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Dataset results
376 results for “scalable”
Transposase-Assisted Tagmentation: An Economical and Scalable Strategy for Single-Worm Whole-Genome Sequencing (elegans)
<p><span>AlphaMissense identifies 23 million human missense variants as likely pathogenic, but only 0.1% have been clinically classified. To experimentally validate these predictions, chemical mutagenesis presents a rapid, cost-effective method to produce billions of mutations in model organisms.</span><span> </span><span>However, the prohibitive costs and limitations in the throughput of whole-genome sequencing (WGS) technologies, crucial for variant identification, constrain its widespread application. Here, we introduce a Tn5 transposase-assisted tagmentation </span><span>technique</span><span> for conducting WGS in <em>C. elegans</em>, <em>E. coli</em>, <em>S. cervisiae</em>, and <em>C. reinhardtii</em>. This method, demands merely 2</span><span>0 minutes of hands-on time for a single worm or single-cell clones and incurs a cost below 10 US dollars. <span>It effectively pinpoints causal mutations in mutants defective in cilia or neurotransmitter secretion and in mutants synthetically sterile with a variant analogous to the oncogenic BRAF(V600E) mutation. Integrated with chemical mutagenesis, our approach can generate and identify missense variants economically and efficiently, facilitating experimental investigations of missense variants in diverse species.</span></span></p>
Core Binding Energy Calculations: A Scalable Approach with the Quantum Embedding Based Equation-of-Motion Coupled-Cluster Method
<p>This data includes the HF-optimized orbitals, coupled cluster amplitudes (T1, T2), and EOM-CCSD left and right eigenvectors at the CC-PCVDZ basis set. It can be used to reproduce the data for "Core Binding Energy Calculations: A Scalable Approach with the Quantum Embedding Based Equation-of-Motion Coupled-Cluster Method."</p>
Ultrarelativistic electron beams accelerated by terawatt scalable kHz laser
<p>This repository contains data for the paper C. M. Lazzarini et al., Phys. Plasmas 31, 030703 (2024); <a href="https://doi.org/10.1063/5.0189051" target="_blank" rel="noopener">https://doi.org/10.1063/5.0189051</a>. The data were obtained by experiment using L1-Allegra laser system (<a href="https://www.eli-beams.eu/facility/lasers/laser-1-allegra-100-mj-1-khz" target="_blank" rel="noopener">https://www.eli-beams.eu/facility/lasers/laser-1-allegra-100-mj-1-khz</a>) and simulation using the EPOCH (v4.18.0) particle-in-cell code (<a href="https://epochpic.github.io/" target="_blank" rel="noopener">https://epochpic.github.io/</a>). The data analysis can be found on GitHub (<a href="https://github.com/valenpe7/5.0189051" target="_blank" rel="noopener">https://github.com/valenpe7/5.0189051</a>).</p>
Scalability Measurement Data for "Efficient Data Flow Constraint Analysis" Master's Thesis
<p>Raw measurement data and plots of performed scalability measurments of our proposed approach for analyzing software systems regarding data flow constraints.</p>
Data for: Scalable Flood Level Trend Monitoring with Surveillance Cameras using a Deep Convolutional Neural Network [Version 2]
<p>This package provides material that can be openly published for the paper "Scalable Flood Level Trend Monitoring with Surveillance Cameras using a Deep Convolutional Neural Network". It consists in the code used to generate results and figures as well as the weights of the deep convolutional neural networks trained to segment water in the surveillance camera images.</p>
Datasets for "Scalable interpolation of satellite altimetry data with probabilistic machine learning"
<p>Elevation (radar freeboard and sea-level anomaly) fields from CryoSat-2, Sentinel-3A, and Sentinel-3B, over the period December 1st 2018 - April 30th 2019. These data were processed for the Arctic domain using the European Space Agency's Grid Processing on Demand (GPOD) service. Processing follows the steps outlined in Lawrence et al., 2021 (<a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.asr.2019.10.011" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.asr.2019.10.011</a>). These data are provided at along-track, 5 km and 50 km resolution, where gridded data follow the EASE grid definition (<a href="https://doi.org/10.3390/ijgi1010032">https://doi.org/10.3390/ijgi1010032</a>).</p> <p>These data were used to develop the open-source Python programming library GPSat (https://github.com/CPOMUCL/GPSat), which uses local Gaussian Process models to perform scalable interpolation of non-stationary satellite altimetry data. The 'Source_data.xlsx' file contains the data corresponding to figures in the published Nature Communications article 'Scalable interpolation of satellite altimetry data with probabilistic machine learning'.</p>
Datasets supplementing journal article "Scalable Bottom-up Synthesis of Co-Ni-Doped Graphene"
<p>Datasets supporting the journal article "Scalable Bottom-up Synthesis of Co-Ni-Doped Graphene".</p>
Measurement-free, scalable and fault-tolerant universal quantum computing
<p>The repository is supporting the publication "Measurement-free, scalable and fault-tolerant universal quantum computing". </p> <p>It includes the data shown in the manuscript as well as the simulation code and circuits that were used to obtain this data. </p>
Supplementary material 7 from: {"en": "Buchner D, Haase P, Leese F (2021) Wet grinding of invertebrate bulk samples – a scalable and cost-efficient protocol for metabarcoding and metagenomics. Metabarcoding and Metagenomics 5: e67533. https://doi.org/10.3897/mbmg.5.67533"}
Figure S2. Baseline-corrected amplification curves (left half) and melting -curves (right half) for A & B) kick-net samples and C & D) malaise trap samples
Scalable Randomized Benchmarking of Quantum Computers using Mirror Circuits
<p>This is supplemental data and code for: T. Proctor et al., <em><a href="http://https://doi.org/10.48550/arXiv.2112.09853">Scalable randomized benchmarking of quantum computers using mirror circuits</a>, </em>arXiv 2112.09853 (2021).</p> <p>This folder contains all the data and the analysis code to generate the results presented in that paper. The core data analysis routines use PyGSTi, which can be found at <a href="https://github.com/pyGSTio/pyGSTi">https://github.com/pyGSTio/pyGSTi</a>.</p> <p>Please direct any questions to Timothy Proctor (tjproct@sandia.gov).</p>
Combinatorial Barcode Scripts for the manuscript 'Scalable Combinatorial Synthesis of Synthetic DNA Barcode Sequences'
<p>Raw data and custom Python code for the manuscript 'Scalable Combinatorial Synthesis of Synthetic DNA Barcode Sequences'.</p>
Supporting data for "Scalable wavelength-multiplexing photonic reservoir computing"
<p>Supporting data for "Scalable wavelength-multiplexing photonic reservoir computing"</p>
Testing a Scalable Model For ACEs-Related Care Navigation
ClinicalTrials.gov study NCT05567250. IPD Sharing: YES. Countries: 1. Publications: 2.
A Scalable Nutrition Adherence Intervention
ClinicalTrials.gov study NCT06121986. IPD Sharing: Not stated. Countries: 1. Publications: 0.
A Scalable Psychological Intervention for Earthquake Survivors in Türkiye
ClinicalTrials.gov study NCT06026306. IPD Sharing: NO. Countries: 1. Publications: 2.
A Scalable Weight Control Intervention for Adolescents
ClinicalTrials.gov study NCT02426346. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Impact of a Transportable Customizable and Scalable Sitting Positioning Device on Swallowing Disorders
ClinicalTrials.gov study NCT02457052. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Scalable Psychological Intervention to Reduce Psychological Distress Among Healthcare Workers
ClinicalTrials.gov study NCT04980326. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Scalability of a Home Health Navigator Program to Reduce Arsenic, Nitrate, and Lead in Private Well Water
ClinicalTrials.gov study NCT05395663. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Advancing Student Suicide Interventions With Scalable Technologies
ClinicalTrials.gov study NCT07211373. IPD Sharing: YES. Countries: 1. Publications: 7.
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.