducting spatial data analyses in R by providing functions which download, process, and calculate covariates from various publicly available environmental data sources. The functions promote interoperability with popular spatial data R packages and integration across other programming languages. Created and maintained with test-driven development, amadeus supports the reproducibility of environmental data acquisition and preparation. The amadeus package has diverse data access and integration applications, ranging from health-oriented studies in environmental epidemiology to ecology and climatology.
期刊介绍:
Environmental Modelling & Software publishes contributions, in the form of research articles, reviews and short communications, on recent advances in environmental modelling and/or software. The aim is to improve our capacity to represent, understand, predict or manage the behaviour of environmental systems at all practical scales, and to communicate those improvements to a wide scientific and professional audience.