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EDGELESS: orchestration domain with 5 VM nodes
<div> <h2>Scenario</h2> <a href="https://github.com/edgeless-project/cnr-experiments/tree/main/experiments/003-tid-benchmark-varload#scenario"></a></div> <ul> <li>ε-ORC, ε-CON, Redis in a VM</li> <li>5 nodes in other VMs: <ul> <li>4 with 2 cores</li> <li>1 with 8 cores</li> </ul> </li> </ul> <p>Factors:</p> <ul> <li>load, in terms of the interarrival between consecutive workflows, with the average ranging from 10 s to 60 s</li> <li>orchestration policy, i.e., either <code>Random</code> or <code>RoundRobin</code></li> </ul> <p>Each experiment lasts 1 hour and is repeated 10 times.</p> <p>Workload generated with <code>edgeless_benchmark</code>, see <code>run.sh</code> for the complete set of parameters.</p> <div> <h2>Repeatability</h2> <p>Check GitHub repository with the EDGELESS <a href="https://github.com/edgeless-project/edgeless/">reference implementation</a> and <a href="https://github.com/edgeless-project/cnr-experiments">experiments</a> (experiment <code>003-tid-benchmark-varload</code>)</p> <a href="https://github.com/edgeless-project/cnr-experiments/tree/main/experiments/003-tid-benchmark-varload#repeatability"></a></div> <ol> <li>Install a Redis server</li> <li>Update the ε-ORC and ε-CON configuration in <code>conf/</code></li> <li>Install a cluster of EDGELESS nodes</li> <li>Use <code>run.sh</code> to run experiments by saving the output data in <code>dataset</code></li> </ol> <h2>Dataset content</h2> <p>The compressed archive <code>003-tid-benchmark-varload.zip</code> contains the following files, all with CSV entries:</p> <table> <tbody><tr> <th>Filename</th> <th>Format</th> </tr> </tbody><tbody> <tr> <td><code>health_status.csv</code></td> <td>timestamp,node_id,node_health_status</td> </tr> <tr> <td><code>capabilities.csv</code></td> <td>timestamp,node_id,node_capabilities</td> </tr> <tr> <td><code>mapping_to_instance_id.csv</code></td> <td>timestamp,logical_id,node_id1,physical_id1,...</td> </tr> <tr> <td><code>performance_samples.csv</code></td> <td>metric,identifier,value,timestamp</td> </tr> <tr> <td><code>application_metrics.csv</code></td> <td>entity,identifier,value,timestamp</td> </tr> </tbody> </table> <p>Notes:</p> <ul> <li>The timestamp format is always A.B, where A is the Unix epoch in seconds and B is the fractional part in nanoseconds.</li> <li>All the identifiers (node_id, logical_id, and physical_id) are UUID.</li> <li>The field entity in the application metrics can be <code>f</code> (function) or <code>w</code> (workflow).</li> </ul>
TN412 SCOPE-Gradients-5 CTD data
<p>The SCOPE-Gradients program was designed to test conceptual and mathematical models of biogeochemical organization across the transition zone between different ecosystems. The studies were grounded by three hypotheses derived from ecological theory and manifested by the Darwin ecosystem model output. 1) Optimal supply ratios of fixed nitrogen and iron result in enhanced productivity across the transition zone. 2) The size distribution of organisms (PSD) across the transition zone are predictable under steady state conditions. 3) Trends in elemental and macro-molecular composition of primary producers will vary predictably across the transition zone. We employed a suite of interdisciplinary sampling strategies on process cruises transecting across the North Pacific subtropical gyre, the transition zone and into the subpolar gyres in order to determine whether the theory underlying our models and driving hypotheses are sufficiently robust to predict in situ observations.</p>
Data for: Drying-Induced Deformation in Concrete: Insights from a 5-Year Study
<p>The experimental dataset from Havlasek, Smilauer, and Nezerka's paper "Drying-Induced Deformation in Concrete: Insights from a 5-Year Study" is available in five archives. These archives contain the data organized according to the specified structure and accompanying Gnuplot source files for easier data visualization.</p> <p>Full experimental description is given in the paper and in the preceding publication entitled "Shrinkage-induced deformations and creep of structural concrete: 1-year measurements and numerical prediction accessible from https://www.sciencedirect.com/science/article/pii/S000888462100051X</p> <p>All Gnulot input files (*.gnu) generate a corresponding *.pdf file with the same base name. These PDFs are included, but they're not listed.<br>Usage: $ gnuplot < plot_name_of_gnuplot_file.gnu</p> <p>The full experimental dataset is reduced to 100 time points using resampling. Initially, geometric progression is applied, but once the time step reaches 30 days, it remains constant.</p> <p>*** FILES STRUCTURE ***</p> <p>|--- ambient_conditions<br>| | # history of ambient humidity and temperature<br>| |<br>| |--- plot_large_beams_ambient.gnu<br>| | # time "0" corresponds to the onset of drying of the beams, i.e. concrete age 34.063 days<br>| |<br>| |--- data<br>| | # time "0" corresponds to concrete set<br>| |--- humidity_function.dat<br>| |--- temperature_function.dat <br>|<br>|--- creep_shrinkage_prisms<br>| | # development of creep and shrinkage of beams 100x100x400 mm exposed to drying at the concrete age of 28 days; concrete specimens are drying from two lateral surfaces only<br>| |<br>| |--- axial_shrinkage.gnu<br>| | # total shrinkage<br>| |--- eccentric_creep.gnu<br>| | # total creep in axial direction and bending <br>| |<br>| |--- data<br>| |--- Jtot_ecc_axial_toi.csv<br>| |--- Jtot_ecc_bending_toi.csv<br>| |--- shrinkage_toi.csv<br>|<br>|--- curvature_beams<br>| # curvature of concrete beams with various sizes and sealing configurations, time "0" = installation time = onset of drying = concrete age of 34.063 days<br>| | <br>| |--- beam_1.gnu<br>| | # 100 Top: breadth B = 0.10 m, height H = 0.10 m, span = 2.50 m, length = 2.70 m, drying from the top surface<br>| |--- beam_2.gnu<br>| | # 100 Sealed: breadth B = 0.10 m, height H = 0.10 m, span = 2.50 m, length = 2.70 m, sealed surface<br>| |--- beam_3.gnu<br>| | # 100 Bottom: breadth B = 0.10 m, height H = 0.10 m, span = 2.50 m, length = 2.70 m, drying from the bottom surface<br>| |--- beam_4.gnu<br>| | # 100 Both: breadth B = 0.10 m, height H = 0.10 m, span = 2.50 m, length = 2.70 m, drying from the top and bottom surfaces<br>| |--- beam_5.gnu<br>| | # 50 Top: breadth B = 0.10 m, height H = 0.05 m, span = 1.75 m, length = 1.95 m, drying from the top surface<br>| |--- beam_6.gnu<br>| | # 200 Top: breadth B = 0.10 m, height H = 0.20 m, span = 3.00 m, length = 3.20 m, drying from the top surface, loading with external weights 2 x 30 kg placed 0.6 m from the support<br>| |--- beam_7.gnu<br>| | # 150 Top: breadth B = 0.10 m, height H = 0.15 m, span = 3.00 m, length = 3.20 m, drying from the top surface<br>| |--- beams_100_ABC.gnu<br>| | # individual responses of the beams with height 100 and different sealing configurations<br>| |--- beams_100_mean.gnu<br>| | # mean responses of the beams with height 100 and different sealing configurations<br>| |--- beams_sizes_ABC.gnu<br>| | # individual responses of the beams with different height and drying from the top surface<br>| |--- beams_sizes_mean.gnu<br>| | # mean responses of the beams with different height and drying from the top surface<br>| |<br>| |--- data<br>| |--- automatic<br>| | # measurement with post-mounted linear potentiometer installed at midspan of each specimen<br>| | |--- 1_mean_curvature_toi.csv<br>| | | # columns: time, mean curvature and standard deviation of curvature; the values are calculated from specimens A, B and C<br>| | |--- 1A_curvature_toi.csv<br>| | | # columns: time and curvature<br>| | |--- 1B_curvature_toi.csv<br>| | |--- 1C_curvature_toi.csv<br>| | |--- 2_mean_curvature_toi.csv<br>| | |--- 2A_curvature_toi.csv<br>| | |--- 2B_curvature_toi.csv<br>| | |--- 2C_curvature_toi.csv<br>| | |--- 3_mean_curvature_toi.csv<br>| | |--- 3A_curvature_toi.csv<br>| | |--- 3B_curvature_toi.csv<br>| | |--- 3C_curvature_toi.csv<br>| | |--- 4_mean_curvature_toi.csv<br>| | |--- 4A_curvature_toi.csv<br>| | |--- 4B_curvature_toi.csv<br>| | |--- 4C_curvature_toi.csv<br>| | |--- 5_mean_curvature_toi.csv<br>| | |--- 5A_curvature_toi.csv<br>| | |--- 5B_curvature_toi.csv<br>| | |--- 5C_curvature_toi.csv<br>| | |--- 6_mean_curvature_toi.csv<br>| | |--- 6A_curvature_toi.csv<br>| | |--- 6B_curvature_toi.csv<br>| | |--- 6C_curvature_toi.csv<br>| | |--- 7_mean_curvature_toi.csv<br>| | |--- 7A_curvature_toi.csv<br>| | |--- 7B_curvature_toi.csv<br>| | |--- 7C_curvature_toi.csv<br>| |--- dial_gauge<br>| | # measurement using a set of 5 digital indicators with a fixed position from the left support, only the front specimens (A) is measured using this technique<br>| | # columns: time, curvature and standard devidation of curvature. Weights corresponding to theoretical normalized deflections are applied.<br>| | |--- 1a_weighted_toi.csv<br>| | |--- 2a_weighted_toi.csv<br>| | |--- 3a_weighted_toi.csv<br>| | |--- 4a_weighted_toi.csv<br>| | |--- 5a_weighted_toi.csv<br>| | |--- 6a_weighted_toi.csv<br>| | |--- 7a_weighted_toi.csv<br>| |--- DIC<br>| | # measurement using digital image correlation, locations of the reference plates are above the support and at quarter-spans<br>| |--- 1_stat_toi.csv<br>| | # columns: time, mean curvature and standard deviation of curvature. The values are calculated from specimens A, B and C. The weights are given by the standard deviation of curvature of the individual specimens<br>| |--- 1a_stat_toi.csv<br>| | # columns: time, mean curvature and standard deviation of curvature.<br>| |--- 1b_stat_toi.csv<br>| |--- 1c_stat_toi.csv<br>| |--- 2_stat_toi.csv<br>| |--- 2a_stat_toi.csv<br>| |--- 2b_stat_toi.csv<br>| |--- 2c_stat_toi.csv<br>| |--- 3_stat_toi.csv<br>| |--- 3a_stat_toi.csv<br>| |--- 3b_stat_toi.csv<br>| |--- 3c_stat_toi.csv<br>| |--- 4_stat_toi.csv<br>| |--- 4a_stat_toi.csv<br>| |--- 4b_stat_toi.csv<br>| |--- 4c_stat_toi.csv<br>| |--- 5_stat_toi.csv<br>| |--- 5a_stat_toi.csv<br>| |--- 5b_stat_toi.csv<br>| |--- 5c_stat_toi.csv<br>| |--- 6_stat_toi.csv<br>| |--- 6a_stat_toi.csv<br>| |--- 6b_stat_toi.csv<br>| |--- 6c_stat_toi.csv<br>| |--- 7_stat_toi.csv<br>| |--- 7a_stat_toi.csv<br>| |--- 7b_stat_toi.csv<br>| |--- 7c_stat_toi.csv<br>|<br>|--- drying_cylinders<br>| # data of moisture loss measured on cylinders drying from the top and bottom surface and with a sealed circumference, onset of drying at concrete age 28 days<br>| |<br>| |--- drying_cylinders_average.gnu<br>| |<br>| |--- data<br>| | # columns: duration of drying, average moisture loss [kg/m^3], standard deviation of moisture loss<br>| |--- dwdV_25_toi.csv<br>| |--- dwdV_50_toi.csv<br>| |--- dwdV_100_toi.csv<br>| |--- dwdV_150_toi.csv<br>| |--- dwdV_200_toi.csv<br>|<br>|---isotherm<br>| # data for sorption isotherm expressed as a dependence of moisture content [kg/m^3] on relative humidity [-]<br>| |<br>| |--- isotherm.gnu<br>| | experimental data and a least-squares fit with a VanGenuchten expression<br>| |<br>| |--- data<br>| | # columns: relative humidity, average moisture content and standard deviation of moisture content<br>| |--- isotherm_beam.dat<br>| | # data measured on specimens cut from a spare concrete beam<br>| |--- isotherm_cube.dat<br>| | # data measured on specimens cut from a standard concrete cube </p>
Antibacterial Cu or Zn-MOFs Based on the 1,3,5-Tris-(styryl)benzene Tricarboxylate
<p>Metal–organic frameworks (MOFs) are highly versatile materials. Here, two novel MOFs, branded as IEF-23 and IEF-24 and based on an antibacterial tricarboxylate linker and zinc or copper cations, and holding antibacterial properties, are presented. The materials were synthesized by the solvothermal route and fully characterized. The antibacterial activity of IEF-23 and IEF-24 was investigated against Staphylococcus epidermidis and Escherichia coli via the agar diffusion method.<br>These bacteria are some of the most broadly propagated pathogens and are more prone to the development of antibacterial resistance. As such, they represent an archetype to evaluate the efficiency of novel antibacterial treatments. MOFs were active against both strains, exhibiting higher activity against Staphylococcus epidermidis. Thus, the potential of the developed MOFs as antibacterial agents was proved.</p>
Stellar Evolution Models from "Finding the Fuse: Prospects for the Detection and Characterization of Hydrogen-Rich Core-Collapse 5 Supernova Precursor Emission with the LSST"
<p>These data consist of all runs from the Modules for Experiments in Stellar Astrophysics (MESA; Paxton et al. 2011, 2013, 2015, 2018, 2019) code, used to construct radius priors for modeling supernova precursor emission in<em> <a href="https://arxiv.org/abs/2408.13314">Finding the Fuse: Prospects for the Detection and Characterization of Hydrogen-Rich Core-Collapse 5 Supernova Precursor Emission with the LSST</a></em> (Gagliano+2024, submitted). </p> <p>The contents of the data files are detailed in the file <strong>ReadmeMESA.txt</strong>. Additional detail concerning the simulations can be found in Section 2.2 of the linked publication. </p>
Cyclin-Dependent Kinase-Like 5 (CDKL5); A Target Enabling Package
<p>The protein kinase CDKL5 is best known for harbouring loss of function mutations that cause a variant of Rett syndrome, CDKL5 deficiency disorder, that predisposes to seizures and mental retardation. Recent kinome-wide screening has identified CDKL5 as a therapeutic target to ameliorate acute kidney injury, which is a common complication of hospitalisation that can lead to chronic kidney disease. There are currently no proven treatments. We prepared recombinant proteins for the kinase domains of CDKL1, CDKL2, CDKL3 and CDKL5 and solved the structures of these kinases in complex with identified inhibitors at resolutions from 1.5 to 2.4 Å. Overall, the structures capture the kinases with both active and inactive conformations and provide a model to explain the effects of CDKL5 mutation. An <em>in vitro</em> kinase assay showed the importance of a C-terminal αJ helix for the activity of CDKL2 and CDKL3, but not CDKL1 and CDKL5. Functional analyses of the single orthologue in C. elegans CDKL-1 also suggested that CDKL proteins can limit cilia length, which could potentially contribute to the neurological defects in CDKL5 deficiency syndrome. AST-487 and ASC67 present inhibitors of CDKL5 that could be developed for treating acute kidney injury. However, future work is needed to improve selectivity in this drug development.</p>
DCASE 2021 Task 5: Few-shot Bioacoustic Event Detection Evaluation Set
<p><strong>General Description</strong></p> <p>The evaluation set for task 5 of DCASE 2021 "Few-shot Bioacoustic Event Detection" consists of 31 audio files acquired from different bioacoustic sources. </p> <p>In Evaluation_Set_Annotations: the first 5 annotations are provided for each file, with events marked as positive (POS) for the class of interest. This is the same setup used during the DCASE 2021 challenge.</p> <p>In Evaluation_Set_Full_Annotations: the full annotations are provided for each file, with events marked as positive (POS) or unknown (UNK) for the class of interest.</p> <p> </p> <p><strong>Folder Structure</strong></p> <p><em>Evaluation_Set.zip contains audio files and annotation files with 5 first POS events (as used during DCASE 2021 challenge)</em></p> <p>|__Evaluation_Set/</p> <p> |___DC/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___ME/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___ML/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p><em>Evaluation_Set_Audio.zip</em> has the same structure but contains only the *.wav files.</p> <p><em>Evaluation_Set_Annotations.zip</em> has the same structure but contains only the *.csv files with first 5 POS annotations.</p> <p><em>Evaluation_Set_Full_Annotations.zip</em> has the same structure but contains only the *.csv files with all POS annotations.</p> <p>The subfolders denote different recording sources and there may or may not be overlap between classes of interest from different wav files.</p> <p> </p> <p><strong>Annotation structure</strong></p> <p>Each line of the annotation csv represents an event in the audio file. The column descriptions are as follows:<br> [ Audiofilename, Starttime, Endtime, Q ]</p> <p> </p> <p><strong>Classes</strong></p> <p>DCASE2021_task5_evaluation_set.csv provides a table with class code correspondance to class name for all the recordings of the Evaluation set.</p> <p>DCASE2021_task5_evaluation_set.csv<br> -------------------<br> dataset, recording, class_code, class_name</p> <p> </p> <p><strong>Development Set</strong></p> <p>The development set for the same task can be found at: <a href="https://doi.org/10.5281/zenodo.5412896">https://doi.org/10.5281/zenodo.5412896</a></p> <p> </p> <p><strong>Open Access</strong></p> <p>This dataset is available under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.<br> </p> <p><strong>Contact info</strong></p> <p>Please send any feedback or questions to:<br> Veronica Morfi: g.v.morfi@qmul.ac.uk</p>
DCASE 2021 Task 5: Few-shot Bioacoustic Event Detection Development Set
<p><strong>General Description</strong></p> <p>The development set for task 5 of DCASE 2021 "Few-shot Bioacoustic Event Detection" consists of 19 audio files acquired from different bioacoustic sources. The dataset is split into training and validation Sets. </p> <p>Multi-class annotations are provided for the training set with positive (POS), negative (NEG) and unkwown (UNK) values for each class. UNK indicates uncertainty about a class. </p> <p>Single-class (class of interest) annotations are provided for the validation set, with events marked as positive (POS) or unkwown (UNK) provided for the class of interest. </p> <p> </p> <p><strong>Folder Structure</strong></p> <p><em>Development_Set.zip</em></p> <p>|_Development_Set/</p> <p> |__Training_Set/</p> <p> |___BV/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___HT/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___JD/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___MT/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |__Validation_Set/</p> <p> |___HV/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___PB/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> </p> <p><em>Development_Set_Audio.zip</em> has the same structure but contains only the *.wav files.</p> <p><em>Development_Set_Annotations.zip</em> has the same structure but contains only the *.csv files</p> <p> </p> <p><strong>Dataset statistics</strong></p> <p>Some statistics on this dataset are as follows, split between training and validation set and their sub-folders:</p> <p>-----------------------------------------------------<br> TRAINING SET<br> -----------------------------------------------------<br> Number of audio recordings | 11<br> Total duration | 14 hours and 20 mins<br> Total classes (excl. UNK) | 19<br> Total events (excl. UNK) | 4,686<br> -----------------------------------------------------<br> TRAINING SET/BV<br> -----------------------------------------------------<br> Number of audio recordings | 5<br> Total duration | 10 hours<br> Total classes (excl. UNK) | 11<br> Total events (excl. UNK) | 2,662<br> Sampling rate | 24,000 Hz<br> -----------------------------------------------------<br> TRAINING SET/HT<br> -----------------------------------------------------<br> Number of audio recordings | 3<br> Total duration | 3 hours<br> Total classes (excl. UNK) | 3<br> Total events (excl. UNK) | 435<br> Sampling rate | 6,000 Hz<br> -----------------------------------------------------<br> TRAINING SET/JD<br> -----------------------------------------------------<br> Number of audio recordings | 1<br> Total duration | 10 mins<br> Total classes (excl. UNK) | 1<br> Total events (excl. UNK) | 355<br> Sampling rate | 22,050 Hz<br> -----------------------------------------------------<br> TRAINING SET/MT<br> -----------------------------------------------------<br> Number of audio recordings | 2<br> Total duration | 1 hour and 10 mins<br> Total classes (excl. UNK) | 4<br> Total events (excl. UNK) | 1,234<br> Sampling rate | 8,000 Hz<br> -----------------------------------------------------</p> <p><br> -----------------------------------------------------<br> VALIDATION SET<br> -----------------------------------------------------<br> Number of audio recordings | 8<br> Total duration | 5 hours<br> Total classes (excl. UNK) | 4<br> Total events (excl. UNK) | 310<br> -----------------------------------------------------<br> VALIDATION SET/HV<br> -----------------------------------------------------<br> Number of audio recordings | 2<br> Total duration | 2 hours<br> Total classes (excl. UNK) | 2<br> Total events (excl. UNK) | 50<br> Sampling rate | 6,000 Hz<br> -----------------------------------------------------<br> VALIDATION SET/PB<br> -----------------------------------------------------<br> Number of audio recordings | 6<br> Total duration | 3 hours<br> Total classes (excl. UNK) | 2<br> Total events (excl. UNK) | 260<br> Sampling rate | 44,100 Hz<br> -----------------------------------------------------</p> <p> </p> <p><strong>Annotation structure</strong></p> <p>Each line of the annotation csv represents an event in the audio file. The column descriptions are as follows:</p> <p>TRAINING SET<br> ---------------------<br> Audiofilename, Starttime, Endtime, CLASS_1, CLASS_2, ...CLASS_N</p> <p>VALIDATION SET<br> ---------------------<br> Audiofilename, Starttime, Endtime, Q</p> <p> </p> <p><strong>Classes</strong></p> <p>DCASE2021_task5_training_set_classes.csv and DCASE2021_task5_validation_set_classes.csv provide a table with class code correspondace to class name for all classes in the Development set.</p> <p>DCASE2021_task5_training_set_classes.csv<br> ---------------------<br> dataset, class_code, class_name</p> <p>DCASE2021_task5_validation_set_classes.csv<br> ---------------------<br> dataset, recording, class_code, class_name</p> <p> </p> <p><strong>Evaluation Set</strong></p> <p>The Evaluation set for the same task can be found at: <a href="https://doi.org/10.5281/zenodo.5413149">https://doi.org/10.5281/zenodo.5413149</a></p> <p> </p> <p><strong>Open Access</strong></p> <p>This dataset is available under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.</p> <p><br> <strong>Contact info</strong></p> <p>Please send any feedback or questions to:<br> Veronica Morfi: g.v.morfi@qmul.ac.uk<br> </p>
Bihar Kotra Graffito 5
<p>Figure 47 in</p> <p><em>To engrave his virtues on the disc of the moon… Inscriptions of the Aulikaras and Their Associates</em></p> <p>Dániel Balogh, 2019</p> <p>Bihar Kotra graffito 5</p> <p>Digital photograph by the author, 2017.</p>
Surfalex HF formability study - Workflow 5 - Simulate Marciniak-Kuczynski
<p>This group of MatFlow workflows is the fifth in a set of eight workflow groups developed during our formability study of the Surfalex HF (AA6016A) material. In these workflows, we used Abaqus to perform deformation of a grooved sheet using the finite element method, for different displacement boundary conditions and different groove angles (relative to the major loading direction). Thus, we performed a numerical Marciniak-Kuczynski analysis on the Surfalex material, which allowed us to predict the forming limit curve. We also include various sensitivity studies.</p> <p>This workflow can be downloaded and explored in a Jupyter notebook, as explained in the <a href="https://github.com/LightForm-group/surfalex_data_explorer">GitHub repository here</a>.</p>
Doodleverse/Segmentation Zoo Res-UNet models for 4-class (water, whitewater, sediment and other) segmentation of Sentinel-2 and Landsat-7/8 5-band (RGB+NIR+SWIR) images of coasts.
<p>These Residual-UNet model data are based on 5-band RGB+NIR+SWIR (red, green, blue, near-infrared, and short-wave infrared) images of coasts and associated labels.</p> <p> </p> <p>Models have been created using Segmentation Gym* using the following dataset**: <a href="https://doi.org/10.5281/zenodo.7344571">https://doi.org/10.5281/zenodo.7344571 </a></p> <p>Classes: {0=water, 1=whitewater, 2=sediment, 3=other}</p> <p><strong>File descriptions</strong></p> <p>For each model, there are 5 files with the same root name:</p> <p>1. <strong>'.json' </strong>config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse.</p> <p>2.<strong> '.h5'</strong> weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function `seg_images_in_folder.py`. Models may be ensembled.</p> <p>3.<strong> '_modelcard.json'</strong> model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model</p> <p>4. <strong> '_model_history.npz'</strong> model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py`</p> <p>5. <strong> '.png'</strong> model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py`</p> <p>Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU</p> <p> </p> <p><strong>References</strong></p> <p>*Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. <a href="https://doi.org/10.1029/2022EA002332">https://doi.org/10.1029/2022EA002332</a> See: <a href="https://github.com/Doodleverse/segmentation_gym">https://github.com/Doodleverse/segmentation_gym</a></p> <p>** Buscombe, Daniel. (2022). Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, whitewater, sediment, other) (v1.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.7344571">https://doi.org/10.5281/zenodo.7344571</a></p>
Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 5 of 14
<p>This dataset is part of the work <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the fifth part of 14 parts of the full dataset (5/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively repetition time (TR) = 300ms, 400ms, 500ms, and echo time (TE) = 30ms. Under <strong>each</strong> simulated scanning sequence, there are 500 brain volumes.</p> <p>The segmentation labels for each tissue are contained in the first part which you may find at <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb <a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data <a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a> and randomly select 500 brains as anatomical references.</p> <p>Other details of this dataset can be found at <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a> where the details of the data construction are discussed.</p> <p>All parts of the whole dataset can be found at:</p> <p>Part 1: <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>Part 2: <a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a></p> <p>Part 3: <a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a></p> <p>Part 4: <a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a></p> <p>Part 5: <a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a></p> <p>Part 6: <a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a></p> <p>Part 7: <a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a></p> <p>Part 8: <a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a></p> <p>Part 9: <a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a></p> <p>Part 10: <a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a></p> <p>Part 11: <a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a></p> <p>Part 12: <a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a></p> <p>Part 13: <a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a></p> <p>Part 14: <a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a></p>
sohamphanseiitb/BIG_Data_5MSEC: BIG Data Analysis of NASA's 5 Millennium Solar Eclipse Database
<p>Solar eclipses are a topic of interest among astronomers, astrologers and the general public as well. There were and will be about 11898 eclipses in the 5 millennia from 2000 BC to 3000 AD. Data visualization and regression techniques offer a deep insight into how various parameters of a solar eclipse are related to each other. Physical models can be verified and can be updated based on the insights gained from the analysis.</p> <p>The study covers the major aspects of data analysis including data cleaning, pre-processing, EDA, distribution fitting, regression and machine learning based data analytics. We provide a cleaned and usable database ready for EDA and statistical analysis.</p>
Daily time series of spatially enhanced relative humidity for Europe at 1000 m resolution (Set 5: 2020 - 2021) derived from ERA5-Land data
<p>Overview:<br> ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past.</p> <p>Processing steps:<br> The original hourly ERA5-Land air temperature 2 m above ground and dewpoint temperature 2 m data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (<a href="https://chelsa-climate.org/">https://chelsa-climate.org/</a>). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically:<br> 1. spatially aggregate CHELSA to the resolution of ERA5-Land<br> 2. calculate difference of ERA5-Land - aggregated CHELSA<br> 3. interpolate differences with a Gaussian filter to 30 arc seconds<br> 4. add the interpolated differences to CHELSA</p> <p>Subsequently, the temperature time series have been aggregated on a daily basis. From these, daily relative humidity has been calculated for the time period 01/2000 - 07/2021.</p> <p>Relative humidity (rh2m) has been calculated from air temperature 2 m above ground (Ta) and dewpoint temperature 2 m above ground (Td) using the formula for saturated water pressure from Wright (1997):</p> <p><code>maximum water pressure = 611.21 * exp(17.502 * Ta / (240.97 + Ta))</code></p> <p><code>actual water pressure = 611.21 * exp(17.502 * Td / (240.97 + Td))</code></p> <p><code>relative humidity = actual water pressure / maximum water pressure</code></p> <p>Data provided is the daily averages of relative humidity. This set provides data for the years 2020 - 2021. For other time periods, please see further linked data sets.</p> <p>Resultant values have been converted to represent percent * 10, thus covering a theoretical range of [0, 1000].</p> <p>The data have been reprojected to EU LAEA.</p> <p>File naming scheme (YYYY = year; MM = month; DD = day):<br> <code>ERA5_land_rh2m_avg_daily_YYYYMMDD.tif</code></p> <p>Projection + EPSG code:<br> EU LAEA (EPSG: 3035)</p> <p>Spatial extent:<br> north: 6874000<br> south: -485000<br> west: 869000<br> east: 8712000</p> <p>Spatial resolution:<br> 1000 m</p> <p>Temporal resolution:<br> Daily</p> <p>Pixel values:<br> Percent * 10 (scaled to Integer; example: value 738 = 73.8 %)</p> <p>Software used:<br> GDAL 3.2.2 and GRASS GIS 8.0.0</p> <p>Original ERA5-Land dataset license:<br> <a href="https://apps.ecmwf.int/datasets/licences/copernicus/">https://apps.ecmwf.int/datasets/licences/copernicus/</a></p> <p>CHELSA climatologies (V1.2):<br> Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. <a href="http://dx.doi.org/doi:10.5061/dryad.kd1d4">http://dx.doi.org/doi:10.5061/dryad.kd1d4</a><br> Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. <a href="https://doi.org/10.1038/sdata.2017.122">https://doi.org/10.1038/sdata.2017.122</a></p> <p>Processed by:<br> mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p> <p>Reference: Wright, J.M. (1997): Federal meteorological handbook no. 3 (FCM-H3-1997). Office of Federal Coordinator for Meteorological Services and Supporting Research. Washington, DC</p> <p>Data is also available in Latitude-Longitude/WGS84 (EPSG: 4326) projection: <a href="http://https://doi.org/10.5281/zenodo.6344125">https://doi.org/10.5281/zenodo.6344125</a></p>
Global Hourly, 5-km, All-sky Land Surface Temperature (GHA-LST) from 2011 - now
<p>GHA-LST is a global, hourly, 5-km, all-sky, gap-free, and all-weather land surface temperature (LST) dataset. The manuscript describing this dataset has been accepted by <em>Earth System Science Data (ESSD)</em> (<a href="https://doi.org/10.5194/essd-15-869-2023" target="_new" rel="noopener">https://doi.org/10.5194/essd-15-869-2023</a>). Due to storage limitations on Zenodo, the full dataset is available at <a href="http://glass.umd.edu/allsky_LST/GHA-LST" target="_new" rel="noopener">glass.umd.edu/allsky_LST/GHA-LST</a>. The dataset is updated annually. For further details, please contact Dr. Aolin Jia at <a rel="noopener">aolin@terpmail.umd.edu</a>.</p>
Datasets for figures in Implementation and evaluation of Wet Bulb Globe Temperature within non-urban environments in the Community Land Model version 5
<p>The files contain 4 scripts and 6 netcdf files. </p> <p>"laborCap_200400.ncl" uses "Lancet_LRF.nc" to create Figure 1.</p> <p>Script "world_plot_ensemble_Avg.I2000.csh", drives a NCL script, "plot_modern.I2000.WBGT.ncl" to make figures 3 and 4, using the netcdf files, "I2000_PR_22_x1_60_5.exceed.WBGT.20yrs.75_99.nc," "I2000_PR_22_x1_60_5.exceed.WBGT_BG_R.20yrs.75_99.nc," "I2000_PR_22_x1_60_5.exceed.WBGT_BC_R.20yrs.75_99.nc," and "I2000_PR_22_x1_60_5.exceed.WBGT_AC_R.20yrs.75_99.nc."</p> <p>"heatmap.wbgt.v4.org.ncl" uses netcdf "I2000_PR_23_Chicago_x1_60_1.11-17.Chicago.allvars.nc" to create figures 5-7. </p>
Dataset and codebook for the article by Gaume J, Bertholet N, McCambridge J, et al. Effect of a Novel Brief Motivational Intervention for Alcohol-Intoxicated Young Adults in the Emergency Department: A Randomized Clinical Trial. JAMA Netw Open. 2022;5(10):e2237563. doi: 10.1001/jamanetworkopen.2022.37563
<p>Dataset and codebook for the article Gaume J, Bertholet N, McCambridge J, et al. <strong>Effect of a Novel Brief Motivational Intervention for Alcohol-Intoxicated Young Adults in the Emergency Department: A Randomized Clinical Trial</strong>. JAMA Netw Open. 2022;5(10):e2237563. doi: <a href="http://jamanetwork.com/article.aspx?doi=10.1001/jamanetworkopen.2022.37563">10.1001/jamanetworkopen.2022.37563</a></p> <p>The dataset contains all data needed to reproduce the results in the above cited article.</p> <p>Variable description and labels can be found in the codebook.</p> <p>Please refer to the published article and supplemental online content for further information about the data and the study procedures.</p>
Urbanus and Cosgrove et al. Nature Communications (2023) - CellRanger outputs for scRNAseq experiments in Figures 2,5, and 6
<p>Attached are the outputs of the cell ranger pipeline for 10x 3' scRNAseq of HSPCs. For any questions regarding this dataset please contact Leila Perie (leila.perie@curie.fr)</p>
Urbanus and Cosgrove et al. Nature Communications (2023) - 12 months scRNAseq fastq files (mouse 1) for Figures 5 and 6
<p>This dataset contains .fastq files for the 12 month timepoint (mouse 1) scRNAseq dataset used in Figures 5 and 6. For any questions about this dataset please contact Leila Perie (leile.perie@curie.fr)</p>
Urbanus and Cosgrove et al. Nature Communications (2023) - 12 months scRNAseq fastq files (mouse 2) for Figures 5 and 6
<p>This dataset contains .fastq files for the 12 month timepoint (mouse 2) scRNAseq dataset used in Figures 5 and 6. For any questions about this dataset please contact Leila Perie (leile.perie@curie.fr)</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.