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306 results for “data archive”
FluView 2018 HHS Region 1 Outpatient Illness and Viral Surveillance Data - Epidemiological Week 40 (archived by MIDAS-ISG)
<p><strong>Other(s)</strong></p> <p>MIDAS Informatics Service Group</p> <p><strong>Rights holder(s)</strong></p> <p>Centers for Disease Control and Prevention</p> <p>Description from the FluView Interactive web application (from which this file was downloaded):</p> <p>Viral Surveillance — Data collection from both the U.S. World Health Organization (WHO) Collaborating Laboratories and National Respiratory and Enteric Virus Surveillance System (NREVSS) laboratories began during the 1997-98 season. The volume of tested specimens has greatly increased during this time due to increased participation and increased testing. During the 1997-98 season 43 state public health laboratories participated in surveillance, and by the 2004-05 season all state public health laboratories were participating in surveillance. The addition of NREVSS data during the 1997-98 season roughly doubled the amount of virologic data reported each week. </p> <p>The number of specimens tested and % positive rate vary by region and season based on different testing practices including triaging of specimens by the reporting labs, therefore it is not appropriate to compare the magnitude of positivity rates or the number of positive specimens between regions or seasons. </p> <p>The U.S. WHO and NREVSS collaborating laboratories report the total number of respiratory specimens tested and the number positive for influenza types A and B each week to CDC. Most of the U.S. WHO collaborating laboratories also report the influenza A subtype (H1 or H3) of the viruses they have isolated, but the majority of NREVSS laboratories do not report the influenza A subtype. </p> <p>For more information on virologic surveillance please visit:http://www.cdc.gov/flu/weekly/overview.htm#Viral</p> <p>Outpatient Illness Surveillance — Information on patient visits to health care providers for influenza-like illness is collected through the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet). This collaborative effort between CDC, state and local health departments, and health care providers started during the 1997-98 influenza season when approximately 250 providers were enrolled. Enrollment in the system has increased over time and there were >3,000 providers enrolled during the 2010-11 season.</p> <p>The number and percent of patients presenting with ILI each week will vary by region and season due to many factors, including having different provider type mixes (children present with higher rates of ILI than adults, and therefore regions with a higher percentage of pediatric practices will have higher numbers of cases). Therefore it is not appropriate to compare the magnitude of the percent of visits due to ILI between regions and seasons.</p> <p>Baseline levels are calculated both nationally and for each region. Percentages at or above the baseline level are considered to be elevated.</p> <p>For more information on ILI surveillance and baselines please visit:http://www.cdc.gov/flu/weekly/overview.htm#Outpatient</p>
FluView 2018 National Outpatient Illness and Viral Surveillance Data - Epidemiological Week 40 (archived by MIDAS-ISG)
<p><strong>Other(s)</strong></p> <p>MIDAS Informatics Service Group</p> <p><strong>Rights holder(s)</strong></p> <p>Centers for Disease Control and Prevention</p> <p>Description from the FluView Interactive web application (from which this file was downloaded):</p> <p>Viral Surveillance — Data collection from both the U.S. World Health Organization (WHO) Collaborating Laboratories and National Respiratory and Enteric Virus Surveillance System (NREVSS) laboratories began during the 1997-98 season. The volume of tested specimens has greatly increased during this time due to increased participation and increased testing. During the 1997-98 season 43 state public health laboratories participated in surveillance, and by the 2004-05 season all state public health laboratories were participating in surveillance. The addition of NREVSS data during the 1997-98 season roughly doubled the amount of virologic data reported each week. </p> <p>The number of specimens tested and % positive rate vary by region and season based on different testing practices including triaging of specimens by the reporting labs, therefore it is not appropriate to compare the magnitude of positivity rates or the number of positive specimens between regions or seasons. </p> <p>The U.S. WHO and NREVSS collaborating laboratories report the total number of respiratory specimens tested and the number positive for influenza types A and B each week to CDC. Most of the U.S. WHO collaborating laboratories also report the influenza A subtype (H1 or H3) of the viruses they have isolated, but the majority of NREVSS laboratories do not report the influenza A subtype. </p> <p>For more information on virologic surveillance please visit:http://www.cdc.gov/flu/weekly/overview.htm#Viral</p> <p>Outpatient Illness Surveillance — Information on patient visits to health care providers for influenza-like illness is collected through the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet). This collaborative effort between CDC, state and local health departments, and health care providers started during the 1997-98 influenza season when approximately 250 providers were enrolled. Enrollment in the system has increased over time and there were >3,000 providers enrolled during the 2010-11 season.</p> <p>The number and percent of patients presenting with ILI each week will vary by region and season due to many factors, including having different provider type mixes (children present with higher rates of ILI than adults, and therefore regions with a higher percentage of pediatric practices will have higher numbers of cases). Therefore it is not appropriate to compare the magnitude of the percent of visits due to ILI between regions and seasons.</p> <p>Baseline levels are calculated both nationally and for each region. Percentages at or above the baseline level are considered to be elevated.</p> <p>For more information on ILI surveillance and baselines please visit:http://www.cdc.gov/flu/weekly/overview.htm#Outpatient</p>
enriched_piles_paper_data_archive
<p>Archive for data from:<br> Effects of heat-producing elements on the stability of deep mantle thermochemical piles<br> R.I. Citron, D.L. Lourenco, A.J. Wilson, A.G. Grima, S.A. Wipperfurth, M.L Rudolph, S. Cottaar, L. Montesi</p>
Archive of aggregate data on work and parenthood
<p>These are aggregate files of labor force participation for mothers, fathers, and women not living with own children. Read ReadMe.txt file first for definitions and codes. Generated using the Current Population Survey monthly files from ipums.org. These files are meant to encourage discussion and analysis of labor force participation of mothers in comparison to fathers and women without own children in their home.</p>
Data archive for paper "Copula-based synthetic data augmentation for machine-learning emulators"
<p><strong>Overview</strong></p> <p>This is the data archive for paper "<a href="https://doi.org/10.5194/gmd-14-5205-2021">Copula-based synthetic data augmentation for machine-learning emulators</a>". It contains the paper’s data archive with model outputs (see <code>results</code> folder) and the Singularity image for (optionally) re-running experiments.</p> <p>For the Python tool used to generate synthetic data, please refer to <a href="https://github.com/dmey/synthia">Synthia</a>.</p> <p><strong>Requirements</strong></p> <ul> <li><a href="https://sylabs.io/singularity/">Singularity</a> >= 3</li> <li><a href="https://en.wikipedia.org/wiki/Portable_Batch_System">Portable Batch System</a> (PBS) job scheduler*</li> <li>Today's high-performance computer (e.g. ~ 32 CPUs @ 2 500 MHz with 64 GB of RAM )</li> </ul> <p>*Although PBS in not a strict requirement, it is required to run all helper scripts as included in this repository. Please note that depending on your specific system settings and resource availability, you may need to modify PBS parameters at the top of submit scripts stored in the <code>hpc</code> directory (e.g. <code>#PBS -lwalltime=72:00:00</code>).</p> <p><strong>Usage</strong></p> <p>To reproduce the results from the experiments described in the paper, first fit all copula models to the reduced NWP-SAF dataset with:</p> <pre><code>qsub hpc/fit.sh</code></pre> <p>then, to generate synthetic data, run all machine learning model configurations, and compute the relevant statistics use:</p> <pre><code>qsub hpc/stats.sh qsub hpc/ml_control.sh qsub hpc/ml_synth.sh</code></pre> <p>Finally, to plot all artifacts included in the paper use:</p> <pre><code>qsub hpc/plot.sh</code></pre> <p><strong>Licence</strong></p> <p>Code released under <a href="./LICENSE.txt">MIT license</a>. Data from the reduced NWP-SAF dataset released under <a href="./data/LICENSE.txt">CC BY 4.0</a>.</p>
Data for: Harnessing the Hubble Space Telescope Archives: A Catalogue of 21,926 Interacting Galaxies
<p>This repository contains the data released in the paper "Harnessing the Hubble Space Telescope Archives: A Catalogue of 21,926 Interacting Galaxies"<em> </em>(DOI: <a href="https://ui.adsabs.harvard.edu/abs/2023ApJ...948...40O/abstract">10.3847/1538-4357/acc0ff</a>).</p> <p>We release the catalogue of interacting galaxies found in this work, as well as any objects of interest found during the visual inspection stage of de-contamination. Each catalogue contains the SourceID, Right Ascension, Declination and the "Status" of each object. The status shows whether an object has appeared in the astrophysical literature prior to this work. <strong>Each catalogue is contained in a .csv file</strong>.</p> <p>The exception to this is the interacting galaxy catalogue itself, which also contains the Zoobot prediction of each source to be an interacting galaxy.</p> <p>We also include the images that correspond to each catalogue. <strong>These are contained within the .tar.gz files in the repository.</strong> Each .tar.gz filename corresponds to the catalogue .csv filename. The individual filenames are the SourceIDs of the corresponding objects.</p> <p>Please note: These images have been saved as 150x150 gray images using only the F814W filter of the <em>HST</em>. Different dimension images/multi-band photometry will have to be found by the user. When input into our Zoobot model, these had been resized to 300x300.</p> <p>Please also note: <strong>Each sub-category of object has</strong> <strong>only been classified visually</strong>.</p> <p>A list of the catalogue definitions is below:</p> <ol> <li>agn-catalogue - Catalogue of sources containing an Active Galactic Nuclei or a Quasar.</li> <li>groups-catalogue - Catalogue of sources containing a Galaxy Group.</li> <li>high_z-catalogue - Catalogue of sources containing high redshift galaxies.</li> <li>interacting-catalogue - Catalogue of sources predicted to be interacting galaxies. This is the main catalogue.</li> <li>jellyfish-catalogue - Catalogue of sources containing a Jellyfish galaxy.</li> <li>jets-catalogue - Catalogue of sources containing a galaxy with a jet.</li> <li>lenses-catalogue - Catalogue of sources containing a gravitational lense.</li> <li>ly-catalogue - Catalogue of sources which are Lyman-Alpha galaxies.</li> <li>overlap-catalogue - Catalogue of sources which contain two galaxies which overlap by projection, but are not interacting.</li> <li>planetary-catalogue - Catalogue of sources which contain edge on planetary disks.</li> <li>radio-catalogue - Catalogue of sources which contain a radio source/jet.</li> <li>ring-catalogue - Catalogue of sources which contain ring galaxies.</li> <li>submillimetre-dusty-catalogue - Catalogue of sources which contain submillimetre or dusty galaxies.</li> <li>supernova-catalogue - Catalogue of sources which contain a supernova remnant.</li> <li>unknown-catalogue - Catalogue of sources which could not be classified visually.</li> <li>ysc-catalogue - Catalogue of sources which contain a Young Stellar Cluster.</li> <li>yso-catalogue - Catalogue of sources which contain a Young Stellar Object.</li> </ol>
Hetero-VZ Data Archive
<p>This is the data archive.</p>
Data Archive for "Latent diffusion models for generative precipitation nowcasting with accurate uncertainty quantification"
<p>This repository contains the training data and pretrained models for the paper "Latent diffusion models for generative precipitation nowcasting with accurate uncertainty quantification".</p> <p>To use the data, clone the repository at <a href="https://github.com/MeteoSwiss/ldcast">https://github.com/MeteoSwiss/ldcast</a>. Unzip the files as follows:</p> <ul> <li>Demo files "ldcast-demo-20210622.zip" to the "data" directory</li> <li>Training and evaluation data archive "ldcast-datasets.zip" to the "data" directory</li> <li>Pretrained model archive "models-genforecast.zip" to the "models" directory</li> </ul>
Archival bundle of the data used for "A 2-phase Strategy For Intelligent Cloud Operations"
<p>This archive contains the data used for the paper</p> <p><strong>A 2-phase Strategy For Intelligent Cloud Operations</strong><br> <a href="mailto:giacomo.lanciano@sns.it">Giacomo Lanciano</a>*, Remo Andreoli, Tommaso Cucinotta, Davide Bacciu, Andrea Passarella</p> <p>Follow the instructions provided in the <a href="https://github.com/giacomolanciano/intelligent-cloud-operations">companion repo</a> to automatically download and decompress the archive. The following files are included:</p> <table> <tbody> <tr> <td><strong>File</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td> <p>amphora-x64-haproxy.qcow2</p> </td> <td> <p>Image used to create Octavia amphorae</p> </td> </tr> <tr> <td> <p>distwalk-disk-load-<INCREMENTAL-ID>/</p> </td> <td> <p>distwalk runs data</p> </td> </tr> <tr> <td> <p>FINAL-rl3-*/</p> </td> <td> <p>Cassandra runs data</p> </td> </tr> <tr> <td> <p>model_dumps/*</p> </td> <td> <p>Dumps of the models used for the validation</p> </td> </tr> <tr> <td> <p>test_load_disk_01-2tpi.dat</p> </td> <td> <p>distwalk load trace used to generate the workload</p> </td> </tr> <tr> <td> <p>ubuntu-20.04-server-distwalk-683d9e7.img</p> </td> <td> <p>Image used to create Nova instances for the scaling group</p> </td> </tr> </tbody> </table> <p>* <em>contact author</em></p>
Archive of biochemical data sets contained in: Guanine-containing ssDNA and RNA induce dimeric and tetrameric SAMHD1 in cryo-EM and binding studies
<p>This archive contains all the biochemical data for the study "Guanine-containing ssDNA and RNA induce dimeric and tetrameric SAMHD1 in cryo-EM and binding studies". Further data supporting the structural data in this study is found in pdb and EMD accession numbers PDB ID 8TDV and EMD-41174 (RNA complex SAMHD1-T*<sub>cl</sub>) and PDB ID 8TDW and EMD-41175 (RNA complex SAMHD1-T*<sub>op</sub>). Further proteomics data is found in the Protein Exchange Database (PXD043587). </p>
Data Archive for James and Ross, 2023 (submitted): The Timing of the ENSO Spring Barrier in the Copernicus Dynamical Models
<p>This archive contains data used in creating figures for James and Ross, 2023 (submitted): The Timing of the ENSO Spring Barrier in the Copernicus Dynamical Models.</p> <p>The data are provided in csv files, and the file "README" explains the contents of each file.</p> <p> </p>
Supplement to zenodo doi 10.5281/zenodo.8122347 Data Archive for the NASA DAILI CubeSatMission:NASA Award Number: 80NSSC18K0973
<p>In the zenodo archive 10.5281/zenodo.8122347 the idl save sets include an array, raw_image, that had no actual data. The save-sets here fill that array with the actual data. These images are the actual images obtained with each DAILI exposure. In the original DAILI archive the portions of each exposure needed for retrieval of the science parameters were correctly included. Hence, the arrays here are just for completeness although they do document the dark counts between the regions where light is admitted.</p>
InTheMED WP3 Data Archive - Climate Projections in the Case Studies -
<p>The data archive InTheMED_WP3_DS_ClimateData is part of Task 3.3 “Downscaling of future climate projections at the case-study scale and their transfer to the Partners” and contains the climate projections at the five pilot sites under two emission scenarios (RCP4.5 and RCP8.5).</p>
Identification of a Resting Bold Connectome Associated with Cognitive Reserve - Data and code archive
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Data from: Return of a giant: DNA from archival museum samples helps to identify a unique cutthroat trout lineage formerly thought to be extinct
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Archival and modern DNA SNP data of 13 Baltic salmon populations
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Data from: Resurrecting an extinct salmon evolutionarily significant unit: archived scales, historical DNA, and implications for restoration
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Data from: MADA: Malagasy Animal trait Data Archive
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Archive data for: Loss of predation risk from apex predators can exacerbate marine tropicalization caused by extreme climatic events
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Data from: Differentiation in neutral genes and a candidate gene in the pied flycatcher: using biological archives to track global climate change
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ScienceDex guides
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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.