Making messy data work for conservation
Conservationists 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 “mes...
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Cell Press
2020
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| _version_ | 1797073778549719040 |
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| author | Dobson, ADM Milner-Gulland, EJ Aebischer, NJ Beale, C Brozovic, R Coals, P Critchlow, R Dancer, A Greve, M Hinsley, A Ibbett, H Johnston, A Kuiper, T Le Comber, S Mahood, S Moore, J Nilsen, EB Pocock, MJO Quinn, A Travers, H Wilfred, P Wright, J Keane, A |
| author_facet | Dobson, ADM Milner-Gulland, EJ Aebischer, NJ Beale, C Brozovic, R Coals, P Critchlow, R Dancer, A Greve, M Hinsley, A Ibbett, H Johnston, A Kuiper, T Le Comber, S Mahood, S Moore, J Nilsen, EB Pocock, MJO Quinn, A Travers, H Wilfred, P Wright, J Keane, A |
| author_sort | Dobson, ADM |
| collection | OXFORD |
| description | Conservationists 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. |
| first_indexed | 2024-03-06T23:26:55Z |
| format | Journal article |
| id | oxford-uuid:6aaf66bb-1d1b-409e-a26d-0796008bc8a8 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T23:26:55Z |
| publishDate | 2020 |
| publisher | Cell Press |
| record_format | dspace |
| spelling | oxford-uuid:6aaf66bb-1d1b-409e-a26d-0796008bc8a82022-03-26T18:59:17ZMaking messy data work for conservationJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:6aaf66bb-1d1b-409e-a26d-0796008bc8a8EnglishSymplectic ElementsCell Press2020Dobson, ADMMilner-Gulland, EJAebischer, NJBeale, CBrozovic, RCoals, PCritchlow, RDancer, AGreve, MHinsley, AIbbett, HJohnston, AKuiper, TLe Comber, SMahood, SMoore, JNilsen, EBPocock, MJOQuinn, ATravers, HWilfred, PWright, JKeane, AConservationists 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. |
| spellingShingle | Dobson, ADM Milner-Gulland, EJ Aebischer, NJ Beale, C Brozovic, R Coals, P Critchlow, R Dancer, A Greve, M Hinsley, A Ibbett, H Johnston, A Kuiper, T Le Comber, S Mahood, S Moore, J Nilsen, EB Pocock, MJO Quinn, A Travers, H Wilfred, P Wright, J Keane, A Making messy data work for conservation |
| title | Making messy data work for conservation |
| title_full | Making messy data work for conservation |
| title_fullStr | Making messy data work for conservation |
| title_full_unstemmed | Making messy data work for conservation |
| title_short | Making messy data work for conservation |
| title_sort | making messy data work for conservation |
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