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Dataset results
264 results for “batch”
Neural-Parareal dataset JOREK blob runs with electrostatic model batch_0001-0500
<p>Please refer to journal paper from S.J.P.Pamela, titled</p> <p>"Neural-Parareal: Self-improving acceleration of fusion MHD simulations using time-parallelisation and neural operators"</p> <p>Available on ArXiV and on Comp.Phys.Comm.: https://doi.org/10.1016/j.cpc.2024.109391</p> <p> </p> <p>Data produced by the JOREK code, https://jorek.eu</p> <p>All runs created using the electrostatic model, model-ID "model003"</p> <p>Data downsampled by saving every 10th timestep, on a regular 2D grid of 100x100.</p> <p>To reproduce full runs, use corresponding input files.</p> <p> </p> <p>Note: variables names are the same as in the JOREK code:</p> <p>u = electric potential</p> <p>omega = toroidal vorticity</p> <p>rho = density</p> <p>T = temperature</p> <p> </p> <p>The create_gif.py can be used to convert data into movies.</p> <p> </p>
Neural-Parareal dataset JOREK blob runs with electrostatic model batch_0501-1000
<p>Please refer to journal paper from S.J.P.Pamela, titled</p> <p>"Neural-Parareal: Self-improving acceleration of fusion MHD simulations using time-parallelisation and neural operators"</p> <p>Available on ArXiV and on Comp.Phys.Comm.: https://doi.org/10.1016/j.cpc.2024.109391</p> <p> </p> <p>Data produced by the JOREK code, https://jorek.eu</p> <p>All runs created using the electrostatic model, model-ID "model003"</p> <p>Data downsampled by saving every 10th timestep, on a regular 2D grid of 100x100.</p> <p>To reproduce full runs, use corresponding input files.</p> <p> </p> <p>Note: variables names are the same as in the JOREK code:</p> <p>u = electric potential</p> <p>omega = toroidal vorticity</p> <p>rho = density</p> <p>T = temperature</p> <p> </p> <p>The create_gif.py can be used to convert data into movies.</p>
Neural-Parareal dataset JOREK blob runs with electrostatic model batch_1501-2000
<p>Please refer to journal paper from S.J.P.Pamela, titled</p> <p>"Neural-Parareal: Self-improving acceleration of fusion MHD simulations using time-parallelisation and neural operators"</p> <p>Available on ArXiV and on Comp.Phys.Comm.: https://doi.org/10.1016/j.cpc.2024.109391</p> <p> </p> <p>Data produced by the JOREK code, https://jorek.eu</p> <p>All runs created using the electrostatic model, model-ID "model003"</p> <p>Data downsampled by saving every 10th timestep, on a regular 2D grid of 100x100.</p> <p>To reproduce full runs, use corresponding input files.</p> <p> </p> <p>Note: variables names are the same as in the JOREK code:</p> <p>u = electric potential</p> <p>omega = toroidal vorticity</p> <p>rho = density</p> <p>T = temperature</p> <p> </p> <p>The create_gif.py can be used to convert data into movies.</p>
Neural-Parareal dataset JOREK blob runs with electrostatic model batch_1001-1500
<p>Please refer to journal paper from S.J.P.Pamela, titled</p> <p>"Neural-Parareal: Self-improving acceleration of fusion MHD simulations using time-parallelisation and neural operators"</p> <p>Available on ArXiV and on Comp.Phys.Comm.: https://doi.org/10.1016/j.cpc.2024.109391</p> <p> </p> <p>Data produced by the JOREK code, https://jorek.eu</p> <p>All runs created using the electrostatic model, model-ID "model003"</p> <p>Data downsampled by saving every 10th timestep, on a regular 2D grid of 100x100.</p> <p>To reproduce full runs, use corresponding input files.</p> <p> </p> <p>Note: variables names are the same as in the JOREK code:</p> <p>u = electric potential</p> <p>omega = toroidal vorticity</p> <p>rho = density</p> <p>T = temperature</p> <p> </p> <p>The create_gif.py can be used to convert data into movies.</p> <p> </p>
CLAIRE: contrastive learning-based batch correction framework for better balance between batch mixing and preservation of cellular heterogeneity
<p>Source code, datasets and outputs. </p>
Lung CT COVID-19 batch 1
<p>This data set is part of the public development data for the <a href="http://auc23.grand-challenge.org/">2023 Automated Universal Classification Challenge</a> (AUC23). The data set concerns COVID-19 RT-PCR outcome prediction and prediction of severe COVID-19, defined as death or intubation after one month, from computed tomography (CT). The data set was previously introduced and described by <a href="https://pubs.rsna.org/doi/full/10.1148/radiol.2021210384">Revel, M. et al (2021)</a>. Data was restructured in compliance with the <a href="https://auc23.grand-challenge.org/">AUC23</a> challenge format. The STOIC project collected CT images of 10,735 individuals suspected of being infected with SARS-COV-2 during the first wave of the pandemic in France, from March to April 2020. For each patient in the training set, the dataset contains binary labels for COVID-19 presence based on RT-PCR test results, and COVID-19 severity, defined as intubation or death within one month from the acquisition of the CT scan. This data set contains the training sample of the STOIC dataset as used in the <a href="https://stoic2021.grand-challenge.org/">STOIC2021</a> challenge.</p> <p>Images are 3D tensors:</p> <ul> <li>0: 3D CT scan</li> </ul> <p>Classification labels:</p> <ul> <li>COVID-19: <ul> <li>0: Negative RT-PCR</li> <li>1: Positive RT-PCR<br> </li> </ul> </li> <li>Severe COVID-19: <ul> <li>0: Alive and no intubation after one month</li> <li>1: Death or intubation after one month</li> </ul> </li> </ul> <p>imagesTr (root folder with all patients and studies)<br> ├── covid19severity_6_0000.mha (3D CT for study 6)<br> ├── covid19severity_17_0000.mha (3D CT for study 17)<br> ├── ...</p> <p>Please cite the following article if you are using the <a href="https://registry.opendata.aws/stoic2021-training/">STOIC2021 training dataset</a>:</p> <pre><code>STOIC2021 Training was accessed on DATE from https://registry.opendata.aws/stoic2021-training. STOIC2021 Training was documented in Thoracic CT in COVID-19: The STOIC Project, Revel, Marie-Pierre, et al. Radiology, 2021, https://doi.org/10.1148/radiol.2021210384.</code></pre> <p>Due to upload size limits, the data set was split into six batches.</p> <p>Batch 2: <a href="https://zenodo.org/record/8042589">https://zenodo.org/record/8042589</a></p> <p>Batch 3: <a href="https://zenodo.org/record/8042817">https://zenodo.org/record/8042817</a></p> <p>Batch 4: <a href="https://zenodo.org/record/8043089">https://zenodo.org/record/8043089</a></p> <p>Batch 5: <a href="https://zenodo.org/record/8043216">https://zenodo.org/record/8043216</a></p> <p>Batch 6: <a href="https://zenodo.org/record/8043218">https://zenodo.org/record/8043218</a></p>
Characterization and Catalytic Datasets for the Multi-Batch Screening Photoreactor
<p>Datasets obtained during characterization of the Multi-Batch_Screening_Photoreactor</p> <p>The folder structure is listed below.</p> <p>The documentation, software and all required files for manufacturing are available at GitHub: <br> https://github.com/photonZfeed/modularPhotoreactor<br> https://github.com/photonZfeed/Py4ModPhotoreactor</p>
Trial to Evaluate Bioequivalence of LCB01-0371 in Different Batches
ClinicalTrials.gov study NCT04939779. IPD Sharing: NO. Countries: 1. Publications: 1.
Batch n ° 2 of the Public Market Evaluation "Medico-economic Demonstrator IsereADOM"
ClinicalTrials.gov study NCT03457623. IPD Sharing: NO. Countries: 1. Publications: 8.
Batch Enrollment for AI-Guided Intervention to Lower Neurologic Events in Unrecognized AF
ClinicalTrials.gov study NCT04208971. IPD Sharing: Not stated. Countries: 1. Publications: 2.
A Study to Qualify an In-house Reference Standard Batch of Sci-B-Vac™
ClinicalTrials.gov study NCT04179786. IPD Sharing: NO. Countries: 0. Publications: 1.
Clinical Evaluation for Batch Consistency of Ad5-nCoV in Chinese Healthy Adults
ClinicalTrials.gov study NCT05313646. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Protectivity and Safety of DTP/HB/Hib (Bio Farma) Vaccines in Infants, Batch Consistency, Multi Center Trial
ClinicalTrials.gov study NCT01986335. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: Batch spawning facilitates transfer of an essential nutrient from diet to eggs in a marine fish
Open the record for dataset details and reuse information.
Data from: Batch effects in a multi-year sequencing study: false biological trends due to changes in read lengths
Open the record for dataset details and reuse information.
Cambridge butterfly wing collection - Patricio Salazar PhD wild specimens batch 3
<p>EN:</p> <p>This upload contains photographs taken by Gabriela Montejo-Kovacevich and a visiting school student in the Butterfly Genetics Group at the University of Cambridge from the 9h July 2019 (student) and the 2nd September 2020 (GMK). </p> <p>This batch contains Patricio Salazar's wild specimen collection. The wild specimens are from mostly across the H. m. plesseni/malleti and H. e. notabilits/lativitta hybrid zones in the Eastern slope of the Andes collected in 2009 - 2011. </p> <p>These individuals have been whole-genome sequenced (haplotyping) and published in Meier, Salazar, Kucka, Chan, et al (2020)<br> <br> Images across the two batches here differ in background colours. They have a white reflectance standard for calibration. Information on the individual IDs can additionally be found in the csv.</p> <p>Nomenclature</p> <p>CAMXXXXXX : unit ID corresponding to individual samples<br> _v _d: ventral or dorsal<br> </p> <p>Information on individual samples from the Butterfly Genetics Group Collection can be found on the public database Earthcape (click <a href="https://heliconius.ecdb.io/#ViewID=ContentPage_DetailView&ObjectKey=843e8ec1-41a6-4706-9622-f643132da859&ObjectClassName=EarthCape.Module.Core.ContentPage&mode=View">here</a> for the database, and <a href="http://heliconius.zoo.cam.ac.uk/databases/earthcape-specimen-database/">here</a> for FAQ)</p> <p>Please contact Chris Jiggins (c.jiggins[at]zoo.cam.ac.uk) or Ian Warren (iaw22[at]cam.ac.uk) for requests.</p> <p> </p> <p>------------------------------------------------------</p> <p>ES:</p> <p>Este repositorio contiene fotografías tomadas en el Butterfly Genetics Group de la Universidad de Cambridge</p> <p>Este lote contiene la colección de especímenes silvestres colectados por Patricio Salazar. Los especímenes silvestres son principalmente de la zona híbrida y alrededores de H. m. plesseni / malleti y H. e. notabilits / lativitta en la vertiente oriental de los Andes recogidas en 2009 - 2011. </p> <p>Nomenclatura</p> <p>CAMXXXXXX: ID de unidad correspondiente a muestras individuales<br> _v _d: ventral o dorsal<br> </p> <p>Puede encontrar información sobre muestras individuales de Butterfly Genetics Group Collection en la base de datos pública Earthcape (haga clic <a href="https://heliconius.ecdb.io/#ViewID=ContentPage_DetailView&ObjectKey=843e8ec1-41a6-4706-9622-f643132da859&ObjectClassName=EarthCape.Module.Core.ContentPage&mode=View">aquí</a> para la base de datos, y <a href="http://heliconius.zoo.cam.ac.uk/databases/earthcape-specimen-database/">aquí</a> para preguntas frecuentes)</p> <p>Por favor, póngase en contacto con Chris Jiggins (c.jiggins [arroba] zoo.cam.ac.uk) o Ian Warren (iaw22 [arroba] cam.ac.uk) con sus preguntas o peticiones.</p>
High-order correction of persistent batch effects in correlation networks
<p>This upload contains data to reproduce the results and figures in our work High-order correction of persistent batch effects in correlation networks. The code is publicly available on <a href="https://github.com/QuackenbushLab/cobra-experiments">GitHub</a>.</p>
Online repository for: "Dielectrophoretic particle chromatography: From batch processing to semi-continuous high throughput separation"
<p>Please check the readme.odt file for details.</p>
Macroinvertebrate ring test raw data: batch 1
<p>Raw data from MiSeq runs from labs 5, 6 and 7.</p>
Macroinvertebrate ring test raw data: batch 3
<p>Raw data from MiSeq runs from labs 3 and 4.</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.