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426 results for “stimuli”
A Stimuli Set of Forty Popular Music Drum Patterns with Perceived Complexity Estimates (Data set)
<p>Stimuli and datasets for the study "A Stimuli Set of Forty Popular Music Drum Patterns with Perceived Complexity Estimates"</p>
Predictive physiological anticipation preceding seemingly unpredictable stimuli: a meta-analysis
<p>The research that created this data is described in:</p> <ul> <li>Mossbridge, J., Tressoldi, P.E. and Utts, J. (2012). Predictive physiological anticipation preceding seemingly unpredictable stimuli: a meta-analysis. Frontiers in Psychology 3:390. doi: 10.3389/fpsyg.2012.00390.</li> </ul> <p>Two files are included:</p> <ul> <li>PPAA_MA.xlsx - The original file, Excel format</li> <li>PPAA_MA.csv - The file converted to CSV format by Adrian Ryan; comments converted to columns.</li> </ul>
LEIRO. The Leipzig kit for testing irony comprehension - Visual Stimuli (examples)
<p>Examples of visual stimuli for LEIRO - The Leipzig kit for testing irony comprehension.</p> <p>Drawings were created by <a href="https://simonefass.de/" target="_blank" rel="noopener">Simone Fass</a> (illustrator) under <a href="https://creativecommons.org/licenses/by-sa/4.0/deed.en" target="_blank" rel="noopener">CC-BY SA Licence</a>. Picture elements were remixed by the authors for presentation in the study.</p>
Imprinting on time-structured acoustic stimuli in ducklings
<p><span><span><span><span><span><span><span><span><span><span><span>Filial imprinting is a dedicated learning process that lacks explicit reinforcement. The phenomenon itself is narrowly heritably canalized, but its content, the representation of the parental object, reflects the circumstances of the newborn. Imprinting has recently been shown to be even more subtle and complex than previously envisaged, since ducklings and chicks are now known to select and represent for later generalization abstract conceptual properties of the objects they perceive as neonates, including movement pattern, heterogeneity, and inter-component relationships of same or different. Here we investigate day-old Mallard (<i>Anas platyrhynchos</i>) ducklings' bias towards imprinting on acoustic stimuli made from mallards' vocalizations as opposed to white noise, whether they imprint on the temporal structure of brief acoustic stimuli of either kind, and whether they generalize timing information across the two sounds. Our data are consistent with a strong innate preference for natural sounds, but do not reliably establish sensitivity to temporal relations. This fits with the view that imprinting includes the establishment of representations of both primary percepts and selective abstract properties of their early perceptual input, meshing together genetically transmitted prior predispositions with active selection and processing of the perceptual input.</span></span></span></span></span></span></span></span></span></span></span></p>
Data for: Specificity of California mouse pup vocalizations in response to olfactory stimuli
<p>To investigate flexibility in vocal signaling by rodent pups, we examined whether olfactory stimuli influence characteristics of pup calls and how these calls may be affected by sex and litter size in California mice (<i>Peromyscus californicus</i>). Pups were isolated and recorded during a 3-minute baseline period followed by a 5-minute exposure to bedding containing scent from their home cage, scent from the home cage of an unfamiliar family, coyote urine, or no scent (control). Latency to call, call rate, and call characteristics (duration, frequency, and amplitude) were compared between the baseline and scent-exposure periods and among olfactory conditions. Compared to the control condition, pups from 2-pup litters called more quietly when exposed to odor from a predator, while pups from 3-pup litters called more loudly. Additionally, pups showed non-significant tendencies to reduce call rates in response to odors from their home cage and to increase call rates when exposed to predator urine. Lastly, males produced higher-frequency calls and more ultrasonic vocalizations than females. These results indicate that pup calling behavior in this species can be influenced by acute olfactory stimuli as well as litter size and sex. The flexibility of pup calling in response to these three variables potentially increases the communication value of pup calls and help shape the parents' responses.</p>
Breaking photoswitch activation depth limit using ionising radiation stimuli adapted to clinical application
<p>This deposit gathers the main raw data and custom codes related to the manuscript "Breaking photoswitch activation depth limit using ionising radiation stimuli adapted to clinical application".</p>
Data for: Individual differences in song plasticity in response to social stimuli and singing position
<p>Individual animals can react to the changes in their environment by exhibiting behaviours in an individual-specific way leading to individual differences in phenotypic plasticity. However, the effect of multiple environmental factors on multiple traits is rarely tested. Such a complex approach is necessary to assess the generality of plasticity and to understand how among-individual differences in the ability to adapt to changing environments evolve. This study examined whether individuals adjust different song traits to varying environmental conditions in the collared flycatcher (Ficedula albicollis), a passerine with complex song. We also aimed to reveal among-individual differences in behavioural responses by testing whether individual differences in plasticity were repeatable. The presence of general plasticity across traits and/or contexts was also tested. To assess plasticity, we documented 1) short-scale temporal changes in song traits in different social contexts (after exposition to male stimulus, female stimulus or without stimuli), and 2) changes concerning the height from where the bird sang (singing position), used as a proxy of predation risk and acoustic transmission conditions. We found population-level relationships between singing position and both song length and complexity, as well as social context-dependent temporal changes in song length and maximum frequency. We found among-individual differences in plasticity of song length and maximum frequency along both the temporal and positional gradients. These among-individual differences in plasticity were repeatable. Some of the plastic responses correlated across different song traits and environmental gradients. Overall, our results show that the plasticity of bird song 1) depends on the social context, 2) exists along different environmental gradients and 3) there is evidence for trade-offs between the responses of different traits to different environmental variables. Our results highlight the need to consider individual differences and to investigate multiple traits along multiple environmental axes when studying behavioural plasticity. </p>
Visual stimuli elicit feedforward and feedback waves in mouse cortex (data and code)
<p>See the readme file for details of the information contained therein.</p> <p>There are also separate readme files for publicly available github repositories from Lyle Muller and the circular statistics toolbox (both for matlab). </p> <p>This dataset includes both the raw data and the analysis code used to process them in Aggarwal et al, Nature Communications, 2022. </p>
IKA CI Music Preprocessing Listening Experiment Stimuli (2022)
<p><strong>IKA CI Music Preprocessing Listening Experiment Stimuli (2022)</strong></p> <p>This dataset contains the audio stimuli that have been presented to both cochlear implant (CI) and normal hearing (NH) listeners in the listening experiments in a study named</p> <p><em>“A Subjective Evaluation of Different Music Preprocessing Approaches in CI Listeners”</em></p> <p>It comprises the excerpts from the <strong>IKA CI Pop Music Dataset (IKA-CI-PMD)</strong> (<a href="https://doi.org/10.5281/zenodo.7060282">10.5281/zenodo.7060282</a>) both as unprocessed references and as processed versions where different music preprocessing strategies have been<br> applied.</p> <p>The <strong>IKA CI Pop Music Dataset</strong> is a dataset of music excerpts that has especially compiled to evaluate different music signal preprocessing strategies for CI listeners. The excerpts taken are from the <strong>MedleyDB </strong>multitrack dataset (<a href="https://medleydb.weebly.com/">https://medleydb.weebly.com/</a>) curated by <a href="https://steinhardt.nyu.edu/marl/research/resources/medleydb">Rachel Bittner et. al.</a>.</p> <p>This dataset is split into a 3-piece “training” set (excerpts T01 to T03) that has been used to familiarize the listeners with the experimental setup, and a 12-piece “test” set (E01 to E12) used in actual experiments.</p> <p>The following music preprocessing strategies are included:</p> <ul> <li>HPCA+P: Harmonic/percussive sound separation (HPSS) combined with PCA-based spectral complexity reduction [1]</li> <li>Cspl and Dspl: DNN-based remix of the harmonic and percussive portions of 4 source stems [2]</li> <li>HALCA: Remix based on a probabilistic model for melody extraction using shift invariant kernels in CQT domain [3] (accompaniment attenuated by 12 dB)</li> <li>MT remix: Oracle remixes of multitrack stems (other accompaniment attenuated by 12 dB)</li> </ul> <p>All stimuli are normalized to a loudness level of -27 LUFS. The signals are stored in the lossless FLAC format. The files contain stereo signals, where both channels are identical.</p> <p>The dataset has been compiled at the Ruhr University Bochum <a href="https://www.ruhr-uni-bochum.de/ika/index_en.html">Institute of Communication Acoustics</a> in 2022 by Johannes Gauer (johannes.gauer@rub.de) in collaboration with the fellow researchers Anil Nagathil, Benjamin Lentz, and Rainer Martin. Like MedleyDB and the IKA CI Pop Music Dataset, it is licensed under a <a href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.</p> <p>For further details on the included excerpts refer to the IKA CI Pop Music Dataset (IKA-CI-PMD) (<a href="https://doi.org/10.5281/zenodo.7060282">10.5281/zenodo.7060282</a>).</p> <p>[1] B. Lentz, A. Nagathil, J. Gauer, and R. Martin, “Harmonic/Percussive sound separation and spectral complexity reduction of music signals for cochlear implant listeners,” in Proc IEEE Int Conf Acoust Speech Signal Process ICASSP, Barcelona, Spain, May 2020, pp. 8713–8717.</p> <p>[2] J. Gauer, A. Nagathil, K. Eckel, D. Belomestny, and R. Martin, “A versatile deep-neural-network-based music preprocessing and remixing scheme for cochlear implant listeners,” J. Acoust. Soc. Am., vol. 151, no. 5, pp. 2975–2986, May 2022.</p> <p>[3] B. Fuentes, R. Badeau, and G. Richard, “Harmonic Adaptive Latent Component Analysis of Audio and Application to Music Transcription,” IEEE Trans. Audio Speech Lang. Process., vol. 21, no. 9, pp. 1854–1866, Sep. 2013.</p>
Rendered Stimuli for "Spatial Analysis and Synthesis Methods: Subjective and Objective Evaluations Using Various Microphone Arrays in the Auralization of a Critical Listening Room"
<h2>Rendered Stimuli from the Subjective Evaluation</h2> <p>This archive (<code>Stimuli.zip</code>) contains the rendered stimuli used in the subjective evaluation of various spatial analysis and synthesis methods, as described in the paper "Spatial Analysis and Synthesis Methods: Subjective and Objective Evaluations Using Various Microphone Arrays in the Auralization of a Critical Listening Room" by Alan Pawlak, Hyunkook Lee, Aki Mäkivirta, and Thomas Lund.</p> <p>The stimuli are provided to improve the reproducibility of the study and to allow readers to listen to the same audio samples used in the subjective evaluation.</p> <h2>File Naming Convention:</h2> <p><code>SYSTEM_PROGRAMMEMATERIAL_AZIMUTH_ELEVATION_-26LUFS.wav</code></p> <p>- <code>SYSTEM</code>: The spatial analysis and synthesis method used (e.g., BSDM-6OM1-Omni, HO-SIRR, SDM-em32, etc.)<br>- <code>PROGRAMMEMATERIAL</code>: The anechoic audio sample used (Bongo, Speech, Orchestra)<br>- <code>AZIMUTH</code>: The azimuth angle of the sound source (e.g., 0, 30, 45, 90, 135)<br>- <code>ELEVATION</code>: The elevation angle of the sound source (e.g., 0, 45)</p> <h2>Audio File Specifications:</h2> <p>- Format: WAV<br>- Sample Rate: 48 kHz<br>- Bit Depth: 32-bit<br>- Loudness Normalization: -26 LUFS</p> <p>To use these stimuli, simply load the desired WAV file into your audio playback software.</p> <p>For more information about the study, please refer to the full paper.</p> <p>Pawlak, A., Lee, H., Mäkivirta, A. and Lund, T., 2024. Spatial Analysis and Synthesis Methods: Subjective and Objective Evaluations Using Various Microphone Arrays in the Auralization of a Critical Listening Room.</p>
Stimuli of Shuttlerun Test
<p>Stimuli used in the shuttle run pilot study</p> <p>Files:</p> <p>Stimuli_components_online.zip: Stimuli used during the Shuttle Run test</p> <p>Stimuli_ShuttleRun.zip: Stimuli used in the online listening test (excerpts of the stimuli used during the shuttle run)</p> <p>Stimuli_shuttlerun_excerpts_online.zip: Stimuli used in the online listening test (sound qualita rating of individual sound components)</p>
Creating Object-Based Stimuli to Explore Media Device Orchestration Reproduction Techniques
<p>Dataset containing Object-based versions and rendered out MDO loudspeaker feeds of two programme items, adapted from existing material to explore Media Device Orchestration reproduction techniques. </p> <p>This forms part of the PhD research of Craig Cieciura. This was experiment-based research to determine how to render object-based audio in the domestic environment using ad-hoc, audio-capable devices.</p> <p><strong>References</strong></p> <p>Cieciura, C., Mason, R., Coleman, P. and Paradis, M. 2018. Creating Object-Based Stimuli to Explore Media Device Orchestration Reproduction Techniques, Audio Engineering Society Preprint, 145th Convention, Engineering Brief 463.</p> <p> </p>
Visual stimuli used in fMRI experiment on processing of real and illusory surfaces
<p>These videos contain samples of visual stimuli used in an fMRI experiment on the processing of real and illusory surfaces in human early visual cortex [the experiments are part of my PhD thesis, in preparation]. Please note that these videos are short sample segments from the experiment, and that in the actual experiment the duration of rest blocks was much longer.</p>
Does evaluative learning depend on the statistical relationship between stimuli? On the sensitivity of evaluative cue conditioning to ecological contingencies
<p>Evaluative conditioning (EC) is concerned with the transfer of valence from an unconditioned stimulus (US) to a conditioned stimulus (CS). Only recently the notion of EC was extended from individual CSs to categories of CSs that share certain cues. That research shows that a contingency implemented between a cue dimension and US valence has a direct effect on the evaluation of stimuli carrying values of this cue dimension. This phenomenon was coined “evaluative cue conditioning” (ECC). The present research tests whether ECC is sensitive to the contingency between a cue dimension and US valence. The present work thereby investigates the impact of ecological contingencies that define contingency by reference to all other CS-US pairings in the learning environment. Two experiments demonstrate that ECC effects are sensitive to the strength of the ecological contingency, suggesting that ECC is a relative phenomenon that depends on the valence of the CS-US pairings in the reference set. Moreover, these findings call for more research on the contingency sensitivity of standard EC effects using this revised definition of contingency.</p>
Investigating the Impact of Visual Stimuli on the Auditory Selective Attention in a VR Classroom
<h2>General</h2> <p>The audio-visual Auditory Selective Attention - visual Priming (avASAvisPrim) project serves to investigate the impact of visual stimuli on auditory selective attention switch in a close-to-real-life classroom setting. This dataset consists of a Unity project and Matlab code used to collect data as well as the collected data and R scripts used for evaluation.</p> <p><strong>The dataset contains:</strong></p> <p> Unity project for audiovisual display and the experiment structure<br> Matlab code for experiment preparation and HpFT measurement<br> Data collected in the experiment (experiment performance)<br> R code for data evaluation</p> <h2>Experiment preparation using Matlab</h2> <p>The code and software used to prepare the experiment is provided in the folder "matlab_avASAvisPrim".</p> <p>The Matlab code used to prepare the trials for each participant as well as to measure the HpTFs. For the HpTF measurements, the ITA Toolbox for Matlab was used and is provided (https://git.rwth-aachen.de/ita/toolbox). A developmental version of Virtual acoustics (VA) 2021a (https://www.virtualacoustics.org/VA/overview/) is provided.</p> <p><strong>Software requirements:</strong></p> <p> Matlab 2020a or higher<br> ITA Toolbox for Matlab installed</p> <h2><br>Experiment conduction in Unity</h2> <p>The Unity project is provided in the folder "unity_avASAvisPrim".</p> <p>Therefore, a virtual classroom with some basic furniture is provided. The used models, prefabs and plugins can be found in the Assets folder.</p> <p>The acoustic stimuli for the task are taken from Loh and Fels 2023 "ChildASA dataset: Speech and Noise Material fpr Child-appropriate Paradigms on Auditory Selective Attention" https://doi.org/10.18154/RWTH-2023-00740. </p> <p>This Unity project was intended for the use in virtual reality using an HMD and respective controllers for input. However, it can also be used on a desktop pc. The mode can be changed using the "VRMode" toggle as described below.<br>The audio reproduction is realized using the Unity plugin for Virtual Acoustics (VA, http://www.virtualacoustics.org/ and https://git.rwth-aachen.de/ita/vaunity_package).</p> <p><strong>Software requirements:</strong></p> <p> Unity 2019.4.21.f1.<br> SteamVR 1.19.7<br> Virtual Acoustics v2021a, VAUnity: https://git.rwth-aachen.de/ita/VAUnity</p> <h2><br>Data evaluation</h2> <p>The collected data is provided in the folder "dataEvaluation_avASAvisPrim". This folder contains three types of data: the raw data collected in the experiment (reaction times and error rates). R code for the evaluation of the head tracking data and the questionnaires is provided.</p>
EEG and eyetracking response to static and moving stimuli
<p>This dataset contains processed EEG and eyetracking recordings from an experiment in which twelve participants viewed a black disk either flashed in one position or moving in a straight line in one of six directions. Static stimuli were presented at nodes on a hexagonal grid, while moving stimuli moved in straight lines along the axes of the grid.</p>
Raw data and stimuli for assessing the perceived reverberation in different rooms for a set of musical instrument sounds
<p>This set of data and sound stimuli was used in the study by Osses, McLachlan, and Kohlrausch (2020) to assess the perceived reverberation --including measurements and simulations-- for different instrument sounds in eight different rooms. The following are the directories that are provided:</p> <ul> <li><strong>00-Experiment-WAE_GM_201712</strong>: Web Audio Evaluation tool (WAE) used to run the listening experiment with 24 participants. Follow the instructions in README.txt to get the experiment running.</li> <li><strong>01-Stimuli</strong>: Sound stimuli as exactly used during the listening experiments.</li> <li><strong>02-Raw-data</strong> and <strong>03-Results-summary</strong>: Outputs from WAE for each of the participants. The raw data contained in these XML files were extracted and stored in '03-Results-summary'</li> <li><strong>04-Stimuli-9s-for-simulations</strong>: Same sounds as in '01-Stimuli' but truncated to have a duration of 9 s. These sounds were used as input to an implementation (Osses et al. 2017, 2020) of the model by van Dorp et al. (2013).</li> </ul> <p>The paper figures can be reproduced in two MATLAB toolboxes: fastACI (script: publ_osses2020a_JASA_EL_figs.m) and AMT (script: exp_osses2020.m, availability as of 2023).</p>
Genetic control of the dynamic transcriptional response to immune stimuli and glucocorticoids at single cell resolution
<p>Supplementary Tables for article "Genetic control of the dynamic transcriptional response to immune stimuli and glucocorticoids at single cell resolution"<br> </p>
Dataset: Imaging the columnar functional organization of human area MT+ to axis-of-motion stimuli using VASO at 7 Tesla
<p>First release upon acceptance of the paper. </p>
Tapping to drumbeats in an online experiment changes our perception of time and expressiveness - Stimuli
<p>The video and audio material here are the experiment stimuli for the study <em>Tapping to drumbeats in an online experiment changes our perception of time and expressiveness</em> on Psychological Research.</p>
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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.