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644 results for “data visualization”
Mouse data sample for Pergola documentation - Shiny visualization
<p>Data sample of feeding and drinking behavior recorded during three weeks of C57BL6/J male mice. The data correspond to 2 groups (9 control mice and 8 high-fat diet mice). Each animal was tracked individually on Phecomp cages for 9 weeks. During the first experimental week all animals were given <em>ad libitum</em> access to a standard chow (habituation phase). After this first week, control mice continued with the same diet regime while high-fat mice were exclusively given <em>ad libitum</em> access to a high-fat chow. Data was used originally in this publication <a href="http://onlinelibrary.wiley.com/doi/10.1111/adb.12595/abstract">10.1111/adb.12595.</a> The recordings were processed using Pergola to BED and BedGraph file formats.</p> <p>The data set consist in:</p> <p>- a exp_info.txt file setting mouse membership to the control or the HF mice.</p> <p>- a files folder containing BED and BedGraph files of mouse feeding behavior.</p>
Figure 4. After merging, overview is more transparent. Tens of persons were merged together into clusters in order to clarify the visualization. Firms and persons are recognized based on their icons.-Browsing Semantic Data in Slovakia
<p>The usefulness of such visualization has its key points regarding connections. Thanks to SBR browsing module, we were able to get 22 firm records for “Váhostav” query. Between any 2 companies, connections may be (and often are) not bidirectional, so, in order to navigate through connections, we have refined all 22 records. Although, even being filtered, graph is still complex. And it is possible to further navigate and search for outgoing connections, for example firm “MERLIN TRADE, a.s.” on Fig.4 contains item on “Ján Kato”, which is already included in our graph and connected to “VÁHOSTAV&SK&DEVELOPEMENT” on bottom left side and “VÁHOSTAV&SK, a.s.” in the center. Edge coloring and drawing is helpful with overlapped edges. For methods of visualization, including coloring, we refer to studies of H. Omote and K. Sugiyama (2006), and I. Herman, G. Melanon, and M. S. Marshall (2000) or our study on graph clutter filtering and connectivity distance (Mojzis & Laclavik, 2014).</p>
BRAIN Journal-Personality Questionnaires as a Basis for Improvement of University Courses in Applied Computer Science and Informatics-Figure 4. Radial visualization of scores across the EPQ–R scales for clustered data about all the participants
<p>On the other hand, the division of data points by gender might not be the only useful strategy when visually inspecting the analyzed sample in a coordinate system. Numerous clustering algorithms may be used to determine which data points share similar scores across the EPQ–R scales, i.e., which data points belong to the same cluster of similar entities based on their corresponding EPQ–R scores. A radial visualization in which data points were organized into three clusters is given in Figure 4. Each cluster is marked by a different color: cluster 1 by red, cluster 2 by green, and cluster 3 by blue. </p>
Prototyping 3D Virtual Learning Environments with X3D-based Content and Visualization Tools-Figure 11. Online accessible repository of digital data on cultural heritage with X3D models (STARC Web Repository, 2017, © Copyright 2017, STARC, Cyprus Institute. Used with permission)
<p>Prototyping can also include the development of toolkits for automatic content generation simulator, but in the case of an architectural environment, the components are too complex to be automatically generated. Furniture elements or the learning artifacts (i.e. content created by learners) can be converted to be viewed in X3D compatible browsers or included in online galleries (Figure 11). After functional and 3D content prototyping, certain components of the virtual campus can be easily modified and adapted as needed.</p>
Pergola: boosting visualization and analysis of longitudinal data by unlocking genomic analysis tools - Mouse feeding behavior dataset
<p>Dataset contains feeding and drinking behavioral recordings of C57BL6/J male mice. Mice were distributed into 2 groups (9 control mice and 8 high-fat diet mice) and tracked individually on Phecomp cages for 9 weeks. During the first experimental week all animals were given <em>ad libitum</em> access to a standard chow (habituation phase). After this first week, control mice continued with the same diet regime while high-fat mice were exclusively given <em>ad libitum</em> access to a high-fat chow. Data was used originally in this publication <a href="http://onlinelibrary.wiley.com/doi/10.1111/adb.12595/abstract">10.1111/adb.12595.</a></p> <p>The data set consist in:</p> <p>- a "mouse_recordings" folder containing a CSV file containing mouse recordings and the files.</p> <p>- a "mappings" folder containing all the mappings used by the pergola in the pipeline to convert data.</p> <p>- a "phases" folder containing a CSV file containing experimental phases.</p> <p>- a "chromHMM_files" folder containing a cellmarkfiletable table used by chromHMM to learn a HMM model</p>
Pergola: boosting visualization and analysis of longitudinal data by unlocking genomic analysis tools - D. melanogaster behavior dataset obtained with JAABA
<p>Dataset contains <em>Drosophila melanogaster </em>behavioral annotations of chasing. The dataset is formed by 20 GAL4 line flies group from a TrpA activation screen with increased propensity to chasing and by 19 pBDPGAL4U line (control) flies group. Ctrax motor trajectories derived from the original video-recordings (1000 seconds) were downloaded from this <a href="https://sourceforge.net/projects/jaaba/files/Sample%20Data/sampledata_v0.1.zip/download">link</a> and used to obtain the chasing behavioral annotations using <a href="http://jaaba.sourceforge.net/">JAABA</a> <a href="https://www.nature.com/articles/nmeth.2281">10.1038/nmeth.2281</a>: </p> <p>The data set consist in:</p> <p>- a "mappings" folder containing all the mappings used by the pergola in the pipeline to convert data.</p> <p>- a "perframe_TrpA" folder containing mat files with three Ctrax derived variables (dnose2ell, dtheta and velmag) from the motor trajectories of the GAL4 line.</p> <p>- a "perframe_pBDPGAL4" folder containing mat files with three Ctrax derived variables (dnose2ell, dtheta and velmag) from the motor trajectories of the control line.</p> <p>- a "scores" folder including the JAABA chasing annotations in two mat file one for each fly line.</p>
Data and Code for "A lasting impact of serotonergic psychedelics on visual processing and behavior"
<p>Processed data and analysis code for "<span><span>A lasting impact of serotonergic psychedelics on visual processing and behavior". <span>https://doi.org/10.1101/2024.07.03.601959 </span></span></span></p>
Source code and experimental data of human brain tissue (visual cortex, corona radiata) for poro-viscoelastic parameter identification
<p>Computer code and experimental data that we used for our inverse parameter identification of poro-viscoelastic material parameters for two different brain regions: visual cortex (gray matter) and corona radiata (white matter). The experimental data comprises large-strain cyclic loading and compression/tension relaxation. For details see the corresponding publication: "Model-driven exploration of poro-viscoelasticity in human brain tissue: Be careful with the parameters!".</p> <p>Further explanation regarding the specimen preparation, experimental setup, as well as the assignment of regions and governing regions can be found in Hinrichsen, J., Reiter, N., Bräuer, L. et al. Inverse identification of region-specific hyperelastic material parameters for human brain tissue. Biomech Model Mechanobiol (2023). <a href="https://doi.org/10.1007/s10237-023-01739-w" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s10237-023-01739-w</a>.</p> <p>The file "<span>nonlinear-poro-viscoelasticity.cc</span>" contains our C++ Finite Element code based on the open source library deal.II. It is accompanied by an exemplary parameter file.</p> <p><strong>Funding:</strong> The support from the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) through the grants BU 3728/1-1, BU 3728/3-1, STE 544/70-1 as well as through project number 460333672 CRC1540 Exploring Brain Mechanics is gratefully acknowledged.</p>
Multi-segment phase coupling to oscillatory visual drive, Gait & Posture (2021): Data
<p>Data set accompanying the publication:</p> <p>Engel, D., Schwenk, J., Schütz, A., Morris, A. P., & Bremmer, F. (2021). Multi-segment phase coupling to oscillatory visual drive. <em>Gait & posture</em>, <em>86</em>, 132–138.</p> <p><a href="https://doi.org/10.1016/j.gaitpost.2021.03.010">https://doi.org/10.1016/j.gaitpost.2021.03.010</a></p>
Datastes for NeuroDAVIS: A neural network model for data visualization
<p>These are the datasets used in the work NeuroDAVIS: A neural network model for data visualization.</p>
Data for: Outcomes of multifarious selection on the evolution of visual signals
<p><span>Multifarious sources of selection shape visual signals and can produce phenotypic divergence. Theory predicts variance in warning signals should be minimal due to purifying selection, yet polymorphism is abundant. While in some instances divergent signals can evolve into discrete morphs, continuously variable phenotypes are also encountered in natural populations. Notwithstanding, we currently have an incomplete understanding of how combinations of selection shape fitness landscapes, particularly those which produce polymorphism. We modeled how combinations of natural and sexual selection act on aposematic traits within a single population to gain insights into what combinations of selection favor the evolution and maintenance of phenotypic variation. With a rich foundation of studies on selection and phenotypic divergence, we reference the poison frog genus <em>Oophaga</em> to model signal evolution. </span><span>Multifarious selection on aposematic traits created the topology of our model's fitness landscape by approximating different scenarios found in natural populations. </span><span>Combined, the model produced all types of phenotypic variation found in frog populations, namely monomorphism, continuous variation, and discrete polymorphism. </span><span>Our results afford advances into how multifarious selection shapes phenotypic divergence, which, along with additional modelling enhancements, will allow us to further our understanding of visual signal evolution.</span></p>
Supplementary data for: Selection on visual opsin genes in diurnal Neotropical frogs and loss of the SWS2 opsin in poison frogs
<p><span></span></p> <p><span></span></p> <p>Amphibians are ideal for studying visual system evolution because their biphasic (aquatic and terrestrial) life history and ecological diversity expose them to a broad range of visual conditions. Here we evaluate signatures of selection on visual opsin genes across Neotropical anurans and focus on three diurnal clades that are well-known for the concurrence of conspicuous colors and chemical defense (i.e., aposematism): poison frogs (Dendrobatidae), Harlequin toads (Bufonidae: <em>Atelopus</em>), and pumpkin toadlets (Brachycephalidae: <em>Brachycephalus</em>). We found evidence of positive selection on 44 amino acid sites in LWS, SWS1, SWS2, and RH1 opsin genes, of which one in LWS and two in RH1 have been previously identified as spectral tuning sites in other vertebrates. Given that anurans have mostly nocturnal habits, the patterns of selection revealed new sites that might be important in spectral tuning for frogs, potentially for adaptation to diurnal habits and for color-based intraspecific communication. Furthermore, we provide evidence that SWS2, normally expressed in rod cells in frogs and some salamanders, has likely been lost in the ancestor of Dendrobatidae, suggesting that under low-light levels, dendrobatids have inferior wavelength discrimination compared to other frogs. This loss might follow the origin of diurnal activity in dendrobatids and could have implications for their chemical ecology, biodiversity, and behavior. Our analyses show that assessments of opsin diversification in understudied groups could expand our understanding of the role of sensory system evolution in ecological adaptation.</p>
Data from: Speakers of different languages remember visual scenes differently
<p>Language can have a powerful effect on how people experience events. Here, we examine how the languages people speak guide attention and influence what they remember from a visual scene. When hearing a word, listeners activate other similar-sounding words before settling on the correct target. We tested whether this linguistic co-activation during a visual search task changes memory for objects. Bilinguals and monolinguals remembered English competitor words that overlapped phonologically with a spoken English target better than control objects without name overlap. High Spanish proficiency also enhanced memory for Spanish competitors that overlapped across languages. We conclude that linguistic diversity partly accounts for differences in higher cognitive functions like memory, with multilinguals providing a fertile ground for studying the interaction between language and cognition.</p>
Data for "Reversal learning of visual cues in Heliconiini butterflies"
<p>Here I provide the data and R code used in the analyses included in the paper "Reversal learning of visual cues in Heliconiini butterflies".</p>
Data for AJ paper: Mass ratio of single-line spectroscopic binaries with visual orbits using Bayesian inference and suitable priors
<p>Data and plots for paper "Mass ratio of single-line spectroscopic binaries with visual orbits using Bayesian inference and suitable priors" accepted for publication in The Astronomical Journal.</p>
Data for: Probing visual sensitivity and attention in mice using reverse correlation
<p>Visual attention allows the brain to evoke behaviors based on the most important visual features. Mouse models offer immense potential to gain a circuit-level understanding of this phenomenon, yet, how mice distribute attention across features and locations is not well understood. Here, we describe a new approach to address this limitation, by training mice to detect weak vertical bars in a background of checkerboard noise while spatial cues manipulated their attention. By adapting a reverse correlation method from human studies, we linked behavioral decisions to stimulus features and locations. We show that mice voluntarily deploy attention to a small rostral region of the visual field. Within this region, mice attended to multiple features (orientation, spatial frequency, contrast) that indicated the presence of weak vertical bars. This attentional tuning grew with training, multiplicatively scaled behavioral sensitivity, approached that of an ideal observer, and resembled the effects of attention in humans. Taken together, we demonstrate that mice can simultaneously attend to multiple features and locations of a visual stimulus.</p>
Data from: Prospection of potential actions during visual working memory starts early, is flexible, and predicts behavior.
<p>Raw data (EEG and behavior) reported in the manuscript "Prospection of potential actions in visual working memory starts early, is flexible, and predicts behavior" by Rose Nasrawi, Sage E.P. Boettcher, and Freek van Ede</p>
Data for: Assessing the Cognition of Movement Trajectory Visualizations: Interpreting Speed and Direction
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Data from: Repeated evolution of reduced visual investment at the onset of ecological speciation in high-altitude <em>Heliconius</em> butterflies
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Data for: Coordination and persistence of aggressive visual communication in Siamese fighting fish
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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.