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71 results for “dynamic lighting”

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zenodo48/100

Dataset for "Light Scalar Meson and Decay Constant in SU(3) Gauge Theory with Eight Dynamical Flavors"

Decoding File Names: Consider the file name f8l24t48b48m00889_S0.csv.  We will break down the meaning of the various pieces of the filename <ul> <li>"f8" means 8 Dirac flavors.</li> <li>"l24t48" means 243×48 lattice.</li> <li>"b48" means beta=4.8, related to the inverse bare gauge coupling.</li> <li>"m00889" means fermion mass m=0.00889.</li> <li>"S" means flavor-singlet scalar meson. Other options are "P" for flavor non-singlet pseudoscalar meson and "C" for flavor non-singlet scalar meson.</li> <li>"0" an integer from 0 to 4 proportional to the squared length of the spatial momentum vector of the correlation function.</li> </ul> Columns of the CSV files: Each line of the CSV file should contain 41 entries, separated by commas. Refer to the Eq. (8) which defines model A in the accompanying paper to understand the physical interpretation of these parameters. <ol> <li>Model number: 1 is model A, 2 is model B, 3 is model C.</li> <li>nmax: the number of non-oscillating states in the fit.</li> <li>jmax: the number of oscillating states in the fit.</li> <li>tmin: the minimum t value used in the fit.</li> <li>tmax: the maximum t value used in the fit.</li> <li>𝜒2 of the fit.</li> <li>\(\log\ p\left(\left.M\right|D\right)\): log of model probability used in Bayesian model averaging.</li> <li>fit value for c0 (model A) or \(\overline{c}_0\) (model B).</li> <li>fit error for c0 (model A) or \(\overline{c}_0\) (model B).</li> <li>fit value for c1.</li> <li>fit error for c1.</li> <li>fit value for c2.</li> <li>fit error for c2.</li> <li>fit value for c3.</li> <li>fit error for c3.</li> <li>fit value for c4.</li> <li>fit error for c4.</li> <li>fit value for \(c_1^\prime\).</li> <li>fit error for \(c_1^\prime\)</li> <li>fit value for \(c_2^\prime\).</li> <li>fit error for \(c_2^\prime\).</li> <li>fit value for \(c_3^\prime\).</li> <li>fit error for \(c_3^\prime\).</li> <li>fit value for \(c_4^\prime\).</li> <li>fit error for \(c_4^\prime\).</li> <li>fit value for \(\log(E_2 - E_1)\).</li> <li>fit error for \(\log(E_2-E_1)\).</li> <li>fit value for \(\log(E_3-E_2)\).</li> <li>fit error for \(\log(E_3-E_2)\).</li> <li>fit value for \(\log(E_4-E_3)\).</li> <li>fit error for \(\log(E_4-E_3)\).</li> <li>fit value for \(\log(E_2^\prime - E_1^\prime)\).</li> <li>fit error for \(\log(E_2^\prime - E_1^\prime)\).</li> <li>fit value for \(\log(E_3^\prime - E_2^\prime)\).</li> <li>fit error for \(\log(E_3^\prime - E_2^\prime)\).</li> <li>fit value for \(\log(E_4^\prime - E_3^\prime)\).</li> <li>fit error for \(\log(E_4^\prime - E_3^\prime)\).</li> <li>fit value for \(\log(E_1)\).</li> <li>fit error for \(\log(E_1)\).</li> <li>fit value for \(\log(E_1^\prime)\).</li> <li>fit error for \(\log(E_1^\prime)\).</li> </ol>

opencc-by-4.0Jun 2023View details →

zenodo44/100

The DR-Train dataset: dynamic responses, GPS positions and environmental conditions of two light rail vehicles in Pittsburgh

Note: Downloading the large data file could have a timeout issue. If you cannot directly download it here, please use the following link as a complementary method for getting the data.  <a href="https://drive.google.com/drive/folders/1oKn7IN7zznQuhwjDCDdjq8r9wHJYBEhj?usp=sharing">https://drive.google.com/drive/folders/1oKn7IN7zznQuhwjDCDdjq8r9wHJYBEhj?usp=sharing</a>   This dataset contains the dynamic responses (acceleration records) of two passenger trains with corresponding GPS positions, environmental conditions and track maintenance schedules for a light rail network in the city of Pittsburgh, Pennsylvania in the United States of America. In particular, two light rail vehicles were instrumented (identified as LRV4306 and LRV4313):  LRV 4306 has 5 acceleration channels, corresponding to the two uni-axial accelerometers inside the train and the three channels of the tri-axial accelerometer on the wheel truck. - The last digit of each acceleration file: 1, 2, 3, 4, 5 - Corresponding sensor channels: tri-axial x, tri-axial y, tri-axial z, front cabinet uni-axial, back cabinet uni-axial LRV 4313 has 8 acceleration channels, corresponding to the two uni-axial accelerometer and the two tri-axial accelerometers inside the train. - The last digit of each acceleration file: 1, 2, 3, 4, 5, 6, 7, 8 - Corresponding sensor channels: front cabinet uni-axial, back cabinet uni-axial, front tri-axial x, front tri-axial y, front tri-axial z, back tri-axial x, back tri-axial y, back tri-axial z. - x longitudinal (vehicle moving direction); y-axis, transverse; z-axis, vertical. The dataset contained in this repository is a condensed version of the original raw data. While the accelerometers on the train were sampled continuously, this dataset contains only those measurements for when the train was actually moving along the track (i.e. not idling at a terminal). The data is stored in binary MAT-files (a MATLAB/Octave data format). These files contain MATLAB objects of the class "pass", which is defined in the file pass.m that can be found in the "code" folder. Specifically, two MAT-files named "obj_dic.mat", and found in the "LRV4306" and "LRV4313" folders, contain the "pass" objects of the two trains, respectively. Each category is described in detail. For more detail on the regions of the track, refer to the 'region.fig' file in this folder. The track was divided into distinct regions so that the data over specific sections of track could be compared. These regions were chosen for two reasons:  (1) within a region, the train always followed the same track and  (2) there are no tunnels in them so the GPS data is relatively consistent.  To get started, using MATLAB or Octave try running "main_script.m" in the "code" folder. A data descriptor paper with details of the data collection process was published. Please cite as Liu, J., Chen, S., Lederman, G., Kramer, D. B., Noh, H. Y., Bielak, J., Garrett, J. H., Kovačević, J., & Berges, M. Dynamic responses, GPS positions and environmental conditions of two light rail vehicles in Pittsburgh. Scientific Data, 6, 146. <a href="https://doi.org/10.1038/s41597-019-0148-9">https://doi.org/10.1038/s41597-019-0148-9</a>(2019) Liu, J., Chen, S., Lederman, G., Kramer, D. B., Noh, H. Y., Bielak, J., Garrett, J. H., Kovačević, J., & Berges, M. The DR-Train dataset: dynamic responses, GPS positions and environmental conditions of two light rail vehicles in Pittsburgh. Zenodo, <a href="https://doi.org/10.5281/zenodo.1432702">https://doi.org/10.5281/zenodo.1432702</a>(2018). For questions or suggestions please e-mail Jingxiao Liu <liujx@stanford.edu>

opencc-by-4.0Oct 2018View details →

zenodo40/100

Supplemental Figures for: "The SDSS-V Black Hole Mapper Reverberation Mapping Project: Multi-Line Dynamical Modeling of a Highly Variable Active Galactic Nucleus with Decade-long Light Curves"

Additional figures for the paper The SDSS-V Black Hole Mapper Reverberation Mapping Project: Multi-Line Dynamical Modeling of a Highly Variable Active Galactic Nucleus with Decade-long Light Curves.  <h2> </h2> <h2>Interactive Figure Data</h2> Data files used to create the intreactive version of Figure 5 in the publication. There is a version of each file for each line species in the plot (i.e., Hα, Hβ, and MgII). clouds_{line_name}.csv: A CSV file containing the cloud positions, line-of-sight velocities, and weights. The columns of the file are x [light-day], y [light-day], z [light-day], velocity [km/s], and weight. transfer_function_velocity_{line_name}.csv: A CSV file containing x-axis of the transfer function panels, the rest-frame velocity. transfer_function_tau_{line_name}.csv: A CSV file containing the y-axis of the transfer function panels, the rest-frame time delay τ in days. transfer_function_{line_name}.csv: A CSV file containing the transfer function Ψ.   <h2>Model-Related Figures</h2> fitplot_low.pdf: Same as Figure 4 in the publication, but for the low state. fitplot_high.pdf: Same as Figure 4 in the publication, but for the high state. geoplot_low.pdf: Same as Figure 5 in the publication, but for the low state. geoplot_high.pdf: Same as Figure 5 in the publication, but for the high state. lagplot_low.pdf: Same as Figure 6 in the publication, but for the low state. lagplot_high.pdf: Same as Figure 6 in the publication, but for the high state.    <h2>Spectral Reduction Method Comparison</h2> spec_decomp_pyqsofit.pdf: A figure showing the spectral decomposition performed in PyQSOFit for the processed line profiles for Hβ, Hα, and MgII for an example epoch. The total spectrum is shown in black, and each of the decomposed elements are shown, color-coded using the legend above the three panels. input_method_comp.pdf: A figure showing the processed multi-epoch line profiles for each spectral reduction method (PyQSOFit and PrepSpec). Each column corresponds to a given line (labeled above), and each row corresponds to a given spectral reduction method (labeled on the right). Note that the scales for each panel are different.   <h2>Published Value Comparison</h2> pubval_table.pdf: A table comparing the values obtained for certain physically relevant parameters obtained from our BRAINS modeling to those obtained in Shen et al. (2024).    <h2>Joint Posterior Analysis</h2> joint_line_posterior_table.pdf: A table containing the median values (and their uncertainties) extracted from the joint posteriors for a few key model parameters. These joint posteriors are produced for a given state, across all line species.    <h2>Virial Factor Analysis</h2> fcomp.pdf: A comparison of the virial factor values obtained by using the line dispersion (σ) and FWHM of each of the lines in each of the states. fcorr_table.pdf: A table showing the correlations between the virial factor and model parameters (i.e., the slopes obtained using <a href="https://github.com/jmeyers314/linmix">LinMix</a> assuming a linear relationship, and the correlation coefficients). Values are given for virial factors obtained using both the line dispersion (σ) and FWHM.

opencc-by-4.0Aug 2024View details →

dryad40/100

Unveiling the genetic networks: Exploring the dynamic interaction of photosynthetic phenotypes in woody plants across varied light gradients

Background: Understanding the mechanisms by which genes control and regulate complex quantitative traits during periods of fluctuating resources remains a challenging and uncertain task in photosynthesis studies. Most studies have focused on the structure of photosynthesis, the photosynthetic response under stress, or the genetic mechanisms involved in photosynthetic effects and neglected the interactive genetic mechanism that governs various traits through significant quantitative trait loci (QTLs). Results In this study, we have developed a differential dynamic system that enables the identification of QTLs based on the photosynthetic phenotypic and genotypic data under varying levels of light intensity gradients. The framework not only allows for the assessment of the direct effects of QTLs on phenotypes but also captures how they influence interactions among phenotypes as light intensities change. We have analyzed the genetic effects and genetic variance, visualized the genetic network associated with photosynthesis interactions, and validated the effectiveness and stability of the DDS framework. Pivotal QTLs were identified individually to uncover the process and pattern of interaction. Through functional annotation, we made an intriguing discovery that seemingly unimportant QTLs can still have significant genetic effects on phenotypic changes through their regulation with other QTLs. Conclusions This finding emphasizes the significance of considering the interactive genetic architecture when seeking to understand the genetic interaction mechanism of photosynthesis in natural populations of woody plants. Moreover, our research provides a novel framework that can be extended to explore the interactive genetic architecture among organisms, contributing to a deeper understanding of stress resistance mechanisms in woody plants.

opencc-zeroNov 2023View details →

zenodo40/100

Scanning dynamic light scattering optical coherence tomography for measurement of high omnidirectional flow velocities

This repository contains raw data and analysis routines of the publication “Scanning dynamic light scattering optical coherence tomography for measurement of high omnidirectional flow velocities” in Optics Express (<a href="https://doi.org/10.1364/OE.456139">doi.org/10.1364/OE.456139</a>). The reader is free to use the scripts and data in this depository if the manuscript is correctly cited in their work. For further questions, feel free to contact the corresponding author. Python 3.7 was used for programming. Keep in mind that running files with larger time series length may take up to 5-10 minutes. For ideal scanning alignment each dataset includes diffusion, focus (beam waist) calibration, and flow measurements (using both M-scan and B-scan methods) for all used sample lengths. The names “M-scan” and “A-scan” are used interchangeably. The analysis process is as follows: firstly, the diffusion coefficient is determined for every sample size (time series length) to be analyzed using the script ‘Diffusion.py’. Secondly, the beam waist (focus) calibration is performed using the script ‘Beam Waist.py’. Since the beam waist should be constant for each dataset, choose the value obtained from the file with a largest time series length for minimizing the statistical uncertainty and fix it for a given dataset. Beam scanning for our setup is not exactly perpendicular to the optical axis. Therefore, for B-scan Doppler flow measurements the calibration parameter v_d, quantifying the axial scan bias, must be used. This calibration parameter varies with time series length and needs to be obtained for each sample size. This is done with the same script as the beam waist calibration. Thirdly, the Doppler angle is determined using M-scan measurement with the lowest discharge rate using the script ‘Angle.py’. Finally, the flow profiles are obtained both for M-scan and B-scan methods with predetermined calibration parameters using the script ‘Flow.py’. All file names are sufficiently descriptive, showing sample size, scan mode, measurement type and discharge rate. The number on the file name represents the time series length. For arbitrary scanning alignment, the dataset includes one diffusion and one focus (beam waist) measurements for calibration purposes. The diffusion measurement is used only for the beam waist calibration and not for flow measurements. It also contains several B-scan flow measurements (with different scan speeds) for every discharge rate. The analysis process is same as before but without the angle calibration step. Use the script ‘Omnidirectional.py’ for this step. The table below summarizes all datasets and Python scripts uploaded to this repository. <table align="center"> <tbody> <tr> <td> Name </td> <td> Applicability </td> <td> Description </td> </tr> <tr> <td> Dataset, 12-04-2021.zip </td> <td> Ideal scan alignment </td> <td> Dataset for Doppler angle of 0.39 deg and alignment angle of 0 deg. </td> </tr> <tr> <td> Dataset, 16-04-2021.zip </td> <td> Ideal scan alignment </td> <td> Dataset for Doppler angle of 0.94 deg and alignment angle of 0.94 deg. </td> </tr> <tr> <td> Dataset, 20-04-2021.zip </td> <td> Ideal scan alignment </td> <td> Dataset for Doppler angle of 1.58 deg and alignment angle of 2.26 deg. </td> </tr> <tr> <td> Dataset, 18-05-2021.zip </td> <td> Arbitrary alignment </td> <td> Dataset for alignment angle of 2.7 deg. </td> </tr> <tr> <td> Chirp.data </td> <td> Both methods </td> <td> File containing k-interpolation data </td> </tr> <tr> <td> ReadOCTFile.py </td> <td> Both methods </td> <td> Written by Jos de Wit, this module reads and imports spectra from raw OCT files. </td> </tr> <tr> <td> DataProcessing.py </td> <td> Both methods </td> <td> This module contains all analysis and processing routines. </td> </tr> <tr> <td> Diffusion.py </td> <td> Both methods </td> <td> This script determines diffusion coefficient from raw OCT spectra. </td> </tr> <tr> <td> Beam Waist.py </td> <td> Both methods </td> <td> This script determines focus beam waist from raw OCT spectra. </td> </tr> <tr> <td> Angle.py </td> <td> Ideal scan alignment </td> <td> This script determines Doppler angle from raw OCT spectra. </td> </tr> <tr> <td> Flow.py </td> <td> Ideal scan alignment </td> <td> This script determines M-scan and B-scan flow profiles from raw OCT spectra. </td> </tr> <tr> <td> Omnidirectional.py </td> <td> Arbitrary alignment </td> <td> This script determines flow profiles for arbitrary scan alignment. </td> </tr> </tbody> </table>

opencc-by-4.0Jun 2022View details →

zenodo40/100

Diffusion Coefficient Analysis by Dynamic Light Scattering Enables Determination of Critical Micelle Concentration

This upload contains dynamic light scattering data files obtained from the work described in the manuscript that is published by Lena Nielinger and co-workers in ChemPlusChem (<a href="https://doi.org/10.1002/cplu.202400645">https://doi.org/10.1002/cplu.202400645</a>) (WILEY). The files in this repository contain dynamic light scattering data obtained from the analysis of different detergents series and can be downloaded and analysed with a Zetasizer software according to the instructions procied in the manuscript. For information on how to obtain the the Zetasizer software, we refer to the customer support and/or website of the company Malvern Panalytical.

opencc-by-4.0Sep 2024View details →

zenodo40/100

Temporal dynamics of biodiversity effects and light-use related traits in two intercropping systems

Dataset for "Temporal dynamics of biodiversity effects and light-use related traits in two intercropping systems"

opencc-by-4.0Jun 2021View details →

zenodo40/100

Dynamic light scattering datasets used to assess the Raynals software

Updated - 23/04/2023 This folder contains the associated data from Burastero et al., 'Raynals, an online tool for the analysis of dynamic light scattering' The experimental datasets can be found at ./experimentalData/ The artificially generated datasets at ./SimulatedDataDLS_case1 and ./SimulatedDataDLS_case2 To produce the experimental datasets, we performed measurements on Carbonic Anhydrase, Bovine Serum Albumin, Gold Nanoparticles, and three in-house samples: A protein with a beta-Propeller domain, a coiled-coil like protein, and an intrinsically disordered protein. Additionally, you'll find R scripts to generate Fig. 3, Fig. 5, and Fig. S1 to S4.

opencc-by-4.0Apr 2023View details →

dryad40/100

Unveiling the genetic networks: Exploring the dynamic interaction of photosynthetic phenotypes in woody plants across varied light gradients

Open the record for dataset details and reuse information.

publicNov 2023View details →

dryad40/100

Data from: Experimental study on the impact of continuous and dynamic artificial light at night on moths originating from different skyglow conditions

Open the record for dataset details and reuse information.

publicSep 2025View details →

edi40/100

Light Availability:Nutrient Network: A cross-site investigation of bottom-up control over herbaceous plant community dynamics and ecosystem function.

This experiment is one implementation of a globally distributed experiment, known as the Nutrient Network. At Cedar Creek, as in over 70 other sites in grasslands around the world, the experiment aims to describe impacts of increased nutrients (nitrogen, phosphorus, potassium, sulfur and other metals) and decreased herbivory (removal of mammals by fencing). Two overarching questions are being explored with these manipulations: 1. To what extent are plant production and diversity co-limited by multiple nutrients in herbaceous-dominated communities? 2. Under what conditions do grazers or fertilization control plant biomass, diversity, and composition? By utilizing identical protocols at diverse grassland sites around the world, NutNet aims to uncover both the generalities in ecosystem functioning, and the contingencies or differences which can obscure those common mechanisms. In addition to the standard NutNet protocol, e247 includes an additional low Nitrogen gradient (1 gram Nitrogen per meter squared per year and 5 grams Nitrogen per meter squared per year in addition to the standard 10 grams Nitrogen per meter squared per year).

openCC0May 2021View details →

dryad36/100

Data from: Effects of soil type and light on height growth, biomass partitioning, and nitrogen dynamics on 22 species of tropical dry forest tree seedlings: comparisons between legumes and nonlegumes

PREMISE OF THE STUDY: The seedling stage is particularly vulnerable to resource limitation, with potential consequences for community composition. We investigated how light and soil variation affected early growth, biomass partitioning, morphology, and physiology of 22 tree species common in tropical dry forest, including eight legumes. Our hypothesis was that legume seedlings are better at taking advantage of increased resource availability, which contributes to their successful regeneration in tropical dry forests. METHODS: We grew seedlings in a full-factorial design under two light levels in two soil types that differed in nutrient concentrations and soil moisture. We measured height biweekly and, at final harvest, biomass partitioning, internode segments, leaf carbon, nitrogen, δ 13 C, and δ 15 N. KEY RESULTS: Legumes initially grew taller and maintained that height advantage over time under all experimental conditions. Legumes also had the highest final total biomass and water-use efficiency in the high-light and high-resource soil. For nitrogen-fixing legumes, the amount of nitrogen derived from fixation was highest in the richer soil. Although seed mass tended to be larger in legumes, seed size alone did not account for all the differences between legumes and nonlegumes. Both belowground and aboveground resources were limiting to early seedling growth and function. CONCLUSIONS: Legumes may have a different regeneration niche, in that they germinate rapidly and grow taller than other species immediately after germination, maximizing their performance when light and belowground resources are readily available, and potentially permitting them to take advantage of high light, nutrient, and water availability at the beginning of the wet season.

opencc-zeroAug 2019View details →

zenodo36/100

Photoactivation of the Orange Carotenoid Protein Requires Two Light-Driven Reactions Mediated by a Metastable Monomeric Intermediate – Absorption Spectra and Global Analysis Results, Molecular Dynamics Simulations

Time-resolved absorption and molecular dynamics trajectory datasets associated with: Rose, J. B.; Gascón, J. A.; Sutter, M.; Sheppard, D. I.; Kerfeld, C. A.; Beck, W. F. Photoactivation of the Orange Carotenoid Protein Requires Two Light-Driven Reactions Mediated by a Metastable Monomeric Intermediate. Phys. Chem. Chem. Phys. 2023, DOI: 10.1039/d3cp04484j.

opencc-by-4.0Dec 2023View details →

zenodo36/100

Precision and bias in dynamic light scattering optical coherence tomography measurements of diffusion and flow

This repository contains raw data and analysis routines of the publication “Precision and bias in dynamic light scattering optical coherence tomography measurements of diffusion and flow” in Biomedical Optics Express (doi.org/10.1364/BOE.505847). The reader is free to use the scripts and data in this depository if the manuscript is correctly cited in their work. For further questions, feel free to contact the corresponding author. Python 3.7 was used for programming. Kindly note that simulating autocorrelation functions from extensive time series data, especially with a high repetition rate, can be time-consuming, often requiring more than 5-10 minutes. Despite parallelized processing routines for the measurement data, the full analysis may still take up to an hour. Please restart the kernel and run the code again if the parallelization fails. For the diffusion measurement under static conditions, there is only one file. However, for experiments involving both flowing and diffusing particles, the dataset comprises diffusion calibration, focus (beam shape) calibration, and flow measurement files. Due to the upload size limitations of the Zenodo repository, only the flow measurements corresponding to one discharge rate have been uploaded. Furthermore, only the non-dilute flow dataset has been uploaded for the same reason. However, for the dilute flow, the analysis logic remains the same, but users will need to utilize the complete g2 formula outlined in Section 2.2 of our article. All file names are sufficiently descriptive, showing whether it is diffusion, focus (waist) calibration or flow measurement. To conduct the analysis, it's essential to have information regarding the time series length (number of A-scans), the number of repeats (B-scans), and the acquisition rate. The results are plotted at the end of our analysis routines. The parameters are displayed as a function of depth. Users can readily compute the Signal-to-Noise Ratio (SNR) at each depth by utilizing the fitted autocorrelation amplitudes. Occasionally, the fitted amplitudes may surpass unity. In such instances, users can assume an extremely high (even infinite) SNR. <div> <table> <tbody> <tr> <td> Name </td> <td> Description </td> <td> Parameters </td> </tr> <tr> <td> Diffusion_03032023.oct </td> <td> Diffusion measurement file. </td> <td> Na=4096, Nb=1100, 5.5 kHz </td> </tr> <tr> <td> Diffusion_07032023.oct </td> <td> Diffusion calibration file for flow measurement. </td> <td> Na=4096, Nb=10, 36 kHz </td> </tr> <tr> <td> Waist_07032023.oct </td> <td> Beam waist calibration file for flow measurement. </td> <td> Na=4096, Nb=40, 36 kHz </td> </tr> <tr> <td> Q=2_07032023.oct </td> <td> Flow measurement file for a discharge rate of 2 ml/min. </td> <td> Na=4096, Nb=1000, 36 kHz </td> </tr> <tr> <td> Chirp.data </td> <td> File containing k-interpolation data. </td> <td>   </td> </tr> <tr> <td> ReadOCTFile.py </td> <td> Written by Jos de Wit, this module reads and imports spectra from raw OCT files. </td> <td>   </td> </tr> <tr> <td> Data_processing.py </td> <td> This module contains all analysis, simulation and processing routines. </td> <td>   </td> </tr> <tr> <td> Simulation_diffusion.py </td> <td> This script is for simulating and fitting g1 and g2 from diffusive particles. </td> <td>   </td> </tr> <tr> <td> Simulation_flow.py </td> <td> This script is for simulating and fitting g1 and g2 from flowing and diffusive particles. </td> <td>   </td> </tr> <tr> <td> Diffusion_parallel.py </td> <td> This script is for analyzing static diffusion measurements performed using Thorlabs Ganymede OCT system. </td> <td>   </td> </tr> <tr> <td> Flow_parallel.py </td> <td> This script is for analyzing flow measurements performed using Thorlabs Ganymede OCT system. </td> <td>   </td> </tr> </tbody> </table> </div>

opencc-by-4.0Jan 2024View details →

zenodo36/100

3D dataset of root soil bacteria dynamics obtained using large field of view light sheet microscope

A tailor made dual-illumination light-sheet system acquired photons scattered from the plant whilst fluorescence emissions were simultaneously captured from transparent soil particles and labelled microorganisms, allowing the generation of quantitative data on samples approximately 3600 mm3 in size with as good as 5 µm resolution at a rate of up to one scan every 30 minutes. The dataset shows the dynamics of Bacillus subtilis populations in the rhizosphere of lettuce plants in real time.

opencc-by-4.0Nov 2021View details →

zenodo36/100

Dynamic light scattering differentiate parameters of blood flow

This dataset demonstrates blood perfusion recordings measurements on the 3rd fingers and wrists simultaneously (sitting position) in volunteers of three groups: healthy volunteers younger group (20 years old), healthy volunteers younger group (~55 years old), patients with Diabetes Type 2 (~55 years old).

opencc-by-4.0Aug 2021View details →

zenodo36/100

Supporting experimental data for "Z-average and Polydispersity Index of Dynamic Light Scattering" by Bazzoni et al.

Data (auto-correlation functions used for preparing Figure 5-8) accompanying manuscript "Z-average and Polydispersity Index of Dynamic Light Scattering" by Bazzoni et al.   1.       “Figure 5_ACFs.zip” Large sets of autocorrelation functions (ASCII format) recorded to demonstrate heteroscedastic and angle-dependent uncertainties. Each file may list the basic parameters, instrument’s estimation of Z-average (not evaluated and not used by us), the auto-correlation function, and a low temporal-resolution trace of the scattering intensity. Figure 5 is based on the analyses of subsets of this data set. 2.       “Figure 6 & 7_ACFs.zip” Large sets of autocorrelation functions (ASCII format) recorded to demonstrate the impact of uncertainty on precision, and the impact of duration and photon counting rate on the signal-to noise ratio. Each file may list the basic parameters, instrument’s estimation of Z-average (not evaluated and not used by us), the auto-correlation function, and a low temporal-resolution trace of the scattering intensity. Figure 6 and 7 are based on the analyses of subsets of this data set. 3.       “Figure 8_ACFs.zip” Large sets of autocorrelation functions (ASCII format) recorded to demonstrate the positive impact of data averaging on precisions. Each file may list the basic parameters, instrument’s estimation of Z-average (not evaluated and not used by us), the auto-correlation function, and a low temporal-resolution trace of the scattering intensity. Figure 8 is based on the analyses of subsets of this data set.

opencc-by-4.0Aug 2022View details →

zenodo36/100

Wavenumber-dependent dynamic light scattering optical coherence tomography measurements of collective and self-diffusion

This repository contains raw data and analysis routines of the publication “Wavenumber-dependent dynamic light scattering optical coherence tomography measurements of collective and self-diffusion” in Optics Express (doi.org/10.1364/OE.521702). The reader is free to use the scripts and data in this depository if the manuscript is correctly cited in their work. For further questions, feel free to contact the corresponding author. Python 3.11 was used for programming. Kindly note that simulating autocorrelation functions from extensive time series data, especially with a high repetition rate, can be time-consuming, often requiring more than 20-30 minutes. Despite parallelized processing routines for the measurement data, the full analysis may still take up to an hour. Please restart the kernel and run the code again if the parallelization fails. Also, keep in mind the significant RAM usage. We've conducted measurements using both a custom-built OCT system and the Thorlabs OCT system. The custom setup specifically focused on measuring diffusion in concentrated suspensions, while the Thorlabs OCT system was used to analyze both concentrated and dilute suspensions. To analyze the data from the custom setup, we require an additional dark measurement file. Conversely, analyzing the Thorlabs measurements necessitates a chirp interpolation file. All filenames, whether for raw data or analysis files, are sufficiently descriptive. Files obtained with the Thorlabs OCT system are easily identifiable as they contain “Thorlabs” in their names. To conduct the analysis of Thorlabs measurements, it's essential to have information regarding the time series length (number of A-scans), the number of repeats (B-scans), and the acquisition rate. The results are plotted at the end of our analysis routines, with the parameters displayed as a function of depth or wavenumber. Raw measurement files and analysis routines are described below. <div> <table> <tbody> <tr> <td> Name </td> <td> Description </td> <td> Parameters </td> </tr> <tr> <td> 10050, 10 us.mat </td> <td> Interference intensity from the custom setup for the concentrated Kostrosöl 10050 sample. </td> <td> Na=8192, Nb=20, 4.5 kHz </td> </tr> <tr> <td> CS50-28, 10 us.mat </td> <td> Interference intensity from the custom setup for the concentrated Levasil CS50-28 sample. </td> <td> Na=8192, Nb=20, 4.5 kHz </td> </tr> <tr> <td> Mix, 10 us.mat </td> <td> Interference intensity from the custom setup for the concentrated mixed sample. </td> <td> Na=8192, Nb=20, 4.5 kHz </td> </tr> <tr> <td> Dark, 10 us.mat </td> <td> Background interference intensity from a custom setup. </td> <td> Na=2048, Nb=5, 4.5 kHz </td> </tr> <tr> <td> Concentrated 8050, Thorlabs.oct </td> <td> Interference intensity from the Thorlabs OCT for the concentrated Kostrosöl 8050 sample. </td> <td> Na=65536, Nb=10, 36 Khz </td> </tr> <tr> <td> Concentrated 9550, Thorlabs.oct </td> <td> Interference intensity from the Thorlabs OCT for the concentrated Kostrosöl 9550 sample. </td> <td> Na=65536, Nb=10, 36 Khz </td> </tr> <tr> <td> Concentrated mix, Thorlabs.oct </td> <td> Interference intensity from the Thorlabs OCT for the concentrated mixed sample. </td> <td> Na=65536, Nb=10, 36 Khz </td> </tr> <tr> <td> Dilute 8050, Thorlabs.oct </td> <td> Interference intensity from the Thorlabs OCT for the dilute Kostrosöl 8050 sample. </td> <td> Na=32768, Nb=20, 36 Khz </td> </tr> <tr> <td> Dilute 9550, Thorlabs.oct </td> <td> Interference intensity from the Thorlabs OCT for the dilute Kostrosöl 9550 sample. </td> <td> Na=32768, Nb=20, 36 Khz </td> </tr> <tr> <td> Dilute mix, Thorlabs.oct </td> <td> Interference intensity from the Thorlabs OCT for the dilute mixed sample. </td> <td> Na=32768, Nb=20, 36 Khz </td> </tr> <tr> <td> Chirp.data </td> <td> File containing k-interpolation data for the Thorlabs OCT measurements. </td> <td>   </td> </tr> <tr> <td> ReadOCTFile.py </td> <td> Written by Jos de Wit, this module reads and imports spectra from raw Thorlabs OCT files. </td> <td>   </td> </tr> <tr> <td> Data_processing.py </td> <td> This module contains all analysis functions. </td> <td>   </td> </tr> <tr> <td> Custom_concentrated.py </td> <td> The script is for analyzing raw concentrated measurement files from the custom setup. </td> <td>   </td> </tr> <tr> <td> Thorlabs_concentrated.py </td> <td> The script is for analyzing raw concentrated measurement files from the Thorlabs setup. </td> <td>   </td> </tr> <tr> <td> Thorlabs_dilute.py </td> <td> The script is for running analysis of raw dilute measurement files from the Thorlabs setup. </td> <td>   </td> </tr> </tbody> </table> </div>

opencc-by-4.0May 2024View details →

zenodo36/100

Repository for "Light-induced cortical excitability reveals programmable shape dynamics in starfish oocytes"

Data and code repository for paper "Light-induced cortical excitability reveals programmable shape dynamics in starfish oocytes". DOI tbd.

opencc-by-4.0Nov 2024View details →

zenodo36/100

Dynamic light scattering data

The upload archive consists of all the obtained results based on DLS measurements ( commonly known as dynamic light scattering) . More detain you could find in the cited article above.

opencc-by-4.0Jun 2021View details →

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