Perspectives on Citizen Science Data Quality

Robert R. Downs,Hampapuram K. Ramapriyan,Yaxing Wei,Ge Peng

doi:10.3389/fclim.2021.615032

Abstract

Information about data quality helps potential data users to determine whether and how data can be used and enables the analysis and interpretation of such data. Providing data quality information improves opportunities for data reuse by increasing the trustworthiness of the data. Recognizing the need for improving the quality of citizen science data, we describe quality assessment and quality control (QA/QC) issues for these data and offer perspectives on aspects of improving or ensuring citizen science data quality and for conducting research on related issues.

Highlights

Citizen science (CS) is recognized as having broad potential benefits to society
Citizen science data contributes to many scientific endeavors that are important for environmental science and for the well-being of society, including sustainable development, humanitarian efforts, and disaster prevention and response (Hicks et al, 2019; Fraisl et al, 2020)
Recognizing a perceived bias among scientists regarding the use of Citizen science data (CSD), Albus et al (2019) reviewed comparison studies that were conducted on volunteer and professional data collection efforts for large-scale water quality projects, concluding that more comparison studies are needed and that such studies should include accuracy, while controlling for variations among the datasets that are compared

Summary

INTRODUCTION

Citizen science (CS) is recognized as having broad potential benefits to society. Citizen science projects are providing unique and sometimes fundamental scientific insights and offer a wide variety of scientific outcomes (Pettibone et al, 2017; Paul et al, 2018; Wiggins et al, 2018; BautistaPuig et al, 2019; Miller et al, 2019; van Etten et al, 2019). Recognizing a perceived bias among scientists regarding the use of CSD, Albus et al (2019) reviewed comparison studies that were conducted on volunteer and professional data collection efforts for large-scale water quality projects, concluding that more comparison studies are needed and that such studies should include accuracy, while controlling for variations among the datasets that are compared Considering such concerns about the quality of CSD, as well as other data, and how data quality can affect data and their use, the Earth Science Information Partners (ESIP) Information Quality Cluster (IQC) is attempting to provide recommendations on practices to help ensure or improve CSD quality and build trust for CSD in the scientific community. The terms, stages 1–4, as defined, above, in terms of the four quality dimensions, are used in sections Recruitment, Selection, SelfSelection, and Training of CSD Contributors, Transparency in Information about QA/QC Practices during the Data Production Process, Documenting Data Quality to Facilitate Discovery and Reuse, and Establishing Rubrics for Evaluating Quality Levels of CSD to indicate when the recommended actions need to be taken during CSD projects. It should be noted that the development of rubrics should be initiated very early during stage 1, and that such rubrics will support users during stage 4

DISCUSSION

DATA AVAILABILITY STATEMENT