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38 results for “extraction technique”
DrCyZ: Techniques for analyzing and extracting useful information from CyZ.
<p>DrCyZ: Techniques for analyzing and extracting useful information from CyZ.</p> <p>Samples from NASA Perseverance and set of GAN generated synthetic images from Neural Mars.</p> <p>Repository: <a href="https://github.com/decurtoidiaz/drcyz">https://github.com/decurtoidiaz/drcyz</a></p> <p><br> Subset of samples from (includes tools to visualize and analyse the dataset):</p> <p>CyZ: MARS Space Exploration Dataset. [<a href="https://doi.org/10.5281/zenodo.5655473">https://doi.org/10.5281/zenodo.5655473</a>]</p> <p>Images from NASA missions of the celestial body.</p> <p>Repository: <a href="https://github.com/decurtoidiaz/cyz">https://github.com/decurtoidiaz/cyz</a></p> <p>Authors:</p> <p>J. de Curtò c@decurto.be</p> <p>I. de Zarzà z@dezarza.be</p> <p>------------------------------------------<br> File Information from DrCyZ-1.1<br> ------------------------------------------</p> <p> • Subset of samples from Perseverance (drcyz/c).<br> ∙ png (drcyz/c/png).<br> PNG files (5025) selected from NASA Perseverance (CyZ-1.1) after t-SNE and K-means Clustering. <br> ∙ csv (drcyz/c/csv).<br> CSV file.</p> <p> • Resized samples from Perseverance (drcyz/c+).<br> ∙ png 64x64; 128x128; 256x256; 512x512; 1024x1024 (drcyz/c+/drcyz_64-1024).<br> PNG files resized at the corresponding size. <br> ∙ TFRecords 64x64; 128x128; 256x256; 512x512; 1024x1024 (drcyz/c+/tfr_drcyz_64-1024).<br> TFRecord resized at the corresponding size to import on Tensorflow.</p> <p> • Synthetic images from Neural Mars generated using Stylegan2-ada (drcyz/drcyz+).<br> ∙ png 100; 1000; 10000 (drcyz/drcyz+/drcyz_256_100-10000)<br> PNG files subset of 100, 1000 and 10000 at size 256x256.</p> <p> • Network Checkpoint from Stylegan2-ada trained at size 256x256 (drcyz/model_drcyz).<br> ∙ network-snapshot-000798-drcyz.pkl</p> <p> • Notebooks in python to analyse the original dataset and reproduce the experiments; K-means Clustering, t-SNE, PCA, synthetic generation using Stylegan2-ada and instance segmentation using Deeplab (<a href="https://github.com/decurtoidiaz/drcyz/tree/main/dr_cyz+">https://github.com/decurtoidiaz/drcyz/tree/main/dr_cyz+</a>).<br> ∙ clustering_curiosity_de_curto_and_de_zarza.ipynb<br> K-means Clustering and PCA(2) with images from Curiosity.<br> ∙ clustering_perseverance_de_curto_and_de_zarza.ipynb<br> K-means Clustering and PCA(2) with images from Perseverance.<br> ∙ tsne_curiosity_de_curto_and_de_zarza.ipynb<br> t-SNE and PCA (components selected to explain 99% of variance) with images from Curiosity.<br> ∙ tsne_perseverance_de_curto_and_de_zarza.ipynb<br> t-SNE and PCA (components selected to explain 99% of variance) with images from Perseverance.<br> ∙ Stylegan2-ada_de_curto_and_de_zarza.ipynb<br> Stylegan2-ada trained on a subset of images from NASA Perseverance (DrCyZ).<br> ∙ statistics_perseverance_de_curto_and_de_zarza.ipynb<br> Compute statistics from synthetic samples generated by Stylegan2-ada (DrCyZ) and images from NASA Perseverance (CyZ).<br> ∙ DeepLab_TFLite_ADE20k_de_curto_and_de_zarza.ipynb<br> Example of instance segmentation using Deeplab with a sample from NASA Perseverance (DrCyZ).</p>
Figure 2. Macrobrachium tenellum adult male who underwent a second spermatophore extraction using the electrostimulation technique. A in Sperm viability in wild-caught males of Macrobrachium tenellum (Smith, 1871) (Decapoda: Caridea: Palaemonidae) fed with different diets
Figure 2. Macrobrachium tenellum adult male who underwent a second spermatophore extraction using the electrostimulation technique. A= The dark brown, melanized spermatophore is different from that observed in healthy males.
Image for the dataset in "Extraction of stratigraphic exposures on visible images using a supervised machine learning technique"
<p>This is the original and hand-masked image for the dataset used in a research paper "Extraction of stratigraphic exposures on visible images using a supervised machine learning technique".</p> <p>The content is</p> <ul> <li>Original images with hand-masked images (original_images_NOGUCHIandShoji.zip) <ul> <li>training/* : original images used for the training dataset generation (60 files)</li> <li>training_masks/* : hand-masked images for the training dataset generation (60 files)</li> <li>validation/* : original images used for the training dataset generation (10 files)</li> <li>validation_masks/* : hand-masked images for the validation dataset generation (10 files)</li> <li>test/* : original images used as the test data (5 files)</li> <li>test_masks/* : hand-masked images used as the test data (5 files).</li> </ul> </li> </ul> <p>Note that original images include images obtained using <em>google-image-download</em>, a Python script published on GitHub (<a href="https://github.com/Joeclinton1/google-images-download/tree/patch-1">https://github.com/Joeclinton1/google-images-download/tree/patch-1</a>, Copyright © 2015-2019 Hardik Vasa). The whole images we obtained by <em>google-image-download</em> were labeled as noncommercial reuse with modification.</p> <p>For more details, please refer to a research paper "Extraction of stratigraphic exposures on visible images using a supervised machine learning technique".</p> <p>Correspondence: Rina Noguchi (r-noguchi@env.sc.niigata-u.ac.jp)</p>
Dataset: Improving the prediction of fertilizer phosphorus availability to plants with simple, but non-standardized extraction techniques
<p>Dataset: Improving the prediction of fertilizer phosphorus availability to plants with simple, but non-standardized extraction techniques</p>
A novel technique to simulate and characterize a yarn's mechanical behavior based on a geometrical fiber model extracted from micro-CT imaging: geometry and simulation data
<p>This dataset contains the original µCT scan data, the scripts and intermediate results for the generation of the geometrical fiber model, as well as the structural simulation files and their experimental validation data described in the paper <a href="https://journals.sagepub.com/doi/10.1177/00405175221137009">"A novel technique to simulate and characterize a yarn's mechanical behavior based on a geometrical fiber model extracted from micro-CT imaging"</a>, published in Textile Research Journal.</p>
Comparison of different gas extraction techniques to analyze CH4 and N2O compositions in gas trapped in permafrost ice wedges
<p>Methane (CH4) and nitrous oxide (N2O) compositions in ground ice may provide information on their production mechanisms in permafrost. However, existing gas extraction methods have not been well tested. We tested conventional wet and dry gas extraction methods using ice wedges from Alaska and Siberia, finding that both methods can extract gas from the easily extractable parts of the ice (e.g., gas bubbles), and yield similar results for CH4 and N2O mixing ratios. We also found insignificant effects of microbial activity during wet extraction. However, both techniques were unable to fully extract gas from the ground ice, presumably because gas molecules adsorbed onto or enclosed in soil aggregates are not easily extractable. Estimation of gas production in subfreezing environment of permafrost should consider such incomplete gas extraction.</p>
From Microorganisms to Biosignatures: Subcritical Water Extraction as a Sample Preparation Technique for Future Life Detection Missions
<p>Data for all figures included in "From Microorganisms to Biosignatures: Subcritical Water Extraction as a Sample Preparation Technique for Future Life Detection Missions". Amino acid concentrations, enantiomeric excesses, and relative distributions for E. coli cells and B. subtilis spores after subcritical water extraction for 30 min at 200 °C using water and dilute acid.</p>
Resulting videos from the article: A Novel Technique for the Extraction of Dynamic Events in Extreme Ultraviolet Solar Images
<p>Videos called 'MFM PCP DMD ...' correspond to Figure 5 in the mentioned article. They show comparison of MFM, PCP, and DMD algorithms. Upper images in each year correspond to the separated background matrix, the lower images correspond to matrix of dynamic component. The videos cover 1.7 hours of observations starting at 06:00:00 UTC on 2011 June 7, 16:50:00 UTC on 2012 April 16 and 18:30:11 UTC on 2014 October 2.<br><br>The other videos correspond to Figure 7 in the article. They compare the results from PCP algorithm applied to the 30.4 and 17.1 nm AIA bandpasses. The videos starting at 16:50:00 UTC on 2012 April 16 and 18:30:11 UTC on 2014 October 2, respectively, and cover 1.7 hours of observations.</p>
Dataset for "Extraction of stratigraphic exposures on visible images using a supervised machine learning technique"
<p>This is the dataset used in a research paper "Extraction of stratigraphic exposures on visible images using a supervised machine learning technique".</p> <p>The content is</p> <ul> <li>Augmented images used in the U-Net training (aug_images.zip) <ul> <li>train/*.png: augmented original images (14,219 files)</li> <li>train_masks/*.png: augmented hand-masked images (14,219 files).</li> </ul> </li> </ul> <p>Note that original images include images obtained using <em>google-image-download</em>, a Python script published on GitHub (<a href="https://github.com/Joeclinton1/google-images-download/tree/patch-1">https://github.com/Joeclinton1/google-images-download/tree/patch-1</a>, Copyright © 2015-2019 Hardik Vasa). The whole images we obtained by <em>google-image-download</em> were labeled as noncommercial reuse with modification.</p> <p>For more details, please refer to a research paper "Extraction of stratigraphic exposures on visible images using a supervised machine learning technique".</p> <p>Correspondence: Rina Noguchi (r-noguchi@env.sc.niigata-u.ac.jp)</p>
Developing a Method to Automatically Extract Road Boundary and Linear Road Markings from MMS Point Cloud using OBB Collision Detection Techniques
<p>This video demonstrates the application of our method in a software tool for constructing road boundaries and lane marking data.</p>
Alveolar Ridge Preservation Techniques After Tooth Extraction
ClinicalTrials.gov study NCT04934813. IPD Sharing: NO. Countries: 1. Publications: 1.
Hand Forceps vs. Conventional One-hand Technique for Fetal Head Extraction During Cesarean Section
ClinicalTrials.gov study NCT04159857. IPD Sharing: NO. Countries: 1. Publications: 1.
A Modified-simple Technique for Managing Moderate and Severe Subluxated Lens Extraction
ClinicalTrials.gov study NCT06627062. IPD Sharing: NO. Countries: 1. Publications: 1.
Clinical Efficacy of a New Piezoelectric Technique for Wisdom Teeth Extraction
ClinicalTrials.gov study NCT03619460. IPD Sharing: UNDECIDED. Countries: 1. Publications: 7.
The Preliminary Application of Socket-shield Technique in Orthodontic Extraction and Fixed Orthodontic Treatment
ClinicalTrials.gov study NCT06510621. IPD Sharing: NO. Countries: 1. Publications: 1.
Evaluation of the Effect of Leucocyte Platelet-Rich Fibrin (L-PRF) Technique Applied in Post-extraction Sockets Before Placement of Dental Implants
ClinicalTrials.gov study NCT07387913. IPD Sharing: NO. Countries: 1. Publications: 8.
Socket Preservation Using the Ice Cream Cone Technique Versus Spontaneous Healing in Fresh Extraction Sockets.
ClinicalTrials.gov study NCT04013425. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Evaluation of a New Technique for Periodontal Pocket Reduction in the Extraction of Wisdom Teeth
ClinicalTrials.gov study NCT05722509. IPD Sharing: NO. Countries: 1. Publications: 8.
Data from: Optimizing techniques to capture and extract environmental DNA for detection and quantification of fish
Few studies have examined capture and extraction methods for environmental DNA (eDNA) to identify techniques optimal for detection and quantification. In this study, precipitation, centrifugation and filtration eDNA capture methods and six commercially available DNA extraction kits were evaluated for their ability to detect and quantify common carp (Cyprinus carpio) mitochondrial DNA using quantitative PCR in a series of laboratory experiments. Filtration methods yielded the most carp eDNA, and a glass fibre (GF) filter performed better than a similar pore size polycarbonate (PC) filter. Smaller pore sized filters had higher regression slopes of biomass to eDNA, indicating that they were potentially more sensitive to changes in biomass. Comparison of DNA extraction kits showed that the MP Biomedicals FastDNA SPIN Kit yielded the most carp eDNA and was the most sensitive for detection purposes, despite minor inhibition. The MoBio PowerSoil DNA Isolation Kit had the lowest coefficient of variation in extraction efficiency between lake and well water and had no detectable inhibition, making it most suitable for comparisons across aquatic environments. Of the methods tested, we recommend using a 1.5 μm GF filter, followed by extraction with the MP Biomedicals FastDNA SPIN Kit for detection. For quantification of eDNA, filtration through a 0.2–0.6 μm pore size PC filter, followed by extraction with MoBio PowerSoil DNA Isolation Kit was optimal. These results are broadly applicable for laboratory studies on carps and potentially other cyprinids. The recommendations can also be used to inform choice of methodology for field studies.
MySQL dump for finding optimal parameters for data augmentation techniques in publication "Leveraging Data Augmentation for Process Information Extraction"
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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)
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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.