TY - JOUR
KW - unstructured observational data
KW - Bias
KW - Citizen Science
KW - observation process
KW - volunteer data
KW - crowd sensing
AU - Dobson A.D.M.
AU - Milner-Gulland E.J.
AU - Aebischer Nicholas J.
AU - Beale Colin M.
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 Timothy
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
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 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.
BT - One Earth
DA - 05/2022
DO - https://doi.org/10.1016/j.oneear.2020.04.012
IS - 5
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 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.
PY - 2020
SN - 2590-3322
SP - 455
EP - 465
T2 - One Earth
TI - Making Messy Data Work for Conservation
UR - https://www.sciencedirect.com/science/article/pii/S2590332220301998
VL - 2
ER -