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649 results for “Citizens”
Reporting Database of the INCENTIVE project's monitoring and evaluation activities of the Citizen Science Hubs pilot operation (M16-M31)
<p>The current excel file constitutes the INCENTIVE Reporting Database, meaning a repository of the data that were accumulated in WP4 (described within Deliverable 4.1). The results presented here are the main input for the elaboration of Deliverable 4.2 and Deliverable 4.3.</p>
SUPPLEMENTARY DATA TO: Using a citizen science approach to assess nanoplastics pollution in remote high-altitude glaciers
<p>This is the repository of the supplementary data, and it contains the following files: </p> <p>Raw data files as the original output of TD-PTR-ToF-MS for all the samples, all the blanks, all the spikes and all the calibration runs (.h5 files in three zip arcives)</p> <p>Polymer library files (a zip archive including csv files.</p> <p>A data analysis file including raw data, blank subtraction and LOD correction of all measurements (xlsx file).</p> <p>A fingerprinting result file for each plastic type (xlsx file)</p> <p>A data analysis file after plastic fingerprinting (xlsx file). </p> <p> </p> <p> </p>
Literature review of the enablers and barriers to stakeholder and citizen engagement in climate change adaptation process (as part of Adaptation AGORA project)
<p>This dataset is the result of collaborative work for Deliverable 4.1 (WP4; T4.1) of the Adaptation AGORA project. This database was used to conduct a literature review of the enablers and barriers to stakeholder and citizen engagement in climate change adaptation process. It contains 123 papers retrived from Web of Science Databse in June 2023. <span>We used a keyword search to identify and select articles that fell within the scope of our research, with each article containing at least one keyword related to climate change adaptation solutions, climate change, co-production, citizen and stakeholder involvement and factors (enablers and barriers). </span></p> <p><span>We divided the coding framework into four main sections:</span></p> <ul> <li> <p><span>Section 1 collected basic information about the paper (i.e., date, journal, authors, type of study and methods for data collection). </span></p> </li> <li> <p><span>Section 2 sought to better understand the adaptation initiatives treated in the paper. Here, we analysed 5 variables (the adaptation solutions type, sectors, benefits, scale, and location). </span></p> </li> <li> <p><span>Section 3 collected characteristics of the climate change adaptation co-production process, including the definition of co-production, the type of the co-production process, its outputs, and the methods used to engage stakeholders. </span></p> </li> <li> <p><span>Section 4 described the factors that enable or hinder the co-production process and their influence on different aspects of the process. After naming and defining each driver, we recorded the main type of factor, its impact, origin, and spatial and temporal scale of influence; the stakeholders who were responsible for and influenced by the factor, and the impacts on the various steps and outcomes of the co-production process.</span></p> <span> </span></li> </ul>
WeCount final citizen survey dataset
<p>WeCount final citizen survey dataset from all cities</p>
Citizen science at public libraries: Data on librarians and users perceptions of participating in a citizen science project in Catalunya, Spain
<p>As libraries struggle to keep pace with the changing societal landscape, emerging practices such as citizen science (CS) initiatives are being incorporated to reinforce the idea of public libraries as gathering, meeting, and collaboration spaces within the context of shared community and shared learning resources. However, there is little empirical evidence of whether the most open and participatory ways that CS puts forward can converge with and be nurtured by the essence of public libraries. Also, the roles of librarians and users in the ‘next generation public library’ have been under-developed. As the number of CS initiatives at public libraries grows, so does the need to collect evidence on the impact and the capacity of assimilation of CS practices. The data describes librarians and users' perceptions of participating in a citizen science project. Two hands-on activities for librarians of the Barcelona Network of Public Libraries were implemented. One was a training course for 30 librarians from 24 libraries which allowed them to envisage citizen science implementation in each library. The second activity consisted in the co-creation of a citizen social science project. 40 library users, 7 librarians from 3 different cities, and professional scientists, were involved. The data on librarians and users' perception was collected through participant observation, surveys, and a focus group to identify strengths and challenges of implementing citizen science at public libraries. The data covers librarians and users attitudes towards citizen science, their motivations to participate, their perceived ability to implement a citizen science project (as for librarians) or to contribute to science (as for library users), and the participants intention to keep engaged with citizen science, drawing on the Theory of Planned Behavior. Responses to closed-ended survey questions are analyzed at a descriptive level. The qualitative feedback from the focus group and the open-ended survey question on motivations is subjected to a thematic analysis. The data offers interesting insights to identify opportunities and challenges of implementing citizen science at public libraries, contributing to the debate over the public library's mission as local community hub.</p> <p>The dataset is formed by 5 tables:</p> <ol> <li>Librarians_pre.csv: data on librarians profiles, attitudes towards citizen science, expected impact of the project and self-efficacy collected at the beginning of the Citizen Science Lab.</li> <li>Librarians_post.csv: data on librarians profiles, attitudes towards citizen science, perceived impact of the project and self-efficacy collected at the end of the Citizen Science Lab.</li> <li>Users_first_phase.csv: data on users profiles, motivation, attitudes towards the library, confidence to perform scientific tasks and self-efficacy collected at the beginning of the Science and Citizen Action.</li> <li>Users_second_phase.csv: data on users profiles and motivation collected at the middle of the Science and Citizen Action.</li> <li>Users_last_phase.csv: data on users profiles, attitudes towards the library, confidence to perform scientific tasks and perceived impact of the project collected at the end of the Science and Citizen Action.</li> </ol> <p><strong>Citizen Science Lab Questionnaire (Librarians_pre)</strong></p> <table> <tbody> <tr> <td> <p><strong>Personal information</strong></p> </td> </tr> <tr> <td> <p>1. [rol_1] What is your role at the library?</p> </td> <td> <ul> <li>Director</li> <li>Library technician</li> <li>Support technician</li> <li>Service support</li> </ul> </td> </tr> <tr> <td> <p>2. [years_1] How long have you been working at the library?</p> </td> <td> <ul> <li>2 or less</li> <li>3 to 5 years</li> <li>6 to 10 years</li> <li>11 to 20 years</li> <li>more than 20 years</li> </ul> </td> </tr> <tr> <td> <p>3. [back_1] Do you have a scientific background?</p> </td> <td> <ul> <li>Yes</li> <li>No</li> </ul> </td> </tr> <tr> <td> <p>4. [know_1] Have you already heard about citizen science?</p> </td> <td> <ul> <li>Yes</li> <li>No</li> </ul> </td> </tr> <tr> <td> <p>5. [part_1] Have you already participated in a citizen science project?</p> </td> <td> <ul> <li>Yes</li> <li>No</li> </ul> </td> </tr> <tr> <td> <p><strong>Attitudes towards users engagement</strong></p> </td> </tr> <tr> <td> <p>6. [att_lib_pre1] Do you believe that library users are able to participate in a citizen science project?</p> </td> <td> <ul> <li>1 [Not at all]</li> <li>2</li> <li>3</li> <li>4 [Totally]</li> </ul> </td> </tr> <tr> <td> <p>7. [att_lib_pre2] Do you believe that library users will commit to participating in a citizen science project?</p> </td> <td> <ul> <li>1 [Not at all]</li> <li>2</li> <li>3</li> <li>4 [Totally]</li> </ul> </td> </tr> <tr> <td> <p><strong>Expected impacts</strong></p> </td> </tr> <tr> <td> <p>8. [exp_lib_pre] To what extent do you believe that citizen science may bring positive impacts to your library?</p> <p> </p> </td> <td> <ul> <li>1 [Not at all]</li> <li>2</li> <li>3</li> <li>4</li> <li>5 [Totally]</li> </ul> </td> </tr> <tr> <td> <p><strong>Self-efficacy</strong></p> </td> </tr> <tr> <td> <p>9. [se_lib_pre1] Right now, do you feel able to recommend any citizen science project to library users?</p> </td> <td> <ul> <li>Yes</li> <li>No</li> </ul> </td> </tr> <tr> <td> <p>10. [se_lib_pre2] Right now, do you feel able to implement yourself and lead a citizen science project?</p> </td> <td> <ul> <li>1 [Not at all]</li> <li>2</li> <li>3</li> <li>4 [Totally]</li> </ul> </td> </tr> </tbody> </table> <p><strong>Citizen Science Lab Questionnaire (Librarians_post)</strong></p> <table> <tbody> <tr> <td> <p><strong>Personal information</strong></p> </td> </tr> <tr> <td> <p>1. [years_2] How long have you been working at the library?</p> </td> <td> <ul> <li>2 or less</li> <li>3 to 5 years</li> <li>6 to 10 years</li> <li>11 to 20 years</li> <li>more than 20 years</li> </ul> </td> </tr> <tr> <td> <p>2. [back_2] Do you have a scientific background?</p> </td> <td> <ul> <li>Yes</li> <li>No</li> </ul> </td> </tr> <tr> <td> <p>3. [sat_1] To what extent does the project meet your initial expectations?</p> </td> <td> <ul> <li>1 [Not at all]</li> <li>2</li> <li>3</li> <li>4</li> <li>5 [Totally]</li> </ul> </td> </tr> <tr> <td> <p><strong>Attitudes towards users engagement</strong></p> </td> </tr> <tr> <td> <p>4. [att_lib_post1] Do you believe that library users will commit to participating in a citizen science project?</p> </td> <td> <ul> <li>1 [Not at all]</li> <li>2</li> <li>3</li> <li>4 [Totally]</li> </ul> </td> </tr> <tr> <td> <p>5. [att_lib_post2] What are/could be the potential barriers to users engagement in citizen science?</p> </td> <td> <p>[open]</p> </td> </tr> <tr> <td> <p><strong>Perceived impact</strong></p> </td> </tr> <tr> <td> <p>6. [imp_lib] What do you believe that citizen science may bring to public libraries and users?</p> <p>1 [Not at all] …… 5 [Totally]</p> <p> </p> </td> <td> <p>a. Knowledge of the scientific process</p> <p>b. New connections among participants </p> <p>c. Fun</p> <p>d. New knowledge of the local environment</p> <p>e. Scientific evidence on a common concern</p> <p>f. Social cohesion</p> <p>g. Positive attitudes towards science</p> <p>h. Willingness to learn</p> <p>i. Critical thinking and self-efficacy</p> </td> </tr> <tr> <td> <p><strong>Self-efficacy</strong></p> </td> </tr> <tr> <td> <p>8. [se_lib_post1] Right now, do you feel able to recommend any citizen science project to library users?</p> </td> <td> <ul> <li>Yes</li> <li>No</li> </ul> </td> </tr> <tr> <td> <p>9. [se_lib_post2] Right now, do you feel able to implement yourself and lead a citizen science project?</p> </td> <td> <ul> <li>1 [Not at all]</li> <li>2</li> <li>3</li> <li>4 [Totally]</li> </ul> </td> </tr> <tr> <td> <p><strong>Intention to keep engaged</strong></p> </td> </tr> <tr> <td> <p>10. [eng_lib] To what extent are you motivated to keep engaged with citizen science?</p> <p> </p> </td> <td> <ul> <li>1 [Not at all]</li> <li>2</li> <li>3</li> <li>4</li> <li>5 [Totally]</li> </ul> </td> </tr> </tbody> </table> <p> </p> <p><strong>Science and Citizens Action Focus group guide (Librarians)</strong></p> <p><strong>Opening questions</strong></p> <p><strong>1.</strong> To start with…. Are you satisfied with the project?</p> <p><strong>Probe</strong><strong>:</strong> Yes, no, why? Was it fun/interesting/challenging/enriching….</p> <p><strong>2.</strong> Do you feel you have learned something new?</p> <p><strong>Probe</strong><strong>:</strong> About your library’s environment, users, science and citizen science... Is there anything special that you will take with you after the project?</p> <p><strong>Reflections on the cocreation process</strong></p> <p><strong>3.</strong> At what time during the project have you felt most comfortable?</p> <p><strong>Probe:</strong> For example, has it been easier to lead the activity and/or involve and retain the community? Did you find it entertaining?</p> <p><strong>4.</strong> At what time during the project have you felt less at ease?</p> <p><strong>Probe</strong><strong>:</strong> What was challenging during the cocreation process?</p> <p><strong>5.</strong> To what extent do you feel more capable of implementing and leading a citizen science project in your library right now?</p> <p><strong>Probe: </strong>For example, in the case of both more crowdsourcing and of cocreated projects that actively involve the community</p> <p><strong>Reflections on the perceived impact</strong></p> <p><strong>6. </strong>To what extent does the project meet your initial expectations?</p> <p><strong>Probe</strong><strong>:</strong> in line with what you discussed at the beginning of the project, you expected it to promote participation, new connections among participants, improve the library perceptions and stimulate the participants’ critical thinking...Do you think that citizen science may meet these expectations?</p> <p><strong>Reflections on citizen science at public libraries</strong></p> <p><strong>7.</strong> To what extent can citizen science (in its most ‘extreme’ form of participation) be imagined as an activity within the library that promotes more active user participation?</p> <p><strong>Probe</strong><strong>:</strong> Through for example cocreation, experimentation, and hands-on learning activities...</p> <p><strong>8. </strong>Do you think that the activity has brought new knowledge? What new knowledge has the activity brought from your perspective?</p> <p><strong>Probe</strong><strong>:</strong> Knowledge of the scientific process, knowledge of the community or new users...</p> <p>9. What could be the opportunities and barriers of introducing citizen science at public libraries? And the barriers?</p> <p><strong>Probe:</strong> Like for example improving the perception of the library, actively involving certain users...What could be the ‘return’ for the community? What impact can citizen science projects have on making the environment more dynamic from libraries?</p> <p><strong>10. </strong>More generally, what could be the ‘added value’ of the introduction of citizen science within the library’s range of activities?</p> <p><strong>Probe:</strong> Is it a fun activity that promotes socialization, for example? Or that allows to generate new knowledge? Or that highlights the library’s social value? Or, also, that may offer new uses and new roles to the library? Can it foster a sense of community with the library as a connector? What other impacts can be generated in your environment?</p> <p><strong>Closing</strong></p> <p><strong>11.</strong> Do you see yourselves the next year, implementing a citizen science project as part of the library’s range of activities? And adopting an existing one?</p> <p><strong>Probe: </strong>Are you motivated to get more involved with citizen science projects? What kind of projects? What level of user involvement do you expect? What barriers do you see to users’ involvement? What benefits and opportunities do you think you can bring to the library?</p> <p><strong>12.</strong> We have now reached the end of the discussion. Anyone want to add anything else?</p> <p><strong>Science and Citizens Action Questionnaire (Users_first_phase)</strong></p> <table> <tbody> <tr> <td> <p><strong>Personal information</strong></p> </td> </tr> <tr> <td> <p>1. [gen_1] Are you..?</p> </td> <td> <ul> <li>Woman</li> <li>Man</li> <li>NA</li> </ul> </td> </tr> <tr> <td> <p>2. [years_3] How old are you?</p> </td> <td> <ul> <li>18-25</li> <li>26-35</li> <li>36-45</li> <li>46-55</li> <li>56-65</li> <li>66+</li> </ul> </td> </tr> <tr> <td> <p>3. [rol_2] What is your role at the library?</p> </td> <td> <ul> <li>Library user not associated with local associations</li> <li>Library technician</li> <li>Member of a local association</li> <li>Representative of public administrations</li> <li>Representative of the private sector</li> <li>Others:</li> </ul> </td> </tr> <tr> <td> <p>4. [back_3] Do you have a scientific background?</p> </td> <td> <ul> <li>Yes</li> <li>No</li> </ul> </td> </tr> <tr> <td> <p>5. [part_2] Have you already participated in a citizen science project?</p> </td> <td> <ul> <li>Yes</li> <li>No</li> </ul> </td> </tr> <tr> <td> <p><strong>Motivations to participate</strong></p> </td> </tr> <tr> <td> <p>6. [mot_us] What did motivate you to participate in the project?</p> </td> <td> <p>[open]</p> </td> </tr> <tr> <td> <p><strong>Attitudes towards the library</strong></p> </td> </tr> <tr> <td> <p>7. [att_us_pre1] To what extent do you believe that your library is responsive to the community needs?</p> </td> <td> <ul> <li>1 [Not at all]</li> <li>2</li> <li>3</li> <li>4 [Totally]</li> </ul> </td> </tr> <tr> <td> <p>8. [att_us_pre2] To what extent to you believe your library is able to face local challenges based on users' active participation?</p> </td> <td> <ul> <li>1 [Not at all]</li> <li>2</li> <li>3</li> <li>4</li> <li>5 [Totally]</li> </ul> </td> </tr> <tr> <td> <p><strong>Confidence to perform scientific tasks</strong></p> </td> </tr> <tr> <td> <p>9. [conf_us_pre] To what extent do you feel able to contribute to perform the following scientific tasks:</p> <p>1 [Not at all] …… 4 [Totally]</p> </td> <td> <p>a. Formulate the research question</p> <p>b. Data collection</p> <p>c. Analysis and interpretation of the results</p> <p>d. Propose concrete actions based on scientific evidence</p> </td> </tr> <tr> <td> <p><strong>Self-efficacy</strong></p> </td> </tr> <tr> <td> <p>10. [se_us_pre] To what extent do you feel able to positively contribute to the library and your community?</p> </td> <td> <ul> <li>1 [Not at all]</li> <li>2</li> <li>3</li> <li>4 [Totally]</li> </ul> </td> </tr> </tbody> </table> <p><strong>Science and Citizens Action Questionnaire (Users_second_phase)</strong></p> <table> <tbody> <tr> <td> <p><strong>Personal information</strong></p> </td> </tr> <tr> <td> <p>1. [gen_3] Are you..?</p> </td> <td> <ul> <li>Woman</li> <li>Man</li> <li>NA</li> </ul> </td> </tr> <tr> <td> <p>2. [years_5] How old are you?</p> </td> <td> <ul> <li>18-25</li> <li>26-35</li> <li>36-45</li> <li>46-55</li> <li>56-65</li> <li>66+</li> </ul> </td> </tr> <tr> <td> <p>3. [rol_4] What is your role at the library?</p> </td> <td> <ul> <li>Library user or technician not associated with local associations</li> <li>Member of a local association</li> <li>Representative of public administrations</li> <li>Representative of the private sector</li> <li>Others:</li> </ul> </td> </tr> <tr> <td> <p>3. [back_5] Do you have a scientific background?</p> </td> <td> <ul> <li>Yes</li> <li>No</li> </ul> </td> </tr> <tr> <td> <p>4. [mot_us2] To what extent are you motivated to carry out the experiment?</p> </td> <td> <ul> <li>1 [Not at all]</li> <li>2</li> <li>3</li> <li>4</li> <li>5 [Totally]</li> </ul> </td> </tr> </tbody> </table> <p><strong>Science and Citizens Action Questionnaire (Users_last_phase)</strong></p> <table> <tbody> <tr> <td> <p><strong>Personal information</strong></p> </td> </tr> <tr> <td> <p>1. [gen_2] Are you..?</p> </td> <td> <ul> <li>Woman</li> <li>Man</li> <li>NA</li> </ul> </td> </tr> <tr> <td> <p>2. [years_4] How old are you?</p> </td> <td> <ul> <li>18-25</li> <li>26-35</li> <li>36-45</li> <li>46-55</li> <li>56-65</li> <li>66+</li> </ul> </td> </tr> <tr> <td> <p>3. [rol_3] What is your role at the library?</p> </td> <td> <ul> <li>Library user or technician not associated with local associations</li> <li>Member of a local association</li> <li>Representative of public administrations</li> <li>Representative of the private sector</li> <li>Others:</li> </ul> </td> </tr> <tr> <td> <p>3. [back_4] Do you have a scientific background?</p> </td> <td> <ul> <li>Yes</li> <li>No</li> </ul> </td> </tr> <tr> <td> <p>4. [part_3] To how many cocreation sessions have you participated?</p> </td> <td> <ul> <li>None</li> <li>1</li> <li>2</li> <li>3</li> </ul> </td> </tr> <tr> <td> <p>5. [sat_2] To what extent are you satisfied with the experiment?</p> </td> <td> <ul> <li>1 [Not at all]</li> <li>2</li> <li>3</li> <li>4</li> <li>5 [Totally]</li> </ul> </td> </tr> <tr> <td> <p><strong>Attitudes towards the library</strong></p> </td> </tr> <tr> <td> <p>6. [att_us_post] To what extent do you believe that the project has positively changed your perception of the library?</p> </td> <td> <ul> <li>1 [Not at all]</li> <li>2</li> <li>3</li> <li>4</li> <li>5 [Totally]</li> </ul> </td> </tr> <tr> <td> <p><strong>Confidence to perform scientific tasks</strong></p> </td> </tr> <tr> <td> <p>7. [conf_us_post] To what extent do you feel able to contribute to perform the following scientific tasks:</p> <p>1 [Not at all] …… 5 [Totally]</p> </td> <td> <p>a. Formulate the research question</p> <p>b. Data collection</p> <p>c. Analysis and interpretation of the results</p> <p>d. Propose concrete actions based on scientific evidence</p> </td> </tr> <tr> <td> <p><strong>Perceived impact</strong></p> </td> </tr> <tr> <td> <p>8. [imp_us] What do you believe that citizen science may bring to public libraries and users?</p> <p>1 [Not at all] …… 5 [Totally]</p> <p> </p> </td> <td> <p>a. Knowledge of the scientific process</p> <p>b. New connections among participants </p> <p>c. Fun</p> <p>d. New knowledge of the local environment</p> <p>e. Scientific evidence on a common concern</p> <p>f. Social cohesion</p> <p>g. Positive attitudes towards science</p> <p>h. Willingness to learn</p> <p>i. Critical thinking and self-efficacy</p> </td> </tr> </tbody> </table> <p> </p>
Quantitative raw data for D1.3 - "Requirements and motivations of quadruple helix stakeholders for active engagement in the Citizen Science"
<p>This dataset presents the quantitative raw data that was collected under the H2020 INCENTIVE project for the D1.3 - “Requirements and motivations of quadruple helix stakeholders for active engagement in the Citizen Science Hubs”. The dataset includes the answers that were provided by almost 2,000 participants from 4 pilot European countries (Greece, Lithuania, Spain, and the Netherlands) regarding the general public's perceptions, attitudes, concerns, motivational factors and obstacles with regard to participation in Citizen Science activities. The original survey questionnaire was created and disseminated through the EUSurvey platform, and data collection took place from April to June 2021. For the statistical analysis of the data and the conclusions drawn from the analysis, you can access the D1.3 - "Requirements and motivations of quadruple helix stakeholders for active engagement in the Citizen Science Hubs”.</p> <p>Under INCENTIVE, four Citizen Science Hubs will be established and tested during the life-span of the project in the facilities of four Research Performing and Funding Organisations (RPFOs): University of Twente (the Netherlands), Autonomous University of Barcelona (Spain), Aristotle University of Thessaloniki (Greece) and Vilnius Gediminas Technical University (Lithuania). Essentially, the Hubs will aim to bring different stakeholders together and bridge society with science under the emerging paradigm of Citizen Science, in an institutionalised way.</p>
Quantitative raw data for "Large scale regional citizen surveys report" (D1.4)
<p>This dataset presents the quantitative raw data that was collected under the H2020 RRI2SCALE project for the D1.4 - “Large scale regional citizen surveys report”. The dataset includes the answers that were provided by almost 8,000 participants from 4 pilot European regions (Kriti, Vestland, Galicia, and Overijssel) regarding the general public's views, concerns, and moral issues about the current and future trajectories of their RTD&I ecosystem. The original survey questionnaire was created by White Research SRL and disseminated to the regions through supporting pilot partners. Data collection took place from June 2020 to September 2020 through 4 different waves – one for each region. Based on the conclusion of a consortium vote during the kick-off meeting, it was decided that instead of resource-intensive methods that would render data collection unduly expensive, to fill in the quotas responses were collected through online panels by survey companies that were used for each region. For the statistical analysis of the data and the conclusions drawn from the analysis, you can access the "Large scale regional citizen surveys report" (D1.4).</p>
Data supporting "Large-scale citizen science programs can support ecological and climate change assessments"
<p>Text file of phenology observations pulled from the USA National Phenology Network's database (www.usanpn.org) and used in this analysis. </p>
Citizen science for traffic counts: WeCount project dataset for inner-city streets of Ljubljana
<p>The Horizon 2020 project WeCount is a citizen science project that involves citizens in all steps from problem definition to data collection and analysis. This is currently one of the most common methods of citizen participation. The ethical criteria that such a project must meet in order to be classified as citizen science, and the form of transparency or informed consent that should be a necessary part of the ethical conduct of citizen science projects, were on Telraam platform for examination at the international and national level during the collection of data on traffic flows for WeCount Ljubljana. Engaged citizens were given low-cost sensors which they placed on the inside of the windowpane in their home or office facing the street at different distances (from 3 to 15 meters). </p>
Challenges of the New Accreditation in Digital Competences for Citizens (ACTIC): Update, Validation and Results
<p>The aim of this study is to explain the process used to update and validate the Catalan Accreditation of Information and Communications Technology Skills (ACTIC), conducted by the Open University of Catalonia and the Blanquerna Foundation of Ramon Llull University, at the initiative of the Catalan Department for Digital Policy and Public Administration, Government of Catalonia.</p> <p>Using a mixed method, the study is divided into 3 phases and presents the definitions of the 5 competence areas, 19 competences, 3 achievement levels and a total of 233 indicators. The definitions were validated by a group of experts. As a result of the study, three key dimensions have emerged that improve digital awareness among citizens while empowering them and providing them with a way of having their skills accredited and acknowledged in the workplace: (i) identity, centred around participatory, social, ethical and civic issues; (ii) skills learning among citizens; and (iii) technological development and digital autonomy of citizens.</p>
Do Carefully-timed Email Messages Increase Accuracy and Precision in Citizen Scientists' Reports of Events?
<p>Periodic messages are a commonly used tactic for reminding citizen science program participants to take a desired action such as collecting observations. In this study, we evaluate the impact of such messages on the accuracy and precision of observations contributed to <em>Nature’s Notebook</em>, a citizen science phenology observing program.</p> <p>To encourage participants in <em>Nature’s Notebook</em> to log the timing of leaf-out and flowering with maximum accuracy and precision, we email observers three days prior to when the events were expected to occur based on forecast models. Unplanned interruptions to the scripts driving these email prompts allowed us to evaluate whether the messages had the intended impacts.</p>
Citizen Science Initiatives in Bulgaria
<p>This dataset contains original mapping data developed in the process of the Bulgarian Citizen Science landscape review. In total, 20 Bulgarian Citizen Science projects were analysed according to a common framework. During the mapping process, we weren’t always able to find information on things that interested us e.g. project impact, stakeholder engagement, the size of the volunteering force. But just because we couldn’t find something does not mean the results or activities did not happen. It goes without saying that absence of evidence is not evidence of absence. Failure to find some information on our part can be explained by the fact that we worked primarily with internet sources, so we had to make do with whatever publicly available information we could find within reasonable time.</p>
Citizen Science Initiatives in Germany
<p>This dataset contains original mapping data developed in the process of the Germany Citizen Science landscape review. In total, 29 German Citizen Science projects were analysed according to a common framework. During the mapping process, we weren’t always able to find information on things that interested us e.g. project impact, stakeholder engagement, the size of the volunteering force. But just because we couldn’t find something does not mean the results or activities did not happen. It goes without saying that absence of evidence is not evidence of absence. Failure to find some information on our part can be explained by the fact that we worked primarily with internet sources, so we had to make do with whatever publicly available information we could find within reasonable time.</p>
Supplementary information for "Reassessment of French breeding bird population sizes using citizen science and accounting for species detectability"
<p>Reproducibility data for the manuscript "<em>Reassessment of French breeding bird population sizes using citizen science and accounting for species detectability</em>", it contains data and script for :</p> <ol> <li> <p>The R script <code>01_HDSfreq_Calibration.R</code> of the developed approach to estimate national breeding bird population size using Hierarchical Distance Sampling (HDS) and the secondary candidate set model selection method (Morin et al., 2020)</p> </li> <li> <p>The R script <code>02_pglmm_figures.R</code> for the calibration of the Phylogenetic Generalised Mixed Model (PGLMM) used in the manuscript to compare previous population size estimates to ones modelled using <code>01_HDSfreq_Calibration.R</code>, while accounting for species phylogenetic relatedness</p> </li> <li> <p>The R script <code>03_results_tables.R</code>, used to generate supplementary tables S2.1-3 and S6.1-2.</p> </li> </ol> <ul> <li> <p>Column names are highlighted in italics.</p> </li> </ul> <h2>Data description</h2> <h5>A. BirdPhylo_Burleigh_et_al.tre</h5> <p>A phylogenetic tree from Burleigh et al., 2015. Phylogenetic distances are used as random effect for the PGLMM in script <code>02_pglmm_figures.R</code></p> <h5>B. Conservation_status.txt</h5> <p>A <code>.txt</code> file of the conservation status for France (<em>Statut_FR</em>) and Europe (<em>Statut_EU</em>) for the studied species retrieved from (UICN France et al., 2016). Only <em>Statut_FR</em> is used for the table S6.1.</p> <h5>C. FBBS_trends_20122023.txt</h5> <p>A <code>.txt</code> file containing species trend of the French Breeding Bird Survey data from 2012 to 2023.</p> <ul> <li> <p>Species names (English, French) associated with FBBS trend estimated using data collected from 2012 - 2023</p> </li> <li> <p><em>Hab_specialization</em>, determined from Julliard et al. 2006 approach</p> </li> <li> <p><em>infPrec, supPerc, estimate, se, pval</em> : Species trends over 2012-2023 period in % | lower and upper confidence intervals, mean, standard error and significance</p> </li> </ul> <h5>D. PrepData_HDS.RData</h5> <p>A file containing <code>.RData</code> environment required to run <code>01_HDSfreq_Calibration.R</code> script, it contains :</p> <ul> <li> <p><strong>ATLAS12</strong> : A dataframe with breeding status information from 2012 breeding bird atlas (used to restrict model prediction grid, in regard of 2012 known breeding locations)</p> </li> <li> <p><strong>ConcordTBL</strong> : A concordance table for species names (English, French and scientific notation)</p> </li> <li> <p><strong>EPOC_ODF</strong> : observation dataset, each line corresponds to detected individuals</p> <ul> <li> <p><em>UUID, Ref, ID_liste, ID, ID_place, Grid_10x10</em> : Columns used to identify observations, lists, sites, locations, 10x10 grids</p> </li> <li> <p><em>ID_species_Biolovision, Nom_espece, english_name, scientific_name</em> : Species ID and names</p> </li> <li> <p><em>Date, Day, Month, Year, Julian_date, Obs_hour, Hour_list, Complete_checklist, Commentary, Project_name, Scheme, Observer, List_time, List_diversity, List_abundance</em> : Lists and Observation related effort covariates and metadata</p> </li> <li> <p><em>X_Lambert93_m, Y_Lambert93_m</em> : Observation locations in <code>(crs = 2154)</code></p> </li> <li> <p><em>GPS_loc_observer</em> : Logical, TRUE : location of observers corresponds to true GPS information ; FALSE : observer's location approximated as the barycenter of observations</p> </li> <li> <p><em>X_barycentre_L93, Y_barycentre_L93</em> : Observers location in <code>(crs = 2154)</code></p> </li> <li> <p><em>Use_distance_sampling, Observation_distance_m, Distance_bin_logical, Distance_class_0_25, Distance_class_25_100, Distance_class_100_200, Distance_class_200_more</em> : Distance sampling related informations</p> </li> <li> <p><em>Abudance_brut, Estimate, Number, Nb_male_identified, Nb_female_identified, Nb_juvenile_identified, Nb_grounded, Nb_flying, Nb_auditory, Nb_NA</em> : Observation metadata, used in case of <em>a priori</em> filter over male detection.</p> </li> </ul> </li> <li> <p><strong>grid_pred_envvar</strong> : Prediction grid with environmental covariates, see appendix S3 of the manuscript, covering metropolitan France</p> </li> <li> <p><strong>grid_pred.sf</strong> : corresponding sf object</p> </li> <li> <p><strong>L93_10x10</strong> : sf object corresponding to 10x10 grid used in 2012 atlas</p> </li> <li> <p><strong>ObsVar_EPOCODF</strong> : dataframe specifying lists effort covariates</p> </li> <li> <p><strong>OCCU_EPOC_ODF</strong> : Environmental covariate agregated over lists</p> </li> <li> <p><strong>OCCU_EPOC_ODF_sites_envvar</strong> : Environmental covariate agregated over sites</p> </li> <li> <p><strong>table.pheno</strong> : species table specifying related phenology filter</p> </li> </ul> <h5>E. ReadOutput_HDSfreq_comparison.csv</h5> <p>A <code>.csv</code> table of species population size estimated using 2021-2023 EPOC-ODF data over areas determined as breeding in the 2012 atlas. <strong>Predictions were restrained over location known as breeding in 2012 for the sake of comparison.</strong></p> <ul> <li> <p><em>HDS_estimUnfenced_XXX</em> : average pop. size estimated with confidence interval before prediction post-treatment (describe in fig 2. of the manuscript)</p> </li> <li> <p><em>HDS_estim_ExtrapolFence_XXX</em> : average pop. size estimated with confidence interval after prediction post-treatment</p> </li> <li> <p><em>NB_data_calib</em> : Number of observations (distance data, not sites) used for calibration</p> </li> <li> <p><em>MALE_FILTERING</em> : (logical) indicating if female individuals could be detected in the same proportion of males during list recording. FALSE : we considered that estimated pop.size corresponded to the number of individuals leading to a division by 2 for the comparison with the previous atlas (in pairs). (cf . line 88-90 in <code>02_pglmm_figures.R</code>)</p> </li> <li> <p><em>EcartDTF_filtrage_maleOnly</em> : If MALE_FILTERING == T, proportion of the remaining data used for calibration after removal of list with individual tagged as female/juvenile (in %)</p> </li> <li> <p><em>Max_dist_breaks</em> : Maximal distance for detection function, after right-side truncation of 5%</p> </li> <li> <p><em>Chat</em> : Coefficient of overdisperion of the best model in the second candidate set</p> </li> <li> <p><em>MED_MEAN_Prob_Detect</em> (.._SE) : weighted averaged median of intercept from the availability state from HDS models, weigthed AICc-wise</p> </li> <li> <p><em>MED_MEAN_Density</em> : weighted averaged median of intercept from the abundance state from HDS models, weigthed AICc-wise</p> </li> <li> <p><em>Significant_phi/lambda</em> : Categorial (Significant/Near/Not), are availability/abundance intercepts significatively different from 0 (significant : alpha = 0.05, near : alpha = 0.1)</p> </li> <li> <p><em>KeyFun_used</em> : Key function used for distance sampling</p> </li> <li> <p><em>Mixtured_used</em> : Mixture used in the abundance state for HDS</p> </li> </ul> <h5>F. ReadOutput_HDSfreq_comparison_20212022.csv</h5> <p>A <code>.csv</code> table of species population size estimated using 2021-2022 EPOC-ODF data over areas determined as breeding in the 2012 atlas. Used for the robustness analysis of HDS estimated population size, see appendix S2 and table S2.2 of the manuscript.</p> <h5>G. ReadOutput_HDSfreq_EstimMetropole.csv</h5> <p>A <code>.csv</code> table of species population size estimated using 2021-2023 EPOC-ODF data over <strong>metropolitan France</strong>.</p> <ul> <li> <p><em>HDS_estimUnfenced_XXX</em> : average pop. size estimated with confidence interval before prediction post-treatment (describe in fig 2. of the manuscript)</p> </li> <li> <p><em>HDS_estim_ExtrapolFence_XXX</em> : average pop. size estimated with confidence interval after prediction post-treatment</p> </li> <li> <p><em>MALE_FILTERING</em> : (logical) indicating if female individuals could be detected in the same proportion of males during list recording. FALSE : we considered that estimated pop.size corresponded to the number of individuals leading to a division by 2 for conversion to pop. size in breeding pairs</p> </li> <li> <p><em>Chat</em> : Coefficient of overdisperion of the best model in the second candidate set</p> </li> <li> <p><em>KeyFun_used</em> : Key function used for distance sampling</p> </li> <li> <p><em>Mixtured_used</em> : Mixture used in the abundance state for HDS</p> </li> </ul> <h5>H. TABLE_SpeciesFilters_and_2012Estimates.txt</h5> <p>A <code>.txt </code>table containing species names (English, French and scientific notation), filters and 2012 French atlas pop. size estimates</p> <ul> <li> <p><em>debut_jour</em> : starting day of the month for phenology filter</p> </li> <li> <p><em>debut_mois</em> : starting month for phenology filter</p> </li> <li> <p><em>fin_jour</em> : ending day of the month for phenology filter</p> </li> <li> <p><em>fin_mois</em> : ending month for phenology filter</p> </li> <li> <p><em>Estim_low/up_Atlas2012</em> : Lower and Upper interval of estimated pop. size in 2012 (number in breeding pairs)</p> </li> <li> <p><em>gregarious</em> : logical (0,1) specifying if the species is considered gregarious during its breeding season</p> </li> </ul> <h5>I. sessionInfo_script_XX</h5> <p>User R session information, obtained from <code>sessionInfo()</code> R function, used for running R script.</p> <h2>Code</h2> <h5>A. <code>01_HDSfreq_Calibration.R</code></h5> <p>R script showcasing data formatting and model calibration of the HDS based upon frequentist aproach from <code>unmarked</code> R package. For more details of the model calibration approach, see appendix S4 of the manuscript.</p> <h5>B. <code>02_pglmm_figures.R</code></h5> <p>Script for the calibration of the PGLMM and generation of figure 5 of the manuscript.</p> <h5>C. <code>03_results_tables.R</code></h5> <p>Script to generate tables depicted in Appendices S2 (S2.1-3) and S6 (S6.1-2)</p> <h5>D. <code>HDS_functions.R</code></h5> <p>R script called in <code>01_HDSfreq_Calibration.R</code>, contains 2 functions:</p> <ul> <li> <p><code>Try_HDS()</code> : Function implementing a try-catch permitting calibration of multiple species in a loop.</p> <ul> <li> <p>Species with non convergent models are skipped sending a notification to the user R interface.</p> </li> <li> <p>Used in all sub-candidate sets (i.e. "null", "p", "phi", "lambda")</p> </li> <li> <p>When phase="ALL" corresponding to the second candidate set (i.e. ensemble of best model candidates, with delta_AIC <= 10, from previous sub-candidate sets), it permits the use of previous sub-candidates set coefficients as starting values, with <code>StartValues </code>argument</p> </li> <li> <p>Later part of the function hack the call of the unmarkedFit class, in order to accommodate from calibrating a gdistsamp using characters formulas</p> </li> </ul> </li> <li> <p><code>fitstats()</code> : Function from unmarked::parboot(), available with <code>help(parboot)</code>. Allow estimation of multiple goodness-of-git statistic (Freeman-Tukey, Chi-squared and Sum of Squared Estimate of errors) through parametric bootstrap. In the manuscript, only chi-squared metric is used.</p> </li> </ul> <h5>E. <code>dsmextra_modif_function.R</code></h5> <p>R script called in <code>01_HDSfreq_Calibration.R</code>. Miscellaneous adjustment of core function from <code>dsmextra </code>package (main change being the integration of tolerance argument (<code>tol</code>) in the chain of function.</p> <h5>F. <code>misc_unmarked.R</code></h5> <p>R script called in <code>01_HDSfreq_Calibration.R</code>. modify Setmethods for unmarked function, in particular for <code>unmarked::parboot</code>, allowing parallelization of parametric bootstrap with prior unmarked version (<code>unmarked < 1.3.0</code>).</p> <h2>References</h2> <p>Data was derived from the following sources:</p> <ul> <li> <p>Burleigh, J.G., Kimball, R.T., Braun, E.L., 2015. Building the avian tree of life using a large-scale, sparse supermatrix. Molecular Phylogenetics and Evolution 84, 53–63. <a href="https://doi.org/10.1016/j.ympev.2014.12.003">https://doi.org/10.1016/j.ympev.2014.12.003</a></p> </li> </ul> <p>Other sources :</p> <ul> <li> <p>Julliard, R., Clavel, J., Devictor, V., Jiguet, F., Couvet, D., 2006. Spatial segregation of specialists and generalists in bird communities. Ecology Letters 9, 1237–1244. <a href="https://doi.org/10.1111/j.1461-0248.2006.00977.x">https://doi.org/10.1111/j.1461-0248.2006.00977.x</a></p> </li> <li> <p>Morin, D.J., Yackulic, C.B., Diffendorfer, J.E., Lesmeister, D.B., Nielsen, C.K., Reid, J., Schauber, E.M., 2020. Is your ad hoc model selection strategy affecting your multimodel inference? Ecosphere 11, e02997. <a href="https://doi.org/10.1002/ecs2.2997">https://doi.org/10.1002/ecs2.2997</a></p> </li> <li> <p>UICN France, MNHN, LPO, SEOF, ONCFS, 2016. La Liste rouge des espèces menacées en France - Chapitre Oiseaux de France métropolitaine. Paris, France.</p> </li> </ul>
The Potential of Deep Learning Object Detection in Citizen-Driven Snail Host Monitoring to Map Putative Disease Transmission Sites
<p><a name="_Hlk158802145"></a><span>Schistosomiasis is a neglected tropical disease caused by parasitic flukes transmitted by freshwater snails. Despite increasing efforts of mass drug administration, schistosomiasis remains a public health concern and the World Health Organization recommends complementary snail control. To address the need of broad-scale and actual snail distribution data to guide snail control, we adopted a citizen science approach and recruited citizen scientists (CS) to perform weekly snail sampling in the endemic setting in Uganda. Snails were identified, sorted and counted according to genus, photographed and uploaded for expert-led validation and feedback. However, expert validation is time-consuming and introduces a delay in verified data output. Thus, artificial intelligence could provide a solution by means of automated detection and counting of multiple snails collected from the field. Trained on approximately 2500 citizen-collected images, the resulting model can simultaneously detect and count Biomphalaria and Radix snails with average precision of 98.1% and 98.8% respectively. The object detection model also agreed with the expert’s decision averagely for 98.8% of the test images and could be ran in real-time (24.6 images per second). We conclude that the automatic and instant detection can rapidly and reliably validate data submitted by CS in the field, ultimately minimizing the expert validation efforts and thereby facilitating the mapping of putative schistosomiasis transmission sites. An extension to a mobile application could equip citizen scientists in remote areas with instant learning opportunities and expert-like identification skills, overcoming the need for on-site training and extensive expert intervention. </span></p>
Dataset: First Citizens BancShares, Inc. (FCNCA) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: First Citizens BancShares, Inc. (FCNCO) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: First Citizens BancShares, Inc. (FCNCP) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Citizens Community Bancorp, Inc. (CZWI) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Citizens & Northern Corporation (CZNC) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
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.