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426 results for “stimuli”
Data from: Central place foragers and moving stimuli: a hidden-state model to discriminate the processes affecting movement
1. Human activities can influence the movement of organisms, either repelling or attracting individuals depending on whether they interfere with natural behavioural patterns or enhance access to food. To discern the processes affecting such interactions, an appropriate analytical approach must reflect the motivations driving behavioural decisions at multiple scales. 2. In this study, we developed a modelling framework for the analysis of foraging trips by central place foragers. By recognising the distinction between movement phases at a larger scale and movement steps at a finer scale, our model can identify periods when animals are actively following moving attractors in their landscape. 3. We applied the framework to GPS tracking data of northern fulmars Fulmarus glacialis, paired with contemporaneous fishing boat locations, to quantify the putative scavenging activity of these seabirds on discarded fish and offal. We estimated the rate and scale of interaction between individual birds and fishing boats and the interplay with other aspects of a foraging trip. 4. The model classified periods when birds were heading out to sea, returning towards the colony or following the closest boat. The probability of switching towards a boat declined with distance and varied depending on the phase of the trip. The maximum distance at which a bird switched towards the closest boat was estimated around 35 km, suggesting the use of olfactory information to locate food. Individuals spent a quarter of a foraging trip, on average, following fishing boats, with marked heterogeneity among trips and individuals. 5. Our approach can be used to characterise interactions between central place foragers and different anthropogenic or natural stimuli. The model identifies the processes influencing central place foraging at multiple scales, which can improve our understanding of the mechanisms underlying movement behaviour and characterise individual variation in interactions with a range of human activities that may attract or repel these species. Therefore, it can be adapted to explore the movement of other species that are subject to multiple dynamic drivers.
GazeMining: A Dataset of Video and Interaction Recordings on Dynamic Web Pages. Labels of Visual Change, Segmentation of Videos into Stimulus Shots, and Discovery of Visual Stimuli.
<p><strong>Recording setup</strong><br> Recordings have been taken place on 12th March 2019. Gaze data has been recorded with a Tobii 4C eye tracker with Pro license at 90 Hz. Resolution of the viewport was set to 1024x768. The display had a size of 24 inches and a resolution of 1680x1050 pixels. We polled the DOM tree every 50 milliseconds for fixed elements. We recorded the Web browsing of four participants, who followed the protocol as stored under "Dataset_visual_change/Instructions.doc".</p> <p><strong>Description of the dataset</strong><br> The dataset consists of following three subsets.</p> <p><em>1. Dataset_visual_change</em><br> The recordings of each participant p1-p4 on twelve Web sites are in the corresponding directories. For each Web site, there are nine to eleven files:</p> <ul> <li><site>.json: datacast</li> <li><site>.webm: video recording</li> <li><site>.features.csv: computer-vision features per observation</li> <li><site>.features_meta.csv: meta information about features</li> <li><site>.labels-l<X>.csv: labels of observations</li> <li><site>_meta.csv: meta information about recording</li> <li><site>_scroll_cache.csv: cache of estimated scrolling</li> <li><site>_scroll_cache_map.csv: mapping of observations to scroll cache entries</li> <li><site>_times.csv: timestamps of frames in the video recording</li> <li><site>_layer_pixels.csv: first row is the pixel count of root layer, second row is pixel count of all fixed elements</li> </ul> <p><em>2. Dataset_stimuli</em><br> Stimulus shots and visual stimuli computed with the framework. Value-based, edge-based, signal-based, and SIFT-based features have been used. The labels of the first participant's session had been used to train a random forest classifier with 100 trees for visual change classification, using the named features. The discovery has been performed on each Web site from the dataset and<br> the results are placed in the respective directories. Inside each directory, there is one directory for the detected shots and one for the discovered stimuli. In the shots directory, there is one overview as <participant>_<site>.csv file. For each shot, there are four further files:</p> <ul> <li><participant>_<site>_<shot>.png: stitched frame of the stimulus shot</li> <li><participant>_<site>_<shot>-blind.csv: frames from animations that are not contributing to the stitched frame</li> <li><participant>_<site>_<shot>-gaze.csv: gaze data (in stitched frame space)</li> <li><participant>_<site>_<shot>-mouse.csv: mouse data (in stitched frame space)</li> </ul> <p>The shots have been merged to stimuli, which are placed in the stimuli directory. The stimuli are grouped per layer (scrollable, fixed elements, etc.) and meta information is available in <layer_index>-<xpath>-meta.csv files. Furthermore, there are directories per layer, storing the discovered stimuli. Each discovered visual stimulus is represented by four files:</p> <ul> <li><stimulus_id>.png: stitched frame of the visual stimulus</li> <li><stimulus_id>-gaze.csv: gaze data (in stitched frame space)</li> <li><stimulus_id>-mouse.csv: mouse data (in stitched frame space)</li> <li><stimulus_id>-shots.csv: contained stimulus shots</li> </ul> <p><em>3. Dataset_evaluation</em><br> We have performed two evaluations of the visual stimuli discovery. One computational estimating the quality of stimuli. One case-study of an expert's task. There are two respective directories with the annotation data.</p> <p><strong>Changelog</strong><br> [1.0.2] Add counts of layer pixels per participant.<br> [1.0.1] Change to CC0 license.<br> [1.0.1] Add labels of third annotator "l3".<br> [1.0.0] Initial release.</p>
Sample Stimuli Presentation for a Remote Speech Segmentation Study
<p>A video used in a remote eye-tracking study to assess speech segmentation abilities in children with Down Syndrome.</p>
Data from: Ongoing habenular activity is driven by forebrain networks and modulated by olfactory stimuli
<p>The data was collected using volumetric two-photon calcium imaging of the forebrain and habenula of juvenile zebrafish expressing a transgenic calcium indicator. The data provided is organized according to the figures of our manuscript and contains the necessary codes and data to replicate the figures presented. In short, included repository contains 6 datasets of experiments. Data structure includes neuronal data as normalised to the baseline fluorescence for each neuron over time (DF/F), 2 or 3 dimensional coordinates of each cell and information about duration and samplings rate of experiments, and appropriate information about stimulation either microstimulation or odor stimulation (if any was performed within the experiment).</p>
Suplemental data for the article: Influence of envelope fluctuation on the lateralization of interaurally delayed low-frequency stimuli.
<p>Original as well as derived data for the paper: Encke, Jörg, and Dietz Mathias (2021)</p>
Binaural auralizations of listening experiment stimuli (Envelopment / Engulfment / Spatial Granular Synthesis)
<p>Binaural auralizations of experiment stimuli with KU100 BRIRs, measured at the listening position of the experiments in the IEM CUBE (25-channel loudspeaker hemisphere). The files are 2-channel WAVs for headphone playback.</p>
Img2brain: Predicting the neural responses to visual stimuli of naturalistic scenes using machine learning
<p>The data for this project is part of the <a href="https://doi.org/10.1038/s41593-021-00962-x">Natural Scenes Dataset</a> (NSD), a massive dataset of 7T fMRI responses to images of natural scenes coming from the <a href="https://cocodataset.org/#home">COCO dataset</a>. The training dataset consists of brain responses measured at 10.000 brain locations (voxels) to 8857 images (in jpg format) for one subject. The 10.000 voxels are distributed around the visual pathway and may encode perceptual and semantic features in different proportions. The test dataset comprises 984 images (in jpg format), and the goal is to predict the brain responses to these images.</p> <p>The zip file contains the following folders:</p> <p>1. <strong>trainingIMG</strong>: contains the training images (8857) in jpg format. The numbering corresponds to the order of the rows in the brain response matrix.</p> <p>2. <strong>testIMG</strong>: contains test images (984) in jpg format.</p> <p>3. <strong>trainingfMRI</strong>: contains a npy file with the fMRI responses measured at 10000 brain locations (voxels) to the training images. The matrix has 8857 rows (one for each image) and 10000 columns (one for each voxel).</p>
Materials - audio stimuli for COLOR project
<p>These recordings are auditory stimuli associated to an experimental data about English-like word learning with color cues</p>
Datasets for "GABAergic miR-34a regulates Dorsal Raphè inhibitory transmission in response to aversive, but not rewarding, stimuli"
<p>Datasets for "GABAergic miR-34a regulates Dorsal Raphè inhibitory transmission in response to aversive, but not rewarding, stimuli"</p>
Fig. 4 in Metabolite profiles of brown planthopper-susceptible and resistant rice (Oryza sativa) varieties associated with infestation and mechanical stimuli
Fig. 4. Normalized intensity of significant metabolites affected by BPH infestation (n = 3/group) with different response patterns: (A). similar response in BPHsusceptible KD and BPH-resistant RH, (B). late induction in KD variety, (C). early induction in RH but late induction in KD, (D). induction in RH in both BPH and mechanical piercing by a needle common response in KD and RH, (E). higher level in KD than RH, and (F). higher level in RH than KD. Unpaired-t test was used to compare the control (black bar) and the BPH-treated (white bar) groups and to compare the control (black bar) and the mechanical-treated (grey bar) groups (*p- value<0.05, **p-value<0.01 and ***p-value<0.001) per time per genotype. HAT represents hour after treatment. Error bars represent one standard deviation of the mean value. Normalized intensity of each feature was relative to the intensity of a reference sample.
Fig. 3 in Metabolite profiles of brown planthopper-susceptible and resistant rice (Oryza sativa) varieties associated with infestation and mechanical stimuli
Fig. 3. Clustergram analysis of BPH-susceptible KD and BPH-resistant RH varieties between control (no treatment) and BPH treatment at 0, 6, 24 and 96 h using hierarchical clustering. (A). Venn's diagram represents numbers of metabolite features of KD and RH from three different treatments. (B). Clustergrams obtained from 213 metabolite features of ESI+with % relative standard deviation (RSD) less than 30% at least half of total sample numbers and six different alteration patterns (46 metabolite features) with their level changes greater than 1.5 folds comparing between KD and RH, and between treatment and control in each timepoint. B indicates BPH treatment. M indicates mechanical piercing by a needle. C indicates no treatment.
Fig. 2 in Metabolite profiles of brown planthopper-susceptible and resistant rice (Oryza sativa) varieties associated with infestation and mechanical stimuli
Fig. 2. PCA score plot of metabolite profiles (6413 metabolite features) obtained from ESI + mode among control (C, red), mechanical piercing by a needle (M, blue) and BPH (B, green) treatment at (A). all timepoints (0, 6, 24 and 96 h) between BPH-susceptible KD and BPH-resistant RH varieties. QC indicates pooled samples (n = 70) of rice leave extracts (orange star), (B). 0 h, (C). 6 h, (D). 24 h and (E) 96 h between BPH-susceptible KD and BPH-resistant RH varieties. The numbers above the circles indicate time points. Open and filled circles indicate data from KD and RH varieties. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Corvids optimize working memory by categorizing continuous stimuli
<p>This repository contains numerical source data for graphs and charts (in a MATLAB struct) presented in our main manuscript: Corvids optimize working memory by categorizing continuous stimuli (Apostel, Panichello, Buschman, Rose).</p> <p>Contact: aylin.klarer@ruhr-uni-bochum.de, jonas.rose@ruhr-uni-bochum.de</p>
Acellular Matrix From Human Dermis in Combination With Orthobiologic Stimuli for Augmentation of Massive Rotator Cuff Tears
ClinicalTrials.gov study NCT05855759. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Feasibility Study of a Virtual Reality Cognitive-motor Task Based on Positive Stimuli for Stroke Rehabilitation
ClinicalTrials.gov study NCT02539914. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Metabolic Activation of Brain Areas With Emotional Stimuli Compared With Score in Psychological Test
ClinicalTrials.gov study NCT04974437. IPD Sharing: YES. Countries: 1. Publications: 7.
Accuracy in the Evaluation of Brain Response to Mechanical and Radiofrequency Stimuli in Humans
ClinicalTrials.gov study NCT06183593. IPD Sharing: NO. Countries: 1. Publications: 4.
Reduction of Visual and Auditory Stimuli to Reduce Pain During Venipuncture in Premature Infants.
ClinicalTrials.gov study NCT04041635. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Perception of Electrical Stimuli in Individuals with Stroke
ClinicalTrials.gov study NCT05465005. IPD Sharing: NO. Countries: 1. Publications: 5.
Locating Nociceptive Stimuli on Digital Body Chart
ClinicalTrials.gov study NCT03463109. IPD Sharing: YES. Countries: 1. Publications: 2.
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.