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24 results for “real-time dynamics”
Strong coupling electron-photon dynamics: a real-time investigation of energy redistribution in molecular polaritons - Dataset
<p>Dataset complement to "Strong coupling electron-photon dynamics: a real-time investigation of energy redistribution in molecular polaritons" - includes output and video files obtained using the <a href="https://etprogram.org/">eT program</a>, an open-source electronic (and molecular-polaritonic) structure program.</p> <p>See the paper at <a href="https://doi.org/10.1103/PhysRevResearch.6.033283">https://doi.org/10.1103/PhysRevResearch.6.033283</a></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>
Real-time benchmark dynamics of the Ohmic Spin-Boson Model computed with Time-Dependent Variational Matrix Product States. (TDVMPS) coupling strength and temperature parameter space
<p>Data describing the complete propagators (maps) for the evolution of the Ohmic Spin-Boson Model are made available, here. Using a time-dependent variotnal matrix product states (TDVMPS) respresentation of the complete spin-environment wave function, non -perturbative results are presented over a wide range of coupling strengths, temperatures and initial conditions. The results in this repository are associated with the article: </p> <p>https://www.preprints.org/manuscript/202012.0016/v1 </p> <p>A mathematica notebook that allows the data to be visualised and manipulated is also provided. </p>
Code and Data for "Real-time dynamic single-molecule protein sequencing on an integrated semiconductor device"
<p><strong>Code and Data for "Real-time dynamic single-molecule protein sequencing on an integrated semiconductor device".</strong></p> <pre>Code to analyze data produced by the Quantum-Si benchtop device and semiconductor chip is provided in a Python library <strong>qsi_algo</strong> under several submodules: - <strong>rs_caller.py</strong>: Algorithm for calling RS segments (also called ROI segments throughout code). - <strong>rs_caller_controller.py</strong>: Code framework for executing RS calling and property computation in a distributed manner - <strong>rs_properties</strong>: Code for computing properties of identified RS - <strong>rs_classifier</strong>: Algorithms for identifying peptide states (i.e. residue calls) associated with an RS - <strong>utils.py</strong>: shared helper code - <strong>pulse_reader</strong>: reader for binary pulse file - <strong>filters</strong>: ROI and pulse filtering utilities - <strong>plotting</strong>: functions for visualization of data relevant to the analyses presented Jupyter notebooks (<strong>.ipynb</strong>) files are named according to the manuscript figure they are associated with. Analysis code inside uses provided RS (recognition segment) data to demonstrate filtering and residue-calling techniques required to replicate analyses shown in manuscript figures. Please note: several methods rely on randomization for model initialization and/or data sampling which can cause small deviations from equivalent analyses in published figures. The raw data produced from the Quantum-Si benchtop device and semiconductor chip for the assays presented in the accompanying study is presented in a pulse-called binary file format. Pulses can be used as input for RS identification and peptide state identification. Pre-segmented (RS-identified) files are included for convenience. The data contained in the files include: <strong>{run_id}.bin</strong>: Binary format for storing pulse info. The reader provided in <strong>qsi_algo.pulse_reader</strong> produces the following columns: - <strong>aperture_index</strong>: unique aperture index on chip - <strong>start_f</strong>: index of first frame in pulse, counted from the beginning of the run - <strong>end_f</strong>: index of last frame in pulse, counted from the beginning of the run - <strong>dur_f</strong>: duration of pulse in frames - <strong>dur_s</strong>: duration of pulse in seconds - <strong>ipd_f</strong>: interpulse duration in frames (number of frames since end of preceding pulse) - <strong>ipd_s</strong>: interpulse duration in seconds (time in seconds elapsed since end of preceding pulse) - <strong>snr</strong>: signal-to-noise ratio (bin1_intensity / bin1_bg_std) - <strong>intensity</strong>: intensity of pulse (counts above baseline in bin1) - <strong>bin0_intensity</strong>: counts above baseline in bin0 - <strong>intensity_display</strong>: bin1_intensity + bin1_bg_mean - <strong>binratio</strong>: bin0_intensity / bin1_intensity - <strong>bg_mean</strong>: bin1 background mean in region of pulse - <strong>bg_std</strong>: bin1 background standard deviation in region pulse - <strong>bin0_bg_mean</strong>: bin0 background mean in region of pulse - <strong>bin0_bg_std</strong>: bin0 background standard deviation in region pulse <strong>{run_id}.csv.gz</strong>: Compressed comma-separated value file containing RS/ROI properties computed from raw pulses.bin file by included RS caller (example in <strong>rs_caller.py</strong>). - <strong>ap</strong>: unique aperture index on chip - <strong>ROI</strong>: ordinal ROI number in the aperture, 0-indexed - <strong>start_p</strong>: index (.loc) of first pulse in the ROI (inclusive) in pulse dataframe - <strong>end_p</strong>: index (.loc) of last pulse in the ROI (inclusive) in pulse dataframe - <strong>start_f</strong>: first frame of the first pulse in the ROI (inclusive) - <strong>end_f</strong>: Last frame of the last pulse in the ROI (exclusive) - <strong>start_s</strong>: Time (in seconds elapsed from beginning of run) of the start of the ROI - <strong>end_s</strong>: Time (in seconds elapsed from beginning of run) of the end of the ROI - <strong>dur_f</strong>: Duration in frames of the ROI - <strong>dur_s</strong>: Duration in seconds of the ROI - <strong>num_pulses</strong>: Number of pulses in the ROI (that also passed filtering during ROI-calling) - <strong>pw_mean</strong>: Mean pulse duration (in seconds) of pulses in the ROI - <strong>ipd_mean</strong>: Mean inter-pulse duration (in seconds) of pulses in the ROI - <strong>snr_mean</strong>: Mean signal-to-noise ratio of pulses in the ROI - <strong>intensity_mean</strong>: Mean intensity above baseline of pulses in the ROI - <strong>binratio_norm</strong>: Estimated pulse bin ratio of pulses in the ROI, according to the following equation: sum(bin0_intensity*dur_f) / np.sum(bin1_intensity*dur_f) - <strong>ROI_score</strong>: ROI quality score (0-1 from least to most likely to contain recognizer-peptide recognition pulsing) - <strong>binratio_skew</strong>: bin ratio correction factor accounting for binning signal timing differences across the chip. This factor has already been applied to the binratio_norm column</pre>
Real-time dynamics of nanoplasmonic dimer, distance d = 0.5 nm
<p>In Ref. <a href="http://doi.org/10.5281/zenodo.1476721">http://doi.org/10.5281/zenodo.1476721 </a>we provide a movie that shows the real-time dynamics of the nanoplasmonic dimer with distance $ d_1=0.5 $ nm. The time-evolution in the movie corresponds to the runs that we discuss in section VI. In the figure, we show a frame of the movie at time 8.33 fs. The upper two panels show contour plots of matter variables, the absolute value of the current density and the electron localized function (ELF). The most relevant Maxwell field variables, the electric field along the laser polarization direction z and the total Maxwell energy are presented in the lower panels. In the top of the figure, we show the incident laser pulse and at the center the geometry of the nanoplasmonic dimer.</p>
Real-time dynamics of nanoplasmonic dimer, distance d = 0.1 nm
<p>In Ref. <a href="http://dx.doi.org/10.5281/zenodo.1476719">http://doi.org/10.5281/zenodo.1476719 </a>we provide a movie that shows the real-time dynamics of the nanoplasmonic dimer with distance $ d_1=0.1 $ nm. The time-evolution in the movie corresponds to the runs that we discuss in section VI. In the figure, we show a frame of the movie at time 6.89 fs. The upper two panels show contour plots of matter variables, the absolute value of the current density and the electron localized function (ELF). The most relevant Maxwell field variables, the electric field along the laser polarization direction z and the total Maxwell energy are presented in the lower panels. In the top of the figure, we show the incident laser pulse and at the center the geometry of the nanoplasmonic dimer.</p>
Real-time dynamics of nanoplasmonic dimer, distance d = 0.5 nm
<p>In Ref. we provide a movie that shows the real-time dynamics of the nanoplasmonic dimer with distance $ d_1=0.5 $ nm. The time-evolution in the movie corresponds to the runs that we discuss in section VI. In the figure, we show a frame of the movie at time 8.33 fs. The upper two panels show contour plots of matter variables, the absolute value of the current density and the electron localized function (ELF). The most relevant Maxwell field variables, the electric field along the laser polarization direction z and the total Maxwell energy are presented in the lower panels. In the top of the figure, we show the incident laser pulse and at the center the geometry of the nanoplasmonic dimer.</p>
Real-time dynamics of nanoplasmonic dimer, distance d = 0.1 nm
<p>In http://dx.doi.org/10.5281/zenodo.1482739 we provide a movie that shows the real-time dynamics of the nanoplasmonic dimer with distance $ d_1=0.1 $ nm. The time-evolution in the movie corresponds to the runs that we discuss in section VI. In the figure, we show a frame of the movie at time 6.89 fs. The upper two panels show contour plots of matter variables, the absolute value of the current density and the electron localized function (ELF). The most relevant Maxwell field variables, the electric field along the laser polarization direction z and the total Maxwell energy are presented in the lower panels. In the top of the figure, we show the incident laser pulse and at the center the geometry of the nanoplasmonic dimer.</p>
Optimizing agents with genetic programming - An evaluation of hyper-heuristics in dynamic real-time logistics - datasets and results
<p>This directory contains the data and results that we used and obtained during the realization of the following paper:</p> <blockquote> <p>Optimizing agents with genetic programming - An evaluation of hyper-heuristics in dynamic real-time logistics. Rinde R.S. van Lon, Juergen Branke, and Tom Holvoet. Genetic Programming and Evolvable Machines (2017).</p> </blockquote> <p>The code that has been used can be found on GitHub (https://github.com/rinde/vanLon17-GPEM-code), an archive is available at https://doi.org/10.5281/zenodo.260130.</p> <p>This repository contains the following files:</p> <ul> <li>evo.zip (expands to 21.2 GB) A zip file containing all results of the training experiment and tuning experiment.</li> <li>JAAMAS.zip (expands to 227.6 MB) A zip file containing the main results of the JAAMAS experiment, also available at https://doi.org/10.5281/zenodo.209760</li> <li>realtime.zip (expands to 913.5 MB) A zip file containing the main results</li> <li>dataset10k.zip (expands to 4.9 GB) Contains the scenarios used for the training and tuning experiments.</li> <li>overview.zip (expands to 42 MB) Contains the following files: <ul> <li>overview/experiment-overview.csv - table containing basic overview of the 40 main evolutionary runs that were performed for this paper.</li> <li>overview/heuristics/ - folder with a tree visualization for every evolved heuristic.</li> <li>overview/makefile - executes the analysis scripts that were used in the paper (requires the R programming language, https://www.r-project.org/). All tables and figures will be placed in results/generated/.</li> <li>readme.txt - this file.</li> <li>overview/results/ - contains the scripts</li> <li>overview/results/data/ - empty directory in which evo.zip, JAAMAS.zip, and realtime.zip have to be unpacked for the analysis scripts to work.</li> </ul> </li> </ul>
Test data for MetIVA ( An XR-based Interactive Visualization Platform for Real-time Exploring Dynamic Earth Science Data)
<p>Here,we presented the minimum demanded dataset to test the basic fuction of the software of MetIVA, which an XR-base interactive visualization platform for real-time exploring dynamic earth science data. The dynamic realtime data of traffic information (such as traffic volume, traffic spped, jam conditions) is directy obtained from the third-party supplier, such as Mapbox in the test version of MetIVA, the users can change it to other sources. The users have their own accounts on the cloud computing platform to run the numerical models (such as WRF), the outputed results (in NetCDF format) can be sent to cloud storage and the link address need to be provided in the MetIVA.</p>
Supplementary Data:Risk Analysis for Real-time Flood Control Operation of a Multi-reservoir System Using a Dynamic Bayesian Network
<p>The files in this record contain data for risk analysis for real-time flood control operation of a multi-reservoir system using a dynamic bayesian network considered for publication in Water Resources Research.</p> <p>The files consist of:</p> <ul> <li>Reservoir data and river flood routing parameters</li> <li>Flood data</li> <li>Code and results of the Monte Carlo simulations</li> <li>Code and results of the Bayesian network</li> </ul>
Raw dataset for "Rehybridization dynamics into the pericyclic minimum of an electrocyclic reaction imaged in real-time"
<p>The dataset contains raw diffraction images in .tiff format. Each image file name contains three numbers, separated by "_". The first number refers to the order in which the images were taking in laboratory time. The second number refers to the absolute translation stage position in millimeters in the optical beam path of the pump beam. The stage position can be converted into a pump-probe delay time (taking into account the speed of light and a factor of 2 for the beam path, since the pulses move back and forth on the stage). Larger stage position values correspond to the optical pump pulse arriving later with respect to the probe pulse. Time zero was determined to be at 156.26 mm using a solid reference sample.</p>
Ab initio multiple spawning simulations for "Rehybridization dynamics into the pericyclic minimum of an electrocyclic reaction imaged in real-time"
<p>60 ICs a(0.82)-2SA-cas(6,4)-SCF/6-31G* using AIMS/uPBE0-D3 for 1st ps of alpha-terpinene photochemistry. 20 for each<br> conformer upon rotation around the isopropyl group. Each initial condition (IC) is saved in a<br> separate folder labeled after conformers m, p, t, and with the index number of the initial condition.<br> The folder contains the positions and amplitudes of all trajectory basis functions (TBF) arising from<br> the initial condition as well as information about their coupling. The TBF index 1 always refers to<br> the TBF launched in the Franck-Condon region of the excited state based on an initial condition<br> sampled from a ground state Wigner distribution. </p> <p>Description of the folders and subsequent files in each IC folder:</p> <p>Simulation_data <br> X-YYYY : X is the isomer and YYYY is the initial condition number<br> "Positions.x.xyz" : Files containing the geometries of each TBF in cartesian coordinates in Angstroms<br> at each time step. The "x" in the filename corresponds to the index of the TBF.<br> "Amp.x" : Files containing the TBF amplitudes for each timestep.<br> "Spawn.log" : File containing timing information about the spawning events.<br> "S.dat" : File containing coupling matrices between the TBFs for every time step.<br> "ext_x" : Folders containing extensions of TBFs on DFT level. Each folder contains a file<br> "coors.xyz" with cartesian coordinates in Angstroms at each time step. The<br> timestep size of the DFT trajectories is uniformly 0.5 femtoseconds. Only TBFs in<br> the groundstate are extended on DFT level. Therefore, there is no folder "ext_1".</p> <p>Simulation_Parameters : Parameters for FMS and TeraChem nonadiabatic dynamics <br> X-rotamer : x is isomer containing parameter files for all X rotamers<br> "c0.casscf" : Binary file with the alpha(0.82)-SA2-CAS(6,4)-SCF/6-31G* orbitals<br> "Geometry.dat" : Initial condition (position and momentum) to start the FMS/TeraChem nonadiabatic dynamics<br> "Control.dat" : Parameter file for running FMS<br> "misc_options" : Parameter file for running TeraChem <br> "DFT-tc.in" : TeraChem adiabatic dynamics on ground electronic state (ext_x)<br> </p>
Data and code to create figures of "Rehybridization dynamics into the pericyclic minimum of an electrocyclic reaction imaged in real-time"
<p>Instruction of making Fig 4 in the main text:<br> 04/07/2023, SLAC, CA<br> Yusong (Liu), on behalf of Thomas (Wolf)</p> <p>1. Making Figure 1:<br> Figure 1 is fully vectorized with file 'Figure-1.svg', also saved in a png version, 'Figure-1.png'</p> <p>2. Making Figure 2:<br> The data plots in panels a and b are produced from a Matlab script: 'MakingFig2MainText.m'<br> This script loads data saved in 'TerpNatCommFig2.mat' and produce several files if using the saving condition:<br> 'PDFStaticTwoDelaysExpSim_22.fig'<br> 'PDFStaticTwoDelaysExpSim_22.png'<br> 'PDFStaticTwoDelaysExpSim_22.svg'<br> The final Figure 2, 'Figure-2.svg', then was assembled using 'PDFStaticTwoDelaysExpSim_22.svg' and 'Molskeleton.svg'.</p> <p>3. Making Figure 3:<br> The data plots in panels a, b, and c are produced from a Matlab script: 'MakingFig3MainText.m'<br> This script loads data saved in 'TerpNatCommFig3.mat' and produce several files if using the saving condition:<br> 'Fig3PDFFalseCMapLineOutExpSim_V10.fig'<br> 'Fig3PDFFalseCMapLineOutExpSim_V10.png'<br> 'Fig3PDFFalseCMapLineOutExpSim_V10.svg'<br> The final Figure 3, 'Figure-3.svg', then was assembled using 'Fig3PDFFalseCMapLineOutExpSim_V10.svg' and 'MolCartoonsFig3.svg'.</p> <p>4. Making Figure 4:<br> (1). Drawing the signal in Fig. 4 panel a<br> Fig. 4a was generated from a py script: 'MakingFig4aMainText.ipynb'<br> Run this script and it will load the data set 'MainFig4aData.npy'generate a figure showing Fig. 4a.<br> Change the figure saving condition to decide whether or not save the figure to both .svg and .png files as below:<br> 'Figure-4a.png'<br> 'Figure-4a.svg'<br> (2). Drawing the signals in Fig. 4 panels b and c<br> These two panels, the signals are drew from a Matlab script: 'MakingFig4bcMainText.m'<br> Run this script and it will load data set 'TerpNatCommFig4.mat' and plot panels b and c<br> Change the figure saving conditions to decide whether or not save to both .svg and .png files as below:<br> 'Fig4DataPanelsbc_v11.fig'<br> 'Fig4DataPanelsbc_v11.png'<br> 'Fig4DataPanelsbc_v11.svg'<br> (3). Fig 4 is then assembled with the 3 data plot panels, molecular cartoons, and equations:<br> Data plot panels:<br> 'Figure-4a.svg'<br> 'Fig4DataPanelsbc_v11.svg'<br> Molecular cartoons:<br> 'Fig4aCartoon.svg' 'Fig4bCartoon.svg' 'Fig4cCartoon.svg'<br> And equations:<br> 'Fig4bEquPhi.svg' 'Fig4cEquPsi.svg'<br> Fig 4 is fully vectorized with file: 'Figure-4.svg' and also saved as a .png version 'Figure-4.png'</p> <p>5. Making Figure-5:<br> The whole Fig. 5 is produced from a single .py script: 'MakingFig5MainText.ipynb'<br> Run the script and it will plot Figure-5. If choosing the save conditions, then it will save Figure-5 to 'Figure-5.svg' and 'Figure-5.png'.</p>
Feasibility of Real-time Ultrasound-guided Spinal Anesthesia Using Dynamic Needle Tip Positioning
ClinicalTrials.gov study NCT04001387. IPD Sharing: NO. Countries: 1. Publications: 8.
Machine learning to extract muscle fascicle length changes from dynamic ultrasound images in real-time
Open the record for dataset details and reuse information.
Data from: Long-term live imaging of the Drosophila adult midgut reveals real-time dynamics of division, differentiation, and loss
Open the record for dataset details and reuse information.
Data from: Real-time social selection maintains honesty of a dynamic visual signal in cooperative fish
Open the record for dataset details and reuse information.
Real-time Evolutionary Landscape of the Bronchial Epithelium and Corresponding Dynamic Immune Cell Alterations in Lung Squamous Cell Carcinogenesis
GEO Series GSE287159. Rattus norvegicus. 10 samples. Type: Expression profiling by high throughput sequencing.
Dynamic Heart Failure Prediction With Real-time Functional Status Data in the Ambulatory Setting
ClinicalTrials.gov study NCT03702062. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
ScienceDex guides
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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)
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.