Find research datasets worth reusing

opencc-by-4.0May 2022View details →

Functionalizing Aromatic Compounds with Optical Cycling Centers

Molecular design principles provide guidelines for augmenting a molecule with a smaller group of atoms to realize a desired property or function. We demonstrate that these concepts can be used to create an optical cycling center that can be attached to a number of aromatic ligands, allowing the scattering of many photons from the resulting molecules without changing the molecular vibrational states. We provide further design principles that indicate the ability to expand this work. This represents a significant step towards a quantum functional group, which may serve as a generic qubit moiety that can be attached to a wide range of molecular structures and surfaces.

opencc-by-4.0Jun 2022View details →

Comprehensive Automatic Processing and Analysis of Adaptive Optics Flood Illumination Retinal Images

A collaborative research group has established this database to support AO-FIO image utilization and evaluation of photoreceptor detection.  Please cite the following publication when using the database: Eva Valterova, Jan D. Unterlauft, Mike Francke, Toralf Kirsten, Radim Kolar, and Franziska G. Rauscher, "Comprehensive automatic processing and analysis of adaptive optics flood illumination retinal images on healthy subjects," Biomed. Opt. Express 14, 945-970 (2023) The database can be utilized in connection with our application MATADOR for AO-FIO image registration and analysis, which is freely available on: https://github.com/evavalterova/MATADOR.git The database includes <ul> <li>over 200 flood illumination adaptive optics images of 10 normal healthy subjects. Each folder includes 10 images of the right eye (denoted by OD) and 10 images of the left eye (denoted by OS) with their preliminary determined retinal position during image acquisition.</li> <li>foveal and peripheral patches. Each consists of 40 cropped regions from the set of 200 images. In each cropped region are manually labeled positions of photoreceptors by three evaluators.</li> <li>axial lengths of 10 subjects in ".xlsx" file  </li> </ul>

opencc-by-4.0Jun 2022View details →

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>

DATA: Comparison between optical tissue clearing methods for detecting administered mesenchymal stromal cells in mouse lungs

This data set includes all the raw data collected for the following article: "Comparison between optical tissue clearing methods for detecting administered mesenchymal stromal cells in mouse lungs".

Image sensing with multilayer, nonlinear optical neural networks

This data repository contains the information necessary to reproduce the main results of the paper “Image sensing with multiplayer, nonlinear optical neural networks”. This repository contains the data and the code for generating the figures in the manuscript "Image sensing with multilayer, nonlinear optical neural networks", including figures in the main text and in supplementary materials. The repository also contains the code for controling the experiment setup and running the experiments conducted in the paper: <ul> <li>Folder 'Data_Collection_Example' and 'Data_Extraction_Example' contain example scripts for instrument control and data collection using the multilayer optical-neural-network sensor.</li> <li>Other folders are organized according to the figure panels in the main text, each containing the data and the code required to reproduce the plots in a main figure panel and its associated supplementary figures. In each of these folders, there is a README.txt file that summarizes the role of each file in the folder. </li> </ul>

Global Positioning System Based on Optical Flow and Convolutional Neural Network

Two datasets used in the paper Global Positioning System Based on Optical Flow and Convolutional Neural Network, to evaluate the proposed CNN model to the task of position estimation. The datasets cover 2 different modes of motion of a drone, and were captured using Google API.

Data from: Optical coherence tomography reveals retinal thinning in schizophrenia spectrum disorders

This dataset contains supporting data for the publication: Boudriot, E., Schworm, B., Slapakova, L. et al. Optical coherence tomography reveals retinal thinning in schizophrenia spectrum disorders. Eur Arch Psychiatry Clin Neurosci (2022). https://doi.org/10.1007/s00406-022-01455-z

SED templates for "Dwarf AGNs from Variability for the Origins of Seeds (DAVOS): Intermediate-mass black hole demographics from optical synoptic surveys"

FITS file containing the pre-computed grid of Done or Nemmen model SEDs. See Table 2 in the publication for details.

Supporting information for the paper: The temporal relationship between Terrestrial Gamma-ray flashes and associated optical pulses from lightning

Supporting information for the paper: The temporal relationship between Terrestrial Gamma-ray flashes and associated optical pulses from lightning, consisting of 2 data files and 221 presentations of TGF-Optical emission events observed by ASIM between end of March 2019 and November 2020. See 0_READ_ME for information about the individual files and variables.

Insight into the Mechanical Coupling Behavior of Loose Sediment and Embedded Fiber-optic Cable using Discrete Element Method

The dataset contains the simulation codes and generated data in the manuscript titled "Insight into the mechanical coupling behavior of loose sediment and embedded fiber-optic cable using discrete element method". The codes (M files) were written in MatDEM, version 3.0 (free access at www.matdem.com), and the data is stored in MAT files. <ul> <li>Test2D_2L1.m - codes for initial compacted elements</li> <li>Test2D_2L1.mat - generated data for initial compacted elements</li> <li>Test2D_2L2.m - codes for compacted elements with embedded fiber-optic cable</li> <li>Test2D_2L2.mat - generated data for compacted elements with embedded fiber-optic cable</li> <li>Test2D_2L3.m – codes for confining pressure setting</li> <li>Test2D_2L-0MPa3.mat ~ Test2D_2L-1.0MPa3.mat - generated data for confining pressure setting</li> <li>Test2D_2L4.m – codes for fiber-optic cable pullout tests under various confining pressures</li> <li>Test2D_2L-05-26-20mm-0MPa-un-No1-4.mat ~ Test2D_2L-07-21-20mm-1MPa-un-No1-4.mat - generated data for fiber-optic cable pullout tests under various confining pressures</li> </ul>

opencc-by-4.0Sep 2022View details →

Coherent enhancement of optical remission in diffusive media

Experimental data sets associated with "Coherent enhancement of optical remission in diffusive media" (https://doi.org/10.1073/pnas.2207089119).

Quantifying the Sensitivity and Unclonability of Optical Physical Unclonable Functions (DATA and Python codes)

The data contain experimental and numerical challenge response pairs (CRPs) collected by experimental setup and using Puffraction homebuilt code. In the data set is available all CRPs referred to the paper entitled "Quantifying the Sensitivity and Unclonability of Optical Physical Unclonable Functions" by Giuseppe Emanuele Lio, Sara Nocentini, Lorenzo Pattelli, Eleonora Cara, Diederik Sybolt Wiersma,  Ulrich R"uhrmair, and Francesco Riboli.    The puffractio python code used to generate and process the numerical data is available at the following link <a href="https://github.com/lpattelli/puffractio.git">https://github.com/lpattelli/puffractio.git</a>   Please cite the following paper:  Quantifying the Sensitivity and Unclonability of Optical Physical Unclonable Functions <a href="https://onlinelibrary.wiley.com/action/doSearch?ContribAuthorRaw=Lio%2C+Giuseppe+Emanuele">Giuseppe Emanuele Lio</a>, <a href="https://onlinelibrary.wiley.com/action/doSearch?ContribAuthorRaw=Nocentini%2C+Sara">Sara Nocentini</a>, <a href="https://onlinelibrary.wiley.com/action/doSearch?ContribAuthorRaw=Pattelli%2C+Lorenzo">Lorenzo Pattelli</a>, <a href="https://onlinelibrary.wiley.com/action/doSearch?ContribAuthorRaw=Cara%2C+Eleonora">Eleonora Cara</a>, <a href="https://onlinelibrary.wiley.com/action/doSearch?ContribAuthorRaw=Wiersma%2C+Diederik+Sybolt">Diederik Sybolt Wiersma</a>, <a href="https://onlinelibrary.wiley.com/action/doSearch?ContribAuthorRaw=R%C3%BChrmair%2C+Ulrich">Ulrich Rührmair</a>, <a href="https://onlinelibrary.wiley.com/action/doSearch?ContribAuthorRaw=Riboli%2C+Francesco">Francesco Riboli</a> <a href="https://onlinelibrary.wiley.com/doi/full/10.1002/adpr.202200225">https://onlinelibrary.wiley.com/doi/full/10.1002/adpr.202200225</a>

opencc-by-4.0Oct 2022View details →

SCM-CNN: A Robust Deep Learning Matting Model for Cloud Removal in Optical Imagery

This is a dataset that can be used for cloud detection and cloud opacity estimation. The data is saved in python-numpy form, and stored in dictionary :dict_keys(['OriginImage', 'Gimage', 'Alpha', 'Trimap', 'CloudMaxDN']) represents the cloud-free remote sensing image, cloud remote sensing image, cloud opacity, trilateration information and cloud brightness respectively. The command {np.load("Path",allow_pickle=True).item()} is used to read, where "Path" is the corresponding path to the file.

opencc-by-4.0Dec 2022View details →

Interseismic and Coseismic Slip Behaviors Along the Tuolaishan-Lenglongling Faults From InSAR, GPS and Optical Observations

This repository contains: (1) The coseismic horizontal displacements measured from Planet-Lab and Landsat-9 optical data in grd format. The matlab script (i.e., grdread2.m) can be used to read the data in grd format. (2) Interseismic fault-parallel and fault-norm veloctiy profiles projected by the east-west and north-south velocity maps, the vertical and InSAR-derived descening (Track 33) LOS velocity profiles perpendicular to the seismogenic fault of the 2022 Menyuan Mw 6.7 earthquake. The  data in profile files is formated as longitude, latitude, velocity and fault-perpendicular distance.

dryad40/100

Supplementary data for: Comparison of optical flow derivation techniques for retrieving tropospheric winds from satellite image sequences

This study introduces a validation technique for quantitative comparison of algorithms which retrieve winds from passive detection of cloud- and water vapor-drift motions, also known as Atmospheric Motion Vectors (AMVs). The technique leverages airborne wind-profiling lidar data collected in tandem with 1-min refresh rate geostationary satellite imagery. AMVs derived with different approaches are used with accompanying numerical weather prediction model data to estimate the full profiles of lidar-sampled winds which enables ranking of feature tracking, quality control, and height-assignment accuracy and encourages meso-scale, multi-layer, multi-band wind retrieval solutions. The technique is used to compare the performance of two brightness motion, or "optical flow," retrieval algorithms used within AMVs, 1) Patch Matching (PM; used within operational AMVs) and 2) an advanced Variational Optical Flow (VOF) method enabled for most atmospheric motions by new-generation imagers. The VOF AMVs produce more accurate wind retrievals than the PM method within the benchmark in all imager bands explored. It is further shown that image regions with low texture and multi-layer-cloud scenes in visible and infrared bands are tracked significantly better with the VOF approach, implying VOF produces representative AMVs where PM typically breaks down. It is also demonstrated that VOF AMVs have reduced accuracy where the brightness texture does not advect with the mean wind (e.g. gravity waves), where the image temporal noise exceeds the natural variability, and when the height-assignment is poor. Finally, it is found that VOF AMVs have improved performance when using fine-temporal refresh rate imagery, such as 1-min versus 10-min data.

opencc-zeroOct 2022View details →

opencc-by-4.0Oct 2022View details →

WORCC-PMOD/WRC quality assured aerosol optical depth and Ångström exponent for Ny-Ålesund GAW station (2002- present)

WORCC-PMOD/WRC quality assured aerosol optical depth and Ångström exponent for Ny-Ålesund GAW station (2002- present) Aerosol optical  depth (AOD) measurements have been performed within the frame of Global Atmospheric Watch Precision Filter Radiometer (GAW-PFR) network in Ny-Ålesund (79N,11E) since 2002.  The measurements are performed from March to October, with PFR (PrecisionFilterRadiometer) instruments provided by Physikalisch-Meteorologisches Observatorium Davos, World Radiation Center (PMOD/WRC). The solar tracker and  infrastructure  are provided by provided by NILU(Norsk institutt for luftforskning) in collaboration with the  Norwegian Polar Institute. PFR manufactured by PMOD/WRC is a temperature stabilized instrument at 20o C equipped with four narrow band interference filters with nominal centroid wavelengths 368 nm, 412 nm, 500 nm and 862 nm and bandpass (fullwidth half maximum) 4 nm for 368 nm channel and 5 nm for the rest. The instrument is calibrated in yearly bases against the WMO-AOD reference at PMOD/WRC during the polar winter. The operation is done remotely by PMOD/WRC with the installation and onsite maintenance (cleaning, alignment adjustments) done by personnel of Norwegian Polar Institute and NILU in collaboration with PMOD/WRC. The processing and quality assurance of the data is done following  the  protocols of  World Optical depth Research and Calibration Center (WORCC, PMOD/WRC) (2018), and the data are submitted to the WDCA database (EBAS-NILU) as hourly mean AOD and AE values. This dataset contains the high-resolution data (1 min) cloud screened and quality assured since 2002. The provided Ångström exponent is retrieved from the 4 wavelengths.     SUN_PFR_AOD_AE_V1.0.dat : AOD and AE for the period 2002-2021 Lunar_PFR_AOD_AE_V1.0.dat : AOD and AE for the period 2018-2021     KN:  Calibration, operation, and processing since 2014, WORCC quality assurance protocols, quality assurance of the presented dataset KS: , WORCC quality assurance protocols, consulting on quality assurance of the presented dataset WC: calibration, operation, and processing 2002- 2014 NS: operation and processing 2005- 2012 HGH: principal  investigator  of hosting  institute NILU SK: principal  investigator  of hosting   institute  NILU Acknowledgement: Special  thanks  the  personnel  of  the  Norwegian Polar Institute  at  Ny-Ålesund for  all  valuable the  technical  support  for  the  solar and  lunar  measurements.     1. Kazadzis, S., Kouremeti, N., Nyeki, S., et al. (2018) The World Optical Depth Research and Calibration Center (WORCC) quality assurance and quality control of GAW-PFR AOD measurements 10.5194/gi-7-39-2018 <a href="https://gi.copernicus.org/articles/7/39/2018/">https://gi.copernicus.org/articles/7/39/2018/</a>

opencc-by-4.0Jul 2017View details →

Data and code for figures: Breathing Dissipative Solitons in Optical Microresonators

This dataset contains the data presented in the Figures of the paper Breathing dissipative solitons in optical microresonators (doi:10.1038/s41467-017-00719-w). The data for figure X is gathered in one matlab dataset file FigureX_Dataset.mat, under a structure variable figX whose fields are the panels of the figure in the manuscript (a,b,c,...). In each of the panel field, you find subfields X, Y, Z that each are cell arrays containing the (X,Y,Z) data for all the lines / surfaces presented in the panel. In order to plot the line #1 of panel b of figure 3 you can proceed as follow: load Figure3_Dataset.mat plot(fig3.b.X{1}, fig3.b.Y{1}) A minimal script FigureX_process.m is provided for each figure in order to plot all the panels. For some insets of Figures 1,2,4, the structure is slightly modified, please refer to the scripts for detail access of the data. The datasets and scripts were generated and tested using Matlab 2017 or 2014.

opencc-by-4.0Dec 2017View details →

Probabilistic Shaping: Experimental Optical Back-to-Back Data Set

Data set containing experimental results on B2B operation of single-carrier probabilistic shaping optical signals spanning from net bit-rates of 200G to 250G.

opencc-by-4.0Dec 2017View details →

Single-Carrier: Experimental Optical Back-to-Back Data Set

Data set containing experimental results on B2B operation of single-carrier optical signals spanning from net bit-rates of 200G (PM-16QAM) to 250G (PM-32QAM).