Making Messy Data Work for Conservation

Author
Dobson A.D.M.
Milner-Gulland E.J.
Aebischer Nicholas J.
Beale Colin M.
Brozovic Robert
Coals Peter
Critchlow Rob
Dancer Anthony
Greve Michelle
Hinsley Amy
Ibbett Harriet
Johnston Alison
Kuiper Timothy
Le Comber Steven
Mahood Simon P.
Moore Jennifer F.
Nilsen Erlend B.
Pocock Michael J.O.
Quinn Anthony
Travers Henry
Wilfred Paulo
Wright Joss
Keane Aidan
Keywords
Abstract

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.

Year of Publication
2020
Journal
One Earth
Volume
2
Issue
5
Number of Pages
455-465
Date Published
05/2022
ISBN Number
2590-3322
URL
DOI
https://doi.org/10.1016/j.oneear.2020.04.012
Research themes