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1,880 results for “Dimensionality”
Data for "Three-Dimensional Broadband Interferometric Mapping and Polarization (BIMAP-3D) Observations of Lightning Discharge Processes" by Shao et al.
<p>Data set for manuscript of “Three-Dimensional Broadband Interferometric Mapping and Polarization (BIMAP-3D) Observations of Lightning Discharge Processes” by Shao et al. submitted to Journal of Geophysical Research-atmosphere</p>
Capillary networks and follicular marginal zones in the human spleen. Three-dimensional models based on immunostained serial sections - Supplementary videos
<p>We regard ROIs, regions of interest, from a human spleen specimen in single (four ROIs) and double (three ROIs) staining. The ROIs with the same number correspond to each other. Below we map references in manuscript (<strong>bold</strong>) to file names in this repository (<em>italics</em>).</p> <ul> <li>File <em>colour-deconvolution.png</em> – settings of colour deconvolution in Fiji for double staining.</li> <li>File <em>comments to videos.odt</em> – a commentary to S3[c,d] Video.</li> <li><strong>S1a,b Video to S3a,b Video</strong>: files <em>video_[1,2,3][a,b].mov</em> – sequence of section with single (a) and double (b) staining for ROI 1 to 3 in the main text.</li> <li><strong>S1c Video to S3c Video</strong>: files <em>video_[1,2,3]c.mov</em> – video of the reconstruction, single staining, special blood vessels highlighted.</li> <li><strong>S1d Video to S4d Video</strong>: files <em>video_[1,2,3]d.mov</em> – an overview video of the reconstruction, double staining.</li> <li><strong>S4 Video</strong>: file <em>video_4.mov</em> – quality control in virtual reality.</li> <li><strong>S1 Figure</strong>: a supplementary figure <em>fig_S1.tiff</em> and its caption <em>fig_S1_legend.odt</em></li> <li><strong>S2 Figure</strong>: a supplementary figure <em>fig_S2.tiff</em> and its caption <em>fig_S2_legend.odt</em></li> </ul> <p>This data corresponds to the publication "Capillary networks and follicular marginal zones in the human spleen. Three-dimensional models based on immunostained serial sections" by B. S. Steiniger, C. Ulrich, M. Berthold, M. Guthe, and O. Lobachev, 2017.</p>
Data to Three-Dimensional Binocular Eye-Hand Coordination in Normal Vision and with Simulated Visual Impairment
<p>This record contains experimental and analysis scripts (written in Matlab) as well as raw and processed data to reproduce the results shown in:</p> <p>Maiello, G., Kwon, M. & Bex, P.J. (2018) Three-dimensional binocular eye--hand coordination in normal vision and with simulated visual impairment. <em>Experimental Brain Research</em>. https://doi.org/10.1007/s00221-017-5160-8</p>
Resistive switching memories with enhanced durability enabled by mixed-dimensional perfluoroarene perovskite heterostructures
<p><span>Characterisation dataset for “</span><span>Resistive switching memories with enhanced durability enabled by mixed-dimensional perfluoroarene perovskite heterostructures”</span><span>, DOI:</span><span>10.1039/d4nh00104d</span><span>. Data for main and supporting figures provided as *.xlsx and *.txt files.</span></p> <p> </p> <p> </p> <p> </p>
Three-dimensional Reconstructions and Quantitative Indicators for colloidal particles in Dry and Liquid Conditions in Scanning Transmission Electron Microscope (STEM)
<p>This dataset accompanies the research presented in the paper:</p> <div>Esteban, D.A., Wang, D., Kadu, A., Olluyn, N., Iglesias, A.S., Perez, A.G., Casablanca, J.G., Nicolopoulos, S., Liz-Marzán, L.M. and Bals, S., 2023. Liquid phase fast electron tomography unravels the true 3D structure of colloidal assemblies. <em>arXiv preprint arXiv:2311.05309</em>. [<a href="https://arxiv.org/pdf/2311.05309" target="_blank" rel="noopener">link</a>]</div> <p>It provides a comprehensive collection of three-dimensional reconstructions and quantitative descriptors for small colloidal particles. These gold nanoparticles are arranged in tetrahedral and other intricate geometries under both dry and liquid conditions. The dataset contains 3D reconstructions and quantitative indicators such as centroids, volumes, surface areas, solidity measures, and principal axis lengths for assemblies with 4, 5, and 6 particles. </p> <p>The dataset includes: <code>N4_dry_dart.rec</code> and <code>N4_liquid_dart.rec</code> for the 3D reconstructions of an assembly with 4 particles in dry and liquid conditions respectively; <code>N4_quant_descriptors_dry.mat</code> and <code>N4_quant_descriptors_liquid.mat</code> providing quantitative descriptors for these conditions. Similar files are provided for assemblies with 5 and 6 particles, such as <code>N5_dry_dart.rec</code>, <code>N5_liquid_dart.rec</code>, <code>N5_quant_descriptors_dry.mat</code>, <code>N5_quant_descriptors_liquid.mat</code>, and the corresponding files for N6. </p> <p>This dataset can be used to study the structural dynamics of nanoparticle assemblies and studies in colloidal chemistry, materials science, and nanotechnology. The <code>.rec</code> files can be visualized using volume rendering software (e.g. Amira or Avizo), while the <code>.mat</code> files contain structured data for analysis in MATLAB. The supporting code and scripts for this dataset are available on the GitHub repository: <a href="https://github.com/ajinkyakadu/LiquidET_NatComm2024" target="_new" rel="noreferrer">https://github.com/ajinkyakadu/LiquidET_NatComm2024</a>. </p>
Three-Dimensional Characterization of Deformation-induced Damage in Dual Phase Steel using Deep Learning
<p>High performance sheet metals with a multi-phase microstructure suffer from deformation induced damage formation during forming in the constituent phases but importantly also where these intersect. To capture damage in terms of the physical processes in three dimensions (3D) and its stochastic nature during deformation, two challenges remain to be tackled: First, bridging high resolution analysis towards large scales to consider statistical data and, second, characterising in 3D with a resolution appropriate for sub-micron sized voids at a large scale. Here, we present how this can be achieved using panoramic scanning electron microscopy (SEM), metallographic serial sectioning, and deep-learning assisted automatic image analysis. This brings together the 3D evolution of active damage mechanisms with volumetric and environmental information for thousands of individual damage sites. We also assess potential surface preparation artefacts in 2D analyses. Overall, we find that for the material considered here, a dual phase (DP800) steel, martensite cracking is the dominant but not sole origin of deformation induced damage and that for a quantitative comparison of damage density, metallographic preparation can induce additional surface damage density far exceeding what is commonly induced between uniaxial straining steps.</p> <p>https://doi.org/10.1016/j.matdes.2023.112108</p>
Nanolaminography dataset: Three-dimensional imaging of integrated circuits with a macro to nanoscale zoom
<p>Here we present a ptychographic X-ray laminography (PyXL) dataset. It is a new approach for nano-imaging that combines a coherent diffractive imaging technique called ptychography with laminography, which is a generalization of tomography. This allows achieving sub-20 nm resolution over large sample volumes. </p> <p>Non-destructive three-dimensional imaging over large volumes with nano-scale resolution is a challenge faced in many fields, perhaps most acutely in mapping the natural neural connectome and artificial silicon-based integrated circuits. For the latter, such inspection is of interest for quality control and security acquiring particular importance due to the delocalized nature of the chain connecting chip design, manufacture and use. A hierarchy of probes are used to image at length scales from that of the entire chip (millimeters) to those of individual features (nanometers) of the underlying transistors, starting with optical microscopy and finishing with transmission electron microscopy on thin slices prepared using a focused ion beam. What has been missing until now is a single technique yielding a three-dimensional image of the entire chip volume with zooming capability to produce high-resolution images of arbitrarily chosen sub-regions, including virtual delayering.</p> <p>The related publication can be accessed via ShareIt <a href="https://mail.ethz.ch/owa/redir.aspx?C=NzB9-v6wS5uDOPXWL0cW_Vu4ys_MIpbvUqKcmezLt5AHOlhZD1DXCA..&URL=https%3a%2f%2frdcu.be%2fbTudW">https://rdcu.be/bTudW</a></p>
Three-dimensional subnanoscale imaging of unit cell doubling due to octahedral tilting and cation modulation in strained perovskite thin films
<p>Transmission electron microscopy data used in the journal publication <a href="https://doi.org/10.1103/PhysRevMaterials.3.063605">"Three-dimensional subnanoscale imaging of unit cell doubling due to octahedraltilting and cation modulation in strained perovskite thin films"</a></p> <p><strong>Data files</strong></p> <p>There are two data types:</p> <ul> <li>Scanning TEM (STEM) diffraction patterns acquired with a Medipix3 detector (Merlin): m004_LSMO_LFO_STO_medipix.hdf5 <ul> <li>Acquired on a probe corrected Jeol ARM200CF</li> <li>Acceleration voltage: 200 kV</li> <li>Convergence semi-angle: 20.4 mrad (calibrated using the SrTiO<sub>3</sub> substrate HOLZ ring)</li> <li>Detector calibration: 1.357 mrad per pixel (calibrated using the SrTiO<sub>3</sub> substrate HOLZ ring)</li> </ul> </li> <li>Atomic resolution STEM data, both annular dark field (ADF) and annular bright field (ABF), which were acquired simultaneously: s007_ADF.hdf5, s007_ABF.hdf5</li> </ul> <p>The data can be loaded in python using h5py.</p> <p>For the Medipix3 data:</p> <pre><code class="language-python">import h5py f = h5py.File('m004_LSMO_LFO_STO_medipix.hdf5', mode='r') data = f['fpd_expt/fpd_data/data'] data_subset = data[0:16, 0:16, :, :]</code></pre> <p>For the STEM-ADF or STEM-ABF data:</p> <pre><code class="language-python">import h5py f = h5py.File('s007_ADF.hdf5', mode='r') data = f['Experiments/__unnamed__/data']</code></pre> <p>Exploring the Medipix3 dataset lazily, i.e. without loading the whole dataset into memory at the same time. Using pixStem:</p> <pre><code class="language-python">import pixstem.api as ps s = ps.load_ps_signal("003_stripe1.hdf5", lazy=True) s.plot()</code></pre> <p>Loading the STEM-ADF or STEM-ABF data using HyperSpy, which automatically loads the probe scaling:</p> <pre><code class="language-python">import hyperspy.api as hs s = hs.load("s007_ADF.hdf5") s.plot()</code></pre> <p><br> <strong>Processing files</strong></p> <p>All the TEM data has been processed using python scripts, which is named based on the type of processing:</p> <ul> <li>d00N_...: Medipix3 data processing</li> <li>a00N_...: Atomic resolution STEM-ADF and STEM-ABF processing using Atomap</li> </ul> <p>The scripts generate intermediate files, which are saved in folders with the same prefix as the scripts. So the d001_... script makes a folder named d001_... . These intermediate files are included here as zip-files, since Zenodo doesn't support folder structures.</p> <p>The python libraries required to run the scripts are listed in requirements.txt. Newer versions of the libraries will most likely also work.</p> <p>To setup the python environment with the required libraries, and run all the scripts:</p> <pre><code class="language-bash">pip3 install -r requirements.txt python3 run_all_scripts.py</code></pre> <p> </p>
Data from "Predicting Global Ground Geoelectric Field With Coupled Geospace and Three‐Dimensional Geomagnetic Induction Models"
<p>Data presented in http://dx.doi.org/10.1029/2018SW001859 excluding the first and last hours which were determined to contain partially unphysical results and probably should not be used.</p> <p>Each file contains the external ground magnetic field components calculated on 5x5 degree geographic grid and the results of induction modeling using 1d and 3d ground conductivity models: total ground magnetic field components, horizontal ground electric field components. Times are given in UTC.</p> <p>To reproduce Figure 8 in above reference use:</p> <p> </p> <p>import numpy<br> import matplotlib.pyplot<br> data = numpy.load('2006-12-14T22:58:00.npz') # or 2006-12-14T22_58_00.npz<br> matplotlib.pyplot.colorbar(<br> matplotlib.pyplot.imshow(<br> data['B_3D_north'],<br> cmap = matplotlib.pyplot.get_cmap('bwr'),<br> vmin = -800, vmax = 800,<br> extent = (-180, 180, -90, 90)<br> ),<br> format = '%.0f', fraction = 0.02, pad = 0.03<br> )<br> matplotlib.pyplot.show()</p> <p> </p> <p>and substitute B_3D_east, E_3D_north, etc. for the different panels.</p>
Dimensional crossover in a quantum gas of light: Datasets
<p>This repository contains the data presented in the manuscript titled "Dimensional crossover in a quantum gas of light" by K. Karkihalli Umesh et al. published in Nature Physics, https://doi.org/10.1038/s41567-024-02641-7</p> <p>Files "Figxy_data.zip" contain the data required to reproduce Figure xy</p> <p>The file "Raw Data.zip" contains the raw data used to determine chemical potential, internal energy, photon number shown in the manuscript in Fig. 4 anf Fig. 5. It also contains a python script which can be used to plot the raw as well as the transmission corrected spectra.</p> <p>Each zip-folder contains a readme with more detailed information.</p> <p>Files "ExtDataFigxy.zip" contain the data for additional images not in the main manuscript. </p> <p>Compared to version 1, the scaling in the figures was changed (the definition of \tilde{N} was changed slightly), and the additional images where added. </p>
Stellar Mass Black Hole Formation and Multimessenger Signals from Three-dimensional Rotating Core-collapse Supernova Simulations
<p>Gravitational waveforms from <a href="https://ui.adsabs.harvard.edu/abs/2021ApJ...914..140P/abstract">Pan et al. (2021) .</a></p> <p>They are the 40 solar mass model from Woosley & Heger 2007 with different<br>initial rotational speeds:</p> <p>Model NR: Omega_0 = 0.0 rad/sec<br>Model SR: Omega_0 = 0.5 rad/sec<br>Model FR: Omega_0 = 1.0 rad/sec</p> <p>/* File content */</p> <p>They are 5 files for each simulation.</p> <p>Files "data_s40_[model]_d3_[Cross/Plus][Equator/Pole].d" are GW strains for different<br>mode of polarization [h_plus or h_cross] and viewing angles [equator or pole].</p> <p>1st column is time [s] in postbounce. <br>2nd column s the GW strain, assuming d=10 kpc.<br> <br>Files "data_s40_[model]_d3_Idotdot.d" are the second time derivative of the<br>quadruple moments.</p> <p>1st: postbounce time [s] <br>2nd: Idd_xx [cgs]<br>3rd: Idd_xy = Idd_yx [cgs]<br>4th: Idd_yy = Idd_yy [cgs]<br>5th: Idd_zx = Idd_zx [cgs]<br>6th: Idd_zy = Idd_zy [cgs]<br>7th: Idd_zz = Idd_zz [cgs]</p> <p> </p>
QMC Raw Data for "Friedel oscillations in one-dimensional 4He"
<p>Raw path integral quantum Monte Carlo data associted with "Friedel oscillations in one-dimensional 4He". Connected github repository with associated analysis scripts can be found at <a title="Github repo" href="https://github.com/DelMaestroGroup/papers-code-HourglassNanopores">https://github.com/DelMaestroGroup/papers-code-HourglassNanopores.</a> Path integral software is available: <a title="PIMC" href="https://github.com/DelMaestroGroup/pimc">https://github.com/DelMaestroGroup/pimc</a></p> <h2><strong>Contents</strong></h2> <p><code>dR_eq_{δR}.tar.gz</code> includes raw QMC data where <code>{δR} = {0.0,2.0,3.0.4.0} Å</code> is the perturbed radius of the hourglass. All simulations include a perturbation with width <code>w = 3.0 Å</code>. Temperature is fixed at <code>T = 2.0 K</code>. </p> <p>Each <code>.tar.gz</code> contains <code>log</code> and <code>estimator</code> files for the simulations described in the associated paper. </p> <p><code>U_hourglass.npz</code>: generated xy mesh for potential plotting. </p> <p><code>gce-position-dR_eq_{δR}.npz</code>: 3D local densities inside the nanopore</p> <p> </p> <h3>Notes</h3> <p>v3: This version contains <code>gce-position-dR_eq_{δR}.npz</code> which used to have to be regenerated from raw QMC data.</p> <p>v2: This version contains an updated <code>dR_eq_2.0.tar.gz</code> (the one in v1 was corrupted).</p> <p> </p>
Insights into non-axisymmetric instabilities in three-dimensional rotating supernova models with neutrino and gravitational-wave signatures
<p>Those are movies of numerical supernova models, which appear in Takiwaki, Kotake, and Foglizzo, (2021), Monthly Notices of the Royal Astronomical Society, Volume 508, Issue 1, pp.966-985</p>
Codes and datasets for a brief introduction to clustering and dimensionality reduction
<p>Python codes and datasets for the examples on unsupervised learning presented in Joris Paret's doctoral thesis « Hidden order in disordered materials » (2021).</p>
Datasets for 'Mandrake: visualising microbial population structure by embedding millions of genomes into a low-dimensional representation'
<p>Datasets for the paper '<strong>Mandrake: visualising microbial population structure by embedding millions of genomes into a low-dimensional representation</strong>'</p> <p>Files:</p> <ul> <li>616k* - Files for the analysis of 661k bacterial genomes from the SRA (note typo 616-661k). Includes mandrake output and input files (.npz)</li> <li>gps_acc - Files for the analysis of 20k S. pneumoniae accessory genomes from the GPS project. Original accessory matrix is gps_gene_presence_absence.Rtab</li> <li>sc2million_v1* - Files for the analysis of ~1M SARS-CoV-2 genomes. sc2million_v3.npz are the input distances.</li> <li>sce<commit hash>.qdrep - Nvidia systems profile of code at that commit hash</li> <li>sce<commit hash>.ncu-rep - Nvidia kernel profile of code at that commit hash</li> </ul>
Emission line models for the lowest mass core-collapse supernovae - I. Case study of a 9 M⊙ one-dimensional neutrino-driven explosion
<p>Model spectra of the 9 Msun iron-core model, and the pure H toy model, 200-600d. Distance 10 Mpc assumed.</p>
A computer program to calculate discrete wavelet transform for one-dimensional signals
<p>This is the most recent version of the True Basic program 'NDHAAR.TRU', which was part of the supplementary materials for the following publication: X. Dong, P. Nyren, B. Patton, A. Nyren, J. Richardson and T. Maresca, 2008. Wavelets for agriculture and biology: A tutorial with applications and outlook. BioScience 58: 445-453.</p> <p>The original version (1.0, April 8, 2008) accepts a one-dimensional signal with 1024 data points. It was previously posted at http://www.ag.ndsu.edu/CentralGrasslandsREC/wavelets-for-agriculture-and-biology</p> <p>Version 1.1 (June 1, 2010) accepts signals with a length of 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384,<br> 32768, or 65536. This version with documentation was initially posted at www.infoclearinghouse.com. Later the website was closed. Now the documentation can still be accessed at https://www.scss.tcd.ie/Khurshid.Ahmad/Research/Wavelets/wva.pdf.</p> <p>Version 1.2 is posted in this current upload. A major change in this version is the correction of a few typos existing in Version 1.1, so that the program can correctly process signals longer than 4096 (that is, with signal length as either of 8192, 16384, 32768, or 65536). Note that Version 1.1 is fine in correctly processing signals with a length at or shorter than 4096.</p> <p>Two sample input data files are included. Also included is the original supplemental material Suppl_dong_2008.pdf. The first input data file 'pdsi.txt' has a length of 1024, and the related output files are OO1.txt, OO2.txt, OO3.txt, OO4.txt and OO5.txt. These data files are discussed in the original BioScience paper as well as in Suppl_dong_2008.pdf. </p> <p>The second sample input file 'warm.txt' has a length of 65536 and the related output files are OUT_1.txt, OUT_2.txt, OUT_3.txt, OUT_4.txt, and OUT_5.txt. The sample input file warm.txt contains NDVI values of winter wheat measured at Uvalde, TX, USA, from about 8 am to 10 am on April 12, 2018. The measurement was made using an ACS-430 Crop Circle sensor mounted to a push-wheel cart. This file and the associated output files are part of the intermediate results for Supplementary Figure S2 to the article entitled "Leaf water potential of field crops estimated using NDVI in ground-based remote sensing - opportunities to increase prediction precision" (<em>PeerJ</em>. 9:e12005 DOI 10.7717/peerj.12005), which can be accessed at https://zenodo.org/record/4574674#.YD7kI2hKiUk</p>
Database of Planar and Three-Dimensional Periodic Orbits and Families Near the Moon
<p>The lunarPOdatabase.zip is the digital database accompanying the paper:<br> <br> C. Franz and R. P. Russell, “Database of planar and three-dimensional periodic orbits and families near the Moon,” The Journal of the Astronautical Sciences, DOI 10.1007/s40295-022-00361-9 (accepted Nov. 2022).</p> <p>Please see the paper for details, and cite the paper as appropriate. The database is accessible and permanently archived with the following DOI <a href="https://nam12.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.6411980&data=05%7C01%7C%7C1770e232817c4c99fc1b08dad0a0e3ad%7C31d7e2a5bdd8414e9e97bea998ebdfe1%7C0%7C0%7C638051686701542157%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=Spb%2FFj5YL8QKaBoojr09MCCIdloAkzLiGKHoUo3uVGE%3D&reserved=0">https://doi.org/10.5281/zenodo.6411980</a>. See accompanying license.txt and gpl-3.0.txt for license information, applying to all files included in the .zip distribution.</p> <p>The database contains over 13 million planar and three-dimensional solutions in the Earth-Moon circular restricted three body problem, grouped into 34,000 family and sub-family clusters. The database exists as human readable text files with periodic orbits organized by clusters and other dynamical characteristics. The database contains the clustered data, a README file describing the output format, an interactive GUI, and a simple MATLAB script as a basic interface with the database. The data are split into five files, one for each of the planar prograde, planar retrograde, axial prograde, axial retrograde, and x-z cases. The results (i.e. initial conditions and relevant dynamical parameters of each converged periodic orbit) are contained in a human-readable text file where each row is a new solution. The data are sorted by cluster and ordered inside the cluster to form a smooth curve. Summary files are included for both the grid search and the clustering for each run. The input parameters to the grid search software are also included with each case for reproducibility. File sizes range from approximately 1.1GB to 2.6GB, with a total uncompressed file size of 5.4GB and a total compressed file size of 1.2GB.</p> <p>It is emphasized that the GUI and other MATLAB interface files are only a preliminary capability to ease interaction with the database. They may not be stable under future releases of MATLAB. On the initial use of the GUI, we recommend to restrict the data to a single value of N (say N=1 or N=16), otherwise the number of solutions may overwhelm the system memory. If a user has difficulties using the GUI, the user is encouraged to use the MATLAB code interfaces or interface with the text files directly. The text files containing the database are the primary product provided here, with the GUI and test scripts provided as a courtesy to help ease the database's use.</p> <p>Please send questions to <a href="mailto:cfranz21@gmail.com">cfranz21@gmail.com</a> and/or <a href="mailto:ryan.russell@utexas.edu">ryan.russell@utexas.edu</a>.</p>
Data for: Two-dimensional infrared-Raman spectroscopy as a probe of water's tetrahedrality
<p>Data for publication: T. Begusic and G. A. Blake, Two-dimensional infrared-Raman spectroscopy as a probe of water’s tetrahedrality (2022).</p> <p>Contains raw data, processed data, and processing and plotting scripts for the results presented in the manuscript. See README files enclosed in the dataset for details about the files and directories. Main results were produced with codes available at https://github.com/tbegusic/i-pi and https://github.com/tbegusic/encorr.</p>
Bandgap fluctuations and robustness in two-dimensional hyperuniform dielectric materials
<p>These are the data associated with the paper "Bandgap fluctuations and robustness in two-dimensional hyperuniform dielectric materials" published in <a href="http://doi.org/10.1364/OE.484232"><em>Optics Express</em> <strong>31</strong>, 18509 (2023)</a></p> <ul> <li>gapsFrequenciesAndWidths.h5: each line of the table lists the largest and second largest gap widths and frequencies in GHz for all 5000 samples studied in the paper <table align="center"> <thead> <tr> <th scope="col">Column</th> <th scope="col">Quantity</th> </tr> </thead> <tbody> <tr> <td>0</td> <td><span class="math-tex">\(\chi\)</span></td> </tr> <tr> <td>1</td> <td>largest gap frequency (GHz)</td> </tr> <tr> <td>2</td> <td>largest gap width (GHz)</td> </tr> <tr> <td>3</td> <td>second largest gap frequency (GHz)</td> </tr> <tr> <td>4</td> <td>second largest gap width (GHz)</td> </tr> </tbody> </table> </li> <li>DOS.h5: normalized density of states as a function of the frequency (GHz) averaged over all samples for all <span class="math-tex">\(\chi\)</span> values studied in the paper <table align="center"> <thead> <tr> <th scope="col">Column</th> <th scope="col">Quantity</th> </tr> </thead> <tbody> <tr> <td>0</td> <td><span class="math-tex">\(\chi\)</span></td> </tr> <tr> <td>1</td> <td>Frequency (GHz)</td> </tr> <tr> <td>2</td> <td>nDOS</td> </tr> </tbody> </table> <p> </p> </li> </ul>
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.