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Dataset results
376 results for “scalable”
Accurate, scalable, and fully automated inference of species trees from raw genome assemblies using ROADIES
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Data from: A scalable model for simulating multi-round antibody evolution and benchmarking of clonal tree reconstruction methods
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InterFlex WP3 data set for scalability and replicability analysis of InterFlex demonstrators
<p>This set of data includes the data for the scalability and replicability task of InterFlex project under GA 731289 for the functional (grid operation) and ICT conducted analyses of the demonstrated use cases. The results and the detailed report can be found in deliverable D3.8</p>
Supplementary Figures for the manuscript 'Robust and Scalable Learning of Complex Intrinsic Dataset Geometry via ElPiGraph' by Albergante et al.
<p>Multidimensional datapoint clouds representing large datasets are frequently characterized by non‐trivial low‐dimensional geometry and topology which can be recovered by unsupervised machine learning approaches, in particular, by principal graphs. Principal graphs approximate the multivariate data by a graph injected into the data space with some constraints imposed on the node mapping. Here we present ElPiGraph, a scalable and robust method for constructing principal graphs. ElPiGraph exploits and further develops the concept of elastic energy, the topological graph grammar approach, and a gradient descent‐like optimization of the<br> graph topology. The method is able to withstand high levels of noise and is capable of approximating data point clouds via principal graph ensembles. This strategy can be used to estimate the statistical significance of complex data features and to summarize them into a single consensus principal graph. ElPiGraph deals efficiently with large datasets in various fields such as biology, where it can be used for example with single‐cell transcriptomic or epigenomic datasets to infer gene expression dynamics and recover differentiation landscapes.</p> <p> </p>
Scalable Synthesis of Esp and Rhodium(II) Carboxylates from Acetylacetone and RhCl3.xH2O
<p>Primary data for the article with the title above.</p>
Scalability Benchmarking of Apache Flink
<p>Results from scalability benchmarking of Apache Flink using the Theodolite benchmarking method.</p> <p>The dataset includes the raw record lag data, numbers of instances and partitions, as well as the minimum required instances that we determined. The resulting scalability graphs are provided as well.</p>
CSR++: A Fast, Scalable, Update-Friendly Graph Data Structure (video)
Full video presentation of the paper: CSR++: A Fast, Scalable, Update-Friendly Graph Data Structure.<br><br>Appears in Session 5 of the 24th International Conference on Principles of Distributed Systems OPODIS 2020<br><a href="https://opodis2020.unistra.fr">https://opodis2020.unistra.fr</a>
Data from: A rapid and scalable method for multilocus species delimitation using Bayesian model comparison and rooted triplets
Multilocus sequence data provide far greater power to resolve species limits than the single locus data typically used for broad surveys of clades. However, current statistical methods based on a multispecies coalescent framework are computationally demanding, because of the number of possible delimitations that must be compared and time-consuming likelihood calculations. New methods are therefore needed to open up the power of multilocus approaches to larger systematic surveys. Here, we present a rapid and scalable method that introduces 2 new innovations. First, the method reduces the complexity of likelihood calculations by decomposing the tree into rooted triplets. The distribution of topologies for a triplet across multiple loci has a uniform trinomial distribution when the 3 individuals belong to the same species, but a skewed distribution if they belong to separate species with a form that is specified by the multispecies coalescent. A Bayesian model comparison framework was developed and the best delimitation found by comparing the product of posterior probabilities of all triplets. The second innovation is a new dynamic programming algorithm for finding the optimum delimitation from all those compatible with a guide tree by successively analyzing subtrees defined by each node. This algorithm removes the need for heuristic searches used by current methods, and guarantees that the best solution is found and potentially could be used in other systematic applications. We assessed the performance of the method with simulated, published, and newly generated data. Analyses of simulated data demonstrate that the combined method has favorable statistical properties and scalability with increasing sample sizes. Analyses of empirical data from both eukaryotes and prokaryotes demonstrate its potential for delimiting species in real cases.
Scalable Co-Optimization of Morphology and control in Embodied Machines
<p>Raw Data for: Scalable Co-Optimization of Morphology and control in Embodied Machines</p> <p>Trials from Nov 2, 2015 represent phase offset controllers for resolution 1000 (10^3), trails from Nov 9, 2015 represent phase offset controllers of resolution 125 (5^3), and trails from 2016 represent neural network controllers with resolutions 1000 (10^3). </p> <p>This data was produced by the code found at: https://github.com/ncheney/morphological-innovation-protection</p>
Generic, Scalable and Decentralized Fault Detection for Robot Swarms
<p>This raw data archive includes the data on fault detection in a simulated swarm of 20 e-puck robots. The data was used in the paper Generic, Scalable and Decentralized Fault Detection for Robot Swarms by D. Tarapore et al. (2017).</p> <p>See readme.txt for more details.</p>
Models and data for "Amortized reparametrization: Efficient and Scalable Variational Inference for Latent SDEs"
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R workspace: A scalable and transferable approach to combining emerging conservation technologies to identify biodiversity change after large disturbances
<p>R workspace and associated code used in the the manuscript '<span>A scalable and transferable approach to combining emerging conservation technologies to identify biodiversity change after large disturbances'</span></p>
Replication package for Fine-grained, accurate and scalable source differencing
<p>Replication package for the article <em><strong>Fine-grained, accurate and scalable source differencing</strong></em> accepted at the 46th International Conference on Software Engineering (ICSE 2024).</p> <p>List of files:</p> <ul> <li><strong>dataset</strong>: the folder containing the four datasets used in the paper, including the file-pairs and the script to regenerate them.</li> <li><strong>qualitative_experiment_diffs</strong>: the folder containing the GUI used by the participants of the qualitative experiment on the 100 cases.</li> <li><strong>gumtree</strong>: the folder containing the code of GumTree used to run the experiments.</li> <li><strong>analysis</strong>: the folder containing the CSV files and notebooks for the analysis of the results.</li> </ul>
Data for: Passive Daytime Cooling Foils for Everyone: A Scalable Lamination Process Based on Upcycling Aluminum-Coated Chips Bags
<p>Raw data of the measurements presented in the affiliated publication. The data are organized in folders corresponding to the respective characterization techniques. The employed techniques comprise: optical spectroscopy, indoor and field testing of the passive cooling performance, and scanning electron microscopy imaging.</p>
Data repository for "Scalable Quantum Monte Carlo with Direct-Product Trial Wave Functions"
<p>This Zenodo deposit provides a comprehensive collection of raw data, processed data, and scripts associated with the research paper available at <a href="https://arxiv.org/abs/2306.15186" target="_new">https://arxiv.org/abs/2306.15186</a>.</p> <p>Contents:</p> <p>- data.zip includes all the QMCpack inputs and outputs, which can be used in the blocking analysis to obtain the AFQMC energy. Processed Data "SI for DP-MSD-AFQMC.xlsx" contains the AFQMC results after the blocking analysis in XLSX format. "SI for DP-MSD-AFQMC.xlsx" also includes the energies from all other methods presented in the paper.</p> <p>- scripts.zip contains all the Python scripts used in running calculations for the paper. This includes scripts to generate LAS-AFQMC and CAS-AFQMC trials (under scripts/utils) in QMCpack format, as well as inputs for each compound.</p>
Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis
<p>A detailed list of the selected scRNA-seq datasets used in the paper, also provided in Additional file <a href="https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1898-6#MOESM1">1</a>: Table S1-S2.</p>
Data & code supplementary to the paper "Matchgate benchmarking: Scalable benchmarking of a continuous family of many-qubit gates"
<p>Code that executes and interprets the experiment displayed in fig 1 of the manuscript, as well as pickle files containing the actual data collected during the experimental run.</p>
Data Set (Song et al., Scalable pillar[5]arene-integrated poly(arylate-amide) molecular sieve membranes to separate light gases. Chem. Mater. 2022)
<p>Data Set for the following publication.</p> <p>Song et al., Scalable pillar[5]arene-integrated poly(arylate-amide) molecular sieve membranes to separate light gases. Chem. Mater. 2022.</p>
Data of "Universal platform for scalable semiconductor-superconductor nanowire networks '
<p>Data of "Universal platform for scalable semiconductor-superconductor nanowire networks '</p>
Scalable mixed model approaches for set-based association studies on large-scale categorical data analysis and its application to 450k exome sequencing data in UK Biobank
<p>The ongoing release of large-scale sequencing data in the UK Biobank allows for identifying associations between rare variants and complex traits. SAIGE-GENE+ is a valid approach to conducting set-based association tests for quantitative and binary traits. However, for ordinal categorical phenotypes, applying SAIGE-GENE+ with treating the trait as quantitative or binarizing the trait can cause inflated type I error rates or power loss. In this study, we propose a novel method for rare-variant association tests, POLMM-GENE, in which a proportional odds logistic mixed model was used to characterize ordinal categorical phenotypes while adjusting for sample relatedness. POLMM-GENE fully utilizes the categorical nature of phenotypes and thus can well control type I error rates while remaining powerful. In the analyses of UK Biobank 450k whole exome-sequencing data for 5 ordinal categorical traits, POLMM-GENE identified 54 gene-phenotype associations.</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.