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18 results for “citizen engagement”
[Dataset] Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects - Raw Data
<p><strong>Explanation/Overview:</strong></p> <p>Corresponding raw data for the analyses 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 raw data is in the <code>.json</code> format and can be read by most languages/tools. It is recommended to import the data into a MongoDB to work with it.</p> <p><strong>Purpose:</strong></p> <p>The purpose of this dataset is to provide the basis for possible further examinations, involving additional (not yet analysed) features such as the content of the comments etc. and also new ways of extracting networks.</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 three files:</p> <ul> <li><code>Comments.json</code> <ul> <li>contains the basic data representation with multiple fields (e.g., <code>time_created</code>, <code>user_login</code>). Each data field represents a comment.</li> </ul> </li> <li><code>Discussions.json</code> <ul> <li><code></code>contains all discussions. Each data field is a discussion, with multiple fields (e.g., <code>comments_count</code>, <code>user_login</code>)</li> </ul> </li> <li><code>Projects.json</code> <ul> <li><code></code>contains all projects. Each data field is a project, with multiple fields (e.g., <code>project_id</code>, <code>description</code>)</li> </ul> </li> </ul> <p><strong>Grouping:</strong></p> <p>The projects (and thus the corresponding discussions and comments) were collected on the basis of common forum features such as the discussion boards.</p>
[Dataset] Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects
<p><strong>Explanation/Overview:</strong></p> <p>Corresponding dataset for the analyses and results achieved in the CS Track project in the research line on participation analyses, which is also reported in 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>. The usernames have been anonymised.</p> <p><strong>Purpose:</strong></p> <p>The purpose of this dataset is to provide the basis to reproduce the results reported in the associated deliverable, and in the above-mentioned publication. As such, it <strong>does not</strong> represent <strong>raw data</strong>, but rather files that already include certain analysis steps (like calculated degrees or other SNA-related measures), ready for analysis, visualisation and interpretation with R.</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>In this Zenodo entry, several files can be found. The structure is as follows (<code>files</code> and <strong>folders </strong>and<strong> </strong><em>descriptions</em>).</p> <ul> <li><code>corresponding_calculations.html</code> <ul> <li><em>Quarto-notebook to view in browser</em></li> </ul> </li> <li><code>corresponding_calculations.qmd</code> <ul> <li><em>Quarto-notebook to view in RStudio</em></li> </ul> </li> <li><strong>assets</strong> <ul> <li><strong>data</strong> <ul> <li><strong>annotations</strong> <ul> <li><code>annotations.csv</code> <ul> <li><em>List of annotations made per day for each of the analysed projects</em></li> </ul> </li> </ul> </li> <li><strong>comments</strong> <ul> <li><code>comments.csv </code> <ul> <li><em>Total list of comments with several data fields (i.e., comment id, text, reply_user_id)</em></li> </ul> </li> </ul> </li> <li><strong>rolechanges</strong> <ul> <li><code>478_rolechanges.csv</code> <ul> <li><em>List of roles per user to determine number of role changes </em></li> </ul> </li> <li><code>1104_rolechanges.csv</code> <ul> <li><em>...</em></li> </ul> </li> <li><code>...</code></li> </ul> </li> <li><strong>totalnetworkdata</strong> <ul> <li><strong>Edges</strong> <ul> <li><code>478_edges.csv</code> <ul> <li><em>Network data (edge set) for the given projects (without time slices)</em></li> </ul> </li> <li><code>1104_edges.csv</code> <ul> <li><em>...</em></li> </ul> </li> <li><code>...</code></li> </ul> </li> <li><strong>Nodes</strong> <ul> <li><code>478_nodes.csv</code> <ul> <li><em>Network data (node set) for the given projects (without time slices)</em></li> </ul> </li> <li><code>1104_nodes.csv</code> <ul> <li><em>...</em></li> </ul> </li> <li><code>...</code></li> </ul> </li> </ul> </li> <li><strong>trajectories</strong> <ul> <li><em>Network data (edge and node sets) for the given projects and all time slices (Q1 2016 - Q4 2021)</em></li> <li><strong>478</strong> <ul> <li><strong>Edges</strong> <ul> <li> <p><code>edges_4782016_q1.csv</code></p> </li> <li> <p><code>edges_4782016_q2.csv</code></p> </li> <li> <p><code>edges_4782016_q3.csv</code></p> </li> <li> <p><code>edges_4782016_q4.csv</code></p> </li> <li> <p><code>...</code></p> </li> </ul> </li> <li><strong>Nodes</strong> <ul> <li><code>nodes_4782016_q1.csv</code></li> <li> <p><code>nodes_4782016_q4.csv</code></p> </li> <li> <p><code>nodes_4782016_q3.csv</code></p> </li> <li> <p><code>nodes_4782016_q2.csv</code></p> </li> <li> <p><code>...</code></p> </li> </ul> </li> </ul> </li> <li> <p><strong>1104</strong> </p> <ul> <li> <p><strong>Edges</strong> </p> <ul> <li> <p><code>...</code></p> </li> </ul> </li> <li> <p><strong>Nodes</strong> </p> <ul> <li> <p><code>...</code></p> </li> </ul> </li> </ul> </li> <li> <p>...</p> </li> </ul> </li> </ul> </li> <li><strong>scripts</strong> <ul> <li><code>datavizfuncs.R</code> <ul> <li><em>script for the data visualisation functions, automatically executed from within </em><code>corresponding_calculations.qmd</code></li> </ul> </li> <li><code>import.R</code> <ul> <li><em>script for the import of data, automatically executed from within </em><code>corresponding_calculations.qmd</code></li> </ul> </li> </ul> </li> </ul> </li> <li><strong>corresponding_calculations_files</strong> <ul> <li>f<em>iles for the html/qmd view in the browser/RStudio</em></li> </ul> </li> </ul> <p><strong>Grouping:</strong></p> <p>The data is grouped according to given criteria (e.g., <code>project_title </code>or <code>time</code>). Accordingly, the respective files can be found in the data structure</p>
[Dataset] Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects
<p>Corresponding dataset for 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 structure of the dataset is as follows:</p> <p><strong>Annotations</strong> </p> <p><em>List of annotations made per day for each of the analysed projects.</em></p> <p><code>annotations.csv </code></p> <p><strong>Comments </strong></p> <p><em>Total list of comments with several data fields (i.e., comment id, text, reply_user_id)</em></p> <p><code>comments.csv </code></p> <p><strong>Rolechanges</strong> </p> <p><em>List of roles per user to determine number of role changes </em></p> <p><code>478_rolechanges.csv</code></p> <p><code>1104_rolechanges.csv</code></p> <p><code>...</code></p> <p><strong>Totalnetworkdata</strong> </p> <p><em>Network data (edge and node sets) for the given projects (without time slices).</em></p> <p>Edges </p> <ul> <li> <p><code>478_edges.csv</code></p> </li> <li> <p><code>1104_edges.csv</code></p> </li> </ul> <p>Nodes </p> <ul> <li> <p><code>478_nodes.csv</code> </p> </li> <li> <p><code>1104_nodes.csv</code> </p> </li> </ul> <p><strong>Trajectories</strong> </p> <p><em>Network data (edge and node sets) for the given projects and all time slices (Q1 2016 - Q4 2021)</em></p> <p>478 </p> <ul> <li>Edges <ul> <li> <p><code>edges_4782016_q1.csv</code></p> </li> <li> <p><code>edges_4782016_q2.csv</code></p> </li> <li> <p><code>edges_4782016_q3.csv</code></p> </li> <li> <p><code>edges_4782016_q4.csv</code></p> </li> </ul> </li> <li> <p>...</p> </li> <li>Nodes <ul> <li><code>nodes_4782016_q1.csv</code></li> <li> <p><code>nodes_4782016_q4.csv</code></p> </li> <li> <p><code>nodes_4782016_q3.csv</code></p> </li> <li> <p><code>nodes_4782016_q2.csv</code></p> </li> <li> <p><code>...</code></p> </li> </ul> </li> </ul> <p> </p> <p>1104 </p> <ul> <li> <p>Edges </p> <ul> <li> <p><code>...</code></p> </li> </ul> </li> <li> <p>Nodes </p> <ul> <li> <p><code>...</code></p> </li> </ul> </li> <li> <p><code>...</code></p> </li> </ul> <p> </p>
Literature review of the enablers and barriers to stakeholder and citizen engagement in climate change adaptation process (as part of Adaptation AGORA project)
<p>This dataset is the result of collaborative work for Deliverable 4.1 (WP4; T4.1) of the Adaptation AGORA project. This database was used to conduct a literature review of the enablers and barriers to stakeholder and citizen engagement in climate change adaptation process. It contains 123 papers retrived from Web of Science Databse in June 2023. <span>We used a keyword search to identify and select articles that fell within the scope of our research, with each article containing at least one keyword related to climate change adaptation solutions, climate change, co-production, citizen and stakeholder involvement and factors (enablers and barriers). </span></p> <p><span>We divided the coding framework into four main sections:</span></p> <ul> <li> <p><span>Section 1 collected basic information about the paper (i.e., date, journal, authors, type of study and methods for data collection). </span></p> </li> <li> <p><span>Section 2 sought to better understand the adaptation initiatives treated in the paper. Here, we analysed 5 variables (the adaptation solutions type, sectors, benefits, scale, and location). </span></p> </li> <li> <p><span>Section 3 collected characteristics of the climate change adaptation co-production process, including the definition of co-production, the type of the co-production process, its outputs, and the methods used to engage stakeholders. </span></p> </li> <li> <p><span>Section 4 described the factors that enable or hinder the co-production process and their influence on different aspects of the process. After naming and defining each driver, we recorded the main type of factor, its impact, origin, and spatial and temporal scale of influence; the stakeholders who were responsible for and influenced by the factor, and the impacts on the various steps and outcomes of the co-production process.</span></p> <span> </span></li> </ul>
Quantitative raw data for D1.3 - "Requirements and motivations of quadruple helix stakeholders for active engagement in the Citizen Science"
<p>This dataset presents the quantitative raw data that was collected under the H2020 INCENTIVE project for the D1.3 - “Requirements and motivations of quadruple helix stakeholders for active engagement in the Citizen Science Hubs”. The dataset includes the answers that were provided by almost 2,000 participants from 4 pilot European countries (Greece, Lithuania, Spain, and the Netherlands) regarding the general public's perceptions, attitudes, concerns, motivational factors and obstacles with regard to participation in Citizen Science activities. The original survey questionnaire was created and disseminated through the EUSurvey platform, and data collection took place from April to June 2021. For the statistical analysis of the data and the conclusions drawn from the analysis, you can access the D1.3 - "Requirements and motivations of quadruple helix stakeholders for active engagement in the Citizen Science Hubs”.</p> <p>Under INCENTIVE, four Citizen Science Hubs will be established and tested during the life-span of the project in the facilities of four Research Performing and Funding Organisations (RPFOs): University of Twente (the Netherlands), Autonomous University of Barcelona (Spain), Aristotle University of Thessaloniki (Greece) and Vilnius Gediminas Technical University (Lithuania). Essentially, the Hubs will aim to bring different stakeholders together and bridge society with science under the emerging paradigm of Citizen Science, in an institutionalised way.</p>
[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>
TRANSFORM video: Citizen engagement TRANSFORMing regional R&I policy making
<p>Over the past three years, the TRANSFORM project (funded under H2020) has explored and tested citizen engagement practices in the governance of regional research and innovation (R&I), in particular in the context of Smart Specialisation Strategies (S3) in three regional clusters: Lombardy (Italy), Catalonia (Spain), Brussels-Capital (Belgium). Under the common framework of Responsible Research and Innovation (RRI), each territory has implemented different methodologies and pilot actions with the common goal of better aligning regional R&I priorities with societal needs and aspirations.</p> <p>This 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. The European Research Executive Agency (REA) and the European Commission are not responsible for any use that may be made of the information it contains.</p>
TRANSFORM final conference: RRI and citizen engagement to open up regional R&I policymaking
<p>Three regions, three different approaches testing citizen and stakeholder engagement in regional R&I policy making... TRANSFORM partners from three regions - Catalonia, Brussels-Capital and Lombardy on their experiences, activities and impact beyond the project. The video was filmed during the TRANSFORM Final Conference which took place on 1 and 2 December in a hybrid format in Miland and online via Zoom. An opportunity to share project results and outcomes, learn and exchange with the RRI community.</p> <p>This 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. The European Research Executive Agency (REA) and the European Commission are not responsible for any use that may be made of the information it contains.</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>
Data for: Citizen science as an ecosystem of engagement: Implications for learning and broadening participation
<p>The bulk of research on citizen science participants is project-centric, based on an assumption that volunteers experience a single project. Contrary to this assumption, survey responses (n=3,894) and digital trace data (n=3,649) from volunteers, who collectively engaged in 1,126 unique projects, revealed that multi-project participation was the norm. Only 23% of volunteers were singletons (who participated in only one project), and multi-project participants split evenly between disciplines specialists (39%) and discipline spanners (38% joined projects with different disciplinary topics), and unevenly between mode specialists (67%) and mode spanners (33% participated in online and offline projects). Public engagement was narrow: multi-project participants were eight times more likely to be white, and five times more likely to hold advanced degrees, than the general population. We propose a volunteer-centric framework that explores how the dynamic accumulation of experiences in a project ecosystem can support broad learning objectives and inclusive citizen science. </p>
Data for: Citizen science as an ecosystem of engagement: Implications for learning and broadening participation
Open the record for dataset details and reuse information.
TURAS and Citizen Engagement
<p>Video of the TURAS project citizen engagement using the mobile green living room.</p>
Smell Pittsburgh: Engaging Community Citizen Science for Air Quality
<p>Link to the files and description of the Smell Pittsburgh Dataset –<br> <a href="https://eur04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2FCMU-CREATE-Lab%2Fsmell-pittsburgh-prediction%2Ftree%2Fmaster%2Fdataset%2Fv2&data=05%7C01%7Cy.c.hsu%40uva.nl%7C89562067341d40d0bad308da2c475652%7Ca0f1cacd618c4403b94576fb3d6874e5%7C0%7C0%7C637870982141190827%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=gWn0nGRUl5EAHvDfJwTlTNJN%2BWFH62NX6Mw%2B2web6XE%3D&reserved=0">https://github.com/CMU-CREATE-Lab/smell-pittsburgh-prediction/tree/master/dataset/v2</a></p> <p>Smell Pittsburgh (<a href="https://smellpgh.org">https://smellpgh.org</a>) is a mobile application for crowdsourcing reports of bad odors, such as those generated from air pollution. The data is used to train a machine learning model to predict the presence of bad smell and create push notifications to inform citizens about the bad smell. The motivation, background, and design of the Smell Pittsburgh application is described in the following paper.</p> <ul> <li>Yen-Chia Hsu, Jennifer Cross, Paul Dille, Michael Tasota, Beatrice Dias, Randy Sargent, Ting-Hao (Kenneth) Huang, and Illah Nourbakhsh. 2020. Smell Pittsburgh: Engaging Community Citizen Science for Air Quality. ACM Transactions on Interactive Intelligent Systems. 10, 4, Article 32. DOI:<a href="https://doi.org/10.1145/3369397">https://doi.org/10.1145/3369397</a>. Preprint:<a href="https://arxiv.org/pdf/1912.11936.pdf">https://arxiv.org/pdf/1912.11936.pdf</a>.</li> </ul>
Efficacy of Peer Counseling, Social Engagement, and Combination Interventions in Improving Depressive Symptoms of Filipino Senior Citizens
ClinicalTrials.gov study NCT03989284. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Dataset of Tweets for the paper: "Hashtag activism on Twitter: The effects of who, what, when and how a tweet is sent for promoting citizens' engagement with climate change"
Open the record for dataset details and reuse information.
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>
SPARCS_WP3_Espoo_City_SPARCS-WP3 engagement of citizens and stakeholders in Espoo
<p>Engagement level of citizens and stakeholders during different actions completed within SPARCS Work Package 3 (WP3)</p>
Pinterest as a facilitator of Transactive Memory Systems for Citizen Engagement in Earth Science
<p>This is a test to see how Zenodo works.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
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.