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dc.contributor.authorDobson, A.D.M.
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 Birkeland
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.accessioned2020-07-09T10:04:38Z
dc.date.available2020-07-09T10:04:38Z
dc.date.created2020-05-26T15:03:16Z
dc.date.issued2020
dc.identifier.issn2590-3330
dc.identifier.urihttps://hdl.handle.net/11250/2661594
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.en_US
dc.language.isoengen_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.no*
dc.titleMaking Messy Data Work for Conservationen_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.rights.holder©2020 The Author(s).en_US
dc.subject.nsiVDP::Matematikk og naturvitenskap: 400en_US
dc.subject.nsiVDP::Mathematics and natural scienses: 400en_US
dc.source.volume2en_US
dc.source.journalOne Earthen_US
dc.identifier.doi10.1016/j.oneear.2020.04.012
dc.identifier.cristin1812693
dc.relation.projectAndre: Natural Environment Research Councilen_US
cristin.ispublishedtrue
cristin.fulltextoriginal


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Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal