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Dataset results
65 results for “information visualization”
Data from: Zebrafish retinal ganglion cells asymmetrically encode spectral and temporal information across visual space
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Stiffness reprogrammable magnetorheological metamaterials inspired by the spine for multi-bit visual mechanical information processing
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Logical inferences from visual and auditory information in ruffed lemurs and sifakas
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Data from: Influence of visual information on sniffing behavior in a routinely trichromatic primate
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When text simplification is not enough: Could a graph-based visualization facilitate consumers’ comprehension of dietary supplement information?
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Supporting information for: Discrimination ability of central visual field testing using stimulus size I, II, and III and relationship with macular ganglion cell thickness in chiasmal compression
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Data from: Analysis and visualization of H7 influenza using genomic, evolutionary and geographic information in a modular web service
We have reported previously on use of a web-based application, Supramap (http://supramap.org) for the study of biogeographic, genotypic, and phenotypic evolution. Using Supramap we have developed maps of the spread of drug-resistant influenza and host shifts in H1N1 and H5N1 influenza and coronaviruses such as SARS. Here we report on another zoonotic pathogen, H7 influenza, and provide an update on the implementation of Supramap as a web service. We find that the emergence of pathogenic strains of H7 is labile with many transitions from high to low pathogenicity, and from low to high pathogenicity. We use Supramap to put these events in a temporal and geospatial context. We identify several lineages of H7 influenza with biomarkers of high pathogenicity in regions that have not been reported in the scientific literature. The original implementation of Supramap was built with tightly coupled client and server software. Now we have decoupled the components to provide a modular web service for POY (http://poyws.org) that can be consumed by a data provider to create a novel application. To demonstrate the web service, we have produced an application, Geogenes (http://geogenes.org). Unlike in Supramap, in which the user is required to create and upload data files, in Geogenes the user works from a graphical interface to query an underlying dataset. Geogenes demonstrates how the web service can provide underlying processing for any sequence and metadata database.
Data from: How lovebirds maneuver through lateral gusts with minimal visual information
Flying birds maneuver effectively through lateral gusts, even when gust speeds are as high as flight speeds. What information birds use to sense gusts and how they compensate is largely unknown. We found that lovebirds can maneuver through 45-degree lateral gusts similarly well in forest, lake, and cave-like visual environments. Despite being diurnal and raised in captivity, the birds fly to their goal perch with only a dim point light source as a beacon, showing that they do not need optic flow or a visual horizon to maneuver. To accomplish this feat, lovebirds primarily yaw their bodies into the gust while fixating their head on the goal using neck angles of up to 30 degrees. Our corroborated model for proportional yaw reorientation and speed control shows how lovebirds can compensate for lateral gusts informed by muscle proprioceptive cues from neck twist. The neck muscles not only stabilize the lovebirds' visual and inertial head orientations by compensating low-frequency body maneuvers, but also attenuate faster 3D wingbeat-induced perturbations. This head stabilization enables the vestibular system to sense the direction of gravity. Apparently, the visual horizon can be replaced by a gravitational horizon to inform the observed horizontal gust compensation maneuvers in the dark. Our scaling analysis shows how this minimal sensorimotor solution scales favorably for bigger birds, offering local wind angle feedback within a wingbeat. The way lovebirds glean wind orientation may thus inform minimal control algorithms that enable aerial robots to maneuver in similar windy and dark environments.
Information-Theoretic Distraction-Free Representation Learning for Visual Offline RL
<p>Cheetah and Walker dataset with Clean, Single Video, Multiple Videos, and 2x2 Grid distractions used as one of the tasks in the paper.</p> <p>We also provide the pretrained encoder for the given dataset.</p> <p>Since the dataset is quite large, we will release the code to generate the dataset. However, we are still in the process of cleaning the code.</p>
Figure 10 in Visual Comparison for Information Visualization
Figure 10: Topographic BGPlay [29] uses explicit encoding and superposition to visualize ISP prefix data. Relationships between the prefixes are shown as a network, while an overlying topographic map encodes region information for the ISPs. Used with permission.
Figure 6 in Visual Comparison for Information Visualization
Figure 6: Multi-image view of the voltage dataset series of Malik et al. [92]. This design addresses the problem of multi-way superposition by breaking the space into hexagonal regions. Each region depicts data from the different series, as indicated by the key in the lower left. Used with permission.
Figure 7 in Visual Comparison for Information Visualization
Figure 7: Mauve [31] uses an additive explicit encoding design to compare conservation trends over a set of aligned genomic sequences. Subsequences (contigs) not conserved by the reference are removed, while matching contigs are explicitly linked. If these links are removed, the conservation patterns are no longer visible. Used with permission.
Figure 5 in Visual Comparison for Information Visualization
Figure 5: Jianu et al. [75] present an example of superimposition. In (a), protein interactions of a specific condition are superimposed over the canonical model. In (b), the data is superimposed over a user-constructed model. By showing data in the same space, the related parts are related visually. Used with permission.
Figure 9 in Visual Comparison for Information Visualization
Figure 9: Vdiff [11] demonstrates explicit encoding combined with juxtaposition. Files are shown juxtaposed to provide the viewer with context, and specific relationships are shown through the use of lines to highlight insertion, similarity and emission. Used with permission.
Figure 4 in Visual Comparison for Information Visualization
Figure 4: Sequence Surveyor [2, 3] uses juxtaposition to compare aligned genomic sequences. Each row represents the sequence of genes of an organism. Homologs (groups of matching genes) are assigned the same color. Colors are assigned based on the position of genes in the reference genome (indicated by the green rectangle). In such a juxtaposition design, each object (here a row representing a genome) is displayed independently: the viewer must make the connections between objects. In contrast, Figure 7 shows similar data in a design where the connections are explicitly encoded.
Figure 8 in Visual Comparison for Information Visualization
Figure 8: 2.5D proteomic network comparison [15] uses extra dimensions to simultaneously use juxtaposition and superposition for network comparison. Manipulating the viewing perspective changes how much each comparison technique is used. Used with permission.
Collective detection based on visual information in animal groups
<p></p><p> We investigate key principles underlying individual, and collective, visual detection of stimuli, and how this relates to the internal structure of groups. While the individual and collective detection principles are generally applicable, we employ a model experimental system of schooling golden shiner fish ( Notemigonus crysoleucas ) to relate theory directly to empirical data, using computational reconstruction of the visual fields of all individuals. This reveals how the external visual information available to each group member depends on the number of individuals in the group, the position within the group, and the location of the external visually detectable stimulus. We find that in small groups, individuals have detection capability in nearly all directions, while in large groups, occlusion by neighbours causes detection capability to vary with position within the group. To understand the principles that drive detection in groups, we formulate a simple, and generally applicable, model that captures how visual detection properties emerge due to geometric scaling of the space occupied by the group and occlusion caused by neighbours. We employ these insights to discuss principles that extend beyond our specific system, such as how collective detection depends on individual body shape, and the size and structure of the group. </p><p></p>
VeloViz: RNA-velocity informed embeddings for visualizing cellular trajectories
<p>Single cell transcriptomic technologies enable genome-wide gene expression measurements in individual cells but can only provide a static snapshot of cell states. RNA velocity analysis can infer cell state changes from single cell transcriptomics data. To interpret these cell state changes as part of underlying cellular trajectories, current approaches rely on visualization with principal components, t-distributed stochastic neighbor embedding, and other 2D embeddings derived from the observed single cell transcriptional states. However, these 2D embeddings can yield different representations of the underlying cellular trajectories, hindering the interpretation of cell state changes. We developed VeloViz to create RNA-velocity-informed 2D and 3D embeddings from single cell transcriptomics data. Using both real and simulated data, we demonstrate that VeloViz embeddings are able to consistently capture underlying cellular trajectories across diverse trajectory topologies, even when intermediate cell states may be missing. By taking into consideration the predicted future transcriptional states from RNA velocity analysis, VeloViz can help visualize a more reliable representation of underlying cellular trajectories. Source code is available on GitHub (https://github.com/JEFworks-Lab/veloviz) and Bio- conductor (https://bioconductor.org/packages/veloviz) with additional tutorials at https://JEF.works/veloviz/.</p> <p> </p> <p>Here, we have included the data used in the package vignettes: <br> <br> 1) Pancreas endocrinogenesis data was obtained from Bergen et. al. Nature Biotechnology 2020 and Bastidas-Ponce et. al. Development 2019 via the scVelo package. </p> <p>- pancreas.rda contains spliced and unspliced count matrices, list of cluster IDs, the first 50 principal components, cell-cell distances used in RNA velocity calculation, and the velocity object resulting from calculating velocity using velocyto.R</p> <p>- pancreasWithGap.rda includes the same data as in pancreas rda but with a subset of intermediate cells () removed to simulate data with missing intermediates. </p> <p>2) MERFISH data was obtained from Xia et. al. PNAS 2019</p> <p>- MERFISH.rda contains nuclear and cytoplasmic counts, colors used for plotting based on Louvain clustering, the first 50 principal components, cell-cell distances used in RNA velocity calculation, and the velocity object resulting from calculating velocity using velocyto.R</p>
Supplementary Information: Tracking an Occluded Visual Target with Sequences of Saccades
<p>Supplementary movie for the article "Tracking an Occluded Visual Target with Sequences of Saccades", showing the design and gaze behaviour in visual tracking with intermittent occlusions. Raw gaze data also provided.</p>
Community Salt Testing and Relation of Iodine Intake to Visual Information Processing of Ethiopian Infants
ClinicalTrials.gov study NCT03889431. IPD Sharing: NO. Countries: 1. Publications: 20.
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.