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Dataset results
608 results for “ensembles”
Lightning Prediction in the Tehran Region Using the WRF Model with Multiple Physical Parameterizations and an Ensemble Approach
<p><span>The Grid Analysis and Display System (</span>GrADS)<span> </span><span>and</span><span> </span><span>Python</span><span> </span><span>scripts and the output data from simulations that we used in this study.</span></p>
Large ensemble simulations of Holocene temperature and volcanic forcing
<p>Simulations of the global monthly mean volcanic Stratospheric Aerosol Optical Depth (gmSAOD) for 6755 BCE - 1900 CE performed with EVA_H (code available from <span><a href="https://github.com/thomasaubry/EVA_H">https://github.com/thomasaubry/EVA_H</a></span>) and associated Effective Radiative Forcing (ERF).</p> <p>Simulations of the Holocene global annual mean temperature for 6755 BCE - 1900 CE performed with FaIR (code available from <span><a href="https://github.com/OMS-NetZero/FAIR/tree/v2.1.4">https://github.com/OMS-NetZero/FAIR/tree/v2.1.4</a></span>) using volcanic, greenhouse gases (CO2, CH4, N2O), solar, orbital, ice sheets, and anthropogenic land use forcings.</p>
Conformational Ensembles Reveal the Origins of Serine Protease Catalysis - auxiliary data and code
<p>EnsemblePDB.zip - package version used to create pseudo-ensembles in the paper "Conformational Ensembles Reveal the Origins of Serine Protease Catalysis"</p> <p>serine_protease_ensembles.zip - data and code used to generate and analyze the data presented in the paper "Conformational Ensembles Reveal the Origins of Serine Protease Catalysis"</p>
Data - Quantifying dynamic linkages between precipitation, groundwater recharge, and streamflow using ensemble rainfall‐runoff analysis
<p>The data support the analysis conducted in "Quantifying Dynamic Linkages Between Precipitation, Groundwater Recharge, and Streamflow Using Ensemble Rainfall‐Runoff Analysis", accepted for publication in Water Resources Research (https://doi.org/10.1029/2024WR037821) by Huibin Gao, Qin Ju, Dawei Zhang, Zhenlong Wang, Zhenchun Hao, and James Kirchner.</p> <p> </p>
Ensembl IDs with associated gene-level metadata
<p>A small table of ensembl gene IDs with associated symbol and gene biotype.<br><br>It's often useful to download this information from biomart (and this table was generated with biomart), so this is a static version of the same data.<br><br>All versions will be listed here, with the date they were retrieved from BioMart:<br>- V1: 2024-04-08</p>
A Unified Ensemble Soil Moisture Dataset Across the Continental United States
<p>A unified ensemble soil moisture (SM) package has been developed over the Continental United States (CONUS). The data package includes 19 products from land surface models, remote sensing, reanalysis, and machine learning models. All datasets are unified to a 0.25-degree and monthly spatiotemporal resolution, covering various temporal spans and providing a comprehensive view of surface SM dynamics. The statistical analysis of the datasets leverages the Koppen-Geiger Climate Classification to explore surface SM’s spatiotemporal variabilities. The extracted SM characteristics highlight distinct patterns, with the western CONUS showing larger coefficient of variation values and the eastern CONUS exhibiting higher SM values. Remote sensing datasets tend to be drier, while reanalysis products present wetter conditions. In-situ SM observations serve as the basis for wavelet power spectrum analyses to explain discrepancies with respect to temporal scales across the 16 datasets facilitating daily SM records. This study provides a comprehensive soil moisture data package and an analysis framework that can be used for Earth system model evaluations and uncertainty quantification, quantifying drought impacts and land–atmosphere interactions, and making recommendations for drought response planning.</p> <p>Data details: 1. scripts: 1) process data from original spatial resolution to 0.25 degree; 2) process data from original temporal resolution to monthly; 3) process the monthly data to seasonal mean analysis; 4) wavelet analysis. 2. data: 1) monthly 0.25deg data processed from raw datasets; 2) monthly and seasonal climatology data for comparison; 3) site data for wavelet analysis.</p> <p>Reference: The data manuscript is under review now and will add it here later.</p> <p>Contact: Mingjie Shi <mingjie.shi@pnnl.gov>; Lingcheng Li <lingcheng.li@pnnl.gov></p>
Development of Ensemble Steric and Electrostatic Chirality (ESEC) descriptors for modelling chromatographic enantioseparations
<p>For example for the Excel file with the title "Borate_chiral_explicit_biased_charged_uncharged.xlsx":</p> <p>- In the first tab, titled "Exp_water_acn_biased_charge", you can find the biased chiral descriptors for the molecules in their pH 9 state, simulated in explicit solvent. </p> <p>- In the second tab, titled "Exp_water_acn_biased_uncharged", you can find the biased chiral descriptors for the molecules in their uncharged state, simulated in explicit solvent. </p> <p>- The third tab, titled "Chiral log alfaRS charge uncharge", includes the responses (log αRS and αRS), along with the retention times and retention factors (k) for each molecule. In addition, this tab contains the descriptors from both the first and the second tab.</p> <p>- The fourth and the fifth tabs present the experimental and predicted responses (log αRS in the fourth tab and αRS in the fifth tab) for the models that were built. </p> <p>The structure of the other files follows a similar pattern: the first tabs provide the descriptor values, followed by a tab containing the modelling information (response(s) and various descriptor sets) and finally, a tab is included with the predicted response values. </p>
Gene/Protein BridgeDb ID Mapping Database (Ensembl 85)
<p>Ensembl 85 derived ID mapping database for use with <a href="https://bridgedb.github.io/">BridgeDb</a> and was created with <a href="https://github.com/bridgedb/create-bridgedb-genedb">custom code</a>.</p>
EnGRaiN : A Supervised Ensemble Learning Method for Recovery of Large-scale Gene Regulatory Networks
<p>EnGRaiN is a supervised machine learning method to construct ensemble networks. To benefit from the typical accuracy advantages of supervised learning methods while taking into account the impossibility of knowing true networks for training, we devised a method that uses small training datasets of true positives and true negatives among gene pairs.</p> <p>The datasets used to evaluate the performance of EnGaiN include (i) simulated datasets generated from Yeast networks and (ii) A. thaliana gene expression datasets.</p>
Conformational ensembles used in "Refining conformational ensembles of flexible proteins against SAXS data; Pesce F. and Lindorff-Larsen K."
<p>Conformational ensembles of Histatin5, Sic1, Tau generated with Flexible-meccano and converted to all-atom models with PULCHRA. TIA-1 ensemble from https://doi.org/10.1371/journal.pcbi.1007870</p> <p>These ensembles have been used for SAXS calculations and reweighting against experimental SAXS data to study the effect of the hydration layer description on the reweighted ensembles.</p>
Distributed sensing via the ensemble spectra of uncoupled electronic chaotic oscillators
<p>These are time-domain and frequency-domain data recorded from 4 realizations of a chaos-generating integrated circuit as a function of a control voltage and various input signals. They are provided to support replication of the results reported in the associated publication, as well as any further public-domain academic research in the field of chaotic oscillators, distributed sensing and related aspects, in compliance with the specified license terms and all applicable legal clauses.</p> <p>The following reference must be cited when using these data: Minati L, Tokgoz KK, Ito H, Distributed sensing via the ensemble spectra of uncoupled electronic chaotic oscillators, <em>Chaos Solitons & Fractals,</em> vol. 155, 111749, 2022, DOI 10.1016/j.chaos.2021.111749</p> <p>This work was partially supported by JSPS KAKENHI Grant Number 19H02191. Device realization was also supported by SCOPE (No. 0159-0013) from the Japan Ministry of Internal Affairs and Communications (MIC), with the assistance of the National Institute of Information and Communications Technology (NICT), and through the activities of VDEC, the University of Tokyo, in collaboration with Cadence Design Systems and Mentor Graphics.</p>
Daily NOAA Global Ensemble Forecasting System forecasts for six National Ecological Observatory Network lakes (2021--05-18 to 2021-10-24)
<p>NOAA Global Ensemble Forecasting System output generated at 00 UTC that has been subsetted and temporally downscaled from 6-hr to 1-hr for six lakes in the National Ecological Observatory Network. The files include all ensembles and the set of variables required to run the General Lake Model. The NEON siteID for the lakes are BARC, SUGG, CRAM, LIRO, PRLA, PRPO. See https://www.neonscience.org for more information about each lake.</p>
CM2.1 Hist ensemble mean
<p>20-member ensemble of CM2.1 historical simulations, each forced by historical and RCP4.5 radiative forcing and different initial conditions</p>
Ensemble Refinement of Stable-5-LOX and AlphaFold2 predictions including variants
<p>Ensemble Refinement of Stable-5-LOX in "closed" (PDB code: 7TTK) and "open" (PDB code: 7TTJ) conformations. AlphaFold2 models of Stable-5-LOX and variants from the research article "Helical remodeling augments 5-lipoxygenase activity."</p>
Supplementary Information: Effects of cryo-EM cooling on structural ensembles
<p>This data set contains a pdb file with the ribosome-EF-Tu complex atoms used for analysis. The trajectories (xtc files) contain the ensembles of structures before cooling and after cooling with various cooling time spans.</p> <p>model3_training.zip contains the code to train and and analyse kinetic model3 as well as the rmsf quantiles obtained from MD simulations, and the temperature drop estimates used for the model.</p>
Dataset for I. Latella, A. Campa, L. Casetti, P. Di Cintio, J. M. Rubi, and S. Ruffo, Monte Carlo simulations in the unconstrained ensemble, Phys. Rev. E 103, L061303 (2021)
<p>This dataset contains data associated to plots published in the paper I. Latella, A. Campa, L. Casetti, P. Di Cintio, J. M. Rubi, and S. Ruffo, Monte Carlo simulations in the unconstrained ensemble, Phys. Rev. E 103, L061303 (2021), https://doi.org/10.1103/PhysRevE.103.L061303</p>
Ensembles of knowledge graph embedding models improve predictions for drug discovery
<p>This contains data described in detail in our paper, "Ensembles of knowledge graph embedding models improve predictions for drug discovery". The metadata involves the different trained models that were used for prediction analysis as well as all the predictions from the trained models.</p>
Pin1 two-state structural ensembles of apo, FFpSPR-bound and pCDC25c-bound form
<p>Pin1 is a two-domain cell regulator that isomerizes peptidyl-prolines. The catalytic domain (PPIase) and the other ligand-binding domain (WW) sample extended and compact conformations. Ligand binding changes the equilibrium of the interdomain conformations through an interdomain allosteric mechanism. We have described ligand-specific conformational changes that occur upon binding of pCDC25c and FFpSPR. pCDC25c binding doubles the population of the extended states compared to the virtually identical populations of the apo and FFpSPR-bound forms. pCDC25c binding to the WW domain triggers conformational changes to propagate via the interdomain interface to the catalytic site, while FFpSPR binding displaces a helix in the PPIase that leads to repositioning of the PPIase catalytic loop.</p> <p>Here, we deposit the entire magnetic resonance-based CYANA structure calculation protocols of Pin1 two-state structural ensembles of apo, FFpSPR-bound and pCDC25c-bound form that allowed us to determine the coupling of intra- and interdomain structural sampling Pin1.</p>
First-order coherence of light emission from inhomogeneously broadened mesoscopic ensembles
<p>Data from all figures corresponding to the article from A. Delteil, V. Blondot, S. Buil, and J.-P. Hermier, "First-order coherence of light emission from inhomogeneously broadened mesoscopic ensembles", <a href="https://journals.aps.org/prb/abstract/10.1103/PhysRevB.106.115302">Phys. Rev. B. <strong>106</strong>, 115302 (2022)</a> – <a href="https://arxiv.org/abs/2209.01137">arXiv:2209.01137</a></p> <p> </p> <p>Data are in tab-separated table format, with a header indicating the variable and unit for each column.</p>
HOIP-ensembles-Kausas-et-al
<p>Supplementary data for "Characterisation of HOIP RBR E3 ligase conformational dynamics using integrative modelling"</p>
ScienceDex guides
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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.