Making Messy Data Work for Conservation

doi:10.1016/j.oneear.2020.04.012

Making Messy Data Work for Conservation

2020-05-01

59

OA

AI

A

Andrew D. M. Dobson *

E

E.J. Milner‐Gulland

N

Nicholas J. Aebischer

DOI：10.1016/j.oneear.2020.04.012

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Abstract

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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.

Keywords:

DATA-INTENSIVE SCIENCE

CITIZEN SCIENCE

BIG DATA

IMPROVE

BIAS

INFORMATION

TRADE

BIODIVERSITY

MANAGEMENT

SEIZURES

Making Messy Data Work for Conservation

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