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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 - 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>
2020 Citizen Science Event Book: #LongCovid
<p>The Chinese Version "2020 年公民科學事件簿:#長新冠(#Long Covid) ": <a href="https://pansci.asia/archives/370282">https://pansci.asia/archives/370282</a></p><p>The English full text : <a href="https://details-or-fragments.blogspot.com/2023/12/LCEventBook.html">https://details-or-fragments.blogspot.com/2023/12/LCEventBook.html</a></p><p>Long Covid comes from a "patient-created term" in the spring of 2020. On October 6, 2021, the WHO announced its official definition. Although it used "post-COVID-19 condition", the Long Covid is still the most common term. This bottom-up grassroots movement of public participation in scientific concepts in online communities has reached the social conscience of the public and driven scientific development, and finally led to the establishment of relevant policies and scientific progress. This is what sociologists called "citizen science". </p><ul><li>How did it all begin?</li><li>Patient symptom stories: COVID-19 affects more than just the lungs</li><li>Long COVID Citizen Campaign: Responses from health services</li><li>The openness of online social media</li></ul><p>The positive actions of these online community and the collective consensus reached are enough to convincingly prove to medical institutions, including the WHO, that Long Covid is a real disease despite the lack of traditional evidence-based medicine. A group of online citizens collectively wrote the first textbook on Long Covid in 2020. At this moment, we are witnessing the mass power of the online community, which not only promotes real changes in the real world, ensures recognition of medical care supply, but also stimulate a new scientific <i>research</i> stage.</p>
Wild for Orchids Citizen Science Campaign Records 2020 - 2023
<p>This dataset represents the geographic distribution of wild orchids in the Maltese Islands, as recorded by the Wild For Orchids Citizen Science Initiative between January 2020 and December 2023. It includes data obtained through citizen science contributions and has undergone rigorous two-stage quality control for species identification and GPS accuracy. Species identification was carried out according to Mifsud (2018). The location data of each records is provided as a shapefile format projected in ETRS89-extended / LAEA Europe (EPSG:3035), and exact GPS location were transformed in 1 km square grid according to the <span>European Forum for Geography and Statistics (EFGS).<br></span></p> <p><span>Wild for Orchids is a Citizen Science Initiative designed and managed by Green House Malta.</span></p>
DATASET OF RESPONSIBLE RESEARCH AND INNOVATION IN CITIZEN SCIENCE
<p><span>The research aim was to explore what aspects of citizen science (CS) make the involvement of researchers (the ones who implement CS projects) meaningful in terms of responsible research and innovation (RRI) principles. The following research questions were formulated:</span></p> <p><span>1) How does RRI contribute to the meaningfulness of CS projects and in which CS aspects?</span></p> <p><span>2) What motivates researchers to accommodate RRI principles in CS projects? </span></p> <p><span>3) What impedes researchers in accommodating RRI principles in CS projects?</span></p> <p><span>To answer these research questions, a qualitative research approach was employed using individual semi-structured interviews for data collection. Using a purposive criterion-based sample, inclusion criteria were the following:</span></p> <p><span>(i) European researchers (principal investigators/project managers) that are running (at least) one CS project; </span></p> <p><span>(ii) researchers who may represent different organisational settings with scientific orientation (e.g. academia, museums, and others) within Europe; </span></p> <p><span>(iii) the CS project, started before 2013 (year of introducing the concept of RRI into European Union Research and Innovation (EU R&I) policy) should be ongoing during the research conduct, or the CS project started in the period of 2014–2018 (the year 2014 was a starting point since it is the date of embedding RRI in the EU R&I policy as a mandatory component of all research activities) should be still ongoing; and </span></p> <p><span>(iv) the CS project covers any academic discipline.</span></p> <p><span>To identify potential informants, we used the list of CS projects publicised in Wikipedia (</span><span><a href="https://en.wikipedia.org/wiki/List_of_citizen_science_projects"><span>https://en.wikipedia.org/wiki/List_of_citizen_science_projects</span></a></span><span>) and added CS projects from authors’ home countries. In addition, we posted the invitation to participate in the study in a newsletter within the citizen science community (e.g. ECSA) and in social media targeting specific groups and using hashtags, namely on Facebook and Twitter.</span><span> </span><span>At the end, we identified 117 CS projects relevant to our research aim.</span><span> </span><span>20 CS projects (five females and fifteen males)</span><span> </span><span>consented to take part in the study.</span><span> </span><span>CS projects covered different academic disciplines, such as psychology, zoology, biology, ecology, linguistics, palaeontology, history and others.</span></p> <p><span>We constructed a questionnaire consisting of four items: self-identity and ties with CS, enablers of RRI in CS, limitations of RRI in CS and impact of RRI on CS. Interviews were conducted remotely. The interview language was English, except for one interview that was held in the participant’s first language and then translated into English. Though some interviews had minor language-specific flaws (for most informants English is not a native language), they did not interfere with understanding an informant.</span></p> <p><span>Each interview was audio-recorded, transcribed, and pseudonymized if such request was expressed in the informed consent. Average length of interview was 53 minutes. Non-pseudonymised full interviews contained an average of 6,409 words.</span></p> <p><span>Different strategies were used to validate all interview transcripts for purposes of data accuracy and clarifying inaudible responses (e.g. validation of half of transcripts involved two researchers, then validation of eleven transcripts involved interviewees). </span></p> <p><span>Nine informants allowed to publish pseudonymised transcripts while eight informants preferred to have non-pseudonymised transcripts published. Three informants disagreed to make publish a pseudonymised transcript as open research data.</span></p> <p><span> </span></p> <p><span>The complete research is published as Tauginienė, L., Butkevičienė, E., Heinisch, B., Massetti, L., Ugolini, F., Popov, S. (2024). Making Responsible Research and Innovation Meaningful in Citizen Science. </span><em><span>Science and Public Policy</span></em><span>. https://doi.org/10.1093/scipol/scae078 </span></p>
MARCSI - Inventory of Marine Citizen Science Initiatives and the FAIRness of the data they produce
<p>Inventory (data set) of Marine Citizen Science Intiatives collected and described in the publication entitled "Past and present marine citizen science around the globe: a cumulative inventory of initiatives and data produced" co-authored by Uta Wehn, Ane Bilbao, Luke Somerwill, Torsten Linders, Joan Maso, Stephen Parkinson, Christina Semasingha,<sup> </sup>Sasha Woods.</p>
Survey Study about Motivation for Participants in Citizen Science Projects
<p>The survey study about motivation for participants in Citizen Science projects was designed within the ongoing H2020 project named <a href="https://actionproject.eu/">ACTION</a> (pArticipatory sCience Toolkit agaInst pollutiON). Volunteers participate to citizen science initiatives for multiple reasons: personal enjoyment, desire for improvement or achievement, establishment of personal relationships, care for the environment, etc.<br> Studying motivation and investigating the factors influencing people participation to citizen science projects is an essential aspect in the analysis of citizen science communities. Understanding the reasons that foster people to engage can support the successful design and implementation of effective participant involvement tasks, as well as pave the way for long-term engagement.<br> The goal of the study is to analyse the motivation to participate of a specific citizen science community and the structure of the survey proposed shuold be customised considering the topic of the activities.</p> <p><br> This research object describes the studies performed within the ACTION project to investigate motivations of different citizen science communities: the TESS Network (<a href="https://tess.stars4all.eu/">https://tess.stars4all.eu/</a>); the 6 ACTION pilots (<a href="https://actionproject.eu/citizen-science-pilots">https://actionproject.eu/citizen-science-pilots</a>) Mapping Mobility, Open Soil Atlas, Water Sentinels, Restart Data Workbench, Wow Nature, Walk Up Aniene.<br> The surveys were designed and administered through Coney (<a href="https://coney.cefriel.com">https://coney.cefriel.com</a>) and made available as linked data exploiting the Survey Ontology (<a href="https://w3id.org/survey-ontology">https://w3id.org/survey-ontology</a>).</p> <p>The research object adopts the <a href="https://www.researchobject.org/ro-crate/1.0/">RO-Crate</a> specification. Files made available within the research object are:</p> <ul> <li><em>*-procedure.ttl</em> contains the RDF representation of the <strong>template</strong> structure of the conversational survey (questions, answers, etc.) using the <a href="https://w3id.org/survey-ontology">Survey Ontology</a></li> <li>*-<em>mean-var-motivating-questions.csv </em>contains the computed mean and average for each question considered (observable variables) comparing all the surveys performed</li> <li>*-<em>mean-var-motivating-factor.csv </em>contains the computed mean and average for each motivation factor considered (latent variables) comparing all the surveys performed</li> <li>*-<em>correlation-factors-global-motivation.csv </em>contains the correlation analysis between each motivation factor and the global motivation comparing all the surveys performed</li> </ul> <p>The Research Object also references all the RO-Crates describing the different survey motivation studies in details.</p>
Citizen Science projects on Alien Species in Europe
<p><strong>Context</strong></p> <p>This survey relates to COST (European Cooperation in Science and Technology) Action CA17122 - Alien CSI - Increasing understanding of alien species through citizen science (see https://alien-csi.eu/). The main aim of this survey was to collect information on Citizen Science projects/initiatives involving alien species in European Member States and some neighbouring countries. The survey was performed using a google forms. Survey respondents/contributors are mentioned in this dataset as data collectors. </p> <p><strong>Definitions</strong></p> <p>We defined Citizen Science projects as project which actively involved citizens in scientific enquiry generating new knowledge or understanding on alien species. Citizens may act as contributors, collaborators, or as project leader and have a meaningful role in the project. 'Alien Species' are defined as any live specimen of a species, subspecies or lower taxon of animals, plants, fungi or micro-organisms introduced outside its natural range; it includes any part, gametes, seeds, eggs or propagules of such species, as well as any hybrids, varieties or breeds that might survive and subsequently reproduce. Alien Species thus includes both species that are invasive and species that are alien but not invasive. An 'Invasive Alien Species' is defined as an alien species whose introduction or spread has been found to threaten or adversely impact upon biodiversity and/or related ecosystem services.</p> <p><strong>Survey methodology</strong></p> <p>The survey was made available on Google Forms and disseminated online, collecting responses from June 27, 2019 to April 6, 2020. It was shared with all COST Action CA17122 participants and in each country one person coordinated contacts with existing citizen science projects involving alien and/or invasive species and requested that they complete the survey. Thus, all projects were active in EU member states and neighbouring countries, though some may also be active outside of Europe. To increase reach, the survey was also disseminated through the European Citizen Science Association (ECSA) newsletter and mailing list and respondents were asked to share it with colleagues and local networks via snowball sampling.</p> <p><strong>Questions and attribute values</strong></p> <p>Survey questions and attribute values were developed using JRC metadata standards for CS projects (Bio Innovation Service 2018) and the project metadata model of PPSR Core, a set of global, transdisciplinary data and metadata standards for Public Participation in Scientific Research (https://core.citizenscience.org/). The survey included 62 questions in nine sections:</p> <ol> <li>Contact information of the respondent;</li> <li>General characterization of the project, including a brief summary, geographical scope, time scale, hosting entities, funding, etc.; </li> <li>Information on project scope, including target audience, taxonomic and environmental scope, project aims, type of data collected, etc.;</li> <li>Policy-related information, namely if the project has policy relevance and inclusion of species listed in the EU IAS Regulation;</li> <li>Information on engagement, such as type of involvement of citizens in the design of the project, engagement methods and social media used, skills needed to participate and frequency of contributions;</li> <li>Information on feedback and support provided to participants by the project, e.g., if projects provide materials for species identification, guidelines, training activities, information on how data from the project are used, feedback mechanisms and support; </li> <li>Data quality and data management, namely validation mechanism for records, registration type, methods of recording, whether data are open and accessible to citizen scientists, data form used to store data, data standards and data licence used, whether a public data management plan was drafted for the project, and the vocabulary used with respect to biological invasions (origin, occurrence status, degree of establishment and pathway of introduction);</li> <li>Performance indicators of projects, namely, usage of apps, number of participants and number of records, whether learning is assessed, number and type of publications using data from the project; </li> <li>Notes and remarks.</li> </ol> <p><strong>Files</strong></p> <ul> <li><strong>raw_data.xlsx</strong>: includes the non-processed survey responses, supplemented with a project_ID. All GDPR sensitive data such as email addresses were omitted. Each row represents one project.</li> <li><strong>projects_excluded.csv: </strong>includes all projects that were omitted from the analysis and the specific criteria for this exclusion. </li> <li><strong>processed_data.csv</strong>: includes the cleaned, processed survey responses, used for analysis. The R code used for the analysis is available on <a href="https://github.com/alien-csi/inventory-analysis/blob/master/src/analysis.Rmd">this github repository</a>.</li> <li><strong>survey.pdf: </strong>a pdf extract from the original Google Forms, including all questions and their specifications. </li> <li><strong>analysis.Rmd</strong>: Rmarkdown script for statistical analysis. Also available on <a href="https://github.com/alien-csi/inventory-analysis/blob/master/src/analysis.Rmd">this github repository</a>.</li> </ul> <p> </p> <p> </p>
Vortex Catalog from Jovian Vortex Hunter Zooniverse citizen science project
<p>This dataset contains the aggregated results from the <a href="https://www.zooniverse.org/projects/ramanakumars/jovian-vortex-hunter/" target="_blank" rel="noopener">Jovian Vortex Hunter citizen science project</a>, where citizen science volunteers labeled images from the JunoCam instrument and determined locations and sizes of vortices.</p> <p>The CSV file contains results from the first workflow and details the consensus from volunteers on different features in each image (given by the Zooniverse Subject ID). There are five categories to choose from:</p> <ol> <li>Vortex</li> <li>Turbulent (i.e. Folded Filamentary Regions: FFRs)</li> <li>Cloud bands</li> <li>Blurry (i.e., image issues)</li> <li>Featureless (there is no discernable feature in the image)</li> </ol> <p>The CSV file also contains an additional column defining the number of classifications of the subject.</p> <p>The JSON file contains the aggregated catalog of vortices and their properties from the second workflow. The format of the JSON file is as below:</p> <p>Each entry contains a dictionary of vortex properties, as shown below. The entry is determined by aggregating the vortex properties across multiple Zooniverse images which share the vortex, and building a consensus from multiple volunteer responses.</p> <pre><code>{ "subject_ids": [array of Zooniverse subject ID for each vortex], "perijove": the perijove corresponding to the image from this vortex was determined "color": the aggregated color of the vortex determined from volunteer responses, "lon": System III planetographic longitude [degree], "lat": planetographic latitude [degree], "x0", "y0": reference coordinate on the Zooniverse crop image for longitude/latitude, "x", "y": coordinate of the vortex center on the Zooniverse crop image "rx", "ry": radius of the vortex in pixel coordinates on the Zooniverse crop image "angle": angle in degree from horizontal of the orientation of the vortex in the Zooniverse crop image, "sigma": 1sigma error in the scale of the vortex, "angular_width": width of the vortex in degrees on the planet, "angular_height": height of the vortex in degrees on the planet, "physical_width", "physical width": width/height of the vortex in km, "extracts": [ array of dictionaries consisting of individual ellipses that make up the vortex consensus ] "colors": { "brown": consensus on the vortex being brown [0-1], "red": consensus on the vortex being red [0-1], "dark": consensus on the vortex being dark [0-1], "white": consensus on the vortex being white [0-1], "white-brown": consensus on the vortex being white and brown [0-1], "white-red": consensus on the vortex being white and red [0-1], "red-brown": consensus on the vortex being red and brown [0-1], } }</code></pre> <p>Each extract is a dictionary containing the following properties. An extract is a single aggregated ellipse on a single Zooniverse image.</p> <pre><code>{ "subject_id": Zooniverse subject ID, "perijove": the perijove when the JunoCam image was taken, "color": the color of the vortex with the highest vote fraction, "lon": System III longitude of the vortex [degree], "lat": planetographic latitude of the vortex [degree], "x0", "y0": reference coordinate on the Zooniverse crop image for longitude/latitude, "x", "y": coordinate of the vortex center on the Zooniverse crop image "rx", "ry": radius of the vortex in pixel coordinates on the Zooniverse crop image "angle": angle in degree from horizontal of the orientation of the vortex in the Zooniverse crop image, "probability": the 1sigma error in the scale of the vortex, "angular_width": width of the vortex in degrees on the planet, "angular_height": height of the vortex in degrees on the planet, "physical_width", "physical width": width/height of the vortex in km, }</code></pre>
[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>
SCShores: time-series of shorelines from Spanish Sandy beaches from citizen-science monitoring program.
<p>This repository contains 5 years of sandy beaches shorelines deriverd from a citizen-science monitoring program in the Spanish coast. The methodology and the dataset are described in:</p> <p><em><strong>González-Villanueva, R., Soriano-González, J., Alejo, I., Criado-Sudau, F., Plomaritis, T., Fernàndez-Mora, À., Benavente, J., Del Río, L., Nombela, M. Á., and Sánchez-García, E.: SCShores: a comprehensive shoreline dataset of Spanish sandy beaches from a citizen-science monitoring programme, Earth System Science Data. V. 15, 4613-4629 , <a href="https://essd.copernicus.org/articles/15/4613/2023/essd-15-4613-2023.html">https://doi.org/10.5194/essd-15-4613-2023</a>, 2023. </strong></em></p> <p>The shoreline dataset is provided in 1 GEOJSON file: SCShores.geojson. This dataset covers five<strong> </strong>sandy beaches located on the Atlantic and Mediterranean coasts of Spain where CoastSnap stations were available, and it includes a total of 1721 shorelines. The coordinate system for the geospatial layer is WGS84.</p> <ul> <li><strong><em>SCShores.geojson</em></strong>: this layer contains the sandy shorelines . Each feature in this layer is a multipoint with the following attributtes: <ul> <li><strong>site</strong>: CoastSnap station name id, e.g. agrelo, samarador, cadiz, ….</li> <li><strong>date</strong>: date and time of the shoreline, yyyyy-mm-dd hh:mm:ss</li> <li><strong>timezone</strong>: Coordinated Universal Time, UTC</li> <li><strong>timestampQuality</strong>: quality flag indicating the confidence in the date-time indicated by the image provider, e.g. 1, 2</li> <li><strong>imageSource</strong>: source of the original image from which the shoreline has been derived, e.g. Instagram, Twitter, Facebook, Email, CoastSnapApp</li> <li><strong>elevation_m:</strong> same as Z coordinate, defined by the observed tide and the tidal offset, in meters, Tide+tide offset</li> <li><strong>verticalDatum</strong>: mean sea level in Alicante, which is considered the zero topographic reference in the Spanish territory, NMMA</li> <li><strong>geometry</strong>: type of geometry used in the file, MultiPoint</li> <li><strong>coordinates</strong>: Geographic WGS84 coordinates for each point in the geometry, longitude, latitude, Z</li> </ul> </li> </ul> <p> </p>
Data from “A Mixed Method Approach to Understanding the Public Health Impact of a School-Based Citizen Science Program to Reduce Arsenic in Private Well Water”
Objectives We have approached the problem of low well water testing rates in Maine and New Hampshire communities by developing the All About Arsenic (AAA) project, which engages secondary school teachers and students as citizen scientists in collecting well water samples for analysis of arsenic and other toxic metals and supports their outreach efforts to their communities. Methods We assessed this project’s public health impact by analyzing student data relative to existing well water quality datasets in both states. In addition, we surveyed private well owners who contributed well water samples to the project to determine the actions taken to mitigate arsenic in well water. Data The data presented here are used in the analyses performed for the publication: "A Mixed Method Approach to Understanding the Public Health Impact of a School-Based Citizen Science Program to Reduce Arsenic in Private Well Water.” Additional data may be available at: The Anecdata Project Page: https://anecdata.org/projects/view/299 The project website: https://www.allaboutarsenic.org/
SuperWASP Variable Stars: Classifying Light Curves Using Citizen Science
<p>Table of 301 previously unidentified SuperWASP stellar variables and related characteristics, not including rotators and unknown variables. The variable type has been decided by citizen scientists through the SuperWASP Variable Stars Zooniverse project. The types and periods of each object have been assessed by the authors to correct for mis-classifications; whilst they have been corrected as much as possible, some types periods remain best guesses. All periods have an uncertainty of 0.1%.</p>
Preliminary table of Citizen Science initiatives for monitoring soil health
<p>This matrix is the result of collaborative work for Deliverable 1.1 (WP1; T1.1) of the ECHO project. It facilitated the creation of an overview of the current state of the art in projects, initiatives, or activities that have already involved citizens in monitoring soil health, from both inside and outside the European Union. From this, strategic recommendations for ECHO were derived, ensuring that this project not only makes a valuable contribution to the field of soil health monitoring but also sets a precedent for future citizen science endeavors.</p>
Video 6 - Open Science: science for and with citizens.
<p><span>An interview on Open Science funding with Ignasi Labastida, director of the Office for the Dissemination of Knowledge, University of Barcelona; Bregt Saenen, Senior Policy Officer for Open Science at Science Europe; Victoria Tsoukala, Policy Officer – Open Science at the European Commissions, DG-Reearch and Innovation; and Sumithra Vellupilai, Senior Research Officer at the Swedish Research Council.</span></p> <p><span>Universities should fund Open Science as a means of sharing knowledge, and as a means of providing tools for all of society to access knowledge. All parts of society should be able to benefit from the knowledge produced through the scientific process. Making openness the norm is a way for including society’s stakeholders in the research process. This level of openness and transparency requires funding infrastructure for sharing articles, data and other research results.</span></p> <p><span> </span></p>
Citizen science snow measurements
<p>Data set includes citizen science observations of snow collected mainly from Finland and Sweden. Data set is collected in CHARTER project with a simplified protocol which follows the international snow observational standards. Data set includes 47 measurement occasions. The protocol includes background information such as measurement date and time, location, description of surroundings, reindeer pasture type, and visible trampling or digging in snow. Measurements includes snow depth in 1-5 points and definition of ice and crust layers at 1-2 of the points. A hardness hand test is used for layer detection (pushing snow first with fist, then with 4 fingers, 1 finger, pencil and knife blade, until snow is too hard to be pierced). For each ice and crust layer, distance of the layer top and bottom from the ground is measured. In addition, it was optional to measure properties of all layers in snowpack (hardness, grain type and distances from the ground) and the snow water equivalent by using cylindrical tube to extract and weight sample of snow.</p> <p>Data set includes date, time, location, longitude, latitude, air temperature, signs of foraging, description of surroundings, type of reindeer pasture, ground, snow height, description of snow conditions with your own language, layer distances from ground, grain type for layers, hardness for layers, snow water equivalent (tube diameter, snow height, weight), recent rain on snow events, comments and links to photos.</p>
Estimating the carbon footprint of citizen science biodiversity monitoring
<p>Datasets used in the production of the paper Gillings, S. & Harris, S.J. 2022. Estimating the carbon footprint of citizen science biodiversity monitoring. People & Nature.</p> <p>The dataset comprises a) the estimated round-trip distances from approximate locations of observers to survey locations for the UK Breeding Bird Survey and b) questionnaire responses concerning mode of travel used to access survey locations. Data have been anonymised and locations have been coarsened to preserve anonymity.</p> <p>We would also greatly appreciate if you could fill out <a href="https://forms.gle/DCc58VXpdmqnTmTk8" target="_blank" rel="noopener">this very short form</a> to tell us how you intend to use these data. Thanks in advance!</p> <p> </p> <p> </p>
CSI-COP Dataset of Organisations to Approach in Citizen Science Projects
<p>This dataset complements CSI-COP project deliverable D2.3 report: '<strong>Framework for Engaging Citizen Scientists</strong>'.</p> <p>The D2.3 deliverable was produced in 2020 by partners in CSI-COP work package 2 led by <strong>Professor Olga Stepankova </strong>of Czech Technical University, Prague (<strong>CTU</strong>).</p> <p>The Stepankova et al. (2020) report is available on this Zenodo platform here:</p> <p><a href="https://zenodo.org/record/4066515#.Yrx9DezMLb0">https://zenodo.org/record/4066515#.Yrx9DezMLb0</a></p>
TIME4CS WP4 Mapping of citizen science training resources
<p>This dataset was compiled as part of the TIME4CS project, WP4, and lists identified citizen science training resources, as of July 2022.</p> <p>The <a href="https://eu-citizen.science/">EU-citizen.science</a> platform provided the basis for mapping CS training in Europe, as the team behind the platform has put considerable effort into compiling, and encouraging the CS community to contribute, CS training resources. Additionally, training courses were identified based on the case studies in WP1, as most universities do not list their courses on the EU-citizen.science platform.</p>
Unprocessed data from the Jungle Weather Zooniverse citizen science project
<p>The <a href="https://www.zooniverse.org/projects/khufkens/jungle-weather">Jungle Weather project</a> aimed to transcribe weather observations recorded between 1949 and 1958 in the tropical rainforest of the Democratic Republic of the Congo. Long-term observations of tropical weather are rare. The Jungle Weather, as part of the COBECORE project, contains observations of three decades of data of weather in the central African tropical forest, and are therefore an extraordinary source of information to support our understanding of for example drought resilience of trees species.</p> <p><strong>Summary</strong></p> <p>Both input and output of the citizen science transcriptions are provided in this data set. This includes the original cut-outs as used in the Zoonivese project, and the output as generated by the Zooniverse data export routines. The data export routines provided CSV output with JSON subfields on the content of each classification made. In addition, we provided the exported subject list and the details of each workflow.</p> <p>In total the project output constitutes of four files:</p> <ul> <li>transcribe-climate-data-classifications.csv (annotations of the table cells)</li> <li>transcribe-meta-data-classifications.csv (annotations of table headers)</li> <li>jungle-weather-workflows.csv (description of the citsci workflow)</li> <li>jungle-weather-subjects.csv (list of all images transcribed, and their online location for validation / referencing)</li> </ul> <p>and roughly ~3GB in data volume.</p> <p><strong>Context</strong></p> <p>Our understanding of forest ecosystem responses to climate change relies on consistent long-term observations to provide baseline measurements. In the central Congo Basin established long-term observation programs are rare. In terms of meteorological observations, the central Congo Basin is currently represented by only a few rain gauges, limiting climate forecasts across the Congo Basin and the central African continent. This lack of long-term (historical) climatological data leaves the central Congo Basin spatially and temporally under-represented. However, old climate records could provide valuable information about previous growing conditions of the forest.</p> <p>Large amounts of ecological and climatological data, approximately five decades (~1910 – 1960), exists as unexplored heritage, stored in various Belgian federal archives and collections. As part of a larger project called Congo Basin eco-climatological data recovery and valorization (COBECORE, see) the Jungle Weather project will help transcribe historical climatological data as measured throughout the Congo Basin. These data will in part complement the completed <a href="https://www.zooniverse.org/projects/khufkens/jungle-rhythms">Jungle Rhythms Zooniverse project</a>, further valorizing these transcribed data.</p> <p><strong>Historical data</strong></p> <p>Within this project we will focus on data records as recorded throughout the tropical part of what is currently the Democratic Republic of the Congo (DRC). The area which we will cover is shown above in the map as an open polygon. The project will not cover the southern province of Katanga (red crosshatches) as this area transitions here from tropical to a humid subtropical climate.</p> <p>The historical data is archived and stored in the Belgian State Archives. The Belgian State Archive harbour almost all data regarding colonial affairs, ranging from communications about trade to the raw data as digitized within the context of the Jungle Weathers project. Row upon row of data is stored in the basement. Below you see a part of the INEAC (Institut National pour l’Etude Agronomique du Congo belge) archive, which holds all climatological records.</p> <p>These climatological records were noted rigorously on carbon copy paper. However, due to the hand written nature of the data (and the volume involved) automated processing is not possible. Although optical character recognition (OCR) works wonderfully on printed data the high variability in characters and the low contrast pencil markings contribute to the failure of current automated approaches. Similar to the <a href="https://www.oldweather.org/">Old Weather project</a> and in spirit of the Jungle Rhythms project, a keen eye is required to decipher the numbers written down on these sheets.</p> <p><strong>Pre-processing / digitization</strong></p> <p>The project provided citizen scientists with digital pictures of the original sheets. Scanning these climate data sheets was a laborious process. In total more than 70 000 records were digitized. Unlike the Old Weather project we did not require citizen scientists to outline valid sections of the sheet. This part of the processing has been automated. We refer to our<a href="https://doi.org/10.5281/zenodo.3378864"> Jungle Weather pre/post-processing repository </a>for more details and example code</p> <p>As such, once digitized and properly aligned the whole record was divided into an estimated 30 million cells and 70 000 header files. Below you find an example of a header file and a table cell. During the Jungle Weather project we selected a subset of ~300K table cells for transcription in efforts to validate further Machine Learning based, automated, transcriptions approaches. All data were transcribed by citizen scientists in the spring/summer of 2020.</p> <p><strong>Notes</strong></p> <p>The provided data is raw data, and expert knowledge is required for the correct interpretation of this data. Please contact the authors for the proper context if you are interested in using this data in your project.</p>
Birdwatching, eBird and citizen science in India: qualitative interviews with participants, practitioners and ecologists
<h1>Abstract</h1> <p>This study consists of qualitative interviews about birdwatching, citizen science, and the use of the birdwatching data platform <em>eBird </em>in India. Interview partners are birdwatchers, citizen science practitioners, and ecologists who have used eBird data. Some of the main topics covered include: the nature of the birdwatching community and styles of birdwatching in India; the history of the adoption of eBird in India; the value of birdwatching and citizen science; challenges involved in conducting or participating in citizen science; opportunities and limitations of using data from eBird and citizen science; processes of data collection and quality control in eBird; and ecological research, conservation priorities, and environmental activism in India. This study is part of the project A Philosophy of Open Science for Diverse Research Environments (PHIL_OS).</p> <h1>Methods</h1> <p>The data in this study was collected using semi-structured qualitative interviews.</p> <p>Interview partners were recruited by snowball sampling through their engagement with eBird India and related organisations. There were 17 interview partners, interviewed either once or several times. 19 interviews were conducted in total.</p> <p>Interview guides/questionnaires were designed for each interviewee depending on their status as birdwatchers, citizen science coordinators, and eBird data users.</p> <p>Interviews were conducted between April 2022 and June 2023. The interviews took place online using Zoom videoconferencing software. Interviews lasted 35-70 minutes. When participants provided their written consent, interviews were audio-recorded and transcribed smart verbatim using otter.ai and manual proofreading. Sensitive information was removed before publishing transcripts.</p> <p>Transcripts were analysed using semi-grounded coding. Codes were organised into parent codes using an inductive approach based on emergent categories.</p> <h1>Description of the data and file structure</h1> <p>Documentation files include interview guides, the information sheet and consent form, ethics approval, and the data narrative. Documentation files are named according to the structure: authorname_filename_DOCUMENTATION.</p> <p>Data files consist of a summary of participants, 17 of the interview transcripts, and a code list. Interview transcript files are named according to the structure: authorname_interviewnumber_date.</p> <p>A full list of files is provided in the README file.</p> <h1>Notes</h1> <p>This study was conducted as part of the project A Philosophy of Open Science for Diverse Research Environments (PHIL_OS). More information can be found at <a href="https://opensciencestudies.eu/">https://opensciencestudies.eu</a></p> <p>This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 101001145).</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.