Find research datasets worth reusing

opencc-by-4.0Dec 2022View details →

ICEBEAR 3D coherent scatter radar data for 2020, 2021

Daily ICEBEAR 3D data for 2020, 2021, organized in nx12 matrices where n is the number of observations (echoes) in any given day. The columns are <ol> <li>Year (UT)</li> <li>Month (UT)</li> <li>Day (UT)</li> <li>Hour (UT)</li> <li>Minute (UT)</li> <li>Second (UT)</li> <li>Longitude [degrees]</li> <li>Latitude[degrees]</li> <li>Altitude [km]*</li> <li>Doppler velocity [m/s]</li> <li>SNR [dB]</li> <li>Beam number (1 = east, 2 = center, 3 = west)</li> </ol> * The west-beam altitudes are anomalous.

opencc-by-4.0Jan 2023View details →

Sub-diffusion flow velocimetry with number fluctuation optical coherence tomography

This repository contains raw data and analysis routines of the publication “Sub-diffusion flow velocimetry with number fluctuation optical coherence tomography” in Optics Express (doi.org/10.1364/OE.474279). 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 10 minutes and 2D flow profile analysis may take up to one hour. For 1D depth-resolved measurements each dataset includes diffusion, focus (beam shape) calibration, and flow measurements for different discharge rates, Q. For all measurements time series length is 31000 points and the sampling rate is 5.5 kHz. Diffusion measurements are performed on a static sample with a stationary beam. Focus (waist) calibration measurements are performed by moving the OCT beam over the static sample with a known velocity. Flow measurements are performed on the flowing sample with the stationary beam. Each measurement is averaged 6 times. The analysis process is as follows: Firstly, the beam waist (focus) calibration is performed using the script ‘Beam Shape.py’. For improved accuracy it is preferable to perform several measurements and average beam waist values at every depth. Secondly, the Doppler angle is determined using a flow measurement with the largest discharge rate using the script ‘Doppler Angle.py’. Thirdly, the flow profiles are obtained with predetermined calibration parameters using the script ‘Flow Profile.py’. Finally, the particle number density is calculated using the script ‘Number Density.py’. This requires knowledge of particle size for calculating the theoretical number density values. The particle size can be determined using the script ‘Diffusion.py’. All file names are sufficiently descriptive, showing whether it is diffusion, focus (waist) calibration or flow measurement. For 2D depth and laterally resolved measurements each dataset includes diffusion, focus (beam shape) calibration, M-scan and B-scan flow measurements for different discharge rates, Q. Diffusion and focus calibration measurements are same as in 1D. M-scan flow measurements are performed on a flowing sample with a stationary beam. They are same as flow measurements in 1D and are only used for determining the Doppler angle. B-scan flow measurements are performed by moving the OCT beam over the flowing sample with a known velocity. 2D flow profiles can be determined using the script ‘2D Flow Profile.py’. The table below summarizes all datasets and Python scripts uploaded to this repository. <table align="center"> <tbody> <tr> <td> Name </td> <td> Usability </td> <td> Description </td> </tr> <tr> <td> Dataset, 15-03-2022.zip </td> <td> 1D measurements </td> <td> Dataset for Doppler angle of 0.34 deg and alignment angle of 0 deg. </td> </tr> <tr> <td> Dataset, 16-03-2022.zip </td> <td> 1D measurements </td> <td> Dataset for Doppler angle of 1.74 deg and alignment angle of 2.3 deg. </td> </tr> <tr> <td> Dataset, 22-03-2022.zip </td> <td> 1D measurements </td> <td> Dataset for Doppler angle of 1.00 deg and alignment angle of 1.15 deg. </td> </tr> <tr> <td> Dataset, 08-07-2022.zip </td> <td> 2D measurements </td> <td> Dataset for Doppler angle of 1.84 deg and alignment angle of 0 deg. </td> </tr> <tr> <td> Chirp.data </td> <td> All measurements </td> <td> File containing k-interpolation data </td> </tr> <tr> <td> ReadOCTFile.py </td> <td> All measurements </td> <td> Written by Jos de Wit, this module reads and imports spectra from raw OCT files. </td> </tr> <tr> <td> Processing.py </td> <td> All measurements </td> <td> This module contains all analysis and processing routines. </td> </tr> <tr> <td> Diffusion.py </td> <td> All measurements </td> <td> This script determines particle size from raw OCT spectra. </td> </tr> <tr> <td> Beam Shape.py </td> <td> All measurements </td> <td> This script determines axial beam shape from raw OCT spectra. </td> </tr> <tr> <td> Doppler Angle.py </td> <td> All measurements </td> <td> This script determines Doppler angle from raw OCT spectra. </td> </tr> <tr> <td> Flow Profile.py </td> <td> 1D measurements </td> <td> This script determines flow profiles from raw OCT spectra. </td> </tr> <tr> <td> Number Density.py </td> <td> 1D measurements </td> <td> This script determines particle number density raw OCT spectra. </td> </tr> <tr> <td> 2D Flow Profile.py </td> <td> 2D measurements </td> <td> This script determines 2D flow profiles from raw OCT spectra. </td> </tr> </tbody> </table>

opencc-by-4.0Mar 2023View details →

SPIRIT Checklist & Model Consent for 'Predicting Acute and Post-Recovery Outcomes in Cerebral Malaria and Other Comas by Optical Coherence Tomography (OCT in CM) – A protocol for an observational cohort study of Malawian children'

This dataset contains the SPIRIT checklist (adapted to a observational trial) and model consent forms for the OCT in CM study protocol. The protocol will be submitted as a paper to Wellcome Open Research.

opencc-by-4.0Dec 2022View details →

Data for the article "Emission of coherent THz magnons in an antiferromagnetic insulator triggered by ultrafast spin-phonon interactions "

Data for the article "Emission of coherent THz magnons in an antiferromagnetic insulator triggered by ultrafast spin-phonon interactions"  URL: https://arxiv.org/abs/2205.11965 DOI: In press in Nature Communications

opencc-by-4.0May 2023View details →

Data: Modification of ground state chemical reactivity via light-matter coherence in infrared cavities

Data to reproduce Figures in paper "Modification of ground state chemical reactivity via light-matter coherence in infrared cavities" published in DOI:0000/0000000

opencc-by-4.0May 2023View details →

Raw data: Coherent control of a high-orbital hole in a semiconductor quantum dot

This is the raw data supporting the findings in the research article titled "Coherent control of a high-orbital hole in a semiconductor quantum dot". DOI: 10.1038/s41565-023-01442-y

opencc-by-4.0Jun 2023View details →

New Zealand InSAR coherence based on nationwide ascending Sentinel-1 datasets acquired during 2015 to 2021

New Zealand Sentinel-1 coherence using ascending datasets acquired during 2015 to 2021 with ~100m resolution.

opencc-by-4.0Mar 2023View details →

Precise, NLL-SSST-coherence hypocenter catalog for the 2023 Mw 7.8 and Mw 7.6 SE Turkey earthquake sequence.

CSV catalog file and visualizations of NLL-SSST-coherence earthquake relocations for the 2023 Mw 7.8 and Mw 7.6, Kahramanmaraş - Gaziantep, Turkey earthquake sequence (28,264 events, M≥1.5, 2023-01-01 to 2023-06-24). 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. Relocations performed with merged phase arrival data available from AFAD (<a href="https://deprem.afad.gov.tr/event-catalog">https://deprem.afad.gov.tr/event-catalog</a>) and KOERI (<a href="http://www.koeri.boun.edu.tr/sismo/2/bultenler">http://www.koeri.boun.edu.tr/sismo/2/bultenler</a>) downloaded on 2023-06-23. All events in the AFAD catalog with M≥1.5 are used for relocation; for each AFAD event, arrival data for the corresponding events from KOERI is merged when available (epicenter within 10km, origin time within 10 sec). Seismic velocity model is a smoothed version of the "Final 1-D" velocity model from: Acarel, D., Cambaz, M.D., Turhan, F., Mutlu, A.K. & Polat, R., 2019. Seismotectonics of Malatya Fault, Eastern Turkey. Open Geosciences, 11, 1098–1111. Available at: <a href="https://doi.org/10.1515/geo-2019-0085">https://doi.org/10.1515/geo-2019-0085</a>. [For relocations of events starting from 2020-01-01 through 2023-03-04 and including the 2020 Mw 6.8 Elazığ, Turkey sequence, see v1.0 of this dataset: <a href="https://doi.org/10.5281/zenodo.7699882">https://doi.org/10.5281/zenodo.7699882</a>]   This repository archive file contains: Full catalog in CSV format:  X_Turkey_20230624A_Acarel2019smooth_NLL-SSST-coherence_M1.5.csv 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> Key NLL-SSST-coherence configuration files: NLL-SSST-coherence_config/ Visualization images:     Full catalog:  A_Turkey_20230624A_Acarel2019smooth_NLL-SSST-coherence_M1.5.png     Events with (68% location ellipsoid) err ≤ 8km, origin-time color scale:  B_Turkey_20230624A_Acarel2019smooth_NLL-SSST-coherence_M1.5_se8km_OTIME.png     Events with err ≤ 8km:  B_Turkey_20230624A_Acarel2019smooth_NLL-SSST-coherence_M1.5_se8km.png     Events with err ≤ 8km, depth ≤ 10km:  C_Turkey_20230624A_Acarel2019smooth_NLL-SSST-coherence_M1.5_se8km_z-10.png     Events with err ≤ 8km, depth ≥ 10km:  C_Turkey_20230624A_Acarel2019smooth_NLL-SSST-coherence_M1.5_se8km_z10-.png Symbol size is proportional to event magnitude. AFAD stations shown by light gray inverted pyramids. KOERI stations shown by dark gray inverted pyramids. Origin-time plot event colors: Blue: before 2023-02-06 M 7.8 event Yellow: from 2023-02-06  01h17 UTC M 7.8 event through 2023-02-06 10h24 UTC M 7.6 event Orange to Red: after 2023-02-06 10h24 UTC M 7.6 event Plot data: Background image from https://opentopography.org Mapped surface faults (light purple) from: Emre, Ö., Duman, T.Y., Özalp, S., Şaroğlu, F., Olgun, Ş., Elmacı, H. & Çan, T., 2018. Active fault database of Turkey. Bull Earthquake Eng, 16, 3229–3275. <a href="https://doi.org/10.1007/s10518-016-0041-2">https://doi.org/10.1007/s10518-016-0041-2</a> Surface Rupture Lines (green) from: Reitman, N.G., Briggs, R.W., Barnhart, W.D., Thompson Jobe, J.A., DuRoss, C.B., Hatem, A.E., Gold, R.D., Akçiz, S., Koehler, R.D., Mejstrik, J.D., Collett, C., 2023, Fault rupture mapping of the 6 February 2023 Kahramanmaraş, Türkiye, earthquake sequence from satellite data: U.S. Geological Survey data release, <a href="https://doi.org/10.5066/P985I7U2">https://doi.org/10.5066/P985I7U2</a>.   Thanks to Sinan Ozeren, Didem Cambaz, Fatih Turhan, Dogan Kalafat, Selda Altuncu Poyraz, Kıvanç Kekovalı, Onur Tan, Alberto Michelini and Pierre Henry for assistance and discussions.

opencc-by-4.0Dec 2022View details →

Computational 3D resolution enhancement for optical coherence tomography with a narrowband visible light source

This repository contains the code and data underlying the publication "Computational 3D resolution enhancement for optical coherence tomography with a narrowband visible light source" in Biomedical Optics Express 14, 3532-3554 (2023) (doi.org/10.1364/BOE.487345). The reader is free to use the scripts and data in this depository, as long as the manuscript is correctly cited in their work. For further questions, please contact the corresponding author.  Description of the code and datasets Table 1 describes all the Matlab and Python scripts in this depository. Table 2 describes the datasets. The input datasets are the phase corrected datasets, as the raw data is large in size and phase correction using a coverslip as reference is rather straightforward. Processed datasets are also added to the repository to allow for running only a limited number of scripts, or to obtain for example the aberration corrected data without the need to use python. Note that the simulation input data (input_simulations_pointscatters_SLDshape_98zf_noise75.mat) is generated with random noise, so if this is overwritten de results may slightly vary. Also the aberration correction is done with random apertures, so the processed aberration corrected data (exp_pointscat_image_MIAA_ISAM_CAO.mat and exp_leaf_image_MIAA_ISAM_CAO.mat) will also slightly change if the aberration correction script is run anew. The current processed datasets are used as basis for the figures in the publication. For details on the implementation we refer to the publication. <table> <caption>Table 1: The Matlab and Python scripts with their description</caption> <tbody> <tr> <td>Script name</td> <td>Description</td> </tr> <tr> <td>MIAA_ISAM_processing.m</td> <td>This scripts performs the DFT, RFIAA and MIAA processing of the phase-corrected data that can be loaded from the datasets. Afterwards it also applies ISAM on the DFT and MIAA data and plots the results in a figure (via the scripts plot_figure3, plot_figure5 and plot_simulationdatafigure).</td> </tr> <tr> <td>resolution_analysis_figure4.m</td> <td>This figure loads the data from the point scatterers (absolute amplitude data), seeks the point scatterrers and fits them to obtain the resolution data. Finally it plots figure 4 of the publication.</td> </tr> <tr> <td>fiaa_oct_c1.m, oct_iaa_c1.m, rec_fiaa_oct_c1.m, rfiaa_oct_c1.m </td> <td>These four functions are used to apply fast IAA and MIAA. See script MIAA_ISAM_processing.m for their usage.</td> </tr> <tr> <td>viridis.m, morgenstemning.m</td> <td>These scripts define the colormaps for the figures.</td> </tr> <tr> <td>plot_figure3.m, plot_figure5.m, plot_simulationdatafigure.m</td> <td>These scripts are used to plot the figures 3 and 5 and a figure with simulation data. These scripts are executed at the end of script MIAA_ISAM_processing.m.</td> </tr> <tr> <td>Python script: computational_adaptive_optics_script.py</td> <td>Python script that applied computational adaptive optics to obtain the data for figure 6 of the manuscript.</td> </tr> <tr> <td>Python script: zernike_functions2.py</td> <td>Python script that gives the values and carthesian derrivatives of the Zernike polynomials.</td> </tr> <tr> <td>figure6_ComputationalAdaptiveOptics.m</td> <td>Script that loads the CAO data that was saved in Python, analyzes the resolution, and plots figure 6.</td> </tr> <tr> <td>Python script: OCTsimulations_3D_script2.py</td> <td>Python script simulates OCT data, adds noise and saves it as .mat file for use in the matlab script above.</td> </tr> <tr> <td>Python script: OCTsimulations2.py</td> <td>Module that contains a python class that can be used to simulate 3D OCT datasets based on a Gaussian beam.</td> </tr> <tr> <td>Matlab toolbox DIPimage 2.9.zip</td> <td>Dipimage is used in the scripts. The toolbox can be downloaded online or this zip can be used.</td> </tr> </tbody> </table> <table> <caption>The datasets in this Zenodo repository</caption> <tbody> <tr> <td>Name</td> <td>Description</td> </tr> <tr> <td>input_leafdisc_phasecorrected.mat</td> <td>Phase corrected input image of the leaf disc (used in figure 5).</td> </tr> <tr> <td>input_TiO2gelatin_004_phasecorrected.mat</td> <td>Phase corrected input image of the TiO2 in gelatin sample.</td> </tr> <tr> <td>input_simulations_pointscatters_SLDshape_98zf_noise75</td> <td>Input simulation data that, once processed, is used in figure 4.</td> </tr> <tr> <td> exp_pointscat_image_DFT.mat exp_pointscat_image_DFT_ISAM.mat exp_pointscat_image_RFIAA.mat exp_pointscat_image_MIAA_ISAM.mat exp_pointscat_image_MIAA_ISAM_CAO.mat </td> <td>Processed experimental amplitude data for the TiO2 point scattering sample with respectively DFT, DFT+ISAM, RFIAA, MIAA+ISAM and MIAA+ISAM+CAO. These datasets are used for fitting in figure 4 (except for CAO), and MIAA_ISAM and MIAA_ISAM_CAO are used for figure 6.</td> </tr> <tr> <td> simu_pointscat_image_DFT.mat simu_pointscat_image_RFIAA.mat simu_pointscat_image_DFT_ISAM.mat simu_pointscat_image_MIAA_ISAM.mat </td> <td>Processed amplitude data from the simulation dataset, which is used in the script for figure 4 for the resolution analysis.</td> </tr> <tr> <td> exp_leaf_image_MIAA_ISAM.mat exp_leaf_image_MIAA_ISAM_CAO.mat </td> <td>Processed amplitude data from the leaf sample, with and without aberration correction which is used to produce figure 6.</td> </tr> <tr> <td> exp_leaf_zernike_coefficients_CAO_normal_wmaf.mat exp_pointscat_zernike_coefficients_CAO_normal_wmaf.mat </td> <td>Estimated Zernike coefficients and the weighted moving average of them that is used for the computational aberration correction. Some of this data is plotted in Figure 6 of the manuscript.</td> </tr> <tr> <td>input_zernike_modes.mat</td> <td>The reference Zernike modes corresponding to the data that is loaded to give the modes the proper name.</td> </tr> <tr> <td> exp_pointscat_MIAA_ISAM_complex.mat exp_leaf_MIAA_ISAM_complex </td> <td>Complex MIAA+ISAM processed data that is used as input for the computational aberration correction. </td> </tr> </tbody> </table>

opencc-by-4.0Aug 2023View details →

Data release for "Measurements of the muon-neutrino and muon-antineutrino-induced coherent charged pion production cross sections on Carbon-12 by the T2K experiment"

The T2K experiment reports the measurement of the flux averaged charged current coherent pion production cross section for neutrino and anti-neutrino scattering from  a Carbon nucleus.  These results are at a mean (anti)neutrino energy of 0.85~GeV in a restricted final state kinematic phase space. The neutrino measurement is an update to a previous result with systematic uncertainties reduced by a half. The antineutrino measurement is the first measurement of this cross section to be made at these energies. We find that the neutrino and antineutrino cross sections are consistent, as expected from theory, and that both agree with the current theoretical models, the Rein-Sehgal and Berger-Sehgal models. The data release contains a summary of these results as well as neutrino and antineutrino flux histograms with which the reader can make their own flux averaged cross section calculation. The paper is published in <a href="https://doi.org/10.1103/PhysRevD.108.092009">Physical Review D</a> and is available on the <a href="https://arxiv.org/abs/2308.16606">arXiv:2308.16606 [hep-ex]</a>.

opencc-by-4.0Sep 2023View details →

Data for submitted manuscript "Connections between Sub-cloud Coherent Structures and the Life Cycle of Shallow Cumulus Clouds: Evidence from Large Eddy Simulation"

This is the dataset for a submitted manuscript "Connections between Sub-cloud Coherent Structures and the Life Cycle of Shallow Cumulus Clouds: Evidence from Large Eddy Simulation" for peer review. The NetCDF file includes masked objects for sub-cloud coherent structures and cloud for tracking.

opencc-by-4.0Sep 2023View details →

Alpha-frequency feedback to early visual cortex orchestrates coherent naturalistic vision (Rawdata)

During naturalistic vision, the brain generates coherent percepts by integrating sensory inputs scattered across the visual field. Here, we asked whether this integration process is mediated by rhythmic cortical feedback. In EEG and fMRI experiments, we experimentally manipulated integrative processing by changing the spatiotemporal coherence of naturalistic videos presented across visual hemifields. Our EEG data revealed that information about incoherent videos is coded in feedforward-related gamma activity while information about coherent videos is coded in feedback-related alpha activity, indicating that integration is indeed mediated by rhythmic activity. Our fMRI data identified scene-selective cortex and hMT as likely sources of this feedback. Analytically combining our EEG and fMRI data further revealed that feedback-related representations in the alpha band shape the earliest stages of visual processing in cortex. Together, our findings indicate that the construction of coherent visual experiences relies on cortical feedback rhythms that fully traverse the visual hierarchy.

restrictedIPD-UNDECIDEDFeb 2026View details →

Evaluation of HealinG of Polymer-Free Biomlimus A9-Coated Stent by Optical Coherence Tomography (EGO-BIOFREEDOM)

ClinicalTrials.gov study NCT01760876. IPD Sharing: Not stated. Countries: 1. Publications: 7.

restrictedIPD-UNDECIDEDFeb 2026View details →

Optical Coherence Tomography Angiography Evaluation of Ocular Changes in Patients With Carotid Artery Stenosis

ClinicalTrials.gov study NCT04326842. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.

Measurement of Anterior Chamber Cell Grading Using Ocular Coherence Tomography

ClinicalTrials.gov study NCT02907814. IPD Sharing: NO. Countries: 1. Publications: 6.

restrictedIPD-UNDECIDEDFeb 2026View details →

In Vivo Assessment of the Tooth-Resin Composite Interface Using Optical Coherence Tomography

ClinicalTrials.gov study NCT05145322. IPD Sharing: Not stated. Countries: 1. Publications: 3.

controlledIPD-YESFeb 2026View details →

Stress-physiology Coherence, Interoception, and Well-being Following Mindfulness Training or Tracking Time Spent on Mobile Device

ClinicalTrials.gov study NCT04125758. IPD Sharing: YES. Countries: 1. Publications: 19.

Sutureless Glueless Technique Versus Interrupted Suturing for Conjunctival Autograft Fixation After Primary Pterygium Excision: Anterior Segment Optical Coherence Tomography (ASOCT) Study

ClinicalTrials.gov study NCT07307820. IPD Sharing: NO. Countries: 1. Publications: 1.

Evaluation of Imaging of Peripheral Arteries by Optical Coherence Tomography and Intravascular Ultrasound Imaging

ClinicalTrials.gov study NCT03480685. IPD Sharing: NO. Countries: 1. Publications: 1.

Novel EEG Coherence Biomarkers for PTSD Assessment

ClinicalTrials.gov study NCT04209387. IPD Sharing: NO. Countries: 1. Publications: 1.