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1,891 results for “Field data”
Data for MILP algorithm for control strategy optimisation of a hybrid solar thermal plant consisting of two different solar fields working for two processes at different temperatures, a three tanks storage and a boiler for SHIP applications
<p>This repository contains the results of simulations exposed in the article <strong>MILP algorithm for control strategy optimisation of a hybrid solar thermal plant consisting of two different solar fields working for two processes at different temperatures, a three tanks storage and a boiler for SHIP applications.</strong></p> <p>Notebooks for easy plotting of the results are contained in this repository. The README.txt contains light explainations regarding how to use those notebooks.</p> <p>This article is yet to be published at the time of writing.</p> <p> </p>
Seismic Magnitude Clustering is Prevalent in Field and Laboratory Catalogs [DATA]
<p>Catalogs for Nature Communications article: Seismic Magnitude Clustering is Prevalent in Field and Laboratory Catalogs.</p> <p> </p> <p>Update: In DataVariableExplanation, two catalogs from University of Minnesota</p> <p>Mixed mode and mode I bending data description needs to show that the third column is in seconds.</p>
QMC Raw Data for Stable Auxiliary Field Quantum Monte Carlo Algorithm in the Canonical Ensemble
<p><strong>Data Summary</strong></p> <p>Raw data of 'A Stable, Recursive Auxiliary Field Quantum Monte Carlo Algorithm in the Canonical Ensemble: Applications to Thermometry and the Hubbard Model'.</p> <p>Random seeds are generated using the default Julia RNG, with the seed number being 1234+the file ID.</p> <p>For more details, please check README.md on the GitHub repository.</p> <p> </p> <p><strong>Fidelity_Lx6Ly6_U(2.0|4.0).zip</strong></p> <ul> <li>Raw data for fidelity measurements that correspond to plot_fidelity.ipynb</li> <li>The random seeds for numerator measurements ('Fidelity(CE|GCE)_num.*') are 5678+the file ID</li> </ul> <p><strong>Purity_(CE|GCE)_Lx6Ly6_U(2.0|4.0).zip</strong></p> <ul> <li>Raw data for purity measurements that correspond to plot_purity.ipynb</li> <li>The random seeds for numerator measurements ('Purity(CE|GCE)_num.*') are 5678+the file ID</li> </ul> <p><strong>StructFactGCE_Lx6_Ly6_U2.0.zip</strong></p> <ul> <li>Raw data for the momentum distribution and structural factor measurements that correspond to plot_nk.ipynb and plot_Cq.ipynb</li> </ul>
Two data sets of gravitational field degree-2 order-1 Stokes coefficient
<p>The SLR Stokes coefficient data are provided by the Space Research Institute (IWF) of the Austrian Academy of Sciences based on SLR observations coordinated by ILRS. The GRACE satellite data are from the Center for Space Research RL06 solutions. All ocean tides, Earth tides and polar tides have been removed.</p>
Supplementary Data to "Simulation of dendritic-eutectic growth with the phase-field method" by Seiz et al.
<p>Video files for several simulations conducted for the paper, showing more of the dynamic time evolution than possible in the paper itself.</p> <p> </p> <p>Update 24/04/2023: A few additional simulations were conducted to test for the applicability of the theory delineating the dendritic-eutectic regime from the eutectic regime. Videos of these plus some additional data is deposited at</p> <p> </p> <p>https://zenodo.org/record/7858461</p> <p> </p> <p><br> All videos show the Cu composition field, with the color ranging from 0.02 (pure black) to 0.33 (pure white).<br> Thus black represents the fcc Al crystal, whitish-grey the Al2Cu intermetallic phase and grey shades in between the liquid melt, with lighter shades being richer in Cu.<br> Excluding the complete directional solidification videos (full*webm), all videos show regions of 280x250um^2, with the far-field to the right being cut off to emphasize the structure.<br> <br> {close,far}_d+e.webm:<br> Complete simulations resulting in a eutectic structure either growing close to the dendrite tip or far from it, cropped to slightly above the solidification front.<br> The same speed v=160um/s and melt composition c_0=0.12 are used, but two different gradients: 99K/mm for close growth and 24.7K/mm for far growth; at the even smaller gradient the eutectic is no longer in the moving window.<br> <br> traveling_oscillation.webm:<br> Complete simulation resulting in a eutectic with traveling oscillations. (v=160um/s, c_0=0.13, G=6.18K/mm)<br> <br> jump_d+e_e.webm:<br> Jumps from v = 160um/s to 320um/s at simulation start in order to move from a dendritic-eutectic morphology to a eutectic morphology.<br> After a eutectic morphology is obtained, the jump is reversed (around 17s into the video) and only a coarsening of the eutectic is observed.<br> <br> jump_e_d+e.webm:<br> Jumps from v=320um/s to 20um/s at simulation start in order to move from a eutectic morphology to a dendritic-eutectic morphology.<br> <br> full_cropped*webm:<br> Complete directional solidification for different alloy compositions and processing conditions yielding different structures. Cropped to slightly above the final maximum position of any solid phase, showing a 970x500um^2 domain.<br> A scaling to 50% of the original resolution is performed as some players/browsers have trouble with large resolutions.<br> e: primarily eutectic (v=320um/s, G=24.7K/mm, c_0 = 0.12)<br> d+e: dendritic-eutectic (v=160um/s, G=24.7K/mm, c_0 = 0.12)<br> <br> full_d.webm:<br> Same as above, only non-cropped as the structure fills the entire simulation box (1500x500um^2). (v=320um/s, G=24.7K/mm, c_0 = 0.08)</p>
Data from: Assessing the deep carbon release in an active volcanic field using hydrochemistry, δ13CDIC and Δ14CDIC
<p><span>Volcanic activities have great implications on geological carbon cycle, and ascertaining the deep carbon contribution in earth surface that run along the volcanic edifices is important to understand the relationship between earth degassing and global climate change. This study reports analytical results of major dissolved ions, stable carbon isotope (δ<sup>13</sup>CDIC) and radiocarbon (Δ<sup>14</sup>CDIC) of dissolved inorganic carbon (DIC) of rivers, cold springs and hot springs from Changbaishan volcanic area, Northeast China. </span></p>
MD Simulation data for a pure DOPC bilayer without salt with AMOEBA force field + OpenMM
<p>MD simulation data for the DOPC bilayer with the AMOEBA-based force field developed by Li (<a href="https://doi.org/10.1080/00268976.2018.1436201">https://doi.org/10.1080/00268976.2018.1436201</a>).</p> <p>The simulation contains 72 DOPC lipids and 2880 water molecules. The trajectory is 201,61 ns long (20161 frames with 10 ps saving frequency).</p> <p><strong>It has been discovered that (https://github.com/NMRLipids/Databank/issues/2#issuecomment-1357871243) the previously uploaded trajectory did not have the correct timestamp: the timestep between two consecutive simulation frames was not correctly embedded into the trajectory information. Therefore, with the latest version we are uploading the "unwrapped_all_fixed_dt.xtc" which has the correct timestamp. The frame saving frequency in this trajectory is 10 ps. </strong></p> <p><strong>This new update should not invalidate any previous calculations that did not explicitly read the timestamp information from the trajectory.</strong></p> <p><strong>This simulation consists of 12 sub-trajectories, each of which starts from the last frame of the previous one. These trajectories (originally in dcd format) were concatenated and saved in xtc format with MDAnalysis.</strong></p> <p> </p>
MD Simulation data for a pure POPE bilayer with AMOEBA force field + OpenMM
<p>MD simulation data for the POPE bilayer with the AMOEBA-based force field developed by Li (<a href="https://doi.org/10.1080/00268976.2018.1436201">https://doi.org/10.1080/00268976.2018.1436201</a>).</p> <p>The simulation contains 72 POPE lipids and 2880 water molecules. The trajectory is 305,94 ns long (30594 frames with 10 ps saving frequency).</p> <p><strong>It has been discovered that (https://github.com/NMRLipids/Databank/issues/2#issuecomment-1357871243) the openmm_combined.dcd trajectory did not have the correct timestamp: the timestep between two consecutive simulation frames was not correctly embedded into the trajectory information. Therefore, with the latest version we are uploading the "wrapped_full.xtc" which has the correct timestamp. The frame saving frequency in this trajectory is 10 ps. The correction to the timestamp was done via MDAnalysis.</strong></p> <p><strong>This new update should not invalidate any previous calculations that did not explicitly read the timestamp information from the trajectory.</strong></p> <p> </p>
Raw data for 'Signature of spin triplet exciton condensations in LaCoO3 at ultrahigh magnetic fields up to 600 T'
<p>Raw data for the paper below</p> <p>Signature of spin triplet exciton condensations in LaCoO3 at ultrahigh magnetic fields up to 600 T<br> A. Ikeda, Y. H. Matsuda, K. Sato, Y. Ishii, H. Sawabe, D. Nakamura, S. Takeyama, J. Nasu<br> arXiv:2201.02704</p> <p> </p>
Data of homogeneity of the photocathode in the Hamamatsu R15458-02 PMT under different magnetic field orientations
<p>The Hamamatsu R15458-02 PMT's photocathode was scanned with a collimated beam at 777 different positions to determine the relative transit time, TTS and gain. This measurement was performed at 192 different magnetic field orientations with a strength of 54.7 microtesla. The analyzed data is available in a JSON file.<br> To access the results for example in Python, you can import the data to a dictionary, and retrieve the values as follows:</p> <ul> <li> <p>The relative transit time is stored in the "RelTransitTime" key, TTS in "TTS," and gain in "Gain." Each of these keys contains a list of 192 lists, with each list containing 777 values.</p> </li> <li> <p>The 192 lists for each key correspond to scans measured at different magnetic fields. The "B_field" key retrieves the corresponding normalized magnetic field direction, which is a list of 192x3 elements.</p> </li> <li> <p>The 777 values in each list correspond to the value at different positions on the photocathode. You can access these positions using the "x" and "y" keys.</p> </li> </ul> <p>For further details, you can refer to Chapter 7 of M. Unland thesis "Development, simulation, and characterisation of a novel multi-PMT optical module for IceCube Upgrade with emphasis on detailed understanding of photomultiplier performance parameters" published in 2023.</p>
Data from: Developing a database of Australian grasshopper occurrences from historic field survey notebooks spanning 54 years (Orthoptera: Acrididae, Morabidae, Pyrgomorphidae, Tetrigidae)
<p>The baseline distribution data for all species of a given group in a region can provide fundamental insights into biogeographic questions about historic patterns of species richness, population trends, and extinction. Grasshoppers are one major group of insects for which a continent-wide perspective on their geographic distribution can be obtained. This is because they were extensively surveyed in Australia for 54 years (1936-1989) as part of Commonwealth expeditions to obtain specimens for the Australian National Insect Collection (ANIC). Field notebooks recorded from those surveys, under the direction of ANIC curator and director K. H. L. Key, form the principal source of historic distribution records for grasshoppers in Australia. We digitized all 223 notebooks (2486 pages) and transcribed all the field trips conducted in Western Australia (WA) and Tasmania (47 notebooks, 590 pages). We then carefully geocoded all sampling sites of the transcribed notebooks, following the odometer readings and descriptions of routes from a suitable reference point using historic topographic maps and Google Earth. In total, we extracted 8975 geographic coordinates for 477 species having a confirmed or putative taxonomic name of (only 170 of these species have been formally described). We found that species richness varied spatially, with highest richness in arid interior and north of WA. Historic grasshopper surveys were non-randomly distributed across both WA and Tasmania with the highest survey intensity around coastal regions. Variation was observed among surveyors in terms of the number of species detected per site, between-site distance, and the season of survey being conducted. Overall, however, the dataset is among the most comprehensive continent-wide surveys of Australian invertebrates and will greatly facilitate future work on their ecology, biogeography, conservation, and responses to environmental change.</p>
Relative Phase Data to 'Experimental observation of curved light-cones in a quantum field simulator', arXiv:2209.09132
<p><strong>Relative phase profiles and averaged density profiles for the results shown in arXiv:2209.09132</strong></p> <p>Each file "phase_and_mean_density_scan_X.mat" contains data for a measurement presented in the manuscript, where "X" is the corresponding scan number.<br> The following table shows the relevant scan number to measurement descriptions mentioned in the manuscript (see Table S1 in the SI Appendix).</p> <table align="center"> <thead> <tr> <th scope="col">Measurement description</th> <th scope="col">Scan number</th> </tr> </thead> <tbody> <tr> <td> <p> Homogeneous (main text)</p> </td> <td> 9185</td> </tr> <tr> <td> <p> Inhomogeneous with sharp edges </p> </td> <td> 10419</td> </tr> <tr> <td> <p> Inhomogeneous with smoothed edges</p> </td> <td> 8935</td> </tr> <tr> <td> <p> Homogeneous 2 (SI Appendix)</p> </td> <td> 10455</td> </tr> </tbody> </table> <p> </p> <p><strong>File Contents</strong></p> <p>Each file contains the following variables:</p> <ul> <li>"phase": A MATLAB cell containing all the phase profiles for every time step. Thus, "phase{t_ind}" is a matrix where rows represent experimental realizations and columns the spatial grid points. For example, "phase{5}(1,:)" would be a one-dimensional phase profile, representing the first realization of the fifth time step. To learn more about the extraction of phase profiles, read Section 2 and see Fig. S5 in SI Appendix.</li> <li>"z_grid_phase_si": Vector. Grid points for phase profiles in SI units (m).</li> <li>"averaged_density_si": Vector. Averaged initial linear density in SI units (m^-1). See Fig. 1(a).</li> <li>"z_grid_density_si": Vector. Grid points for averaged density in SI units (m).</li> <li>"times_si": Vector. Time points in SI units (s).</li> </ul> <p> </p> <p><strong>Matlab script calculating the velocity field</strong></p> <p>In addition to the data, a MATLAB script (velocity_field_calculation.m) loads a data file and calculates the velocity field and its correlations following the equations in the manuscript:</p> <ul> <li>"u": MATLAB cell. Velocity field for every time step.</li> <li>"u_u_corr": MATLAB cell. Second-order correlations of the velocity field for every time step.</li> <li>"std_u_u_corr": MATLAB cell. Standard deviation of second-order correlations of the velocity field for every time-step.</li> </ul> <p>Finally, the script plots "u_u_corr" for all the time steps and plots the averaged linear density.</p>
Data From: B1 Field inhomogeneity correction for qDESS T2 mapping: application to rapid bilateral knee imaging
<p><strong>Purpose</strong>: T2 mapping is a powerful tool for studying osteoarthritis (OA) changes and bilateral imaging may be useful in investigating the role of between-knee asymmetry in OA onset and progression. The quantitative double-echo in steady-state (qDESS) can provide fast simultaneous bilateral knee T2 and high-resolution morphometry for cartilage and meniscus.<br> The qDESS uses an analytical signal model to compute T2 relaxometry maps, which require knowledge of the flip angle (FA). <br> In the presence of B1 inhomogeneities, inconsistencies between the nominal and actual FA can affect the accuracy of T2 measurements.<br> We propose a pixel-wise B1 correction method for qDESS T2 mapping exploiting an auxiliary B1 map to compute the actual FA used in the model.<br> <br> <strong>Methods</strong>: The technique was validated in a phantom and in vivo with simultaneous bilateral knee imaging. <br> T2 measurements of femoral cartilage (FC) of both knees of six healthy participants were repeated longitudinally to investigate the association between T2 variation and B1.</p> <p><strong>Results</strong>: The results showed that applying the B1 correction mitigated T2 variations that were driven by B1 inhomogeneities. <br> Specifically, T2 left-right symmetry increased following the B1 correction (rc = 0.74 > rc = 0.69). Without the B1 correction, T2 values showed a linear dependence with B1. The linear coefficient decreased using the B1 correction (from 24.3 <span class="math-tex">\(\pm\)</span> 1.6 ms to 4.1 <span class="math-tex">\(\pm\)</span> 1.8) and the correlation was not statistically significant after the application of the Bonferroni correction (p-value > 0.01).<br> <br> <strong>Conclusion</strong>: the study showed that B1 correction could mitigate variations driven by the sensitivity of the qDESS T2 mapping method to B1, therefore increasing the sensitivity to detect real biological changes. The proposed method may improve the robustness of bilateral qDESS T2 mapping, allowing for an accurate and more efficient evaluation of OA pathways and pathophysiology through longitudinal and cross-sectional studies.</p> <p><strong>This repository contains the raw data for reproducing the results reported in the paper. The data are provided in NIFTI format. The repository also contains the MALTAB scripts used to compute B<sub>1</sub> maps from the NIFTI data.</strong></p>
Supplementary Data for "Prediction of solar wind speed by applying convolutional neural network to potential field source surface (PFSS) magnetograms"
<p>These are supplementary data for the paper "Prediction of solar wind speed by applying convolutional neural network to potential field source surface (PFSS) magnetograms". They are:</p> <p>- Python code to construct a neural network model</p> <p>- Saved optimal models (for 8-fold validation)</p> <p>- Selected y-label data (solar wind speed) and corresponding dates, which we eliminate the data identified as ICME</p>
Strong lensing mass models for the paper "Model-Independent Mass Reconstruction of the Hubble Frontier Field Clusters with MARS \\ Based on Self-Consistent Strong Lensing Data"
<p><strong>Codes for evaluating multiple image scatters</strong></p> <p>The code "eval_source_scatter.py" generates the source plane scatters for multiple images.</p> <p>In the folder "Image_plane_scatters", there are codes for computing image plane scatters.<br> "lens_rms.py": to find the location of the multiple images in the image plane.<br> "plot_result.py": to plot the result rms scatters.<br> We uploaded our results and code for computing scatters in both the source and the image planes.</p> <p> </p> <p><strong>Results from the MARS algorithm</strong></p> <p>In each folder, there are 'result_fits.fits', 'resut_kappa_w_header.fits', 'deflection_angle_w_header.fits', and 'catalog.txt' files.<br> All kappa and deflection angle maps are scaled to Dds/Ds= 1.</p> <p>"result_fits.fits" contains all parameters produced by MARS and has a size of (140x140 + alpha), where alpha is the number of model redshifts.<br> "result_kappa_w_header.fits" is the 100x100 convergence map.<br> "deflection_angle_w_header.fits" is the 100x100 deflection angle map in the unit of arc second.<br> "catalog.txt" is the multiple image catalog. The positions are given in pixel unit.</p> <p>For more details, readers are referfed to arXiv:2301.08765. Also, feel free to contact us (<a href="mailto:sang6199@yonsei.ac.kr">sang6199@yonsei.ac.kr</a>) if you have any questions.</p> <ul> <li>We found errors in WCS for the fits files and re-uploaded corrected files on 2023-01-27. We thank Jori Liesenborgs for pointing this out.</li> <li>We updated files on 2023-04-06.</li> </ul>
Supporting Data for "Does a Machine-Learned Potential Perform Better Than an Optimally Tuned Traditional Force Field? A Case Study on Fluorohydrins"
<p>Supporting Data for "Does a Machine-Learned Potential Perform Better Than an Optimally Tuned Traditional Force Field? A Case Study on Fluorohydrins"</p>
Data from: Behavioral thermoregulation of caterpillars is altered by temperature, but not parasitism: an empirical field study
<p>Laboratory assays show that parasites often have lower heat tolerance than their hosts. But how physiological tolerances and behavioral responses of hosts and parasites combine to affect their ecological interactions in heterogeneous field environments is largely unknown. We addressed this challenge using the model insect system of the braconid wasp parasitoid, <em>Cotesia congregata</em>, and its caterpillar host, <em>Manduca sexta</em>. We used experimental manipulations of microclimate in the field to determine how elevated daytime temperatures altered the behavior, performance, and survival of the host and parasite. Our experimental manipulation increased daily maximum temperatures on host plants, but had negligible effects on overall mean temperature. These increased maximum temperatures resulted in subtle, biologically relevant, changes in physiology and behavior of the host and parasitoid. We found that parasitism by the wasp did not significantly alter caterpillar thermoregulatory behavior, while experimentally increased daily maximum temperatures resulted in both parasitized and unparasitized caterpillars being found more frequently in cooler microhabitats. Overall, we did not observe the complete parasitoid mortality seen at extreme temperatures in laboratory studies, but gained insight into sublethal effects of increased daily maximum temperatures on host and parasitoid behavior and physiology. Climate change will alter both the biotic and abiotic environments that organisms face, and we show here that empirical experiments in the field are important for understanding organismal response to these new environments. </p>
Review data for: SnowQM 1.0: A fast R Package for bias-correcting spatial fields of snow water equivalent using quantile mapping
<p>Climatology of snow water equivalent of Switzerland between winters 1962 and 2021. Obtained using quartile mapping between a model using data assimilation since 1998 and a model without data assimilation. This version of the dataset corresponds to the publication revision time. The publication has been submitted to GMD Copernicus journal as: <em>SnowQM 1.0: A fast R Package for bias-correcting spatial fields of snow water equivalent using quantile mapping</em></p>
Data for: Improvements in the Land and Crop Modeling over Flooded Rice Fields by Incorporating the Shallow Paddy Water (Submitting to the Journal of Advances in Modeling Earth Systems)
<p>We incorporated the shallow paddy surface water layer into the Noah-MP land surface model to improve its performance of surface heat fluxes over flooded rice paddies. Field measurements from two crop sites, i.e., SAITO (early rice) and SAGA (late rice), were used to initialize and evaluate the modified Noah-MP model (Maruyama, 2021). Additionally, we investigated the roles of some key parameters in the land and crop modeling. </p> <p>Note that, all numerical experiments in this study were conducted at the field scale using the offline version of Noah-MP (Niu et al., 2011) running within the High-Resolution Land Data Assimilation System (HRLDAS v3.9; Chen et al., 2007). Please refer to the official HRLDAS/Noah-MP unified Github repository (<a href="https://github.com/NCAR/hrldas">https://github.com/NCAR/hrldas-release</a>) for the original model codes.</p> <p>The related model code modifications and model outputs were included in this dataset. Surface observations for the nearest AMeDAS or meteorological observatory stations were obtained from the Japan Meteorological Agency website (<a href="https://www.jma.go.jp/jma/indexe.html">https://www.jma.go.jp/jma/indexe.html</a>), and were also provided in this dataset.</p> <p> </p> <p>References</p> <p>Chen, F., Manning, K. W., LeMone, M. A., Trier, S. B., Alfieri, J. G., Roberts, R. D., et al. (2007). Description and evaluation of the characteristics of the NCAR high‐resolution land data assimilation system. <em>Journal of Applied Meteorology and Climatology</em>, 46(6), 694-713. <a href="https://doi.org/10.1175/JAM2463.1">https://doi.org/10.1175/JAM2463.1</a></p> <p>Maruyama, A. (2021). Data for: Coupling land surface and crop models to estimate the effects of changes in the growing season on energy balance and water use of rice paddies (version 2) [Data set]. Mendeley Data. <a href="https://doi.org/10.17632/tv23z95r5g.2">https://doi.org/10.17632/tv23z95r5g.2</a></p> <p>Niu, G., Yang, Z., Mitchell, K., Chen, F., Ek, M., Barlage, M., et al. (2011). The community Noah land surface model with multiparameterization options (Noah‐MP): 1. Model description and evaluation with local‐scale measurements. <em>Journal of Geophysical Research, </em>116, D12109. <a href="https://doi.org/10.1029/2010JD015139">https://doi.org/10.1029/2010JD015139</a></p>
Data from: Ecological genetics of local adaptation in Arabidopsis: an 8-year field experiment
<p>There is considerable evidence for local adaptation in nature, yet important questions remain regarding its genetic basis. How many loci are involved? What are their effect sizes? What is the relative importance of conditional neutrality vs. genetic trade-offs? Here we address these questions in the self-pollinating, annual plant <em>Arabidopsis thaliana</em>. We used 400 recombinant inbred lines (RIL) derived from two locally adapted populations in Italy and Sweden, grew the RILs and parents at the parental locations, and mapped quantitative trait loci (QTL) for mean fitness (fruits/seedling planted). We previously published results from the first three years of the study, and here add five additional years, providing a unique opportunity to assess how temporal variation in selection might affect QTL detection and classification. We found ten adaptive and one maladaptive QTL in Italy, and six adaptive and four maladaptive QTL in Sweden. The discovery of maladaptive QTL at both sites suggests that even locally adapted populations are not always at their genotypic optimum. Mean effect sizes for adaptive QTL, 0.97 and 0.55 fruits in Italy and Sweden respectively, were large relative to the mean fitness of the RILs (approximately 8 fruits/seedling planted at both sites). Both genetic trade-offs (four cases) and conditional neutrality (seven cases) contribute to local adaptation in this system. The eight-year dataset provided greater power to detect QTL and to estimate their locations compared to our previous three-year study, identifying one new genetic trade-off and reclassifying one QTL from genetic trade-off to conditionally neutral.</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.