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1,903 results for “Perceptions”
Thermal Comfort and Perception of Different Materials for Tabletops: Datasets, Processing Code, and Supplementary Tables.
<p>In this data repository, data related to the study <em>Thermal Comfort and Perception of Different Materials for Tabletops </em> is deposited, and includes datasets, processing code, and supplementary tables.</p> <p> </p>
Climate change perception interview questions for Kunene, Namibia
<p>The files contain interview question used to collect data on climate change perception in Kunene Region Namibia, from a pastoralist Himba tribe.</p>
Sesquialtera in the Colombian bambuco: Perception and estimation of beat and meter
<p> Supplementary material for the ISMIR2020 paper titled: " Sesquialtera in the Colombian bambuco: Perception and estimation of beat and meter". The data in this repository includes the audio files, transcriptions and annotations of the 10 bambucos used in the study.</p> <p>This work was conducted in the context of the ACMus project. You can find more information about our project <a href="https://acmus-mir.github.io/">here.</a></p> <p>For more information about ACMUS-MIR, our annotated dataset of Colombian music from the Andes region, you can visit our <a href="https://acmus-mir.github.io/acmus-mir/">website</a> and our zenodo <a href="https://zenodo.org/record/3268961#.XShkTo8RVPZ">repository</a>.</p> <p> </p>
Perception Sensor Dataset For Bioinspired Landing Trajectories Of An Ornithopter Robot
<p>The dataset contains the measurements captured by several onboard sensors during the landing maneuvers of an ornithopter robot. Each dataset contains a ROS bag file with the sensor measurements, a file with the bioinspired trajectory, a file with the events generated by the simulated event-based sensor, and a README file with the instructions to use the dataset.</p> <p>The bioinspired landing trajectories are computed using Tau Theory. Each landing trajectory test was performed in a simulated scenario. The object models of each scene can be found in the /model/meshes folder of each scene. There are two testing scenes: (i) a warehouse and (ii) a refinery. The file object_pose.csv includes the position and orientation of each object in the scene. The sensor measurements were saved in rosbag file that contains a topic for each sensor measurement. The dataset includes information from the following simulated sensors:</p> <ul> <li>Velodyne HDL-32E</li> <li>Sonar sensor with a range of 20 m</li> <li>IMU</li> <li>Frame based monocular camera</li> <li>Event camera</li> </ul>
Audio data from thesis Perception and Production of Nanning Mandarin Fourth Tone
<p>Recordings of 4 female speakers (S1, S2, S3, and S4) of Nanning-accented Mandarin Chinese reading preconstructed sentences. Recordings of those 4 female speakers telling a story based on 5 pages from Mercer Mayer's wordless picture book <em>Frog on His Own</em> (FOHO). Recording of perception test and perception test warm-up given to 26 Chinese living in Nanning, Guangxi. List of corpus sentences read, test prompts, and test sheet.</p>
Vehicle driving actions for loudness and annoyance perception
<p>This dataset contains 360º videos of 36 driving actions. The videos are organized by vehicles: a white car (Opel Corsa 2016), a dark red motorbike (Suzuki VX 800 800cc 1994), a dark blue van (Fort Transit FT100 1999) and a street sweeper (Kärcher MC 50).</p> <p>The recordings were done with a 360º camera (Xiami Mi Sphere Camera) and a tethraedral microphone (Core Sound TetraMic). The microphone recordings were synthesized to stereo recordings (as if the microphones were pointing at +-60º azimuth) with VVMic from VVAudio. The sound pressure level was measured with a level meter.</p> <p>The driving actions are the following. The sound pressure level was calculated as the fast maximum level (maximum dB SPL in windows of 125ms).</p> <ul> <li>Car <ol> <li><a href="https://www.youtube.com/watch?v=zvmhiE3NXx8&t=0s">00:00</a> Scene 1 - Stand by (close) - 72.5 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=zvmhiE3NXx8&t=18s">00:18</a> Scene 2 - Accelerate (close) LR - 84.9 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=zvmhiE3NXx8&t=36s">00:36</a> Scene 3 - 30 km/h (far) RL - 70.4 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=zvmhiE3NXx8&t=54s">00:54</a> Scene 4 - 50 km/h (close) LR - 81.0 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=zvmhiE3NXx8&t=72s">01:12</a> Scene 5 - Break and stop (far) RL - 79.8 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=zvmhiE3NXx8&t=90s">01:30</a> Scene 6 - Stand by (far) - 67.8 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=zvmhiE3NXx8&t=108s">01:48</a> Scene 7 - Accelerate (far) RL - 79.3 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=zvmhiE3NXx8&t=126s">02:06</a> Scene 8 - 30 km/h (close) LR - 82.4 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=zvmhiE3NXx8&t=144s">02:24</a> Scene 9 - 50 km/h (far) RL - 75.7 dB SP</li> <li><a href="https://www.youtube.com/watch?v=zvmhiE3NXx8&t=162s">02:42</a> Scene 10 - Break and stop (close) LR - 75.9 dB SPL</li> </ol> </li> <li>Motorbike <ol> <li><a href="https://www.youtube.com/watch?v=3wa6zeEbJ5w&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=2&t=0s">00:00</a> Scene 1 - Stand by (close) - 83.7 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=3wa6zeEbJ5w&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=2&t=18s">00:18</a> Scene 2 - Accelerate (close) LR - 92.5 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=3wa6zeEbJ5w&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=2&t=36s">00:36</a> Scene 3 - 30 km/h (far) RL - 83.2 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=3wa6zeEbJ5w&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=2&t=54s">00:54</a> Scene 4 - 50 km/h (close) LR - 90.2 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=3wa6zeEbJ5w&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=2&t=72s">01:12</a> Scene 5 - Break and stop (far) RL - 81.8 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=3wa6zeEbJ5w&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=2&t=90s">01:30</a> Scene 6 - Stand by (far) - 78.1 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=3wa6zeEbJ5w&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=2&t=108s">01:48</a> Scene 7 - Accelerate (far) RL - 86.8 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=3wa6zeEbJ5w&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=2&t=126s">02:06</a> Scene 8 - 30 km/h (close) LR - 91.2 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=3wa6zeEbJ5w&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=2&t=144s">02:24</a> Scene 9 - 50 km/h (far) RL - 84.3 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=3wa6zeEbJ5w&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=2&t=162s">02:42</a> Scene 10 - Break and stop (close) LR - 83.4 dB SPL</li> </ol> </li> <li>Van <ol> <li><a href="https://www.youtube.com/watch?v=DdaWvKfVILo&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=3&t=0s">00:00</a> Scene 1 - Stand by (close) - 84.4 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=DdaWvKfVILo&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=3&t=18s">00:18</a> Scene 2 - Accelerate (close) LR - 93.8 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=DdaWvKfVILo&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=3&t=36s">00:36</a> Scene 3 - 30 km/h (far) RL - 81.4 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=DdaWvKfVILo&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=3&t=54s">00:54</a> Scene 4 - 50 km/h (close) LR - 92.3 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=DdaWvKfVILo&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=3&t=72s">01:12</a> Scene 5 - Break and stop (far) RL - 81.4 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=DdaWvKfVILo&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=3&t=90s">01:30</a> Scene 6 - Stand by (far) - 79.8 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=DdaWvKfVILo&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=3&t=108s">01:48</a> Scene 7 - Accelerate (far) RL - 85.0 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=DdaWvKfVILo&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=3&t=126s">02:06</a> Scene 8 - 30 km/h (close) LR - 85.9 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=DdaWvKfVILo&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=3&t=144s">02:24</a> Scene 9 - 50 km/h (far) RL - 85.6 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=DdaWvKfVILo&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=3&t=162s">02:42</a> Scene 10 - Break and stop (close) LR - 83.1 dB SPL</li> </ol> </li> <li>Street sweeper <ol> <li><a href="https://www.youtube.com/watch?v=4ssqZk78JYc&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=4&t=0s">00:00</a> Scene 1 - Stand by (close) - max 81.9 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=4ssqZk78JYc&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=4&t=18s">00:18</a> Scene 2 - Sweeper on (close) - max 93.7 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=4ssqZk78JYc&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=4&t=36s">00:36</a> Scene 3 - Move forward (close) LR - max 94.6 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=4ssqZk78JYc&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=4&t=54s">00:54</a> Scene 4 - Stand by (far) - max 79.6 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=4ssqZk78JYc&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=4&t=72s">01:12</a> Scene 5 - Sweeper on (far) - max 84.4 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=4ssqZk78JYc&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=4&t=90s">01:30</a> Scene 6 - Move forward (far) RL - max 84.3dB SPL</li> </ol> </li> </ul> <p> </p> <p>You can also find the videos in <a href="https://www.youtube.com/playlist?list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB">Youtube</a>.</p> <p>Reference:</p> <p>Llorach, Gerard, Matthias Vormann, Volker Hohmann, Dirk Oetting, Christina Fitschen, Markus Meis, Melanie Krüger, and Michael Schulte. "Vehicle noise: Loudness ratings, loudness models and future experiments with audiovisual immersive simulations." In <em>INTER-NOISE and NOISE-CON Congress and Conference Proceedings</em>, vol. 259, no. 3, pp. 6752-6759. Institute of Noise Control Engineering, 2019.</p>
UI Dark Patterns and Where to Find Them: A Study on Mobile Applications and User Perception
<p>UI Dark Patterns and Where to Find Them: A Study on Mobile Applications and User Perception</p> <p> </p> <p>This dataset contains:</p> <ul> <li>survey_data.xlsx: Read-only spreadsheet containing the answers of 541 participants of our online survey (48 participants opted to not make their answers publicly available);</li> <li>classification_data.xlsx: Read-only spreadsheet containing the overall and the individual categorization of 240 mobile apps with respect to the presence of dark patterns; and,</li> <li>Videos.zip: videos of 15 apps (10 minutes each) used to classify the apps. The complete set of videos is considerably large and can be provided upon request.</li> </ul>
Dataset: Information content of ultraviolet-reflecting color patches and visual perception of body coloration in the Tyrrhenian wall lizard Podarcis tiliguerta
<p>These are the data sets and R script corresponding to the scientific publication with the same title and authors.</p> <p>Description of these files is available in the file Note.pdf</p>
Perception of shape and space across rigid transformations
<p>Dataset relative to the following publication:</p> <p>Schmidt, F., Spröte, P., & Fleming, R. W. (2016). Perception of shape and space across rigid transformations. <em>Vision Research, 126</em>, 318-329. <a href="http://dx.doi.org/10.1016/j.visres.2015.04.011"> http://dx.doi.org/10.1016/j.visres.2015.04.011 </a></p> <p>Each folder contains the data relative to one experiment and a text file with comments.</p>
Categorical perception for red and brown
<p>This data supplements the article:</p> <p>Witzel, C., & Gegenfurtner, K. R. (2016). Categorical perception for red and brown. Journal of Experimental Psychology: Human Perception & Performance, 42(4), 540-570. doi:10.1037/xhp0000154</p> <p>The Excell-file with the data includes 3 sheets:</p> <p><strong>Sheet 1 (jnd): </strong>JND data from Figure 4.a of the above article.</p> <p>- columns = 20 test colours.</p> <p>- rows = 14 observers.</p> <p><strong>Sheet 2 (rt): </strong>Response time data from Figure 6.a of the above article.</p> <p>- columns = three kinds of colour pairs (AB, BC, & CD) and the location of the target (left vs. right).</p> <p>- rows = 15 observers.</p> <p><strong>Sheet 3 (er): </strong>Error rates from Figure 6.b of the above article.</p> <p>- columns and rows as in sheet 2.</p>
Dataset supplementing "Marx, S., & Einhäuser, W. (2015). Reward modulates perception in binocular rivalry. Journal of Vision, 15(1):11, 1–13, http://www.journalofvision.org/content/15/1/11, doi:10.1167/15.1.11."
<p>These data supplement the publication</p> <p>Marx, S., & Einhäuser, W. (2015). Reward modulates perception in binocular rivalry. Journal of Vision, 15(1):11, 1–13, http://www.journalofvision.org/content/15/1/11, doi:10.1167/15.1.11.</p> <p>and be used freely for scientific purposes provided the aforementioned paper is appropriately cited.</p> <p>exp1_data.mat contains data of experiment 1</p> <p>exp2_data.mat contains data of experiment 2</p> <p>figure2_3.m and figure4_5.m exemplify usage of the data and reproduce the figures 2-5 of the aforementioned article.</p>
Integration of serial sensory information in haptic perception of softness
<p>Redundant estimates of an environmental property derived simultaneously from different senses or cues are typically integrated according to the Maximum Likelihood Estimation model (MLE): Sensory estimates are weighted according to their reliabilities, maximizing the percept"s reliability. Mechanisms underlying the integration of sequentially derived estimates from one sense are less clear. Here we investigate the integration of seriallysampled redundant information in softness perception. We developed a method to manipulate haptically perceived softness of silicone rubber stimuli during bare finger exploration. We then manipulated softness estimates derived from single movement segments (indentations) in a multi-segmented exploration to assess their contributions to the overall percept. Participants explored two stimuli in sequence, using 2-5 indentations and reported which stimulus felt softer. Estimates of the first stimulus' softness contributed to the judgments similarly, whereas for the second stimulus estimates from later as compared to earlier indentations contributed less. In line with unequal weighting, the percept"s reliability increased with increasing exploration length less than predicted by the MLE model. This pattern of results is well explained by assuming that the representation of the first stimulus fades when the second stimulus is explored, which fits with a neurophysiological model of perceptual decisions (Deco et al., 2010).</p> <p> </p> <p>There are zip files for every experiment (1 & 2a-d), which contain all data relative to the publication. The data of each participant is contained in a separate folder. This folder contains a *.raw file for each session of the experiment and a "data" folder, which contains movement trajectories (*.trj files) and the staircase reversals for each condition (*.pse files) in separate folders for each session. In every experiment folder there is a list of trials which were excluded from the analyses.</p> <p>Variables of Experiment 1 are described in the file VARIABLE_CODES_EXP1.txt and the variables of Experiment 2a-d are described in the file VARIABLE_CODES_EXP2.txt.</p> <p> </p>
Database for article: "Privacy Perceptions in Digital Games: A Study with Information Technology (IT) Undergraduates"
<p>This database is an addendum to the article "<strong>Privacy Perceptions in Digital Games: A Study with Information Technology (IT) Undergraduates</strong>" to provide information regarding the anonymously collected data.</p><p><strong>Abstract of the article</strong></p><p>This study explores the perceptions and practices of undergraduates in Information Technology (IT) regarding privacy issues in digital games. This topic becomes relevant in the current scenario where artificial intelligence (AI) is increasingly integrated into digital games, providing an enhanced experience for players. However, this integration poses security and privacy challenges, the understanding of which is crucial for both players and developers.<br>The primary objective of this research is to comprehend the participants' perceptions and understandings of privacy in digital games. We employed a qualitative and quantitative methodology to address our research inquiries. Through an online form of data collection, we obtained 61 responses. Among the obtained information, we observed that 40\% of the students are interested in pursuing a career in game development, and 49.18% would consider this possibility. Noteworthy among the identified issues is the necessity for companies to devise more effective means of communicating their privacy policies to players/users, adapting the language to their target audience. Participants reported attacks related to online multiplayer games and expressed concerns about the security of personal data.</p>
Les processus de socialisation du Hezbollah auprès des jeunes mineurs et les différentes perceptions de la communauté libanaise et internationale du sujet.
<p>Ce mémoire de recherche examine la situation et le rôle polymorphe du Hezbollah au Liban, en mettant en lumière ses diverses actions de socialisation, particulièrement auprès des jeunes mineurs. L'analyse se concentre sur la manière dont ces informations sont interprétées à l'échelle internationale et au sein de la société libanaise. Il est important de souligner que chaque culture a sa propre perception de la vérité, et par conséquent, une approche objective et une certaine distance critique sont essentielles.</p><p>Une attention particulière est consacrée à l'exploration de la sémantique entourant ce sujet, soulignant l'importance de la perspective à partir de laquelle on aborde cette question.</p>
Dataset and analysis file for 3-factor solution for heat pump perception study using Q-methodology in Groningen, the Netherlands
<p>Dataset and analysis using KEN-Q method for a 3-factor solution for heat pump perception study using Q-methodology in Groningen, the Netherlands</p>
Data for: Collective signalling is shaped by feedbacks between signaller variation, receiver perception, and acoustic environment in a simulated communication network
<p>Communication takes place within a network of multiple signallers and receivers. Social network analysis provides tools to quantify how an individual's social positioning affects group dynamics, and the subsequent biological consequences. However, network analysis is rarely applied to animal communication, likely due to the logistical difficulties of monitoring natural communication networks. We generated a simulated communication network to investigate how variation in individual communication behaviours generates network effects, and how this communication network's structure feeds back to affect future signalling interactions. We simulated competitive acoustic signalling interactions among chorusing individuals and varied several parameters related to communication and chorus size to examine their effects on calling output and social connections. Larger choruses had higher noise levels, and this reduced network density and altered the relationships between individual traits and communication network position. Hearing sensitivity interacted with chorus size to affect both individuals' positions in the network and the acoustic output of the chorus. Physical proximity to competitors influenced signalling, but a distinctive communication network structure emerged when signal active space was limited. Our model raises novel predictions about communication networks that could be tested experimentally, and identifies aspects of information processing in complex environments that remain to be investigated. </p>
Has the Supreme Court become just another political branch? Public perceptions of Court approval and legitimacy in a post-Dobbs world
<p>Have perceptions of the U.S. Supreme Court polarized, much like the rest of American politics? Because of the Court's unique role, for many years it remained one of the few institutions respected by both Democrats and Republicans alike. But the Court's dramatic shift to the right in recent years—highlighted by its <em>Dobbs</em> decision in 2022—potentially upends that logic. Using both 8 waves of panel data and 18 nationally representative surveys spanning two decades, we show that while there was little evidence of partisan polarization in earlier years, in 2022 and 2023, such patterns are clear in favorability, trust, legitimacy, and support for reform. Factors that used to protect the Court—like knowledge about it and support for key democratic values—no longer do so. The Court has also become more important to voters, and will likely remain a political flashpoint, with disquieting implications for the Court's place in our polity.</p>
Public perceptions of trophy hunting are pragmatic, not dogmatic
<div> <div> <div> <div> <p>Fierce international debates rage over whether trophy hunting is socially acceptable, especially when people from the Global North hunt well-known animals in sub-Saharan Africa. We used an online vignette experiment to investigate public perceptions of the acceptability of trophy hunting in sub-Saharan Africa among people who live in urban areas of the USA, UK and South Africa. Acceptability depended on specific attributes of different hunts as well as participants' characteristics. Zebra hunts were more acceptable than elephant hunts, hunts that would provide meat to local people were more acceptable than hunts in which meat would be left for wildlife, and hunts in which revenues would support wildlife conservation were more acceptable than hunts in which revenues would support either economic development or hunting enterprises. Acceptability was generally lower among participants from the UK and those who more strongly identified as an animal protectionist, but higher among participants with more formal education, who more strongly identified as a hunter, or who would more strongly prioritize people over wild animals. Overall, acceptability was higher when hunts would produce tangible benefits for local people, suggesting that members of three urban publics adopt more pragmatic positions than are typically evident in polarized international debates.</p> </div> </div> </div> </div>
Repeatedly experiencing the McGurk effect induces long-lasting changes in auditory speech perception
<p>In the McGurk effect, presentation of incongruent auditory and visual speech evokes a fusion percept different than either component modality. We show that repeatedly experiencing the McGurk effect for 14 days induces a change in auditory-only speech perception: the auditory component of the McGurk stimulus begins to evoke the fusion percept, even when presented on its own without accompanying visual speech. This perceptual change, termed fusion-induced recalibration (FIR), was talker-specific and syllable-specific and persisted for a year or more in some participants without any additional McGurk exposure. Participants who did not experience the McGurk effect did not experience FIR, showing that recalibration was driven by multisensory prediction error. A causal inference model of speech perception incorporating multisensory cue conflict accurately predicted individual differences in FIR. Just as the McGurk effect demonstrates that visual speech can alter the perception of auditory speech, FIR shows that these alterations can persist for months or years. The ability to induce seemingly permanent changes in auditory speech perception will be useful for studying plasticity in brain networks for language and may provide new strategies for improving language learning.</p>
Effects of relational and instrumental messaging on human perception of rattlesnakes
<p>We tested the effects of relational and instrumental message strategies on US residents' perception of rattlesnakes—animals that tend to generate feelings of fear, disgust, or hatred but are nevertheless key members of healthy ecosystems. We deployed an online survey to social media users (n=1,182) to describe perceptions of rattlesnakes and assess the change after viewing a randomly selected relational or instrumental video message. An 8–item, pre– and post– Rattlesnake Perception Test (RPT) evaluated perception variables along emotional, knowledge, and behavioral gradients on a 5–point Likert scale; the eight responses were combined to produce an Aggregate Rattlesnake Perception (ARP) score for each participant. We found that people from Abrahamic religions (i.e., Christianity, Judaism, Islam) and those identifying as female were associated with low initial perceptions of rattlesnakes, whereas agnostics and individuals residing in the Midwest region and in rural residential areas had relatively favorable perceptions. Overall, both videos produced positive changes in rattlesnake perception, although the instrumental video message led to a greater increase in ARP than the relational message. The relational message was associated with significant increases in ARP only among females, agnostics, Baby Boomers (age 57–75), and Generation–Z (age 18–25 to exclude minors). The instrumental video message was associated with significant increases in ARP, and this result varied by religious group. ARP changed less in those reporting prior experience with a venomous snake bite (to them, a friend, or a pet) than in those with no such experience. Our data suggest that relational and instrumental message strategies can improve people's perceptions of unpopular and potentially dangerous wildlife, but their effectiveness may vary by gender, age, religious beliefs, and experience. These results can be used to hone and personalize communication strategies to improve perceptions of unpopular wildlife species.</p>
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.