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750 results for “coherence”
Data of publication Coherent optical and spin spectroscopy of nanoscale Pr3+ : Y2O3
<p>Data corresponding to the figures of the publication " Coherent optical and spin spectroscopy of nanoscale Pr3+: Y2O3" by D. Serrano et al. (file:///C:/Users/diana.serrano/Zotero/storage/86IZ7H73/PhysRevB.100.html). A text file describes data in each compressed folder, please refer to the publication for more details. </p>
Revealing real-time 3D in vivo pathogen dynamics in plants by label-free optical coherence tomography
<p>This repository contains all data and code underlying the publication: J. de Wit et al. "<em>Revealing real-time 3D in vivo pathogen dynamics in plants by label-free optical coherence tomography</em>" in Nature Communications (2024) (https://doi.org/10.1038/s41467-024-52594-x)</p> <p><strong>--------------Code description------------------</strong></p> <p>The set of scripts is largely organized around the figures. For each (sub)figure, also from supplementary materials, that involves data and plotting, there is a script that generates the plot from data that can be found in the different zip files that are present in the Zenodo repository under https://doi.org/10.5281/zenodo.11428245.</p> <p>The scripts use the data that is contained in the ZIP folders. The ZIP folders are organized by experiment (Experiment 1, including contrast optimization; Experiment 2), one for the other data (OtherData, the validation for with Trypan blue, and the Arabidopsis, Radish and nematode) and one as a smaller dataset to explain the method on a single B-scan (Example_Bscan_dynamicOCT).</p> <p>IMPORTANT: The folder where the ZIP files are unzipped should be put in the file '<em>basepath.txt</em>', such that the data can be automatically loaded.</p> <p>Besides the figures that mention 'MakeFig...' there are a few more scripts:</p> <ul> <li><em>pointcloud_generation_experiment1.py</em>: this file makes the point clouds from the dynamic OCT images as described in Fig 2b. The resulting data is saved as maximum intensity projections and axial sums(forming the basis for Fig S4, S6 and S7) and as voxel counts (forming the basis of Fig.2c and Fig S5)</li> <li><em>pointcloud_generation_timelapses.py</em>: this file does the segmentation for experiment 2 and saves the maximum intensity projections and axial sums of the different stages in the segmentation (forming the basis of Fig3a,d,e and FigS9a,b), and saves the point clouds of the data. These point clouds were refined manually in CloudCompare as described in methods. These segmented point clouds are contained in the data zip folder of experiment 2.</li> <li><em>StatisticalTests.R</em>: This R file calculates the statistical tests for Fig.2cd and Fig.S5d. Here the path is not automatically updated, and should be manually set. The input file is contained in "Experiment1/SegmentationData/segmentationdata_samples.csv" and the output of the file is "D:/DataZenodo/Experiment1/SegmentationData/data_combined_Rstats_output.csv"</li> <li><em>example_dynamic_Bscan.py</em>: This script gives an example of the dynamic OCT processing as proposed in this paper. First it shows the process from an OCT interference spectrum to a B-scan. Then it loads 100 B-scans and applies dynamic OCT, including normalization with histograms. Finally it gives a dynamic B-scan and plots this against the average normal OCT image. This script can be used with only the zip folder "Example_Bscan_dynamicOCT", which reduces the amount of data needed to download/unzip.</li> </ul> <p>The list of other script files to load the data and generate the figures (guiding to the path of uncropped figures) is:</p> <ul> <li><em>MakeFig1bce_Fig2e.py</em></li> <li><em>MakeFig1d.py</em></li> <li><em>MakeFig1agraphs_FigureS1.py</em></li> <li><em>MakeFig3acde_S9ab.py</em></li> <li><em>MakeFigS2_determine_dynamic_range_experiment1.py</em></li> <li><em>MakeFigS8.py</em></li> <li><em>MakeFigureS4-S6-S7.py</em></li> <li><em>MakeFigureS5.py</em></li> <li><em>MakeHistFig2b_makeFigS3b-e.py</em></li> <li><em>MakePlotsFig2ab.py</em></li> <li><em>MakePlotsFig2cd.py</em></li> </ul> <p>Code was all run in Python 3 using Anaconda Spyder.</p> <p>Moreover, the zip file with the code contains the folder '<em>figures</em>' with all subfigures. Some of them are automatically saved from the scripts, others (like photos, icons, but also the Trypan blue microscopy figure) are added in the respective folder. The are logically organized by figure number.</p> <p><strong>--------------Dataset Description-----------------</strong></p> <p>As mentioned above, the data is organized in four zip folders for both experiments, the other data (validation with Trypan blue, other plant-pathogens) and one for the dynamic OCT B-scan example. The data contain the following:</p> <p><strong>Experiment 1: </strong></p> <ul> <li>DynamicOCTimages whose subfolders (organized by date) contain a folder per volume dataset in experiment 1 with a z-stack of .tif files that form the imaged volume. The lateral sampling is 3 um and the axial sampling is 1.37 um. </li> <li>ContrastOptimization: This folder contains <ul> <li><em>Bscans_with_segmentation</em>: segmented B-scans for contrast optimization (Fig S3)</li> <li><em>Bscan_figS1_fig1</em>: The B-scans and segementation for Figure S1.</li> <li><em>histogramdata_dynamicrange</em>: The histograms, bins and deducted reference data for determining the dynamic range per color channel for experiment 1 (Fig S2)</li> <li><em>logcompressed_3value_dOCT_example</em>: An example data stack for obtaining histograms (see script MakeFigS2_determine_dynamic_range_experiment1.py)</li> <li><em>overlaps_threshold-100-98-95-92-90-85-80-75-70-65-60-55-50-45perc_red-1_2_blue_-3_0_green1_filt.npy</em>: A file with intermediate data for the contrast optimization, which can also be generated with the script "<em>MakeHistFig2b_makeFigS3b-e.py</em>"</li> </ul> </li> <li>SegmentationData: This folder contains <ul> <li><em>MIP_segmentation_stages</em>: maximum intensityp projections and axial sums for all images at different stages in the segmentation (basis for Fig S4,6,7)</li> <li><em>processed_masks and StackMasks</em>: the manually obtained masks (segmented in StackMasks, made into masks in the folder 'processed_masks') for filtering out stomata, veins and artefacts.</li> <li><em>Unmasked_axialsum_th34_formanualsegmentation</em>: This folder contains the images of Fig.S4 and were used for the segmentation (we addes a small offset, such that the in segmentation we could set it to 0 and have a unique mask). </li> <li><em>overview_samples_bremiayn.csv</em>: A dataframe with the data for all the samples in experiment 1 that is used as input for the segmentation. It also contains the result of the manual check whether it has infection (Fig2c, left).</li> <li><em>segmentationdata_samples.csv</em>: This supplements the file of overview_samples_bremiayn.csv with the results from the segmentation and is output to script "<em>pointcloud_generation_experiment1.py</em>". It forms the basis of Fig.2a-c, and FigS5, as well as for the R-script to do the statistical testing.</li> <li><em>qPCR_dOCT.csv</em>: This script contains the qPCR data and is input to Fig2d. </li> </ul> </li> </ul> <p><strong>Experiment 2:</strong></p> <ul> <li><em>DynamicOCTimages</em>: This contains the z-stacks of .tif files of the volumes for experiment 2 (and one extra, where a z-slice is used in Fig.1b, bottom). Sampling step size is here again 3 um in lateral direction and 1.37 um in axial direction.</li> <li><em>.npy files </em>with the histograms (with same bins as Experiment 1), maxvalues and reference values for the dynamic range calculation.</li> <li><em>segmentation_data</em>: this folder contains: <ul> <li><em>quantification_volume_disc160_33_10.csv</em> and <em>quantification_volume_disc160_33_10.xlsx</em>: data from the manually segmented point clouds that form the basis of Fig.3c.</li> <li><em>timelapse_sampleoverview.csv</em>: overview of the samples that is used as input in the file "<em>pointcloud_generation_timelapses.py</em>"</li> <li><em>pointclouds</em>: Folder with segmented point clouds for the three leaf discs. These files could be loaded in CloudCompare.</li> <li><em>overviewMIPs</em>: folder with overview maximum intensity projections for the different steps in segmentation, which also forms the input of Fig.3a, FigS9ab.</li> <li>rawpointclouds: folder with the automatically generated point clouds from file <em>pointcloud_generation_timelapses.py </em>which were imported into CloudCompare as the basis for the segmented point clouds.</li> </ul> </li> </ul> <p><strong>OtherData:</strong></p> <p>This folder contains the z-stacks of dynamic OCT tif images for Arabidopsis (here both a normal contrast and one that has been increased to only contain the original 0-180 range); nematodes, radish (called radijs_test_PP_py_0002), spores for Fig1c (SporesImaging) and the dynamic OCT image of Fig1d. </p> <p><strong>example_Bscan_dynamicOCT:</strong></p> <p>This folder contains data to run the script example_dynamic_Bscan.py to show the dynamic OCT imaging process from raw OCT spectra.</p> <ul> <li><em>raw_spectra_exampleframe:</em> contains interference spectra, a reference spectrum and interpolation grid to show how to get from a raw OCT spectrum to a normal single B-scan.</li> <li><em>abs_images:</em> contains 100 subsequent B-scans that can be used to generate a dynamic OCT image as done in example_dynamic_Bscan.py</li> </ul> <p> </p> <p> </p>
Intermediate data products for: Moored Turbulence Measurements using Pulse-Coherent Doppler Sonar (Zippel et al. 2021, Journal of Atmospheric and Oceanic Technology)
<p>This repository contains some of the intermediate data products needed to reproduce the results in the <em>Journal of Atmospheric and Oceanic Technology</em> article "Moored Turbulence Measurements using Pulse-Coherent Doppler Sonar" by S.F. Zippel, J. T. Farrar, C. J. Zappa, U. Miller, L. St. Laurent, T. Ijichi, R. A. Weller, L. McRaven, S. Nylund, and D. Le Bel. Specifically, this material should allow reproduction of Figures 3, 5-7, 12 and 13. Reproduction of Figures 8-11 also requires data from associated glider deployments nearr the SPURS-1 mooring, which may be requested from co-author L. St. Laurent.</p> <p>Code to do the analysis and make the plots is here: https://github.com/zippelsf/MooredTurbulenceMeasurements</p> <p>Matlab data files:</p> <p>(1) 677404_burst1865.mat</p> <p>Single-burst data used for the example spectral fit in Figure 7. The burst was collected during the SPURS-1 project at 21.5m depth. The data collection and processing methods are described in detail in Section 2. </p> <p>(2) 811604_burst0510.mat (Single-burst data used in the unwrapping example, Figure 5)</p> <p>(3) 8116_dissipation_timeseries.mat (Used for associated ancillary data in Figure 6)</p> <p>(4) 913411_burst2879.mat (Single-burst data, used for ancillary data to make Figure 3).</p> <p>(5) BuoyancyFlux_b.mat</p> <p>Ocean buoyancy flux estimates for SPURS-2 dataset, created from the 1-hr "met" and "flux" files available on the UOP website, and using the Gibbs SeaWater (GSW) toolbox to estimate "alpha" and "beta". The estimated buoyancy fluxes were used for Figure 12.</p> <p>(6) BuoyancyFlux_c.mat</p> <p>Ocean buoyancy flux estimates for SPURS-1 dataset, created from the 1-hr "met" and "flux" files available on the UOP website, and using the Gibbs SeaWater (GSW) toolbox to estimate "alpha" and "beta". The estimated buoyancy fluxes were used for Figure 12.</p> <p>(7) SPURS1_dissipation_grid_v1d.mat</p> <p>Gridded TKE dissipation rates for SPURS-1 dataset. Processing of these data is described extensively in Section 2. Data used in Figures 8-13. Dissipation rates also available on NASA's PODAAC.</p> <p>(8) spurs1_met_1hr.mat (Processed met data from SPURS-1 mooring. Also available on WHOI's UOP website.)</p> <p>(9) SPURS2_dissipation_grid_v1c.mat</p> <p>Gridded TKE dissipation rates for SPURS-2 dataset. Processing of these data is described extensively in Section 2. Data used in Figures 12. Dissipation rates also available on NASA's PODAAC.</p>
Raw Data: Gold Coated ZnO Microstructures by Bragg Coherent X-Ray Diffraction Imaging
<p>Two sets of raw data from gold coated ZnO microstructure (rod) investigated by Bragg coherent X-ray diffraction imaging used in publication: "Visualizing Intrinsic 3D-Strain Distribution in Gold Coated ZnO Microstructures by Bragg Coherent X-Ray Diffraction Imaging and Transmission Electron Microscopy with Respect to Piezotronic Applications" (<a href="https://doi.org/10.1002/aelm.202100546">https://doi.org/10.1002/aelm.202100546</a>)</p> <p>Included is data from two different spatial positions along the c-axis of the ZnO rod. Futher on called position 1 (P1) and position 2 (P2). For each position there is a .nxs file of a rocking scan around the {10-10} Bragg reflection, collected by a 2D detector and other recorded values, e.g. motor positions, counter values. </p>
LASSO coherent seismic wavefield reconstruction and source imaging
<p>Coherent wavefield reconstruction and source imaging has been performed for 4 cataloged seismic events recorded with the Large-N Seismic Survey in Oklahoma (LASSO). The array consists of almost 2,000 densely spaced seismic stations and the corresponding raw time sries data have been made freely accessible by the Incorporated Research Institutions for Seismology (IRIS). The results for the 4 seismic events are accompanied with results gained for controlled seismic simulations for two of these events Reconstruction results and source images are provided in HDF5 and MAT file formats, respectively. File names were giving according to the following pattern: <br> <br> "LASSO_<<em>event name>_<reconstruction mode>_<result type>"</em></p> <p>where <<em>reconstruction mode</em>> refers either to "enhancement" (reconstruction performed for the original station layout) or "regularization" (reconstruction perfomed for a new, sense and regular station layout). <<em>result type</em>> denotes either reconstructed waveforms ("wavefield"), waveform coherence ("coherence"), or spatial source images. For the HDF5 files, mportant meta information like spatial coordinates and temporal sampling parameters are stored in a symbolic dictionary named "META", whereas the time series data is saved as a 2D matrix. Important META fields include "ntrac" (number of traces), "nt" (number of time samples), "dt" (dampling interval), "gx" (stations x coordinates), "gy" (stations y coordinates).<br> <br> The MAT files (result type "images") contain raw waveform and STA/LTA images, which are stored as 3D regular arrays named "recm1z_Enh_5_raw" (enhancement) / "recm1z_Reg5_5_raw" (regularization) and "recm1z_Enh_5_slta" (enhancement) / "recm1z_Reg5_5_slta" (regularization), respectively. For comparison, source images generated for the raw field data (without reconstruction are included in every MAT file and can be accessed through fields "recm1z_Raw_raw" and "recm1z_Raw_slta".</p>
Data Analysis files for "Coherent optical control of a superconducting microwave cavity via electro-optical dynamical back-action"
<p>Data analysis files for the manuscript "Coherent optical control of a superconducting microwave cavity via electro-optical dynamical back-action", <a href="https://www.nature.com/articles/s41467-023-39493-3#data-availability">Nature Communications <strong>14</strong>, 3784 (2023)</a>, or <a href="https://arxiv.org/abs/2210.12443">arXiv:2210.12443 (2022)</a></p> <p>This contains the raw data, the data analysis files, and the figure generation files of the manuscript, which includes the following three parts,</p> <p>0. Data preparation</p> <p>The total size of the raw dataset is around 120GB. The raw data is pre-processed via digital down-conversion at 40MHz to obtain the optical/microwave transient response of the electro-optical device in the presence of strong optical pulses at different powers and frequencies.</p> <p>The processed data is adopted for data analysis of the response measurements for convenience.</p> <p>1. Data Analysis</p> <ul> <li>Detailed data analysis of the electro-optical (microwave and optical) responses in presence of the optical pump pulses for different mode and probing configurations.</li> </ul> <p>2. Figures for the manuscripts.</p> <ol> <li>Figures for the optical characterizations</li> <li>Figures for the coherent responses for different configurations</li> <li>Figures for the excess back-action</li> <li>Figures for the theoretical curves in the Supplementary Information </li> </ol>
OPTICAL COHERENCE TOMOGRAPHY (OCT) IMAGE DATASET OF RADIATION DERMATITIS
<p><strong>Optical Coherence Tomography (OCT) Image dataset of radiation dermatitis </strong></p> <p><strong>Citing the Dataset</strong></p> <p>The dataset is released under a Creative Commons Attribution license, so please cite the dataset if it is used in your work in any form. Published academic papers should use the academic paper citation for our paper. Personal works, such as projects or blog posts, should provide a URL to this Zenodo page, though a reference to our paper would also be appreciated.</p> <p><em>Academic paper citation</em></p> <p>Photiou C., Cloconi C. & Strouthos I. Feature-Based vs. Deep-Learning Fusion Methods for the In Vivo Detection of Radiation Dermatitis Using Optical Coherence Tomography, a Feasibility Study. <em>J Digit Imaging. Inform. med.</em> (2024). https://doi.org/10.1007/s10278-024-01241-4</p> <p><em>Personal use citation</em></p> <p>Include a link to this Zenodo page - 10.5281/zenodo.8238140</p> <p><strong>ACKNOWLEDGMENT</strong></p> <p>This research is funded by the European Union’s Horizon 2020 research and innovation program under grant agreement No. 739551 (KIOS CoE) and from the Republic of Cyprus through the Directorate General for European Programs, Coordination and Development.</p> <p><strong>Contact Information</strong></p> <p>If you would like further information about the dataset, or if you experience any issues downloading files, please contact us at photiou.christos@ucy.ac.cy.</p> <p><strong>Dataset Description</strong></p> <p>This dataset consists of Optical Coherence Tomography (OCT) images from 22 head and neck cancer patients undergoing radiotherapy. Specifically, this dataset includes OCT images of five stages of Acute Radiation Dermatitis (ARD), labelled by an expert oncologist as Grade 0 (0), Grade 1 (1), Grade 2a (2), Grade 2b (3) and Grade 3 (4). Twenty-two head and neck cancer patients who were scheduled to receive radiation therapy at the German Oncology Center (GOC) in Limassol, Cyprus, participated in this proof-of-concept trial. The trial has received bioethics approval from the Cyprus National Bioethics Committee (Cyprus National Bioethics Committee 2020/61) and informed consent was collected. Patients under the age of 18 or with disabilities, expectant women, those who had recently undergone radiation therapy in the same area, and patients with autoimmune diseases were excluded from the study. After informed consent, the irradiated side of the neck of the subjects, was imaged with OCT. The imaging was performed with a swept-source OCT system (Santec IVS300), with a center wavelength of 1300 nm, an axial resolution of 12 micrometers in tissue, and an A-scan rate of 40 kHz. Six images were acquired at 1 cm intervals, covering the region from the mandibular angle to the clavicle. Imaging was repeated prior to every radiation therapy session, twice per week, until the conclusion of the therapy, resulting in a dataset of 1487 images. During each visit, the patient's ARD grade, at each of the imaging sites, was determined and recorded by a senior oncologist.</p> <p>Dataset<br>The data consists of two items: (1) the excel file 'Description.xlsx' with the patient information and (2) the zip file 'Dataset.zip' containing the images, as described below.</p> <p>1) Description.xlsx<br>This excel file contains patient information such as age, habits, etc, in the sheet 'Patient_Info'. The sheet 'Image_Info' contains the information for each image, such as the patient number (1-22), week number, visit number (usually one or two visits per week), image number (six images per visit with some exceptions), and classification (0-4). </p> <p>2) Dataset.zip <br>This zip file contains the OCT images. Each patient's folder has sub-folders corresponding to each week, within which there are sub-folders corresponding to each visit, which contain the image folders. Each image folder contains two excel files: OCT Data (demodulated and logarithmic intensity image) and Raw Data (resampled interferometric data). </p> <p> </p>
NLL-SSST-coherence high-precision earthquake location catalog for the 2023 Ojai, California earthquake sequence
<p><strong>Hypocenter catalog files and visualizations of high-precision, NLL-SSST-coherence earthquake locations for the 2023 M5.1 Ojai, California earthquake sequence and background seismicity (2128 events, 1980-01-01 to 2023-08-25).</strong></p> <p>NLL-SSST-coherence (<a href="https://doi.org/10.1029/2021JB023190">Lomax and Savvaidis, 2022</a>; <a href="https://doi.org/10.26443/seismica.v2i1.324">Lomax and Henry, 2023</a>) is an enhanced, absolute-timing earthquake location procedure which 1) iteratively generates spatially varying travel-time corrections to improve multi-scale location precision and 2) uses waveform similarity to improve fine-scale location precision.</p> <p>Relocations performed with phase arrival data available from <a href="http://service.scedc.caltech.edu">http://service.scedc.caltech.edu</a></p> <p>Visualizations include topography from <a href="https://opentopography.org">https://opentopography.org</a> and surface fault traces from <a href="https://usgs.maps.arcgis.com/apps/webappviewer/index.html?id=5a6038b3a1684561a9b0aadf88412fcf">https://usgs.maps.arcgis.com</a></p> <p> </p> <p>This repository contains:</p> <p><strong>Full catalog in CSV format</strong>:<br> CSV file data columns correspond to selected fields of the of NonLinLoc Hypocenter format output <a href="http://alomax.free.fr/nlloc/soft7.00/formats.html#_location_hypphs_">http://alomax.free.fr/nlloc/soft7.00/formats.html#_location_hypphs_</a></p> <p><strong>Full catalog in NonLinLoc hyp format</strong>:<br> NonLinLoc Hypocenter format output <a href="http://alomax.free.fr/nlloc/soft7.00/formats.html#_location_hypphs_">http://alomax.free.fr/nlloc/soft7.00/formats.html#_location_hypphs_</a></p> <p><strong>Key NLL-SSST-coherence configuration files</strong>: NLL-SSST-coherence_config/*</p> <p><strong>Selected Visualization images</strong></p> <p> </p>
Coherent manipulation of nuclear spins in the strong driving regime
<p>Data for <a href="https://iopscience.iop.org/article/10.1088/1367-2630/ad0c0b">manuscript</a> with the same name. Consists of four parts:</p><p>(1) DC characterization: all files having a format corresponding to "20230129*.dat"</p><p>(2) Finite element analysis: all files having a format corresponding to "B_field_*.txt"</p><p>(3) Proton Rabi oscillations: all files having a format corresponding to "20230112*.dat", "20230119*.dat" and "20230120*.dat"</p><p>(4) Spiral transmission: a CSV file</p><p> </p>
A method for generating coherent spatially explicit maps of seasonal palaeoclimates from site-based reconstructions
<p>Reconstruction of climate anomalies in southern Europe for the Last Glacial Maximum (LGM, ca 21,000 years ago), made by combining pollen based reconstructions (from Bartlein et al. 2011) and averaged outputs of LGM simulations from the 3rd round of the Palaeoclimate Model Intercomparison Project (PMIP, Braconnot et al. 2011), under a variational data assimilation technique. Reconstructions made using this technique are designed to be used for data-model comparison, specifically against the results of PMIP4. The dataset consists of 6 variables: moisture index (the ratio precipitation and equilibrium evapotranspiration), mean annual precipitation (mm), mean annual temperature (degrees C), mean temperature of the coldest month (degrees C), mean temperature of the warmest month (degrees C), growing degree days above 5 degrees C (day degrees C). The standard deviation of these variables is also given.</p>
Supporting data for "Spatial Noise Correlations in a Si/SiGe Two-Qubit Device from Bell State Coherences"
<p>Datasets, analysis scripts and simulations for "Spatial Noise Correlations in a Si/SiGe Two-Qubit Device from Bell State Coherences".</p> <p>For more information and instructions, see READ ME.txt.</p> <p>For questions, contact Jelmer Boter (j.m.boter@tudelft.nl) or Lieven Vandersypen (l.m.k.vandersypen@tudelft.nl).</p>
Final results from McCoy et al., 'Global Observations of Submesoscale Coherent Vortices in the Ocean', submitted to Progress in Oceanography.
<p>Submesoscale coherent vortices (SCVs) are small-scale, subsurface eddies that are ubiquitous in the ocean. Observations suggest that they efficiently trap and transport water, nutrients, and other properties thousands of kilometers away from their formation regions. However, the weak sea-surface signature restricts SCV observations to mostly chance encounters with shipboard subsurface instrumentation. Thus, the global occurrence, properties, and generation frequency of SCVs remain poorly constrained. Here we present results from a new algorithm used to identify SCVs from Argo float data, applied to roughly 2 million profiles conducted globally from August 1997 to January 2020.</p> <p>After application of the SCV detection algorithm to the global Argo array, we identify 2501 casts piercing spicy-core SCVs (those with anomalously hot and salty water mass characteristics), and 1583 casts piercing minty-core SCVs (anomalously cold and fresh cores) over more than 20 years of available data. The Matlab file 'final_individual_scvs.mat' contains various data for each SCV identified.</p> <p>By grouping detections from consecutive Argo casts, we are also able to record 383 spicy-core SCV time-series and 169 minty-core SCV time-series. The Matlab file 'final_timeseries_scvs.mat' contains the data for these time-series. </p> <p>For a more detailed description of each Matlab file, please see 'README.rtf'. </p> <p>Reach out to Daniel McCoy (dmccoy801@gmail.com) or Daniele Bianchi (dbianchi@atmos.ucla.edu) for inquiries. </p>
Quantification of plant morphology and leaf thickness with optical coherence tomography
<p>The uploaded scripts and data are used to obtain the figures 2, 4, 5, 6 and 7 in the publication. </p> <p>The code has been run with Python 3.7 in Spyder (Anaconda).</p> <p>There are three scripts, each needing specific datasets to run the code.</p> <p>1. The core is the segmentation of the leaf surface and this is subsequently used to calculate leaf thickness and obtain en-face images.</p> <p><a href="https://zenodo.org/api/files/89412f06-4c84-4516-9e7d-113796b42834/3D_segmentation_thickness_enface.py">3D_segmentation_thickness_enface.py</a>: This file loads the 3D processed OCT data, does the leaf surface segmentation and calculates the en face images. It needs the files processed_3Ddata.npy and videoim.npy</p> <p><a href="https://zenodo.org/api/files/89412f06-4c84-4516-9e7d-113796b42834/processed_3Ddata.npy">processed_3Ddata.npy</a>: This file contains the processed 3D OCT dataset (linear amplitude data), with respectively dimensions z,x,y. The data is saved as uint16 to save memory, and should be converted to double before further processing, as done in the script.</p> <p><a href="https://zenodo.org/api/files/89412f06-4c84-4516-9e7d-113796b42834/videoim.npy">videoim.npy</a>: This file contains the RGB image of Fig. 6(a) as image matrix.</p> <p>2. The non-infiltrated and infiltrated image (Figure 4)</p> <p><a href="https://zenodo.org/api/files/89412f06-4c84-4516-9e7d-113796b42834/2D_fig4.py">2D_fig4.py</a>: This script produces Figure 4 of the paper and also shows the two RGB images that indicate the scan location on the leaf. It needs the files OCTdata_figure4.npy (containing OCT data) and videoimages_figure4.npy</p> <p><a href="https://zenodo.org/api/files/89412f06-4c84-4516-9e7d-113796b42834/OCTdata_figure4.npy">OCTdata_figure4.npy</a>: This file contains the processed 2D OCT dataset (linear amplitude data), with respectively dimensions (a/b),z,x. The data is saved as uint16 to save memory, and should be converted to double before further processing, as done in the script.</p> <p><a href="https://zenodo.org/api/files/89412f06-4c84-4516-9e7d-113796b42834/videoimages_figure4.npy">videoimages_figure4.npy</a> This file contains the two RGB images that show the scan area of the data in Figure 4.</p> <p>3. The calculation of the refractive index and making Figure 5</p> <p><a href="https://zenodo.org/api/files/89412f06-4c84-4516-9e7d-113796b42834/refractiveindex_fig5.py">refractiveindex_fig5.py</a>: this script segments the cuvette wall and leaf surface on 2D images and calculates the refractive index by evaluating equation 1 of the publication. It needs the file images_refractiveindex.npy</p> <p><a href="https://zenodo.org/api/files/89412f06-4c84-4516-9e7d-113796b42834/images_refractiveindex.npy">images_refractiveindex.npy</a>: This file contains the processed 2D OCT dataset (linear amplitude data), with respectively dimensions (leaf/empty),z,x. The data is saved as uint16 to save memory, and should be converted to double before further processing, as done in the script.</p>
Data set on the main text of "A bright and fast source of coherent single photons"
<p>The data set that is presented in the main text is uploaded to the repository. Please note that all the data is scaled according to the axis on the paper, that means if the axis has a multiplication by 1e3 then the data is divided by 1e3.</p> <p>Each file is named after the corresponding subfigure.</p> <p>The preprint version of the article can be found in: https://arxiv.org/abs/2007.12654</p>
Data of the publication: Nuclear spin coherence properties of 151Eu3+ and 153Eu3+ in a Y2O3 transparent ceramic by J. Karlsson et al.
<p>Data corresponding to the figures of the publication "Nuclear spin coherence properties of 151Eu3+ and 153Eu3+ in a Y2O3 transparent ceramic" by J. Karlsson et al., (https://doi.org/10.1088/1361-648X/aa529a). A text file describes data in each compressed folder, please refer to the caption in the publication for more details. </p>
Supporting Data for "Coherent superpositions of three states for phosphorous donors in silicon prepared using THz radiation"
<p>Supporting data for the publication "Coherent superpositions of three states for phosphorous donors in silicon prepared using THz radiation", describing experimental results from Figures 4-7. The data are in .csv format; columns and units for each file are described in the relevant readme file. The data are given in sufficiently compact states for a reader of the paper to reproduce the figures.</p> <p>Spectral data presented are related to their interferograms by simple Fourier Transform methods. The source interferograms are derived from explicit records of oscilloscope traces, which are verbose and inefficient. Consequently, these raw data files are not included in the dataset. Raw oscilloscope traces will be made available by the authors on request.</p>
Figures 2-6. Calusamyia hribari. 2 in A New Genus and Species of North American Robsonomyiini (Diptera: Sciaroidea: Keroplatidae: Macrocerinae) from the Florida Keys Edward I. Coher Emeritus Prof. Long Island Univ. 10203 Greentrail Drive N. Boynton Beach, FL 33436
Figures 2-6. Calusamyia hribari. 2) Wing. 3) Thorax, lateral. 4) Male terminalia, dorsal view. 5) Female terminalia, ventral view. 6) Female terminalia, lateral view.
Coherent Charge Oscillations in a Bilayer Graphene Double Quantum Dot
<p>This repository contains the experimental data and the scripts for evaluating the data of the publication "Coherent Charge Oscillations in a Bilayer Graphene Double Quantum Dot".</p>
Radiofrequency to Microwave Coherent Manipulation of an Organometallic Electronic Spin Qubit Coupled to a Nuclear Qudit
<p>Dataset containing ASCII files for Figures 2-8 of the paper </p><p>Radiofrequency to Microwave Coherent Manipulation of an Organometallic Electronic Spin Qubit Coupled to a Nuclear Qudit</p><p>Inorg. Chem. 2021, 60, 11273−11286</p>
Experimental data for "Exact inversion of partially coherent dynamical electron scattering for picometric structure retrieval"
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