Next-Generation Field Guides

doi:10.1525/bio.2013.63.11.8

Cited by 62 publications

(23 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For instance, the Decision tree interface has design elements (single access key design) similar to the Merlin Bird ID mobile application () and the Natural History Museum Bumblebee ID key (), while the Feature selection interface has design elements of multi-access keys such as interactive keys on iSpot () and Discover Life (). Some citizen science projects even provide multiple types of interactive tools, such as Go Botany (), which offers a dichotomous key as well as a multi-access key based design (Farnsworth et al, 2013). For the purpose of this study, we looked more closely at design choices in relation to data quality and user-learning, thus contributing empirical findings to this growing area of practice.…”

Section: Methodsmentioning

confidence: 99%

Designing online species identification tools for biological recording: the impact on data quality and citizen science learning

Sharma

Colucci‐Gray

Siddharthan

et al. 2019

PeerJ

View full text Add to dashboard Cite

In recent years, the number and scale of environmental citizen science programmes that involve lay people in scientific research have increased rapidly. Many of these initiatives are concerned with the recording and identification of species, processes which are increasingly mediated through digital interfaces. Here, we address the growing need to understand the particular role of digital identification tools, both in generating scientific data and in supporting learning by lay people engaged in citizen science activities pertaining to biological recording communities. Starting from two well-known identification tools, namely identification keys and field guides, this study focuses on the decision-making and quality of learning processes underlying species identification tasks, by comparing three digital interfaces designed to identify bumblebee species. The three interfaces varied with respect to whether species were directly compared or filtered by matching on visual features; and whether the order of filters was directed by the interface or a user-driven open choice. A concurrent mixed-methods approach was adopted to compare how these different interfaces affected the ability of participants to make correct and quick species identifications, and to better understand how participants learned through using these interfaces. We found that the accuracy of identification and quality of learning were dependent upon the interface type, the difficulty of the specimen on the image being identified and the interaction between interface type and ‘image difficulty’. Specifically, interfaces based on filtering outperformed those based on direct visual comparison across all metrics, and an open choice of filters led to higher accuracy than the interface that directed the filtering. Our results have direct implications for the design of online identification technologies for biological recording, irrespective of whether the goal is to collect higher quality citizen science data, or to support user learning and engagement in these communities of practice.

show abstract

Section: Methodsmentioning

confidence: 99%

Designing online species identification tools for biological recording: the impact on data quality and citizen science learning

Sharma

Colucci‐Gray

Siddharthan

et al. 2019

PeerJ

View full text Add to dashboard Cite

show abstract

“…The Merlin project demonstrates the potential for revolutionary change in ecological identification (Farnsworth et al, 2013). Merlin is the joint project from Visipedia and the Cornell Laboratory of Ornithology to identify 600 common North American bird species (Van Horn et al, 2015).…”

Section: Case Study: Merlin a Bird Identification App Powered By Dmentioning

confidence: 99%

A computer vision for animal ecology

Weinstein

2017

Journal of Animal Ecology

332

287

View full text Add to dashboard Cite

A central goal of animal ecology is to observe species in the natural world. The cost and challenge of data collection often limit the breadth and scope of ecological study. Ecologists often use image capture to bolster data collection in time and space. However, the ability to process these images remains a bottleneck. Computer vision can greatly increase the efficiency, repeatability and accuracy of image review. Computer vision uses image features, such as colour, shape and texture to infer image content. I provide a brief primer on ecological computer vision to outline its goals, tools and applications to animal ecology. I reviewed 187 existing applications of computer vision and divided articles into ecological description, counting and identity tasks. I discuss recommendations for enhancing the collaboration between ecologists and computer scientists and highlight areas for future growth of automated image analysis.

show abstract

“…For example, patient data [S8, neurologist], lab animal data [S5, neurologist], and student data [S14, psychologist] were never shared due to strict ethics regulations. The whereabouts of certain endangered plants [29] and archaeological sites [S15, archaeologist] were not shared due to potential for looting or destruction.…”

Section: Data Uncertaintiesmentioning

confidence: 99%

Crowdsourcing as a Tool for Research

Law

Gajos

Wiggins

et al. 2017

Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing

View full text Add to dashboard Cite

Numerous crowdsourcing platforms are now available to support research as well as commercial goals. However, crowdsourcing is not yet widely adopted by researchers for generating, processing or analyzing research data. This study develops a deeper understanding of the circumstances under which crowdsourcing is a useful, feasible or desirable tool for research, as well as the factors that may influence researchers' decisions around adopting crowdsourcing technology. We conducted semi-structured interviews with 18 researchers in diverse disciplines, spanning the humanities and sciences, to illuminate how research norms and practitioners' dispositions were related to uncertainties around research processes, data, knowledge, delegation and quality. The paper concludes with a discussion of the design implications for future crowdsourcing systems to support research.

show abstract

Next-Generation Field Guides

Cited by 62 publications

References 24 publications

Designing online species identification tools for biological recording: the impact on data quality and citizen science learning

Designing online species identification tools for biological recording: the impact on data quality and citizen science learning

A computer vision for animal ecology

Crowdsourcing as a Tool for Research

Contact Info

Product

Resources

About