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608 results for “ensembles”
Large ensemble climate modelling time series for the Rhine catchment, including drought2018 storylines
<p>Dataset associated with <strong>Van der Wiel, Lenderink, De Vries (2021): Physical storylines of future European drought events like 2018 based on ensemble climate modelling, <em>Weather and Climate Extremes, </em>DOI <a href="http://doi.org/10.1016/j.wace.2021.100350">10.1016/j.wace.2021.100350</a>.</strong></p> <p>Large ensemble climate modelling time series for the Rhine catchment. The dataset contains three ensembles (present-day, pre-industrial + 2C-warming, pre-industrial + 3C-warming) of 2000 years each, various variables related to drought are included. All data is derived from the EC-Earth global climate model (v2, Hazeleger et al. 2012, DOI <a href="https://doi.org/10.1007/s00382-011-1228-5">10.1007/s00382-011-1228-5</a>). Descriptions of large ensemble experimental setup can be found in Van der Wiel et al. (2019, DOI <a href="http://doi.org/10.1029/2019GL081967">10.1029/2019GL081967</a>). Files: *_d_ECEarth_??_Rhine.tar.gz</p> <p>Additionally, three sets of storylines of droughts similar to the western European drought of 2018 are included. These are the simulated events selected from the large ensembles, using metrics 1, 2 and 3 of Van der Wiel et al. (2021, DOI <a href="http://doi.org/10.1016/j.wace.2021.100350">10.1016/j.wace.2021.100350</a>). Files: drought18_m[123]_Rhine.tar.gz</p>
Hypothetical ensemble dispersion model runs with statistical verification
<p>This dataset contains output from the dispersion model NAME (Numerical Atmospheric-dispersion Modelling Environment) generated by modelling the hypothetical eruption of two volcanoes (Hekla and Oraefajokull both in Iceland) and a radiological release from 12 different locations across Europe. The hypothetical eruption of Hekla is a 12km eruption lasting 24 hours and the hypothetical eruption of Oraefajokull is a 25km eruption lasting 24 hours. Each of the radiological releases is a 1PBq Cs-137 release over 6 hours. The scenarios were modelling using three different sets of met data from the Met Office unified model; data from the global deterministic forecast, data from the global ensemble forecast and data constructed to form a global analysis. </p> <p>Output from the runs is stored in gzipped tar files labelled <name>_scenario_<year><month>.tar.gz where <name> is hekla, orae (short for oraefajokull) or radiological. Each of these tarballs contains the output from all the runs with a release start date in the year and month given in the file name. The data is stored in NetCDF format with each NetCDF file containing the run output from a single run with one type of met data. For example the NetCDF file: 20181109T1200Z_engl_members.nc contains all the output from the run started at 12 UTC on 9 November 2018 using the ensemble global forecast met data. </p> <p>For the radiological scenario output is the total integrated air concentration and the total deposition after 48 hours. For the volcanic eruption scenarios output is the hourly air concentration of volcanic ash on 22 vertical levels, hourly ash column load and hourly ash deposits.</p> <p>Two additional files are included. These contain the Brier skill score computed by comparing the ensemble and deterministic output to the analysis output and the maximum distance at which threshold concentrations are exceeded. Full details of the computation can be found in a paper submitted to Atmospheric Chemistry and Physics.</p> <p>@Crown Copyright, Met Office</p>
Coordinates activities of retrosplenial ensembles during resting-state encode spatial landmarks. Part 1 of 2
<p>The brain likely uses off-line periods to consolidate recent memories. One hypothesis holds that the hippocampal output provides a unique, global linking or 'index' code for each memory, and that this code is stored in the cortex in association with locally encoded attributes of each memory. Activation of the index code is hypothesized to evoke coordinated memory trace reactivation thus facilitating consolidation. Retrosplenial cortex (RSC) is a major recipient of hippocampal outflow and we have described populations of neurons there with sparse and orthogonal coding characteristics that resemble hippocampal 'place' cells, and whose expression depends on an intact hippocampus. Using two-photon Ca<sup>2+</sup> imaging, we recorded ensembles of neurons in the RSC during periods of immobility before and after active running on a familiar linear treadmill track. Synchronous bursting of distinct groups of neurons occurred during rest both prior to and after running. In the second rest epoch, these patterns were associated with the locations of tactile landmarks and reward. Complementing established views on the functions of the RSC, our findings indicate that the structure is involved with processing landmark information during rest.</p>
Long-term stability of cortical ensembles
<p>Neuronal ensembles, coactive groups of neurons found in spontaneous and evoked cortical activity, are causally related to memories and perception, but it still unknown how stable or flexible they are over time. We used two-photon multiplane calcium imaging to track over weeks the activity of the same pyramidal neurons in layer 2/3 of the visual cortex from awake mice and recorded their spontaneous and visually evoked responses. Less than half of the neurons were commonly active across any two imaging sessions. These "common neurons" formed stable ensembles lasting weeks, but some ensembles were also transient and appeared only in one single session. Stable ensembles preserved ~68 % of their neurons up to 46 days, our longest imaged period, and these "core" cells had stronger functional connectivity. Our results demonstrate that neuronal ensembles can last for weeks and could, in principle, serve as a substrate for long-lasting representation of perceptual states or memories.</p>
Coordinates activities of retrosplenial ensembles during resting-state encode spatial landmarks. Part 2 of 2
<p>The brain likely uses off-line periods to consolidate recent memories. One hypothesis holds that the hippocampal output provides a unique, global linking or 'index' code for each memory, and that this code is stored in the cortex in association with locally encoded attributes of each memory. Activation of the index code is hypothesized to evoke coordinated memory trace reactivation thus facilitating consolidation. Retrosplenial cortex (RSC) is a major recipient of hippocampal outflow and we have described populations of neurons there with sparse and orthogonal coding characteristics that resemble hippocampal 'place' cells, and whose expression depends on an intact hippocampus. Using two-photon Ca<sup>2+</sup> imaging, we recorded ensembles of neurons in the RSC during periods of immobility before and after active running on a familiar linear treadmill track. Synchronous bursting of distinct groups of neurons occurred during rest both prior to and after running. In the second rest epoch, these patterns were associated with the locations of tactile landmarks and reward. Complementing established views on the functions of the RSC, our findings indicate that the structure is involved with processing landmark information during rest.</p>
Controlling the emission time of photon echoes by optical freezing of exciton dephasing and rephasing in quantum-dot ensembles
<p>Dataset of the publication “Controlling the emission time of photon echoes by optical freezing of exciton dephasing and rephasing in quantum-dot ensembles“, <a href="https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11684/2576887/Controlling-the-emission-time-of-photon-echoes-by-optical-freezing/10.1117/12.2576887.short?SSO=1">Proc. SPIE 11684,116840X (2021)</a> ( <a href="https://doi.org/10.1117/12.2576887">https://doi.org/10.1117/12.2576887</a> ). The zip file includes the data on which the figures are based, the gnuplot files for the figures, and an explaining readme.txt.</p>
Orbits of Milky Way satellites in an ensemble of Galactic potentials including the LMC
<p>This archive contains the samples from the MCMC analysis of Milky Way potential,<br> including the dynamical perturbation from the LMC, and corresponding orbits<br> of other Galactic satellites in each choice of the MW+LMC potential.<br> <br> There are 1000 samples of the MW+LMC potential parameters from the chain,<br> and for each of them, one sample from the posterior distribution of present-day<br> position/velocity for each satellite (i.e., sampled from its measurement<br> uncertainties and weighted by the probability of finding this phase-space point<br> in the DF corresponding to the given potential in the chain).<br> The archive contains pre-computed trajectories for 63 objects,<br> stored as a 4d numpy array orbits.npy with shape<br> (63 objects, 1000 samples, 151 timesteps, 6 phase-space coordinates).<br> The timesteps are equally spaced between -3 Gyr and now (also stored in<br> orbit_times.npy).<br> Object names are listed in names.npy; LMC comes 0th.<br> The potential parameters for each of these 1000 samples are stored in<br> potential_params.npy - each row has 6 parameters, 5 for the MW halo<br> and the last one is the LMC mass.<br> The script integrate_orbits.py contains a routine for constructing<br> the MW potential with the given parameters (taken from the chain),<br> computing the past trajectories of MW+LMC and constructing the time-dependent<br> potential of both galaxies, which can then be used to integrate orbits of<br> test particles, such as other satellites. Doing this for all 1000 samples<br> and 62 objects would take some time, that's why they are provided in already<br> pre-computed form.<br> </p>
Data of publication Cavity-enhanced spectroscopy of a few-ion ensemble in Eu3+:Y2O3
<p>Data corresponding to the figures of the publication "Cavity-enhanced spectroscopy of a few-ion ensemble in Eu3+:Y2O3" by B. Casabone et al. (New J. Phys. 20(2018) 095006 https://doi.org/10.1088/1367-2630/aadf68). A text file describes data in each compressed folder, please refer to the caption in the publication for more details. </p> <p>These data can be also found together with the article manuscript at https://zenodo.org/record/1546139</p>
Spectral Engineering of Cavity-Protected Polaritons in an Atomic Ensemble with Controlled Disorder
<p>The paradigm of N quantum emitters coupled to a single cavity mode appears in many situations ranging from quantum technologies to polaritonic chemistry. The ideal case of identical emitters is elegantly modeled in terms of symmetric states, and understood in terms of polaritons. In the practically relevant case of an inhomogeneous frequency distribution, this simple picture breaks down and new and surprising features appear. Here we leverage the high degree of control in a strongly coupled cold atom system, where for the first time the ratio between coupling strength and frequency inhomogeneities can be tuned. We directly observe the transition from a disordered regime to a polaritonic one with only two resonances. The latter are much narrower than the frequency distribution, as predicted in the context of ''cavity protection''. We find that the concentration of the photonic weight of the coupled light-matter states is a key parameter for this transition, and demonstrate that a simple parameter based on statistics of transmission count spectra provides a robust experimental proxy for this theoretical quantity. Moreover, we realize a dynamically modulated Tavis-Cumming model to produce a comb of narrow polariton resonances protected from the disorder, with potential applications to quantum networks.</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>
Daily surface temperature and current from a 10 members ensemble simulation of June-September 2018 over the South China Sea
<p>This file contains the outputs from an ensemble of 10 members of simulation performed over the South China Sea for summer 2018.</p> <p>Members are numbered from 09 to 18.</p> <p>member_11_daily_surface_tem_u_v_JJAS.nc contains the surface daily temperature and current simulated by member 11</p> <p>grid.nc contains all information about the Arakawa C grid (longitude, latitude, mask, mesh size etc).</p> <p>wstress_surf_2018_JJAS_daily.nc contains the daily wind stress for summer 2018</p>
Customized gtf file from Ensembl version 102 mm10
<p>The gtf from Ensembl version 102 (mm10) 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.<br> In this new version, genes on contigs have been kept as well as CDS information. The procedure to generate the gtf is described in the bash file attached.<br> The version used in Amandio et al. 2021 is the previous version of this record available <a href="https://zenodo.org/record/4596490">here</a>.</p>
Speos: An ensemble graph representation framework to predict core genes for complex diseases (Datasets)
<p>the "data.tar.gz" tarball contains the unprocessed or minimally processed data used by Speos. If you intend to use the framework or want to inspect the data, download this part of the dataset.</p> <p>The "final_datasets.tar.gz" tarball contains tsv-formatted, processed data matrices which are directly used as input features for the ensemble models. </p> <p>There are two tsv-files per disease, one labeled "normal", which contains the p input features alongside the gene identifiers and a column which indicates if the gene is labeled as Mendelian or not, and another file labeled "with_n2v_vectors", which also contains the 100-dimensional vectors produced by Node2Vec so the N2V+MLP method can be reproduced with the exact same parameters. </p> <p>All files contain a header row which describes the column and no index column.</p> <p>The "model_parameters.tar.gz" tarball contains the model parameters for all ensemble models used to produce the candidate genes.</p>
Artifact for "MAAT: A Novel Ensemble Approach to Addressing Fairness and Performance Bugs for Machine Learning Software"
<p>This artifact is for the paper entitled “MAAT: A Novel Ensemble Approach to Addressing Fairness and Performance Bugs for Machine Learning Software”, which is accepted by ESEC/FSE 2022. MAAT is a novel ensemble approach to improving the fairness-performance trade-off for ML software. It outperforms state-of-the-art bias mitigation methods. The artifact has also been placed on GitHub (https://github.com/chenzhenpeng18/FSE22-MAAT) under the Apache License, publicly accessible to other researchers. In this artifact, we provide the source code of MAAT and other existing bias mitigation methods that we use in our study, as well as the intermediate results, the installation instructions, and a replication guideline (included in the README). The replication guideline provides detailed steps to replicate all the results for all the research questions.</p>
Multivariate projected ensemble of "Downscaling CORDEX through deep learning to daily 1 km multivariate ensemble in complex terrain"
<p>Multivariate statistically downscaled projected ensemble for different combinations of GCM-RCMs of both stochastic and deterministic runs for eight historical runs, eight RCP85 runs and one RCP26 run. Selection of good perfoming GCM-RCM combinations in NetCDF format. Variables: precipitation, water vapour pressure, radiation, wind speed, and, maximum, mean and minimum temperature.</p>
Engineering Graph States of Atomic Ensembles by Photon-Mediated Entanglement
<p>This is data associated with the paper "Engineering Graph States of Atomic Ensembles by Photon-Mediated Entanglement" (<a href="https://arxiv.org/abs/2212.11961">arXiv</a>).</p>
Data for manuscript "Adaptive Ensemble Refinement of Protein Structures in High Resolution Electron Microscopy Density Maps with Radical Augmented Molecular Dynamics Flexible Fitting"
<p>The tar file contains the input files for RADICAL augmented MDFF implementation (R-MDFF) for two protein systems, Adenylate Kinase (ADK) and Carbon Monoxide Dehydrogenase (CODH). These examples demonstrate the implementation of R-MDFF using RADICAL-Cybertools to flexibly fit biomolecules in cryo-EM density maps with on-the-fly decision making.</p> <p>All molecular simulations were performed using CUDA enabled NAMD 2.14 installed on OLCF Summit HPC resource. The CHARMM36 force field parameters were used for the proteins. Synthetic density maps were prepared at 1.8, 3 and 5 Å for ADK and 1.8 and 3 Å for CODH using VMD 1.9.3 software installed on OLCF Summit HPC resource. During the analysis stage, the cross correlation coefficients between density maps and atomic model were computed using VMD 1.9.3 on Summit HPC as part of the R-MDFF workflow.</p> <p>The source code is publicly available on GitHub: <a href="https://github.com/radical-collaboration/MDFF-EnTK">https://github.com/radical-collaboration/MDFF-EnTK </a></p> <p>The preprint of this research is submitted on bioRxiv, doi: <a href="https://doi.org/10.1101/2021.12.07.471672">https://doi.org/10.1101/2021.12.07.471672 </a></p> <p>To obtain maximum compression of the data, the tar command used to generate this tarball was:</p> <pre><code class="language-bash">GZIP=-9 tar --exclude='last.pdb' --exclude='*last_from_prev_iter.pdb' --exclude='*old' --exclude='*log' --exclude='*coor' --exclude='*vel' --exclude='*xsc' --exclude='*dcd' --exclude='lastframepdbs_fix' --exclude='*out' --exclude='*sl' --exclude='*rs' --exclude='*prof' --exclude='*err' --exclude='*dx' --exclude='*grid.pdb' --exclude='*txt' -cvzf rmdffv2.tar.gz rmdff-zenodo/</code></pre> <p> </p>
Gene/Protein BridgeDb ID Mapping Database (Ensembl Fungi 52)
<p>Mapping databases derived from Ensembl Fungi 52. These files can be used with BridgeDb.<br> This version doesn't have the issue of not could be searched using gene names (e.g. in PathVisio).</p> <p>The scripts which were used to create these databases based on Ensembl BioMart can be found at <a href="https://github.com/bridgedb/create-bridgedb-genedb">https://github.com/bridgedb/create-bridgedb-genedb</a>.</p>
Gene/Protein BridgeDb ID Mapping Database (Ensembl 106)
<p>Ensembl 106 derived ID mapping databases for use with BridgeDb.<br> This version doesn't have the issue of not could be searched using gene names (e.g. in PathVisio).</p> <p>The scripts used to create these databases based on Ensembl BioMart can be found at <a href="https://github.com/bridgedb/create-bridgedb-genedb">https://github.com/bridgedb/create-bridgedb-genedb</a>.</p>
Gene/Protein BridgeDb ID Mapping Database (Ensembl Plants 52)
<p>Mapping databases derived from Ensembl Plants 52. These files can be used with BridgeDb.<br> This version doesn't have the issue of not could be searched using gene names (e.g. in PathVisio).</p> <p>The scripts which were used to create these databases based on Ensembl BioMart can be found at <a href="https://github.com/bridgedb/create-bridgedb-genedb">https://github.com/bridgedb/create-bridgedb-genedb</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.