Find research datasets worth reusing

opencc-by-4.0Apr 2020View details →

Data on spatial distribution of tracers for optical sensing of stream surface flow

Here, we present the numerical and field data used in the manuscript entitled Spatial distribution of tracers for optical sensing of stream surface flow. Numerical data were synthetically generated considering different values of seeding density and aggregation levels of tracers for image-velocimetry analyses. In total, 33,600 synthetic images were generated. Field data correspond with the Basento River case study located in southern Italy. The respective footage at 12 fps, pre-processed and stabilised frames, and reference velocity data are provided in this dataset.

opencc-by-4.0May 2020View details →

Measurement of Absolute Retinal Blood Flow Using a Laser Doppler Velocimeter Combined with Adaptive Optics

Purpose: Development and validation of an absolute laser Doppler velocimeter (LDV) based on an adaptive optical fundus camera which provides simultaneously high definition images of the fundus vessels and absolute maximal red blood cells (RBCs) velocity in order to calculate the absolute retinal blood flow.\newline Methods: This new absolute laser Doppler velocimeter is combined with the adaptive optics fundus camera (rtx1, Imagine Eyes$^\copyright$,Orsay, France) outside its optical wavefront correction path. A 4 seconds recording includes 40 images, each synchronized with two Doppler shift power spectra. Image analysis provides the vessel diameter close to the probing beam and the velocity of the RBCs in the vessels are extracted from the Doppler spectral analysis. Combination of those values gives an average of the absolute retinal blood flow. An in vitro experiment consisting of latex microspheres flowing in water through a glass-capillary to simulate a blood vessel and in vivo measurements on six healthy humans were done to assess the device.\newline Results: In the in vitro experiment, the calculated flow varied between 1.75µl/min and 25.9µl/min and was highly correlated (r2= 0.995) with the imposed flow by a syringe pump. In the in vivo experiment, the error between the flow in the parent vessel and the sum of the flow in the daughter vessels was between -11% and 36% (mean±sd 5.7±18.5%). Retinal blood flow in the main temporal retinal veins of healthy subjects varied between 0.9 µL/min and 13.2µL/min. Conclusion: This adaptive optics LDV prototype (aoLDV) allows the measurement of absolute retinal blood flow derived from the retinal vessel diameter and the maximum RBCs velocity in that vessel.

opencc-zeroNov 2021View details →

The impulse response of optic flow sensitive descending neurons to roll m-sequences

When animals move through the world, their own movements generate widefield optic flow across their eyes. In insects, such widefield motion is encoded by optic lobe neurons. These lobula plate tangential cells (LPTCs) synapse with optic flow sensitive descending neurons, which in turn project to areas that control neck, wing and leg movements. As the descending neurons play a role in sensori-motor transformation, it is important to understand their spatio-temporal response properties. Recent work shows that a relatively fast and efficient way to quantify such response properties is to use m-sequences or other white noise techniques. We therefore here used m-sequences to quantify the impulse responses of optic flow sensitive descending neurons in male Eristalis tenax hoverflies. We focused on roll impulse responses as hoverflies perform exquisite head roll stabilizing reflexes, and the descending neurons respond particularly well to roll. We found that the roll impulse responses were fast, peaking after 16.5-18.0 ms. This is similar to the impulse response time-to-peak (18.3 ms) to widefield horizontal motion recorded in hoverfly LPTCs. We found that the roll impulse response amplitude scaled with the size of the stimulus impulse, and that its shape could be affected by the addition of constant velocity roll or lift. For example, the roll impulse response became faster and stronger with the addition of excitatory stimuli, and vice versa. We also found that the roll impulse response had a long return to baseline, which was significantly and substantially reduced by the addition of either roll or lift.

opencc-by-4.0Apr 2022View details →

Estimation of groundwater flow rate by an actively heated fiber optics based thermal response test in a grouted borehole

The dataset contains the numerical data and the in-situ measurements in the manuscript titled "Estimation of groundwater flow rate by an actively heated fiber-optics-based thermal response test in a grouted borehole". The data is stored in MAT files, which are Binary MATLAB files. There are a series of codes used in this manuscript to estimate groundwater flow rates. The codes were written in MATLAB Live Script, version 2021b. <ul> <li>The numerical data contains temperatures of the heating stage in different thermal response tests in a numerical model, which considers the borehole effects. The model is set up by COMSOL Multiphysics, and a series of flow rates is set to the model respectively for different thermal response tests.</li> <li>The in-situ measurements include temperatures of the heating stage in an actively heated fiber-optics-based thermal response test, which was performed in July 2021 in the grouted borehole, which is located in the lower section of the Sima bend of the Yangtze River. The temperature for the flow rate estimation was thinned to 120 s records from 10s records for the limited computing resources.</li> <li>The data_process.mlx provides pre-processing for the observational data recorded by Silixa Ultima-M MK2 DTS. The estimation_process.mlx gives a groundwater flow estimation case in a grouted borehole.</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>

opencc-by-4.0Aug 2022View details →

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.

opencc-zeroOct 2022View details →

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.

publicOct 2022View details →

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

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publicDec 2025View details →

Using optical flow temporal interpolation of satellite imagery to assist multi-sensor global cloud product composites

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publicSep 2025View details →

Supplementary data for: Nowcasting 3D cloud fields using forward warping optical flow

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publicMar 2024View details →

The impulse response of optic flow sensitive descending neurons to roll m-sequences

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dryad36/100

Data from: Spatio-temporal dynamics of impulse responses to figure motion in optic flow neurons

White noise techniques have been used widely to investigate sensory systems in both vertebrates and invertebrates. White noise stimuli are powerful in their ability to rapidly generate data that help the experimenter decipher the spatio-temporal dynamics of neural and behavioral responses. One type of white noise stimuli, maximal length shift register sequences (m-sequences), have recently become particularly popular for extracting response kernels in insect motion vision. We here use such m-sequences to extract the impulse responses to figure motion in hoverfly lobula plate tangential cells (LPTCs). Figure motion is behaviorally important and many visually guided animals orient towards salient features in the surround. We show that LPTCs respond robustly to figure motion in the receptive field. The impulse response is scaled down in amplitude when the figure size is reduced, but its time course remains unaltered. However, a low contrast stimulus generates a slower response with a significantly longer time-to-peak and half-width. Impulse responses in females have a slower time-to-peak than males, but are otherwise similar. Finally we show that the shapes of the impulse response to a figure and a widefield stimulus are very similar, suggesting that the figure response could be coded by the same input as the widefield response.

opencc-zeroDec 2014View details →

opencc-by-4.0Jan 2024View details →

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-4.0Apr 2024View details →

Optical Flow FLDAS Climate Velocity for 2001-2021

Climate velocity estimated using an optical flow method using global surface temperature data of the NASA FLDAS model at 0.1×0.1-degree grid for 2000-2021

opencc-by-4.0Apr 2022View details →

Dataset - Generalization of deep recurrent optical flow estimation for particle-image velocimetry data

This is the official test datasets of "Generalization of deep recurrent optical flow estimation for particle-image velocimetry data" published in Measurement Science and Technology. Particle-Image Velocimetry (PIV) is one of the key techniques in modern experimental fluid mechanics to determine the velocity components of flow fields in a wide range of complex engineering problems. Current  PIV processing tools are mainly handcrafted models based on cross-correlations computed across interrogation windows. Although widely used, these existing tools have a number of well-known shortcomings, including limited spatial output resolution and peak-locking biases. Recently, new approaches for PIV processing leveraging a novel neural network architecture for optical flow estimation called Recurrent All-Pairs Field Transforms (RAFT) have been developed. These have matched or exceeded the performance of classical, handcrafted models. While the RAFT-PIV method is a promising approach, it is important for the broader fluids community to more completely understand its empirical behavior and performance. To this end, in this study, we thoroughly investigate the performance of RAFT-PIV under varying image and lighting conditions. IWe consider applications spanning synthetic and experimental data, with a breadth and depth going far beyond currently available empirical results. The results for the wide variation of experiments included in this dataset shed new light on the capabilities of deep learning for PIV processing. This dataset is given as binary TFRECORD format.

dryad36/100

Gait behavioral and neuromuscular characterization in response to increasing working memory load while walking under optic flow perturbations in young adults

The precise role of cognitive control on optic flow processing during locomotion has rarely been investigated. Therefore, this study aimed to determine whether coping with unreliable visual inputs during walking requires attentional resources. Twenty-four healthy young adults walked on an instrumented treadmill in a virtual environment under two optic flow conditions: normal (NOF) and perturbed (POF, continuous mediolateral pseudo-random oscillations). Each condition was performed under single-task and dual-task conditions of increasing difficulty (1-, 2-, 3-back). In all conditions, subjective mental workload was estimated (raw NASA-TLX). For kinematic variables, mean, standard deviation, statistical persistence and step-to-step error correction were computed from gait time series in mediolateral and anteroposterior directions. For EMG variables of soleus and gluteus medius, the full width at half maximum and the variance ratio were calculated. Performance on N-back tasks was assessed using mean reaction time and d-prime. Cognitive performance was not affected by simultaneous walking in any optic flow condition. Gait variability was altered under POF compared to NOF, so that young adults sought to counteract those perturbations by adopting an effortful gait control strategy, independently of concurrent working memory (WM) load. Increasing WM load led changes first at the neuromuscular level, then at the behavioral level, with a prioritization of gait control in the mediolateral direction. Interestingly, dual-tasking lowered the effects of POF but in the anteroposterior direction only. These findings and their theoretical implications provide valuable insight into the complex interaction effects of cognitive and visual constraints on gait control during treadmill walking.

opencc-zeroAug 2022View details →

dryad36/100

Optic flow and odometry data from intelrealsense camera

Insects rely on the perception of image motion, or optic flow, to estimate their velocity relative to nearby objects. This information provides important sensory input for avoiding obstacles. However, certain behaviors, such as estimating the absolute distance to a landing target, accurately measuring absolute distance travelled, and estimating the ambient wind speed require decoupling optic flow into its component parts: absolute ground velocity and distance to nearby objects. Behavioral experiments suggest that insects perform these calculations, but their mechanism for doing so remains unknown. Here we present a novel algorithm that combines the geometry of dynamic forward motion with known features of insect visual processing to provide a hypothesis for how insects might \textit{directly} estimate absolute ground velocity from a combination of optic flow and acceleration information. Our robotics-inspired-biology approach reveals three critical requirements. First, absolute ground velocity can only be directly estimated from optic flow during times of active acceleration and deceleration. Second, spatial pooling of optic flow across a receptive field helps to alleviate the effects of noise and/or low resolution visual systems. Third, averaging velocity estimates from multiple receptive fields further helps to reject noise. Our algorithm provides a hypothesis for how insects might estimate absolute velocity from vision during active maneuvers, and also provides a theoretical framework for designing fast analog circuitry for efficient state estimation that can be applied to insect-sized robots.

opencc-zeroAug 2021View details →