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649 results for “Citizens”

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edi56/100

Upper Midwest Great Lakes Region Citizen Secchi Data 1938 - 2012

Upper MidwestorGreat Lakes Region Citiizen Secchi Data includes 239,741 citizen Secchi monitoring records (1938 – 2012) from Illinois Volunteer Lake Monitoring Program, Indiana Clean Lakes Program, Iowa Secchi Dip-In Project, Michigan Clean Water Corps, Lakes of Missouri Volunteer Program, Minnesota Citizen Lake Monitoring Program, Ohio Citizen Lake Awareness Program, and Wisconsin Citizen Lake Monitoring Records. Data were obtained from above monitoring groups and merged with the high resolution National Hydrography Dataset (www.nhd.usgs.gov) based on citizen proved latitudeorlongitude coordinates to verify the location of individual lakes and size of lake (hectare). Code used to estimate annual average Secchi depth (m) provided in metadata. These citizen-collected, publically available Secchi depth measurements were collected to answer two questions: (1) what are the long-term trends in lake water quality across a broad geographic region?; (2) how do trends differ as a function of spatial location, size of lake monitored, and when Secchi records were collected. Data collection and analysis were funded by the National Science Foundation (MSB- 1065786, EF-1065818, EF-1065649), NTL-LTER (DEB-0822700), STRIVE grant 2011-W-FS-7 from the Environmental Protection Agency. GLERL contribution number (1703).

openCC (other)Dec 2022View details →

zenodo52/100

[Dataset] Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects - Raw Data

Explanation/Overview: 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. 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. Purpose: 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. Relatedness: 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'. Content: The dataset contains three files: <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> Grouping: The projects (and thus the corresponding discussions and comments) were collected on the basis of common forum features such as the discussion boards.

opencc-by-4.0Nov 2022View details →

edi52/100

CLA Yahara Lakes Citizen Offshore Water Quality Monitoring 2016 - 2017

In 2013, Clean Lakes Alliance (CLA) launched a Citizen Water Quality Monitoring pilot. Objectives included evaluating and tracking nearshore water quality conditions on all five Yahara lakes: Lakes Mendota, Monona, Waubesa, Kegonsa and Wingra. In 2016, in order to fully understand the interaction between the offshore and nearshore environment, CLA volunteers will begin sampling the deepest point (deep hole) of all Yahara lakes. The offshore monitoring program will focus on two components: water clarity sampling and dissolved oxygen and temperature measurement. Data from the offshore monitoring program will be compared to data from the nearshore program.

openCC (other)Dec 2022View details →

zenodo48/100

2020 Citizen Science Event Book: #LongCovid

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>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>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". <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>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 research stage.

opencc-by-4.0Dec 2023View details →

zenodo48/100

Wild for Orchids Citizen Science Campaign Records 2020 - 2023

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 European Forum for Geography and Statistics (EFGS). Wild for Orchids is a Citizen Science Initiative designed and managed by Green House Malta.

opencc-by-4.0Dec 2022View details →

zenodo48/100

DATASET OF RESPONSIBLE RESEARCH AND INNOVATION IN CITIZEN SCIENCE

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: 1) How does RRI contribute to the meaningfulness of CS projects and in which CS aspects? 2) What motivates researchers to accommodate RRI principles in CS projects? 3) What impedes researchers in accommodating RRI principles in CS projects? 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: (i) European researchers (principal investigators/project managers) that are running (at least) one CS project; (ii) researchers who may represent different organisational settings with scientific orientation (e.g. academia, museums, and others) within Europe; (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 (iv) the CS project covers any academic discipline. To identify potential informants, we used the list of CS projects publicised in Wikipedia (<a href="https://en.wikipedia.org/wiki/List_of_citizen_science_projects">https://en.wikipedia.org/wiki/List_of_citizen_science_projects</a>) 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. At the end, we identified 117 CS projects relevant to our research aim. 20 CS projects (five females and fifteen males) consented to take part in the study. CS projects covered different academic disciplines, such as psychology, zoology, biology, ecology, linguistics, palaeontology, history and others. 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. 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. 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). 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.   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. Science and Public Policy. https://doi.org/10.1093/scipol/scae078

opencc-by-4.0Nov 2024View details →

zenodo48/100

MARCSI - Inventory of Marine Citizen Science Initiatives and the FAIRness of the data they produce

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, Sasha Woods.

opencc-by-sa-4.0Nov 2024View details →

zenodo48/100

Survey Study about Motivation for Participants in Citizen Science Projects

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. 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. 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. 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. 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>). 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: <ul> <li>*-procedure.ttl contains the RDF representation of the template structure of the conversational survey (questions, answers, etc.) using the <a href="https://w3id.org/survey-ontology">Survey Ontology</a></li> <li>*-mean-var-motivating-questions.csv contains the computed mean and average for each question considered (observable variables) comparing all the surveys performed</li> <li>*-mean-var-motivating-factor.csv contains the computed mean and average for each motivation factor considered (latent variables) comparing all the surveys performed</li> <li>*-correlation-factors-global-motivation.csv contains the correlation analysis between each motivation factor and the global motivation comparing all the surveys performed</li> </ul> The Research Object also references all the RO-Crates describing the different survey motivation studies in details.

opencc-by-4.0Dec 2021View details →

zenodo48/100

Citizen Science projects on Alien Species in Europe

Context 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.  Definitions 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. Survey methodology 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. Questions and attribute values 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: <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> Files <ul> <li>raw_data.xlsx: 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>projects_excluded.csv: includes all projects that were omitted from the analysis and the specific criteria for this exclusion. </li> <li>processed_data.csv: 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>survey.pdf: a pdf extract from the original Google Forms, including all questions and their specifications. </li> <li>analysis.Rmd: 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>

opencc-by-4.0Apr 2021View details →

zenodo48/100

Vortex Catalog from Jovian Vortex Hunter Zooniverse citizen science project

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. 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: <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> The CSV file also contains an additional column defining the number of classifications of the subject. 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: 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. <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> Each extract is a dictionary containing the following properties. An extract is a single aggregated ellipse on a single Zooniverse image. <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>

opencc-by-4.0Jun 2024View details →

zenodo48/100

[Dataset] Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects

Explanation/Overview: 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. Purpose: 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 does not represent raw data, 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. Relatedness: 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'. Content: In this Zenodo entry, several files can be found. The structure is as follows (<code>files</code> and folders and descriptions). <ul> <li><code>corresponding_calculations.html</code> <ul> <li>Quarto-notebook to view in browser</li> </ul> </li> <li><code>corresponding_calculations.qmd</code> <ul> <li>Quarto-notebook to view in RStudio</li> </ul> </li> <li>assets <ul> <li>data <ul> <li>annotations <ul> <li><code>annotations.csv</code> <ul> <li>List of annotations made per day for each of the analysed projects</li> </ul> </li> </ul> </li> <li>comments <ul> <li><code>comments.csv </code> <ul> <li>Total list of comments with several data fields (i.e., comment id, text, reply_user_id)</li> </ul> </li> </ul> </li> <li>rolechanges <ul> <li><code>478_rolechanges.csv</code> <ul> <li>List of roles per user to determine number of role changes </li> </ul> </li> <li><code>1104_rolechanges.csv</code> <ul> <li>...</li> </ul> </li> <li><code>...</code></li> </ul> </li> <li>totalnetworkdata <ul> <li>Edges  <ul> <li><code>478_edges.csv</code> <ul> <li>Network data (edge set) for the given projects (without time slices)</li> </ul> </li> <li><code>1104_edges.csv</code> <ul> <li>...</li> </ul> </li> <li><code>...</code></li> </ul> </li> <li>Nodes  <ul> <li><code>478_nodes.csv</code>  <ul> <li>Network data (node set) for the given projects (without time slices)</li> </ul> </li> <li><code>1104_nodes.csv</code>  <ul> <li>...</li> </ul> </li> <li><code>...</code></li> </ul> </li> </ul> </li> <li>trajectories <ul> <li>Network data (edge and node sets) for the given projects and all time slices (Q1 2016 - Q4 2021)</li> <li>478  <ul> <li>Edges  <ul> <li> <code>edges_4782016_q1.csv</code> </li> <li> <code>edges_4782016_q2.csv</code> </li> <li> <code>edges_4782016_q3.csv</code> </li> <li> <code>edges_4782016_q4.csv</code> </li> <li> <code>...</code> </li> </ul> </li> <li>Nodes  <ul> <li><code>nodes_4782016_q1.csv</code></li> <li> <code>nodes_4782016_q4.csv</code> </li> <li> <code>nodes_4782016_q3.csv</code> </li> <li> <code>nodes_4782016_q2.csv</code> </li> <li> <code>...</code> </li> </ul> </li> </ul> </li> <li> 1104  <ul> <li> Edges  <ul> <li> <code>...</code> </li> </ul> </li> <li> Nodes  <ul> <li> <code>...</code> </li> </ul> </li> </ul> </li> <li> ... </li> </ul> </li> </ul> </li> <li>scripts <ul> <li><code>datavizfuncs.R</code> <ul> <li>script for the data visualisation functions, automatically executed from within <code>corresponding_calculations.qmd</code></li> </ul> </li> <li><code>import.R</code> <ul> <li>script for the import of data, automatically executed from within <code>corresponding_calculations.qmd</code></li> </ul> </li> </ul> </li> </ul> </li> <li>corresponding_calculations_files <ul> <li>files for the html/qmd view in the browser/RStudio</li> </ul> </li> </ul> Grouping: 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

opencc-by-4.0Nov 2022View details →

zenodo48/100

Data on the Netherlands and United Kingdom's Citizens Juries on New Plant Breeding Techniques

This dataset contains the codebooks, code references, and code items for the Netherlands and United Kingdom citizens' juries on new plant breeding techniques.  The main folders 01_NLJury_Codes & codebook and 02_UKJury_Codes contain the data for the Netherlands and United Kingdom citizens' juries and the codebook respectively. The juries were four days long and each main folder has four sub-folder which contains the code references and code items for each day of the citizens' jury. Both the main folders also contain a Word document that provides the codebooks for the respective citizens' jury.

opencc-by-4.0Dec 2022View details →

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SCShores: time-series of shorelines from Spanish Sandy beaches from citizen-science monitoring program.

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: 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.  The shoreline dataset is provided in 1 GEOJSON file: SCShores.geojson. This dataset covers five 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. <ul> <li>SCShores.geojson: this layer contains the sandy shorelines . Each feature in this layer is a multipoint with the following attributtes: <ul> <li>site: CoastSnap station name id, e.g. agrelo, samarador, cadiz, ….</li> <li>date: date and time of the shoreline, yyyyy-mm-dd hh:mm:ss</li> <li>timezone: Coordinated Universal Time, UTC</li> <li>timestampQuality: quality flag indicating the confidence in the date-time indicated by the image provider, e.g. 1, 2</li> <li>imageSource: source of the original image from which the shoreline has been derived, e.g. Instagram, Twitter, Facebook, Email, CoastSnapApp</li> <li>elevation_m: same as Z coordinate, defined by the observed tide and the tidal offset, in meters, Tide+tide offset</li> <li>verticalDatum: mean sea level in Alicante, which is considered the zero topographic reference in the Spanish territory, NMMA</li> <li>geometry: type of geometry used in the file, MultiPoint</li> <li>coordinates: Geographic WGS84 coordinates for each point in the geometry, longitude, latitude, Z</li> </ul> </li> </ul>

opencc-by-4.0Apr 2023View details →

zenodo48/100

Characterization of investments profiles on the energy transition for european citizens

<ul> <li>Name: Characterization of investments profiles on the energy transition for european citizens</li> <li>Summary: The dataset contains: (1) surveyee consent form for the study, (2) different scenarios about the energy transition, (3) determinant factors about those scenarios, (4) socioeconomic description of the surveyee, (5) investment decisions, (6) and household characterization/description. </li> <li>License: cc-BY-SA</li> <li>Acknowledge: These data have been collected in the framework of the WHY project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 891943.</li> <li>Disclaimer: The sole responsibility for the content of this publication lies with the authors. It does not necessarily reflect the opinion of the Executive Agency for Small and Medium-sized Enterprises (EASME) or the European commission (Ec). EASME or the Ec are not responsible for any use that may be made of the information contained therein.</li> <li>Collection Date: 22/07/2022</li> <li>Publication Date: 15/10/2023</li> <li>DOI: 10.5281/zenodo.4455198</li> <li>Other repositories:</li> <li>Author: University of Deusto</li> <li>Objective of collection: This data was originally collected to analyze quantitatively the decisions of everyday people in relation to their energy consumption and their reactions to specific political interventions.</li> <li>Description: The dataset contains a CSV file file containing data collected from a survey about energy consumption investments. The fields that can be found for each entry are (1) Different scenarios about the energy transition and reactions to those scenarios, (money spent on energy investments, decisions about scenarios, actions taken under a blackout, etc.) (2) Determinant factors about the chosen scenarios in the previous question, which include different choices that could affect your decision about a scenario (3) socioeconomic information about the user (age, country of residence, studies), (4) estimation of the prices of various technologies related to the energy transition and (5) descriptive statistics about the household living situation (gender of user, people living in household, yearly rent, average savings per month, type of house, size of house) and also includes questions about climate change expertise. Next you can found a description of each field in the dataset <ul> <li>Section 1 - Scenarios for energy transition. <ul> <li>ID90. Rank in order of priority, from top to bottom, in which scenario you will be willing to live or to contribute/invest to make it possible. </li> <li>ID36, ID38, ID43, ID44, ID72. Percentage of money people are willing to spend/save out of their income per scenario</li> <li>ID191, ID192.. Amount of money people would spend based on an assumed case.</li> <li>ID191, ID192. Priority service provision in case of Intermittent energy service. Rating energy services from 0 to 10 stars, where 0 stars means it is extremely low priority for you and 10 stars means it is absolutely necessary for you.</li> <li>[ID325, ID326, ID327, ID328, ID329, ID330, ID331, ID332, ID333, ID334, ID335, ID336, ID337, ID338, ID339, ID340, ID341, ID133, ID242]. Priority service provision in case of Intermittent energy service. Rating energy services from 0 to 10 stars, where 0 stars means it is extremely low priority and 10 stars means it is absolutely necessary.</li> <li>[ID251, ID256, ID257, ID292, ID293, ID294, ID295, ID296, ID297, ID298, ID299, ID301, ID302, ID303, ID304, ID305, ID306, ID250, ID251]. Priority service provision in case of full black-outs. Rating energy services from 0 to 10 stars, where 0 stars means it is extremely low priority and 10 stars means it is absolutely necessary.</li> <li>[ID141, ID5, ID147]. Used for statements that best represent survey responder</li> </ul> </li> <li>Section 2 - Determinants (factors). Questions used to rate (from 0 to 100) factors that may influence the decision-making process contributing to make an ideal scenario possible. <ul> <li>ID100 Risk profile</li> <li>ID101 Added value</li> <li>ID102 Self-Satisfaction</li> <li>ID103 Technical Fit</li> <li>ID104 Own competence</li> <li>ID105 Knowledge</li> <li>ID106 Cost-Efficiency</li> <li>ID107 Safety</li> <li>ID108 Trust</li> <li>ID109 Autarky</li> <li>ID110 Legal</li> <li>ID111 Climate Protection</li> <li>ID112 Wellbeing</li> <li>ID113 Coziness</li> <li>ID114 Rights and Duties</li> <li>ID115 Peer-Pressure</li> <li>ID116 Socialising</li> <li>ID117 Support</li> <li>ID118 Agreement</li> <li>ID119 Brag</li> <li>ID120 Fun</li> <li>ID121 Novelty</li> <li>ID122 Trends</li> <li>ID123 Authority</li> <li>ID124 Own Significance</li> <li>ID125 Poseur</li> <li>ID2 Frugality</li> <li>ID3 Environmental concerns</li> <li>ID31 Adherence</li> <li>ID52 Commitment</li> <li>ID97 Profits</li> <li>ID99 Credit Score</li> </ul> </li> <li>Section 3 - “Socio-economic” description. Questions about the socio-economic information of the survey respondents for data stratification. The indentation represents the dependency of questions and whether this data was asked <ul> <li>ID164 Understanding of questions</li> <li>ID300 Country of residence</li> <li>ID137 Age</li> <li>ID178 Highest level of education</li> <li>ID136 Willingness to provide data on the investment decision (respond apply for -Investment decision section)</li> </ul> </li> <li>Section 4 - Investment decision. Questions about specific prices of potential purchases-decisions related to four scenarios (respondent's lifestyle) <ul> <li>Appliances <ul> <li>ID42 Affordable cost of a Regular refrigerator</li> <li>ID45 Energy efficient refrigerator costs</li> <li>ID50 Willingness to purchase an energy efficient refrigerator <ul> <li>ID65 Why no</li> <li>ID66 affordable cost of an energy efficient option</li> <li>ID67 Years to amortize an efficient option</li> </ul> </li> </ul> </li> <li>Insulation <ul> <li>ID47 Affordable cost of updating to a state of the art insulation on the facade</li> <li>ID56 Willingness for paying/invest <ul> <li>ID74 Why no?</li> <li>ID20 affordable cost of an energy efficient option</li> <li>ID34 Years to amortize an energy efficient option</li> </ul> </li> </ul> </li> <li>Energy Generation <ul> <li>ID68 Affordable cost of a solar photovoltaic system</li> <li>ID76 Willingness for paying/invest <ul> <li>ID84 Why no?</li> <li>ID132 Affordable cost of a photovoltaic system</li> <li>ID138 Years that amortize a  photovoltaic system</li> </ul> </li> </ul> </li> <li>Energy Storage <ul> <li>ID142 Affordable cost of an energy storage system</li> <li>ID146 Willingness for paying/invest <ul> <li>ID181 Why no? </li> <li>ID182 Affordable cost of an energy storage system </li> <li>ID183 Years that amortize an energy storage systems</li> </ul> </li> </ul> </li> <li>Heating <ul> <li>ID140 Affordable cost of a gas boiler</li> <li>ID209 Affordable cost of an energy efficient heating system</li> <li>ID217 Willingness for paying/invest <ul> <li>ID238 Why no?</li> <li>ID239 Affordable cost of a energy efficient option</li> <li>ID241 Years that amortize a heat pumps</li> </ul> </li> </ul> </li> <li>Mobility <ul> <li>ID41 Average kilometers traveled a typical day</li> <li>ID51 Usual travel option</li> <li>ID264 Affordable cost of a diesel or gasoline mid-range brand new car</li> <li>ID265 Affordable cost of a mid-range brand new electric car</li> <li>ID281 Willingness to buy an electric car <ul> <li>ID289 Why no?</li> <li>ID290 Affordable price of an electric car</li> <li>ID291 Years that amortize an electric car</li> </ul> </li> </ul> </li> </ul> </li> <li>Section 5 - Household characterization <ul> <li>ID127 Selecting an asked value</li> <li>ID189 Type of living area</li> <li>ID202 Gender identity</li> <li>ID1 Those living in the house</li> <li>ID32 Number of inhabitants</li> <li>ID220 Average neat yearly income</li> <li>ID229 Average monthly saving</li> <li>ID240 Type of housing</li> <li>ID249 Owner / co-owner</li> <li>ID255 Usable area of the property (m²)</li> <li>ID263 Insulation level</li> <li>ID270 Climate zone</li> <li>ID86 Level of self-awareness about climate change. On scale of 0-10, where 0 is “climate change does not exist” and 10 is “I am a climate change expert/activist”</li> <li>ID87 Level of awareness of climate change among your peers or relatives, On a scale of 0-10, where 0 is “climate change does not exist” and 10 is “They are climate change experts/activists”</li> <li>ID88 Level of self-awareness about  energy transition. On a scale of 0-10, where 0 is “It is the first time I hear about it” and 10 is “I am an expert or activist”</li> <li>ID89 Level of awareness of energy transition among your peers or relatives On a scale of 0-10, where 0 is “It is the first time they hear about it” and 10 is “They are experts or activists”</li> <li>ID190 feedback about survey</li> </ul> </li> </ul> </li> <li>5 star: ⭐⭐⭐</li> <li>Preprocessing steps: anonymization, data fusion, imputation of gaps.</li> <li>Reuse: NA</li> <li>Update policy: No more updates are planned</li> <li>Ethics and legal aspects: Spanish electric cooperative data contains the CUPS (Meter Point Administration Number), which is personal data. A pre-processing step has been carried out to substitute the CUPS by a random value hash.</li> <li>Technical aspects: </li> <li>Other:</li> </ul>

opencc-by-4.0Nov 2022View details →

edi48/100

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/

openCC (other)Apr 2024View details →

zenodo44/100

Artificial Intelligence for EU Decision-Making: Effects on Citizens Perceptions of Input, Throughput and Output Legitimacy

The uploaded dataset was used for the statistical analysis of the pre-print "Artificial Intelligence for EU Decision-Making: Effects on Citizens’ Perceptions of Input, Throughput and Output Legitimacy" (Permanent identifier: <a href="https://arxiv.org/abs/2003.11320">arXiv:2003.11320</a>) A lack of political legitimacy undermines the ability of the European Union (EU) to resolve major crises and threatens the stability of the system as a whole. By integrating digital data into political processes, the EU seeks to base decision-making increasingly on sound empirical evidence. In particular, artificial intelligence (AI) systems have the potential to increase political legitimacy by identifying pressing societal issues, forecasting potential policy outcomes, informing the policy process, and evaluating policy effectiveness. This paper investigates how citizens’ perceptions of EU input, throughput, and output legitimacy are influenced by three distinct decision-making arrangements: (1) independent human decision-making (HDM); (2) independent algorithmic decision-making (ADM) by AI-based systems; and (3) hybrid decision-making by EU politicians and AI-based systems together. The results of a pre-registered online experiment (n = 572) suggest that existing EU decision-making arrangements are still perceived as the most democratic (input legitimacy). However, regarding the decision-making process itself (throughput legitimacy) and its policy outcomes (output legitimacy), no difference was observed between the status quo and hybrid decision-making involving both ADM and democratically elected EU institutions. Where ADM systems are the sole decision-maker, respondents tend to perceive these as illegitimate. The paper discusses the implications of these findings for (a) EU legitimacy and (b) data-driven policy-making.

opencc-by-4.0Mar 2020View details →

zenodo44/100

SuperWASP Variable Stars: Classifying Light Curves Using Citizen Science

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%.

opencc-by-4.0Jan 2021View details →

zenodo44/100

Preliminary table of Citizen Science initiatives for monitoring soil health

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.

opencc-by-4.0Nov 2023View details →

zenodo44/100

Video 6 - Open Science: science for and with citizens.

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. 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.

opencc-by-4.0Jan 2024View details →

zenodo44/100

Citizen science snow measurements

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. 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.

opencc-by-4.0Dec 2024View details →

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