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1,903 results for “Perceptions”
Integration of data sets from different sources for modeling gender violence and perception of insecurity
<p>The dataset is composed of three distinct files which aggregate processed data derived from open datasets of three cities: Dublin, San Francisco, and Valencia. The data has been mapped to a grid of 25m² for Valencia and 50m² for Dublin and San Francisco. The respective files are named DATA_ES_VLC.csv, DATA_IE_DUB.csv, and DATA_US_SFO.csv. Additionally, there is a dataset for tweets named DATA_TWT.csv, which contains tweets collected through web scraping and analysed using natural language processing (NLP) algorithms and neural networks. The aim is to identify and classify tweets that discuss gender-based violence in the city of Valencia. Another file, MAP_ES_VLC.csv, includes points collected during various mapathons conducted by the Polytechnic University of Valencia campus for a science project aimed at identifying potentially insecure locations.</p>
Dataset on consumers' perception of different types of sustainability levies, Swiss agriculture and farmers and willingness to choose suboptimal potatoes in different settings
<p><em><span>This dataset includes survey data from 481 Swiss consumers. Data were collected in the German-speaking parts of Switzerland in February and March 2024. The survey includes three independent main parts. </span></em></p> <p><em><span>In a first part, we collected qualitative and quantitative data on participants’ perception of Swiss agriculture and farmers. Specifically, participants’ trust in crop and livestock production farmers and their perceived knowledge about production methods and their affect towards farmers was assessed. </span></em></p> <p><em><span>In a second part, we collected quantitative data on participants’ preference for different sustainability levies. For this, six different products were used (i.e., fresh/processed vegetables, dairy, and meat). For each of these six products, participants were shown four levy options from which they had to choose the one that they found most appealing. For vegetables, the options were: (A) reduction of risks related to plant protection products, (B) more support for local farmers, (C) support for environmental sustainability, and (D) sustainability projects in general. For the animal products, option (A) was an increase in animal welfare, whilst options (B), (C) and (D) were the same as for the vegetable products.</span></em></p> <p><em><span>In a third part, we collected qualitative and quantitative data on participants preferences for suboptimal or optimal potatoes. Here, a 2 × 2 experimental design (setting × information) was used. This means that participants were presented with either a supermarket or farm shop setting and with or without food waste information. Participants then chose between two potatoes: optimal potato A, suboptimal potato B, or neither. Both potatoes were equally expensive.</span></em></p>
Perceptions of green facades among residents of buildings with and without a greened envelope – Data from a household survey in Leipzig, Germany
<p>The data set stems from a survey of residents in two neighborhoods of Leipzig, Germany, and was implemented in April and May of 2022. The primary aim of the study was to better understand resident perceptions of green facades, including their (perceived) benefits as well as concerns. Additionally, residents were asked for a number of other perceptions, including heat stress, noise and air pollution. The sample includes both residents of buildings with and without an existing green facade.</p> <p>All variables included in this data publication are described in the codebook. The original German language wording of the survey questions can be found in the questionnaire enclosed with the data set. We include responses to all questions from the survey that were close-ended or had a numerical response. Open-ended questions were excluded from this publication for data privacy reasons. </p>
AVbook, a high-frame-rate corpus of narrative audiovisual speech for investigating multimodal speech perception
<p><strong>Please cite</strong><br> Varano E, Guilleminot P, Reichenbach T. <em>AVbook, a high-frame-rate corpus of narrative audiovisual speech for investigating multimodal speech perception</em>. J Acoust Soc Am. 2023 May 1;153(5):3130. doi: 10.1121/10.0019460. PMID: 37249407.<br> <br> Seeing a speaker's face can help substantially in understanding them, in particular in challenging listening conditions. Research into the neurobiological mechanisms behind the audiovisual integration has recently begun to employ continuous natural speech. However, these efforts are impeded by a lack of high-quality audiovisual recordings of a speaker narrating a longer text. Here we seek to close this gap by developing AVbook, an audiovisual speech corpus designed for cognitive neuroscience studies and audiovisual speech recognition. The corpus consists of 3.6 hours of audiovisual recordings of two speakers, one male and one female, reading 59 passages from a narrative English text. The recordings were acquired at a high frame rate of 119.88 frames per second. The corpus includes a sets of multiple-choice questions to test attention to the different passages. We verified the efficacy of these questions in a pilot study. A short written summary is also provided for each recording. To enable audiovisual synchronization when presenting the stimuli, four videos of an electronic clapperboard were recorded with the corpus. The corpus is available for download to support research into the neurobiology of audiovisual speech processing as well as the development of computer algorithms for audiovisual speech recognition.</p>
Monitoring knowledge, risk perceptions, preventive behaviours and trust to inform pandemic outbreak response.
<p>The study is part of the large project promoted by WHO Regional Office for Europe called “<em>Monitoring knowledge, risk perceptions, preventive behaviours and trust to inform pandemic outbreak response</em>” and carried out in over 30 countries of the WHO European Region (Registered ISRCTN on 11/05/2021, ID: ISRCTN26200758). In Italy, the survey was conducted administering an online questionnaire developed <em>ad hoc</em> by the WHO in four waves (January-May 2021) to a sample of 10.000 individuals aged 18-70 years. A detailed sampling plan was developed to obtain a representative sample of the Italian adult population. The following variables were taken into account for stratification of the participants: gender by age (four age groups: 18-34 years, 35-44 years, 45-54 years, 55-70 years); geographical area (four areas: North West, North East, Centre, South and Islands); size of living centers (two classes: above and below 100,000 inhabitants); level of education (up to lower middle school, beyond lower middle school); and employment situation (employed, not employed). At the end of each survey’s wave, a weighting procedure has been applied to accurately restore the proportionality of the total sample examined with the reference population, according to the most recent data of the Italian Statistics Institute (ISTAT, 12/31/2019). In particular, data have been weighted for the main socio-demographic and geographic variables (e.g., sex by age by geographical area, occupation, educational qualification, geographical area by size of living centers). The sample size made it possible to maintain a sampling error of less than 2% (at the significance level of 95%) and to control the error of estimates within groups or subgroups of interest. The interviews were conducted by Doxa S.p.a. and carried out with the CAWI technique (Computer Assisted Web Interviewing) on an online panel and on the Confirmit software platform used by Doxa S.p.a. The average administration time was about 18-20 minutes. This study was approved by the Ethics Committee of the IRCCS San John of God Fatebenefratelli of Brescia (n° 72-2020), and all participants provided written informed consent.</p> <p>The primary objectives are to:</p> <p>● Monitor variables that are critical for population behaviour to control transmission of the novel coronavirus, including risk perceptions, knowledge, self-efficacy, confidence in institutions, behaviours, rumours, affect, worry, resilience, trust in/use of information sources and more.<br> ● Document changes over time in these factors to understand the effect of the pandemic process, new developments, events or measures taken.<br> ● Monitor possible issues, e.g. related to misinformation or distrust, as they emerge, to allow early response.<br> ● Identify relationships between variables to identify levers for effective and appropriate responses.<br> ● Explore the relationship of psychological variables (e.g. worry, resilience, trust, affect) with the epidemiological situation and the events and measures taken.<br> ● Identify gaps between perceived and actual knowledge.<br> ● Evaluate the effectiveness of pandemic response measures, and the acceptance and effectiveness of policies and restrictions implemented, including the easing of such restrictions.<br> The secondary objectives are to:<br> ● Contribute to post-outbreak evaluation, thereby contributing to the continued regional/global efforts to better understand mechanisms of crisis response.<br> ● If additional research capacity is available, the data can be triangulated with data on media reporting, COVID-19 cases and other.● If additional research capacity is available, the data can be triangulated with data on media reporting, imported or confirmed cases, etc.: The relationship between psychological variables and characteristics of the outbreak situation can be explored (i.e. how closely the perceived risk mirrors reported cases, relative import risk, media reports).<br> This approach allows a citizen-centred approach where insights into population perceptions and behaviours inform COVID-19 actions, alongside epidemiological data and considerations of economic, cultural, ethical, structural political nature and other.</p> <p>The WHO questionnaire includes 21 different thematic areas noteworthy for the investigation of COVID-19 experience. The questionnaire was translated into specific country language by each recruiting site, following the WHO’s guidelines for translations of tools into other languages. The process included the following steps: forward translation, panel experts, back-translation, pre-test and cognitive interviews and, finally, development of the final version. Variables being surveyed include the following:<br> • Socio-demography;<br> • COVID-19 personal experience;<br> • Health literacy;<br> • COVID-19 risk perception;<br> • Probability and Severity;<br> • Preparedness and Perceived self-efficacy;<br> • Prevention – own behaviours;<br> • Affect;<br> • Trust in sources of information;<br> • Use of sources of information;<br> • Frequency of Information;<br> • Trust in institutions (perceptions);<br> • Policies, interventions (perceptions);<br> • Conspiracies (perceptions);<br> • Resilience (perceptions);<br> • Testing and tracing;<br> • Fairness (perceptions);<br> • Lifting restrictions (pandemic transition phase);<br> • Unwanted behaviour;<br> • Wellbeing;<br> • COVID-19 vaccine.</p> <p> </p>
User Study Data for "Perception of Ultrasound Haptic Focal Point Motion"
<p>Data from two experiments about the perception of ultrasound haptic feedback.</p>
Visualization and perception of data gaps in the context of Citizen Science projects: Gradation of Reporting Activity
<p>Online experiment about the influence of different numbers of levels of representation of reporting activity (total number of reports for all birds in the given time span and region) on proportion of correct responses and subjective evaluation of the task (NASA-TLX). Effects of representation with three (3) levels and effects of representation with five (5) levels are investigated. Two groups of members of ornitho.de were tested: experts - persons with access to database (more than 10 reports per month in average) and novices - persons without access to database (less than 10 reports per month in average). Two different tasks were given. The evaluation of statements on a map and the selection of grid fields that met a given requirement.</p>
Visualization and perception of data gaps in the context of Citizen Science projects: Video tutorial support
<p>Online experiment about the influence of the availability of a video tutorial on proportion of correct responses and subjective evaluation of the task (NASA-TLX). Two different tasks were given. The evaluation of statements on a map and the selection of grid fields that met a given requirement.</p>
Risk-perception, attitudes and behavioural intentions to spend on experiences in the post-Corona crisis: data from Italy, Denmark, China and Japan
<p>A cross-sectional survey conducted in Japan (n=1,111), Denmark (n=1,028), China (n=1,019) and Italy (n=1,014) during 10-24th of July 2020.</p> <p>Data format: sav (SPSS) and csv.</p>
Raw and post-processed data for the microscopic investigation of the effect of random envelope fluctuations on phoneme-in-noise perception
<p>The current dataset consists of three main folders:</p> <ul> <li><strong>01-Stimuli/</strong>: Contains the three sets of noises (white noise, bump noise, MPS noise) for the 12 study participants (S01 to S12).</li> <li><strong>02-Raw-data/fastACI/</strong>: Contains the raw data as obtained for each participant, which are also available within the GitHub repository of the fastACI toolbox, using the same directory tree. The results for each (anonymised) participant (under: <strong>publ_osses2022b/data_SXX/1-experimental_results/</strong>) include their audiometric thresholds (folder: <strong>audiometry</strong>), the results for the Intellitest speech test (folder: <strong>intellitest</strong>), and for the phoneme-in-noise test /aba/-/ada/ for the three noises (savegame files in MAT format).</li> <li><strong>02-Raw-data/ACI_sim/</strong>: Contains the raw data as obtained for the artificial listener, i.e., the model osses2022a.m (available within the fastACI toolbox). Twelve sets of simulations (using the waveforms of participants S01 to S12) were run for the three types of test noises. The results of the simulations of the phoneme-in-noise test are stored in the savegame MAT files. The template derived from 100 repetitions of /aba/ and /aba/ at an SNR=-6 dB in white noise is also included (template-osses2022a-speechACI_Logatome-abda-S43M-trial-1-v1-white-2022-7-15-N-0100.mat). The same template was used in all simulations.</li> <li><strong>03-Post-proc-data/ACI_exp/</strong>: Auditory classification images (ACIs) derived from the participants' data (folder: <strong>ACI_exp</strong>) and from the simulations (folder: <strong>ACI_sim</strong>). For each participant (or artificial listener) there are three ACIs (MAT files) for each of the corresponding noises. Cross predictions are also included with performance predictions across 'participants' (Crosspred.mat, 12 cross predictions for each noise) or across 'noises' (Crosspred-noise.mat, 3 cross predictions for each participant). The cross predictions all have the same names but are stored in dedicated directories.</li> </ul> <p><strong>Use these data:</strong></p> <ol> <li>Download all these data, place them in a local directory of your computer. If you have MATLAB and you downloaded a local copy of the fastACI toolbox (open access at: <a href="http://github.com/aosses-tue/fastACI">GitHub</a>) you can recreate the figures of our paper.</li> <li>After initialising the toolbox (type 'startup_fastACI;', without quotation marks in MATLAB) and then type either of the following commands, to recreate the figure you want. To recreate the figures in the main text:</li> </ol> <pre><code class="language-javascript">publ_osses2022b_JASA_figs('fig1','zenodo'); publ_osses2022b_JASA_figs('fig2a','zenodo'); publ_osses2022b_JASA_figs('fig2b','zenodo'); publ_osses2022b_JASA_figs('fig3','zenodo'); publ_osses2022b_JASA_figs('fig4','zenodo'); publ_osses2022b_JASA_figs('fig5','zenodo'); publ_osses2022b_JASA_figs('fig6','zenodo'); publ_osses2022b_JASA_figs('fig7','zenodo'); publ_osses2022b_JASA_figs('fig8','zenodo'); publ_osses2022b_JASA_figs('fig8b','zenodo'); publ_osses2022b_JASA_figs('fig9','zenodo'); publ_osses2022b_JASA_figs('fig9b','zenodo'); publ_osses2022b_JASA_figs('fig10','zenodo');</code></pre> <p>To generate the figures of the supplementary materials (Appendix in the BioRxiv preprint):</p> <pre><code class="language-javascript">publ_osses2022b_JASA_figs('fig1_suppl','zenodo'); publ_osses2022b_JASA_figs('fig2_suppl','zenodo'); publ_osses2022b_JASA_figs('fig3_suppl','zenodo'); publ_osses2022b_JASA_figs('fig3b_suppl','zenodo'); publ_osses2022b_JASA_figs('fig4_suppl','zenodo'); publ_osses2022b_JASA_figs('fig4b_suppl','zenodo'); publ_osses2022b_JASA_figs('fig5_suppl','zenodo'); publ_osses2022b_JASA_figs('fig5b_suppl','zenodo');</code></pre> <p><strong>References:</strong></p> <ul> <li><strong>Preprint</strong>: Alejandro Osses, Léo Varnet. "A microscopic investigation of the effect of random envelope fluctuations on phoneme-in-noise perception." BioRxiv.</li> <li><strong>fastACI toolbox</strong>: Alejandro Osses, Léo Varnet. fastACI toolbox: the MATLAB toolbox for investigating auditory perception using reverse correlation (v1.2). Zenodo. doi:<a href="https://doi.org/10.5281/zenodo.7314014">10.5281/zenodo.7314014</a>. Supplement to: <a href="http://github.com/aosses-tue/fastACI/tree/v1.2">https://github.com/aosses-tue/fastACI/tree/v1.2</a></li> </ul>
Dataset and stimuli: Perception of saturation in natural objects
<p><strong>This dataset contains observer data and stimulus information for the below publication. Refer to this manuscript for more details.</strong></p> <p>Laysa Hedjar, Matteo Toscani, and Karl R. Gegenfurtner, "Perception of saturation in natural objects," Journal of the Optical Society of American A <strong>40</strong>(3), A190-A198 (2023), doi:10.1364/JOSAA.476874.</p> <p> </p> <p>Participant data is available in two files: <em>fruit_pt_data.csv </em>and <em>blob_pt_data.csv</em></p> <ul> <li><em>fruit_pt_data.csv</em>: <ul> <li>fruit name: name of fruit pair</li> <li>object or swatch: whether the stimulus pair were whole objects or 8x8 swatches</li> <li>participant ID: given participant identification number</li> <li>proportion positive: proportion of trials in which participant chose the positive stimulus as more saturated (out of 10 total trials per stimulus pair)</li> </ul> </li> <li><em>blob_pt_data.csv</em>: <ul> <li>blob hue (radians - LAB): hue in radians of the blob pair, as defined in LAB-LCH color space</li> <li>object or swatch: whether the stimulus pair were whole objects or 8x8 swatches</li> <li>matched or unmatched: whether the stimulus pair were matched in terms of blob ID (refers to spatial configuration)</li> <li>positive stimulus ID: identification number of the positive LC-slope stimulus (refers to spatial configuration)</li> <li>negative stimulus ID: identification number of the negative LC-slope stimulus (refers to spatial configuration) <ul> <li>note that the above two IDs should be identical if the stimulus is a 'matched' pair</li> </ul> </li> <li>participant ID: given participant identification number</li> <li>proportion positive: proportion of trials in which participant chose the positive stimulus as more saturated (out of 5 total trials per stimulus pair)</li> </ul> </li> </ul> <p> </p> <p>Stimuli pngs are in the zip file s<em>timuli.zip</em>. Pngs are not gamma-corrected. Blob and fruit stimulus sets are separated by folder; object and swatch stimulus sets are also separated by folder.</p> <p>Fruit pngs are labeled:</p> <p> fruit_[object/swatch]_[fruitName]-[negative/positive].png</p> <p>For blob pngs, six possible spatial configurations for each hue were used. An ID was given for each configuration. Blob pngs are labeled:</p> <p> blob_[object/swatch]_hue[hueInRadians]_ID[1-6]-[negative/positive].png</p> <p> </p> <p>Statistics of the stimulus images are presented in the files <em>fruit_stats_objects.csv</em>, <em>fruit_stats_swatches.csv</em>, <em>blob_stats_objects.csv</em>, and <em>blob_stats_swatches.csv</em>. Each column represents a different stimulus image. Each row represents a different statistic taken across the distribution of pixels. Calculations were made in CIELAB-LCH color space ('white point' defined as white of monitor: CIE1931 xyY 0.3328, 0.3343, 142.35).</p>
Data on public perceptions of, attitudes towards, and values for managing urban green infrastructure for carbon, biodiversity, and well-being outcomes in Helsinki, Finland
<p>A public participatory GIS -survey dataset detailing public perceptions of, attitudes towards, and values for managing urban green infrastructure for carbon, biodiversity, and well-being outcomes in Helsinki, Finland.</p>
Shear Shock Waves Mediate Haptic Holography via Focused Ultrasound - Human Hand: Wave Patterns and Perception
<p><strong>Human Hand: Wave Patterns and Perception</strong></p> <p>This dataset is part of a larger repository (DOI: 10.5281/zenodo.5248082) which houses links to the data used in the publication "Shear Shock Waves Mediate Haptic Holography via Focused Ultrasound" (<a href="http://www.science.org/doi/10.1126/sciadv.adf2037">Reardon et al., 2023</a>). If you use these data please cite our publication (<a href="http://www.science.org/doi/10.1126/sciadv.adf2037">http://www.science.org/doi/10.1126/sciadv.adf2037</a>).</p> <p>This dataset contains the in vivo response of a single participant's hand to focused ultrasound (UHEV1, Ultrahaptics) scanned in a zigzag path from the wrist to the distal end of digit 2 (and vice-versa). The data is provided as .mat files. The files are separated via longitudinal scanning speed, <em>v<sub>l</sub></em> = 1, 2, 4, 7, 11 m/s. At all speeds, the ultrasound focus was modulated transverse to its primary motion direction at a speed, <em>v<sub>mod</sub></em> of +-2.5 m/s yielding a zigzag path (2 cm path width). The longitudinal speed is designated in the filename. The direction of scanning - either from the wrist to the distal end of digit 2 (Distal direction) or from the distal end of digit 2 to the wrist (Proximal direction) - is also designated in the filename. Written, informed consent was gathered from the participant in this study, and the protocol was approved by the human subjects committee of our institution. Details about our experimental procedure can be found in our publication.</p> <p>IMPORTANT - The data is the unprocessed output from a laser doppler vibrometer (PSV-500, Polytec). The data is NOT time-aligned and must be reconstructed using the reference signal and the map of the measurement locations.</p> <p> </p> <p><strong>Data Fields</strong></p> <p><strong>y</strong> (NxMx2) - 3D array containing the skin velocity normal to the laser doppler vibrometer (in m/s) at N measurement locations for M timepoints and 2 repetitions</p> <p><strong>ref</strong> (NxMx2) - 3D array containing a reference voltage signal taken from the ultrasound phased array. The beginning of the reference signal can be used to time-align each of the measurements and repetitions</p> <p><strong>fs</strong> - Laser doppler vibrometer sampling rate (in Hz)</p> <p><strong>measurementLocations</strong> (Nx3) - 3D locations on the hand (x,y,z; in m) for each of N measurement locations<br> <br> </p> <p> </p> <p><strong>BehavioralDataset.zip</strong></p> <p>Contains the responses from three different perception experiments on tactile motion direction discrimination. The experiments are provided in three separate files; the results are provided as a MATLAB table. Written, informed consent was gathered from all participants in this study, and the protocol was approved by the human subjects committee of our institution. Details about our experimental procedure can be found in our publication.</p> <p>In the first experiment, SSW_PrimaryDataset.mat, participants (N=12) identified the direction of the focused ultrasound as either moving from the wrist to the end of digit 2 (Distal direction) or from the end of digit 2 to the wrist (Proximal direction).</p> <p>The second experiment, SSW_SecondaryDataset-Zigzag.mat, was nearly identical to the first experiment, except we cyclically repeated the stimuli such that the total integrated time in which the stimulus was applied to the skin was approximately constant between all of the different scan speeds. The participants (N=3) identified the motion direction of the focused ultrasound as either "Distal" or "Proximal" under two conditions - one in which there was no delay between our cyclical repeats (No Delay condition) and a second in which there was a 500 ms time delay between subsequent repetitions (With Delay condition).</p> <p>The third file, SSW_SecondaryDataset-Circle.mat, presents the pilot results (N=1) of a similar tactile motion experiment, except with circular trajectories (radius 2.8 cm) drawn on the palm of the hand in either a clockwise or counterclockwise direction. The stimuli were also repeated cyclically (with and without delay between repetitions), similar to experiment two.</p> <p> </p> <p> </p> <p><strong>Table Fields - SSW_PrimaryDataset.mat</strong></p> <p><strong>Participant</strong> - Participant label</p> <p><strong>Speed</strong> - Longitudinal speed, *v<sub>l</sub>*, of the focused ultrasound stimulus (in m/s)</p> <p><strong>Response</strong> - Participant response as a binary 0 (Distal direction) or 1 (Proximal direction)</p> <p><strong>Direction</strong> - True direction of the stimulus as a binary 0 (Distal direction) or 1 (Proximal direction)</p> <p><strong>isCorrect</strong> - Indicates whether the participant's response matches the true stimulus direction</p> <p><strong>Repetition</strong> - Stimuli were block randomized and "Repetition" refers to how many times the participant has seen that specific stimulus</p> <p><strong>ResponseLabel </strong>- Participant response as either "Distal" or "Proximal"</p> <p><strong>DirectionLabel </strong>- True label of the stimulus as either "Distal" or "Proximal"</p> <p><strong>Plays</strong> - Number of times the participant felt the stimulus before selecting a response</p> <p> </p> <p><strong>Table Fields - SSW_SecondaryDataset-Zigzag.mat</strong></p> <p><strong>Participant</strong> - Participant label</p> <p><strong>Speed </strong>- Longitudinal speed, *v<sub>l</sub>*, of the focused ultrasound stimulus (in m/s)</p> <p><strong>Response </strong>- Participant response as a binary 0 (Distal direction) or 1 (Proximal direction)</p> <p><strong>Direction </strong>- True direction of the stimulus as a binary 0 (Distal direction) or 1 (Proximal direction)</p> <p><strong>isCorrect </strong>- Indicates whether the participant's response matches the true stimulus direction</p> <p><strong>Repetition </strong>- Stimuli were block randomized and "Repetition" refers to how many times the participant has seen that specific stimulus</p> <p><strong>ResponseLabel </strong>- Participant response as either "Distal" or "Proximal"</p> <p><strong>DirectionLabel </strong>- True label of the stimulus as either "Distal" or "Proximal"</p> <p><strong>Condition </strong>- Indicates the experimental condition ("NoDelay" or "WithDelay")</p> <p> </p> <p><strong>Table Fields - SSW_SecondaryDataset-Circle.mat</strong></p> <p><strong>Participant </strong>- Participant label</p> <p><strong>Speed </strong>- Linear speed of the focused ultrasound stimulus along the circular trajectory (in m/s)</p> <p><strong>Response </strong>- Participant response as a binary 0 (Counterclockwise direction) or 1 (Clockwise direction)</p> <p><strong>Direction </strong>- True direction of the stimulus as a binary 0 (Counterclockwise direction) or 1 (Clockwise direction)</p> <p><strong>isCorrect </strong>- Indicates whether the participant's response matches the true stimulus direction</p> <p><strong>Repetition </strong>- Stimuli were block randomized and "Repetition" refers to how many times the participant has seen that specific stimulus</p> <p><strong>ResponseLabel </strong>- Participant response as either "Counterclockwise" or "Clockwise"</p> <p><strong>DirectionLabel </strong>- True label of the stimulus as either "Counterclockwise" or "Clockwise"</p> <p><strong>Condition </strong>- Indicates the experimental condition ("NoDelay" or "WithDelay")</p>
Citizen Perception of the NBS in Valladolid city
<p>Citizens’ perceptions, both individuals and communities, are essential when evaluating the well-being benefits from urban green spaces (Kothencz et al, 2017). Public and stakeholder perceptions of urban nature, and specifically the quality or functionality of nature, are critical to our understanding of the “value” people place on local environments (Priego et al., 2008).</p> <p>Exploring visitors’ perceptions of green spaces is challenging as it depends on cognitive, affective, and behavioural components and, therefore, sensory perceptions are individually different (Kothencz et al, 2017).</p> <p>This KPI measures identified green space characteristics by the two following well-being variables and one geolocation variable:</p> <ul> <li>Green space visitors’ level of satisfaction. Directly related with the urban green space (UGS) quality.</li> <li>Self-reported quality of life (QoL).</li> <li>Frequency of green space visitors’ crowd-sourced geo-tagged data in NBS sites.</li> </ul> <p>Visitors’ level of satisfaction and perceived QoL contributions of UGS are key individual-level measures that are subjectively affected by area-based green space characteristics.</p> <p>This KPI will reflect on how people assess change in their local environments in terms of satisfaction, quality of life and citizens’ presence of urban green space (UGS) at a site (NBS), neighbourhood and city scale.</p>
Extensive crowdsourced dataset of in-situ evaluated binaural soundscapes of private dwellings containing subjective sound-related and situational ratings along with person factors to study time-varying influences on sound perception — research data
<p><strong>Abstract:</strong></p> <p>The soundscape approach highlights the role of situational factors in sound evaluations; however, only a few studies have applied a multi‐domain approach including sound‐related, person‐related, and time‐varying situational variables. Therefore, we conducted a study based on the Experience Sampling Method to measure the relative contribution of a broad range of potentially relevant acoustic and non‐auditory variables in predicting indoor soundscape evaluations. Here we present the comprehensive dataset for which 105 participants reported temporally (rather) stable trait variables such as noise sensitivity, trait affect, and quality of life. They rated 6.594 situations regarding the soundscape standard dimensions, perceived loudness, and the saliency of its sound components and evaluated situational variables such as state affect, perceived control, activity, and location. To complement these subject‐centered data, we additionally crowdsourced object‐centered data by having participants make binaural measurements of each indoor soundscape at their homes using a low‐(self‐)noise recorder. These recordings were used to compute (psycho‐)acoustical indices such as the energetically averaged loudness level, the A‐weighted energetically averaged equivalent continuous sound pressure level, and the A‐weighted five‐percent exceedance level. This complex hierarchical data can be used to investigate time‐varying non‐auditory influences on sound perception and to develop soundscape indicators based on the binaural recordings to predict soundscape evaluations.</p> <p><strong>Content:</strong></p> <ul> <li><a href="https://zenodo.org/record/7858848/files/01%20StudyDescription.pdf">01 StudyDescription.pdf </a> <ul> <li>Description of the field study.</li> <li>Information about the methods and materials used.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/02%20Dataset.csv">02 Dataset.csv</a> <ul> <li>The dataset, consisting of 93 variables describing 6594 observations taken by 105 participants.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/03%20VariableDescriptions_EnglishPersonQuestionnaire.pdf">03 VariableDescriptions_EnglishPersonQuestionnaire.pdf</a> <ul> <li>Descriptions of all variables, their measurement scale, scale ranges and levels.</li> <li>Questions and task descriptions of the Experience Sampling Method questionnaire in German language with an English translation.</li> <li>English translations of questions asked in the person questionnaire.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/04%20ESM-Questionnaire.pdf">04 ESM-Questionnaire.pdf</a> <ul> <li>Screenshots of the original Experience Sampling Method questionnaire with English translations.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/05%20PersonQuestionnaire_OriginalGermanVersion.pdf">05 PersonQuestionnaire_OriginalGermanVersion.pdf</a> <ul> <li>Original version of the person questionnaire in German language.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/06%20HelpTexts.pdf">06 HelpTexts.pdf</a> <ul> <li>Descriptions of the study task.</li> <li>Explanations of the scales used in the questionnaire.</li> <li>Explanations of the sound categories and the soundscape composition.</li> <li>Explanation of the operation of the recording device.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/AcousticFeatures_README.md">AcousticFeatures_README.md</a> <a href="https://zenodo.org/api/files/3d784540-c0f4-412f-8742-df1db6f5401d/TimeSeries_and_Spectrograms_README.md?versionId=9291496c-d2c6-4151-96f1-a2ad99e1a540"> </a> <ul> <li>Descriptions of the structure of the AcousticFeatures_xxx.csv and .zip files.</li> <li>Analyis settings used in Artemis Suite to generate the acoustic features.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/AcousticFeatures_SingleValues.csv">AcousticFeatures_SingleValues.csv</a> <ul> <li>All acoustic features, aggregated to single values per feature, recording, and channel.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/AcousticFeatures_Spectra.csv">AcousticFeatures_Spectra.csv</a> <ul> <li>Time-averaged 1/3 octave spectra of each channel of each recording, A-weichted and un-weighted.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/AcousticFeatures_Spectrograms.zip">AcousticFeatures_Spectrograms.zip</a> <ul> <li>13188 .csv files with un-weighted spetrograms of each channel of each recording.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/AcousticFeatures_TimeSeries.zip">AcousticFeatures_TimeSeries.zip</a> <ul> <li>A .csv file containing LAeq and LZeq time series of each channel of each recording.</li> </ul> </li> </ul> <p><strong>Publications refering to this dataset:</strong></p> <p>Versümer, Siegbert; Steffens, Jochen; Weinzierl, Stefan (currently under review): "The role of loudness predictions, personal and situational factors in day-to-day loudness assessments of indoor soundscapes."</p> <p><strong>Funding:</strong></p> <p>This study was sponsored by the German Federal Ministry of Education and Research. “FHprofUnt” funding code: 13FH729IX6. </p> <p><strong>License: </strong></p> <p>CC 4.0 BY, <a href="https://creativecommons.org/licenses/by/4.0/legalcode">https://creativecommons.org/licenses/by/4.0/legalcode</a></p> <p><strong>Version history:</strong></p> <p>Details can be found in the <a href="https://zenodo.org/api/files/a15d6a91-1a35-4b5e-a7ec-da8a9bcbee2b/Changelog.md">Changelog.md</a> file.</p> <ul> <li> V.01.0. March 7, 2023: Initial publication. <a href="https://doi.org/10.5281/zenodo.7193938">https://doi.org/10.5281/zenodo.7193938</a></li> <li> V.01.1. April 25, 2023. <a href="https://doi.org/10.5281/zenodo.7858848">https://doi.org/10.5281/zenodo.7858848</a></li> </ul>
Datasets for examining perceptions of fire resilient landscapes
<p>Datasets used to answer the question 'what is a fire resilient landscape?'. Included is the data used for a literature review from Scopus and Web of Science containing entries surrounding resilience to landscape fires. Also included is survey responses. Participants came from two groups, students of the Pyrogeography course at Wageningen University, and professionals working within the fire community (both academia and practice). The participants were asked about their perception of a fire resilient landscape, and the responses coded with thematic analysis.</p>
FEDORA. Excerpts from essays, transcript of interviews and group discussions on students' future perception. Part 3: Interviews, Finland.
<p>Finnish-language dataset. Related to a research article that is awaiting acceptance for publication: <em>Future, technology and agency: Students’ experiences from a course on futures thinking and quantum computing</em>.</p> <p>As per ethical concerns and participants' consent, the dataset is given in a fully anonymised form. Instead of students' interviews (the context of which is given in the article). In a nutshell, 21 upper-secondary school students were interviewed in 2018 regarding their experiences on taking an experimental science course that combined ideas from futures thinking and quantum computing. The present dataset contains all 245 transcribed passages from 21 student interviews that were initially marked as relevant to the research goals (i.e. how students saw their conceptions change over the course). Additionally, for each passage the final coding that was used in the analysis for the research paper is shown. The "number-letter codes" were used as shorthands; the full names of the codes correspond closely with the final, English-language codes in the paper.</p> <p>To preserve full anonymity, students are not identified by any marker or pseudonym here; rather, the passages are given alphabetically. The start and end of passages has not been checked for additional or missing first and last characters. Please also note that the character > marks change of speaker. Identifying the interviewer and interviewee should be straighforward based on the context.</p> <p>Please contact the corresponding author for more information.</p> <p> </p> <p>--</p> <p> </p> <p><a href="https://zenodo.org/communities/futuresthinking?page=1&size=20">FEDORA Project</a> README:</p> <p> </p> <p><strong>README</strong></p> <p><strong>Data Set Title:</strong> “FEDORA. Excerpts from essays, transcript of interviews and group discussions on students’ future perception. Finland"</p> <p><strong>Data Set Author/s:</strong> Antti Laherto, Tapio Rasa, Elina Palmgren (University of Helsinki)</p> <p><strong>Data Set Contact Person/s</strong>: Tapio Rasa<strong> </strong>(University of Helsinki), ORCID 0000-0003-1315-5207, tapio.rasa@helsinki.fi;</p> <p><strong>Data Set License</strong>: this data set is distributed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.</p> <p><strong>Publication Year</strong>: 2021</p> <p><strong>Project Info</strong>: FEDORA<strong> </strong>(Future-oriented Science EDucation to enhance Responsibility and engagement in the society of Acceleration and uncertainty<strong> , </strong>funded by European Union, Horizon 2020 Programme. Grant Agreement num.<strong> </strong>872841,<br> www.fedora-project.eu)</p> <p> </p> <p><strong>Data set Contents</strong></p> <p>The data set consists of:</p> <p>One spreadsheet file, provided in two alternative formats (CSV and XLSX).</p> <p>Future_technology_agency_DATA_CSV.csv</p> <p>Future_technology_agency_DATA_XLSX.xlsx</p> <p> </p> <p><strong>Data set Documentation</strong></p> <p><em>Given above this README, on the ZENODO repository. https://zenodo.org/record/4734161</em></p>
Artifacts supplementing the ACM DTRAP 2020 article "Will You Trust This TLS Certificate? Perceptions of People Working in IT (extended version)"
<p>These research artifacts supplement the following two publications:</p> <ul> <li>Will You Trust This TLS Certificate? Perceptions of People Working in IT [ACSAC 2019], DOI 10.1145/3359789.3359800, more details at https://crocs.fi.muni.cz/public/papers/acsac2019</li> <li>Will You Trust This TLS Certificate? Perceptions of People Working in IT (extended version) [ACM DTRAP 2020], DOI 10.1145/3419472, more details at https://crocs.fi.muni.cz/public/papers/dtrap2020</li> </ul> <p>The artifacts contain the full experimental setup (as described in Section 2.1 of the paper) and the complete anonymized dataset underlying the evaluation presented in Sections 3 and 4.</p> <p>The experimental setup contains the documents accompanying the task: the informed consent, pre-task questionnaire, task description, trust scales, and the list of questions posed during the post-task interview (all in PDFs). We further include the custom website with certificate validation documentation for the “redesigned” condition (static HTML). While working on the task, participants in the “redesigned” condition could access this website via a link that was in the redesigned error messages. Furthermore, we provide the software with which the participants interacted. It contains the displayed error messages and validated certificates. These things are available both individually and incorporated in a snapshot of a virtual machine used at the experiment (importable directly into VirtualBox).</p> <p>The collected data is presented in a single dataset (SPSS format; you can use PSPP as a free alternative). It includes the analysis syntax files to obtain the numerical results presented in the paper. For each participant, the dataset contains: 1) pre-task questionnaire answers, 2) reported trust ratings, 3) sub-task timing, 4) information on whether they browsed the Internet and 5) the interview codes assigned. Note that we do not publish the interview transcripts to preserve participant privacy.</p>
Public perceptions of an avian reintroduction aiming to connect people with nature
<p>Full raw dataset (quantitative and qualitative) and R code for quantitative analyses, associated with the publication "Public perceptions of an avian reintroduction aiming to connect people with nature" <em>People and Nature</em>. Survey responses are presented here anonymised. Please note that where the term 'Proactive' sample is used this is referred to as the 'Self selecting' sample in the associated journal article.</p>
InterTVA. A multimodal MRI dataset for the study of inter-individual differences in voice perception and identification.
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