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644 results for βdata visualizationβ
Supplemental data for: Visualization of rank-citation curves for fast detection of possible manipulations with the h-index of the university
<p>This dataset consists of papers of universities in the top 30 Scopus Ranking of Ukrainian Universities (May 2023). The data was obtained from Scopus using the search query "AF-ID (“university name”) AND PUBYEAR < 2023 AND PUBYEAR > 2002". Rank-citation curves were also generated for the publications of each university. In this analysis, the rank of publications was plotted along the horizontal axis, while the corresponding citation counts were depicted on the left axis. All types of documents were included in the dataset.</p>
Data for GECCO2023 Paper "Pareto Local Optimal Solutions Networks with Compression, Enhanced Visualization and Expressiveness"
<p><strong>Data for Paper "Pareto Local Optimal Solutions Networks with Compression, Enhanced Visualization and Expressiveness"</strong></p> <ul> <li><strong>instances.tar.xz</strong> contains πmnk-landscape instances</li> <li><strong>metrics.csv</strong> contains the (C)PLOS-net metric-values</li> <li><strong>performance.csv</strong> contains the performance of the different algorithms on each instance</li> <li><strong>merged.csv</strong> contains the merged data from the 2 csv files above</li> </ul> <p><strong>Reference</strong></p> <p>Arnaud Liefooghe, Gabriela Ochoa, Sébastien Verel, and Bilel Derbel. 2023. <strong>Pareto Local Optimal Solutions Networks with Compression, Enhanced Visualization and Expressiveness</strong>. In Genetic and Evolutionary Computation Conference (GECCO ’23), July 15–19, 2023, Lisbon, Portugal. ACM, New York, NY, USA, 9 pages. <a href="https://doi.org/10.1145/3583131.3590474">https://doi.org/10.1145/3583131.3590474</a></p> <p><strong>Abstract</strong></p> <p>The structure of local optima in multi-objective combinatorial optimization and their impact on algorithm performance are not yet properly understood. In this paper, we are interested in the representation of multi-objective landscapes and their multi-modality. More specifically, we revise and extend the network of Pareto local optimal solutions (PLOS-net), inspired by the well-established local optima network from single-objective optimization. We first define a compressed PLOS-net which allows us to enhance its perception while preserving the important notion of connectedness between local optima. We then study an alternative visualization of the (compressed) PLOS-net that focuses on good-quality solutions, improves the distinction between connected components in the network, and generalizes well to landscapes with more than 2 objectives. We finally define a number of network metrics that characterize the PLOS-net, some of them being strongly correlated with search performance. We visualize and experiment with small-size multi-objective nk-landscapes, and we disclose the effect of PLOS-net metrics against well-established multi-objective local search and evolutionary algorithms.</p>
Data from: Visual feedback influences the consistency of the locomotor pattern in Asian Elephants (Elephas maximus)
<p><span>Elephants are atypical of most quadrupeds in that they maintain the same lateral sequence footfall pattern across all locomotor speeds. It has been speculated that the preservation of the footfall patterns is necessary to maintain a statically stable support polygon. This should be a particularly important constraint in large, relatively slow animals. This suggests that elephants must rely on available sensory feedback mechanisms to actively control their massive pillar-like limbs for proper foot placement and sequencing. How the nervous system of elephants integrates the available sensory information for a stable gait is unknown. Here we explored the role that visual feedback plays in the control of the locomotor pattern in elephants. Four Asian elephants (</span><span><em>Elephas maximus</em>)</span><span> walked with and without a blindfold as we measured their stride time intervals. Coefficient of variation was used to assess changes in the overall variability of the stride time intervals, while approximate entropy was used to measure the stride-to-stride consistency of the time intervals. We show that visual feedback plays a role in the stride-to-stride consistency of the locomotor pattern in elephants. These results demonstrate that elephants likely use visual feedback to correct and maintain proper sequencing of the limbs during locomotion. </span></p>
EFFECT OF VISUAL STIMULI ON THE JUMPING ABILITY OF AMATEUR SOCCER PLAYERS DATA
<p>Data from the study "EFFECT OF VISUAL STIMULI ON THE JUMPING ABILITY OF AMATEUR SOCCER PLAYERS"</p>
Data for: Visualization of the hidden food sources of bass (Micropterus salmoides), crucian carp (Carassius carassius), and minnow (Zacco platypus) using 18S rRNA V9 primers on urban Singal Reservoir in Korea
<p>Fish are the most important consumers in aquatic ecosystems, and the analysis of fish prey is very important for understanding their short-term feeding characteristics and the connectivity of food webs. In this study, DNA metabarcoding was used to identify the prey of the native fish species, <em>Zacco platypus</em> (the pale chub) and <em>Carassius carassius</em>, and an introduced species, <em>Micropterus salmoides</em>, in domestic lentic ecosystems. Prey community composition, selectivity index, prey diversity, and trophic level analyses were performed. The prey composition ratio analysis showed that in August 2020, 85.8% of <em>M. salmoides</em>' prey was fish from the orders Cypriniformes and Perciformes, and in July 2021, 100% of <em>M. salmoides</em>' prey was zooplankton from the orders Anomopoda and Calanoida. Zooplankton was the main prey of <em>Z. platypus</em> collected in August 2020 (69.5%) and <em>C. carassius</em> collected in July 2021 (88.9%). The selectivity index analysis showed that the preferred prey of M. salmoides was fish from the order Cypriniformes, the preferred prey of <em>Z. platypus</em> was phytoplankton from the division Bacillariophyta, and the preferred prey of <em>C. carassius</em> was zooplankton from the order Cladocera. For the prey width analysis, <em>C. carassius</em> had the highest BI index value (0.8), followed by <em>Z. platypus</em> (0.29), while <em>M. salmoides</em> had BI index values of 0.09, 0.19, and 0.001, indicating low prey width. These results provide fundamental data on the utility of DNA metabarcoding for dietary studies of major fish species in lentic ecosystems and for analyzing food web connectivity based on major prey items.</p>
Data and data analysis for "Human Preferences for the Visual Appearance of Desks: Examining the Role of Wooden Materials and Desk Designs"
<p>Data and data analysis for the article "Human Preferences for the Visual Appearance of Desks: Examining the Role of Wooden Materials and Desk Designs".</p>
Supporting data for the EIAS 2023 Image Data Visualization workshop
<p>A collection of scientific images used for demonstration purposes in the Image Data Visualization workshop given in the EPFL EIAS 2023 summer school by the EPFL Center for Imaging.</p> <p>The images are automatically downloaded and used in the code repository <a href="https://gitlab.epfl.ch/center-for-imaging/eias-2023-visualization-workshop">Image Data Visualization with Python and Napari</a> on GitLab.</p> <p>The original provenance of the images is summarized below.</p> <table> <tbody> <tr> <td>cell_tracking_2d.tif</td> <td><a href="http://celltrackingchallenge.net/3d-datasets/">Cell Tracking Challenge</a></td> </tr> <tr> <td>deepslide.png</td> <td><a href="https://zenodo.org/record/1184621">DeepSlides dataset</a></td> </tr> <tr> <td>drosophila_trachea.tif</td> <td>Provided by the <a href="https://www.epfl.ch/labs/lemaitrelab/">Lemaitre lab</a> in EPFL.</td> </tr> <tr> <td>lungs_ct.tif</td> <td>Provided by <a href="https://www.epfl.ch/labs/depalma-lab/">Prof. De Palma's lab</a> in EPFL.</td> </tr> <tr> <td>snow_3d.tif</td> <td>Example data from the Python <a href="https://ttk.gricad-pages.univ-grenoble-alpes.fr/spam/index.html">spam</a> package.</td> </tr> <tr> <td>crystallites.tif</td> <td>Provided by the <a href="https://www.epfl.ch/labs/las/">LAS</a> lab in EPFL</td> </tr> </tbody> </table>
Data from: Electroencephalography Responses to Simplified Visual Signals Reveal Explain Differences in Speech-in-Noise Comprehension
<p>Contents and Folder Structure:</p> <p><strong>EEG Experiment</strong></p> <ul> <li><strong>EEG_stimuli</strong>: these are the videos that were presented to participants in the EEG experiment, and the code that generates them from the original corpus (link)</li> <li><strong>data_2020>split_trials</strong>: contains the raw EEG data starting 1.995s before each trial and ending 1.995s after each trial with naming convention subXx_VV_YY_N.fif where X or Xx is the subject number, YY is the modality condition (AV for audiovisual and V0 for video only), N is the trial number (between 0 and 4 inclusive), and VV is the video condition (1e for the envelope dot, 1m is the mismatched dot, 4v is the cartoon, bw is the edge detection and nh is the natural condition). <ul> <li><strong>unprocessed>raw</strong>: contains the unprocessed raw EEG data <ul> <li><strong>processed>Fs-200>BP-1-80-ASR-INTP-AVR</strong>: contains the pre-processed raw EEG data: the output of <em>run_preprocessing.m</em></li> <li><strong>processed>Fs-200>BP-1-80-ASR-INTP-AVR-ICr</strong>: contains the pre-processed raw EEG data after ICA cleaning: the output of <em>run_reject_ICs.m</em></li> <li><strong>stim>stim_dwnspl</strong>: contains the aligned 200Hz envelopes of the presented speech used as features for the time-lagged models</li> </ul> </li> </ul> </li> <li><strong>EEG_analysis_code </strong>[note: please extract the contents of this folder to match paths] <ul> <li><strong>2_ICA_filt:</strong> this folder contains the MATLAB code that performs the pre-processing of the EEG data, including filtering, downsampling, ICA cleaning etc. The main functions are: <ul> <li><em>run_preprocessing.m: downsampling, filtering, ASR cleaning</em></li> <li><em>run_reject_ICs.m: ICLabel ICA cleaning</em></li> </ul> </li> <li><strong>3_analysis:</strong> this is the Python code that performs the TRF and backward modelling on the EEG data. The main functions are: <ul> <li><em>multisensory_bw.py</em>: backwards model</li> <li><em>multisensory_fw.py</em>: forwards model</li> </ul> </li> </ul> </li> </ul> <p><strong>Behavioural Experiment</strong></p> <ul> <li><strong>behavioural</strong> <ul> <li><strong>0_dataset</strong>: these are the videos that were presented to participants in the behavioural experiment, and the code that generates them from the original corpus (AV GRID corpus)</li> <li><strong>3_analysis</strong>: behavioural data analysis script <ul> <li>main function: <em>data_grid_v3.py</em></li> </ul> </li> </ul> </li> <li><strong>behavioural_data>data_grid</strong>: behavioural results </li> </ul>
Data for A Far-Red Fluorescent Probe to Visualize Staphylococcus aureus in Patient Samples
<p>Raw and processed data supporting the manuscript "A Far-Red Fluorescent Probe to Visualize <em>Staphylococcus aureus</em> in Patient Samples"</p>
Calcium imaging data from: Functional organization of visual responses in the octopus optic lobe
<p>Cephalopods are highly visual animals with camera-type eyes, large brains, and a rich repertoire of visually guided behaviors. However, the cephalopod brain evolved independently from that of other highly visual species, such as vertebrates, and therefore the neural circuits that process sensory information are profoundly different. It is largely unknown how their powerful but unique visual system functions, since there have been no direct neural measurements of visual responses in the cephalopod brain. In this study, we used two-photon calcium imaging to record visually evoked responses in the primary visual processing center of the octopus central brain, the optic lobe, to determine how basic features of the visual scene are represented and organized. We found spatially localized receptive fields for light (ON) and dark (OFF) stimuli, which were retinotopically organized across the optic lobe, demonstrating a hallmark of visual system organization shared across many species. Examination of these responses revealed transformations of the visual representation across the layers of the optic lobe, including the emergence of the OFF pathway and increased size selectivity. We also identified asymmetries in the spatial processing of ON and OFF stimuli, which suggest unique circuit mechanisms for form processing that may have evolved to suit the specific demands of processing an underwater visual scene. This study provides insight into the neural processing and functional organization of the octopus visual system, highlighting both shared and unique aspects, and lays a foundation for future studies of the neural circuits that mediate visual processing and behavior in cephalopods.</p>
Data from: Vomeronasal organ volume increases with body size and is dissociated with loss of a visual signal in Sceloporus lizards
<p>Many organisms communicate using signals in different sensory modalities (multicomponent or multimodal). When one signal or component is lost over evolutionary time, it may be indicative of changes in other characteristics of the signaling system, including the sensory organs used to perceive and process signals. <em>Sceloporus</em> lizards predominantly use chemical and visual signals to communicate, yet some species have lost the ancestral ventral color patch used in male-male agonistic interactions and exhibit increased chemosensory behavior. Here, we asked whether evolutionary loss of this sexual signal is associated with larger vomeronasal organ (VNO) volumes (an organ that detects chemical scents) compared to species that have retained the color patch. We measured VNO coronal section areas of 7β8 adult males from each of 11Β <em>Sceloporus</em> species (4 that lost and 7 that retained the color patch), estimated sensory and total epithelium volume, and compared volumes using phylogenetic ANCOVA, controlling for body size. Contrary to expectations, we found that species retaining the ventral patch had similar relative VNO volumes as did species that have lost the ancestral patch, and that body size explains VNO epithelium volume. Visual signal loss may be sufficiently compensated for by increased chemosensory behavior, and the allometric pattern may indicate sensory system trade-offs for large-bodied species.</p>
Neural and behavioral data from: A dynamic sequence of visual processing initiated by gaze shifts
<p>Animals move their head and eyes as they explore and sample the visual scene. Previous studies have demonstrated neural correlates of head and eye movements in rodent primary visual cortex (V1), but the sources and computational roles of these signals are unclear. We addressed this by combining measurement of head and eye movements with high density neural recordings in freely moving mice. V1 neurons responded primarily to gaze shifts, where head movements are accompanied by saccadic eye movements, rather than to head movements where compensatory eye movements stabilize gaze. A variety of activity patterns immediately followed gaze shifts, including units with positive, biphasic, or negative responses, and together these responses formed a temporal sequence following the gaze shift. These responses were greatly diminished in the dark for the vast majority of units, replaced by a uniform suppression of activity, and were similar to those evoked by sequentially flashed stimuli in head-fixed conditions, suggesting that gaze shift transients represent the temporal response to the rapid onset of new visual input. Notably, neurons responded in a sequence that matches their spatial frequency preference, from low to high spatial frequency tuning, consistent with coarse-to-fine processing of the visual scene following each gaze shift. Recordings in foveal V1 of freely gazing head-fixed marmosets revealed a similar sequence of temporal response following a saccade, as well as the progression of spatial frequency tuning. Together, our results demonstrate that active vision in both mice and marmosets consists of a dynamic temporal sequence of neural activity associated with visual sampling.</p>
Processed data for: Co-registration of heading to visual cues in retrosplenial cortex
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Data for: A morphological basis for path-dependent evolution of visual systems
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Data from: Ontogenetic adaptations in the visual systems of deep-sea crustaceans
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Climate Hazards Data Integration and Visualization for the Climate Adaptations Solutions Accelerator through School-Community Hubs
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Data from: Encoding of cerebellar dentate neuron activity during visual attention in rhesus macaques
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Data from: Building variation in visual displays through discrete modifications of motion
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Data from: Effects of arousal and movement on secondary somatosensory and visual thalamus
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Data from: Fine-grained neural coding of bodies and body parts in human visual cortex
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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.