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1,903 results for “Perceptions”
Figure 3 in Biology, trophic chain, and ethnobiological calendar of the mangrove crab, Ucides cordatus (Linnaeus, 1763) (Brachyura, Ocypodidae), according to the perception of catchers in Itanhaém, São Paulo, Brazil
Figure 3. Ethnobiological calendar of the mangrove crab (Ucides cordatus) showing the months of occurrence of its biological events according to interviews conducted in 2017 with catchers of the mangrove swamp of the Itanhaém River in Itanhaém city (São Paulo State, Brazil).
Figure 2 in Biology, trophic chain, and ethnobiological calendar of the mangrove crab, Ucides cordatus (Linnaeus, 1763) (Brachyura, Ocypodidae), according to the perception of catchers in Itanhaém, São Paulo, Brazil
Figure 2.Representation of a fragment of the mangrove crab trophic chain according to the knowledge of catchers in the Itanhaém- SP mangrove.
Figure 1 in Biology, trophic chain, and ethnobiological calendar of the mangrove crab, Ucides cordatus (Linnaeus, 1763) (Brachyura, Ocypodidae), according to the perception of catchers in Itanhaém, São Paulo, Brazil
Figure 1. Photographic maps of the Itanhaém River Estuary (São Paulo State, southeastern Brazil). Source: Modified from Google® Maps 2020 - Image from 19 Mar 2021.
Dataset: "On the influence of AVAS directivity on electric vehicle speed perception"
<p>This repository contains experiment results and calibrated stimuli recordings accompanying the publication: </p> <blockquote> <p>Leon Müller and Wolfgang Kropp, <em>On the influence of AVAS directivity on electric vehicle speed</em><br><em>perception</em>, submitted for publication in Inter-Noise 2024 proceedings</p> </blockquote> <p>The stimuli recordings were obtained by placing a calibrated artificial head (<em>HEAD Acoustics HMS-V</em>) at the participant listening position in the anechoic chamber.</p> <p>The AVAS sounds were generated using the Electric Vehicle Auralization Toolbox presented in:</p> <blockquote> <p>Müller L. & Kropp W. 2024. Auralization of electric vehicles for the perceptual evaluation of acoustic vehicle alerting systems. Acta Acustica, 8, 27. https://doi.org/10.1051/aacus/2024025</p> </blockquote> <p>The .wav files contain 32-bit float values that correspond to pressure in Pa and are named according to the following table.</p> <table> <tbody> <tr> <td><strong>AVAS Signal</strong></td> <td><strong>Directivity</strong></td> <td><strong>Vehicle Speed</strong></td> </tr> <tr> <td>T: Tonal (VW ID.3)</td> <td>B: BEM</td> <td>10: 10 km/h</td> </tr> <tr> <td>N: Noise (Tesla Model Y)</td> <td>C: Cardioid</td> <td>20: 20 km/h</td> </tr> <tr> <td> </td> <td>S: Star</td> <td> </td> </tr> <tr> <td> </td> <td>O: Omnidirectional</td> <td> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p>
Perception survey 2023-2024 Bibracte tourism observatory
<p>In 2018 and 2019, Bibracte set up a new survey system, EVALTO, which enabled to cross-reference data from questionnaires administered mainly face-to-face to <strong>visitors, elected representatives and socio-professionals, and local residents</strong>. The aim was to go further than traditional visitor surveys by, on the one hand, collecting data on visitor profiles and practices and, on the other, questioning local players about their perceptions of tourism, in order to share an objective view of tourist activity and thus move towards genuine territorial <em>tourism intelligence</em>. The system was reactivated in 2023 and the questionnaire has been completed as part of the INCULTUM project.</p> <p><u>Sets of data available :</u></p> <ul> <li><strong>Visitors to the Bibracte site: collected from April to September </strong><strong>2023</strong><br>Excel document attached extracted from the Sphinx software INCULTUM_BI_visitors-survey_2023._rawdata.csv</li> <li><strong>Hikers survey on the territory of Bibracte-Morvan des Sommets</strong><br>Data for the 2023 survey are in the Excel document extracted from the Sphinx software INCULTUM_BI_hikers_surevy_2023_rawdata.xls</li> </ul> <p><u>Set of data that will be available later in 2024:</u><br>- <strong>Inhabitants of the wider area of the Grand Site de</strong> France: 50 collected in 2023 out of 200 targeted, the rest will be carried out in 2024. These questionnaires are in paper format and have not yet been entered into the Sphinx software.<br>- <strong>socio-professionals and elected representatives</strong>: 34 responses in 2023 out of 200 targeted; the follow-up will be carried out in 2024. These questionnaires are in paper format and have not yet been entered into the Sphinx software.<br>- <strong>motor home owners</strong>: 93 responses in 2023 out of 200 targeted, with a follow-up in 2024. These questionnaires are in paper format and have not yet been entered into the Sphinx software.</p>
Auditory time perception impairment in children with developmental dyscalculia
<p>Each row of the dataset refers to an individual participant, column refers to:</p> <p>1st column: group (1 = group of individuals without dyscalculia, 2 = group of individuals with dyscalculia)</p> <p>2nd colum: gender (1 = female, 2 = male)</p> <p>3rd column: PSE in the milliseconds range</p> <p>4th column: WF in the milliseconds range</p> <p>5th column: PSE in the seconds range</p> <p>6th column: WF in the seconds range</p> <p>7th column: age</p> <p>8th column: non verbal IQ</p> <p>9th column: math index</p> <p>10th column: reading index</p>
Students' perceptions of an environmental education program.
<p>This database contains 185 records. It was developed using a questionnaire to evaluate students' perceptions of an environmental educommunication program. The variables measured are:</p> <p>1 Gender: 1 male, 2 female, 3 non-binary</p> <p>2 Age: 12 to 17</p> <p>3 Type of school. 1: private, 2: public, 3: state-funded private school</p> <p>4 -11: measured using the following scale: </p> <p> 5 totally agree</p> <p> 4 agree</p> <p> 3 neither agree nor disagree</p> <p> 2 disagree</p> <p> 1 strongly disagree</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>
The knowledge, attitudes and perceptions towards the Covid-19 vaccine among dental staff at the University of the Western Cape, South Africa
<p><strong>Background:</strong> Despite the well-known increased risk of exposure to the Covid-19 virus in a dental setting, vaccination rates among staff members are low. This study was aimed at understanding the knowledge, attitudes and perceptions of staff towards the Covid-19 vaccine. This information, as well as the possible associations to demographic profiles, are necessary for authorities to adequately address specific concerns and uncertainties<strong>; (2) Methods:</strong> A descriptive cross-sectional study was conducted by means of an anonymous, online, validated questionnaire.; <strong>(3) Results:</strong> 105 staff members participated. Majority of staff have received the Covid-19 vaccine but stated that they would not take the booster vaccination. Significant associations between the level of education and the knowledge, attitudes and perceptions of staff were found.; <strong>(4) Conclusions:</strong> Majority of the staff members had a positive attitude towards the Covid-19 vaccine. However, specific concerns and uncertainties were identified and will need to be addressed in order to improve vaccination rates among staff members.</p>
IHTApark. Multi-detailed 3D architectural model for sound perception research in Virtual Reality
<p><strong>IHTApark – Multi-detailed 3D architecture model</strong></p> <p>This dataset describes visual and acoustic 3D architectural models of the park next to the IHTA.</p> <p>Institute of Hearing Technology and Acoustics (IHTA), RWTH Aachen, 52056 Aachen, Germany</p> <p>Files are stored in FBX format for geometry, JPEG format for visual textures, and Unreal Engine for the virtual reality scenes.</p> <p><strong>VERSION 1: Visual photogrammetry + Acoustic model</strong></p> <p>As used in the publication:</p> <p>[1] Llorca-Bofí, J. and Vorländer, M. (2021). Multi-Detailed 3D Architectural Framework for Sound Perception Research in Virtual Reality. Front. Built Environ. 7:687237.doi: https://doi.org/10.3389/fbuil.2021.687237</p> <p>Data is available separately for each definition, and for each visual and acoustic cue. The level of detail for each definition is shown here:</p> <ul> <li>Visual cues <ul> <li>Geometries <ul> <li>HighLOD</li> </ul> </li> </ul> </li> <li>Acoustic cues <ul> <li>Geometries <ul> <li>HighLOD</li> </ul> </li> </ul> </li> </ul> <p>This version of the model includes only the modules used for the description of the referenced paper. The authors reserve the right to complete other levels of detail if future applications require them.</p> <p>An additional data file contains a unique file in [IHTApark_UnrealEngine] Unreal Engine format, with the set up scenario. The instructions to open the final scenario are described here:</p> <ol> <li>Download the [IHTApark_UnrealEgine] file, and save it in your working space.</li> <li>Extract the content of the [IHTApark_UnrealEngine]. The folder naming and arrangement are prepared for the scenario.</li> <li>Run the .uproject file.</li> <li>Open a <strong>Content Browser</strong> tab to navigate through the folder hierarchy. You can open the <strong>Content Browser</strong> under the tabs <strong>Window > Content Browser</strong></li> <li>Open the <strong>IHTApark</strong> map under the folder <strong>Content > Maps</strong> by double clicking on it.</li> <li>The scenario will be visible in the <strong>Viewport 1</strong> tab. Go to <strong>Window > Viewports > Viewport 1</strong> to open the tab.</li> <li>Press key <strong>G</strong> to hide or unhide the helpers and editor actors.</li> <li>Press keys <strong>0,</strong> <strong>1</strong>, <strong>2</strong>, <strong>3</strong>… <strong>9</strong> to jump into different saved view positions.</li> <li>Drag the mouse while pressing right click to rotate the viewer direction</li> <li>While pressing right click, press key <strong>W</strong> to navigate through the scenario.</li> </ol> <p><strong>VERSION 2: Object-based visualization in three different weather conditions</strong></p> <p>As used and described in the publication:</p> <p>[2] Submitted to journal.</p> <p>The file [IHTApark_3weath_comp] Unreal Engine format contains the set up scenario. The instructions to open the final scenario are described here:</p> <ol> <li>Download the [IHTApark_3weath_comp] file, and save it in your working space.</li> <li>Extract the content of the [IHTApark_3weath_comp]. The folder naming and arrangement are prepared for the scenario.</li> <li>Run the .uproject file.</li> <li>Open a <strong>Content Browser</strong> tab to navigate through the folder hierarchy. You can open the <strong>Content Browser</strong> under the tabs <strong>Window > Content Browser</strong></li> <li>Open the <strong>IHTApark_warm</strong>, <strong>IHTApark_wet </strong>or<strong> IHTApark_snowy</strong> maps under the folder <strong>Content > Maps</strong> by double clicking on it to visualize each weather condition.</li> <li>The scenario will be visible in the <strong>Viewport 1</strong> tab. Go to <strong>Window > Viewports > Viewport 1</strong> to open the tab.</li> <li>Press key <strong>G</strong> to hide or unhide the helpers and editor actors.</li> <li>Press keys <strong>0,</strong> <strong>1</strong>, <strong>2</strong>, <strong>3</strong>… <strong>9</strong> to jump into different saved view positions.</li> <li>Drag the mouse while pressing right click to rotate the viewer direction</li> <li>While pressing right click, press key <strong>W</strong> to navigate through the scenario.</li> </ol> <p>The folder [IHTApark_3weathers_audio] contains the sound signals, as .wav files, in fist order ambisonics format (B-format).</p> <p> </p>
Dataset: Cross-Sectional National Survey on Risk Perception and Tourism Behaviour (SNF NRP 78)
<p>The data set contains scales (<em>rating items</em>) on the willingness of the Swiss resident population to take risks in connection with touristic travel during the coronavirus pandemic. The data includes a selection of items of the <em>Domain-Specific Risk-Taking Scale</em> (<em>DOSPERT) </em>(Weber et al., 2002). The data contains measures of the <em>health belief model</em> (<em>HBM</em>; Rosenstock, 1960, see also Champion & Skinner, 2008) that is used both in health research and in tourism research to explain and predict the preventive health behaviour of individuals. Furthermore, the data covers all three elements of the t<em>heory of planned behaviour (</em>Ajzen, 1991). This is a representative data set for the Swiss population aged 18 and above. A trilingual and national survey of the Swiss resident population was carried out in the period from March to May 2021. A letter of invitation to participate in the study was sent by post to a total of 4,530 randomly selected persons residing in Switzerland. The address data was provided by the Federal Statistical Office (BfS). Of the total of 4,530 people contacted, 164 were reported as unreachable (no longer at the address, deceased, or due to old age). A total of 1,683 persons participated in the survey. This corresponds to a response rate of 39%. The structure of the respondents corresponds to that of the Swiss resident population 18 years of age and older with regard to gender, age, and language region.</p> <p>Ajzen, I. (1991). The theory of planned behavior. <em>Organizational Behavior and Human Decision Processes</em>, <em>50</em>(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T</p> <p>Weber, E., Blais, A.-R., & Betz, N. E. (2002). A domain-specific risk-attitude scale: Measuring risk perceptions and risk behaviors. <em>Journal of Behavioral Decision Making</em>, <em>15</em>, 263–290. https://doi.org/10.1002/bdm.414</p> <p>Champion, V. L., & Skinner, C. S. (2008). The health belief model. In K. Glanz, B. Rimer, & K. Viswanath (Eds.), <em>Health behavior and health education: Theory, research, and practice</em> (4th ed., pp. 45–65). San Francisco, CA: Jossey-Bass.</p> <p>Rosenstock, I. M. (1960). What research in motivation suggests for public health. <em>American Journal of Public Health, 50</em>(3), 295-302. https://doi.org/10.2105/AJPH.50.3_Pt_1.295</p>
Data and Codes from: Decoding distributed oscillatory signals driven by memory and perception in the prefrontal cortex
<p>These datasets and codes were used for analysis in a manuscript titled "Decoding distributed oscillatory signals driven by memory and perception in the prefrontal cortex." For more details on the experimental and analysis methods, please refer to that manuscript. The raw data was obtained by recording signals from a 64-channel ECoG array implanted on the PFC of monkeys performing a behavioral task using custom software (NS computer service, Japan) running on LabVIEW Real-Time (National Instruments, TX, USA). The recorded raw data were converted into time-series data of power in six frequency bands using FieldTrip. The total file size of all the raw data is over 50GB, so it is not included here. It is available upon request.</p>
Local communities' perceptions of wild edible plant and mushroom change: A systematic review
<p>These datasets are the basis of publication "Local communities’ perceptions of wild edible plant and mushroom change: A systematic review", DOI: https://doi.org/10.1016/j.gfs.2021.100601<br> File GFS_paperIDs.csv lists the articles that are included in the systematic review with their identification numbers.<br> File GFS_data_papers.csv contains data related to the articles reporting on changes of wild edible plants.<br> File GFS_data_plants.csv contains data related to the plant species that are affected by the changes.<br> File GFS_variable_desriptions.txt informs about the variables in files GFS_data_papers.csv and GFS_data_plants.csv.</p>
The Perception of "Intelligent" Design in Visual Structure
<p>Datasets relative to the following publication:</p> <p>Schmidt, F. (2022). The Perception of “Intelligent” Design in Visual Structure. <em>i-Perception, 13(2)</em>, 1-5. https://doi.org/10.1177/20416695221080184</p> <p>Each excel file contains the data relative to (1) the label task and (2) the two ranking tasks, respectively:</p> <p>(1) LabelData.xlsx contains the average frequencies of verbal labels per category across 3 raters, based on accumululation of participants' free naming responses (for experimental details see publication).</p> <p>(2) RankingData.xlsx contains the individual rankings of all experimental stimuli on the continuum from "animate origin" to "inanimate origin", as well as on the continuum from "Straight lines and right angles" to "No straight lines and no right angles" (for experimental details see publication). </p>
Molecular similarity perception based on machine-learning models
<p>Molecular similarity is an particularly important notion for chemical legislation, specifically in the evaluation process for orphan drugs (i.e., drugs for rare diseases). A new molecule needs to be dissimilar from any other existing drug for a given disease to be assigned the financially advantageous status of orphan drug. Currently, there are many ways to define whether two molecules are similar or dissimilar. Thus far, the European Medicines Agency has used experts majority voting on discretional judgments of similarity when assessing new drugs for rare diseases. The decision of individual expert whether two compounds are similar is inherently subjective, depending on factors such as gender, age, state of mind, and previous experiences. It is therefore desirable, in this context, to benefit from an objective measure of similarity. To answer this need, we report a new dataset of molecular similarity assessments, that includes complex and difficult similarity scenarios. As a result, we propose new and improved models for similarity-prediction procedures, including 3D properties. These models are publicly available: <a href="https://chematlas.chimie.unistra.fr/ReadySim/">https://chematlas.chimie.unistra.fr/ReadySim/</a>.</p> <p>Software, 3D structures and pictures are available in the git related to this deposit: <a href="https://github.com/enricogandini/paper_similarity_prediction.git">https://github.com/enricogandini/paper_similarity_prediction.git</a></p> <p>The deposit contains two files.</p> <ul> <li>original_training_set.csv: this is one of the dataset published initially in [doi: 10.1186/1758-2946-6-5].</li> <li>new_dataset.csv: result from a new survey organized in 2020</li> </ul> <p>The columns are the following:</p> <ul> <li>id_pair: unique identifier of the compound pair</li> <li>curated_smiles_molecule_a: first compound of the pair</li> <li>curated_smiles_molecule_b: second compound of the pair</li> <li>tanimoto_cdk_Extended: ECFP similarity measure</li> <li>TanimotoCombo: ComboScore similarity measure</li> <li>pchembl_distance: difference of activity of the compound pair</li> <li>target_name: protein to which the compound pair is binding</li> <li>simil_2D: similar based on ECFP (0 or 1)</li> <li>simil_3D: similar based on ComboScore (0 or 1)</li> <li>dissimil_2D: dissimilar based on ECFP (0 or 1)</li> <li>dissimil_3D: dissimilar based on ComboScore (0 or 1)</li> <li>pair_type: pairs are classified based on ECFP and ComboScore as similar or dissimilar in 2D and 3D - Sim2DSim3D, Sim2DDis3D, Dis2D,Sim3D, Dis2DSim3D</li> <li>n_answers: number of answers from experts</li> <li>n_similar: number of answers labeling the pair as similar compounds</li> <li>frac_similar: n_similar/n_answers</li> </ul>
Dataset: We Do Not Understand What It Says -- Studying Student Perceptions of Software Modelling
<p>The dataset contains two supporting documents for the paper title, "We Do Not Understand What It Says -- Studying Student Perceptions of Software Modelling". The first one is an excel sheet containing interview transcripts of 13 of the participants of this study (who agreed to publish their statements) and the second is an appendix file containing the interview guide (questionnaire used for interviews with students and instructors) used in the case study. </p> <p>The interview transcripts are supported by "in-vivo coding" used by both authors separately during analysis. </p>
Supplementary Material for the paper "Developers' Perception Matters: Machine Learning to Detect Developer-sensitive Smells"
<p>Supplementary material for the paper "Daniel Oliveira; Wesley K. G. Assunção; Alessandro Garcia; Baldoino Fonseca; and Márcio Ribeiro. Developers' Perception Matters: Machine Learning to Detect Developer-sensitive Smells. In: Empirical Software Engineering. 2022. Springer."</p>
REMODEL. WP5. Cable Manipulation Planning, Execution and Interactive Perception. T5-2. Cable grasping. Data related to a paper published in MDPI Electronics 2021
<p>The datasets contain data for the normalization of tactile voltages, the training dataset for the diameter classifier, and data recorded during the experiment related to the publication:</p> <p>A. Cirillo, G. Laudante, and S. Pirozzi, “Tactile sensor data interpretation for estimation of wire features,” Electronics, vol. 10, no. 12, art. 1458, June 2021. (DOI: 10.3390/electronics10121458)</p>
REMODEL. WP5. Cable Manipulation Planning, Execution and Interactive Perception. T5-2. Cable grasping. Data related to a paper for the conference ICPS2022
<p>The datasets contain data related to the experiments presented in the publication:</p> <p>A. Cirillo, G. Laudante, and S. Pirozzi, “Wire Grasping by Using Proximity and Tactile Data,” 5th IEEE International Conference on Industrial Cyber-Physical Systems, ICPS 2022. (DOI: 10.1109/ICPS51978.2022.9816936)</p>
Figure 3 in On the perception of leaf morphology and visible light byTetranychus urticae Koch (Acariformes, Tetranychidae)
Figure 3 Distribution of Tetranychus urticae females (A) and daily oviposition rate (B) on bean leaves: A1, B1 (BNI); A2, B2 (BNS); A3, B3
ScienceDex guides
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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.