Capturing quality

Abstract

The "real world" nature of field-based citizen science involves unique data management challenges that distinguish it from projects that involve only Internet-mediated activities. In particular, many data contribution and review practices are often accomplished "offline" via paper or general-purpose software like Excel. This can lead to integration challenges when attempting to implement project-specific ICT with full revision and provenance tracking. In this work, we explore some of the current challenges and opportunities in implementing ICT for managing volunteer monitoring data. Our two main contributions are: a general outline of the workflow tasks common to field-based data collection, and a novel data model for preserving provenance metadata that allows for ongoing data exchange between disparate technical systems and participant skill levels. We conclude with applications for other domains, such as hydrologic forecasting and crisis informatics, as well as directions for future research.

Community-based Data Validation Practices in Citizen Science

“…These findings bring us to question the appropriateness of current technology trends for the actual community practices [40], as there is currently strong demand for citizen science mobile apps, which are perceived as a promising means of expanding participation [4,42]. Mobile devices can capture temporal and geographic information to submit automatically and precisely [14,28,32].…”

“…Validation is critical to ensuring the usefulness of citizen science data by establishing its quality. For example, in biology and ecology, without validation of species identifications, volunteers' observations are usually argued to have limited value [40]. Given the challenges of scaling and successes of crowdsourcing, it is reasonable to ask if a community can effectively validate the data they create.…”

A Knowledge Perspective on Quality in Complex Citizen Science

“…This results in uncertainty about citizens' knowledge and time availability for projects (Law et al 2017). The second challenge is the weaker control that project leaders have on knowledge flows, as citizens are not employed by the research organization (Kittur et al 2013;Simula 2013) and are thus not subject to formal supervision (Sheppard et al 2014).…”

“…The quality of citizen science outcomes is essential because the reliability of research depends on it. Concerns about quality in citizen science (Oomen and Aroyo 2011;Sheppard, Wiggins and Terveen 2014;Riesch and Potter 2014;Bonney, Cooper and Ballard 2016) are not surprising given the involvement of (usually unknown) diverse and distributed citizens with different levels of expertise versus the complexity of research tasks for which academics have been trained for years (Miller 2001).…”

Still in Need of Norms: The State of the Data in Citizen Science

“…Other researchers have identified and analyzed, for example, data quality practices and fitness-for-use assessments across citizen science initiatives (see for example Specht and Lewandowski 2018;Kelling 2018;Aceves-Bueno et al 2017;Kosmala et al 2016;Lukyanenko et al 2016;Sheppard, Wiggins, and Terveen 2014;Wiggins et al 2011). Still others delved into issues related to standardized data collection (Higgins et al 2018;Sturm et al 2017), data management (Schade et al 2017;Bastin et al 2017) or concepts like fitness to purpose or fitness for use (Parrish et al 2018).…”