Assessing the quality and trustworthiness of citizen science data. (13th September 2012)

• Record Type: Journal Article
• Authors: Hunter, Jane; Alabri, Abdulmonem; van, Catharine; Remick, Carolyn; Hunt, James; Javid, Harold; Bougé, Luc; Lengauer, Christian
• Is Part Of: Concurrency and computation. Volume 25:Number 4(2013:Mar.)
• Page Start: 454
• Page End: 466
• Publication Date: 2012-09-13
• Subjects: Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.2923
• Languages: English
• ISSNs: 1532-0626

Abstract

<abstract abstract-type="main"> <title>SUMMARY</title> <p>The Internet, Web 2.0 and Social Networking technologies are enabling citizens to actively participate in 'citizen science' projects by contributing data to scientific programmes via the Web. However, the limited training, knowledge and expertise of contributors can lead to poor quality, misleading or even malicious data being submitted. Subsequently, the scientific community often perceive citizen science data as not worthy of being used in serious scientific research—which in turn, leads to poor retention rates for volunteers. In this paper, we describe a technological framework that combines data quality improvements and trust metrics to enhance the reliability of citizen science data. We describe how online social trust models can provide a simple and effective mechanism for measuring the trustworthiness of community‐generated data. We also describe filtering services that remove unreliable or untrusted data and enable scientists to confidently reuse citizen science data. The resulting software services are evaluated in the context of the CoralWatch project—a citizen science project that uses volunteers to collect comprehensive data on coral reef health. Copyright © 2012 John Wiley &amp; Sons, Ltd.</p> </abstract>