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Where health and environment meet: the use of invariant parameters in big data analysis
The use of big data to investigate the spread of infectious diseases or the impact of the built environment on human wellbeing goes beyond the realm of traditional approaches to epidemiology, and includes a large variety of data objects produced by research communities with different methods and goa...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Netherlands
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8550214/ https://www.ncbi.nlm.nih.gov/pubmed/34720225 http://dx.doi.org/10.1007/s11229-018-1844-2 |
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author | Leonelli, Sabina Tempini, Niccolò |
author_facet | Leonelli, Sabina Tempini, Niccolò |
author_sort | Leonelli, Sabina |
collection | PubMed |
description | The use of big data to investigate the spread of infectious diseases or the impact of the built environment on human wellbeing goes beyond the realm of traditional approaches to epidemiology, and includes a large variety of data objects produced by research communities with different methods and goals. This paper addresses the conditions under which researchers link, search and interpret such diverse data by focusing on “data mash-ups”—that is the linking of data from epidemiology, biomedicine, climate and environmental science, which is typically achieved by holding one or more basic parameters, such as geolocation, as invariant. We argue that this strategy works best when epidemiologists interpret localisation procedures through an idiographic perspective that recognises their context-dependence and supports a critical evaluation of the epistemic value of geolocation data whenever they are used for new research purposes. Approaching invariants as strategic constructs can foster data linkage and re-use, and support carefully-targeted predictions in ways that can meaningfully inform public health. At the same time, it explicitly signals the limitations in the scope and applicability of the original datasets incorporated into big data collections, and thus the situated nature of data linkage exercises and their predictive power. |
format | Online Article Text |
id | pubmed-8550214 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Springer Netherlands |
record_format | MEDLINE/PubMed |
spelling | pubmed-85502142021-10-29 Where health and environment meet: the use of invariant parameters in big data analysis Leonelli, Sabina Tempini, Niccolò Synthese S.I.: Philosophy of Epidemiology The use of big data to investigate the spread of infectious diseases or the impact of the built environment on human wellbeing goes beyond the realm of traditional approaches to epidemiology, and includes a large variety of data objects produced by research communities with different methods and goals. This paper addresses the conditions under which researchers link, search and interpret such diverse data by focusing on “data mash-ups”—that is the linking of data from epidemiology, biomedicine, climate and environmental science, which is typically achieved by holding one or more basic parameters, such as geolocation, as invariant. We argue that this strategy works best when epidemiologists interpret localisation procedures through an idiographic perspective that recognises their context-dependence and supports a critical evaluation of the epistemic value of geolocation data whenever they are used for new research purposes. Approaching invariants as strategic constructs can foster data linkage and re-use, and support carefully-targeted predictions in ways that can meaningfully inform public health. At the same time, it explicitly signals the limitations in the scope and applicability of the original datasets incorporated into big data collections, and thus the situated nature of data linkage exercises and their predictive power. Springer Netherlands 2018-06-08 2021 /pmc/articles/PMC8550214/ /pubmed/34720225 http://dx.doi.org/10.1007/s11229-018-1844-2 Text en © Springer Nature B.V. 2018 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | S.I.: Philosophy of Epidemiology Leonelli, Sabina Tempini, Niccolò Where health and environment meet: the use of invariant parameters in big data analysis |
title | Where health and environment meet: the use of invariant parameters in big data analysis |
title_full | Where health and environment meet: the use of invariant parameters in big data analysis |
title_fullStr | Where health and environment meet: the use of invariant parameters in big data analysis |
title_full_unstemmed | Where health and environment meet: the use of invariant parameters in big data analysis |
title_short | Where health and environment meet: the use of invariant parameters in big data analysis |
title_sort | where health and environment meet: the use of invariant parameters in big data analysis |
topic | S.I.: Philosophy of Epidemiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8550214/ https://www.ncbi.nlm.nih.gov/pubmed/34720225 http://dx.doi.org/10.1007/s11229-018-1844-2 |
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