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Translating Diverse Environmental Data into Reliable Information -  Daniel A. Vallero

Translating Diverse Environmental Data into Reliable Information (eBook)

How to Coordinate Evidence from Different Sources
eBook Download: EPUB
2017 | 1. Auflage
500 Seiten
Elsevier Science (Verlag)
978-0-12-812447-5 (ISBN)
Systemvoraussetzungen
120,85 inkl. MwSt
(CHF 117,95)
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Translating Diverse Environmental Data into Reliable Information: How to Coordinate Evidence from Different Sources is a resource for building environmental knowledge, particularly in the era of Big Data. Environmental scientists, engineers, educators and students will find it essential to determine data needs, assess their quality, and efficiently manage their findings. Decision makers can explore new open access databases and tools, especially portals and dashboards. The book demonstrates how environmental knowledgebases are and can be built to meet the needs of modern students and professionals. Topics covered include concepts and principles that underpin air, water, and other public health and ecological topics. Integrated and systems perspectives are woven throughout, with clues on how to build and apply interdisciplinary data, which can increasingly be obtained from sources ranging from peer-reviewed research appearing in scientific journals to information gathered by citizen scientists. This opens the door to using vast amounts of open data and the necessary quality assurance and metadata considerations for their countless applications. - Provides tools to manage data of varying sizes and quality - Identifies both opportunities and cautions in using 'other people's data - Updates physical, chemical and biological factors that must be considered in risk evaluations and life cycle assessments - Applies to data collected by academic, governmental, businesses, and citizen scientists across environmental systems - Improves readers' ability to organize and visualize their work in the age of Big Data

Professor Daniel A. Vallero is an internationally recognized author and expert in environmental science and engineering. He has devoted decades to conducting research, teaching, and mentoring future scientists and engineers. He is currently developing tools and models to predict potential exposures to chemicals in consumer products. He is a full adjunct professor of civil and environmental engineering at Duke University's Pratt School of Engineering. He has authored 20 environmental textbooks, with the most recent addressing the importance of physical principles in environmental science and engineering. His books have addressed all environmental compartments and media within the earth's atmosphere, hydrosphere, lithosphere, and biosphere.
Translating Diverse Environmental Data into Reliable Information: How to Coordinate Evidence from Different Sources is a resource for building environmental knowledge, particularly in the era of Big Data. Environmental scientists, engineers, educators and students will find it essential to determine data needs, assess their quality, and efficiently manage their findings. Decision makers can explore new open access databases and tools, especially portals and dashboards. The book demonstrates how environmental knowledgebases are and can be built to meet the needs of modern students and professionals. Topics covered include concepts and principles that underpin air, water, and other public health and ecological topics. Integrated and systems perspectives are woven throughout, with clues on how to build and apply interdisciplinary data, which can increasingly be obtained from sources ranging from peer-reviewed research appearing in scientific journals to information gathered by citizen scientists. This opens the door to using vast amounts of open data and the necessary quality assurance and metadata considerations for their countless applications. - Provides tools to manage data of varying sizes and quality- Identifies both opportunities and cautions in using "e;other people's data- Updates physical, chemical and biological factors that must be considered in risk evaluations and life cycle assessments- Applies to data collected by academic, governmental, businesses, and citizen scientists across environmental systems- Improves readers' ability to organize and visualize their work in the age of Big Data
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