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148 results for “entropy”
Dataset of "Towards 2D van der Waals Entropy Mixture MX2 (M=Mo,W; X=S,Se,Te) for Hydrogen Evolution Electrocatalysis"
<p>High-entropy alloys have emerged as a class of materials, offering unique properties due to their irregular and randomized arrangement of multiple elements in an ordered lattice. This concept has been extended to two-dimensional (2D) van der Waals materials, including transition metal dichalcogenides (TMD), which exhibit promising applications in electrocatalysis. In this work, we have explored the synthesis of entropy mixture crystals (TMDmix) involved the chemical vapor transport of five individual elements, Mo and W as metal elements, S, Se, and Te as chalcogenide elements, resulting in a crystalline structure with a controlled composition Mo0.56W0.44(S0.33Se0.35Te0.32)2, with an estimated ΔSmix of 0.96R. When observed along the [001] zone axis, STEM HAADF images indicate the presence of the different crystal phases of the 2D TMDs (1T, 2H, and 3R). Our findings demonstrate the potential of the entropy TMDmix materials as catalysts for the hydrogen evolution reaction, as an alternative to noble metal-based catalysts. To maximize the potential of TMDmix, we chose the chemical exfoliation with the resulting material being subdivided into size groups, big and small according to their lateral size. In acidic medium, the lowest overpotential of 127 mV and Tafel slope of 79 mV/dec were obtained for the exfoliated sample with a small lateral size (exf-TMDsmall).</p>
Dataset of "Preparation of novel lithiated high-entropy spinel type oxyhalides and their electrochemical performance in Li-ion batteries "
<p>Electrochemical measurements carried out using the 2032-coin cells with the Li-metal anode have shown voltammetric charge capacities of 450, 694, and 593 mAh g-1 for HEOFe, LiHEOFeCl, and LiHEOFeF, respectively.<br>Galvanostatic chronopotentiometry at 1 C rate confirmed high initial charge capacities for all the samples but galvanostatic curves exhibited a capacity decay over 100 charging/discharging cycles. Raman spectroelectrochemistry measured on the LiHEOFeF sample proved the reversibility of the electrochemical process for initial charging/discharging cycles. Electrochemical impedance spectroscopy revealed the lowest initial charge transfer resistance for LiHEOFeCl and its gradual decrease both for LiHEOFeCl and LiHEOFeF during galvanostatic cycling, whereas the charge transfer resistance of HEOFe slightly increases over 100 galvanostatic cycles due to different mechanism of the electrochemical reduction. </p>
Dataset of "High Entropy 2D Metals Sulfides: Fast Synthesis, Exfoliation and Electrochemical Activity in Overall Water Splitting at Alkaline pH"
<p>Novel simple and efficient method for synthesis of high entropy sulfides of iron group metals (Cr, Fe, Ni, Co, Zn) is describedThe created material was investigated as a catalyst for electrochemical water splitting in acidic, neutral and alkaline pH. Investigation of the electrocatalytic activity of the synthesized material shows its high efficiency for overall water splitting in alkaline media. </p>
Simulated galaxy cluster data at z=0 demonstrating the entropy core problem with the SWIFT-EAGLE galaxy formation model
<p>Cluster simulated with the SWIFT hydrodynamic code with the Ref SWIFT-EAGLE model. This dataset contains the redshift 0 snapshot and the VELOCIraptor halo catalogue.</p> <p>Paper reference: https://arxiv.org/abs/2210.09978</p>
Entropy-Driven Crystallization of Hard Colloidal Mixtures of Polymers and Monomers
<p>Data archive corresponding to the publication "Entropy-Driven Crystallization of Hard Colloidal Mixtures of Polymers and Monomers " by O. Bouzid <em>et al</em>., Polymers <strong>16</strong>, 2311 (2024). </p> <p>Preprint available at: 10.20944/preprints202407.0786.v1</p> <p>Please see README.txt for instructions on how to access and read the files from the crystallographic analysis based on the CCE norm descriptor.</p> <p>All system configurations have been generated and successively analyzed by the Simu-D software.</p> <p> </p> <p>This research was funded by MICINN/FEDER (Ministerio de Ciencia, Innovación y Universidades, Fondo Europeo de Desarrollo Regional), grant number “PID2021-127533NB-I00”, by the scholarship program from the Algerian Ministry of Higher Education and Scientific Research and by UPM and Santander Bank, “Programa Propio UPM Santander”.</p>
Ecological Niche Models, in 2019 and across RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100, of 1508 European Marine Species, developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution
<p>Native ecological niche models of 1508 European species (894 fish and 614 non fish) developed with AquaMaps, Artificial Neural Networks, Maximum Entropy, and Support Vector Machines, for 2019 and under RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100, at 0.5° spatial resolution.</p>
Dataset for Accessing Cosmic Radiation as an Entropy Source for a Non-Deterministic Random Number Generator
<p>The dataset contains all gathered data from the experiment from Wednesday, March 16, 2022 11:58:41.929 AM UTC+0 (1647431921929) until Sunday, April 3, 2022 1:08:35.353 PM UTC+0 (1648991315353). The experiment was executed during physical presence within the Arctic Circle in Tromsø, Norway 69° 40' 53.117'' N 18° 58' 36.027'' E at 35m elevation above sea level. The dataset was gathered with a prototype [1] based on the CREDO android application [2]. The main research is to use Ultra High Energy Cosmic Rays (UHECR) as an entropy source for a Random Bit Generator (RBG). </p> <p>The associated publication will probably have the title "Accessing Cosmic Radiation as an Entropy Source for a Non-Deterministic Random Number Generator"</p> <p>In order to reproduce the results the SQLite3 database "mrng_arctic_experiment_2022.db" is needed. To get the visual representations of the detections use "image_decoding_and_codesnippets.py" to generate the cleaned (414 detections / ~15MB) or the uncleaned (5567 detections / ~195 MB) dataset. The compressed folder "raw_data_incl_space_weather.7z" contains all raw data as gathered with the MRNG prototype, unprocessed, uncleaned, and unmerged. </p> <p> </p> <p>[1] https://github.com/StefanKutschera/mrng-prototype, visited on 27.03.2023</p> <p>[2] https://github.com/credo-science/credo-detector-android, visited on 27.03.2023</p>
Biodiversity Index, in 2019 and across RCP 4.5, and 8.5 scenarios in 2050 and 2100 of 1508 European Marine Species based on ensemble Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution
<p>Biodiversity Index in 2019 and across RCP 4.5, and 8.5 scenarios in 2050 and 2100 of 1508 European marine species based on ensemble Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution. The Index counts the number of species (among the 1508) potentially present in each 0.5° cell according to the ensemble models. For each ensemble model, a threshold of at least 3 models agreeing on species presence in the cell was used to indicate species presence.</p>
Ecological Niche Models of 96 European Marine Species, for 2019, developed with AquaMaps, Artificial Neural Networks, Maximum Entropy, and Support Vector Machines at 0.1° Resolution
<p>Native ecological niche models of 96 European marine species of particular commercial and conservation interest developed with AquaMaps, Artificial Neural Networks, Maximum Entropy, and Support Vector Machines, for 2019 at 0.1° spatial resolution.</p>
Ensemble Ecological Niche Models and Biodiversity Index for 2019 of 96 European Marine Species based on Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, AquaMaps, and Support Vector Machines at 0.1° Resolution
<p>Ensemble Ecological Niche Models for 2019 of 96 European marine species of particular commercial and conservation interest, based on Ecological Niche Models developed with (i) Artificial Neural Networks, (ii) Maximum Entropy, (iii) Support Vector Machines, and (iv) AquaMaps at 0.1° Resolution. The data report, for each 0.1° cell, how many models (from 0 to 4) overcome a model-specific decision threshold to assess species presence in the cell. A Biodiversity Index is also provided as the count of the number of species (among the 96) potentially present in each 0.1° cell according to the ensemble models. For each ensemble model, a threshold of at least 3 models agreeing on species presence in the cell was used to indicate species presence.</p>
Underlying data for "Interpretation of Hydrogen-Deuterium Exchange Data by Maximum-Entropy Reweighting of Simulated Structural Ensembles"
<p>This dataset contains code, data, and figures used in the article "Interpretation of Hydrogen-Deuterium Exchange Data<br> by Maximum-Entropy Reweighting of Simulated Structural Ensembles".</p> <p>Contents:</p> <p>code/* - Underlying code used to analyze molecular dynamics trajectories and calculate predicted HDX-MS data, used to reweight structural ensembles to best fit target HDX-MS data, and used to structurally cluster simulation frames after reweighting</p> <p>data/* - Simulation trajectories of the TeaA protein, along with two sub-trajectories corresponding to only 'closed' or 'open' TeaA frames, and predicted HDX-MS deuterated fractions used as target data in simulation reweighting. Also simulation trajectories of the LeuT protein, in either 'outward-facing' or 'inward-facing' conformational states embedded in a DMPC bilayer, and experimental HDX-MS deuterated fractions used as target data in simulation reweighting</p> <p>figures/* - Underlying data and scripts used to create all figures and movies used in the article.</p> <p>Where appropriate, README files include instructions for regenerating data used in the article, and details of the Python packages used to run Python scripts are available in conda_environment.yml</p>
Protein and DNA alignments for ILS and Entropy calculations
<p>Datasets for reproducing the Entropy and ILS calculations of the 3rd Chapter of my PhD.</p><p>Data include Protein, Exon and Intron alignments for 12 genes.</p>
Low-entropy Packed Binary Detection using Hardware Performance Counters
<p><span>Malware analysis faces a critical challenge in accurately identifying packed executables, especially those with low entropy. Existing software-based solutions often fail in detecting packers used by malware, resulting in inaccurate classifications. To address this shortcoming, in this study we introduce a novel method using<br>Hardware Performance Counters (HPCs) to facilitate the classification of binary packers due to HPCs’ minimal access overhead and ability to obviate the necessity for source code. We trained classic machine-learning models by selecting relevant hardware attributes associated with the unpacking procedure for detecting<br>packers used by low-entropy binary programs. Extensive experiments shows the substantial role played by Hardware Performance Counters in detecting binary packing characterized by low entropy,<br>offering a promising avenue for further exploration and refinement of techniques in malware analysis<br><br><br></span></p> <p><span>The following zip files are executables that represent low entropy versions of software packers using byte-padding. The name of the files are the names of the packers which are represened, Acprotect, Armadillo, Aspack, Nspack, Pecompact, Petite, UPX, and Zprotect. These can be used to measure the unpacking process using hardware performance counters in order to test & train machine earning classifiers for accurate classification of low entropy packers.</span></p>
Global Surface Ozone Concentration Dataset 1990-2017 Mapped at Fine Resolution through the Bayesian Maximum Entropy Data Fusion of Observations and Model Output
<p>This global surface ozone concentration dataset corresponds to the data developed in this paper:</p> <p>DeLang, M. N., J. S. Becker, K.-L. Chang, M. L. Serre, O. R. Cooper, M. G. Schultz, S. Schroder, X. Lu, L. Zhang, M. Deushi, B. Josse, C. A. Keller, J.-F. Lamarque, M. Lin, J. Liu, V. Marecal, S. A. Strode, K. Sudo, S. Tilmes, L. Zhang, S. Cleland, E. Collins, M. Brauer, and J. J. West (2021) Mapping yearly fine resolution global surface ozone through the Bayesian Maximum Entropy data fusion of observations and model output for 1990-2017, <em>Environmental Science & Technology</em>, 55, 4389-4398, doi: 10.1021/acs.est.0c07742.</p> <p>Ozone concentrations are estimated as described in the paper, with output shown for the Ozone Season Daily Maximum 8-hr metric (OSDMA8) for each year between 1990 and 2017, at 0.1 degree spatial resolution. Ozone is estimated through data fusion of output from several global models, with observations of ozone collected by TOAR. The data fusion involves application of the M3Fusion method to create a multi-model composite of several global models, followed by BME data fusion, as described in the paper. </p> <p>The *.nc file contains the latitude, longitude, ozone concentration estimate, and estimated variance for each 0.1 x 0.1 degree grid cell.</p> <p>Please contact Jason West (jasonwest@unc.edu) with questions about the dataset. We'd like to hear from you to know how you're using the data!</p> <p> </p> <p> </p>
Example of reverse Monte Carlo simulation for fcc high-entropy alloy CrMnFeCoNi
<p>The data set contains the example of reverse Monte Carlo (RMC) simulation of EXAFS spectra collected for fcc high-entropy alloy CrMnFeCoNi.</p> <p>The simulation was performed by the EvAX code freely available from http://www.dragon.lv/evax/. </p> <p> </p>
Global Surface Ozone Concentration Dataset 1990-2017 Generated by Bayesian Maximum Entropy Data Fusion With RAMP Bias Correction
<p>This dataset reports estimates of surface ozone concentration at fine spatial resolution for 1990 to 2017, at 0.5 degree horizontal resolution. Also reported is the variance. Estimates correspond to this paper:</p> <p><span>Becker, J. S.</span><span>, DeLang, M. N., K.-L. Chang, M. L. Serre, O. R. Cooper, <u>H. Wang</u>, M. G. Schultz, S. Schroder, X. Lu, L. Zhang, M. Deushi, B. Josse, C. A. Keller, J.-F. Lamarque, M. Lin, J. Liu, V. Marecal, S. A. Strode, K. Sudo, S. Tilmes, L. Zhang, M. Brauer, and <span>J. J. West</span> (2023) Using Regionalized Air Quality Model Performance and Bayesian Maximum Entropy data fusion to map global surface ozone concentration, <em>Elementa Science of the Anthropocene</em>, 11: 1, doi: 10.1525/elementa.2022.00025.</span></p> <p>The dataset reports estimates of surface ozone for the OSDMA8 metric (the 6-month ozone-season average of the daily maximum 8-hr concentration), estimated through a data fusion of ozone observations from the Tropospheric Ozone Assessment Report (TOAR) database, and output from multiple global atmospheric models. Estimates are created in each year by a combination of M3Fusion to create a multi-model composite, Regional Air Quality Model Performance (RAMP) regional and nonlinear bias correction, and Bayesian Maximum Entropy (BME) data fusion in space and time. The estimates here are the final results using a weighted RAMP bias correction. </p>
Distinguishing between high entropy bit streams
<p>This dataset contains the curated files, classified by type and extension so that other researchers can compute their features and replicate outcomes. </p> <p> </p> <p>A total of 5 datasets (i.e., one according to each file size denoted as 64, 128, 256, 512, and 1024) each one consisting of exactly 50% encrypted and 50% compressed files.</p> <p>-The encryption algorithms used to generate the files were:</p> <p>AES(128 / 192 / 256) and Camelia(128 / 192 / 256</p> <p> </p> <p>-In the case of compressed files:</p> <p>ZIP RAR BZIP2 GZIP</p> <p> </p> <p>-Different source files were considered to generate the encrypted and compressed files. </p> <p>COCO Dataset (http://cocodataset.org/home) <br> Microsoft Research (https://www.microsoft.com/en-us/research/project/rgb-d-dataset-7-scenes/)<br> ArXiv (https://arxiv.org/)<br> Project Gutenberg (https://www.gutenberg.org/)<br> Several classical music symphonies in MP3 format <br> YouTube-8M dataset<br> Binaries extracted from system32 in Win10 x64 and sbin from Ubuntu 16.04</p> <p> </p> <p> </p> <p> </p> <p> </p>
Dataset for configurational entropy of a finite number of dumbbells close to a wall
<p><strong>Introduction:</strong> Dataset from numerical simulations to quantify the reduction in configurational entropy of dumbbells due to the presence of a wall, as developed and described in detail in the paper</p> <ul> <li>Markus Hütter: Configurational entropy of a finite number of dumbbells close to a wall. Eur. Phys. J. E, 45(1): 6 (19 pages), 2022. DOI: 10.1140/epje/s10189-022-00160-y WWW: https://doi.org/10.1140/epje/s10189-022-00160-y</li> </ul> <p>which should be cited whenever this dataset is used. The data compiled here is the basis for figures 5, 6, and 9 in that paper.</p> <p><strong>Format</strong>: The files are provided in plain-text format (ascii).</p> <p><strong>Filenames</strong>: The nomenclature for the filenames follows the following scheme:</p> <ul> <li>data-normal-Lone{L1}-Ltwo{L2}-N{N}-{method}-{timestamp}.txt</li> </ul> <p>where (see the original paper for details) {L1} and {L2} specify the confining slab, {N} denotes the number of dumbbells, and {method} is either "SPLIT" (for the data presented in figures 5 and 6) or "WangLandau-MERGED" (for the data presented in figure 9).</p> <p><strong>File content</strong>: Each datafile contains 8 headerlines, in which the values of L1, L2, and N are repeated, and furthermore the following quantities are specified: nsteps is the total number of steps for the random sampling; clow = (4*L1^2)/N; cupp = 4*L2^2.</p> <p>After these headerlines, the data is presented in tab-delimited columns, as<br> follows for the "SPLIT"-files:</p> <ul> <li>column 1: conformation value (mid-bin position)</li> <li>column 2: number of successful placings in that bin (i.e., compatible with wall confinement)</li> <li>column 3: number of attempted placings in that bin</li> <li>column 4: (not used)</li> </ul> <p>(where the ratio of column 2 to column 3 gives the partition coefficient), whereas for the "WangLandau-MERGED"-files the columns represent the following:</p> <ul> <li>column 1: conformation value (mid-bin position)</li> <li>column 2: natural logarithm of the partition coefficient</li> </ul> <p>Details are explained in the paper mentioned above.</p>
Materials for Design Open Repository. High Entropy Alloys
<p>The current dataset is composed of a collection of High Entropy Alloys (HEAs). It contains the alloy composition, the number of chemical elements (No), the phase in a simple form (S_Phase), where 4 classes of phases were considered, namely amorphous (AM), intermetallic (IM), solid solution (SS), and solid solution + intermetallic (SS+IM). It contains also a second phase column (Phase), where we added the type of phase present in alloys with SS and repeated the S_Phase entry for the other cases. We have calculated 13 design parameters (see their definition below) used to design HEAs, known as the parametric approach. Finally, a set of columns containing the chemical elements and their corresponding fraction in the alloy is included. This dataset was developed in the framework of the European project ACHIEF for the discovery of novel materials to be used in industrial processes.</p> <ol> <li>Mean atomic radius <em>a</em> (Å) <ul> <li><span class="math-tex">\(a = \displaystyle\sum_{i=1}^{n} c_i r_i\)</span></li> </ul> </li> <li>Atomic size difference δ <ul> <li><span class="math-tex">\(\delta = \sqrt{\displaystyle\sum_{i=1}^{n} c_i \bigg(1 - \dfrac{r_i}{a} \bigg)^2}\)</span></li> </ul> </li> <li>Average melting temperature <em>T<sub>m</sub></em> (K) <ul> <li><span class="math-tex">\(T_m = \displaystyle\sum_{i=1}^{n} c_i T_{mi}\)</span></li> </ul> </li> <li>Average melting temperature standard deviation (K) <ul> <li><span class="math-tex">\(\sigma_{T_m} = \sqrt{\displaystyle\sum_{i=1}^{n} c_i \bigg(1 - \dfrac{T_{mi}}{T_m} \bigg)^2}\)</span></li> </ul> </li> <li>Mixing enthalpy Δ<em>H<sub>mix</sub></em> (kJ/mol) <ul> <li><span class="math-tex">\(\Delta H_{mix} = 4 \displaystyle\sum_{i \neq j} c_i c_j H_{ij}\)</span></li> </ul> </li> <li>Mixing enthalpy standard deviation (kJ/mol) <ul> <li><span class="math-tex">\(\sigma_{\Delta H_{mix}} = \sqrt{\displaystyle\sum_{i \neq j} c_i c_j (H_{ij} - \Delta H_{mix})^2}\)</span></li> </ul> </li> <li>Ideal mixing entropy <em>S<sub>id</sub></em> (<em>R</em>)<strong>*</strong> <ul> <li><span class="math-tex">\(S_{id} = \Delta S_{mix} = -R \displaystyle\sum_{i=1}^{n} c_i \ln c_i\)</span></li> </ul> </li> <li>Electronegativity <em>χ</em> <ul> <li><span class="math-tex">\(\chi = \displaystyle\sum_{i=1}^{n} c_i \chi_i\)</span></li> </ul> </li> <li>Electronegativity difference in a multi-component alloy system <ul> <li><span class="math-tex">\(\Delta\chi = \displaystyle\sqrt{\sum_{i=1}^{n} c_i(\chi_i - \chi)^2}\)</span></li> </ul> </li> <li>Valence electron concentration <em>VEC</em> <ul> <li><span class="math-tex">\(VEC = \displaystyle\sum_{i=1}^{n} c_i \cdot VEC_i\)</span></li> </ul> </li> <li>Valence electron concentration standard deviation <ul> <li><span class="math-tex">\(\sigma_{VEC} = \sqrt{\displaystyle\sum_{i=1}^{n} c_i (VEC_i - VEC)^2}\)</span></li> </ul> </li> <li>Mean bulk modulus <em>K </em>(GPa) <ul> <li><span class="math-tex">\(K = \displaystyle\sum_{i=1}^{n} c_i K_i\)</span></li> </ul> </li> <li>Bulk modulus standard deviation (GPa) <ul> <li><span class="math-tex">\(\sigma_{K} = \sqrt{\displaystyle\sum_{i=1}^{n} c_i (K_i - K)^2}\)</span></li> </ul> </li> <li>Young's modulus <em>E</em> (GPa) <ul> <li><span class="math-tex">\(E = \displaystyle\sum_{i=1}^{n} c_i E_i\)</span></li> </ul> </li> <li>Shear modulus <em>G</em> (GPa) <ul> <li><span class="math-tex">\(G = \displaystyle\sum_{i=1}^{n} c_i G_i\)</span></li> </ul> </li> </ol> <p>where <em>n</em> is the number of components in the alloy system, <em>c<sub>i</sub></em> is the stoichiometric ratio, <em>r<sub>i</sub></em> is the atomic radius, <em>T<sub>mi</sub></em> is the melting temperature, <em>χ<sub>i</sub></em> is the Pauli electronegativity, <em>VEC<sub>i</sub></em> is the valence electron concentration, and <em>K<sub>i</sub></em> is the bulk modulus, <em>E<sub>i</sub></em> is the Young's modulus, and <em>G<sub>i</sub></em> is shear modulus for the <em>i</em>-th component of the alloy. <em>H<sub>ij</sub></em> is the binary mixing enthalpy in the liquid phase, and <em>R</em> is the gas constant.</p> <p><strong>*Note:</strong> the ideal mixing entropy <em>S<sub>id</sub></em> units in the first version of the dataset appear as kJ/mol, but they should be written in terms of the gas constant <em>R</em>, e.g., the compound Ag<sub>2</sub>Al has <em>S<sub>id</sub></em> = 0.636 <em>R</em>, where <em>R</em> = 8.314 J · K<sup>−1</sup> · mol<sup>−1</sup>. The second version the <em>S<sub>id</sub></em> units are corrected and two new features are included.</p>
Development and analysis of entropy stable no-slip wall boundary conditions for the Eulerian model for viscous and heat conducting compressible flows
<p>The database used in the submission of "Development and analysis of entropy stable no-slip wall boundary conditions implementation of the Eulerian model for viscous and heat conducting compressible flows."</p> <p>Abstract: Nonlinear entropy stability analysis is used to derive entropy stable no-slip wall boundary conditions for the Eulerian model proposed by Svärd ( <em>Physica A: Statistical Mechanics and its Applications, 2018 </em>). and its spatial discretization based on entropy stable collocated discontinuous Galerkin operators with the summation-by-parts property for unstructured grids. A set of viscous test cases of increasing complexity are simulated using both the Eulerian and the classic compressible Navier–Stokes models. The numerical results obtained with the two models are compared, and differences and similarities are then highlighted.</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.