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12 results for “Citizen social science”
Survey on the social impact of the citizen science in the projects LIFE MIPP and InNat
<p>The present dataset consists in a sociological survey addressed to the volunteers involved in MIPP and InNat projects, a citizen science initiative aimed at the collection of distribution data of protected species and habitats all over Italian national territory. In particular, two different files are provided: </p> <ol> <li>the original survey (questions and answers) addressed to the volunteers in Italian</li> <li>the English translation of the questionnaire </li> </ol> <p>The survey covers different topics: socio-demographic data (section 1), opinions on the role of volunteering in a citizen science project (section 2), possible previous citizen science experience (section 3), test on the acquired skills on the monitored insect species with MIPP/InNat (section 4), opinions concerning the ecological crisis and the trust in the ability of humankind to solve environmental issues (section 5), possibility to be further contacted for additional interviews (section 6).</p> <p>A total of 364 completed questionnaires have been collected and relative results are provided here. Moreover, the above-mentioned results are thoroughly investigated and analysed in a scientific paper which also explores drivers that might keep volunteers active in a biodiversity monitoring project. Indeed, the engagement of volunteers in citizen science projects is a remarkable issue to address in order to ensure long-term sustainability, scientific relevance and public participation.</p> <p>The MIPP/InNat initiative started in 2012, with the project MIPP “Monitoring of insects with public participation” (LIFE11 NAT/IT/000252) which ended in 2017, and was then continued by the InNat project thanks to Italian National fundings. Data gathered in both projects converged in the same database. This research was supported by the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.4 - Call for tender No. 3138 of 16 December 2021, rectified by Decree n.3175 of 18 December 2021 of Italian Ministry of University and Research funded by the European Union – 1034 of 17 June 2022 adopted by the Italian Ministry of University and Research, CUP B83D21014060006, Project title “National Biodiversity Future Center” – NBFC.</p>
Data from: CoAct Citizen Science chatbot explores social support networks in mental health based on lived experiences
<p>A data set on lived experiences in the context of social support in mental health, created within a Citizen Social Science project. </p> <p><br> Societies around the world increasingly encounter wicked and complex problems, such as those related to mental health, environmental justice, and youth employment. <strong>CoAct as a EU-funded global effort</strong> addresses these problems by deploying Citizen Social Science. </p> <p> </p> <p><strong>Citizen Social Science</strong> is understood here as participatory research co-designed and directly driven by citizen groups sharing a social concern. This methodology wants to give citizen groups an equal ‘seat at the table’ through <strong>active participation in research</strong>, from the design to the interpretation of the results and their transformation into concrete actions. Citizens thus act as <strong>co-researchers</strong> and are recognised as in-the-field competent experts. </p> <p> </p> <p>In Barcelona, a group of <strong>32 co-researchers</strong> work together with the OpenSystems group, Universitat de Barcelona, the Catalan Federation of Mental Health (Federació Salut Mental Catalunya), and with the help of many others on a better understanding of informal <strong>social support networks in mental health</strong> in the project <em>CoActuem per la Salut Mental</em> (lit. “We act together for mental health”). The co-researchers, who are either persons with a personal history of mental health problems or are family members of the latter, contributed their <strong>personal experiences related to social support</strong> in the form of <strong>222 micro-stories</strong>, each shorter than 400 characters, and most accompanied by an illustration by Pau Badia.</p> <p> </p> <p>Those micro-stories form the heart of the first co-created Citizen Science chatbot, the code of which is open on <a href="https://github.com/Chaotique/CoActuem_per_la_Salut_Mental_Chatbot.git">https://github.com/Chaotique/CoActuem_per_la_Salut_Mental_Chatbot.git</a> . The <strong>Telegram chatbot</strong> sends them to participants <strong>on a daily basis over the course of a year</strong> and asks them either, whether they and/ or their close surrounding lived this experience, too (stories of type C), or, how they would or would have reacted in the presented situation (stories of type T). The answers of each participant can be contrasted with the individual participants’ answer to a 32-questions <strong>socio-demographic survey</strong>. Further, the timing of the messages is included to allow for a broader analysis. </p> <p> </p> <p>The chatbot is still running, hence this data set will still be updated. For further information on the project <strong>CoAct</strong>, see <a href="https://coactproject.eu/">https://coactproject.eu/</a>. For further details on the co-creation process and purpose of the chatbot <strong>CoActuem per la Salut Mental</strong>, take a look on <a href="https://coactuem.ub.edu/">https://coactuem.ub.edu/</a>. Please direct your questions regarding the data set to <strong>coactuem[at]ub.edu</strong>.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>The CoAct project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement number 873048. We especially thank the co-researchers for the passion and time invested.</p>
COESO: funding entities of citizen science activities on social sciences and humanities topics
<p>This database has been compiled by the Ibercivis team working in COESO together with contributions from other members of the COESO Consortium and representatives of the COESO Advisory Board, in addition to those who contributed information through the surveys and interviews.</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>
SciStarter: exploring project connections across the citizen science landscape: a social network analysis of shared volunteers
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FIGURE 1 in Ontogeny of an arlequin: morphological and colour pattern changes from juvenile to adult in Gnathophyllum elegans (Risso, 1816) (Decapoda: Palaemonidae), traced through citizen science and social media data mining
FIGURE 1. Morphological and colour pattern changes from juvenile to adult in Gnathophyllum elegans (Risso, 1816). A–C. Specimens from Capo Noli (Italy, Mediterranean Sea) (~44.199232N, 8.420455E), 15–16 m, on anthropogenic debris laying on a detritic bottom, 5–13.IX.2020. A. Photo by Walter Bassi. B–C. Photos by Alessandro Raho. D. Specimen from La Laja beach, Gran Canaria (Spain, Atlantic Ocean) (~28.060335N, -15.418428E), 1 m, amidst algae in a tide pool, 29.VIII.2017. Photo by Alberto Navarro. E. Specimen from Bat Galim reef, Haifa (Israel, Mediterranean Sea) (~32.833317N, 34.97431E), 2 m, under a rock on a rocky bottom, ~2017. Photo by Sarah Ohayon. F. Specimen from Capo Caccia, Sardinia (Italy, Mediterranean Sea) (~40.565506N, 8.165579E), 5 m, detritic bottom with rocks, 28.VIII.2015. Photo by Marco Colombo.
Chimpanzee identification and social Network construction through an online citizen science platform
<p><span><span><span><span><span><span><span><span><span><span><span>Citizen science has grown rapidly in popularity in recent years due to its potential to educate and engage the public while providing a means to address a myriad of scientific questions. However, the rise in popularity of citizen science has also been accompanied by concerns about the quality of data emerging from citizen science research projects. We assessed data quality in the online citizen scientist platform Chimp&See, which hosts camera trap videos of chimpanzees (<i>Pan troglodytes</i>) and other species across Equatorial Africa. In particular, we compared detection and identification of individual chimpanzees by citizen scientists to that of experts with years of experience studying those chimpanzees. We found that citizen scientists typically detected the same number of individual chimpanzees as experts, but assigned far fewer identifications (IDs) to those individuals. Those IDs assigned, however, were nearly always in agreement with the IDs provided by experts. We applied the data sets of citizen scientists and experts by constructing social networks from each. We found that both social networks were relatively robust and shared a similar structure, as well as having positively correlated individual network positions. Our findings demonstrate that, although citizen scientists produced a smaller data set based on fewer confirmed IDs, the data strongly reflect expert classifications and can be used for meaningful assessments of group structure and dynamics. This approach expands opportunities for social research and conservation monitoring in great apes and many other individually identifiable species. </span></span></span></span></span></span></span></span></span></span></span></p>
Data from: Identifying conservation priorities for gorgonian forests in Italian coastal waters with multiple methods including citizen science and social media content analysis
<div> <div> <div> <div> <p>Gorgonian forests are among the most complex of subtidal habitats in the Mediterranean Sea, supporting high biodiversity and providing diverse ecosystem services. Despite their iconic status, the geographical distribution and condition of gorgonian species is poorly known. Using multiple online data sources, our primary aims were to compile, map and analyse observations of gorgonian forests in Italian coastal waters to assess the biological complexity of gorgonian forests; evaluate impacts and vulnerable species, and identify areas of special interest inside and outside of existing MPAs to help prioritise conservation strategies and actions.</p> </div> </div> </div> </div>
Portuguese citizen science and social media images of "Cortaderia Selloana"
<p>In the first dataset (Citizen_science_dataset), we compiled 2,273 images with <em>Cortaderia selloana</em> from the main Portuguese citizen science platform on invasive alien plants (<a href="https://invasoras.pt/">https://invasoras.pt/</a>) and iNaturalist (<a href="https://www.inaturalist.org/">https://www.inaturalist.org/</a>), from which only those containing the species with plumes (1,323) were retained. This set of 1,323 images was manually checked, and low resolution and blurred images were removed (resulting in 1,237 images). A similar number (1,237) of outdoor images without <em>Cortaderia selloana</em> (in similar context to the ones with <em>Cortaderia selloana</em>) was additionally collected from Flickr, resulting in a total of 2,474 images. </p> <p>The second dataset (Social_media_dataset) consists of publicly available images taken in Portugal from four social media platforms: Facebook (<a href="https://www.facebook.com/">https://www.facebook.com/</a>), Flickr (<a href="https://www.flickr.com/">https://www.flickr.com/</a>), Instagram (<a href="https://www.instagram.com/">https://www.instagram.com/</a>) and Twitter (<a href="https://twitter.com/">https://twitter.com/</a>). Image collection was done by searching for common names of <em>C. selloana</em> in Portuguese (e.g., “Erva das pampas”, “Capim das pampas”), as well as the scientific name of the species (i.e., “<em>Cortaderia selloana</em>”), resulting in a total of 400 images. We also collected the same number of outdoor images without the <em>C. selloana</em> from the same social media platforms). The final size of the transferability set was 800 images.</p>
Data from: Identifying conservation priorities for gorgonian forests in Italian coastal waters with multiple methods including citizen science and social media content analysis
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Chimpanzee identification and social Network construction through an online citizen science platform
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Engagement and social impact in tech-based Citizen Science initiatives for achieving the SDGs : A Systematic Literature Review with a perspective on complex thinking
<p>Data set</p>
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