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444 results for “Citizen Science”
Biological soil covers: data on lichen, bryophyte and algae coverage in soils gathered by SoilSkin citizen science program using eBryoSoil app for smartphones
<p>Biological soil covers (BSC) are small-sized topsoil communities composed mainly by lichens, bryophytes and algae that cover the terrestrial surface and play an essential role in maintaining the quality of the soil. However, little is known about their distribution, conservation, and ecosystem functions. The SoilSkin citizen science project aims to expand the scientific knowledge about the distribution of biological soil covers as an important step to evaluate the vulnerability of soil ecosystems of the Iberian Peninsula in the face of global change.</p> <p>The project has a dedicated free of charge app for smartphones (eBryoSoil, available at Google Play <a href="https://play.google.com/store/apps/details?id=com.omarfiz.ebryosoil&hl=ca&gl=US">https://play.google.com/store/apps/details?id=com.omarfiz.ebryosoil&hl=ca&gl=US</a>) that is designed to obtain information about the coverage of the BSC communities. To use this app, users must select a sampling location and capture the three soil pictures required to complete a transect. These photographs are taken at a 27 cm distance from the soil, in a straight line with 15 meters of distance between each picture. After the acquisition of each image, users can quantify the coverage percentage of biological soil covers and select the type of habitat where the transect took place. The transect is complete when all three pictures and their respective information are uploaded.</p> <p>The data presented here contains the records from SoilSkin participants, which mainly include a characterization of the cover patterns of biological soil covers, the type of habitat and the coordinates where each record was taken. The data set is composed by 279 unique records taken by 37 unique users from 28/11/2019 to 12/12/2020, across the Iberian Peninsula. These records specifically detail the percentage of cover occupied by three types of lichen growth forms (crustose, foliose and fruticose); liverworts; two types of moss growth forms (acrocarpous and pleurocarpous); algae; and soil. Moreover, each record also contains a description of the main type of habitat where the transect took place, that was selected from a list contained in the app with the following habitats:</p> <ul> <li>Dense forest - Habitat characterized by trees of more than 2 meters tall and canopy over 60%.</li> <li>Open forest – Habitat characterized by trees with more than 2 meters tall and a canopy below 60%.</li> <li>Shrubland – Habitat characterized by woody vegetation with less than 2 meters tall.</li> <li>Grassland – Habitat characterized by herbaceous plants.</li> <li>Agricultural land – Habitat characterized by temporary or woody crops.</li> <li>Coastal habitat – Habitat characterized by a landscape where land is in contact with the sea, creating a visibly different landscape from inner terrestrial one’s.</li> <li>Urban green spaces – Habitat characterized by a landscape in which man-made structures are present.</li> </ul> <p>The database was revised to correct any possible mistakes (e.g., miscalculation of total percentages; habitat missing in some registers; removal of invalid registers).</p> <p>The data file contains the following columns:</p> <ul> <li>Date: numerical variable indicating the “day”/”month”/”year” when the register was generated.</li> <li>User_ID: categorical variable with the identification number of the user who gathered the record.</li> <li>Transect: categorical variable with the identification of the number of the transect.</li> <li>Photo_number: numeric variable that takes values of 1, 2 or 3 and corresponds with the identification of the photographs within each transect.</li> <li>Photo_label: character string with the identification of the photograph from each record.</li> <li>Register_localization: categorical variable with the identification of the geographic area where the record was done.</li> <li>Latitude: integer, variable indicating the latitude of the sampling location in decimal degrees.</li> <li>Longitude: integer, variable indicating the longitude of the sampling location in decimal degrees.</li> <li>Accuracy: integer, variable indicating the accuracy of the coordinates given by the GPS.</li> <li>Habitat_type: categorical variable with the description of the main type of habitat of the sampling location.</li> <li>Lichen_Crustose: integer, variable indicating the percentage of crustose lichen cover quantified in the record.</li> <li>Lichen_Foliose: integer, variable indicating the percentage of foliose lichen cover quantified in the record.</li> <li>Lichen_Fruticose: integer, variable indicating the percentage of fruticose lichen cover quantified in the record.</li> <li>Total_lichen: integer, variable indicating the sum of all lichen coverage quantified in the record.</li> <li>Liverwort: integer, variable indicating the percentage of liverwort cover quantified in the record.</li> <li>Moss_Acrocarpous: integer, variable indicating the percentage of acrocarpous moss cover quantified in the record.</li> <li>Moss_Pleurocarpous: integer, variable indicating the percentage of pleurocarpous moss cover quantified in the record.</li> <li>Total_ moss: integer, variable indicating the sum of all moss coverage quantified in the record.</li> <li>Algae: integer, variable indicating the percentage of algae cover quantified in the record.</li> <li>Soil: integer, variable indicating the percentage of soil visible in the record.</li> </ul> <p> </p>
SeaPaCS graphic elaboration of the Protocol for marine micro-plastic collection and monitoring in citizen science and for building a L.A.D.I. trawling tool
<p>This is a graphic elaboration (in Italian) of the protocol "SeaPaCS deliverable - protocol for plastic monitoring in citizen science" in English and Italian is a deliverable of the SeaPaCS project (Participatory Citizen Science Against Marine Pollution), funded by IMPETUS (project ID 101058677). The protocol and the visual elaboration has been freely adapted from "<i>LADI and the Trawl</i>" by Coco Coyle with Melissa Novaceski, Emily Wells and Max Liboiron, as published by the Civic Laboratory for Environmental Action Research, August 2016. The graphic elaboration (as the protocol) in both languages, consists of three parts: 1) how to build a DIY low cost manta trawl device (LADI - Low-Tech Aquatic Detection Debris Instrument) to monitor plastic pollution, adjusted to materials availability and costs in Italy; 2) how to monitor (the sampling itself and towing procedure); and 3) how to categorize plastic debris back on land. </p>
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>
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>
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>
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>
Biodiversity on public lands: how citizen science can help
<p>These are the code and data files for "Biodiversity on public lands: how citizen science can help". Included in this are the following:</p> <p>1. taxataxi_opt_multiple_reports.R which is code for running multiple iNaturalist Bioblitz datasets with built in re-setting mechanisms for the various reports run.</p> <p>2. iNat_NPS_comparison_code.R which is code for running a single iNaturalist Bioblitz dataset. This is identical to (1) except for the re-setting mechanisms and some re-organization of parts of the code.</p> <p>3. synonym_links & taxonomic_units which are used in conjunction with (1) & (2) for the synonym identification.</p> <p>4. All_iNat3.csv is all of the iNaturalist entries used in the initial study named above.</p> <p>5. all_parks.csv contains all of the filtered data from the park units through (1) for the initial study named above.</p> <p>6. invasives_for_all_table1.R is code used in the initial study to identify introduced species.</p> <p>7. migration.R is code used in the initial study to identify previously unrecorded species that were recorded less than 50 times in BISON in the USA state that the park unit is located in.</p> <p>8. table3_all_parks.csv is the data used in the migration.R code for identifying these species.</p> <p>9.<span> <span><a href="../api/records/12571055/draft/files/query848species_result-categories_2024JUN27.xlsx/content" target="_blank" rel="noopener noreferrer">query848species_result-categories_2024JUN27.xlsx</a></span></span> is the manual checking of the unmatched species.</p> <p>10. i<span><a href="../api/records/12571055/draft/files/iNatSourceRecords-for-each-of-the-848unmatched-spp.xlsx/content" target="_blank" rel="noopener noreferrer">NatSourceRecords-for-each-of-the-848unmatched-spp.xlsx</a></span> is the unmatched species iNat records.</p>
Figure S1 in Capelin beach spawning diaries: an analysis of 30 years of citizen science data from the island of Newfoundland, Canada
Figure S1. – Histograms of permutation test statistics testing the null hypothesis that the timing of first day of spawning was random amongst the three NAFO divisions (3KLPs). A) First day of spawning in Div. 3Ps was significantly earlier than in Div. 3L (two-tailed permutation test statistic: p = 0.0005) and B) Div. 3K (two-tailed permutation test statistic: p = 0.0005). C) There was no significant difference in first spawning day between Div. 3L and Div. 3K (two-tailed permutation test statistic: p = 0.588). The vertical line in each panel is the original test statistic.
Figure 1. – The 56 in Capelin beach spawning diaries: an analysis of 30 years of citizen science data from the island of Newfoundland, Canada
Figure 1. – The 56 capelin spawning beaches (red dots) that were monitored as part of the citizen science capelin spawning diary program along the southeastern and eastern coasts of Newfoundland, Canada (NAFO Divs. 3KLPs) for the years 1991-2021. FB (Fortune Bay), WB (White Bay), SPM (St. Pierre and Miquelon). There are four capelin stocks in the Northwest Atlantic: NAFO Divs. 2J3KL, NAFO Div. 3Ps, NAFO Div. 4RST, NAFO Divs. 3NO. Grey contours are 100 m and dark grey contours are 500 m bathymetry.
FIGURE 8 in An analysis of fossil identification guides to improve data reporting in citizen science programs
FIGURE 8. Cluster analyses of all subjects as individuals. Subject labels denote which field guide the subject tested, a red 'C' for color photos, a black 'G' for grayscale photos, and a blue 'I' for illustrations. Author names are abbreviated "DaBu" for Dava Butler, "DoEs" for Donald Esker and "KrJu" for Kristopher Juntunen.
FIGURE 1 in An analysis of fossil identification guides to improve data reporting in citizen science programs
FIGURE 1. Map of the United States and surrounding regions, showing the location of the state of Florida and FMMS (Google 2017). 1B: Geologic map of the State of Florida, showing the geographic distribution of rocks and the location of the FMMS (Google 2017; Scott et al. 2001). 1C: Geologic map of the region around the FMMS, showing the distribution of rocks and location of the site (Google 2017; Scott et al. 2001). 1D: Photograph of FMMS, taken by Fred Mazza.
E-platforms for Citizen Science
<p>A short overview and suggestions for choosing a platform and some key elements to pay attention to when setting up a citizen science project’s website or profile, are given in the video by LibOCS partner UL Library. </p>
Beyond the Shelves: Embarking on Citizen Science with Your Library
<p>The video “Beyond the Shelves: Embarking on Citizen Science with your Library” by LibOCS project partner UT Library shows how to make first connections with librarians and get to know the role of university libraries in citizen science.</p>
Fig. 13 in Fig. 5 in Fig. 2 in An Updated Checklist of Sea Slugs (Gastropoda, Heterobranchia) from Hong Kong Supported by Citizen Science.
Fig. 13. Austruca albimana (Kossmann, 1877), SEM images of the telson of zoea II; a) at magnification 6000×; b) at magnification 10000×.
Fig. 15 in Fig. 5 in Fig. 2 in An Updated Checklist of Sea Slugs (Gastropoda, Heterobranchia) from Hong Kong Supported by Citizen Science.
Fig. 15. Austruca albimana (Kossmann, 1877), SEM images of the telson of zoea IV at magnification 40000×.
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.