Porth Ymchwil

Making messy data work for conservation

Allbwn ymchwil: Cyfraniad at gyfnodolyn › Erthygl › adolygiad gan gymheiriaid

StandardStandard

Making messy data work for conservation. / Dobson, Andrew D.M.; Milner-Gulland, EJ; Aebischer, Nicholas J et al.
Yn: One Earth, Cyfrol 2, Rhif 5, 22.05.2020, t. 455-465.

Allbwn ymchwil: Cyfraniad at gyfnodolyn › Erthygl › adolygiad gan gymheiriaid

HarvardHarvard

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, SP, Moore, JF, Nilsen, EB, Pocock, MJO, Quinn, A, Travers, H, Wilfred, P, Wright, J & Keane, A 2020, 'Making messy data work for conservation', One Earth, cyfrol. 2, rhif 5, tt. 455-465. https://doi.org/10.1016/j.oneear.2020.04.012

APA

Dobson, A. D. M., Milner-Gulland, EJ., Aebischer, N. J., 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. P., Moore, J. F., Nilsen, E. B., Pocock, M. J. O., Quinn, A., ... Keane, A. (2020). Making messy data work for conservation. One Earth, 2(5), 455-465. https://doi.org/10.1016/j.oneear.2020.04.012

CBE

Dobson ADM, Milner-Gulland EJ, Aebischer NJ, Beale C, Brozovic R, Coals P, Critchlow R, Dancer A, Greve M, Hinsley A, et al. 2020. Making messy data work for conservation. One Earth. 2(5):455-465. https://doi.org/10.1016/j.oneear.2020.04.012

MLA

Dobson, Andrew D.M. et al. "Making messy data work for conservation". One Earth. 2020, 2(5). 455-465. https://doi.org/10.1016/j.oneear.2020.04.012

VancouverVancouver

Dobson ADM, Milner-Gulland EJ, Aebischer NJ, Beale C, Brozovic R, Coals P et al. Making messy data work for conservation. One Earth. 2020 Mai 22;2(5):455-465. doi: 10.1016/j.oneear.2020.04.012

Author

Dobson, Andrew D.M. ; Milner-Gulland, EJ ; Aebischer, Nicholas J et al. / Making messy data work for conservation. Yn: One Earth. 2020 ; Cyfrol 2, Rhif 5. tt. 455-465.

RIS

TY - JOUR

T1 - Making messy data work for conservation

AU - Dobson, Andrew D.M.

AU - Milner-Gulland, EJ

AU - Aebischer, Nicholas J

AU - Beale, Colin

AU - Brozovic, Robert

AU - Coals, Peter

AU - Critchlow, Rob

AU - Dancer, Anthony

AU - Greve, Michelle

AU - Hinsley, Amy

AU - Ibbett, Harriet

AU - Johnston, Alison

AU - Kuiper, Tomothy

AU - Le Comber, Steven

AU - Mahood, Simon P

AU - Moore, Jennifer F.

AU - Nilsen, Erlend B

AU - Pocock, Michael J.O.

AU - Quinn, Anthony

AU - Travers, Henry

AU - Wilfred, Paulo

AU - Wright, Joss

AU - Keane, Aidan

PY - 2020/5/22

Y1 - 2020/5/22

N2 - 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 pro- pose 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.

AB - 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 pro- pose 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.

U2 - 10.1016/j.oneear.2020.04.012

DO - 10.1016/j.oneear.2020.04.012

M3 - Article

VL - 2

SP - 455

EP - 465

JO - One Earth

JF - One Earth

SN - 2590-3330

IS - 5

ER -