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134 results for “Gaussian”
250MBaud Gaussian CV-QKD in coexistence with 8x200G PM-16QAM
<p>This dataset comprises the simulation data of the VPItoolkit™ QKD application example "250MBaud Gaussian CV-QKD in coexistence with 8x200G PM-16QAM". This demonstrates the possibility of coexisting Gaussian CV-QKD and two times four classical channels. The classical channels (32 GBaud) are simulated with 8 samples per symbol. The modulation variance is swept to illustrate how excess noise and secret fraction depend on the modulation variance.</p>
Global Ocean Heat Content Anomalies and Ocean Heat Uptake based on mapping Argo data using local Gaussian processes
<p>Monthly Ocean Heat Content Anomalies (OHCA) in the top 2000 dbar of the ocean are calculated (during 2004-2024, equatorward of 65 degree latitude) subtracting the mean over the period 2004-2024 from the monthly time series of OHC. Yearly OHCA time series are then calculated that include 1. one point per year, i.e., from averaging Jan to Dec (see files ending in “yearly.nc”), and 2. two points per year, i.e., from averaging Jan to Dec and Jul to Jun, respectively (see files ending in “yearly2.nc”). OHC fields are mapped using locally stationary Gaussian processes (defined over space and time) with data-driven decorrelation scales (Kuusela and Stein, 2018). A linear time trend was included in the estimate of the mean field (along with spatial terms and harmonics for the annual cycle). Mapping is done separately for different vertical sections: 15-20 dbar, 15-300 dbar, 300-700 dbar, 700-1850 dbar, 1800-1850 dbar. The 15-20 dbar (1800-1850 dbar) section is used to estimate OHCA for 0-15 dbar (1850-2000 dbar), where observations are sparser. Different vertical sections are combined to estimate global OHCA time series for 0-2000 dbar, 0-700 dbar, 700-2000 dbar (as indicated in the file names). The attribute "area" is included in the netcdf files and it tells the corresponding surface area for the estimates. Regions of the ocean that are shallower than 300 m or are not sufficiently well sampled by the Argo array are not included. Maps of the ocean masks used for the different vertical sections can be found in the .png files (blue shading indicates the area used for the horizontal integral); the bathymetry mask by Roemmich and Gilson (included in the file RG_ArgoClim_Temperature_2019.nc at https://sio-argo.ucsd.edu/RG_Climatology.html) is also used to define the ocean mask. Ocean Heat Uptake is calculated from the monthly OHCA and then averaged as described above to produce yearly time series included in the files for the different layers.</p> <p>For the uncertainty at each time point, the standard deviation of each OHCA/OHU value in the time series is included. When plotting a time series, the user may consider, e.g., shading plus/minus 1* or 1.96*standard deviation (corresponding to a confidence level of 68% or 95% respectively). These standard deviations in the files are estimated using spatially and temporally dependent conditional simulations of monthly gridded anomalies. When combining different layers, the standard deviation of the sum is conservatively estimated as the sum of the standard deviations. </p> <p>Finally, OHCA/OHU trends are estimated via a least-squares fit and reported in the variable metadata with uncertainties (confidence level of 68%). Trend uncertainties are estimated by repeating the fit for each member of the conditional simulation ensemble described above.</p> <p> </p>
pKaDatabase for Stacking Gaussian Processes to Improve pKa Predictions in the SAMPL7 Challenge
<p>A curated a database of small molecules with experimentally measured pKa values. </p> <p>This pickle file can be loaded into memory using Pandas. In the code block below we will print out the columns of the DataFrame:</p> <pre><code class="language-python">import pandas as pd df = pd.load("pKaDatabase.pkl") print(df.keys()). # print the columns</code></pre> <blockquote> <p>['deprotonated microstate ID', 'protonated microstate ID', 'deprotonated microstate smiles', 'protonated microstate smiles', 'AM1BCC partial charge (prot. atom)', 'AM1BCC partial charge (deprot. atom)', 'AM1BCC partial charge (prot. atoms 1 bond away)', 'AM1BCC partial charge (deprot. atoms 1 bond away)', 'AM1BCC partial charge (prot. atoms 2 bond away)', 'AM1BCC partial charge (deprot. atoms 2 bond away)', 'Gasteiger partial charge (prot. atom)', 'Gasteiger partial charge (deprot. atom)', 'Gasteiger partial charge (prot. atoms 1 bond away)', 'Gasteiger partial charge (deprot. atoms 1 bond away)', 'Gasteiger partial charge (prot. atoms 2 bond away)', 'Gasteiger partial charge (deprot. atoms 2 bond away)', 'Extented Hückel partial charge (prot. atom)', 'Extented Hückel partial charge (deprot. atom)', 'Extented Hückel partial charge (prot. atoms 1 bond away)', 'Extented Hückel partial charge (deprot. atoms 1 bond away)', 'Extented Hückel partial charge (prot. atoms 2 bond away)', 'Extented Hückel partial charge (deprot. atoms 2 bond away)', '∆G_solv (kJ/mol) (prot-deprot)', 'SASA (Shrake)', 'SASA (Lee)', 'Bond Order', 'Change in Enthalpy (kJ/mol) (prot-deprot)', 'pKa','href', 'num ionizable groups', 'Weight', 'pKa source']</p> </blockquote> <p> </p> <p>For more information regarding feature calculations, please read the following paper:</p> <blockquote> <p>Raddi, Robert, and Vincent Voelz. "Stacking Gaussian Processes to Improve pKa Predictions in the SAMPL7 Challenge." (2021). <a href="https://doi.org/10.26434/chemrxiv.14650302.v1">10.26434/chemrxiv.14650302.v1</a></p> </blockquote>
Gaussian Process Model and Sensor Placement for Detroit Green Infrastructure: Datasets and Code
<ol> <li><strong>code.zip: </strong>Zip folder containing a folder titled "code" which holds: <ol> <li>csv file titled "MonitoredRainGardens.csv" containing the 14 monitored green infrastructure (GI) sites with their design and physiographic features;</li> <li>csv file titled "storm_constants.csv" which contain the computed decay constants for every storm in every GI during the measurement period;</li> <li>csv file titled "newGIsites_AllData.csv" which contain the other 130 GI sites in Detroit and their design and physiographic features;</li> <li>csv file titled "Detroit_Data_MeanDesignFeatures.csv" which contain the design and physiographic features for all of Detroit;</li> <li>Jupyter notebook titled "GI_GP_SensorPlacement.ipynb" which provides the code for training the GP models and displaying the sensor placement results;</li> <li>a folder titled "MATLAB" which contains the following: <ol> <li>folder titled "SFO" which contains the SFO toolbox for the sensor placement work</li> <li>file titled "sensor_placement.mlx" that contains the code for the sensor placement work</li> <li>several .mat files created in Python for importing into Matlab for the sensor placement work: "constants_sigma.mat", "constants_coords.mat", "GInew_sigma.mat", "GInew_coords.mat", and "R1_sensor.mat" through "R6_sensor.mat"</li> <li>several .mat files created in Matalb for importing into Python for visualizing the results: "MI_DETselectedGI.mat" and "DETselectedGI.mat"</li> </ol> </li> </ol> </li> </ol>
Datasets of fitted toy Monte Carlo samples for "Gaussian Process-based calculation of look-elsewhere trials factor"
<p>For the paper "Gaussian Process-based calculation of look-elsewhere trials factor", we generated and fitted a number of toy Monte Carlo samples which can be used to reproduce figures from the paper or do tests of hypotheses inspired by the method suggested in the article.</p> <p>Together with the hdf5 files containing the samples, we also provide a Jupyter notebook which was used to produce the figures.</p>
Metrics for two-sample tests: results on Mixture of Gaussians and Correlated Gaussians models
<p>The repository includes version 1.0 (v1.0) of the code and results corresponding to the GitHub repository <a href="https://github.com/TwoSampleTests/GenerativeModelsMetrics">GenerativeModelsMetrics</a>.</p> <p>Publishing information and arXiv identifier will be added after publication of the main manuscript related to the data.</p>
Simulated movies with gaussian-shaped pHluorin signal intensity on the cell surface
<p>Synthetic data mimicking exocytic events across a wide range of features including normalized intensity, apparent size and decay mean lifetime. Numbers and spatial location of simulated events are randomly distributed over time.</p> <p>Events could have:</p> <p>* positive attribute: single exponential decay</p> <p>* negative attribute: constant signal for a random amount of time, damped sine decay signal +/- spatial displacement</p>
Gaussian polarizable-ion tight binding (Supplemental Data)
<p><strong>This is a collection of input and output files for the publication below.</strong></p> <p>Abstract:</p> <p>To interpret Ultrafast Dynamics experiments on large molecules, computer simulation is required due to the complex response to the laser field. We present a method capable of efficiently computing the static electronic response of large systems to external electric fields. This is achieved by extending the density-functional tight binding method to include larger basis sets and by multipole expansion of the charge density into electrostatically interacting Gaussian distributions. Polarizabilities for a range of hydrocarbon molecules are computed for a multipole expansion up to quadrupole order, giving excellent agreement with experimental values, with average errors similar to those from density functional theory, but at a small fraction of the cost. We apply the model in conjunction with the polarizable-point-dipoles model to estimate the internal fields in amorphous Poly(3-hexylthiophene-2,5-diyl).</p>
Gaussian-accelerated Molecular Dynamics simulations of CCR8-CCL1-Gprotein complex in a POPC lipid bilayer
<p>Gaussian-accelerated Molecular Dynamics simulations of the CCR8-CCL1-Gprotein complex in a POPC lipid bilayer. Simulation system was prepared with OpenMM v7.7 and simulations were performed using the GaMD-OpenMM package (https://github.com/MiaoLab20/gamd-openmm) with a modification to include the MDTraj h5 file formate reporter as the output file format. These simulations were then converted to pdb topologies and dcd trajectories using MDTraj. </p><p>Files include:</p><p>CCL1_CCR8_noSer23_oriented_repaired1_system.pdb : system topology</p><p>CCL1_CCR8_config.xml : config for running GaMD-OpenMM</p><p>CCL1_CCR8_N_1ns_imaged_structure.pdb : initial topology/structure</p><p>CCL1_CCR8_N_1ns_imaged_trajectory.dcd : trajectory file</p><p> </p><p>Simulations can be loaded in python using MDTraj:</p><p>import mdtraj</p><p>trj = mdtraj.load(<dcd file>, top=<pdb file>)</p>
Data associated to the paper "Phase diagram detection via Gaussian fitting of number probability distribution"
<p>We investigate the number probability density function that characterizes subportions of a quantum many-body system with globally conserved number of particles. We put forward a linear fitting protocol capable of mapping out the ground-state phase diagram of the rich one-dimensional extended Bose-Hubbard model: The results are quantitatively comparable with more sophisticated traditional and machine learning techniques. We argue that the studied quantity should be considered among the most informative bipartite properties, being moreover readily accessible in atomic gases experiments.<br><br>The dataset contains the entanglement spectra of several configurations of the extended Bose-Hubbard model ground state for different systems' sizes. </p>
Exploring Gaussian processes for short-term forecasting in offshore energy systems: Supplementary material
<p>Two supplementary videos are provided. The first video analyses the performance of wave excitation force forecasting across different horizons in a noise-free case. The second video examines the impact of noise on the forecast. Both videos include results from a Gaussian-based forecaster, an AR forecaster, and show the uncertainty bounds provided by the Gaussian forecaster. The variable analysed and forecasted in these videos is the wave excitation force.</p>
Data release: Searching for binary black hole sub-populations in gravitational wave data using binned Gaussian processes
<p>The data required to reproduce the analyses of "Searching for binary black hole sub-populations in gravitational wave data using binned Gaussian processes" (<a href="https://arxiv.org/abs/2404.03166" target="_blank" rel="noopener">arxiv:2404.03166</a>). The main inference code can be found at <a href="https://github.com/AnaryaRay1/gppop/tree/spin-dev" target="_blank" rel="noopener">https://github.com/AnaryaRay1/gppop/tree/spin-dev </a> (commit: <a href="https://github.com/AnaryaRay1/gppop/commit/ee5ffc421e2c96eeed15a0e0d3839da42b982842">ee5ffc</a>). To reproduce the analyses, follow the instructions at <a href="https://github.com/AnaryaRay1/bbh-subpopulations-scripts">https://github.com/AnaryaRay1/bbh-subpopulations-scripts</a> (commit <a href="https://github.com/AnaryaRay1/bbh-subpopulations-scripts/commit/de88f931d8c1a2cb31ad2fa9d6fdf9a5a00a3c3b">de88f93</a>). Frozen versions of these repositories that were used to generate all the results are available as part of this data release, in the files "gppop_spin_dev_ee5ffc421.tar.gz" and "bbh-subpopulations-scripts_de88f931.tar.gz" respectively.</p>
Optimized Coefficients for the Generalized Karagiannidis–Lioumpas Approximations and Bounds to the Gaussian Q-Function
<p>This is a supplementary dataset for the publication:</p> <p>I. M. Tanash and T. Riihonen, "Generalized Karagiannidis–Lioumpas Approximations and Bounds to the Gaussian Q-Function with Optimized Coefficients," in<em> IEEE Communications Letters</em>, in press.</p> <p>The dataset contains the sets of the optimized coefficients for the novel GKL minimax approximations and bounds of the Gaussian Q-function, and the optimized coefficients for the GKL approximations in terms of the total error. The corresponding optimized coefficients are found up to 10 terms (N=10) for the two variations of the absolute error and for the relative error in terms of the minimax and the total errors.</p> <p>The Matlab function (func_extract_coef.m) extracts the required set of optimal coefficients from the provided dataset according to the selected optimization_criterion, error_type, number of terms, the bound or approximation type, and the variation. See help func_extract_coef for more information.</p> <p>A Matlab script (Example.m) is also provided as an example to illustrate the use of the provided Matlab function in extracting the required coefficients from the dataset, to calculate and plot the corresponding minimax absolute error function which is shown by figure Example.jpg. Another example is given in the same script to extract the coefficients of the total relative error.<br> </p>
Gaussian plume footprints
<p>Supplementary Gaussian plume footprints for the paper "Recovery of sparse urban greenhouse gas emissions". Code used with this dataset is found at <a href="https://doi.org/10.5281/zenodo.5900738">https://doi.org/10.5281/zenodo.5900738</a>.</p> <p>.</p>
Data for "Impact of Gaussian transformation on cloud cover data assimilation for historical weather reconstruction"
<p>This dataset contains the simulation results in "Impact of Gaussian transformation on cloud cover data assimilation for historical weather reconstruction".</p>
Reinforcing Tunnel Network Exploration in Proteins using Gaussian Accelerated Molecular Dynamics (inputs, outputs, analysis)
<ul> <li>00_LinB-Wt.tar.gz - LinB-Wt: contains raw data that are used for analysis, also conatin folder for GaMD testing.</li> </ul> <p> 1. cMD(Classical MD simulation) analysis files :<br> <br> 1. Analysis of catalytic residue’s RMSD, whole protein RMSD and RMSF along with whole protein’s Rg and sasa.<br> 2. Inputs and output files of caver calculations. <br> 3. H-bond raw distance files from all simulations named run1-run5. <br> 4. Distance files used to calculate PCA and cluster analysis.<br> 5. Input files and input structure used to run simulations along with output restart files from each stage of production.<br> <br> 2. GaMD(Gaussian Accelerated MD simulation) analysis files : <br> <br> 1. Analysis of catalytic residue’s RMSD, whole protein RMSD and RMSF along with whole protein’s Rg and sasa.<br> 2. Inputs and output files of caver calculations. <br> 3. H-bond raw distance files from all simulations named run1-run5. <br> 4. Distance files used to calculate PCA and cluster analysis.<br> 5. Input files and input structure used to run simulations along with output restart files from each stage of production.</p> <p> 3. GaMD-testing :</p> <p> 1. Input file of GaMD used to run testing and output gamd.log files for multiple run of σOP 1.2 - 1.4 and σOD 2.5.</p> <p> 4. Initial 200ns cMD simulation files used for cluster analysis :</p> <p> 1. Force field parameters and input coordinates *.inpcrd, parameters *.parm7 and 200ns stripped water and ions simulation in Amber *.nc format<br> 2. Restart files for each stage of the minimization, equilibration and production runs in Amber *.rst format in rst folder.<br> 3. Ouput files from Simulation for each stage of the minimization, equilibration and production runs in Amber *.out format in out folder.</p> <ul> <li>01_LinB-Open.tar.gz - LinB Open mutant: contains raw data that are used for analysis.</li> </ul> <p> 1. cMD(Classical MD simulation) analysis files :<br> <br> 1. Analysis of catalytic residue’s RMSD, whole protein RMSD and RMSF along with whole protein’s Rg and sasa.<br> 2. Inputs and output files of caver calculations. <br> 3. H-bond raw distance files from all simulations named run1-run5. <br> 4. Distance files used to calculate PCA and cluster analysis.<br> 5. Input files and input structure used to run simulations along with output restart files from each stage of production.<br> <br> 2. GaMD(Gaussian Accelerated MD simulation) analysis files : <br> <br> 1. Analysis of catalytic residue’s RMSD, whole protein RMSD and RMSF along with whole protein’s Rg and sasa.<br> 2. Inputs and output files of caver calculations. <br> 3. H-bond raw distance files from all simulations named run1-run5. <br> 4. Distance files used to calculate PCA and cluster analysis.<br> 5. Input files and input structure used to run simulations along with output restart files from each stage of production.</p> <p> 3. Initial 200ns cMD simulation files used for cluster analysis :</p> <p> 1. Force field parameters and input coordinates *.inpcrd, parameters *.parm7 and 200ns stripped water and ions simulation in Amber *.nc format<br> 2. Restart files for each stage of the minimization, equilibration and production runs in Amber *.rst format in rst folder.<br> 3. Ouput files from Simulation for each stage of the minimization, equilibration and production runs in Amber *.out format in out folder.</p> <ul> <li>02_LinB-Closed.tar.gz - LinB Closed mutant: contains raw data that are used for analysis.</li> </ul> <p><br> 1. cMD(Classical MD simulation) analysis files :<br> <br> 1. Analysis of catalytic residue’s RMSD, whole protein RMSD and RMSF along with whole protein’s Rg and sasa.<br> 2. Inputs and output files of caver calculations. <br> 3. H-bond raw distance files from all simulations named run1-run5. <br> 4. Distance files used to calculate PCA and cluster analysis.<br> 5. Input files and input structure used to run simulations along with output restart files from each stage of production.<br> <br> 2. GaMD(Gaussian Accelerated MD simulation) analysis files : <br> <br> 1. Analysis of catalytic residue’s RMSD, whole protein RMSD and RMSF along with whole protein’s Rg and sasa.<br> 2. Inputs and output files of caver calculations. <br> 3. H-bond raw distance files from all simulations named run1-run5. <br> 4. Distance files used to calculate PCA and cluster analysis.<br> 5. Input files and input structure used to run simulations along with output restart files from each stage of production.</p> <p> 3. Initial 200ns cMD simulation files used for cluster analysis :</p> <p> 1. Force field parameters and input coordinates *.inpcrd, parameters *.parm7 and 200ns stripped water and ions simulation in Amber *.nc format<br> 2. Restart files for each stage of the minimization, equilibration and production runs in Amber *.rst format in rst folder.<br> 3. Ouput files from Simulation for each stage of the minimization, equilibration and production runs in Amber *.out format in out folder.</p> <ul> <li>03_TT_analysis.tar.gz - TransportTools: contains config file and all the raw data from all set and subset of reclustered (using in-house python script) caver calculations used for running TT.</li> </ul> <p> 1. Caver input data for comparison between 500ns, 1 us, 2.5 us and 5us between LinB-Wt and it’s mutants.<br> 2. TransportTools log file.<br> 3. Main statistics result of comparative analysis.</p> <ul> <li>04_reweighting.tar.gz: directory contains reweighted .csv files after running in-house reweighting protocol.<br> <br> 1. GaMD log files from each simulation of LinB-Wt and it’s mutants.<br> 2. CSV files from TT result folder.<br> 3. Result *.csv file contained reweighted tunnel properties in folder reweighted_filtered_new.</li> <li>05_caverdock.tar.gz: contains raw data for caverdock calculations uisng 100 best tunnels with four ligands 2-bromoethanol (be), 1,2-dibromoethane (dbe), Bromide ion (br-) and water (h2o).</li> </ul> <p> 1. Top 100 tunnels present in tunnel folder for all three tunnels ST, p1b and p3 with subdirectory containing three variants and four ligand, whichare used for running caverdock.<br> 2. Ligand *.pdbqt file and receptor *.pdbqt are present in each 100 tunnel folder of respective caverdock calculation.<br> 3. Inside each variant and each ligand, there is respective result of migration analysis with energy barrier calculation of respective tunnels *energy_barriers-new.log* and further simplied *.csv files that was used for preparing figure in manuscript.</p> <p> </p>
BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 2. Original image affected by Gaussian noise
<p>In this research work we have detected Gaussian noise (fig 2) pattern in our images based on<br> methodology discussed in [18]. Where statistical moments features are extracted from the noise<br> patterns for noise class detection. This experiment detects the Gaussian noise patterns from images.<br> This leads to applying of wiener filter on noisy images, which gives the best noise removal.</p>
Synthetic Data for Neutrophil Analysis: Sets with regular shapes and Gaussian noise
<p><strong>Synthetic Datasets with regular shapes and Gaussian noise.</strong></p> <p><strong>Part of the PhagoSight neutrophil tracking and analysis package (Henry, et al., PLOS ONE, 2013):</strong></p> <p> </p> <p>https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0072636</p> <p>http://www.phagosight.org</p> <p>https://github.com/phagosight/phagosight</p> <p> </p> <p>A series of synthetic data sets that reproduce different behaviour characteristics of migrating neutrophils were generated in MATLAB. The data sets consisted of six artificial neutrophils that travelled along paths that presented different conditions of tortuosity, times to activation and proximity to other neutrophils during 98 time frames.</p> <p>Numerous data sets of neutrophils in zebrafish were carefully observed before setting the characteristics. Six trajectories were manually determined by setting the row, column positions of the centroids at every time point for 98 time frames. Each trajectory was designed so that it would represent different neutrophil behaviours: some trajectories were very oriented and had movements with uniform distance between time frames, whilst others were less uniform and would move at different velocities, some were tortuous whilst others were straight. The trajectories of cells 1 and 2 collided several times in the second half of the time frames whilst cells 3 and 4 collided at the beginning of the movement. Cell 6 migrated without meandering and then stopped at the end (which represents the wound area of an inflammation-based experiment) whilst 5 presented a delayed activation. </p> <p>Each time frame consisted of 11 slices of z-stack each with 275 x 275 pixels, where the neutrophils were formed by Gaussian distributions of higher intensities than the background and <strong>Gaussian noise </strong>(check the corresponding irregular shapes with Poisson noise plus another set with a <strong>single large neutrophil</strong> and Poisson noise). The orientation of the Gaussians varied according to the displacement of the artificial neutrophils, <em>i.e.</em>they were round when the cells were static, or elongated when in movement. The tracks with the Gaussians were saved as the <em>gold standard</em> and five different data sets were generated by adding varying levels of white Gaussian noise resulting in data sets with distributions with increasing similarity between the neutrophils and the background reflected by the decreasing values of the Bhattacharyya Distance (1.61, 1.25, 1, 0.66, 0.45) as defined by Coleman 1979.</p> <p> </p> <p>Files corresponding to the sets with irregular shapes and Poisson noise (noise increases from 1 to 6):</p> <ul> <li><strong> x,y,t trajectories ThreeDTracks</strong></li> <li><strong> Ground Truth syntheticData0_mat_Re </strong></li> <li><strong> First data set syntheticData1_mat_Re</strong></li> <li><strong> Second data set syntheticData2_mat_Re</strong></li> <li><strong> Third data set syntheticData3_mat_Re</strong></li> <li><strong> Fourth data set syntheticData4_mat_Re</strong></li> <li><strong> Fifth data set syntheticData5_mat_Re</strong></li> <li><strong> Sixth data set syntheticData6_mat_Re</strong></li> </ul> <p> </p> <p>Corresponding GIF files are also included as illustrations of the cells in motion.</p> <p> </p> <p>Main Reference:</p> <p><a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0072636"><strong><em>PhagoSight</em>: An Open-Source MATLAB® Package for the Analysis of Fluorescent Neutrophil and Macrophage Migration in a Zebrafish Model</strong> </a><br> Henry KM, Pase L, Ramos-Lopez CF, Lieschke GJ, Renshaw SA, Reyes-Aldasoro CC. (2013) <em>PhagoSight</em>: An Open-Source MATLAB® Package for the Analysis of Fluorescent Neutrophil and Macrophage Migration in a Zebrafish Model. PLOS ONE 8(8): e72636. <a href="https://doi.org/10.1371/journal.pone.0072636">https://doi.org/10.1371/journal.pone.0072636</a></p>
A new sampling capability for uncertainty quantification in the Ice-sheet and Sea-level System Model v4.19 using Gaussian Markov random fields -- Datasets and results
<p>Data archives for test experiments (Section 3) and Pine Island Glacier application (Section 4) from the manuscript "Kevin Bulthuis and Eric Larour, A new sampling capability for uncertainty quantification in the Ice-sheet and Sea-Level System Model v4.19 using Gaussian Markov random fields"</p> <p>Source code is available at https://doi.org/10.5281/zenodo.5532775.</p>
Data and Results for: Comparing Apples with Apples: Robust Detection Limits for Exoplanet High-Contrast Imaging in the Presence of non-Gaussian Noise
<p>This collection of data and results contains everything needed to reproduce the results in the paper:</p> <p>Comparing Apples with Apples: Robust Detection Limits for Exoplanet \\ High-Contrast Imaging in the Presence of non-Gaussian Noise</p> <p>The <a href="/api/files/53bfc05e-f632-443a-9c07-590b9bf860e1/apples_root_dir.zip?versionId=d41f6d1e-ef44-497c-ae2a-a5b291a8c9b9">apples_root_dir.zip</a> is further needed to run the examples of the python package Applefy.</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.