Reframing the environment in data-intensive health sciences
dc.contributor.author | Canali, S | |
dc.contributor.author | Leonelli, S | |
dc.date.accessioned | 2022-03-28T10:33:31Z | |
dc.date.issued | 2022-05-13 | |
dc.date.updated | 2022-03-28T08:21:19Z | |
dc.description.abstract | In this paper, we analyse the relation between the use of environmental data in contemporary health sciences and related conceptualisations and operationalisations of the notion of environment. We consider three case studies that exemplify a different selection of environmental data and mode of data integration in data-intensive epidemiology. We argue that the diversification of data sources, their increase in scale and scope, and the application of novel analytic tools have brought about three significant conceptual shifts. First, we discuss the EXPOsOMICS project, an attempt to integrate genomic and environmental data which suggests a reframing of the boundaries between external and internal environments. Second, we explore the MEDMI platform, whose efforts to combine health, environmental and climate data instantiate a reframing and expansion of environmental exposure. Third, we illustrate how extracting epidemiological insights from extensive social data collected by the CIDACS institute yields innovative attributions of causal power to environmental factors. Identifying these shifts highlights the benefits and opportunities of new environmental data, as well as the challenges that such tools bring to understanding and fostering health. It also emphasises the constraints that data selection and accessibility pose to scientific imagination, including how researchers frame key concepts in health-related research. | en_GB |
dc.description.sponsorship | Deutsche Forschungsgemeinschaft (DFG) | en_GB |
dc.description.sponsorship | European Research Council (ERC) | en_GB |
dc.description.sponsorship | Alan Turing Institute | en_GB |
dc.identifier.citation | Vol. 93, pp. 203 - 214 | en_GB |
dc.identifier.doi | 10.1016/j.shpsa.2022.04.006 | |
dc.identifier.grantnumber | 254954344/GRK2073 | en_GB |
dc.identifier.grantnumber | 335925 | en_GB |
dc.identifier.grantnumber | EP/N510129/1 | en_GB |
dc.identifier.uri | http://hdl.handle.net/10871/129181 | |
dc.identifier | ORCID: 0000-0002-7815-6609 (Leonelli, Sabina) | |
dc.language.iso | en | en_GB |
dc.publisher | Elsevier | en_GB |
dc.rights | © 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). | en_GB |
dc.subject | epidemiology | en_GB |
dc.subject | big data | en_GB |
dc.subject | environment | en_GB |
dc.subject | exposure | en_GB |
dc.title | Reframing the environment in data-intensive health sciences | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2022-03-28T10:33:31Z | |
dc.identifier.issn | 0039-3681 | |
dc.description | This is the final version. Available on open access from Elsevier via the DOI in this reocrd | en_GB |
dc.identifier.journal | Studies in History and Philosophy of Science | en_GB |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_GB |
dcterms.dateAccepted | 2022-03-22 | |
rioxxterms.version | VoR | en_GB |
rioxxterms.licenseref.startdate | 2022-03-22 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2022-03-28T08:21:22Z | |
refterms.versionFCD | AM | |
refterms.dateFOA | 2022-06-14T12:30:07Z | |
refterms.panel | C | en_GB |
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Except where otherwise noted, this item's licence is described as © 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).