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644 results for “Data Visualization”
Unsupervised functional alignment via data-driven ANN-based visual decoding
<p>This repository is for the under-review paper "Unsupervised functional alignment via data-driven ANN-based visual decoding". Xin-Ya Zhang, Hang Lin, Zeyu Deng, Markus Siegel, Earl K. Miller and Gang Yan.</p>
Dataset for Data Analysis and Visualization with Python for Social Scientists lesson
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Code and data for: A computational model for driver's cognitive state, visual perception and intermittent attention in a distracted car following task
<p>A source code and data dump for analyses of the article "A computational model for driver’s cognitive state, visual perception and intermittent attention in a distracted car following task"</p> <p>Code is under GNU AGPL-v3. Data under CC-BY-4.0</p> <p>Versioned code is available at https://gitlab.com/mulsimco/follow17 and https://gitlab.com/mulsimco/cfmodels</p> <p>See README.md in follow17 for usage.</p>
Supporting data for: "Frequency-tagged visual evoked responses track syllable effects in visual word recognition"
<p>The dataset consists of the original 17 .bdf files.</p>
Data from "Analyzing Emulsion Dynamics via Direct Visualization and Statistical Methodologies"
<p>The data used in "<strong>Analyzing Emulsion Dynamics via Direct Visualization and Statistical Methodologies</strong>".</p>
Source Data for "Direct Visualization of Relativistic Quantum Scars"
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Keywords derived from Publications for SMS "Design Practices in Visualization Driven Data Exploration for Non-Expert Audiences".
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Data from: Context-dependent signaling of coincident auditory and visual events in primary visual cortex
Detecting rapid, coincident changes across sensory modalities is essential for recognition of sudden threats or events. Using two-photon calcium imaging in identified cell types in awake, head-fixed mice, we show that, among the basic features of a sound envelope, loud sound onsets are a dominant feature coded by the auditory cortex neurons projecting to primary visual cortex (V1). In V1, a small number of layer 1 interneurons gates this cross-modal information flow in a context-dependent manner. In dark conditions, auditory cortex inputs lead to suppression of the V1 population. However, when sound input coincides with a visual stimulus, visual responses are boosted in V1, most strongly after loud sound onsets. Thus, a dynamic, asymmetric circuit connecting AC and V1 contributes to the encoding of visual events that are coincident with sounds.
Data from: Contributions of local speech encoding and functional connectivity to audio-visual speech perception
Seeing a speaker's face enhances speech intelligibility in adverse environments. We investigated the underlying network mechanisms by quantifying local speech representations and directed connectivity in MEG data obtained while human participants listened to speech of varying acoustic SNR and visual context. During high acoustic SNR speech encoding by temporally entrained brain activity was strong in temporal and inferior frontal cortex, while during low SNR strong entrainment emerged in premotor and superior frontal cortex. These changes in local encoding were accompanied by changes in directed connectivity along the ventral stream and the auditory-premotor axis. Importantly, the behavioral benefit arising from seeing the speaker's face was not predicted by changes in local encoding but rather by enhanced functional connectivity between temporal and inferior frontal cortex. Our results demonstrate a role of auditory-frontal interactions in visual speech representations and suggest that functional connectivity along the ventral pathway facilitates speech comprehension in multisensory environments.
Data from: Emerging Representational Geometries in the Visual System Predict Reaction Times for Object Categorization
Recognizing an object takes just a fraction of a second, less than the blink of an eye. Applying multivariate pattern analysis, or "brain decoding", methods to magnetoencephalography (MEG) data has allowed researchers to characterize, in high temporal resolution, the emerging representation of objects that underlie our capacity for rapid recognition. Shortly after stimulus onset, exemplar stimuli cluster by category in high-dimensional activation spaces. In these emerging activation spaces, the decodability of exemplar category varies over time, reflecting the brain's transformation of visual inputs into coherent categorical representations. How do these emerging representations relate to categorization behavior? Recently it has been proposed that the distance of an exemplar representation from a categorical boundary in an activation space is critical for perceptual decision-making, and that reaction times should therefore correlate with distance from the boundary. The predictions of this distance hypothesis have been born out in human inferior temporal cortex (IT), an area of the brain crucial for the representation of object categories. The time of peak decoding is the optimal time for category information to be "read out" from the brain's time varying representation of the stimuli. In this study, we tested the distance hypothesis, and specifically whether or not the brain reads out at the optimal time for choice behavior. Using MEG decoding methods, we show that the distance of a pattern of activity from a decision boundary through a high-dimensional activation space correlates with reaction times in a visual categorization task, but only during the period of peak decodability. Our results suggest the brain uses the optimal stimulus representation for choice behavior, and that neural representations for objects are partially constitutive of the decision process in visual perception.
Data from: Opsins in Onychophora (velvet worms) suggest a single origin and subsequent diversification of visual pigments in arthropods
<p>Multiple visual pigments, prerequisites for color vision, are found in arthropods, but the evolutionary origin of their diversity remains obscure. In this study, we explore the opsin genes in five distantly related species of Onychophora, using deep transcriptome sequencing and screening approaches. Surprisingly, our data reveal the presence of only one opsin gene (<em>onychopsin</em>) in each onychophoran species, and our behavioral experiments indicate a maximum sensitivity of onychopsin to blue–green light. In our phylogenetic analyses, the onychopsins represent the sister group to the monophyletic clade of visual r-opsins of arthropods. These results concur with phylogenomic support for the sister-group status of the Onychophora and Arthropoda and provide evidence for monochromatic vision in velvet worms and in the last common ancestor of Onychophora and Arthropoda. We conclude that the diversification of visual pigments and color vision evolved in arthropods, along with the evolution of compound eyes—one of the most sophisticated visual systems known.</p>
Data from: Decoding the influence of anticipatory states on visual perception in the presence of temporal distractors
Anticipatory states help prioritise relevant perceptual targets over competing distractor stimuli and amplify early brain responses to these targets. Here we combine electroencephalography recordings in humans with multivariate stimulus decoding to address whether anticipation also increases the amount of target identity information contained in these responses, and to ask how targets are prioritised over distractors when these compete in time. We show that anticipatory cues not only boost visual target representations, but also delay the interference on these target representations caused by temporally adjacent distractor stimuli—possibly marking a protective window reserved for high-fidelity target processing. Enhanced target decoding and distractor resistance are further predicted by the attenuation of posterior 8–14 Hz alpha oscillations. These findings thus reveal multiple mechanisms by which anticipatory states help prioritise targets from temporally competing distractors, and they highlight the potential of non-invasive multivariate electrophysiology to track cognitive influences on perception in temporally crowded contexts.
Data from: Concurrent visual and motor selection during visual working memory guided action
Visual working memory enables us to hold onto past sensations in anticipation that these may become relevant for guiding future actions. Yet laboratory tasks have treated visual working memories in isolation from their prospective actions and have focused on the mechanisms of memory retention rather than utilization. To understand how visual memories become used for action, we linked individual memory items to particular actions and independently tracked the neural dynamics of visual and motor selection when memories became used for action. This revealed concurrent visual-motor selection, engaging appropriate visual and motor brain areas at the same time. Thus we show that items in visual working memory can invoke multiple, item-specific, action plans that can be accessed together with the visual representations that guide them, affording fast and precise memory-guided behavior.
Research data for "Visualization and Quantification of Geometric Diversity in Metal-Organic Frameworks"
<p>This dataset supports the paper: "Visualization and Quantification of Geometric Diversity in Metal-Organic Frameworks".</p> <p>The coarse-grained scaled and unscaled structures are provided here in CIF format. The code introduced in this work is subject to continuing development (and can be found at: https://github.com/tcnicholas/coarse-graining), therefore we include here the version of the code used for this paper alongside the Python analysis scripts.</p> <p>The original unprocessed CIFs were extracted from the Cambridge Structural Database (CSD).</p>
Figure 4 from: Klein A (2016) Data-Visual Relationships to Subject Performance and Eye Movements. Research Ideas and Outcomes 2: e8814. https://doi.org/10.3897/rio.2.e8814
Figure 4 - Relational operators - w, x, y, and z are all optional and refer to any attribute or operator
Figure 1 from: Klein A (2016) Data-Visual Relationships to Subject Performance and Eye Movements. Research Ideas and Outcomes 2: e8814. https://doi.org/10.3897/rio.2.e8814
Figure 1 - Methods pipeline (A) Each data feature (scale, dimensionality, etc., defined by the data taxonomy), has attributes (e.g., scale may be set to nominal, ordinal, or ratio). The combination of attribute settings form (B) a data structure, which in turn is amenable to certain (C) visualization methods (defined by the visual taxonomy). When a visualization method is used to perform (D) a set of tasks, (E) performance and eye movement data are recorded for each of N subjects.
Empirical study on Visual Attention Characteristics of basketball players of different levels during free-throw shooting original data.
<p>不同水平篮球运动员罚球投篮视觉注意特征实证研究的原始数据,包括注视持续时间、注视次数、瞳孔扩张和扫视幅度。</p>
Data for "Visual feedback modulates the 1/f structure of movement amplitude time series"
<p>The spreadsheet "Time-series, power-spectra, ANOVA, and figure data.xlsx" contains data relevant to the article "Visual feedback modulates the 1/<em>f</em> structure of movement amplitude time series." The file contains movement amplitude time series and the corresponding power spectra for all participants in all conditions. In addition, the file includes data submitted to ANOVA and data used to plot all article figures.</p>
SEM Data Base Microvilli Semantic Segmentation in Microscopic Images Using a Visual Learning Pipeline
<p>SEM images raw data X1, X1, Y1, Y2, Z1 and Z2</p>
Data for "Common dynamic prior for time in auditory and visual interval timing"
<p>Data for "Common dynamic prior for time in auditory and visual interval timing"</p>
ScienceDex guides
Understand access before you commit
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.