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1,880 results for “Dimensionality”
Extended-variable probabilistic computing with probabilistic d-dimensional bits
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Toward three-dimensional DNA industrial nanorobots
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Corrigendum: Dimensionality and modularity of adaptive variation: Divergence in threespine stickleback from diverse environments
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Data from: Functional decline in facial expression generation in older women: a cross-sectional study using three-dimensional morphometry
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Military maritime load planning instances for prioritized two-dimensional orthogonal packing
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Four-dimensional computational ultrasound imaging of brain hemodynamics
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Data from: Three-dimensional single-cell transcriptome imaging of thick tissues
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Data from: High-dimensional imaging of vestibular schwannoma reveals distinctive immunological networks across histomorphic niches in NF2-related schwannomatosis
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Anticipating fluctuations of bigeye tuna in the Pacific Ocean from three-dimensional ocean biogeochemistry
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Data for: Three-dimensional characterization of osteocyte volumes at multiple scales, and its relationship with bone biology and genome evolution in ray-finned fishes
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Easy-to-configure zero-dimensional valley-chiral modes in a graphene point junction
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Dimensional analysis of spring-wing systems reveals performance metrics for resonant flapping-wing flight
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Supporting data for: Three-dimensional genome re-wiring in loci with Human Accelerated Regions
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Data for: EMBER multi-dimensional spectral microscopy enables quantitative determination of disease- and cell-specific amyloid strains
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Low mass planet migration in three dimensional wind-driven inviscid discs: A negative corotation torque - Animations
<p>Animations accompanying the paper "Low mass planet migration in three dimensional wind-driven inviscid discs: A negative corotation torque". Each file corresponds to a panel of Figures 3 and 4. All videos are at a frame rate 10 frames per second with one orbit per frame. Note these animations use a fixed colour scale, while the figures in the paper use colour scales tuned for the panels shown. The prefix of the filenames matches the title for the corresponding sub-panel of the figures in the paper, and the model code given in Table 1 of the paper which describes the details of each model.</p> <p>The video files correspond to the sub-panels of the figures in the paper as given below:</p> <p><strong>Figure 3</strong></p> <ul> <li>2AMS_logV_10fps.mp4 </li> <li>2AMD_logV_10fps.mp4 </li> <li>2AFS_logV_10fps.mp4 </li> </ul> <p><strong>Figure 4</strong></p> <ul> <li>3AMS_logV_10fps.mp4</li> <li>3WMS_logV_10fps.mp4</li> <li>3AFS_logV_10fps.mp4</li> </ul>
Traveltime calculation for qP-, qSV- and qSH- waves in two-dimensional tilted transversely isotropic media
<p>three anisotropic model files in Numerical Examples of the manuscript : Traveltime calculation for qP-, qSV- and qSH- waves in two-dimensional tilted transversely isotropic media. The data is used for reader who wants to reproduce/verify the three methods.</p>
Data and Code from: Using cameras for precise measurement of two-dimensional plant features: CASS
<p>Computer vision explanation: The code (https://github.com/amy-tabb/CASS, referred to as CASS) takes an image of an object on top of an aruco calibration pattern, calibrates the camera using the detected aruco information as well as EXIF tag information, and undistorts and computes the homography from the current location of the aruco calibration pattern in the image to its location in physical space. Then the image is warped to match the coordinate system of the aruco coordinate system, scaled by a user-selected parameter.</p> <p>This dataset provides examples of properly-formatted input images and accompanying text files, as well as a successful run where the option of writing intermediate results has been selected. Details about how to format the directories is found in the README of https://github.com/amy-tabb/CASS.</p> <ul> <li><code>iphone6</code>. is a directory of input files using the camera of a iPhone 6 cellular phone.</li> <li><code>iphone6_results</code>. is the directory of results created from running CASS on <code>iphone6</code>.</li> <li><code>CanonEOS60D</code>. is a directory of input files using a DSLR camera from Canon, model name EOS 60D.</li> <li><code>CanonEOS60D_results.</code> is the directory of results created from running CASS on <code>CanonEOS60D</code>.</li> </ul> <p>See the <code>Write directory format</code> section of CASS's README for details of all of the files; briefly for this example, <code>warped_ORIGINALFILENAME.jpg</code> is the original image, transformed such that 10 pixels corresponds to 1 millimeter on the two dimensional plane of the calibration pattern.</p> <p>https://github.com/amy-tabb/CASS provides code in C++ for processing on this dataset, as well as as Docker image.</p> <p> </p>
IoT device identification - Cut and dimensionality reduced measurements
<p>This repository contains:</p> <p>- Cut measurements corresponding to all the original data, but reduced in bandwidth and augmented as described in the associated paper</p> <p>- The PCA dimensionality reduced data based on the single and multi user data.</p> <p>Frequency: 863-870 MHz (center 866,5 MHz)</p> <p>Sample Frequency: 10 MSPS</p> <p>Date of measurement: 15 November 2018</p> <p>Location: Connectivity Lab, Fredrik Bajers Vej 7C, Aalborg University, Denmark</p>
Noise study data for: Mechanisms of root-reinforcement in soils: an experimental methodology using four-dimensional X-ray computed tomography and digital volume correlation
<p>This dataset contains noise study data used in the paper: Mechanisms of root-reinforcement in soils: an experimental methodology using four-dimensional X-ray computed tomography and digital volume correlation. These include raw CT scans and processed digital volume correlation data.</p> <p>This dataset is part of another dataset which covers other aspects of the paper DOI: <a href="http://www.doi.org/10.5281/zenodo.3352268">10.5281/zenodo.3352268</a></p> <p>The structure of the dataset is as follows:</p> <ul> <li>Noise study CT raw volumes are contained in a zip file. There are four raw files corresponding to the four noise study steps. These files are 8-bit unsigned, dimensions are 1800 x 1800 x 1400 pixels. A txt file giving more details to the data is included. <ul> <li><strong>CT_Raw_data_Noise_Scans.zip</strong></li> </ul> </li> <li>Metadata files generated for each scan given details of scan parameters are found in the zip file: <ul> <li><strong>CT_Scan_Metadata.zip</strong></li> </ul> </li> <li>Tabulated digital volume data for the noise study scans are contained in the zip file. Tabulated data for each subset size is included in subfolders. A .txt file explains the structure of the tab separated .dat files, i.e. what each column of data represents, and what CT scan each of the four .dat files relate to. <ul> <li><strong>DVC_Noise_Study_Data.zip</strong></li> </ul> </li> </ul> <p> </p> <p> </p>
Data from: Low-dimensional spatio-temporal dynamics underlie cortex-wide neural activity
<p>Cognition arises from the dynamic flow of neural activity through the brain. To capture these dynamics, we used mesoscale calcium imaging to record neural activity across the dorsal cortex of awake mice. We found that the large majority of variance in cortex-wide activity (~75%) could be explained by a limited set of ~14 'motifs' of neural activity. Each motif captured a unique spatio-temporal pattern of neural activity across the cortex. These motifs generalized across animals and were seen in multiple behavioral environments. Motif expression differed across behavioral states and specific motifs were engaged by sensory processing, suggesting the motifs reflect core cortical computations. Together, our results show that cortex-wide neural activity is highly dynamic, but that these dynamics are restricted to a low-dimensional set of motifs, potentially allowing for efficient control of behavior.</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.