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dc.contributor.authorDobson, Andrew
dc.contributor.authorMilner-Gulland, E. J.
dc.contributor.authorAebischer, Nicholas J.
dc.contributor.authorBeale, Colin M.
dc.contributor.authorBrozovic, Robert
dc.contributor.authorCoals, Peter
dc.contributor.authorCritchlow, Rob
dc.contributor.authorDancer, Anthony
dc.contributor.authorGreve, Michelle
dc.contributor.authorHinsley, Amy
dc.contributor.authorIbbett, Harriet
dc.contributor.authorJohnston, Alison
dc.contributor.authorKuiper, Timothy
dc.contributor.authorLe Comber, Steven
dc.contributor.authorMahood, Simon P.
dc.contributor.authorMoore, Jennifer F.
dc.contributor.authorNilsen, Erlend B.
dc.contributor.authorPocock, Michael J.O.
dc.contributor.authorQuinn, Anthony
dc.contributor.authorTravers, Henry
dc.contributor.authorWilfred, Paulo
dc.contributor.authorWright, Joss
dc.contributor.authorKeane, Aidan
dc.date.accessioned2021-11-26T12:30:06Z
dc.date.available2021-11-26T12:30:06Z
dc.date.issued2020-05-22
dc.identifier.citationDobson , A , Milner-Gulland , E J , Aebischer , N J , Beale , C M , Brozovic , R , Coals , P , Critchlow , R , Dancer , A , Greve , M , Hinsley , A , Ibbett , H , Johnston , A , Kuiper , T , Le Comber , S , Mahood , S P , Moore , J F , Nilsen , E B , Pocock , M J O , Quinn , A , Travers , H , Wilfred , P , Wright , J & Keane , A 2020 , ' Making messy data work for conservation ' , One Earth , vol. 2 , no. 5 , pp. 455-465 . https://doi.org/10.1016/j.oneear.2020.04.012en
dc.identifier.issn2590-3330
dc.identifier.otherPURE: 276806421
dc.identifier.otherPURE UUID: 715b278b-9a3d-417b-8556-ba081210c8fb
dc.identifier.otherScopus: 85091421139
dc.identifier.urihttp://hdl.handle.net/10023/24408
dc.descriptionFunding: Supported by the Natural Environment Research Council (grant NE/N001370/1).en
dc.description.abstractConservationists increasingly use unstructured observational data, such as citizen science records or ranger patrol observations, to guide decision making. These datasets are often large and relatively cheap to collect, and they have enormous potential. However, the resulting data are generally “messy,” and their use can incur considerable costs, some of which are hidden. We present an overview of the opportunities and limitations associated with messy data by explaining how the preferences, skills, and incentives of data collectors affect the quality of the information they contain and the investment required to unlock their potential. Drawing widely from across the sciences, we break down elements of the observation process in order to highlight likely sources of bias and error while emphasizing the importance of cross-disciplinary collaboration. We propose a framework for appraising messy data to guide those engaging with these types of dataset and make them work for conservation and broader sustainability applications.
dc.format.extent11
dc.language.isoeng
dc.relation.ispartofOne Earthen
dc.rightsCopyright 2020 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).en
dc.subjectBiasen
dc.subjectCitizen scienceen
dc.subjectCrowd sensingen
dc.subjectObservation processen
dc.subjectUnstructured observational dataen
dc.subjectVolunteer dataen
dc.subjectGE Environmental Sciencesen
dc.subjectQA Mathematicsen
dc.subjectEarth and Planetary Sciences (miscellaneous)en
dc.subjectEnvironmental Science(all)en
dc.subject.lccGEen
dc.subject.lccQAen
dc.titleMaking messy data work for conservationen
dc.typeJournal itemen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews. Statisticsen
dc.identifier.doihttps://doi.org/10.1016/j.oneear.2020.04.012
dc.description.statusPeer revieweden


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