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Dataset results
22 results for “computational resource”
Resources to compute TF-IDF weightings on press articles and tweets
<p>These two datasets of features are used in order to compute TF-IDF weightings of documents. It is meant to be used with the <a href="https://pypi.org/project/compute-tf-idf-vectors/">compute-tf-idf-vectors</a> program written in Python and available on Pypi.org.</p> <p>- features_tweets.csv contains features (tokens, lemmas and entities) extracted from Tweets published by press agencies in french, german, spanish and english.</p> <p>- features_news.csv contains features (tokens, lemmas and entities) extracted from articles published by Deutsche Welle in the same languages.</p>
Supplementary datasets for "ARBRE: Computational resource to predict pathways towards industrially important aromatic compounds"
<p>Supplementary datasets accompanying the manuscript "ARBRE: Computational resource to predict pathways towards industrially important aromatic compounds" published in the Metabolic Engineering Journal (<a href="https://doi.org/10.1016/j.ymben.2022.03.013">https://doi.org/10.1016/j.ymben.2022.03.013). </a>In line with the standards of open science, the ARBRE toolbox is freely available to the scientific community on gitHub (<a href="https://github.com/EPFL-LCSB/ARBRE">https://github.com/EPFL-LCSB/ARBRE</a>) and we also provide the web-version at <a href="http://lcsb-databases.epfl.ch/arbre/">http://lcsb-databases.epfl.ch/arbre/</a></p> <p>ARBRE: Aromatic compounds RetroBiosynthesis Repository and Explorer is a new computational resource consisting of a comprehensive biochemical reaction network centered around aromatic amino acid biosynthesis and a computational toolbox for navigating this network. ARBRE encompasses over 33′000 known and 390′000 novel reactions predicted with generalized enzymatic reactions rules and over 74′000 compounds, of which 19′000 are known to biochemical databases and 55′000 only to PubChem. Over 1′000 molecules that were solely part of the PubChem database before and were previously impossible to integrate into a biochemical network are included in the ARBRE reaction network by assigning enzymatic reactions. ARBRE can be applied for pathway search, enzyme annotation, pathway ranking, visualization, and network expansion around known biochemical pathways and products of lignin degradation to predict valuable compound derivations.</p> <p>Supplementary files are organized as follows:</p> <p>- 1-s2.0-S1096717622000490-mmc4.docx contains Supplementary Figures 1-4 and Tables 1, 2, and 4.</p> <p>- 1-s2.0-S1096717622000490-mmc2.xlsx contains Supplementary Table 3.</p> <p>- 1-s2.0-S1096717622000490-mmc1.xlsx contains Supplementary Table 5</p> <p>- 1-s2.0-S1096717622000490-mmc3.xlsx contains Supplementary Table 6</p> <p> </p> <p> </p> <p> </p> <p> </p>
BRAIN Journal-Intelligent Continuous Double Auction method For Service Allocation in Cloud Computing-Figure 6. Comparison on resource utilization.
<p>Figure 6 shows resource utilization in different system loads and as shown in it, in ICDA<br> resource utilization is more efficient than other methods especially in higher system load which is<br> due to tradeoff and sharing factors.</p>
BRAIN Journal-Intelligent Continuous Double Auction method For Service Allocation in Cloud Computing-Figure 4. Sharing and tradeoff factor effect on resource utilization
<p>In Figure 4 we consider tradeoff and sharing factors in providers. The result illustrates that<br> by using these factors providers improve resource utilization. Higher resource utilization motivates<br> more providers to participate in the cloud and also enables the cloud market to handle more<br> consumers which influences market efficiency.</p>
BRAIN Journal-Intelligent Continuous Double Auction method For Service Allocation in Cloud Computing-Figure 1. Resource allocation schema in proposed method
<p>We assume that the resources allocation satisfies the following conditions:<br> • The quantity of a resource can be measured in arbitrary units (e.g. 60 units of resource<br> A).<br> • A resource can be divided into an arbitrary fraction (e.g. a resource of 60 units is divided<br> into 20 units for consumer 1 and 40 units for consumer 2).<br> • A resource request of a service can be divided into sub-requests and acquired from<br> multiple providers (e.g. a resource request of 40 units utilized as 10 units from provider<br> 1 and 30 units from provider 2).<br> Figure 1 shows a cloud computing environment with the proposed mechanism.</p>
Species-informative GT-seq markers for Columbia River salmonid fishes: Genotypic data and computing resources
Open the record for dataset details and reuse information.
A multi-omics systems vaccinology resource to develop and test computational models of immunity: 1st challenge dataset and submissions
<p>The goal of the CMI-PB prediction contest is to foster a collaborative research community that can collectively tackle challenges and accelerates scientific progress beyond the capabilities of individual researchers or groups. The CMI-PB consortium has curated multi-source data from multiple individuals, encompassing Ab titers (around four antibodies/features), cell frequency (approximately 20 cell types/features), gene expression (roughly 50,000 RNA transcripts/features), and plasma proteomics (around 50 proteins/features). The challenge requires integrating these diverse data sources to predict different immune responses or tasks. Specifically, you will utilize multi-source data from several individuals on day 0 (baseline) to predict specific immune responses at later time points (1, 3, 7, and 14 days post-booster vaccination).</p> <p>The first CMI-PB challenge, which is an internal challenge, was conducted using datasets from 2020 (train) and 2021 (test). In the following sections, we provide detailed information on the datasets, challenge tasks, submission format, descriptions, and access to the necessary data files for participants to develop their models and make predictions.</p> <p><br><strong>A) Multiomics CMI-PB dataset:</strong></p> <p>We propose a study design that enables a systems-level understanding of the immune responses through computational modeling. Our cohort comprises aP vs. wP infancy-primed subjects boosted with Tdap. We recruit individuals born before 1995 (wP) and after 1996 (aP), collect baseline plasma and blood samples, and then at 1, 3, 7 and 14 days post booster vaccination.</p> <p>With the obtained samples processed, we generated omics data by:</p> <ul> <li> <p>Bulk PBMCs transcriptomics,</p> </li> <li> <p>Plasma proteomics using Olink, which provides a quantitative readout of cytokines, chemokines, and other immune factors,</p> </li> <li> <p>Cell frequency in PBMCs using flow cytometry,</p> </li> <li> <p>Tdap-specific antibodies levels</p> </li> </ul> <p><strong>B) List of tasks can be accessed using the “List of tasks for challenge 1.docx” file, and submissions need to submit in provided format here: “submission template challenge 1.tsv”</strong></p> <p><strong>C) Datasets for model building and making predictions:</strong></p> <p> Data files are divided into two categories: 1) raw dataset and 2) computable matrices.</p> <ol> <li> <p><strong>Raw dataset: </strong>This raw-most dataset is divided into training and test datasets. </p> </li> <li> <p><strong>Computable matrices: </strong>There are three different types of computable matrices. a) Full: These files are generated by dividing raw files into sub-files specific to planned days specific to vaccination. b) harmonized: These are generated by preserving only overlapping features between train and test datasets. b) imputed: MICE imputation is performed to impute missing values in the dataset.</p> </li> </ol> <p><strong>D) Submission evaluation</strong></p> <p>This folder contains all submitted models with ranking files and code for evaluating these models.</p> <p><strong>To learn more about the CMI-PB prediction challenge, visit our website at www.cmi-pb.org.</strong></p>
Applying innovative cloud computing technology for the effective management of Groundwater resources to promote SUStainable food security within the Sokoto Basin, Nigeria (AGSUS)
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Dataset: Computational resources in the development of e-health IoT applications: A systematic mapping study
<p>Files related to systematic mapping titled Computational resources in the development of e-health IoT applications: A systematic mapping study</p>
A computational toolbox to investigate the metabolic potential and resource allocation in fission yeast
<p>Computational models and figure data for the publication "A computational toolbox to investigate the metabolic potential and resource allocation in fission yeast" (preprint on <a href="https://doi.org/10.1101/2022.05.04.490403"><em>bioRxiv</em></a>). Data put together by Pranas Grigaitis, p.grigaitis [at] vu.nl.</p> <p> </p> <p><em>Abstract</em></p> <p>The fission yeast <em>Schizosaccharomyces pombe</em> is a popular eukaryal model organism for cell division and cell cycle studies. With this extensive knowledge of its cell and molecular biology, <em>S. pombe</em> also holds promise for use in metabolism research and industrial applications. However, unlike the baker’s yeast <em>Saccharomyces cerevisiae</em>, a major workhorse in these areas, cell physiology and metabolism of <em>S. pombe</em> remain less explored. One way to advance understanding of organism-specific metabolism is construction of computational models and their use for hypothesis testing. To this end, we leverage existing knowledge of <em>S. cerevisiae</em> to generate a manually-curated high-quality reconstruction of <em>S. pombe’s</em> metabolic network, including a proteome-constrained version of the model. Using these models, we gain insights into the energy demands for growth, as well as ribosome kinetics in <em>S. pombe</em>. Furthermore, we predict proteome composition and identify growth-limiting constraints that determine optimal metabolic strategies under different glucose availability regimes, and reproduce experimentally determined metabolic profiles. Notably, we find similarities in metabolic and proteome predictions of <em>S. pombe</em> with <em>S. cerevisiae</em>, which indicate that similar cellular resource constraints operate to dictate metabolic organization. With these use cases, we show, on the one hand, how these models provide an efficient means to transfer metabolic knowledge from a well-studied to a lesser-studied organism, and on the other, how they can successfully be used to explore the metabolic behaviour and the role of resource allocation in driving different strategies in fission yeast.</p>
Data supporting "A real-time, scalable, fast and resource-efficient decoder for a quantum computer"
<p>Data includes the circuits (stim_circuits.zip) used to create samples to benchmark CC decoder across different noise rates and code sizes. The resulting accuracy and cycle data is in fpga_accuracy_data.csv. The memory footprint (in KB) of the algorithm for different code sizes is in fpga_memory_data.csv.</p> <p>Weights of syndromes for different noise rates for both phenomenological and circuit-level noise at distance d=23 and d=21 are in noise_rate_sampling_full_d23.csv and noise_rate_sampling_full_d21.csv respectively.</p>
The Effects of Computational Resources on Flaky Tests (Artifact)
<p>This artifact contains resources for reproducing and extending the work "The Effects of Computational Resources on Flaky Tests"</p><p>Contents:</p><ul><li> Analysis and Processed Test Results.tgz: An archive that contains information about the projects analyzed, summarized test results per-throttling configuration per-run, and a Jupyter notebook that detects RAFT (generating all tables and figures in the article). A README in this archive provides further guidance on its contents</li><li>js-results.tar, java-results.tar, python-results.tar: The raw results produced by the test runner when executing each JavaScript, Java and Python project 300 times in each of the throttling configurations.</li></ul><p>See also:</p><p>We have published docker containers that include each project that we studied, along with all of the dependenices for running the tests. These containers can be used to reproduce our results, or to extend our work by running additional tests. The containers are available at <a href="https://hub.docker.com/r/jonbell/raft/tags">https://hub.docker.com/r/jonbell/raft/tags</a></p>
A Systematic Mapping of the Classification of Open Educational Resources for Computer Science Education in Digital Sources (Data)
<p>Data from a Systematic Mapping of the classification of Open Educational Resources for Computer Science Education.</p> <p>Content:</p> <ul> <li>Studies selected</li> <li>Digital sources used to classify Open Educational Resources for Computer Science Education</li> <li>Computer Science domains explored by Open Educational Resources</li> <li>Approaches for the classification of Open Educational Resources for Computer Science Education</li> </ul>
AMPSphere pre-computed resources and auxiliary files for the manuscript codes
<p>AMPSphere is a comprehensive catalog of antimicrobial peptides predicted using Macrel (DOI: 10.7717/peerj.10555) from 63,410 public metagenomes, ProGenomes v2.2 database (82,400 high-quality microbial genomes), and c.a. 4k non-whitelisted microbial genomes from NCBI. Currently, AMPSphere is available as a web resource at https://ampsphere.big-data-biology.org/. AMPSphere v.2022-03 contains 863,498 sequences (avg length: 36 amino acids, range 8-98). DRAMP database was used to find confirmed sequences with strict homology to reference. This approach showed that 2,488 peptides were previously confirmed in our dataset. The present repository is a data dump for the precomputed resources and files needed for its generation and analysis as a complement to the GitHub repository. The complementary documentation is also available for each one of the files. To use the files just download them and apply the command `untar` to decompress the folders.</p>
Dataset for: Buffer Management for Trust Computation in Resource-constrained IoT Networks
<p>Dataset for Buffer Management for Trust Computation in Resource-constrained IoT Networks</p>
Computing Safe Contention Bounds for Multicore Resources with Round-Robin and FIFO Arbitration
<p>This dataset contains processed data in format of an microsoft excel spreadsheet referring to simulations and measurements of multicore contention described in the publication "Computing Safe Contention Bounds for Multicore Resources with Round-Robin and FIFO Arbitration" published in IEEETC.</p>
Wyniki zawarte w Energy-Efficient OFDM Radio Resource Allocation Optimization With Computational Awareness: A Survey
<p>This resource contains the results included in Energy-Efficient OFDM Radio Resource Allocation Optimization With Computational Awareness: A Survey</p>
Data, Code and Computational Environment for: Wild olive tree genetic diversity in Greece: a diverse resource in danger of erosion
<p><strong>Data</strong></p> <p>The csv file "2023-06-27_data.csv" contains genetic profiles for all olive trees analyzed in the publication: Wild olive tree genetic diversity in Greece: a diverse resource in danger of erosion". The genotypes are provided in the GenAlEx format.</p> <p> </p> <p><strong>Code</strong></p> <p>Code is provided in the compressed folder "Tourvas_et_al_Olive". It is structured as a R project and can easily be opened, after decompressing, from the Rstudio interface. If you prefer to review and/or reuse code you can access it from the "analysis" folder inside the "Tourvas_et_al_Olive" folder.</p> <p> </p> <p><strong>Computational Environment</strong></p> <p>A tarball for the Docker image "tourvas_et_al_olive" is also provided. This is the recommended way to reproduce the results of the publication: "Wild olive tree genetic diversity in Greece: a diverse resource in danger of erosion".</p> <p>It is assumed that you already have Docker installed on your system. If not, please visit <a href="https://docs.docker.com/get-started/">https://docs.docker.com/get-started/</a></p> <p>To use it:</p> <ul> <li>download the image file tourvas_et_al_olive.tar</li> <li>load it with docker with the command:</li> </ul> <pre><code class="language-bash">docker load --input tourvas_et_al_olive.tar</code></pre> <ul> <li>then launch the Docker container with the command:</li> </ul> <pre><code class="language-bash">docker run --name popgen --rm -dp 8787:8787 -e ROOT=TRUE -e DISABLE_AUTH=true -v "`pwd`":/home/rstudio/working nikostourvas/tourvas_et_al_olive</code></pre> <ul> <li>start your favorite web browser and go to: http://localhost:8787/</li> <li>from the bottomright pane of the Rstudio server click on the directory "Tourvas_et_al_Olive" and open the project by clicking on the "Tourvas_et_al_Olive.Rproj" file</li> <li>launch the scripts inside the "analysis" directory and run them to reproduce results</li> </ul>
Computer Aided Screening for Tuberculosis in Low Resource Environments
ClinicalTrials.gov study NCT04545164. IPD Sharing: YES. Countries: 1. Publications: 1.
A GENETIC, GENOMIC, AND COMPUTATIONAL RESOURCE FOR EXPLORING NEURAL CIRCUIT FUNCTION
GEO Series GSE116969. Drosophila melanogaster. 266 samples. Type: Expression profiling by high throughput sequencing.
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.