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13,064 results for “predictions”
Data and software from: Decomposition of skill scores for conditional verification – Impact of AMO phases on the predictability of decadal temperature forecasts
<p>Data and software from "Decomposition of skill scores for conditional verification – Impact of AMO phases on the predictability of decadal temperature forecasts"</p> <ul> <li>synthetic data and score computation routine</li> <li>data from MiKlip decadal prediction systems preop-dcpp-HR, preop-LR and observational HadCRUT4 data used for the conditional verification</li> <li>AMO calculation routine</li> <li>AMO data and phases defining the ocean state used in the conditional verification</li> </ul> <p>The computation routines from the conditional verification of decadal predcition systems is publicly available as the Freva plug-in "ProblEMS" at <a href="https://doi.org/10.5281/zenodo.10469658">https://doi.org/10.5281/zenodo.10469658</a></p> <p> </p>
Predicting Publication of Clinical Trials Using Structured and Unstructured Data
<p>This dataset (N=76,950) links metadata from ClinicalTrials.gov (a registry of clinical trials) and MEDLINE (a bibliographic database of academic journal articles), and can be used to model whether a clinical trial will get published or not.</p>
Fig. 2 in Predicting the potential distribution of the subalpine broad-leaved tree species, Betula ermanii Cham. under climate change in South Korea
Fig. 2. ROC curve and AUC value in the current period (1970- 2000).
Fig. 1 in Predicting the potential distribution of the subalpine broad-leaved tree species, Betula ermanii Cham. under climate change in South Korea
Fig. 1. The location of occurrence points of Betula ermanii in South Korea (N = 162).
Predicting Performance and Power Consumption of Parallel Applications
<p><em><strong>Abstract: </strong>Current architectures provide many control knobs for the reduction of power consumption of applications, like reducing the number of used cores or scaling down their frequency. However, choosing the right values for these knobs in order to satisfy requirements on performance and/or power consumption is a complex task and trying all the possible combinations of these values is an unfeasible solution since it would require too much time. For this reasons, there is the need for techniques that allow an accurate estimation of the performance and power consumption of an application when a specific configuration of the control knobs values is used. Usually, this is done by executing the application with different configurations and by using these information to predict its behaviour when the values of the knobs are changed. However, since this is a time consuming process, we would like to execute the application in the fewest number of configurations possible. In this work, we consider as control knobs the number of cores used by the application and the frequency of these cores. We show that on most Parsec benchmark programs, by executing the application in 1% of the total possible configurations and by applying a multiple linear regression model we are able to achieve an average accuracy of 96% in predicting its execution time and power consumption in all the other possible knobs combinations.</em></p> <p>This dataset includes the raw data of the experiments as well as the scripts used to plot them.</p>
Deep learning improves prediction of drug-drug and drug-food interactions
<p>Information on drug-drug interactions predicted by DeepDDI</p> <p> </p> <p> </p>
Lithosphere removal (delamination) following continental collision: shape and complexity of observables predicted from 3D numerical models
<p>Upload contains most essential data files from the manuscript. The raw data amount to several TB and cannot be uploaded.</p> <p><strong>1 Model data</strong></p> <p><em>1.1 Models covered</em></p> <p>Relatively frequent output is available for the reference model (<em>R</em> in paper, see Table S2), prefixed <em>dp10</em>. Limited output is uploaded for models with geometric variations (<em>OSM -> dp19, TSM->dp26</em>).</p> <p><em>1.2 Types of data</em></p> <p>Most files are visualisation files (ending *.<em>vtr</em>). These are intended for loading into the visualisation software Paraview. Files starting with <em>comp_</em> contain the lithological composition field; other visualisation files hold the fields of selected physical parameters. Note that as of the design of the study, the maximum file size allowed for loading into Paraview is ca. 2 GB. This limits the number of fields available according to model size - larger models (geometrical variations) hold therefore less fields.</p> <p>Binary files with ending *.<em>prn</em> are saved model states, from which programs can be rerun.</p> <p>Time information mapping step numbers to years is provided in <em>times...txt</em>.</p> <p> </p> <p><strong>2 Software for reproduction</strong></p> <p>The numerical code is not in the public domain, and its use is restricted. A pre-compiled binary is provided and allows reproduction of the main/reference model. We present the input files; however, these cannot be changed freely (equivalent to software distribution), and changes will lead to termination of the program.</p> <p><em>2.1 Requirements, preparation and use</em></p> <p>The code is compiled on CentOS with gcc 4.8.2. There is no library dependency, apart from the intrinsic OpenMP and glibc (>= 2.17). The following requirements <em>must</em> be satisfied in order to run it:</p> <ul> <li>Linux OS (tested on CentOS and Fedora)</li> <li>> 160 GB shared memory</li> </ul> <p>The following provisions are recommended:</p> <ul> <li>16 cores</li> <li>high bandwidth storage</li> <li>storage space <em>O</em>(TB)</li> </ul> <p>To prepare, simply unzip the provided snapshot <em>clsd_reproduce_gcc_CentOS.zip</em> in an appropriate directory (cluster). The input file <em>init.t3c</em> sets the initial conditions; the input file <em>mode.t3c </em>controls solver and file output. <em>file.t3c</em> is the pointer to the last output step; if a model snapshot is available as binary dump (*.prn file), setting the pointer to its number allows restarting.</p> <p>For normal usage, create initial condition with executable <em>in3mg</em>, and subsequently run <em>i3mg</em>. This will create snapshots and *.vtr visualisation files.</p> <p> </p> <p>High-performance computing facilities, runtime (months), proper software environment, and training in use of the code, or in the use of the visualisation software, are not provided.</p> <p> </p>
Chatter Prediction Data_1
<p>The files contain the machine tool configurations for two robot configurations which are used for milling process. They together can be used to train the ML algorithms to predict chatter. </p>
The Measurement and Prediction of Phase Transformation Kinetics in a Nuclear Steel During Weld-Like Thermal Cycles: supporting data
<p>The Excel files contain the raw dilatometry data for the FGHAZ and CGHAZ experiments, with the spreadsheet used to calculate the modified offset also included. </p> <p>The .tif files are the raw 2D images acquired during synchrotron X-ray diffraction experiments. The sample-detector distance was approximately 1400 mm. Please see the publication for further details (or contact the corresponding author). </p>
Supplementary Data: iterative Random Forests to discover predictive and stable high-order interactions
<p>This repository contains scripts and data for the simulations and case studies described in <em>iterative Random Forests to discover predictive and stable high-order interactions.</em></p>
Data from: Billeci et al. "Patient-specific seizure prediction based on heart rate variability and recurrence quantification analysis"
<p>Dataset of electrocardiogram and electroencephalogram signals (.edf) acquired in epileptic patients (N=15).</p> <p>All the patients were long-term monitored with a Video-EEG, with electrodes arranged on the basis of the international 10-20 system, and with ECG. ECG was measured simultaneously with a sampling rate of 512 Hz.</p> <p>Each data include a descriptor file (.txt) containing all the information related to the acquisition: data, registration start (time), registration end (time), seizure/s start, seizure/s end and the electrodes involved at the seizure onset.</p> <p> </p>
Predicted Human Immunopeptidome
<p>Comprehensive peptide-MHC binding predictions were performed for all 8-11mer peptides in the human proteome to all class I MHC available for prediction using NetMHCpan 3.0.</p> <p>human_immunopeptidome_files.tar.gz, when decompressed, contains a directory of all individual HLA files each containing all self-peptides predicted to bind. Alternatively, if only a subset of HLA alleles are of interest to you, decompressed individual files are available for each HLA. Note that these are identical to the files contained within the .tar.gz archive file.</p> <p>Details on the creation of this dataset can be found here: https://github.com/scottdbrown/self-immunopeptidome_cancer</p>
Amino acid sequences of the proteins predicted from the whole genome of hilsa shad (Tenualosa ilisha) of the Bay of Bengal
<p>Gene prediction was performed by AUGUSTUS (Stanke et al., 2006) from the whole genome sequence of <em>T. ilisha</em> (<a href="https://www.ncbi.nlm.nih.gov/bioproject/400122">PRJNA400122</a>). The data contain amino acid sequences of 37,450 predicted protein coding genes.</p>
Evaluation of Predictive Capabilities of Regression Models and Artificial Neural Networks for Density and Viscosity Measurements of Different Biodiesel-Diesel-Vegetable Oil Ternary Blends
<p>In this section, it was given that Annex Figures and Annex Tables related to the article "Evaluation of Predictive Capabilities of Regression Models and Artificial Neural Networks for Density and Viscosity Measurements of Different Biodiesel-Diesel-Vegetable Oil Ternary Blends" published in "Environmental and Climate Technologies" journal. </p>
Online Optimization in Cloud Resource Provisioning: Predictions, Regrets, and Algorithms: Virtual Machine ID Dataset
<p>The csv files in this dataset contain the virtual machine IDs used in [1] which correspond to the virtual machine traces in the Azure Public Dataset [2]. The file named "vmtable_lifetime_VMoL_1003.csv" holds the IDs used in Section 5 [1] and the file named "vmtable_lifetime_VMoL_55.csv" holds the IDs used in Section 6 [1]. The first column in both files refers to the ID labels in [1], while the second, third, and fourth columns refer to the Virtual Machine IDs, the Subscription IDs, and the Deployment IDs, respectively.</p> <p> </p> <p>[1] Joshua Comden, Sijie Yao, Niangjun Chen, Haipeng Xing, and Zhenhua Liu. 2019. Online Optimization in<br> Cloud Resource Provisioning: Predictions, Regrets, and Algorithms. Proc. ACM Meas. Anal. Comput. Syst. 3, 1,<br> Article 179 (March 2019).</p> <p>[2] Eli Cortez, Anand Bonde, Alexandre Muzio, Mark Russinovich, Marcus Fontoura, and Ricardo Bianchini. 2017. Resource Central: Understanding and Predicting Workloads for Improved Resource Management in Large Cloud Platforms. In Proceedings of SOSP’17. ACM, New York, NY, USA, 15 pages. https://doi.org/10.1145/3132747.3132772 Dataset access: https://github.com/Azure/AzurePublicDataset (August 2018)</p>
Towards improving short-term predictions of fine particulate matter over the United States via assimilation of satellite aerosol optical depth retrievals
<p>This dataset contains paired modeled and observed values of different trace gases and aerosol species over the CONUS for the period of 15 July to 14 August 2014 for the three different data assimilation experiments (BKG, MET_BE and MET+EMIS_BE) described in the paper. </p>
Predicting the Onset of Void Swelling in Irradiated Metals with Machine Learning
<p>Dataset to support the manuscript, "Predicting the Onset of Void Swelling in Irradiated Metals with Machine Learning", which contains all variables for predicting the target variable.</p>
Dataset - Enhanced flux prediction by integrating relative expression and relative metabolite abundance into thermodynamically consistent metabolic models
<p><strong>Simulation data needed to reproduce the results from the manuscript “Enhanced flux prediction by integrating relative expression and relative metabolite abundance into thermodynamically consistent metabolic models”</strong><br> by V. Pandey, N. Hadadi and V. Hatzimanikatis</p> <p>"REMI manuscript - simData" folder contains all simulation data which can be used to generate results of the paper: <br> • Expression_data: This folder contains Transcriptomics data from both studies: Ishii et al (see test_expr.mat) and Holm et al.<br> • Fluxdata: Fluxomics data can be found form the studies Ishii et al and Holm et al.<br> • Metabolomics: This contains metabolomics data of aforementioned both studies.<br> • ModelsSolutions: We generated different models using with thermodynamics (TGex, TGexM, TM) and without thermodynamics models (Gex, GexM, M). Gex indicates integration with only gene expression, GexM indicates gene expression and metabolite, and M indicates only metabolites. ‘T’ is used for thermodynamic models. Models for different mutants and conditions (e.g. pgm, pgi) can be found in the corresponding folders (TGex, TGexM, TM, Gex, GexM, and M). Variables with the ‘store’ tag comprises flux solutions, correlation values and percentage error between simulation and experiment fluxes.<br> • AlternativeMCS: We generated alternative states for MCS and saved results.<br> • FVAMM: This is the result flux variability analysis can be found in this folder.<br> • Scatter_plot: Scatter plots indicates correlation between measured and model predicted fluxes.</p> <p> </p>
Empirical Predictions for the Period Distribution of Planets to be Discovered by the Transiting Exoplanet Survey Satellite
<p>This repository contains the supplement materials for our paper :</p> <p>Jonathan H. Jiang, Xuan Ji, Nicolas Cowan, Renyu Hu, Zonghong Zhu, Empirical Predictions for the Period Distribution of Planets to be Discovered by the Transiting Exoplanet Survey Satellite, Submitted to AJ.</p> <p>Supplemental materials include:</p> <p>1.Data behind the Figures</p> <p>2.Simulation Results to estimate the error range</p> <p> </p>
Deep in the Bowel: Highly Interpretable Neural Encoder-Decoder Predicts Gut Metabolites from Gut Microbiome: Supplemental Data
<p>This upload contains the supplemental data to the manuscript titled, "Deep in the Bowel: Highly Interpretable Neural Encoder-Decoder Predicts Gut Metabolites from Gut Microbiome". It contains the transformed data used to train the model (*-grouped-clr.csv), the latent feature space (latent_z.txt), and the differential abundance results (*DA*.csv).</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.