Dark citizen science.

Citizen science is often celebrated. We interrogate this position through exploration of socio-technoscientific phenomena that mirror citizen science yet are disaligned with its ideals. We term this 'Dark Citizen Science'. We identify five conceptual dimensions of citizen science - purpose, process, perceptibility, power and public effect. Dark citizen science mirrors traditional citizen science in purpose and process but diverges in perceptibility, power and public effect. We compare two Internet-based categorisation processes, Citizen Science project Galaxy Zoo and Dark Citizen Science project Google's reCAPTCHA. We highlight that the reader has, likely unknowingly, provided unpaid technoscientific labour to Google. We apply insights from our analysis of dark citizen science to traditional citizen science. Linking citizen science as practice and normative democratic ideal ignores how some science-citizen configurations actively pit practice against ideal. Further, failure to fully consider the implications of citizen science for science and society allows exploitative elements of citizen science to evade the sociological gaze.

Meteorological data rescue: Citizen science lessons learned from Southern Weather Discovery.

Daily weather reconstructions (called "reanalyses") can help improve our understanding of meteorology and long-term climate changes. Adding undigitized historical weather observations to the datasets that underpin reanalyses is desirable; however, time requirements to capture those data from a range of archives is usually limited. Southern Weather Discovery is a citizen science data rescue project that recovered tabulated handwritten meteorological observations from ship log books and land-based stations spanning New Zealand, the Southern Ocean, and Antarctica. We describe the Zooniverse-hosted Southern Weather Discovery campaign, highlight promotion tactics, and replicate keying levels needed to obtain 100% complete transcribed datasets with minimal type 1 and type 2 transcription errors. Rescued weather observations can augment optical character recognition (OCR) text recognition libraries. Closer links between citizen science data rescue and OCR-based scientific data capture will accelerate weather reconstruction improvements, which can be harnessed to mitigate impacts on communities and infrastructure from weather extremes.

What Do We Know about Young Volunteers? An Exploratory Study of Participation in Zooniverse.

Citizen Science (CS) is an increasingly popular activity enacted either in the field or online. Volunteers participate in research activities such as data processing and analysis by, for example, identifying plants and animals. In this paper we examine young people's participation in online CS projects hosted on the Zooniverse platform. This is an exploratory study, the first of its kind that focuses on young people, mainly 16-19 years old. It uses data analytics and visualisation techniques to capture participation in online CS, and in particular to answer the following questions: (a) What does young people's participation look like in CS projects? (b) What Zooniverse projects do young people choose to participate in? and (3) What Zooniverse projects do young people choose together? Findings revealed five distinct engagement profiles characterising young people's participation and identified certain projects as been more popular across participants. Implications for the design of online citizen science projects targeting young people are discussed.

Galaxy zoo: Science content knowledge of citizen scientists.

We report the results of a large-scale study of the state of science content knowledge of volunteers in Galaxy Zoo ( www.galaxyzoo.org ), an online citizen science project in which public volunteers classify galaxies in an effort to benefit cutting-edge astronomy research. We were interested in whether participating in Galaxy Zoo leads to any increase in participants' astrophysical content knowledge. To assess volunteer content knowledge, we examined the responses of 1476 Galaxy Zoo volunteers to 32 conceptually challenging multiple-choice questions. We looked for any relationships between participants' assessment scores and the number of galaxies classified upon answering the first assessment question, the number of galaxies classified between their first response and their final response to the assessment, and the length of time since they first created their Galaxy Zoo account. All relationships were of small effect size. These results suggest that participation in the project's central galaxy classification task, in and of itself, is not associated with increased astrophysical content knowledge. We strongly recommend that future studies of online citizen science environments examine how volunteers take advantage of opportunities to develop their knowledge and skills outside of the self-contained central task, especially in the context of opportunities for interactions with other volunteers.

Fossil Atmospheres: a case study of citizen science in question-driven palaeontological research.

Palaeontologists increasingly use large datasets of observations collected from museum specimens to address broad-scale questions about evolution and ecology on geological timescales. One such question is whether information from fossil organisms can be used as a robust proxy for atmospheric carbon dioxide through time. Here, we present the citizen science branch of 'Fossil Atmospheres', a project designed to refine stomatal index of Ginkgo leaves as a palaeo-CO2 proxy by involving citizen scientists in data collection through the Zooniverse website. Citizen science helped to overcome a barrier presented by the time taken to count cells in Ginkgo samples; however, a new set of challenges arose as a result. A beta-testing phase with Zooniverse volunteers provided an opportunity to improve instructions to ensure high fidelity data. Exploration of citizen scientists' estimates shows that volunteer counts of stomata are accurate with respect to counts made by the project's lead scientist. However, counts of epidermal cells have a wide range, and mean values tend to underestimate expert counts. We demonstrate a variety of approaches to reducing the inaccuracy in the calculated stomatal index that this variation causes. Zooniverse serves as an ideal tool for collection of palaeontological data where the distribution of fossils would be impossible, but where specimens can be easily imaged. Such an approach facilitates the collection of a large palaeontological dataset, as well as providing an opportunity for citizens to engage with climate research.This article is part of the theme issue 'Biological collections for understanding biodiversity in the Anthropocene'.

A generalized approach for producing, quantifying, and validating citizen science data from wildlife images.

Citizen science has the potential to expand the scope and scale of research in ecology and conservation, but many professional researchers remain skeptical of data produced by nonexperts. We devised an approach for producing accurate, reliable data from untrained, nonexpert volunteers. On the citizen science website www.snapshotserengeti.org, more than 28,000 volunteers classified 1.51 million images taken in a large-scale camera-trap survey in Serengeti National Park, Tanzania. Each image was circulated to, on average, 27 volunteers, and their classifications were aggregated using a simple plurality algorithm. We validated the aggregated answers against a data set of 3829 images verified by experts and calculated 3 certainty metrics-level of agreement among classifications (evenness), fraction of classifications supporting the aggregated answer (fraction support), and fraction of classifiers who reported "nothing here" for an image that was ultimately classified as containing an animal (fraction blank)-to measure confidence that an aggregated answer was correct. Overall, aggregated volunteer answers agreed with the expert-verified data on 98% of images, but accuracy differed by species commonness such that rare species had higher rates of false positives and false negatives. Easily calculated analysis of variance and post-hoc Tukey tests indicated that the certainty metrics were significant indicators of whether each image was correctly classified or classifiable. Thus, the certainty metrics can be used to identify images for expert review. Bootstrapping analyses further indicated that 90% of images were correctly classified with just 5 volunteers per image. Species classifications based on the plurality vote of multiple citizen scientists can provide a reliable foundation for large-scale monitoring of African wildlife.