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Dataset results
17 results for “Distributed computing”
A Modular Quantum Compilation Framework for Distributed Quantum Computing
<p>This repository contains the data used for the plots in "<em>A Modular Quantum Compilation Framework for Distributed Quantum Computing</em>" by D. Ferrari, S. Carretta and M. Amoretti.</p> <p>Data is located in the <em>'data'</em> directory in <em>.csv</em> format, a python script to generate the plots can be found in the main directory. The script was tested with <strong>python3.10</strong> and needs <strong>matplotlib</strong>, <strong>pandas</strong> and <strong>seaborn</strong> packages. Plots are saved as <em>.pdf</em> files in the <em>'figures'</em> directory.</p>
A computer vision pipeline for predicting zoonotic reservoir distribution
<p>Shared here is the complete project repository of code and data supporting the manuscript, "Predicting the fine-scale spatial distribution of zoonotic reservoirs using computer vision."</p> <p>Note that all project subdirectories (with the exception of the "Data" subdirectory) were compressed into single .zip files and thus need to be decompressed upon download. The "Data" subdirectory, which contains numerous large data files, was stored as a split zip archive to facilitate upload to Zenodo and consists of the files "Data.z01", "Data.z02", "Data.z03", and "Data.zip". These files may need to be decompressed using dedicated archiver software (such as The Unarchiver [https://theunarchiver.com/] on macOS). The "Data" subdirectory should be ~17 GB upon decompression. Note that the entire project repository is ~40 GB when uncompressed.</p>
Data from: Mapping coastal redwoods (<em>Sequoia sempervirens</em>) across their natural range: An updateable and field-validated distribution map using Sentinel satellite data and cloud computing
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Quantum-inspired computational wavefront shaping enables turbulence-resilient distributed aperture synthesis imaging
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Output data computed for the solution of Maxwell's equations on a distributed system using the FDTD method
<p>This is an example output dataset from the distributed computation to solve Maxwell's equations using the FDTD method. This is an example from the report describing the implementation of the computer program - Gillan and Fusco (1998). The example is based on the work reported in the book by Kunz and Leubbers on the FDTD method. It consists of scattering a Gaussian pulse from a sphere of dielectric defined with a 3D cube of size 34x34x34 Yee cells. </p>
Hydrate spatial distribution influence on the mechanical behavior hydrate-bearing sediment using computed tomography
<p>This supporting information includes two Movie S1-2, illustrating the deformation evolution of the specimen # O-1 and specimen # O-2.</p> <p>Movie S1 is uploaded with file name Movie S1.gif. Detailed information includes the deformation evolution of the specimen # O-1.</p> <p>Movie S2 is uploaded with file name Movie S2. gif. Detailed information includes the deformation evolution of the specimen # O-2.</p>
Effects of individualized Electrical Impedance Tomography and image reconstruction settings upon the assessment of regional ventilation distribution: Comparison to 4-dimensional Computed Tomography in a porcine model
<p>Reconstruction Models used for identification of optimal settings for comparison to CT images. Forward models are available in the supplement of the article but were removed form the inverse models due to redundance storage within each model.</p> <p>Prior reconstruction in EIDORS, forward models have to be added again to<em> imdl.fwd_model</em> and <em>imdl.jacobian_background.fwd_model</em>.</p>
DistSNE: Distributed computing and online visualization of DNA methylation-based central nervous system tumor classification
<p><strong>The current state-of-the-art analysis of central nervous system (CNS) tumors through DNA methylation profiling relies on the tumor classifier developed by Capper and colleagues, which centrally harnesses DNA methylation data provided by users. Here, we present a distributed-computing-based approach for CNS tumor classification that achieves a comparable performance to centralized systems while safeguarding privacy. We utilize the t-distributed neighborhood embedding (t-SNE) model for dimensionality reduction and visualization of tumor classification results in two-dimensional graphs in a distributed approach across multiple sites (DistSNE). DistSNE provides an intuitive web interface (https://gin-tsne.med.uni-giessen.de) for user-friendly local data management and federated methylome-based tumor classification calculations for multiple collaborators in a DataSHIELD environment. The freely accessible web interface supports convenient data upload, result review, and summary report generation. Importantly, increasing sample size as achieved through distributed access to additional datasets allows DistSNE to improve cluster analysis and enhance predictive power. Collectively, DistSNE enables a simple and fast classification of CNS tumors using large-scale methylation data from distributed sources, while maintaining the privacy and allowing easy and flexible network expansion to other institutes. This approach holds great potentialfor advancing human brain tumor classification and fostering collaborative precision medicine in neuro-oncology. </strong></p>
DEDICAT 6G Robot metrics for different levels of intelligence (computation) distribution
<p>Battery consumption, CPU and RAM metrics for locobots used in testing of intelligence distribution. </p>
Computing Expected Visiting Times and Stationary Distributions in Markov Chains: Fast and Accurate (Artifact)
<p>This artifact contains the raw data of our experiments as well as scripts and benchmarks to reproduce the experiments.<br>Furthermore, the considered version of [Storm](http://stormchecker.org) is included, which contains our implementation.</p> <p>Please also consider the artifact of the conference paper available at [zenodo](https://zenodo.org/records/10438916) which has been accepted by the TACAS Artifact evaluation committee.</p> <p><br>This artifact contains: <br>`LICENSE`: The license document.<br>`README.md`: The instructions.<br>`raw_data.zip`: The raw data obtained during our experiments<br>`raw_data_with_results.zip`: The raw data, also including the resulting stationary distributions and evts in an explicit format. (84 GB!)<br>`reproduce.zip` contains benchmarks and scripts for reproducing the experiments<br>`storm-0b1cae2a94f06984f3cf4cecf5a5090e9bc71a56.zip` is the exact Storm version we considered.</p>
Data from: The demographic history of Atlantic salmon (Salmo salar) across its distribution range reconstructed from approximate Bayesian computations
Understanding the dual roles of demographic and selective processes in the buildup of population divergence is one of the most challenging tasks in evolutionary biology. Here, we investigated the demographic history of Atlantic Salmon across the entire species range using 2035 anadromous individuals from North America and Eurasia. By combining results from admixture graphs, geo-genetic maps and an Approximate Bayesian Computation (ABC) framework, we validated previous hypotheses pertaining to secondary contact between European and Northern American populations, but also identified secondary contacts in European populations from different glacial refugia. We further identified the major sources of admixture from the southern range of North America into more northern populations along with a strong signal of secondary gene flow between genetic regional groups. We hypothesize that these patterns reflects the spatial redistribution of ancestral variation across the entire North American range. Results also support a role for linked selection and differential introgression that likely played an under-appreciated role in shaping the genomic landscape of species in the Northern hemisphere. We conclude that studies between partially isolated populations should systematically include heterogeneity in selective and introgressive effects among loci to perform more rigorous demographic inferences of the divergence process.
Computer codes (in Python) for 'The reproduction number and its probability distribution for stochastic viral dynamics'
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Accelerating the Conjugate Gradient Method on Distributed-Memory Computers: Software and Experiment Data
<p>This Zenodo record consists of supplementary material for journal article 'Accelerating the Conjugate Gradient Method on Distributed-Memory Computers' by Kozický, Šimeček and Rúra. Archive 'software.zip' contains the source code of the software we used to perform the experiments in our article, archive 'data-experiments.zip' contains the raw data from our experiments and archive 'data-manuscript.zip' contains the result data presented in our article.</p>
Structure and distribution of chalky deposits in the Pacific oyster using x-ray computed tomography (CT)
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Data from: The demographic history of Atlantic salmon (Salmo salar) across its distribution range reconstructed from approximate Bayesian computations
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Open, Prospective Pilot Study to Obtain Aerosol Distribution in Asthmatic Patients Using Single Photon Emission Computed Tomography (SPECT) for Comparison With Functional Imaging Using Computer Method
ClinicalTrials.gov study NCT00966758. IPD Sharing: Not stated. Countries: 1. Publications: 0.
A Local Distributed Peer-to-Peer Algorithm Using Multi-Party Optimization Based Privacy Preservation for Data Mining Primitive Computation
This paper proposes a scalable, local privacy-preserving algorithm for distributed peer-to-peer (P2P) data aggregation useful for many advanced data mining/analysis tasks such as average/sum computation, decision tree induction, feature selection, and more. Unlike most multi-party privacy-preserving data mining algorithms, this approach works in an asynchronous manner through local interactions and therefore, is highly scalable. It particularly deals with the distributed computation of the sum of a set of numbers stored at different peers in a P2P network in the context of a P2P web mining application. The proposed optimization-based privacy-preserving technique for computing the sum allows different peers to specify different privacy requirements without having to adhere to a global set of parameters for the chosen privacy model. Since distributed sum computation is a frequently used primitive, the proposed approach is likely to have significant impact on many data mining tasks such as multi-party privacypreserving clustering, frequent itemset mining, and statistical aggregate computation.
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.