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Translational data analytics in exposure science and environmental health: a citizen science approach with high school students
BACKGROUND: Translational data analytics aims to apply data analytics principles and techniques to bring about broader societal or human impact. Translational data analytics for environmental health is an emerging discipline and the objective of this study is to describe a real-world example of this...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7329470/ https://www.ncbi.nlm.nih.gov/pubmed/32611428 http://dx.doi.org/10.1186/s12940-020-00627-5 |
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author | Hyder, Ayaz May, Andrew A. |
author_facet | Hyder, Ayaz May, Andrew A. |
author_sort | Hyder, Ayaz |
collection | PubMed |
description | BACKGROUND: Translational data analytics aims to apply data analytics principles and techniques to bring about broader societal or human impact. Translational data analytics for environmental health is an emerging discipline and the objective of this study is to describe a real-world example of this emerging discipline. METHODS: We implemented a citizen-science project at a local high school. Multiple cohorts of citizen scientists, who were students, fabricated and deployed low-cost air quality sensors. A cloud-computing solution provided real-time air quality data for risk screening purposes, data analytics and curricular activities. RESULTS: The citizen-science project engaged with 14 high school students over a four-year period that is continuing to this day. The project led to the development of a website that displayed sensor-based measurements in local neighborhoods and a GitHub-like repository for open source code and instructions. Preliminary results showed a reasonable comparison between sensor-based and EPA land-based federal reference monitor data for CO and NOx. CONCLUSIONS: Initial sensor-based data collection efforts showed reasonable agreement with land-based federal reference monitors but more work needs to be done to validate these results. Lessons learned were: 1) the need for sustained funding because citizen science-based project timelines are a function of community needs/capacity and building interdisciplinary rapport in academic settings and 2) the need for a dedicated staff to manage academic-community relationships. |
format | Online Article Text |
id | pubmed-7329470 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-73294702020-07-02 Translational data analytics in exposure science and environmental health: a citizen science approach with high school students Hyder, Ayaz May, Andrew A. Environ Health Research BACKGROUND: Translational data analytics aims to apply data analytics principles and techniques to bring about broader societal or human impact. Translational data analytics for environmental health is an emerging discipline and the objective of this study is to describe a real-world example of this emerging discipline. METHODS: We implemented a citizen-science project at a local high school. Multiple cohorts of citizen scientists, who were students, fabricated and deployed low-cost air quality sensors. A cloud-computing solution provided real-time air quality data for risk screening purposes, data analytics and curricular activities. RESULTS: The citizen-science project engaged with 14 high school students over a four-year period that is continuing to this day. The project led to the development of a website that displayed sensor-based measurements in local neighborhoods and a GitHub-like repository for open source code and instructions. Preliminary results showed a reasonable comparison between sensor-based and EPA land-based federal reference monitor data for CO and NOx. CONCLUSIONS: Initial sensor-based data collection efforts showed reasonable agreement with land-based federal reference monitors but more work needs to be done to validate these results. Lessons learned were: 1) the need for sustained funding because citizen science-based project timelines are a function of community needs/capacity and building interdisciplinary rapport in academic settings and 2) the need for a dedicated staff to manage academic-community relationships. BioMed Central 2020-07-01 /pmc/articles/PMC7329470/ /pubmed/32611428 http://dx.doi.org/10.1186/s12940-020-00627-5 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Hyder, Ayaz May, Andrew A. Translational data analytics in exposure science and environmental health: a citizen science approach with high school students |
title | Translational data analytics in exposure science and environmental health: a citizen science approach with high school students |
title_full | Translational data analytics in exposure science and environmental health: a citizen science approach with high school students |
title_fullStr | Translational data analytics in exposure science and environmental health: a citizen science approach with high school students |
title_full_unstemmed | Translational data analytics in exposure science and environmental health: a citizen science approach with high school students |
title_short | Translational data analytics in exposure science and environmental health: a citizen science approach with high school students |
title_sort | translational data analytics in exposure science and environmental health: a citizen science approach with high school students |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7329470/ https://www.ncbi.nlm.nih.gov/pubmed/32611428 http://dx.doi.org/10.1186/s12940-020-00627-5 |
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