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Dataset results
608 results for “ensembles”
ALCC: Temporally consistent annual land cover maps over China from 1985 to 2022 based on an ensemble change detection method
<p><span><span>We develope <span><span> </span>a consistent annual land cover product for China (ALCC) from 1985 to 2022. First, change areas for each year was detected baesd on an ensemble change detection method combing CCDC, BFASTm, and Chow Test. Then, stable training samples were derived from both CLCD and CLUDs. The Random Forest classifier was locally trained and used to classify change areas year by year. Finally, ALCC was generated by updating land cover classification results for change areas and remaining class label the same as the base map forthe unchanged areas. ALCC achieved a mean overall accuracy of 81.11±0.67% nationwide and exceeded 72.00% across seven geographical regions.</span></span></span></p>
Enhancement of the Ensemble Nonlinear Least Squares Algorithm for i4DVar
Open the record for dataset details and reuse information.
Lateral hypothalamic neuronal ensembles regulate pre-sleep nest-building behavior
<p>Data and codes for the paper "Lateral hypothalamic neuronal ensembles regulate pre-sleep nest-building behavior".</p>
Customized gtf file from Ensembl version 99 galGal6
<p>The gtf from Ensembl version 99 (galGal6) was filtered to remove readthrough transcripts and all non-coding transcripts from a protein-coding gene. In addition, all genes with the same gene name which overlaps were merged under the same gene id to avoid ambiguous reads.</p>
Data for "An Ensemble-Based Statistical Methodology to Detect Differences in Weather and Climate Model Executables" Part 1/2
<p>Ensemble simulations from the weather and climate model COSMO. The data has been used for model verification cases in the corresponding paper (https://doi.org/10.5194/gmd-2021-248).</p> <p>The data is partitioned into the following parts:</p> <ol> <li>gpu_dycore.tar.gz<br> 5-day ensemble (600 members) produced with COSMO 5.09 GPU version in double precision.</li> <li>cpu_nodycore.tar.gz<br> 5-day ensemble (200 members) produced with COSMO 5.09 CPU version in double precision.</li> <li>gpu_dycore_sp.tar.gz<br> 5-day ensemble (200 members) produced with COSMO 5.09 GPU version in single precision.</li> </ol> <p>The second part of the dataset with the diffusion ensembles can be found here: https://doi.org/10.5281/zenodo.6355647</p>
HYSPLIT ensemble model output for case studies
<p>Each folder named like a date (YYYYmmddHHMM) has a case study and it's named after eruption start time.</p> <p> * <strong>201208061200 </strong>- Historical case study for eruption starting at 2012-08-06T12:00 UTC<br> * <strong>202107150000</strong> - Operational case study for eruption starting at 2021-07-15T00:00 UTC</p> <p>Each case study folder has :<br> - <strong>input</strong> folder with the eruption source parameters, sampled with Latin Hyspercube technique, used to configure each HYSPLIT run.<br> - <strong>output </strong>folder with the HYSPLIT modelling results processed to cumulative deposited ash thickness netcdf file.</p> <p>Code to process results is in this <a href="http://github.com/yannikbehr/ashfall_forecasts/">Git repository</a></p> <p> </p>
Ensemble of global landslide susceptibility
<p>This ensemble of global landslide susceptibility maps at a 36-km spatial resolution on the EASEv2 grid has been created through a combination of blocked random cross validation and predictor variable perturbation. In total, the ensemble has 2500 members, each obtained from a logistic regression equation with 5 predictor variables. Note that these predictor variables vary between ensemble members, as do the logistic regression parameters (study design of data-driven predictor variable selection with cross validation). For details, please see Felsberg, A., Poesen, J., Bechtold, M., Vanmaercke, M., and De Lannoy, G. J. M. (2022): Estimating global landslide susceptibility and its uncertainty through ensemble modelling, Natural Hazards and Earth System Sciences.</p> <p>Provided are the individual ensemble member maps (FelsbergEtAl2022_LSS_ensMemb_all.nc4c), as well as the ensemble average and standard deviation maps (FelsbergEtAl2022_LSS_ensavg.nc4c, FelsbergEtAl2022_LSS_ensstd.nc4c). More details can be found in the README.</p>
A multi-model ensemble for the Lake Victoria Basin (East-Africa)
<p>Data produced by all CORDEX FPS - <strong>ELVIC</strong> models (COSMO-CLM, HCLIM38, WRF, RegCM, UKMO), covering the larger region surrounding Lake Victoria - East-Africa. This data accompanies ELVIC's evaluation paper (van Lipzig et al., 2022, submitted). More information about ELVIC ("climate Extremes in the Lake Victoria basin") is available via <a href="https://ees.kuleuven.be/en/elvic/">https://ees.kuleuven.be/en/elvic/</a>.</p> <p>In this dataset, zipped files are available per variable (precipitation, top-of-atmosphere short- and longwave radiation and lake surface temperature) for both convection-parametrized (10 km-scale resolution, "PAR") and convection-permitting (km-scale resolution, "CP") simulations. Simulation 10y-period covers the years 2006-2015. Each netcdf file contains 120 monthly diurnal cycle data at 1h or 3h temporal resolution. The full dataset is available by contacting the first author.</p>
Unlocking crowding by ensemble statistics
<p>Dataset from "Unlocking crowding by ensemble statistics"</p>
RNA-FUS MD simulation ensembles
<p>Amber parm files MD simulation <strong>S</strong> ensembles in .xtc format. Water molecules are stripped and only each one ns is stored. Data from Pokorna et al.: Conformational Heterogeneity of RNA Stem-Loop Hairpins Bound to FUS RNA Recognition Motif with Disordered RGG Tail Revealed by Unbiased Molecular Dynamics Simulations, 2022.</p>
Ensembled projection outputs of Lake Sunapee using multiple General Circulation models and Lake models
<p>This dataset contains the ensemble projection output of Lake Sunapee, NH. The conception and methods of the dataset is explained in the manuscript "Uncertainty in projections of future lake thermal dynamics is differentially driven by global climate models and lake models."</p>
Dataset for the paper "Ensemble optimization retrieval algorithm of hydrometeor profiles for the Ice Cloud Imager submillimeter-wave radiometer'
<p>1. "Retrieval_Database" file contains the pre-calculated retrieval database.</p> <p>2. "Algorithm_Input" file contains the input of the ensemble optimization retrieval algorithm.</p> <p>3. "TrueProfiles" file contains the true profiles corresponding to the input brightness temperatures. </p> <p>4. "Algorithm_Output" file contains the output of the ensemble optimization retrieval algorithm.</p>
SUMMA/mizuRoute model configurations and parameters for global ensemble modeling
<p>Meteorological forcing is a major source of uncertainty in hydrological modeling. The recent development of probabilistic large-domain meteorological datasets enables convenient uncertainty characterization, which however is rarely explored in large-domain research. </p> <p>We analyze how uncertainties in meteorological forcing data affect hydrological modeling on the global scale by forcing the Structure for Unifying Multiple Modeling Alternatives (SUMMA) and mizuRoute models with precipitation and air temperature ensembles from the Ensemble Meteorological Dataset for Planet Earth (EM-Earth). EM-Earth probabilistic estimates are used in ensemble simulation for uncertainty analysis. The global land area is divided into ~3 million sub-basins using the MERIT-Basins dataset. </p> <p>This dataset contains the SUMMA and mizuRoute configuration files (e.g., diverse land attributes, model parameters, and model physics decisions) to reproduce the global simulation results. The global land is divided into different continents, including Africa, Arctic, Europe, North America, North Asia, Oceania, SouthAmerica, and SouthAsia. Greenland is not included because of its complexity and low quality of available data. Antarctic is not included in MERIT-Basins. </p>
Optimized Ensemble Algorithm of SVMD-SVR+BiLSTM for Short-Term Forecasting of Earth Rotation Parameters
Open the record for dataset details and reuse information.
Self-Ordering, Cooling and Lasing in an Ensemble of Clock Atoms
<p>Data supporting the findings within the paper. All data have .jld2 file extension using JLD2.jl data package in Julia.</p>
FASTSUM Generation 2 Anisotropic Thermal Lattice QCD Gauge Ensembles
<p><strong>FASTSUM Generation 2 Anisotropic Thermal Lattice QCD Gauge Ensembles</strong></p> <p> </p> <p>The FASTSUM collaboration <a href="https://fastsum.gitlab.io/">[1]</a> Generation 2 Ensembles are lattice Quantum Chromodynamics (QCD) gauge-ensembles used extensively to examine thermal (non-zero temperature) properties of QCD using the first principles methods of lattice QCD. These are anisotropic lattices using the `fixed-scale` approach to thermal ensembles wherein the temperature is changed entirely by changing the number of points in the temporal direction.</p> <p> </p> <p>These ensembles are freely available (see below). The only requirements of use are that this Zenodo page, and the two papers detailing the ensembles, <a href="https://doi.org/10.1007/JHEP02(2015)186">Electrical conductivity and charge diffusion in thermal QCD from the lattice</a> and <a href="https://link.aps.org/doi/10.1103/PhysRevD.105.034504">Properties of the QCD thermal transition with Nf=2+1 flavors of Wilson quark</a> are appropriately cited.</p> <p>These ensembles are characterised by</p> <ul> <li>\(N_f = 2 +1 \) flavour <ul> <li>Degenerate up and down quarks, physical strange quark</li> </ul> </li> <li>spatial lattice spacing \(a_s\) <ul> <li>~0.12 fm</li> </ul> </li> <li>temporal lattice spacing \(a_t\) <ul> <li>~0.035 fm</li> </ul> </li> <li>anisotropy \(\nu = a_s / a_t\) <ul> <li>~3.444</li> </ul> </li> <li>Number of spatial sites NS <ul> <li>NS = 24 or 32</li> </ul> </li> <li>Number of Temporal sites <ul> <li>NT in [16, 48]</li> </ul> </li> <li>Temperatures <ul> <li>117 MeV to 352 MeV</li> </ul> </li> <li>Pseudocritical temperature (from renormalised chiral condensate) <ul> <li>181(1) MeV</li> </ul> </li> <li>Pion mass <ul> <li>\(m_\pi \sim 384\) MeV</li> </ul> </li> <li>Pseudoscalar to vector mass ratio <ul> <li>\( M_\pi / M_\rho \sim 0.446\)</li> </ul> </li> </ul> <p> </p> <p>This choice action and bare parameters follows that of the Hadron Spectrum Collaboration <a href="https://doi.org/10.1103/PhysRevD.78.054501">https://doi.org/10.1103/PhysRevD.78.054501</a>, namely a Symanzik improved gauge action and a tadpole improved Wilson-clover fermion action with stout-smeared links. </p> <p>The main parameters in the lattice action are listed below. The bare fermion anisotropy \(\gamma_f\) is obtained by \(\gamma_f = \gamma_g / \nu\). Full details may be found in <a href="https://doi.org/10.1007/JHEP02(2015)186">Electrical conductivity and charge diffusion in thermal QCD from the lattice</a> and <a href="https://link.aps.org/doi/10.1103/PhysRevD.105.034504">Properties of the QCD thermal transition with Nf=2+1 flavors of Wilson quark</a> which are also attached to this Zenodo record.</p> <ul> <li>gauge coupling <ul> <li>\(\beta = 1.5\)</li> </ul> </li> <li>tree-level coefficients <ul> <li>\(c_0 = 5/3,\, c2=-1/12\)</li> </ul> </li> <li>bare gauge, fermion anisotropy <ul> <li>\(\gamma_g = 4.3,\, \gamma_f = 3.399\)</li> </ul> </li> <li>ratio of bare anisotropies <ul> <li>\(\nu = \gamma_g / \gamma_f = 1.265\)</li> </ul> </li> <li>spatial tadpole (without, with smeared links) <ul> <li>\(u_s = 0.733566, \tilde{u}_s = 0.92674\)</li> </ul> </li> <li>temporal tadpole (without, with smeared links) <ul> <li>\(u_\tau =1, \tilde{u}_\tau =1\)</li> </ul> </li> <li>stout smearing for spatial links <ul> <li>isotropic, 2 steps, \(\rho = 0.14\)</li> </ul> </li> <li>bare light quark mass <ul> <li>\(m_0^l = -0.0840\)</li> </ul> </li> <li>bare strange quark mass <ul> <li>\(m^s_0 = -0.0743\)</li> </ul> </li> <li>light quark hopping parameter <ul> <li>\(\kappa_{light} = 0.2780\)</li> </ul> </li> <li>strange quark hopping parameter <ul> <li>\(\kappa_{strange} = 0.2765\)</li> </ul> </li> </ul> <p> </p> <p>The available ensembles are detailed in the below table</p> <table> <tbody> <tr> <td>\(N_\tau\)</td> <td>16</td> <td>20</td> <td>24</td> <td>28</td> <td>32</td> <td>36</td> <td>40</td> <td>48</td> </tr> <tr> <td>\(N_s\)</td> <td>24, 32</td> <td>24</td> <td>24, 32</td> <td>24, 32</td> <td>24, 32</td> <td>24</td> <td>24</td> <td>32</td> </tr> <tr> <td>\(T\) MeV</td> <td>352</td> <td>281</td> <td>235</td> <td>201</td> <td>176</td> <td>156</td> <td>141</td> <td>117</td> </tr> <tr> <td>\(T / T_{c}\)</td> <td>1.900</td> <td>1.520</td> <td>1.267</td> <td>1.086</td> <td>0.950</td> <td>0.844</td> <td>0.760</td> <td>0.633</td> </tr> <tr> <td>\(N_{cfg}\)</td> <td> <p>1009*, 1172**</p> </td> <td>1000</td> <td> <p>1001, 502</p> </td> <td>1100^, 502</td> <td>1000, 501</td> <td>501</td> <td>523^^</td> <td>501</td> </tr> </tbody> </table> <p> </p> <p>* Only 1000 in openQCD format</p> <p>** Only 591 in openQCD format</p> <p>^ Only 1001 in openQCD format</p> <p>^^ Only 502 in openQCD format</p> <p><strong>Sharing & Usage</strong></p> <blockquote> <p>The ensembles are available on Storj, a decentralised cloud storage service. Our storage here is supported by DiRAC <a href="https://dirac.ac.uk/">[2]</a>. The available data may be viewed and downloaded via web browser or in an automated manner using the S3 interface to Storj <a title="https://rclone.org/storj/" href="https://rclone.org/storj/" target="_blank" rel="noopener">[3]</a>.</p> <p>To view the available data, and download via web browser, the HTTP interface may be used: https://link.storjshare.io/julj4eulkfnqnd26v36des6wvy3a/gen2-configs . To download in an automated manner the S3 interface to Storj may be used [3] with the following Access Key: </p> <p>jxejpzv46zrr3rfnoz66n5px2rca</p> <p>and Secret Key</p> <p>j2o44mi2a76zajpvyv5wjd2wgjqgncfagln6nf6jpwbv7o3npj7t6</p> </blockquote> <blockquote> <p> </p> <p>with the gateway</p> <p><a href="https://gateway.storjshare.io" target="_blank" rel="noopener">https://gateway.storjshare.io</a></p> <p> </p> <p>More details are available from (preferably) the generic email address PhysicsByFASTSUM AT gmail DOT com or one of Chris Allton, Ryan Bignell and Jon-Ivar Skullerud.</p> </blockquote> <p> </p> <p>We supply the gaugefields in at least one of ILDG (Chroma) format and openqcd format. Also supplied are log files, and chroma input files. The gaugefields were generated using Chroma and later converted to openqcd.</p> <p> </p> <p>The ensembles are available for all uses. If they are used, this Zenodo page and two papers detailing the ensembles, <a href="https://doi.org/10.1007/JHEP02(2015)186">Electrical conductivity and charge diffusion in thermal QCD from the lattice</a> and <a href="https://link.aps.org/doi/10.1103/PhysRevD.105.034504">Properties of the QCD thermal transition with Nf=2+1 flavors of Wilson quark</a> must be appropriately cited, i.e. with bibtex</p> <pre><code>@article{Aarts:2014nba, author = "Aarts, Gert and Allton, Chris and Amato, Alessandro and Giudice, Pietro and Hands, Simon and Skullerud, Jon-Ivar", title = "{Electrical conductivity and charge diffusion in thermal QCD from the lattice}", eprint = "1412.6411", archivePrefix = "arXiv", primaryClass = "hep-lat", reportNumber = "HIP-2014-34-TH, INT-PUB-14-060, MS-TP-14-40", doi = "10.1007/JHEP02(2015)186", journal = "JHEP", volume = "02", pages = "186", year = "2015" }</code><br><br>and<br><br><code>@article{PhysRevD.105.034504,</code><br><code> title = {Properties of the QCD thermal transition with ${N}_{f}=2+1$ flavors of Wilson quark},</code><br><code> author = {Aarts, G. and Allton, C. and Glesaaen, J. and Hands, S. and J\"ager, B. and Kim, S. and Lombardo, M. P. and Nikolaev, A. A. and Ryan, S. M. and Skullerud, J.-I. and Wu, L.-K.},</code><br><code> journal = {Phys. Rev. D},</code><br><code> volume = {105},</code><br><code> issue = {3},</code><br><code> pages = {034504},</code><br><code> numpages = {12},</code><br><code> year = {2022},</code><br><code> month = {Feb},</code><br><code> publisher = {American Physical Society},</code><br><code> doi = {10.1103/PhysRevD.105.034504},</code><br><code> url = {https://link.aps.org/doi/10.1103/PhysRevD.105.034504}</code><br><code>}</code><br><br><br></pre> <p> </p> <p><a href="https://fastsum.gitlab.io/">[1] </a>https://fastsum.gitlab.io/</p> <p><a href="https://dirac.ac.uk/">[2]</a> https://dirac.ac.uk/</p> <p><a href="https://rclone.org/storj/">[3]</a> https://rclone.org/storj/</p> <p> </p>
Space Weather Modeling Framework ensemble simulations
<p><strong>Space Weather Modeling Framework ensemble simulations</strong></p> <p>This archive contains folders for 41 simulations with the operational geospace configuration of the University of Michigan's Space Weather Modeling Framework[1]. The operational configuration uses the University of Michigan's BATS-R-US magnetohydrodynamics code[2], the Ridley ionospheric electrodynamics solver[3], and the Rice Convection Model[4] inner magnetosphere model. More details of the operational geospace configuration are given by [5].</p> <p>These simulations were performed for, and used in, the paper</p> <p>> "Perturbed Input Ensemble Modeling with the Space Weather Modeling Framework",<br> > S.K. Morley, D.T. Welling and J.R. Woodroffe,<br> > Space Weather, 2018. doi: <a href="https://doi.org/10.1029/2018SW002000">10.1029/2018SW002000</a></p> <p><em>Directory Structure</em><br> All numbered directories are members of a perturbed input ensemble. The directory labeled "orig" is the reference (unperturbed) simulation. Each directory is structured identically.</p> <p>Each run directory contains `PARAM.in`, `LAYOUT.in` and `magin_GEM.dat` files. This set of files consistutes the required inputs for each run that are invariant. That is, these files are identical between runs and control the setup of the model and the types of outputs generated. Each run directory also contains an `IMF.dat` file that sets the upstream boundary condition. This file differs between each simulation. The values in each ensemble member have been perturbed from the values given in the reference simulation using a block resampling of measurement errors between an L1 solar wind monitor and a near-Earth monitor.</p> <p>Each run directory also contains `GM` and `GM\IO2` subdirectories. The `GM\IO2` subdirectory contains simulation output from the global magnetosphere module. Three files are present for each simulation: `geoindex_e20100404-190000.log`, `magnetometers_e20100404-190000.mag`, and `log_e20100404-190000.log`. These are standard SWMF log files that can be parsed and analyzed using, for example, the `pybats` module in the SpacePy[6] software package[7]. The simulation ouput includes ground magnetic perturbations at a set of magnetic observatory locations, local K indices, an estimated Kp index, a 1 minute resolution Sym-H/Dst index equivalent and simulated auroral electrojet indices.</p> <p><br> <em>Basic Analysis</em><br> To derive the time derivative of the horizontal ground magnetic perturbation (dB/dt) the magnetometer log file can be loaded using SpacePy<br> </p> <pre><code class="language-python">>>> import spacepy.pybats.bats >>> magdata = spacepy.pybats.bats.MagFile('run_001/GM/IO2/magnetometers_e20100404-190000.mag') >>> magdata.calc_h() #calculates horizontal from North and East components >>> magdata.calc_dbdt() #calculates time derivatives</code></pre> <p>To then calculate binned maxima in the dB/dt time series, e.g., for the Yellowknife (YKC) station<br> </p> <pre><code class="language-python">>>> import datetime as dt >>> import numpy as np >>> import spacepy.toolbox as tb >>> dBdt_max20, bintimes = tb.windowMean(magdata['YKC'], time=subset['time'], winsize=dt.timedelta(minutes=20), overlap=dt.timedelta(0), st_time=dt.datetime(2010,4,5), op=np.max)</code></pre> <p><br> and to turn this into a binary event series indicating a threshold crossing<br> </p> <pre><code class="language-python">>>> threshold = 1.1 #nT/s >>> predicted_event = np.asarray(dBdt_max20) >= threshold</code></pre> <p><br> Assuming that the observational data are obtained from NASA's CCMC and similarly processed, the event validation statistics can be calculated and displayed using the PyForecastTools package[8].<br> </p> <pre><code class="language-python">>>> import verify >>> c_table = verify.Contingency2x2.fromBoolean(predicted_event, observed_event) >>> ctable.summary(ci='bootstrap', verbose=True)</code></pre> <p> </p> <p><em>Footnotes</em><br> [1] Tóth, G., I. V. Sokolov, T. I. Gombosi, D. R. Chesney, C. R. Clauer, D. L. D. Zeeuw, K. C. Hansen, K. J. Kane, W. B. Manchester, R. C. Oehmke, K. G. Powell, A. J. Ridley, I. I. Roussev, Q. F. Stout, O. Volberg, R. A. Wolf, S. Sazykin, A. Chan, B. Yu, and J. KÃşta (2005), Space weather modeling framework: A new tool for the space science community, Journal of Geophysical Research: Space Physics, 110(A12), doi:10.1029/2005JA011126.</p> <p>[2] de Zeeuw, D. L., T. I. Gombosi, C. P. T. Groth, K. G. Powell, and Q. F. Stout (2000), An adaptive MHD method for global space weather simulations, IEEE Transactions on Plasma Science, 28(6), 1956–1965, doi:10.1109/27.902224.</p> <p>[3] Ridley, A. J., T. I. Gombosi, and D. L. DeZeeuw (2004), Ionospheric control of the magnetosphere: conductance, Annales Geophysicae, 22(2), 567–584, doi:10.5194/angeo-22-567-2004.</p> <p>[4] Toffoletto, F., S. Sazykin, R. Spiro, and R. Wolf (2003), Inner magnetospheric modeling with the Rice convection model, Space Science Reviews, 107(1), 175–196, doi: 10.1023/A:1025532008047.</p> <p>[5] Haiducek, J. D., D. T. Welling, N. Y. Ganushkina, S. K. Morley, and D. S. Ozturk (2017), SWMF global magnetosphere simulations of January 2005: Geomagnetic indices and cross-polar cap potential, Space Weather, 15(12), 1567–1587, doi: 10.1002/2017SW001695.</p> <p>[6] Morley, S. K., J. Koller, D. T. Welling, B. A. Larsen, M. G. Henderson, and J. T. Niehof (2011), Spacepy - a Python-based library of tools for the space sciences, in Proceedings of the 9th Python in science conference (SciPy 2010), Austin, TX.</p> <p>[7] SpacePy is packaged on PyPI, with the official git repository on SourceForge and an unofficial mirror on github.</p> <p>[8] PyForecastTools is packaged on PyPI and the repository is on github. The latest release is archived on Zenodo with doi: 10.5281/zenodo.1256921. The citation for v1.0.1 is Steve Morley. (2018, June 28). drsteve/PyForecastTools: PyForecastTools: Version 1.0.1 (Version v1.0.1). Zenodo. http://doi.org/10.5281/zenodo.1299389</p> <p> </p>
Supporting data for: A 250-year European drought inventory derived from ensemble hydrologic modelling
<p>Supporting data for visualization of 250-year (1766-2015) inventory of European meteorological, hydrological and agricultural droughts derived from ensemble simulations of the mesoscale Hydrological Model (mHM)</p>
Datasets used in the papers: STree: A Single Multi-class Oblique Decision Tree Based on Support Vector Machines & ODTE - An ensemble of multi-class SVM-based oblique decision trees
<p>These are the 49 datasets used in the benchmark. 45 of them are from the UCI machine learning repository, while the other 4 correspond to a problem about fecundity estimation for fisheries</p>
Intermediate Data Products for "A NASA GISTEMPv4 Observational Uncertainty Ensemble"
<p>Contains the Intermediate data for "A NASA GISTEMPv4 Observational Uncertainty Ensemble" as accepted at JGR:Atmospheres (August 2024).</p> <p>Raw and Results Data can be found here: <a href="https://doi.org/10.5281/zenodo.13343335">https://doi.org/10.5281/zenodo.13343335</a></p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.