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3,690 results for “Computing”
Data from: Elucidating the spatio-temporal dynamics of an emerging wildlife pathogen using approximate Bayesian computation
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Data from: Fast coalescent-based computation of local branch support from quartet frequencies
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Fluid-structure simulations outperform computational fluid dynamics in the differentiation of progressive dilation in Marfan syndrome patients
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Experimental electrical resistivity values and computed thermal conductivities
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Supporting data for Formation of Highly Oxidized Molecules from NO3 Radical Oxidation of Δ-3-Carene: A Computational Mechanism
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Computer-aided solvent mixture design for the crystallisation and isolation of mefenamic acid
<p>The files contain the MINLP formulations presented in this publication.</p> <p>The MINLP problems are implemented and solved in GAMS version 28.2.0, using SBB, a local branch-&-bound MINLP solver.</p>
Computer-aided solvent mixture design for the crystallisation and isolation of mefenamic acid
<p>The files contain the MINLP formulations presented in this publication.</p> <p>The MINLP problems are implemented and solved in GAMS version 28.2.0, using SBB, a local branch-&-bound MINLP solver.</p>
Example computer vision classification training data derived from British Library 19th Century Books Image collection
<p>Example computer vision classification training data derived from British Library 19th Century Books Image collection</p> <p>This dataset provides training data for image classification for use in a computer vision workshop. The images are derived from '<a href="https://doi.org/10.21250/db17">Digitised Books - Images identified as Embellishments. c. 1510 - c. 1900. JPG'</a> from the year '1839'.</p> <p>Currently, included are four folders containing a variety of images derived from the BL books corpus.</p> <ul> <li>'cv_workshop_exercise_data' include images of: 'building', 'people', 'coat of arms'</li> <li>'humancats' contains images of humans and images of cats</li> </ul> <p>The 'fashion' and 'portraits' folders both contain images of people organised into 'female' and 'male'. These labels were annotated by a single annotator and these categories may themselves not be meaningful. They are included in the workshop data as a point of discussion about how we should label data both in general and when working with historical data. </p> <p>This data is intended primarily as an educational resource.</p>
Raw data for "First ptychographic X-ray computed tomography experiment at the NanoMAX beamline"
<p>Raw data used in "First ptychographic X-ray computed tomography experiment at the NanoMAX beamline" by M. Kahnt, S. Sala, U. Johansson, Z. Jiang, S. Kalbfleisch, F. Lenrick, J. H. Pikul and K. Thånell, submitted to the journal of applied crystallography.</p>
Inferring macroevolutionary parameters from on a model of adaptive radiation by Approximate Bayesian Computation - ABC Simulations
<p>Recent advances in DNA sequencing are providing increasingly accurate phylogenetic trees to study, but understanding the evolutionary forces at play in different contexts remains a huge challenge. To tackle this issue, we applied an Bayesian approach to an existing model of phenotypic and species diversification [1] in order to retrieve 11 underlying parameters (such as basal speciation and extinction rates, but also competition strength) from phylogenetic trees with known traits values at the tips.</p> <p>This dataset corresponds to the Approximate Bayesian Computation simulations realized for the inference.</p>
Link to dataset related to article "Development, application and computational analysis of high-dimensional fluorescent antibody panels for single-cell flow cytometry"
<p>The interrogation of single cells is revolutionizing biology, especially our understanding of the immune system. Flow cytometry is still one of the most versatile and high-throughput approaches for single-cell analysis, and its capability has been recently extended to detect up to 28 colors, thus approaching the utility of cytometry by time of flight (CyTOF). However, flow cytometry suffers from autofluorescence and spreading error (SE) generated by errors in the measurement of photons mainly at red and far-red wavelengths, which limit barcoding and the detection of dim markers. Consequently, development of 28-color fluorescent antibody panels for flow cytometry is laborious and time consuming. Here, we describe the steps that are required to successfully achieve 28-color measurement capability. To do this, we provide a reference map of the fluorescence spreading errors in the 28-color space to simplify panel design and predict the success of fluorescent antibody combinations. Finally, we provide detailed instructions for the computational analysis of such complex data by existing, popular algorithms (PhenoGraph and FlowSOM). We exemplify our approach by designing a high-dimensional panel to characterize the immune system, but we anticipate that our approach can be used to design any high-dimensional flow cytometry panel of choice. The full protocol takes a few days to complete, depending on the time spent on panel design and data analysis.</p> <p> </p> <p> </p> <p>link related to dataset: https://flowrepository.org/id/FR-FCM-ZYV3</p>
Transfer Learning for leveraging computer vision in infrastructure maintenance [extracted features]
<p>Dataset containing features extracted from images taken on single case of infrastructure facility for the purpose of training Transfer Learned CNN classifier. It is meant to be used with KrakN framework (https://github.com/MatZar01/KrakN), published with the research paper.</p>
TOADS for Computable Phenotypes
<p>Inventory of existing tools, ontologies, algorithms, data, and standards (TOADS) that makes computable phenotypes and predictive phenomics feasible now. (SW: software; HW: hardware).</p>
GECCO Industrial Challenge 2015 Dataset: A heating system dataset for the 'Recovering missing information in heating system operating data' competition at the Genetic and Evolutionary Computation Conference 2015, Madrid, Spain
<p>Dataset of the 'Industrial Challenge: Recovering missing information in heating system operating data' competition hosted at The Genetic and Evolutionary Computation Conference (GECCO) July 11th-15th 2015, Madrid, Spain</p> <p> </p> <p>The task of the competition was to recover (impute) missing information in heating system operation time series'.</p> <p> </p> <p>Included in zenodo: </p> <p>- dataset of heating system operational time series with missing values</p> <p>- additional material and descriptions provided for the competition</p> <p> </p> <p>The competition was organized by:</p> <p>M. Friese, A. Fischbach, C. Schlitt, T. Bartz-Beielstein (TH Köln)</p> <p> </p> <p>The dataset was provided by:</p> <p>Major German heating systems supplier (S. Moritz)</p> <p> </p> <p> </p> <p>Industrial Challenge: Recovering missing information in heating system operating data</p> <p> </p> <p>The Industrial Challenge will be held in the competition session at the Genetic and Evolutionary Computation Conference. It poses difficult real-world problems provided by industry partners from various fields. Highlights of the Industrial Challenge include interesting problem domains, real-world data and realistic quality measurement</p> <p>Overview</p> <p>In times of accelerating climate change and rising energy costs, increasing energy efficiency and reducing expenses becomes a high priority goal for businesses and private households alike. Modern heating systems record detailed operating data and report this data to a central system. Here, the operating data can be correlated and analyzed to detect potential optimization opportunities or anomalies like unusually high energy consumption. Due to various difficulties this data might be incomplete which makes accurate forecasting even harder.</p> <p>Goal of the GECCO 2015 Industrial Challenge is to develop capable procedures to recover missing information in heating system operating data. Adequate recovery of the missing data enables more accurate forecastings which allow for intelligent control of the heating systems, and therefore contributes to a positive energy balance and reduced expenses.</p> <p> </p> <p><strong>Submission deadline:</strong><br> June 22, 2015</p> <p><strong>Official Webpage:</strong><br> <a href="http://www.spotseven.de/gecco-challenge/gecco-challenge-2015/">www.spotseven.de/gecco-challenge/gecco-challenge-2015/</a></p> <p> </p>
GECCO Industrial Challenge 2018 Dataset: A water quality dataset for the 'Internet of Things: Online Anomaly Detection for Drinking Water Quality' competition at the Genetic and Evolutionary Computation Conference 2018, Kyoto, Japan.
<p>Dataset of the 'Internet of Things: Online Anomaly Detection for Drinking Water Quality' competition hosted at The Genetic and Evolutionary Computation Conference (GECCO) July 15th-19th 2018, Kyoto, Japan</p> <p> </p> <p>The task of the competition was to develop an anomaly detection algorithm for a water- and environmental data set.</p> <p> </p> <p>Included in zenodo: </p> <p>- dataset of water quality data</p> <p>- additional material and descriptions provided for the competition</p> <p> </p> <p>The competition was organized by:</p> <p>F. Rehbach, M. Rebolledo, S. Moritz, S. Chandrasekaran, T. Bartz-Beielstein (TH Köln)</p> <p> </p> <p>The dataset was provided by:</p> <p>Thüringer Fernwasserversorgung and IMProvT research project</p> <p> </p> <p>GECCO Industrial Challenge: 'Internet of Things: Online Anomaly Detection for Drinking Water Quality'</p> <p>Description:</p> <p>For the 7th time in GECCO history, the SPOTSeven Lab is hosting an industrial challenge in cooperation with various industry partners. This years challenge, based on the 2017 challenge, is held in cooperation with "Thüringer Fernwasserversorgung" which provides their real-world data set. The task of this years competition is to develop an anomaly detection algorithm for the water- and environmental data set. Early identification of anomalies in water quality data is a challenging task. It is important to identify true undesirable variations in the water quality. At the same time, false alarm rates have to be very low.<br> Additionally to the competition, for the first time in GECCO history we are now able to provide the opportunity for all participants to submit 2-page algorithm descriptions for the GECCO Companion. Thus, it is now possible to create publications in a similar procedure to the Late Breaking Abstracts (LBAs) directly through competition participation!</p> <p> </p> <p>Accepted Competition Entry Abstracts<br> - Online Anomaly Detection for Drinking Water Quality Using a Multi-objective Machine Learning Approach (Victor Henrique Alves Ribeiro and Gilberto Reynoso Meza from the Pontifical Catholic University of Parana)<br> - Anomaly Detection for Drinking Water Quality via Deep BiLSTM Ensemble (Xingguo Chen, Fan Feng, Jikai Wu, and Wenyu Liu from the Nanjing University of Posts and Telecommunications and Nanjing University)<br> - Automatic vs. Manual Feature Engineering for Anomaly Detection of Drinking-Water Quality (Valerie Aenne Nicola Fehst from idatase GmbH)</p> <p>Official webpage:</p> <p><a href="http://www.spotseven.de/gecco/gecco-challenge/gecco-challenge-2018/">http://www.spotseven.de/gecco/gecco-challenge/gecco-challenge-2018/</a></p>
Computer Aided Design (CAD) files for capillaric circuit with 4 retention burst valves
<p>AutoCAD design file and STL file for capillaric circuit with 4 retention burst valves.</p>
All figures and tables for "Optimized approximate inverse Laplace transform for geo-deformation computation in viscoelastic earth model"
<p>It is the data set of all figures and tables in the paper with title "Optimized approximate inverse Laplace transform for geo-deformation computation in viscoelastic earth model".</p>
Evaluation datasets and pre-computed scores for: "CAPICE: a computational method for Consequence-Agnostic Pathogenicity Interpretation of Clinical Exome variations"
<p>CAPICE is a computational method for predicting the pathogenicity of SNVs and InDels.</p> <p>This new repository added index for CAPICE v1.0 (build37) precomputed files.</p> <p><strong>Repository description:</strong></p> <p>1) "paper_datasets.tar.gz" contains all datasets used in the CAPICE paper;</p> <p>2) "capice_v1.0_build37_indels.tsv.gz" contains the precomputed scores for InDels in genome build 37</p> <p>3) "capice_v1.0_build37_indels.tsv.gz.tbi" contains the index for file"capice_v1.0_build37_indels.tsv.gz"</p> <p>4) "capice_v1.0_build37_snvs.tsv.gz" contains the precomputed scores for all possible SNVs in genome build 37</p> <p>5) "capice_v1.0_build37_snvs.tsv.gz.tbi" contains the index for file "capice_v1.0_build37_snvs.tsv.gz"</p> <p> </p>
Paracoherent Answer Set Computation
<p>Instances used for the experiments of the paper "Paracoherent Answer Set Computation".</p>
Supplementary Data: CosmoBit: A GAMBIT module for computing cosmological observables and likelihoods (arXiv:2009.03286)
<p><strong>Supplementary Data</strong><br> <em>CosmoBit: A GAMBIT module for computing cosmological observables and likelihoods</em></p> <p>This record contains the samples and plotting routines used to create the figures and to derive most of the results in J.J. Renk et al., “CosmoBit: A GAMBIT module for computing cosmological observables and likelihoods” (available on the <a href="http://arxiv.org/abs/2009.03286">arXiv</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.