Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
233
datasets available to search
ShareScore release 0.7.1
Dataset results
233 results for “kernel”
Quantifying the Sensitivity of Sea Level Change in Coastal Localities to the Geometry of Polar Ice Mass Flux -- Supplemental Data Set: Sea Level Sensitivity Kernels
<p><strong>Quantifying the Sensitivity of Sea Level Change in Coastal Localities to the Geometry of Polar Ice Mass Flux<br> SUPPLEMENTAL DATA SET: SEA LEVEL SENSITIVITY KERNELS</strong></p> <p>To accompany</p> <p> Jerry X. Mitrovica, Carling C. Hay, Robert E. Kopp, Christopher Harig, and<br> Konstantin Laytchev (2018). Quantifying the Sensitivity of Sea Level Change<br> in Coastal Localities to the Geometry of Polar Ice Mass Flux. Journal of<br> Climate. doi: 10.1175/JCLI-D-17-0465.1.</p> <p>We provide sea level kernels for ~740 tide gauge sites in the Permanent Service for Mean Sea Level (PSMSL) database (Holgate et al., 2013). Kernels associated with sensitivities to Greenland and Alaskan glacier melt are given on a spatial grid covering the globe, with 512 latitude rows (i=1,512) and 1024 longitude (j=1,1024) columns.</p> <p>Longitude values are evenly spaced moving eastward from Greenwich (the jth grid point has an east longitude value of (j-1)×360°/1024). Latitude values are Gauss-Legendre points beginning close to the North Pole and ending near the South Pole. Kernels associated with sensitivities to Antarctic melt are given on a spatial grid covering the globe, with 256 (Gauss-Legendre) latitude rows (i=1,256) and 512 longitude (j=1,512) columns. Longitude values are evenly spaced moving eastward from Greenwich.</p> <p>The format of the files is: </p> <p> grid_sitenumber_region.txt</p> <p>where “region” is either “green” (Greenland), “ant” (Antarctic) or “Alaska” (Alaska). The list of sites (and site numbers) is provided in the sites.txt file. The first 8 sites in this list were test sites and can be ignored.</p>
MCMC chains for demographic fits presented in "NICMOS Kernel-Phase Interferometry II: Demographics of Nearby Brown Dwarfs"
<p>These files are the data behind the figure for Figure 3 (and the corresponding Figure Set) as well as other fits presented in Table 5. They are saved in <a href="https://numpy.org/doc/stable/reference/generated/numpy.lib.format.html">npy</a> format which can be read into python using numpy according to the code snippet below.</p> <p>The files are flattened and trimmed MCMC chains produced by running emcee (Foreman-Mackey et al. 2013) using 64 walkers for 10,000 steps. The first 1,000 steps were trimmed for burn in and the remaining chains were thinned by 40 steps.</p> <p>The files are named according to the following convention: flatSamples<malm cor><age><prior>.npy where:</p> <p><malm cor> is either 'Malm' or '' (nothing) if the model population was or was not corrected for Malmquist bias (before comparing to the observed population while fitting).</p> <p><age> is '0p9', '1p2', '1p5', '1p9', '2p4', or '3p1' according to that assumed field age (in Gyr).</p> <p><prior> is 'U' or 'I' for uninformed or informed (incorporating the information from Blake et al. 2010 on the unresolved population).</p> <p>The true underlying population corresponds to the flatSamplesMalm<age>I.npy files while the others are included for context and comparison to populations fit to the observed (not Malmquist corrected) population. The uninformed prior chains are dominated by a significant population of unresolved companions which is not consistent with previous RV studies.</p> <p>The files can be read into python using:</p> <pre><code class="language-python">import numpy as np flat_samples0p9I = np.load('flatSamples0p9I.npy') </code></pre> <p>which produces an array with shape 14400 x 4. The rows are the samples and the four columns are the parameters <span class="math-tex">\(F, \gamma, \overline{\log(\rho)}\)</span>, and <span class="math-tex">\(\sigma_{\log(\rho)}\)</span>, respectively.</p>
Arctic Gridded surface cloud fraction radiative kernels (GCF-CRKs)
<p><span>These <a name="OLE_LINK1"></a>gridded surface cloud fraction radiative kernels (GCF-CRKs) are created by integrating refined downwelling surface shortwave radiation (DSSR) estimates and a high-precision cloud fraction (CF). The DSSR is corrected by a CF-dependent model, which leveraging the correlation between the top-of-atmosphere (TOA) shortwave radiative parameters and surface radiation, combined with high-precision fused CF datasets from multiple satellite sources. </span></p> <p><span><span> </span>There are five individual files. “SFC_SW_Kernel_Arc.nc” is for CRKs of all clouds, “SFC_SW_lowcloud_Kernel_Arc.nc” is for CRKs of low-level clouds, “SFC_SW_midlowcloud_Kernel_Arc.nc” is for CRKs of mid-low-level clouds, “SFC_SW_midhighcloud_Kernel_Arc.nc” is for CRKs of mid-high-level clouds, and “SFC_SW_highcloud_Kernel_Arc.nc” is for CRKs of high-level clouds. The four cloud layers are derived from four pressure layers (surface to 700 hPa, 700-500 hPa, 500-300 hPa, and 300-50 hPa, representing low, middle-low, middle-high, and high clouds, respectively) based on the CERES-SYN stratification standard.</span></p> <p><span> </span></p> <p><span>The file format is netcdf4, and was created by Matlab. To read these files, any software supporting netcdf4 can be used. These files only involved sunlit months from Apr to Sep during 2000-2020, with the longitude from -180°~180° and the latitude from 60°N~90°N.</span></p>
Dataset: Mapping intrinsic and scattering attenuation in the southern Aegean crust using S-wave envelope inversion and sensitivity kernels derived from perturbation theory
<p><strong>Data Set S1: </strong>File “ds01.csv” contains the catalogue of relocated events used in this study. The columns in the file represent origin time (in year-month-day’H’hour’M’minute’S’seconds format), event longitude, event latitude, event depth in a sequential manner.</p> <p><strong>Data Set S2: </strong>File “ds02.zip” contains four ASCII data files (ray_prmtrs12.txt, ray_prmtrs24.txt, ray_prmtrs48.txt and ray_prmtrs816.txt). The data files contain scattering coefficient (<em>g<sup>*</sup></em>) and intrinsic coefficient (<em>b</em>) values in 1-2, 2-4 Hz, 4-8 Hz and 8-16 Hz bands respectively. The columns in the text files represent event latitude, event longitude, event depth, station latitude, station longitude, station velocity, envelope duration, <em>g<sup>*</sup></em>, <em>b</em>, early-S window length, percentage error in early-S window, percentage error for full envelope, and event origin time in a sequential manner.</p> <p><strong>Data Set S3: </strong>File “ds03.zip” contains four data files (envnodes15g_3_3_1-2.txt, envnodes15g_3_3_2-4.txt, envnodes15g_3_3_4-8.txt, and envnodes15g_3_3_8-16.txt), one BASH script containing GMT and Octave commands (Qs_envg.gmt), and a lat-long coordinate file (SAegean_poly_coord_extnd.txt) to mask the area outside the seismic network. The data files contain log<sub>10</sub>(<em>Q<sub>sc</sub></em><sup>-1</sup>) values in 1-2, 2-4, 4-8 and 8-16 Hz bands respectively. The columns in the text files represent node latitude, node longitude and log<sub>10</sub>(<em>Q<sub>sc</sub></em><sup>-1</sup>) value of the node sequentially. This GMT script also uses GSHHG coastline data whose path can be added to the script by changing the value of variable GDIR at the beginning of the script. The BASH script file can be run to see the spatial distribution of log<sub>10</sub>(<em>Q<sub>sc</sub></em><sup>-1</sup>) using GMT-6 (Wessel et al., 2019) and Octave version 5.1 and above.</p> <p><strong>Data Set S4: </strong>File “ds04.zip” contains four data files (envnodes15b_3_3_1-2.txt, envnodes15b_3_3_2-4.txt, envnodes15b_3_3_4-8.txt, and envnodes15b_3_3_8-16.txt), one BASH script containing GMT and Octave commands (Qi_env.gmt), and a lat-long coordinate file (SAegean_poly_coord_extnd.txt) to mask the area outside the seismic network. The data files contain log<sub>10</sub>(<em>Q<sub>i</sub></em><sup>-1</sup>) values in 1-2, 2-4, 4-8 and 8-16 Hz bands respectively. The columns in the text files represent node latitude, node longitude and log<sub>10</sub>(<em>Q<sub>i</sub></em><sup>-1</sup>) value of the node sequentially. This GMT script also uses GSHHG coastline data whose path can be added to the script by changing the value of variable GDIR at the beginning of the script. The BASH script file can be run to see the spatial distribution of log<sub>10</sub>(<em>Q<sub>i</sub></em><sup>-1</sup>) using GMT-6 (Wessel et al., 2019) and Octave version 5.1 and above.</p> <p><strong>Data Set S5: </strong>File “ds05.zip” contains four data files (envnodes15a_3_3_1-2.txt, envnodes15a_3_3_2-4.txt, envnodes15a_3_3_4-8.txt, and envnodes15a_3_3_8-16.txt), one BASH script containing GMT and Octave commands (albd_env.gmt), and a lat-long coordinate file (SAegean_poly_coord_extnd.txt) to mask the area outside the seismic network. The data files contain Albedo (<em>B<sub>o</sub></em>) as % values in 1-2, 2-4, 4-8 and 8-16 Hz bands respectively. The columns in the text files represent node latitude, node longitude and <em>B<sub>o</sub></em> value of the node sequentially. This GMT script also uses GSHHG coastline data whose path can be added to the script by changing the value of variable GDIR at the beginning of the script. The BASH script file can be run to see the spatial distribution of <em>B<sub>o</sub></em> using GMT-6 (Wessel et al., 2019) and Octave version 5.1 and above. </p>
Figure Sets and Data Associated with AJ Publication: "NICMOS Kernel-Phase Interferometry I: Catalogue of Brown Dwarfs Observed in F110W and F170M"
<p>Images for Figure Sets 4, 5, 6, 7, and 9 and data behind the figure for Figure 15 from the AJ publication "NICMOS Kernel-Phase Interferometry I: Catalogue of Brown Dwarfs Observed in F110W and F170M" (Currently accepted and in press.). Figure sets and file names are described in the fsREADME file. Data behind the figure is described in the dbfREADME file.</p>
End-condition for solution small angle X-ray scattering measurements by kernel density estimation
<p>The set of python scripts and some datasets for estimating the minimum X-ray exposure time for X-ray solution scattering experiments using statistical and mathematical approaches.</p> <p>We apply a statistical inequality to estimate the kernel density estimation (KDE) method’s error to determine the minimum X-ray exposure time.</p> <p>Please refer to the following article, </p> <p>End-condition for solution small angle X-ray scattering measurements by kernel density estimation<br> Science and Technology of Advanced Materials: Methods, Volume 2 Issue 1, pages 426-434 (2022)<br> DOI: 10.1080/27660400.2022.2140021<br> <a href="https://doi.org/10.1080/27660400.2022.2140021">https://doi.org/10.1080/27660400.2022.2140021</a></p>
NLO QCD Track Evolution Kernels for Fourier and Wavelet Methods
<p>Two datasets of LO and NLO evolution numerical kernels for track functions, one for the Fourier series method and the other for the Legendre wavelet method. We also include the corresponding Julia code to numerically solve the track evolution equation based on these kernels. These datasets are used to build docker images for <a href="https://hub.docker.com/r/haochern/qcd-track-evolution-fourier">Fourier</a> and <a href="https://hub.docker.com/r/haochern/qcd-track-evolution-wavelet">wavelet</a> approaches. More details of instructions, as well as the moment method to the track evolution, can be found on <a href="https://github.com/HaoChern14/Track-Evolution">https://github.com/HaoChern14/Track-Evolution</a>. </p> <p> </p>
Figure reproduction for "Accelerating small angle scattering experiments on anisotropic samples using kernel density estimation"
<p>These datasets and a Jupyter notebook reproduce figures in <a href="https://www.nature.com/articles/s41598-018-37345-5">a publication by Saito et al in Scientific Reports</a>. The notebook also serves as a demo for kernel density estimation (smoothing) of 2D data using Python. Details are described in the notebook. If you have no idea about ipynb format, please see HTML version with your web browser instead. It contains exactly the same codes and results as ipynb version.</p>
Data for Weighted Manifold Alignment using Wave Kernel Signatures for Aligning Medical image Datasets
<p>Data used in MRI experiments in paper 'Weighted Manifold Alignment using Wave Kernel Signatures for Aligning Medical image Datasets'. For each volunteer, breath-hold data (folder bhs) and dynamic free-breathing (folder dyn) data is provided in NIFTI format.</p>
Software, Dataset, and Techreport: Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration
<p>This upload contains a techreport titled "Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration" together with the software (with documentation) and dataset generating the results. The software is also available on GitHub at https://github.com/croci/mpfem-paper-experiments-2024/ . The GitHub version may be updated in the future. This upload corresponds to commit number 8506dd368b84655201c8c72b1307239b9b4e43fd . See README.md file for installation instructions. The manuscript is also available on the arXiv: https://arxiv.org/abs/2410.12614.</p>
Radiative kernels for Isca v1.0
<p>The radiative kernels for <a href="https://github.com/ExeClim/Isca">Isca</a> v1.0, in which the basic states are from simulations (T42, 25 vertical levels) without sea ice and clouds. Here we provided radiative kernels for a hierarchy of radiation schemes in Isca, including:</p> <ul> <li>Frierson scheme (gray radiation scheme without water vapor feedback): temperature radiative kernel at the top of the atmosphere (TOA) and surface temperature radiative kernel.</li> <li>Byrne and O'Gorman (BOG) scheme (gray radiation scheme, but with water vapor feedback): temperature and water vapor radiative kernels at TOA and surface temperature radiative kernel.</li> <li>RRTM scheme (full radiation scheme): Similar as the BOG scheme, but only zonal mean results.</li> </ul> <p>The scripts to calculate the offline radiative kernels can be found at: <a href="https://github.com/lqxyz/Isca_kernels">https://github.com/lqxyz/Isca_kernels</a>, and the input data is available at DOI:<a href="https://doi.org/10.5281/zenodo.4071837">10.5281/zenodo.4071837</a>.</p>
Data used in "Fast metabolite identification with Input Output Kernel Regression"
<p>This repository contains the data used in [1] to evaluate the performance for metabolite identification from tandem mass spectra. These data have been extracted and processed in [2]. We used a subset of 4138 MS/MS spectra extracted from the GNPS public spectral library (https://gnps.ucsd.edu/ProteoSAFe/libraries.jsp) for training and evaluation. For searching, we used molecular structures from PubChem as candidate sets.</p> <p>Please mention and cite GNPS when using these data.</p> <p>The implementation of the method proposed in [1] is available on: https://version.aalto.fi/gitlab/kepaco/Fast-metabolite-identification-with-IOKR</p> <p><strong>Files description:</strong></p> <ul> <li><em>spectra.txt</em>: informations about the MS/MS spectra (GNPS identifier, compound name and INCHI identifier)</li> <li><em>data_GNPS.mat</em>: contains the molecular fingerprints, molecular formula and InCHI corresponding to the MS/MS spectra</li> <li><em>cv_ind.txt</em>: indices of the cross-validation folds</li> <li><em>ind_eval.txt</em>: indices of the examples used for evaluation</li> <li><em>candidates</em>: fingerprints and INCHI for the different candidate sets</li> <li><em>input_kernels</em>: contains 24 input kernel matrices</li> </ul> <p><strong>References:</strong></p> <p>[1] Brouard, C., Shen, H., Dührkop, K., d'Alché-Buc, F., Böcker, S. and Rousu, J.: Fast metabolite identification with Input Output Kernel Regression. In the proceedings of ISMB 2016, Bioinformatics 32(12): i28-i36, 2016. DOI: https://doi.org/10.1093/bioinformatics/btw246</p> <p>[2] Dührkop, K., Shen, H., Meusel, M., Rousu, J. and Böcker, S.: Searching molecular structure databases with tandem mass spectra using CSI:FingerID. PNAS, 112(41), 12580-12585, 2015. doi:10.1073/pnas.1509788112</p> <p> </p> <p> </p>
Datasets for the article "The temperature and density of a solar flare kernel measured from extreme ultraviolet lines of O IV"
<p>This entry contains the following files:</p><p>20120309_030933_kernel_fe8_shift.save<br>20120309_030933_kernel_fe8_shift_fits.txt<br>20110814_055342_qs_offlimb_si10.save<br>20110814_055342_qs_offlimb_si10_fits.txt</p><p>The .save files are IDL save files that can be restored into IDL using the restore command.</p><p>The 20120309 save file contains:</p><p>swspec - An IDL structure containing a 1D spectrum of the flare kernel for the EIS short wavelength (SW) channel. The format is that returned by eis-mask-spectrum.pro.<br>lwspec - As above, but for the long-wavelength (LW) channel.<br>map185 - An IDL map structure containing the Fe VIII 185.21 image that was used to select the flare kernel.<br>mask185 - An IDL structure containing the pixel mask that is used as input to eis-mask-spectrum.pro.</p><p>The Gaussian fits to the spectra (as performed with the routine spec-gauss-eis.pro) are stored in 20120309_030933_kernel_fe8<i>s</i>hift_fits.txt. This file can be read with read_line_fits.pro in Solarsoft.</p><p>The 20110814 dataset is used to obtain an off-limb coronal spectrum for calibration purposes. The save file contains:</p><p>swspec - An IDL structure containing a 1D spectrum of the off-limb region for the EIS SW channel. The format is that returned by eis-mask-spectrum.pro.<br>lwspec - As above, but for the LW channel.<br>map - An IDL map structure containing the Si X 272 image that was used to select off-limb region.<br>mask - An IDL structure containing the pixel mask that is used as input to eis-mask-spectrum.pro.</p><p>The Gaussian fits to the spectra (as performed with the routine spec-gauss-eis.pro) are stored in 20110814_055342_qs_offlimb_si10_fits.txt. This file can be read with read_line_fits.pro in Solarsoft. </p><p> </p><p> </p><p> </p><p> </p><p> </p><p> </p><p> </p>
Error bounds for kernel-based approximations of the Koopman operator
<p>This repository contains python scripts and data to re-create the result shown in</p><p>`Error bounds for kernel-based approximations of the Koopman operator, arxiv:2301.08637`</p><p>See README for detailed instructions on how to re-create these data.</p>
Duferco forecast results using Kernel Ridge
<p>The data uploaded represent thirteen months of Duferco forecast (June 2021 - June 2022) using Kernel Ridge regression with hourly granularity for the Calabria wind farm.</p> <p>The three csv files represents the three different methods developed and tested during the project:</p> <ol> <li><strong>BL:</strong> Kernel Ridge regression with gaussian kernel (Baseline).</li> <li><strong>PC:</strong> The Baseline method with the pre-processing of the data using the power curve.</li> <li><strong>PC+K: </strong>The Baseline method with the pre-processing of the data using the power curve, and the post-processing of the forecast using Kalman smoothing filter.</li> </ol> <p> </p>
DongTing: A Large-scale Dataset for Anomaly Detection of the Linux Kernel
<p>DongTing is the first large-scale dataset dedicated to Linux kernel anomaly detection. The dataset covers Linux kernels released in the last five years and includes a total of 18,966 well-labeled normal and attack sequences. The entire dataset is 85 GB in size (after decompression). The attack data covers 26 major kernel releases and contains a total of 12,116 system call sequences collected from running 17,855 bug-triggering programs. The normal data comes from 6,850 normal programs in four kernel regression test suites. We maintain the dataset and source code in Zenodo and Github, respectively, and back up the dataset and code in Baidu netdisk.</p> <h3><strong>Dataset</strong></h3> <p>The dataset is stored at <a href="http://doi.org/10.5281/zenodo.6627050">http://doi.org/10.5281/zenodo.6627050</a></p> <ul> <li>The data includes `abnormal_data`, `normal_data`, `models`, `npz` and baseline data, with a total volume of nearly 87 GB (including 85 GB for abnormal data and normal data, it's after decompression files size).</li> <li>The `Abnormal_data` directory contains 12,116 files containing system call sequence for 26 kernel releases, and the `Normal_data` directory contains 6,850 files containing system call sequences collected from four regression test suites. All of which are raw sequences.</li> <li>CNN/RNN, LSTM, and Wavenet (three sets of hyperparameters per model) machine learning models are selected, the ECOD model (without hyperparameters) was also chosen for the evaluation of DT. DT_abnormal, DT_normal, ADFA-LD, and PLAID are used for training respectively. The results of DT training models are stored in the directory `Models-DongTing`, and the results of ADFA-LD and PLAID training models are stored in the directory `Models-Comparison`.</li> <li>The directory `npz `stores the encoded dataset of DongTing, ADFA-LD, and PLAID (sequence length varies from 8 to 4495), according to syscall_64.tbl in Linux kernel 5.17, including the training set, validation set, and test set.</li> <li>The file `Baseline.xlsx` contains all the information about DongTing dataset, which can be used in training machine learning models. For example, the whole dataset is randomly divided into three sets with the ratio of 80%:10%:10% (training: validation: test). The implementation of dataset division can be found in the source code.</li> </ul> <h3><strong>Source Code</strong></h3> <p><br>The source code for dataset development is stored at <a href="https://github.com/HNUSystemsLab/DongTing">https://github.com/HNUSystemsLab/DongTing</a> and the following is a brief introduction.</p> <ul> <li>The source code contains three folders, i.e., `Source Code Files`, `Documents` and `DB`, where `Documents `stores the detailed documents related to development, `DB` stores samples data, and `Source Code Files` stores the source code related to the development of our dataset.</li> <li>The detailed description about the source code can be found in `Documents/Documentation.pdf`. The document consists of four parts: environment requirements, database, program structure and working steps, model training and evaluation (including training and evaluation). It details the preparation of the environment, data import method, functional description of each file in the source code directory, how model training and evaluation work and other related contents.</li> </ul> <p>We additionally maintain the dataset and source code on Baidu.com <a href="https://pan.baidu.com/s/1vu1WGZpf2DqMIoyGayNu3w?pwd=dtds">https://pan.baidu.com/s/1vu1WGZpf2DqMIoyGayNu3w?pwd=dtds</a> to facilitate the access from China.</p> <p> </p> <h3>Tips: </h3> <p>If you find DongTing useful for your research, please cite the article as "DongTing: A large-scale dataset for anomaly detection of the Linux kernel".</p> <blockquote> <p><br>@article{DUAN2023111745,<br>title = {DongTing: A large-scale dataset for anomaly detection of the Linux kernel},<br>journal = {Journal of Systems and Software},<br>volume = {203},<br>pages = {111745},<br>year = {2023},<br>issn = {0164-1212},<br>doi = {https://doi.org/10.1016/j.jss.2023.111745},<br>url = {https://www.sciencedirect.com/science/article/pii/S0164121223001401},<br>author = {Guoyun Duan and Yuanzhi Fu and Minjie Cai and Hao Chen and Jianhua Sun}<br>}<br><br></p> </blockquote>
Dataset: Kernel Group Holdings, Inc. (KRNLW) 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: Kernel Group Holdings, Inc. (KRNLU) 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: Kernel Group Holdings, Inc. (KRNL) 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.
Efficient NAS Benchmark Kernels with C++ Parallel Programming Frameworks for Multi-Cores
<p>Benchmarking is a way to study the performance of new architectures and parallel programming frameworks. Well-established benchmark suites such as the NAS Parallel Benchmarks (NPB) comprise legacy codes that still lack portability to C++ language. As consequence, a set of high-level and easy-to-use C++ parallel programming frameworks cannot be tested in NPB. Our goal is to describe a C++ porting of the NPB kernels and to analyze the performance achieved by different parallel implementations written using the Intel TBB, OpenMP and FastFlow frameworks for Multi-Cores. The experiments show an efficient code porting from Fortran to C++ and a good parallel efficiency on average.</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.