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474 results for “fidelity”
Ultraliser: a framework for creating multiscale, high-fidelity and geometrically realistic 3D models for in silico neuroscience
<p><strong>Supplementary Data</strong> </p> <ol> <li><strong>Supplementary Data 1</strong> contains the input (non-watertight) surface meshes of the block (shown in Figure 2a) reconstructed within the context of the EPFL-KAUST collaboration, and the corresponding output (watertight) meshes generated by Ultraliser.</li> <li><strong>Supplementary Data 2 </strong>contains a set of 20 non-watertight meshes that were randomly selected from the block shown in <strong>Supplementary Figure S54</strong> and another set of the their watertight counterparts.</li> <li><strong>Supplementary Data 3</strong> contains a set of 25 neuronal morphologies with different morphological types and their corresponding watertight meshes.</li> <li><strong>Supplementary Data 4</strong> contains a set of 25 synthetic astroglial morphologies 15 and their corresponding watertight meshes.</li> <li><strong>Supplementary Data 5</strong> contains the vascular morphology (shown in <strong>Supplementary Fig. S83</strong>) and a corresponding multi-partitioned watertight mesh.</li> <li><strong>Supplementary Data 6</strong> contains the datasets used for the comparative analysis shown in <strong>Supplementary Section 13</strong>.<br> <br> Neuronal, astrocytic and vascular morphologies are stored in SWC, H5 and VMV file formats respectively. The file structures of the SWC and VMV formats are publicly available online. The H5 files of the complete astrocyte cells can be made available from corresponding authors upon request. All the surface meshes are stored in Wavefront OBJ files. Additional STL meshes are generated to be used for TetGen to create corresponding tetrahedral meshes. All the input and generated data files are publicly available on Zenodo (10.5281/zenodo.7105941).</li> </ol> <p><strong>Data Sources</strong> </p> <ol> <li>Cellular and subcellular NGV meshes segmented from the volume shown in Figure 2 are provided by the collaborating co-authors affiliated with KAUST.</li> <li>Neuronal meshes shown in Figure 3, Supplementary Figures S55 - S75 and Supplementary Figures S85 are publicly available from the MICrONS program.</li> <li>Neuronal morphologies shown in Figure 4, Supplementary Figures S80 - S81 and Supplementary Figure S86 are publicly available from NeuroMorpho.Org.</li> <li>Astrocytic morphologies (Figure 5 and Supplementary Figure S82) are provided by Eleftherios Zisis.</li> <li>Vascular morphologies (rat’s cerebral microvasculature) shown in Figure 6 and Supplementary Figures S83 - S84 are courtesy of Bruno Weber, University of Zürich (UZH).</li> <li>The vascular morphology of the arterial arborizations shown in Supplementary Figure S88 is available from the Brain Vasculature (BraVa) database (cng.gmu.edu/brava).</li> </ol>
Data from: The biogeochemical boomerang: Site fidelity creates nutritional hotspots that may promote recurrent calving site reuse
<p>Animals interact with nutrient cycles by consuming and depositing nutrients, interactions that are studied in the separate fields of nutritional ecology and zoogeochemistry. Recent theoretical work has begun bridging these disciplines, highlighting that animal-driven nutrient recycling could be crucial in helping animals meet nutritional needs. When animals exhibit site fidelity, they consistently deposit nutrients, potentially improving vegetation quality. We investigated this potential feedback by analyzing changes in forage nitrogen stocks following simulated caribou calving. We found that forage nitrogen stocks increased after two weeks and remained elevated after one year, a change due to an increase in forage quality but not quantity. We thus highlight a positive zoogeochemical feedback whereby caribou deposit nutrients during calving that become bioavailable during lactation and provide evidence that site fidelity creates a biogeochemical boomerang in which animals deposit nutrients that can be reused at a later time.</p>
Fig. 2 in Preliminary observations on the circadian variation in site fidelity in Atelopus hoogmoedi (Lescure, 1974) (Anura, Bufonidae)
Fig. 2. Geographical overview of the study area. (A) Map of the Iwokrama Forest Reserve and its location in Guyana (top right corner). The red line crossing Iwokrama corresponds to the Linden-Lethem Road. (B) Relief map of the Iwokrama Mountains with Turu Falls represented by a black triangle (N 4°24.770' W 58°47.061'). (C) Portion of the trail between Turu Falls camp and the Linden-Lethem Road monitored, with dots corresponding to Atelopus individuals (from A1 - N 4°24.742', W 58°47.130' to A14 - N 4°24.750', W 58°47.128'). A and B from Kok et al. (2013).
Fig. 3 in Preliminary observations on the circadian variation in site fidelity in Atelopus hoogmoedi (Lescure, 1974) (Anura, Bufonidae)
Fig. 3. Substrate (shrub or leaf litter) use in Atelopus hoogmoedi during the day (light grey) and night (dark grey) at Turu Falls, Guyana. As indicated, substrate use was significantly different after dark.
Fig. 1 in Preliminary observations on the circadian variation in site fidelity in Atelopus hoogmoedi (Lescure, 1974) (Anura, Bufonidae)
Fig. 1. (A) Orange and (B) Yellow color morphs of Atelopus hoogmoedi, both encountered at the studied locality in the Iwokrama Mountains, Guyana. (C) Typical breeding habitat of A. hoogmoedi in the Iwokrama Mountains. Photos by PJRK.
Dataset: Fidelity Metaverse ETF (FMET) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Fidelity Disruptive Medicine ETF (FMED) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Fidelity Disruptive Communications ETF (FDCF) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Fidelity Crypto Industry and Digital Payments ETF (FDIG) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Fidelity Disruptive Automation ETF (FBOT) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Fidelity Disruptive Technology ETF (FDTX) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Fidelity D & D Bancorp, Inc. (FDBC) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Fidelity Disruptors ETF (FDIF) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Fidelity Disruptive Finance ETF (FDFF) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Fidelity Nasdaq Composite Index ETF (ONEQ) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Data for: Resource landscapes explain contrasting patterns of aggregation and site fidelity by red knots at two wintering sites
<p>This repository contains data for the paper: Oudman et al. 2018. Resource landscapes explain contrasting patterns of aggregation and site fidelity by red knots at two wintering sites. <em>Movement Ecology</em> 6(14) 1-12. https://doi.org/10.1186/s40462-018-0142-4.</p> <p>Please cite the original publication when using this data.</p>
Supporting data for "Benchmarking Gate Fidelities in a Si/SiGe Two-Qubit Device" (arXiv:1811.04002)
<p>Supporting data and analysis scripts for FIG. 2 - FIG. 6 of "Benchmarking Gate Fidelities in a Si/SiGe Two-Qubit Device" (arXiv:1811.04002, Physical Review X in press)</p> <p>Data for FIG. 2 and FIG. 3 and analysis script are in the file 'single_two_qubit_RB.zip'.</p> <p>Data for FIG. 4 and analysis script are in the file 'CRB.zip'.</p> <p>Data for FIG. 5 and FIG. 6 and analysis script are in the file 'Append.zip'.</p> <p>To run the scripts, QCoDeS needs to be installed. Please refer to:</p> <p>http://qcodes.github.io/Qcodes/</p> <p>for instructions about installation and Dataset. If there's a problem, try to use an earlier version 0.1.7.</p> <p>For more information, contact the author: Xiao Xue, x.xue-1@tudelft.nl </p>
Figure 1 in The study of fidelity measures in the context of using them as a threshold criterion in the allocation of diagnostic species
Figure 1. Detection of optimal number of clusters by the Optimclass-2 approach. From left to right: the case study of the test data set 1 and 2.
Figure 2 in The study of fidelity measures in the context of using them as a threshold criterion in the allocation of diagnostic species
Figure 2. Representing distributions of some fidelity measures with different degree of asymmetry with kernel density estimates. From left to right: the case study of the test data set 1 and 2.
ChessRender360: High-Fidelity Rendered Chess Dataset with Multi-Modal Annotations
<p>ChessRender360 is a synthetically crafted dataset featuring 10,000 rendered chess positions. Designed for computer vision and machine learning research, this dataset provides a rich collection of RGB images, depth maps, instance masks, and semantic segmentation masks for each chess piece and board element.</p> <p>Each chess position is rendered in high resolution (2000x2000 pixels), capturing the intricate details of the board and pieces from various angles. The dataset includes:</p> <ul> <li><strong>RGB Images:</strong> High-quality rendered images of chess positions, showcasing a diverse range of board configurations.</li> <li><strong>Depth Maps:</strong> Accurate depth representations of the scene, capturing depth in the range of 20 cm to 120 cm. In the depth maps, black corresponds to a depth of 20 cm, and white corresponds to 120 cm, providing spatial information for each position.</li> <li><strong>Instance Masks:</strong> Unique instance masks for each chess piece, enabling precise identification and localization.</li> <li><strong>Semantic Segmentation Masks:</strong> Segmentation masks that differentiate between piece types and board elements, with distinct hue values assigned to each type.</li> <li><strong>Bounding Boxes:</strong> Each sample has an annotation <code>.json</code> file containing bounding boxes for each piece.</li> <li><strong>Board Corners:</strong> Same annotation <code>.json</code> contains positions of corners of the board in order: white left, white right, black left, black right.</li> <li><strong>FENs:</strong> A CSV file containing the FEN (Forsyth-Edwards Notation) for each chess position in the dataset, listed in order. This allows users to easily recognize and replicate the exact board position from any image.</li> </ul> <p><strong>Bounding Box Generation:</strong></p> <ul> <li>The dataset does not include predefined bounding boxes, but they can be easily generated from the provided semantic and instance masks. This allows for flexible bounding box creation tailored to specific research needs.</li> </ul> <p><strong>Rendering Details:</strong></p> <ul> <li><strong>3D Models and Materials:</strong> The dataset uses a consistent set of 3D models for all chess pieces, with three different material/color schemes applied across the dataset, along with random perturbations in material brightness, contrast and saturation to introduce visual variety.</li> <li><strong>Camera Angles:</strong> Camera angles are randomly selected, with yaw ranging from 0 to 360 degrees and pitch between 30 to 80 degrees, providing diverse perspectives of the chess positions.</li> <li><strong>Background Variability:</strong> The chessboard is randomly placed on different types of tables, with the floor material randomly sampled to create a variety of backgrounds.</li> <li><strong>Lighting:</strong> Lighting conditions are randomly generated, adding further diversity and realism to the rendered scenes.</li> </ul> <p><strong>Augmentation Potential:</strong></p> <ul> <li>The instance and semantic masks can be used to further augment the dataset. Researchers can selectively modify specific parts of the images—such as the board, background, or individual pieces—enabling the creation of new variations and enhancing the dataset's utility for model training and testing.</li> </ul> <p><strong>Color Mapping:</strong></p> <ul> <li>The semantic masks are color-coded using a hue-based system, where the board frame, squares, and each piece type are assigned specific hues. Instances of the same piece type are differentiated by varying the value component, with saturation consistently set to 1. A detailed <code>color_mapping.json</code> file is included, providing a comprehensive guide to interpreting the masks.</li> </ul> <p><strong>Applications:</strong> ChessRender360 is ideal for tasks such as object detection, instance segmentation, depth estimation, and scene understanding in synthetic environments. Researchers and developers can leverage this dataset for training and evaluating models in computer vision, robotics, and artificial intelligence.</p> <p><strong>Dataset Highlights:</strong></p> <ul> <li>10,000 uniquely rendered chess positions</li> <li>High-resolution images (2000x2000 pixels) with diverse visual characteristics</li> <li>Comprehensive annotations with RGB, depth (20 cm to 120 cm), instance, and semantic maps</li> <li>Side identification map for distinguishing white and black sides of the board</li> <li>FEN notation CSV file for easy position recognition and replication</li> <li>Variety introduced through different material schemes and lighting setups</li> <li>Randomized camera angles for enhanced perspective diversity</li> <li>Potential for further augmentation by modifying specific image components</li> <li>Detailed color mapping for easy interpretation of segmentation masks</li> <li>Suitable for a wide range of computer vision and AI applications</li> </ul> <p>ChessRender360 offers a rich and versatile dataset for advancing research and development in the field of computer vision, providing a synthetic yet highly realistic environment for model training and testing.</p> <p>For any questions, feedback, or collaboration opportunities, or if you are interested in custom artificial datasets, please contact me:</p> <ul> <li><strong>Name:</strong> Marko Kojić</li> <li><strong>LinkedIn:</strong> https://www.linkedin.com/in/mmkoya</li> </ul> <p>I welcome inquiries from researchers, developers, and organizations interested in utilizing or collaborating on artificial datasets.</p>
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
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DANDI Archive for NWB datasets
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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.