Evaluating the Fitness for Use of Citizen Science Data for Wildlife Monitoring

Abstract

Contributory citizen science programs focused on ecological monitoring can produce fine-grained and expansive data sets across spatial and temporal scales. With this data collection potential, citizen scientists can significantly impact the ability to monitor ecological patterns. However, scientists still harbor skepticism about using citizen science data in their work, generally due to doubts about data quality. Numerous peer-reviewed articles have addressed data quality in citizen science. Yet, many of these methods are not useable by third-party scientists (scientists who are not directly involved in the citizen science program). In addition, these methods generally capture internal data quality rather than a dataset’s potential to be used for a specific purpose. Assessing data fitness for use represents a promising approach to evaluating data accuracy and quality for different applications and contexts. In this article, we employ a Spatial, Temporal, Aptness, and Application (STAAq) assessment approach to assess data fitness for use of citizen science datasets. We tested the STAAq assessment approach through a case study examining the distribution of caribou in Denali National Park and Preserve. Three different datasets were used in the test, Map of Life data (a global scale citizen science mobile application for recording species observations), Ride Observe and Record data (a program sponsored by the park staff where incentivized volunteers observe species in the park), and conventionally collected radio collar data. The STAAq assessment showed that the Map of Life and Ride Observe and Record program data are fit for monitoring caribou distribution in the park. This data fitness for use approach is a promising way to assess the external quality of a dataset and its fitness to address particular research or monitoring questions. This type of assessment may help citizen science skeptics see the value and potential of citizen science collected data and encourage the use of citizen science data by more scientists.

Wildlife conservation on private land: a social-ecological systems study

“…Despite presenting signi cant logistical, training and data-integrity challenges, the unrealised potential of citizen science in biodiversity research, particularly in lling the data gaps that hinder effective conservation efforts has been emphasised by a substantive literature (Locke et al 2019;Dissanayake et al 2019;Fischer et al 2021). Our ndings corroborate this, showing that citizen scientists were adept at identifying both threatened mammal and bird species across various locations, contributing essential data that might otherwise be unavailable.…”

The role of citizen science mobile apps in facilitating a contemporary digital agora

“…In this regard, adhering to the FAIR principles' characteristic to Data Science and Open Science is imperative for CS projects (Batsaikhan et al 2020;Roman et al 2021). Adhering to the above-mentioned principles in projects will address the limitation highlighted by Fischer et al (2021), which pertains to providing access to CS project data to scientists not directly involved in the project. Literature reviews as independent studies on CS apps should also adhere to the FAIR principles, making possible the continuation of reviews by authors different from those in the initial team.…”

Examining Visitor Collected Species Data from Denali National Park and Preserve

“…This additional analysis is an initial assessment of the data accuracy of the Map of Life data. A comprehensive data fitness-for-use analysis of the full participant collected dataset is presented in Fischer et al (2021).…”

“…These data, however, can be prone to observer error, such as false observations, inaccurate location data, or incorrect species identification (Lukyanenko et al 2014;Ward et al 2015). Previous studies have examined the quality of citizen science data compared with authoritative datasets and have developed various methods to do so (Comber et al 2013;Fischer et al 2021;Haklay 2010;Senaratne et al 2016;Wiggins et al 2011). These methods range from an expert review of data records to data quality flagging, statistical analysis, and determining data fitness for use.…”