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990 results for “quantification”
Supplemental Material to "Consistent quantification of precipitate shapes and sizes in two and three dimensions using central moments"
<p>Supplemental material to manuscript "Consistent quantification of precipitate shapes and sizes in two and three dimensions using central moments" published in IMMJ "Integrating Materials and Manufacturing Innovation" 2022</p>
Liver Micrometastases area quantification using QuPath and pixel classifier
<p><strong>Sample</strong>: Mouse (NSG) liver slices with human colorectal cancer cells metastases, stained with Hematoxylin & Eosin. </p> <p><strong>Image Acquisition</strong>: Images were acquired on an Olympus VS120 Whole Slide Scanner, using a 20x objective (UPLSAPO, N.A. 0.75) and a color camera (Pike F505 Color) with an image pixel size of 0.345 microns.</p> <p><strong>Image Processing and Analysis</strong>: Obtained images were analyzed using the software QuPath [1] (version 0.3.2) using groovy scripts, making use of a pixel classifier to segment and measure cancer cell clusters.</p> <p><strong>Files</strong> :</p> <p><em>Detailed_worflow.pdf</em> : contains a detailed description of how pixel classifier was created</p> <p><em>images_for_classifier_training.zip</em> : contains all the vsi file obtained from the microscope and used for the training</p> <p><em>project_for_classifier_training.zip</em> : contains the QuPath project, with Training Image, annotations, classifiers and scripts for analysis</p> <p><em>PythonCode.txt</em> : code ran to transform output results from QuPath to final results</p> <p> </p> <p>[1] Bankhead, P. et al. <strong>QuPath: Open source software for digital pathology image analysis</strong>. <em>Scientific Reports</em> (2017). <a href="https://doi.org/10.1038/s41598-017-17204-5">https://doi.org/10.1038/s41598-017-17204-5</a></p>
SEESAW quantification data for temporal gene expression across osteoblastogenesis (B6xCAST), n=9
<p>Osteoblast cells mature from a mesenchymal stem cell pool to become cells capable of forming bone matrix and mineralizing this matrix. The goal of this study was to characterize temporal changes in the transcriptome across osteoblast maturation, starting with committed mesenchymal stem cell/ early pre-osteoblast stage through to mature osteoblasts capable of matrix mineralization. Methods: Enriched populations of pre-osteoblast-like cells were obtained from neonatal calvaria from B6xCAST mice expressing CFP under the control of the Col3.6 promoter. These cells were placed into culture for 4 days, removed from culture and subjected to FACS sorting based on the presence/absence of CFP expression. Cells expressing CFP were returned to culture, subjected to an osteoblast differentiation cocktail and RNA was collected at 2, 4, 6, 8, 10, 12, 14, 16 and 18 days post differentiation. Methods II: mRNA profiles for each time point were generated by next generation RNA sequencing, using an Illumina HiSeq 2000. Three technical replicates per sample were sequenced. Overall design: Gene expression in calvarial osteoblasts from neonatal B6xCAST-Col3.6 CFP mice at 9 time points post differentiation.</p> <p>File description: the R data files (.rda) provide outputs of the scripts in the mikelove/osteoblast-quant GitHub repo (July 2022, commit 01d96490), having run the fishpond package function importAllelicCounts() followed by minimal filtering. The `_counts.rda` files contain SummarizedExperiment objects with estimated count, TPM abundance, and effective length, but do not contain inferential replicates (bootstrap counts), although the transcript-level allelic counts object contains bootstrap mean and variances for every isoform, sample, and allele. The other two `.rda` files are summarized to gene level.</p> <p>The `_quant_dirs.tgz` files contain all the Salmon quantification data including bootstraps for the 9 time points. They are grouped into sets of three for convenience. The `CAST_EiJ.diploid.fa.gz` file provides the transcript sequences that were used for Salmon quantification.</p> <p>The `B6xCAST_discordant_global_AI.csv` file contains the same information as presented in Table S1 of Wu et al (2022). These are TSS-level results for 134 genes showing significant and discordant patterns within gene.</p> <p>The other 6 CSV files provide global and dynamic AI testing results at three levels of resolution: gene level, isoform level (txp), and TSS level where TSS within 50bp are combined into a single TSS-group. The significance cutoff is a q-value of 0.05. The code used for generating these results is provided in the GitHub repo: FennecFish/osteoblast-test.</p>
Dataset of "An end-to-end KNN-based PTV approach for high-resolution measurements and uncertainty quantification"
<p>Dataset of the article "An end-to-end KNN-based PTV approach for high-resolution measurements and uncertainty quantification" (https://doi.org/10.1016/j.expthermflusci.2022.110756). Local similarity between non-time-resolved snapshots is enforced by KNN to extract high-resolution velocity fields and estimate the uncertainty of the measurements.</p> <p>The codes processing data here are on https://github.com/erc-nextflow/KNN-PTV.</p> <p>This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No 949085, NEXTFLOW).</p>
A data set on "Utilizing Constant Energy Difference between sp-Peak and C 1s Core Level in Photoelectron Spectra for Unambiguous Identification and Quantification of Diamond Phase in Nanodiamonds"
<p>The data set to paper: </p> <p>Utilizing Constant Energy Difference between sp-Peak and C 1s Core Level in Photoelectron Spectra for Unambiguous Identification and Quantification of Diamond Phase in Nanodiamonds</p> <p>Oleksandr Romanyuk1,*, Štěpán Stehlík1,2, Josef Zemek1, Kateřina Aubrechtová Dragounová1,3 and Alexander Kromka1</p> <p>1 Institute of Physics of the Czech Academy of Sciences, Cukrovarnická 10, 162 00 Prague, Czech Republic<br>2 New Technologies—Research Centre, University of West Bohemia, Univerzitní 8, 306 14 Pilsen, Czech Republic<br>3 Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University in Prague, Břehová 7, 115 19 Prague, Czech Republic</p> <p>* corresponding author: romanyuk@fzu.cz</p> <p>Data manager: Kristýna Dostálová: dostalovak@fzu.cz</p> <p>Date of data collection: 1. 1. 2024 - 15. 03. 2024</p> <p>All the data showed in the pictures are provided in X-Y format with described sample. Always, the respective Figure to which the data belong is provided in high resolution. <br>The data are in the following formats: <br>Figure 1: tiff, csv<br>Figure 2: tiff, csv<br>Figure 3: tiff, csv<br>Figure 4: tiff, csv<br>Figure 5: tiff, csv</p> <p>Data acquistion and processing is provided in the Experimental part in the publication: DOI:10.3390/nano14070590</p>
Quantification of ADHD Medication in Biological Fluids with Liquid Chromatography: A Comprehensive Review - Metadata
<p>This file is the metadata related to the publication "Quantification of ADHD Medication in Biological Fluids with Liquid Chromatography: A Comprehensive Review".</p>
Supplementary files for Machine learning for histological annotation and quantification of cortical layers
<div> <h2>Creators</h2> <ul> <li><a href="https://orcid.org/0009-0000-9093-9385">Meystre Julie</a></li> <li><a href="https://orcid.org/0000-0002-7100-3749">Olivier Burri</a></li> </ul> <h2>Contributors</h2> <ul> <li><a href="https://orcid.org/0009-0002-0029-7951">Jean Jacquemier</a></li> </ul> </div> <h2>Description</h2> <p>This dataset contains 7 <a href="https://qupath.github.io/">QuPath</a> projects. The raw data images linked to these projects and located in other Zenodo datasets need to be downloaded as well.</p> <p>The raw data contains images of 14 hemispheres from height animals.</p> <ul> <li>Nissl_1 : <ul> <li>animal 1413827 Right Hemisphere</li> <li> </li> </ul> </li> <li>Nissl_2 : <ul> <li>animal 1413829 Right Hemisphere</li> <li>animal 1413828 Right Hemisphere</li> <li>animal 1413827 Left Hemisphere</li> <li> </li> </ul> </li> <li>Nissl_3 : <ul> <li>animal 1413828 Left Hemisphere</li> <li> </li> </ul> </li> <li>Nissl_4 : <ul> <li>animal 1443459 Right Hemisphere</li> <li>animal 1443460 Right Hemisphere</li> <li> </li> </ul> </li> <li>Nissl_5 : <ul> <li>animal 1443459 Left Hemisphere</li> <li>animal 1443460 Left Hemisphere</li> </ul> </li> </ul> <ul> <li>Nissl_6 : <ul> <li>animal 1449920 Left Hemisphere</li> <li>animal 1449921 Left Hemisphere</li> <li>animal 1449921 Right Hemisphere</li> <li>animal 1449922 Left Hemisphere</li> <li>animal 1449922 Right Hemisphere</li> <li> </li> </ul> </li> <li>QuPath_LayerBoundaries_GroundTruth_20220927: <ul> <li>This is the QuPath project that contains S1HL layers annotations done by the experts and which have been used to trained the Random forest Machine Learning method for the S1HL brain classification. It contains some images from all the eight animals.</li> </ul> </li> </ul> <p> </p> <div> <h3>Animals</h3> <p>All animal procedures were approved by the Veterinary Authorities and the Cantonal Commission for Animal Experimentation of the Canton of Vaud, according to the Swiss animal protection laws, under license number VD3516.</p> <p>Outbred Wistar Han rats (Janvier Laboratories, France) were ordered with their litter aged eight postnatal days (P8). Dams were housed individually and allowed to raise their own litters until experimentation on male offspring aged fourteen days (P14; N=8 animals; N=3 litters). Animals were housed in standard plastic laboratory cages, with bedding, nesting material and paper tube and ad libitum access to food (SAFE 150 SP-25) and water, cleaned once per week, and kept on a twelve-hour light-dark schedule with lights turned on at 06:30 AM, in rooms under controlled humidity and temperature. The sample size here is greater than those reported in other open source atlases <a href="https://www.zotero.org/google-docs/?1dkN18">(“Allen Reference Atlas - Mouse,” n.d.; “The Rat Brain in Stereotaxic Coordinates - 7th Edition,” n.d.)</a>.</p> <h3>Sample preparation</h3> <p>On postnatal day fourteen, rats were transferred to the experimental room in the morning to acclimate. The described procedure was conducted within a consistent 3-hour window of the day (09:00-12:00). Initially, the rats were deeply anesthetized using pentobarbital (intraperitoneal dose of 150 mg/kg; concentration of 150 mg/ml). This was succeeded by transcardial perfusion with ice cold 0.1 M phosphate buffer (PB; pH 7.4), followed by cold 4% paraformaldehyde (PFA) in 0.1 M PB. Subsequently, the brain was carefully removed from the skull, postfixed at 4°C in 4% PFA overnight, and then rinsed in 0.1 M PB. The brains underwent a sequential storage process: first in a 15% sucrose solution (in 0.1 M PB) at 4°C for approximately 24 hours, followed by a 30% sucrose solution at 4°C for an additional 24 hours. The hemispheres were carefully divided along the midline, after which both right and left hemispheres were precisely sliced sagittally using a cryostat (Leica, VT-1200S) at 50 µm employing an approximate angle rotation of 4 ± 1 degrees along the anterior-posterior axis to optimize alignment with apical dendrites. These brain slices were stored in a cryoprotectant solution (30% v/v ethylene glycol; 30% m/v sucrose in 0.1 M PB) at -20°C, preserving them until immunohistochemistry assays were executed (within a maximum of two weeks from extraction to immunohistochemistry).</p> <p>In order to determine the cell densities in P14 rat, brain slices were immunostained using cresyl violet, a stain specifically targeting cell bodies, including the endoplasmic reticulum, also known as Nissl substance or Nissl bodies. Free-floating sections of 50 µm thickness were transferred from cryoprotectant into 0.1 M PB to thaw and eliminate any cryoprotectant remnants. Subsequently, they were transferred into 0.01 M PB to minimize salt residues before being meticulously mounted onto SuperFrost© glass slides (Thermo Fisher Scientific Inc., Gerhard Menzel B.V. & Co. KG, GE). This mounting was carried out while considering the brain’s orientation relative to the midline, from its external to internal regions. Slide-mounted sections were processed using an automated slide stainer Tissue-Tek® Prisma Plus (Sakura Finetek-Europe, NL). These sections were incubated for 6 minutes at room temperature (RT = 20°C) in a 0.5% cresyl violet solution in water (with pH adjusted to 2.85 using acetic acid), followed by a brief wash in tap water. The sections underwent dehydration through a series of ethanol concentrations (70%, 70%, 96%, 100%, 100%) with each step lasting one minute at RT. Subsequently cleared with two steps of xylene for one minute each at RT, and the sections were mounted using Pertex (Sakura Finetek-Europe, NL) before being cover-slipped using the automated glass coverslipper Tissue-Tek® Glas™ g2 (Sakura Finetek-Europe, NL). A meticulous assessment of the coloration was conducted and if the staining appeared faint, a repeat staining procedure was carried out.</p> <p><strong>Immunostained slides were scanned using an automated slide scanner (Olympus, VS120-L100, GER) equipped with a UPLSAPO 20x/0.75 air objective (Olympus, GER) and a Pike F505 Color camera leading to a pixel size of 0.346 μm/pixel. Each brain slice was entirely scanned. Subsequently, the digital images obtained were meticulously organized and subjected to analysis using the open-source software QuPath v0.3.2 <a href="https://www.zotero.org/google-docs/?jnVnIg">(Bankhead et al., 2017)</a>. </strong></p> <p> </p> <h2>Intructions</h2> <p>The projects contained in this dataset have been created with QuPath v0.3.2 but could be opened with new QuPath version.</p> <ol> <li>Download the dataset</li> <li>untar the tar balls included in this dataset</li> <li>install <a href="https://qupath.github.io">QuPath</a></li> <li>Open QuPath</li> <li>Open a project within QuPath (Files->Project...->Open Project...)</li> </ol> </div>
Towards a more complete quantification of the global carbon cycle
<p>These are the data and IDL code required to create Table3 from the paper.</p> <p><strong>Abstract.</strong></p> <p>The main components of global carbon budget calculations are the emissions from burning fossil fuels, cement production, and net land-use change, partly balanced by ocean CO<sub>2</sub> uptake and CO<sub>2</sub> increase in the atmosphere. The difference between these terms is referred to as the residual sink, assumed to correspond to increasing carbon storage in the terrestrial biosphere through physiological plant responses to changing conditions (Δ<em>B</em><sub>phys</sub>). It is often used to constrain carbon exchange in global earth-system models. More broadly, it guides expectations of autonomous changes in global carbon stocks in response to climatic changes, including increasing CO<sub>2</sub>, that may add to, or subtract from, anthropogenic CO<sub>2</sub> emissions.</p> <p>However, a budget with only these terms omits some important additional fluxes that are important to correctly infer Δ<em>B</em><sub>phys</sub>. They are cement carbonation and fluxes into increasing pools of plastic, bitumen, harvested-wood products, and landfill deposition after disposal of these products, and carbon fluxes to the oceans via wind erosion and non-CO<sub>2</sub> fluxes of the intermediate break-down products of methane and other volatile organic compounds. While the global budget includes river transport of dissolved inorganic carbon, it omits river transport of dissolved and particulate organic carbon, and the deposition of carbon in inland water bodies.</p> <p>Each one of these terms is relatively small, but together they can constitute important additional fluxes that would significantly reduce the size of the inferred Δ<em>B</em><sub>phys</sub>. We estimate here that inclusion of these fluxes would reduce Δ<em>B</em><sub>phys</sub> from the currently reported 3.6 GtC yr<sup>–1 </sup>down to about 2.1 GtC yr<sup>–1</sup> (excluding losses from land-use change). The implicit reduction in the size of ΔB<sub>phys</sub> has important implications for the inferred magnitude of current-day biospheric net carbon uptake and the consequent potential of future biospheric feedbacks to amplify or negate net anthropogenic CO<sub>2</sub> emissions.</p>
Uncertainty quantification of parenchymal tracer distribution using random diffusion and convective velocity fields (data sets)
<p>Supplementary dataset for Uncertainty quantification of parenchymal tracer distribution using random diffusion and convective velocity fields. Output functionals of interest from finite element simulations together with postprocessing source code. </p>
Data set for "Quantification of amorphous siliceous fly ash in hydrating blended cement pastes by X-ray powder diffraction"
<p>The main data is XRD patterns originally collected as xrdml and converted into rd format.</p> <p>The data set for the manuscript:</p> <p>Quantification of amorphous siliceous fly ash in hydrating blended cement pastes by X-ray powder diffraction</p> <p>Xuerun Li<sup>a</sup>, Ruben Snellings<sup>b</sup> and Karen L. Scrivener<sup>a</sup></p> <p><sup>a</sup>Laboratory of Construction Materials, Swiss Federal Institute of Technology in Lausanne (EPFL), Station 12, CH-1015 Lausanne, Switzerland</p> <p><sup>b</sup>Sustainable Materials Management, Flemish Institute of Technological Research (VITO), Boeretang 200, 2400 Mol, Belgium<br> </p>
Research Data/Code for "Scale-bridging within a complex model hierarchy for investigation of a metal-fueled circular energy economy by use of Bayesian model calibration with model error quantification"
<p>This repository contains research data and code for supplementing the manuscript <br>"Scale-bridging within a complex model hierarchy for investigation of a metal-fueled circular energy economy by use of Bayesian model calibration with model error quantification" <br>by L. Gossel, E. Corbean, S. Dübal, P. Brand, M. Fricke, H. Nicolai, C. Hasse, S. Hartl, S. Ulbrich, and D. Bothe. </p> <p>There is a corresponding preprint available on Arxiv: https://doi.org/10.48550/arXiv.2404.13092</p> <p><br>Users are referred to the manuscript for background information. This repository shall enable reproduction of the reported results and does not stand alone. </p> <p>Please read important information in the README in the top-level directory. </p> <p>Funded by the Hessian Ministry of Higher Education, Research, Science and the Arts - cluster project Clean Circles. </p>
Advancing Vanadium Redox Flow Battery Analysis: A Deep Learning Framework for High-Throughput 3D Visualization and Bubble Quantification via Synchrotron X-ray Tomography
<p>Dataset and model of UTILE-Redox - Deep Learning based Tool for Autonomous 3D Bubble Analysis of Vanadium Flow Batteries from Synchrotron X-ray Imaging. This project focuses on the deep learning-based automatic analysis of Vanadium Redox Flow Batteries (VRFB) Synchrotron X-ray tomographies. This repository contains the Python implementation of the UTILE-Redox software for automatic volume analysis, feature extraction, and visualization of the results.</p>
Simulation data for eddy current rail testing - simulation accuracy and evaluation uncertainty quantification
<p>This dataset serve to quantify the simulation error and the evaluation uncertainties in the context of eddy current rail testing. It was obtained during the AIFRI project (Artificial Intelligence for Rail Inspection) with the Faraday software by INTEGRATED Engineering Software, using its BEM Solver. For an analysis see the article below.</p>
Polymer simulations guide the detection and quantification of chromatin loop extrusion by imaging
<p><strong>Dataset description</strong></p> <p>This dataset contains coordinates of the two anchors of a 150 kb simulated loop (static imaging) and anchor-anchor distances of a 150 kb loop in simulations where the extruder was allowed to unbind from the polymer (dynamic imaging).</p> <p><strong>Dataset description - Static imaging</strong></p> <p>This dataset contains coordinates of the two anchors of a 150 kb simulated loop.</p> <p>The subfolder 'Free' contains coordinates from a polymer not submitted to loop extrusion (1,000 independent simulations). The subfolder 'Loop' contains loop anchor coordinates in a polymer submitted to loop extrusion (4,000 independent simulations). In simulations with extrusion, the polymer chain was simulated such as it went through 3 different states : i) Open state (absence of loops) ii) Extruding state where the loop size increases with time (anchor-anchor distance decreases) and iii) Closed state corresponding to a stable loop with the two anchors in contact.</p> <p> </p> <p><strong>Structure of data - Static imaging</strong></p> <p>In each .txt file, the first three columns correspond to the XYZ coordinates of the anchor (in µm), the fourth column indicates the state label (0=Open, 1=Extruding, 2=Closed). Each row corresponds to a simulation timepoint (2991 timepoints in polymers submitted to loop extrusion).</p> <p>The simulation ID is indicated at the end of each .txt file.</p> <p>The two anchors of the loop are bead #275 and bead #324.</p> <p> </p> <p><strong>Structure of data - Dynamic imaging</strong></p> <p>Each .txt file contains the anchor-anchor distance (in µm) as function of time for approximately 10,000 independent simulations. Each column is an independent simulation. Each row is a simulation timepoint (0.3 s / simulation unit). The different .txt files correspond to different localization errors, indicated in µm in XY and Z.</p> <p>'list_deb_closed_10000.p' is a dictionary whose keys are the simulation ID (corresponding to the columns of the .txt files), and the values are the frame at which extrusion begins.</p> <p>'list_end_closed_10000.p' is a dictionary whose keys are the simulation ID (corresponding to the columns of the .txt files), and the values are the frame at which extrusion ends.</p>
Dataset of Machine Learning forecasted VTEC from paper: Uncertainty Quantification for Machine Learning-based Ionosphere and Space Weather Forecasting
<p>The *csv files contain forecasted one-day-ahead Vertical Total Electron Content (VTEC), consisting of the mean/median VTEC values and the upper and lower VTEC bounds of the 95% confidence intervals of 4 models based on machine learning for test data.</p> <p>The first part of the *csv file name corresponds to the type of model: SE stands for the super-ensemble VTEC model, QGB stands for the quantile gradient boosting VTEC model, BNN1 stands for the Bayesian neural network VTEC model, and BNN2 stands for the Bayesian neural network with negative log-likelihood (NLL) loss VTEC model. The second part of the file name refers to the geographic location of the VTEC points for which the forecast is performed, i.e., 10E70N for 10 degree of longitude and 70 degree of latitude, 10E40N for 10 degree of longitude and 40 degree of latitude, and 10E10N for 10 degree of longitude and 10 degree of latitude. The last part of the file name corresponds to the test year, i.e., year 2017.</p> <p>The SE_*_2017.csv file consists of 14 columns. The index column ("Date-time") is expressed in Coordinated Universal Time (UTC) as YYYY-MM-DD. Columns 1-3 contain the VTEC forecast results of Random Forest (RF) trained on three data subsets; columns 4-6 contain the VTEC forecast results of Adaptive Boosting (AB) trained on three data subsets; columns 7-9 contain the VTEC forecast results of Gradient Boosting (XGBoost) trained on three data subsets. Column 10 ("Mean") represents the mean of columns 1-9, i.e., the ensemble mean; column 11 ("Std") represents the standard deviation of columns 1-9, i.e., the ensemble spread; columns 12 ("UB") and 13 ("LB") contain the upper and lower bounds of the 95% confidence interval of VTEC, respectively; and column 14 contains the Global Ionosphere Maps (GIM) values of CODE, i.e., the ground-truth in this study.</p> <p>The QGB_*_2017.csv file consists of 4 columns. The index column ("Date-time") is expressed in UTC as YYYY-MM-DD. Column 1 ("Median") contains the median VTEC forecast, column 2 ("LB") contains the lower VTEC bound of the 95% confidence interval, column 3 ("UB") contains the upper VTEC bound of the 95% confidence interval, and column 4 contains the GIM values of CODE, i.e., the ground-truth in this study.</p> <p>The BNN*_2017.csv file consists of 5 columns. The index column ("Date-time") is expressed in UTC as YYYY-MM-DD. Column 1 ("Mean") contains the mean VTEC forecast, column 2 ("Std") contains the standard deviation, column 3 contains GIM values of CODE, i.e., ground-truth in this study; column 4 ("UB") contains the upper VTEC bound of the 95% confidence interval, and column 5 ("LB") contains the lower VTEC bound of the 95% confidence interval.</p> <p>----------------------------------------------------------------------------------------------------------------------------------------</p> <p>Contact</p> <p>----------------------------------------------------------------------------------------------------------------------------------------</p> <p>If you have any questions regarding these data, please contact:</p> <p>Randa Natras</p> <p>Deutsches Geodätisches Forschungsinstitut (DGFI-TUM)</p> <p>Technical University of Munich</p> <p>Arcisstraße 21</p> <p>80333 München</p> <p>randa.natras@tum.de</p>
Quantification of Giant Unilamellar Vesicle Fusion Products by High-Throughput Image Analysis - Microscopy Data
<p>Microscopy dataset of multipoint-multichannel images of giant unilamellar vesicles (GUVs) suspensions analysed in "Quantification of Giant Unilamellar Vesicle Fusion Products by High-Throughput Image Analysis" (under revision).</p> <p>Three folders concerning different sections of the work are included. "preliminary analysis.zip" contians the raw files and analysis scripts for recall computation and imaging setup optimization as described in the paper. Timelapse data was excluded due to file size restrictions (available upon request at the corresponding authors of the work). "IFC comparison.zip" contains raw files and analysis scripts used to optimize colocalization computation in lipid exchange and content exchange experiments. "GUV fusion analysis" contains raw files and analysis scripts for the quantification of lipid and content exchange upon sodium chloride-induced aggregation.</p> <p>Further details on the analysis are provided in the paper. The R scripts require files saved upon analysis of the raw files by the ImageJ macro "CE_analysis_CPU.ijm" included here. The R environment of the complete analysis are included in each folder to provide easier access to the elaborated data.</p>
Master and Landsat-8 simultaneous acquisition datacubes for the quantification of directional anisotropy in Thermal Infra-Red domain
<p>‎</p> <p>This dataset contains datacubes of simultaneous Landsat-8 and Master<sup><a href="#fn.1">1</a></sup> data as listed in table <a href="#org4c9ba67">1</a>. Those pairs have been identified by cross-searching Landsat-8 and Master archive for Master flight tracks with a Landsat-8 overpass during the flight. The dataset has been collected and analysed in the following paper:</p> <p><em>Julien Michel, Olivier Hagolle, Simon J Hook, Jean-Louis Roujean, Philippe Gamet. Quantifying Thermal Infra-Red directional anisotropy using Master and Landsat-8 simultaneous acquisitions. 2023. <a href="https://hal.science/hal-04073733">⟨hal-04073733⟩</a></em></p> <table> <caption>Table 1: List of valid Master and Landsat-8 pairs</caption> <thead> <tr> <th scope="col"><strong>Id</strong></th> <th scope="col"><strong>Master track id</strong></th> <th scope="col"><strong>Landsat L2 product id</strong></th> </tr> </thead> <tbody> <tr> <td>1</td> <td><code>2013-03-29_18:06:53</code></td> <td><code>LC08_L2SP_038037_20130329_20200912_02_T1</code></td> </tr> </tbody> <tbody> <tr> <td>2</td> <td><code>2013-04-11_18:14:46</code></td> <td><code>LC08_L2SP_041036_20130411_20200912_02_T1</code></td> </tr> </tbody> <tbody> <tr> <td>3a</td> <td><code>2013-05-22_18:13:09</code></td> <td><code>LC08_L2SP_040036_20130522_20200913_02_T1</code></td> </tr> <tr> <td>3b</td> <td><code>2013-05-22_18:13:09</code></td> <td><code>LC08_L2SP_040037_20130522_20200913_02_T1</code></td> </tr> </tbody> <tbody> <tr> <td>4</td> <td><code>2013-12-05_18:23:35</code></td> <td><code>LC08_L2SP_043035_20131205_20200912_02_T1</code></td> </tr> </tbody> <tbody> <tr> <td>5a</td> <td><code>2014-03-31_18:11:16</code></td> <td><code>LC08_L2SP_039035_20140331_20200911_02_T1</code></td> </tr> <tr> <td>5b</td> <td><code>2014-03-31_18:11:16</code></td> <td><code>LC08_L2SP_039036_20140331_20200911_02_T1</code></td> </tr> </tbody> <tbody> <tr> <td>6a</td> <td><code>2014-04-14_18:27:14</code></td> <td><code>LC08_L2SP_041036_20140414_20200911_02_T1</code></td> </tr> <tr> <td>6b</td> <td><code>2014-04-14_18:27:14</code></td> <td><code>LC08_L2SP_041037_20140414_20200911_02_T1</code></td> </tr> </tbody> <tbody> <tr> <td>7</td> <td><code>2014-04-28_18:22:43</code></td> <td><code>LC08_L2SP_043035_20140428_20200911_02_T1</code></td> </tr> </tbody> <tbody> <tr> <td>8a</td> <td><code>2014-06-06_18:25:35</code></td> <td><code>LC08_L2SP_044033_20140606_20200911_02_T1</code></td> </tr> <tr> <td>8b</td> <td><code>2014-06-06_18:25:35</code></td> <td><code>LC08_L2SP_044034_20140606_20200911_02_T1</code></td> </tr> </tbody> <tbody> <tr> <td>9a</td> <td><code>2014-10-21_18:35:15</code></td> <td><code>LC08_L2SP_043034_20141021_20200910_02_T1</code></td> </tr> <tr> <td>9b</td> <td><code>2014-10-21_18:35:15</code></td> <td><code>LC08_L2SP_043035_20141021_20200911_02_T1</code></td> </tr> </tbody> <tbody> <tr> <td>10a</td> <td><code>2015-05-28_18:13:05</code></td> <td><code>LC08_L2SP_040036_20150528_20200909_02_T1</code></td> </tr> <tr> <td>10b</td> <td><code>2015-05-28_18:13:05</code></td> <td><code>LC08_L2SP_040037_20150528_20200909_02_T1</code></td> </tr> </tbody> <tbody> <tr> <td>11</td> <td><code>2018-06-19_18:28:30</code></td> <td><code>LC08_L2SP_042034_20180619_20200831_02_T1</code></td> </tr> </tbody> <tbody> <tr> <td>12a</td> <td><code>2021-03-30_18:32:40</code></td> <td><code>LC08_L2SP_043033_20210330_20210409_02_T1</code></td> </tr> <tr> <td>12b</td> <td><code>2021-03-30_18:32:40</code></td> <td><code>LC08_L2SP_043034_20210330_20210409_02_T1</code></td> </tr> </tbody> </table> <p>Variables of interest are resampled on a common UTM grid at 100m. The resulting datacubes are distributed as netCDF files, and contains the variables listed in table <a href="#org09b0cd2">2</a>. Landsat-8 pixels flagged as cloud and missing pixels are set to NaN.</p> <table> <caption>Table 2: Description of variables in netCDF files</caption> <thead> <tr> <th scope="col"><strong>Variable Name</strong></th> <th scope="col"><strong>Description</strong></th> </tr> </thead> <tbody> <tr> <td><code>ls8_lst</code></td> <td>Landsat-8 Land Surface Temperature (K)</td> </tr> <tr> <td><code>ls8_bt</code></td> <td>Landsat-8 Surface Brightness temperature (K)</td> </tr> <tr> <td><code>ls8_b2</code></td> <td>Landsat-8 B2 Surface reflectance (unitless)</td> </tr> <tr> <td><code>ls8_b3</code></td> <td>Landsat-8 B2 Surface reflectance (unitless)</td> </tr> <tr> <td><code>ls8_b4</code></td> <td>Landsat-8 B2 Surface reflectance (unitless)</td> </tr> <tr> <td><code>ls8_b5</code></td> <td>Landsat-8 B2 Surface reflectance (unitless)</td> </tr> <tr> <td><code>ls8_emis</code></td> <td>Landsat-8 emissivity (unitless)</td> </tr> <tr> <td><code>ls8_water</code></td> <td>Landsat-8 water mask (1 = water, 0 = no water)</td> </tr> <tr> <td><code>ls8_snow</code></td> <td>Landsat-8 snow mask (1 = snow, 0 = no snow)</td> </tr> <tr> <td><code>ls8_view_zenith</code></td> <td>Landsat-8 view zenith angle (degrees)</td> </tr> <tr> <td><code>ls8_view_azimuth</code></td> <td>Landsat-8 view azimuth angle (degrees)</td> </tr> <tr> <td> </td> <td>(0 = north, positive to the east, negative to the west)</td> </tr> <tr> <td><code>ls8_sun_zenith</code></td> <td>Landsat-8 sun zenith angle (degrees)</td> </tr> <tr> <td><code>ls8_sun_azimuth</code></td> <td>Landsat-8 sun azimuth angle (degrees)</td> </tr> <tr> <td> </td> <td>(0 = north, positive to the east, negative to the west)</td> </tr> </tbody> <tbody> <tr> <td><code>master_lst</code></td> <td>Master Land Surface Temperature (K)</td> </tr> <tr> <td><code>master_bt</code></td> <td>Master Surface Brightness Temperature (K)</td> </tr> <tr> <td><code>master_emis3</code></td> <td>Master B47 emissivity (unitless)</td> </tr> <tr> <td><code>master_emis4</code></td> <td>Master B48 emissivity (unitless)</td> </tr> <tr> <td><code>master_emis</code></td> <td>Master interpolated emissivity (unitless)</td> </tr> <tr> <td><code>master_view_zenith</code></td> <td>Master view zenith angle (degrees)</td> </tr> <tr> <td><code>master_view_azimuth</code></td> <td>Master view azimuth angle (degrees)</td> </tr> <tr> <td> </td> <td>(0 = north, positive to the east, negative to the west)</td> </tr> <tr> <td><code>master_sun_zenith</code></td> <td>Master sun zenith angle (degrees)</td> </tr> <tr> <td><code>master_sun_azimuth</code></td> <td>Master sun azimuth angle (degrees)</td> </tr> <tr> <td> </td> <td>(0 = north, positive to the east, negative to the west)</td> </tr> </tbody> </table> <p>Landsat-8 products were downloaded from the collection 2 level 2 archive from the EarthExplorer portal<sup><a href="#fn.2">2</a></sup>. Master L1B products, containing radiances and viewing angles, as well as L2 products, containing LST and geo-location grids, were requested on the Master website<sup><a href="#fn.1">1</a></sup>. Landsat-8 viewing angles have been computed by using a C program publicly available on USGS website<sup><a href="#fn.3">3</a></sup>.</p> <p>Footnotes:</p> <p><sup><a href="#fnr.1">1</a></sup></p> <p><a href="https://masterprojects.jpl.nasa.gov/">https://masterprojects.jpl.nasa.gov/</a>, consulted on 2023.03.01</p> <p><sup><a href="#fnr.2">2</a></sup></p> <p><a href="https://earthexplorer.usgs.gov/">https://earthexplorer.usgs.gov/</a>, consulted on 2023.03.01</p> <p><sup><a href="#fnr.3">3</a></sup></p> <p><a href="https://www.usgs.gov/landsat-missions/solar-illumination-and-sensor-viewing-angle-coefficient-file">https://www.usgs.gov/landsat-missions/solar-illumination-and-sensor-viewing-angle-coefficient-file</a>, consulted on 2022.09.12</p>
LIF-based quantification of the species transport during droplet impact onto thin liquid films (Dataset)
<p>This database includes Supplementary Data and videos for <em>Experiments in Fluids </em>manuscript: LIF-based quantification of the species transport during droplet impact onto thin liquid films.</p> <p>Number of figure in the file name is changed:</p> <ul> <li>fig.2 to fig. 3</li> <li>fig.3 to fig. 4</li> <li>fig.4 to fig. 5</li> <li>fig.5 to fig. 6</li> <li>fig.8 to fig. 11</li> <li>fig.9 to fig. 12</li> <li>fig.10 to fig. 13</li> <li>fig.11 to fig. 14</li> <li>fig.12 to fig. 15</li> <li>fig.13 to fig. 16</li> <li>fig.14 to fig. 17</li> </ul> <p> </p>
A Simple Strategy to Eliminate Glycation Bias in the Relative Quantification of Protein N-glycosylation
<p><strong>Contents</strong></p> <p>Supplementary files for “A Simple Strategy to Eliminate Glycation Bias in the Relative Quantification of Protein <em>N</em>-glycosylation” by Esser-Skala et al (2020):</p> <ul> <li> <p><em>cafog_raw_data.tar.gz</em>: Raw data for denosumab and NISTmAb.</p> </li> <li> <p><em>cafog_source_code.zip</em>: Supplementary file 1 from the manuscript. This ZIP archive contains the source code of CAFOG.</p> </li> <li> <p><em>data.zip</em>: Supplementary file 2 from the manuscript. Files in this ZIP archive allow to reproduce all results presented in the manuscript.</p> </li> </ul> <p> </p> <p><strong>Changelog</strong></p> <ul> <li>1.1.0 – 2023-05-25 <ul> <li>added supplementary files 1 and 2 mentioned in the manuscript, since those were not published along with the manuscript</li> </ul> </li> <li>1.0.0 – 2020-01-30 <ul> <li>initial release</li> </ul> </li> </ul>
UCI and OpenML Data Sets for Ordinal Quantification
<p>These four labeled data sets are targeted at ordinal quantification. The goal of quantification is not to predict the label of each individual instance, but the distribution of labels in unlabeled sets of data.</p> <p>With the scripts provided, you can extract CSV files from the UCI machine learning repository and from OpenML. The ordinal class labels stem from a binning of a continuous regression label.</p> <p>We complement this data set with the indices of data items that appear in each sample of our evaluation. Hence, you can precisely replicate our samples by drawing the specified data items. The indices stem from two evaluation protocols that are well suited for ordinal quantification. To this end, each row in the files <em>app_val_indices.csv</em>, <em>app_tst_indices.csv</em>, <em>app-oq_val_indices.csv</em>, and <em>app-oq_tst_indices.csv</em> represents one sample.</p> <p>Our first protocol is the artificial prevalence protocol (APP), where all possible distributions of labels are drawn with an equal probability. The second protocol, APP-OQ, is a variant thereof, where only the smoothest 20% of all APP samples are considered. This variant is targeted at ordinal quantification tasks, where classes are ordered and a similarity of neighboring classes can be assumed.</p> <p><strong>Usage</strong></p> <p>You can extract four CSV files through the provided script <em>extract-oq.jl</em>, which is conveniently wrapped in a <em>Makefile</em>. The <em>Project.toml</em> and <em>Manifest.toml</em> specify the Julia package dependencies, similar to a requirements file in Python.</p> <p><strong>Preliminaries:</strong> You have to have a working Julia installation. We have used Julia v1.6.5 in our experiments.</p> <p><strong>Data Extraction:</strong> In your terminal, you can call either</p> <pre><code>make</code></pre> <p>(recommended), or</p> <pre><code>julia --project="." --eval "using Pkg; Pkg.instantiate()" julia --project="." extract-oq.jl</code></pre> <p><strong>Outcome: </strong>The first row in each CSV file is the header. The first column, named "class_label", is the ordinal class.</p> <p><strong>Further Reading</strong></p> <p>Implementation of our experiments: <a href="https://github.com/mirkobunse/regularized-oq">https://github.com/mirkobunse/regularized-oq</a></p>
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