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444 results for “Citizen Science”
[Dataset] Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects - Graph Files
<p><strong>Explanation/Overview:</strong></p> <p>Corresponding graph files of the extracted Zooniverse networks described in D3.3 (can be found here), which are the result of our research that culminated into the publication "Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects", a conference paper for the conference CollabTech 2022: <a href="https://link.springer.com/book/10.1007/978-3-031-20218-6">Collaboration Technologies and Social Computing</a> and published as part of the <a href="https://link.springer.com/bookseries/558">Lecture Notes in Computer Science</a> book series (LNCS,volume 13632) <a href="https://link.springer.com/chapter/10.1007/978-3-031-20218-6_5">here</a>. Usernames have been anonymised.</p> <p>The graph files are in <code>.gexf</code> (graph exchange XML format) and <code>.gml</code> (graph modeling language) formats which can be used by common graph/network-analysis and visualisation tools such as Gephi.</p> <p><strong>Purpose:</strong></p> <p>The purpose of this dataset is to provide the basis for possible further examinations of the network structure, involving additional (not yet analysed) features such as the content of the comments etc.</p> <p><strong>Relatedness:</strong></p> <p>The data of the different projects was derived from the forums of 7 Zooniverse projects based on similar discussion board features. The projects are: 'Galaxy Zoo', 'Gravity Spy', 'Seabirdwatch', 'Snapshot Wisconsin', 'Wildwatch Kenya', 'Galaxy Nurseries', 'Penguin Watch'.</p> <p><strong>Content:</strong></p> <p>The dataset contains distinct graph files for each of the analysed projects. For each graph file, there are <em>nodes </em>and <em>edges</em> and their associated attributes (i.e., each edge can have an attribute). For the edges, apart from source and target, we have as attributes:</p> <ul> <li><code>weight</code></li> <li><code>project_title</code></li> <li><code>body </code>(i.e., text)</li> <li><code>created_at</code></li> <li><code>userRoles</code></li> <li><code>discussion_title</code></li> <li><code>discussion_id</code></li> <li><code>user_id</code></li> <li><code>board_title</code></li> <li><code>relation</code></li> <li><code>target_role</code></li> </ul> <p>For the nodes, the attributes are:</p> <ul> <li><code>user_id</code></li> <li><code>userRoles</code></li> <li><code>degree_reply </code>(i.e., degree for the reply relation)</li> <li><code>in_degree_reply</code></li> <li><code>out_degree_reply</code></li> <li><code>degree_comment</code></li> <li><code>in_degree_comment</code></li> <li><code>out_degree_comment</code></li> <li><code>degree_total</code></li> <li><code>in_degree_total</code></li> <li><code>out_degree_total</code></li> <li><code>target_role</code></li> </ul> <p><strong>Grouping:</strong></p> <p>Each graph file represents all the comments for the respective project across its lifespan irrespective of any time slices. Edges represent the comments and users represent the nodes. While the different boards are still contained within the data, all boards occur in the data.</p> <p> </p>
[Dataset] Is it a match? Motivations on citizen science volunteers and recruitment arguments in project descriptions
<p>This dataset contains the necessary details to reproduce the experiments of the paper:</p> <p><em>Kai Nils, W., Gutiérrez Páez,N.F., Sabel, O. and Hämäläinen, R. (2022) “Is It a Match? Motivations on Citizen Science Volunteers and Recruitment Arguments in Project Descriptions.” In Proceedings of the ECSA2022 conference: Citizen Science for Planetary Health, 69–70. <a href="https://2022.ecsa-conference.eu/files/ecsa/Bilder/ECSA2022_Conference_Proceedings.pdf">https://2022.ecsa-conference.eu/files/ecsa/Bilder/ECSA2022_Conference_Proceedings.pdf</a></em></p> <p>Data has been collected by quantitative triangulation. 1076 participants in citizen science projects answered a survey about the 12 motivational factors for participating. They had access to the survey by social media posts or email invitations sent to people in charge of projects. Data regarding motivational arguments in recruitment come from quantitative content analysis of 367 project descriptions of the website Zooniverse. The content analysis of the project descriptions was done manually by two coders independently. Then, both coders analysed their codings and reached consensus.</p>
Citizen Science Initiatives in Greece
<p>This dataset contains original mapping data developed in the process of the Greek Citizen Science landscape review. In total, 20 Greek Citizen Science projects were analysed according to a common framework. During the mapping process, we weren’t always able to find information on things that interested us e.g. project impact, stakeholder engagement, the size of the volunteering force. But just because we couldn’t find something does not mean the results or activities did not happen. It goes without saying that absence of evidence is not evidence of absence. Failure to find some information on our part can be explained by the fact that we worked primarily with internet sources, so we had to make do with whatever publicly available information we could find within reasonable time.</p>
Benefits and limitations of environmental magnetism for completing citizen science on air quality: a case study in a street canyon.
<p>Inside a street canyon in Montpellier (France) a total of 72 deposimeters were deployed in 29 households for a period of 3 months to measure local air quality. This street canyon was chosen because dwellers were already mobilized against the street traffic, and because they were in conflict on this issue with policy makers. The project aimed to include all the stakeholders through co-construction. The closure of the street during the metrological campaign and the absence of agreement curbed their involvement and motivation. However, the feedbacks from the citizen partners promote the fact that this study supported their claims and brought them a deeper understanding on the micro-scale air quality monitoring. Indeed, it is increasingly difficult for citizens, who seemed specifically interested in what is happening right outside their front door, to understand this measure with the emergence of ever more low-cost sensors. For that reason, we examined the citizen’s degree of confidence in magnetic monitoring of air quality and how can this technique be useful in their claims. The results show that magnetism can be a measurement technique favorable to citizen participation because it provides a large amount of data at the micro-scale of the street level, while the data from the certified associations for monitoring air quality requires a spatial interpolation to map variations on a neighborhood scale. In this study, we proposed a magnetic air quality index to standardize and democratize the magnetic monitoring of air quality to facilitate the dialogue with all stakeholders.</p>
TRANSFORM video - Waste pilot using citizen science in Catalonia
<p>A citizen science project to improve the selective waste collection system in Mollet del Vallès Citizens have a key role in political decision-making and the TRANSFORM pilot project is a clear example of this. TRANSFORM Catalan cluster carried out a citizen science pilot in the city of Mollet del Vallès to contribute to improving the selective collection of municipal waste. Citizen science was used as an instrument to move towards a greener, more digital, resilient and fairer socioeconomic model.</p> <p>Results of the pilot project: https://zenodo.org/record/7472655#.Y-IMXS0w2X2</p> <p>TRANSFORM project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 872687. This video reflects only the authors' view and the funding institutions are not responsible for any use that may be made of the information it contains.</p>
Assessment of the acoustic adaptation hypothesis in frogs using large-scale citizen science data
<p>This is the data required to reproduce the results of the manuscript "Assessment of the acoustic adaptation hypothesis in frogs using large-scale citizen science data", including measurements of tree canopy cover extracted from the Global Forest Cover Change dataset (Townshend 2016).</p> <p> </p> <p><strong>Reference</strong></p> <p>Gillard, G. L. & Rowley, J. J. L. (2023). Assessment of the acoustic adaptation hypothesis in frogs using large-scale citizen science data. <em>Journal of Zoology</em>. [In publication].</p> <p> </p> <p><strong>Global Forest Cover Change Dataset</strong></p> <p>Townshend J. 2016. Global Forest Cover Change (GFCC) Tree Cover Multi-Year Global 30 m V003 [Data set]. NASA EOSDIS Land Processes DAAC. Accessed June 22, 2022. doi:10.5067/MEaSUREs/GFCC/GFCC30TC.003.Townshend J. 2016. Global Forest Cover Change (GFCC) Tree Cover Multi-Year Global 30 m V003 [Data set]. NASA EOSDIS Land Processes DAAC. Accessed June 22, 2022. doi:10.5067/MEaSUREs/GFCC/GFCC30TC.003.</p>
Dataset for "Exposure and environmental engagement: A pilot integrating wearable sensors, air quality and citizen science"
<p>The dataset contains anonymised readings of 7 citizens taking air quality measurements using PlumeLabs Flow 2 monitor. Data is for Falmouth/Penryn, and Bristol and it was collected between January 26, 2022 and March 9, 2022.</p> <p>CSV file:</p> <ul> <li>latitude: unit degrees, positive values indicate North hemisphere.</li> <li>longitude, unit degrees, positive values indicate East.</li> <li>AQI: PlumeLabs' Air Quality Index.</li> <li>site: A refers to Falmouth/Penryn(UK), B refers to Bristol (UK).</li> <li>count: auxiliary variable that indicates that the record was comprised of a single reading.</li> </ul> <p>Jupyter notebook: The air quality analysis was conducted with Python 3.9.16 alongside numpy 1.24.3, pandas 2.0.2, matplotlib 3.7.1, and cartopy 0.21.1 (background tiles by OpenStreetMaps).</p>
Selection process of articles for a systematic literature review on citizen science initiatives in school contexts
<p>Selection process of articles for a systematic literature review on citizen science initiatives in school contexts. Initial records are extracted from Web of Science database from 2000 to 2021. We used the keyword "citizen science" in the title mixed with some keywords in the topic of the search: “classroom” or “school” or “student*” or “pupil” or “learning”.</p> <p>The different database sheets inform about the application of the inclusion and exclusion criteria applied.</p>
Leveraging the strengths of citizen science and structured surveys to achieve scalable inference on population size
<ol> <li>Population size is a key metric for management and policy decisions, yet wildlife monitoring programs are often limited by the spatial and temporal scope of surveys. In these cases, citizen science data may provide complementary information at higher resolution and greater extent.</li> <li>We present a case study demonstrating how data from the eBird citizen science program can be combined with regional monitoring efforts by the U.S. Fish and Wildlife Service to produce high-resolution estimates of golden eagle abundance. We developed a model that uses aerial survey data from the western United States to calibrate high-resolution annual estimates of relative abundance from eBird. Using this model, we compared regional population size estimates based on the calibrated eBird information to those based on aerial survey data alone.</li> <li>Population size estimates based on the calibrated eBird information had strong correspondence to estimates from aerial survey data in two out of four regions, and population trajectories based on the two approaches showed high correlations.</li> <li>We demonstrate how the combination of citizen science data and targeted surveys can be used to (a) increase the spatial resolution of population size estimates, (b) extend the spatial extent of inference, and (c) predict population size beyond the temporal period of surveys. Findings based on this case study can be used to refine policy metrics used by the U.S. Fish and Wildlife Service and inform permitting regulations (e.g., mortality/harm associated with wind energy development).</li> <li> <em>Policy implications</em>. Our results demonstrate the ability of citizen science data to complement targeted monitoring programs and improve the efficacy of decision frameworks that require information on population size or trajectory. After validating citizen science data against survey-based benchmarks, agencies can harness strengths of citizen science data to supplement information needs and increase the resolution and extent of population size predictions.</li> </ol>
Data from: Utilising citizen science data to rapidly assess changing associations between wild birds and avian influenza outbreaks in poultry
Open the record for dataset details and reuse information.
Data from: Fun surveys? Developing an innovative approach to assessing learning through citizen science
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Leveraging the strengths of citizen science and structured surveys to achieve scalable inference on population size
Open the record for dataset details and reuse information.
All site data for Citizen Science (Guildford case study)
<p>The collected data during citizen science activities in Guildford using low-cost sensors.</p>
Citizen Science in the Social Sciences and Humanities
<p>The aim of the study was to develop a more integrated understanding of the extent and ways the social sciences and humanities (SSH) are represented and dealt with in current citizen science (CS) practice. To achieve this, a meta-synthesis methodology was adopted to identify and examine all related cases reported in the research literature. </p> <p>Five research questions were formulated as follows:</p> <p>1) What methodological approaches and roles of citizens are used by CS projects and activities claiming to pertain to SSH?</p> <p>2) What disciplinary fields within SSH do these CS projects encompass and what do diverse interdisciplinary synergies piece together?</p> <p>3) What are the SSH topics that have engrossed or attracted most of the interest in CS practice so far?</p> <p>4) What purposes are defined to incorporate SSH in CS projects? and</p> <p>5) What are the benefits of citizen-generated data? </p> <p>To select relevant papers two of the largest databases were opted: a) Clarivate Analytics Core Collection (Science Citation Index Expanded, Social Science Citation Index, Arts & Humanities Citation Index, Conference Proceeding Citation Index - Science Edition & Social Science & Humanities Edition), and b) EBSCOhost research databases.</p> <p>A combination of two keywords only was used as subject terms/topics (e.g. citizen science AND social sciences > TS=(citizen AND science) AND TS=(natural AND sciences)).</p> <p>Search results were retrieved twice, in October 2018 and in January 2019. At the pre-identification stage, 2763 records were retrieved. At the identification stage, 1244 full-text papers were included in the sample. At the screening stage, 344 full-text papers in English, Spanish and French were identified and submitted to a preliminary meta-synthesis. In total, 62 papers were selected for being relevant and providing most of the data needed.</p>
Data & Open Science analysis of citizen science projects related with pollution
<p>These two spreadsheets contain the analysis done from a Data & Open Science perspective of citizen science projects related with pollution</p>
Data from: Responses of sympatric canids to human development revealed through citizen science
<p>Measuring wildlife responses to anthropogenic activities often requires long-term, large-scale datasets that are difficult to collect. This is particularly true for rare or cryptic species, which includes many mammalian carnivores. Citizen science, in which members of the public participate in scientific work, can facilitate collection of large datasets while increasing public awareness of wildlife research and conservation. Hunters provide unique benefits for citizen science given their knowledge and interest in outdoor activities. We examined how anthropogenic changes to land cover impacted relative abundance of two sympatric canids, coyote (<i>Canis latrans</i>) and red fox (<i>Vulpes vulpes</i>) at a large spatial scale. In order to assess how land cover affected canids at this scale, we used citizen science data from bow hunter sighting logs collected throughout New York State, USA, during 2004–2017. We found that the two species had contrasting responses to development, with red foxes positively correlated and coyotes negatively correlated with the percentage of low-density development. Red foxes also responded positively to agriculture, but less so when agricultural habitat was fragmented. Agriculture provides food and denning resources for red foxes, whereas coyotes may select forested areas for denning. Though coyotes and red foxes compete in areas of sympatry, we did not find a relationship between species abundance, likely a consequence of the coarse spatial resolution used. Red foxes may be able to coexist with coyotes by altering their diets and habitat use, or by maintaining territories in small areas between coyote territories. Our study shows the value of citizen science, and particularly hunters, in collection of long-term data across large areas (i.e., the entire state of New York) that otherwise would unlikely be obtained.</p>
Artificial night light helps account for observer bias in citizen science monitoring of an expanding large mammal population
1. The integration of citizen scientists into ecological research is transforming how, where, and when data are collected, and expanding the potential scales of ecological studies. Citizen-science projects can provide numerous benefits for participants, while educating and connecting professionals with lay audiences, potentially increasing acceptance of conservation and management actions. However, for all the benefits, collection of citizen-science data is often biased towards areas that are easily accessible (e.g. developments and roadways), and thus data are usually affected by issues typical of opportunistic surveys (e.g. uneven sampling effort). These areas are usually illuminated by artificial light at night (ALAN), a dynamic sensory stimulus that alters the perceptual world for both humans and wildlife. 2. Our goal was to test whether satellite-based measures of ALAN could improve our understanding of the detection process of citizen scientist-reported sightings of a large mammal. 3. We collected observations of American black bears (Ursus americanus; n = 1,315) outside their primary range in Minnesota, USA, as part of a study to gauge population expansion. Participants from the public provided sighting locations of bears on a website. We used an occupancy modelling framework to determine how well ALAN accounted for observer metrics when compared to other commonly used metrics (e.g. housing density). 4. Citizen scientists reported 17% of bear sightings were under artificially-lit conditions and monthly ALAN estimates did the best job accounting for spatial bias in detection of all observations, based on AIC values and effect sizes (β ^ = 0.81, 0.71 – 0.90 95% CI). Bear detection increased with elevated illuminance; relative abundance was positively associated with natural cover, closer proximity to primary bear range and lower road density. Although the highest counts of bear sightings occurred in the highly illuminated suburbs of the Minneapolis-St. Paul metropolitan region, we estimated substantially higher bear abundance in another region with plentiful natural cover and low ALAN (up to 275% increased predicted relative abundance) where observations were sparse. 5. We demonstrate the importance of considering ALAN radiance when analyzing citizen scientist-collected data, and we highlight the ways that ALAN data provides a dynamic snapshot of human activity. 31-Jul-2020
Replication data for: Consumer preference testing of boiled sweetpotato (Ipomoea batatas (L.) Lam.) using crowdsourced citizen science in Ghana and Uganda
<p>Crowdsourced citizen science is an emerging approach in plant sciences. The triadic comparison of technologies (tricot) approach has been successfully utilised by demand-led breeding programmes to identify varieties for dissemination suited to specific geographic and climatic regions. An important feature of this approach is the independent way in which farmers individually evaluate the varieties on their own farms as ‘citizen scientists’. In this study, we adapted this approach to evaluate consumer preferences to boiled sweetpotato (<em>Ipomoea batatas</em> (L.) Lam) roots of 21 advanced breeding materials and varieties in Ghana and 6 released varieties in Uganda. We were specifically interested in evaluating if a more independent style of evaluation (home tasting) would produce results comparable to an approach that involves control over preparation (centralised tasting). We compiled data from 1,433 participants who individually contributed to a home tasting (de-centralised) and a centralised tasting trial in Ghana and Uganda, evaluating overall acceptability, and indicating the reasons for their preferences. Geographic factors showed important contribution to define consumers’ preference to boiled sweetpotato genotypes. Home and centralised tasting approaches gave similar rankings for overall acceptability, which was strongly correlated to taste. In both Ghana and Uganda, it was possible to robustly identify superior sweetpotato genotypes from consumers’ perspectives. Our results indicate that the tricot approach can be successfully applied to consumer preference studies.</p>
Sample Records. Citizen Science and Citizen Energy Communities: A Systematic Review of Potential Alliances for SDGs
<p>Sample Records. Citizen Science and Citizen Energy Communities: A Systematic Review of Potential Alliances for SDGs</p>
Changes in participant behaviour and attitudes are associated with knowledge and skills gained by using a turtle conservation citizen science app
Citizen science has become a popular way to collect biodiversity data and engage the wider public in scientific research. It has the potential to improve the knowledge and skills of participants, and positively change their behaviour and attitude towards the environment. Citizen science outcomes are particularly valuable for wildlife conservation, as they could help alleviate human impacts on the environment. We used an online questionnaire to investigate the consequences of participating in an Australian turtle mapping app, TurtleSAT, on skills and knowledge gain, and test for any association between these gains with behavioural or attitudinal changes reported by the participants. 148 citizen scientists completed our questionnaire, mostly from the states of New South Wales and Victoria. Participants listed TurtleSAT as the third most common source of knowledge about turtle ecology and conservation, after a talk about turtles and personal observations/research. Citizen scientists who participated more often were more knowledgeable about turtles than infrequent users. Self-reported gains in knowledge and skills were positively linked to attitudinal and behavioural changes, such as being more aware of turtles on roads. However, behaviour and attitude changes were not related to participation rate. Respondents also reported that after learning about the current decline in turtle populations, they adopted several turtle-friendly practices, such as habitat restoration or moving turtles out of harm's way, underlining the importance of increasing people's awareness on species declines. The reported changes in attitudes and behaviours are likely to positively impact the conservation of Australian freshwater turtles. Engagement with citizen science projects like TurtleSAT may result in participants being more interested in the natural world, by learning more about it and being more exposed to it, and therefore contribute more actively to its protection.
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International Brain Laboratory public data
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.