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608
datasets available to search
ShareScore release 0.9.0
Dataset results
608 results for “ensembles”
Machine learning based methods to generate conformational ensembles of disordered proteins (len18, point mutation)
<p>data is organized by bin number (0-9) and mutation location (4, 8, 12). (Note: in the manuscript, we used the nomenclature bins 1-10 and mutation locations 5,8,13. We simply used a 0-index convention when naming our folders). All data is in rep_1, which contains the trajectory (xtc) file, Rg information (in the file Rg.out), pairwise distance information (in the file traj_analysis_data/pairwise_distance_matrix.csv) and the bspline coefficients (in the file bspline_info/xyz_coeff.npy). However, for bin_num=9/mutation_loc=12, the data used is in rep_2, not rep_1. </p>
Machine learning based methods to generate conformational ensembles of disordered proteins (len36)
<p>data is in rep_1 for all sequences, which contains the trajectory (xtc) file, Rg information (in the file Rg.out), pairwise distance information (in the file traj_analysis_data/pairwise_distance_matrix.csv) and the bspline coefficients (in the file bspline_info/xyz_coeff.npy)</p>
Dataset, models and code for "Automating global landslide detection with heterogeneous ensemble deep-learning classification"
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Ensemble Programme an Early Intervention for Informal Caregivers of Psychiatric Patients
ClinicalTrials.gov study NCT04020497. IPD Sharing: NO. Countries: 1. Publications: 3.
Data from: Modelling bat distributions and diversity in a mountain landscape using focal predictors in ensemble of small models
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Data from: Varying dataset resolution alters predictive accuracy of spatially explicit ensemble models for avian species distribution
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Data from: Protocol dependence and state variables in the force-moment ensemble
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Data from: Homogenisation of carnivorous mammal ensembles caused by global range reductions of large-bodied hypercarnivores during the late Quaternary
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A field-validated ensemble species distribution model of Eriogonum pelinophilum, an endangered subshrub in Colorado, USA
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Data from: Accuracy in the prediction of disease epidemics when ensembling simple but highly correlated models
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Data from: Ecomorphology of the African felid ensemble: the role of the skull and postcranium in determining species segregation and assembling history
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Data from: Trajectory-based training enables protein simulations with accurate folding and Boltzmann ensembles in cpu-hours
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Data from: How does spatial resolution affect model performance? A case for ensemble approaches for marine benthic mesophotic communities
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Data from: Ensemble approach for potential habitat mapping of invasive Prosopis in Turkana, Kenya
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Dorsal Periaqueductal gray ensembles represent approach and avoidance states
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Supporting data for "Synthesis and Simulation of Ensembles of Boolean Networks for Cell Fate Decision" by Chevalier et al., 2020
<p>Code, data, and notebooks used for the synthesis and simulations of ensembles of Boolean networks for the tumor invasion model introduced in <a href="https://doi.org/10.1371/journal.pcbi.1004571">(Cohen et al, 2015)</a></p> <p>Visualize online:</p> <ul> <li><a href="https://nbviewer.jupyter.org/urls/zenodo.org/record/3938904/files/Simulations%20-%20Mutant%20analysis.ipynb">Simulations - Mutant analysis.ipynb </a></li> <li> <a href="https://nbviewer.jupyter.org/urls/zenodo.org/record/3938904/files/Tumour%20-%20Synthesis%20with%20BoNesis.ipynb">Tumour - Synthesis with BoNesis.ipynb</a></li> </ul> <p>The notebooks can be executed within the <a href="http://colomoto.org/notebook">CoLoMoTo Docker</a> image 2020-07-01:</p> <pre><code>pip install -U colomoto-docker colomoto-docker -V 2020-07-01 --bind . </code></pre> <p>The synthesis additionally requires executing the following command (within the Docker image):</p> <pre><code>pip install --user bonesis-preview-20200514.zip </code></pre> <p>The ensembles have been generated with the following commands.</p> <pre><code>python synthesis.py synthesis --exact-pkn --globalfps python synthesis.py synthesis --exact-pkn --globalfps --mutant p53 --mutant NICD </code></pre> <p> </p> <ul> </ul>
TS Ensemble for "Evolution of an interaction between disordered proteins resulted in increased heterogeneity of the binding transition state"
<p>These are the input files for a phi-value restrained molecular dynamics associated with this paper "Evolution of an interaction between disordered proteins resulted in increased heterogeneity of the binding transition state"</p> <p> </p>
Replication Package for the Paper: "A Machine Learning Based Ensemble Method for Automatic Multiclass Classification of Decisions: A Study of the Hibernate Developer Mailing List"
<p>This is the replication package for the paper: "A Machine Learning Based Ensemble Method for Automatic Classification of Decisions: A Study of the Hibernate Developer Mailing List". It contains the source code and dataset of our experiment for the replication by other researchers. In the meanwhile, we provide brief description of the files in the replication package below.</p> <p><strong>1. code folder</strong></p> <ul> <li><em>experiment.py </em>contains the source code for our experiment, which is conducted on Windows 10 and Python 3.7.0. <strong>Note that you may get slightly</strong> <strong>different experiment results when conducting the experiments on different environment configurations.</strong></li> <li><em>requirement.txt</em> records all the installation packages and their version numbers needed for the current program to run. You can use "<em>pip install -r requirement.txt</em>" to rebuild the project and install all dependencies. <strong>Note that you may get slightly different experiment results when using different packages or versions. </strong></li> </ul> <p><strong>2. dataset folder</strong></p> <ul> <li><em>decisions.xlsx </em>contains 844 labelled sentence-level decisions from the Hibernate developer mailing list.</li> </ul>
Data from: Ensemble modelling of the potential distribution of the whale shark in the Atlantic Ocean
<p><span>This dataset reports observations of whale shark (<i>Rhincodon typus</i>) in the tropical and subtropical Atlantic Ocean. Data were obtained by the Spanish Institute of Oceanography (IEO) observers program, and were rescaled to the resolution of the Bio-ORACLE 2.0 variables, against which they were modelled along with presences from additional online sources. Our data include both presence and surveyed absence records – i.e., places where observers were active and did not detect whale sharks – within 5 arc-minute pixels spanning the surveyed area.</span></p>
HIV Tropism Ensemble Methods
<p>Prediction of HIV-1 subtype C tropism using ensemble learning with genotypic algorithms. Made using R version 3.6.0.</p> <p>Original data used to generate models is in /originalData</p> <p>--------------------------</p> <p>Previsão do tropismo do HIV-1 subtipo C com stacking de testes genotípicos. Feito com R, versão 3.6.0.</p> <p>Dados originais usados para gerar os modelos em /originalData</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.