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1,880 results for “Dimensionality”
Data accompanying the article "Comparison of six different methods for measuring the equine hoof and recording of its three-dimensional conformation"
<p>Equine hoof measurements data, to be used with <a href="https://gitlab.com/hoofstats/hoofstats">https://gitlab.com/hoofstats/hoofstats</a>. See also <a href="https://zenodo.org/record/6962789">https://zenodo.org/record/6962789</a>.</p> <p>Files:</p> <ul> <li>measures.py - declarative description of hoof measures using point cloud data labels</li> <li>Pointcloud_hoof_coordinates_MicroScribe.xlsx - the point cloud data and their description</li> </ul>
Four-dimensional wind fields retrieved from FY4B GIIRS during 15-minute interval intensified observations from 24 to 26 June 2022
<p>This dataset is the four-dimensional (time, pressure, u, v) wind fields retrieved from FY4B GIIRS observations with a 15-minute interval during a northeastern cold vortex event in China.</p>
Four-dimensional wind fields retrieved from FY4A GIIRS during 30-minute interval intensified observations on typhoons Ampil (2018), Mangkhut(2018) and Lekima(2019)
<p>This dataset includes the four-dimensional (time, pressure, u, v) wind fields retrieved from FY4A GIIRS with 30-minute interval during Ampil (2018), Mangkhut(2018) and Lekima(2019).</p>
Differences in visual-field sensitivity in the inferior hemifield between the eyes affect three-dimensional shape perception in glaucoma
<p><strong>Raw data repository for the article:</strong></p> <p> </p> <p><strong>Differences in visual-field sensitivity in the inferior hemifield between the eyes affect three-dimensional shape perception in glaucoma</strong></p> <p> </p> <p><strong>Hiromasa Sawamura<sup>1</sup>, Ryo Asaoka<sup>1,2,3,4,5</sup>, Hiroshi Murata<sup>1</sup>, Eriko Ando<sup>1</sup>, Céline R. Gillebert<sup>6</sup>, James T. Todd<sup>7</sup>, Guy A. Orban<sup>8</sup></strong></p> <p> </p> <p><sup>1</sup> Department of Ophthalmology, University of Tokyo Graduate School of Medicine, Tokyo, Japan.</p> <p><sup>2</sup> Department of Ophthalmology, Seirei Hamamatsu General Hospital, Hamamatsu, Shizuoka, Japan.</p> <ol> <li>Seirei Christopher University, Hamamatsu, Shizuoka, Japan.</li> </ol> <p><sup>4</sup> Nanovision Research Division, Research Institute of Electronics, Shizuoka University, Shizuoka Japan.</p> <p><sup>5</sup> The Graduate School for the Creation of New Photonics Industries, Shizuoka Japan.</p> <ol> <li>Department of Brain and Cognition, KU Leuven, Leuven, Belgium.</li> <li>Department of Psychology, The Ohio State University, Columbus, OH, USA.</li> <li>Department of Medicine and Surgery, Neuroscience Unit, University of Parma, Parma, Italy.</li> </ol> <p> </p> <p>Excel data:</p> <p>Binocular Visual-filed sensitivity for 20 subjects: BinocularHFAData.xlsx</p> <p>Visual filed sensitivity in the right eye: OD_HFA_data.xlsx </p> <p>Visual filed sensitivity in the left eye: OS_HFA_data.xlsx</p> <p>Error-in-depth for 20 subjects: ErrorinDepth_20subj.xlsx</p> <p>Results of Simple feature discrimination: Result_SinpleFeatureDiscrimination.xlsx</p>
Replication data for: Spatiotemporal Bedload Transport Patterns Over Two-Dimensional Bedforms
<p>Sediment transport images and Acoustic Doppler Velocitmetry data files associated with the manuscript "Spatiotemporal Bedload Transport Patterns Over Two-Dimensional Bedforms"</p> <p>Authors: Kate Potter Leary, Leah Tevis, and Mark Schmeeckle</p>
First-passage probability estimation of high-dimensional nonlinear stochastic dynamic systems by a fractional moments-based mixture distribution approach
<p>First-passage probability estimation of high-dimensional nonlinear stochastic dynamic systems is a significant task to be solved in many science and engineering fields, but remains still an open challenge. The present paper develops a novel approach, termed ‘fractional moments-based mixture distribution’, to address such challenge. This approach is implemented by capturing the extreme value distribution (EVD) of the system response with the concepts of fractional<br> moment and mixture distribution. In our context, the fractional moment itself is by definition a high-dimensional integral with a complicated integrand. To efficiently compute the fractional moments, a parallel adaptive sampling scheme that allows for sample size extension is developed using the refined Latinized stratified sampling (RLSS). In this manner, both variance reduction and parallel computing are possible for evaluating the fractional moments. From the knowledge<br> of low-order fractional moments, the EVD of interest is then expected to be reconstructed. Based on introducing an extended inverse Gaussian distribution and a log extended skew-normal distribution, one flexible mixture distribution model is proposed, where its fractional moments are derived in analytic form. By fitting a set of fractional moments, the EVD can be recovered via the proposed mixture model. Accordingly, the first-passage probabilities under different<br> thresholds can be obtained from the recovered EVD straightforwardly. The performance of the proposed method is verified by three examples consisting of two test examples and one engineering problem.</p>
Supplementary Data for Manuscript : "Application of OSL surface exposure dating with the use of two-dimensional OSL laser scanning instruments and energy-dispersive x-ray spectroscopy'
<p>Contains all supplementary works mentioned in the manuscript.</p>
Modeling polar cap airglow patch intensities using three-dimensional ionospheres produced by data assimilations
<p>Qaanaaq imager data supporting the paper entitled Modeling polar cap airglow patch intensities using three-dimensional ionospheres produced by data assimilations.</p>
High-dimensional multivariate autoregressive model estimation of human electrophysiological data using fMRI priors
<p>Data to reproduce figures in submitted manuscript "High-dimensional multivariate autoregressive model estimation of<br> human electrophysiological data using fMRI priors"</p> <p>https://www.biorxiv.org/content/10.1101/2022.11.18.516669v1</p>
Datasets associated with the publication: "The three-dimensional structure of fronts in mid-latitude weather systems in numerical weather prediction models".
<p>ECMWF HRES and ERA5 datasets for the paper "The three-dimensional structure of fronts in mid-latitude weather systems in numerical weather prediction models".</p> <p>Content: </p> <p>========================================================================================</p> <p>Storm Friederike:</p> <p>ECMWF HRES forecast, 18.01.2018 00:00 UTC - 23:00 UTC, hourly, GRIB-format.</p> <p>ECMWF ERA5 reanalysis, 16.01.2018 12:00 UTC - 19.01.2018 00:00 UTC, twelve-hourly data, GRIB-format.</p> <p>========================================================================================</p> <p>Storm Vladiana:</p> <p>ECMWF HRES analysis, 23.09.2016 00:00 UTC - 23.09.2016 00:00 UTC, six-hourly data, rotated North Pole (latitude: 51˚, longitude: 160˚), NetCDF-format.</p> <p>========================================================================================</p> <p>Storm Egon:</p> <p>ECMWF ERA5 reanalysis, 12.01.2017 00:00 UTC - 13.01.2017 06:00 UTC, six-hourly data, GRIB-format.</p>
Multi-state Data Storage in a Two-dimensional Stripy Antiferromagnet Implemented by Magnetoelectric Effect
<p>Source data for: Multi-state Data Storage in a Two-dimensional Stripy Antiferromagnet Implemented by Magnetoelectric Effect</p>
Supplementary Documents for Manuscript 'Investigating the use of two-dimensional OSL laser scanning instruments and energy-dispersive x-ray spectroscopy for OSL exposure dating'
<p>Appendix Data for Manuscript 'Investigating the use of two-dimensional OSL laser scanning instruments and energy-dispersive x-ray spectroscopy for OSL exposure dating' - with added "Readme's" for relevant databases.</p>
Mixed model-based deconvolution of cell-state abundances along a one-dimensional trajectory [csd-eQTL]
<p><strong>README:</strong></p> <p>The full summary data of the cell-state-dependent eQTLs for GTEx Esophagus Mucosa (n=497) are stored in the .parquet format.</p> <p>An example of the file name:</p> <p><strong>"GTEx_Esophagus_Mucosa_bin1.cis_qtl_pairs.1.parquet.gz"</strong> means the summary data of csd-eQTLs for bin1 of chromosome 1.</p>
Three-dimensional Topside Ionospheric Model and Onboard GNSS Observations from LEO Satellites
<p>Here are three-dimensional topside ionospheric models based on machine learning methods and data from LEO satellites. </p>
Raw results for medium- and high-dimensional settings
<p>Raw results of the Best Subset Selection, Forward Stepwise, Lasso and Elastic-Net methods for medium- and high-dimensional settings.</p>
Observational dataset of snowfall events in 20160121 and 20180104 by polarimetric radar and 2-dimensional vedio disdrometer
<p>Observational dataset of snowfall events in 20160121 and 20180104 by polarimetric radar and 2-dimensional vedio disdrometer used for the Manuscript: "Potential of Snowfall Nowcasting Using Polarimetric Radar Data and Its Link to Ice Microphysics: Study of Two Snowstorms in East China". QVP.zip represents the quasi-vertical profiles composited from NUIST CPOL at the 19.5-degree elevation. VAD.zip is the velocity azimuth displays (VADs) estimated from radar volume scans, using the method by Matejka and Srivastava (1991). Traj_ele_1p5.zip is the reflectivity factor at the trajectory final location estimated by VAD winds at the 1.5-degree elevation. Snow_data.zip is the observations of snow particles by NJU 2DVD. The files that end with .pkl can be read by python joblib module. The files that end with .nc are in Netcdf format.</p>
Assessing the Influence of Graduate Characteristics on Employer Satisfaction: A Multi-dimensional Analysis
<p>Dataset for the paper</p>
Fig. 2 in Global metabolome analysis of Dunaliella tertiolecta, Phaeobacter italicus R11 Co-cultures using thermal desorption - Comprehensive two-dimensional gas chromatography - Time-of-flight mass spectrometry (TD-GC×GC-TOFMS)
Fig. 2. Workflow for sample preparation and injection. Culture samples were filtered and dried (A–B). Dried filter papers were placed in clean vials (C) and then resuspended in methanol (D) before being extracted with Chloroform (E). Water was added (F) and subsequently, the chloroform layer was aliquotted into GC vials (G) for further sample preparation. Extracts were dried (H) and then derivatized using a two-step methoximation/silylation process to yield derivatized extracts (I). 9-μL aliquots of derivatized extracts were automatically transferred to microvial inserts in thermal desorption tubes for injection (J) using an initial solvent vent step to remove excess solvent and derivatisation reagents (K), followed by thermal desorption to a cooled PTV inlet and subsequent splitless injection to the GC × GC-TOFMS system. Non-volatile residues from the extracts remained in the microvial insert for subsequent disposal (L). See text for details.
Fig. 4 in Global metabolome analysis of Dunaliella tertiolecta, Phaeobacter italicus R11 Co-cultures using thermal desorption - Comprehensive two-dimensional gas chromatography - Time-of-flight mass spectrometry (TD-GC×GC-TOFMS)
Fig. 4. From left to right: results of principal component analysis of the raw data (autoscaled), similarly scaled data normalised to class-specific TUPA, and the normalised, scaled data using the selected features from the FS-CR routine. Quality control samples were not included in the feature selection routine, and are displayed as filled icons connected to their corresponding replicate with a straight line, following projection into the optimised principal component space. Confidence ellipses were drawn about each sample class for a confidence interval of 0.95. Note the convention: DUN refers to D. tertiolecta samples, CO refers to co-culture samples, and BAC refers to P. italicus R11 samples.
Direct strain correlations at the single-atom level in three-dimensional core-shell interface structures
<p>The data and source codes for the paper [H.J., D.H.W., T.L., Y.K., C.J., J.L., H.B., A. J. P., W.T., C.O., P.E., Y.-L.L., S.R., S.W.H., and Y.Y., "Direct strain correlations at the single-atom level in three-dimensional core-shell interface structures", <em>Nat. Commun.</em> <strong>13</strong>, 5957 (2022)] are posted below.</p> <p> </p> <p>Nanomaterials with core-shell architectures are prominent examples of strain-engineered materials. The lattice mismatch between the core and shell materials can cause strong interface strain, which affects the surface structures. Therefore, surface functional properties such as catalytic activities can be designed by fine-tuning the misfit strain at the interface. To precisely control the core-shell effect, it is essential to understand how the surface and interface strains are related at the atomic scale. Here, we elucidate the surface-interface strain relations by determining the full 3D atomic structure of Pd@Pt core-shell nanoparticles at the single-atom level via atomic electron tomography. Full 3D displacement fields and strain profiles of core-shell nanoparticles were obtained, which revealed a direct correlation between the surface and interface strain. The strain distributions show a strong shape-dependent anisotropy, whose nature was further corroborated by molecular statics simulations. From the observed surface strains, the surface oxygen reduction reaction activities were predicted. These findings give a deep understanding of structure-property relationships in strain-engineerable core-shell systems, which can lead to direct control over the resulting catalytic properties.<br> <br> </p> <p>1. Raw experimental tilt series images for Particle 1 and Particle 2 (Raw tilt series images.zip).</p> <p>2. Denoised and aligned tilt series images for Particle 1 and Particle 2 (Finalized tilt series images.zip).</p> <p>3. Finalized tilt angles for Particle 1 and Particle 2 (Finalized tilt angles.zip).</p> <p>4. 3D tomographic reconstruction for Particle 1 and Particle 2 (Final reconstruction.zip).</p> <p>5. Final atomic structure for Particle 1 and Particle 2 (Final atomic structure.zip).</p> <p>6. Indices of surface Pd atoms for Particle 1 and Particle 2 (Indices of surface Pd.zip).</p> <p>7. Source codes for the assignment of atomic coordinates, displacement/strain calculation and ORR activity calculation (Code_PdPt.zip).</p> <p> </p> <p>If you use any of the above data or source codes in your publications and/or presentations, our paper should be properly cited: H.J., D.H.W., T.L., Y.K., C.J., J.L., H.B., A. J. P., W.T., C.O., P.E., Y.-L.L., S.R., S.W.H., and Y.Y., "Direct strain correlations at the single-atom level in three-dimensional core-shell interface structures", <em>Nat. Commun.</em> <strong>13</strong>, 5957, (2022).<br> <br> If you have any questions regarding the above data or source codes, please contact Yongsoo Yang, Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea. Email: <a href="mailto:yongsoo.yang@kaist.ac.kr">yongsoo.yang@kaist.ac.kr</a></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.