Find research datasets worth reusing

opencc-by-4.0Nov 2024View details →

Dataset for wave-by-wave particle tracking in the surf zone

This dataset comprises 49 trajectories with 3D positions of buoyant tracers reconstructed from stereo camera imaging using two cameras and a standard triangulation process. The data is extracted from stereo image frames of the sea surface, captured at a rate of 30 frames per second. These images were collected between 15:13:00 and 17:18:59 UTC on September 7, 2019, near the island of Sylt, Germany. <div>An appropriate coordinate system was used to better represent the tracer position time series for the analysis of tracers position, velocity and acceleration. </div> <div>Details about the coordinate system are provided in the associated manuscript and supporting information as well as in </div> <div><a title="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021GL095722" href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021GL095722" target="_blank" rel="noopener noreferrer">https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021GL095722</a></div> <div> </div> <div>The dataset is organized into four columns: the frame time [µs]; the X coordinate [m], defining the horizontal position with the origin at the base of Pole 2 (see above referenced paper) and oriented shoreward; the Y coordinate [m], denoting the transverse position perpendicular to the direction of wave propagation ; </div> <div>and the Z coordinate [m], specifying the vertical position with the axis oriented upward.</div> These coordinates were obtained using a triangulation algorithm and adjusted using a coordinate system transformation to yield a precise, physically meaningful representation of the trajectories. The data is provided in .mat (MATLAB) format

opencc-by-4.0Feb 2022View details →

Simulated X-ray micro-computed tomography based particle tracking velocimetry dataset for validation purposes

Authors: Tom Bultreys, Stefanie Van Offenwert, Wannes Goethals, Matthieu N. Boone, Jan Aelterman and Veerle Cnudde; Ghent University (Belgium) Date: 8th February 2022 For any usage, please cite the accompanying publication: T. Bultreys, S. Van Offenwert, W. Goethals, M. N. Boone, J. Aelterman and V. Cnudde, "X-ray Tomographic Micro-Particle Velocimetry in Porous Media", Physics of Fluids, 34, 042008 (2022). https://doi.org/10.1063/5.0088000 ----------------------------- Validation dataset for micro-computed tomography based particle tracking velocimetry: a simulated micro-CT based velocimetry experiment with associated ground-truth particle trajectories - The ground truth trajectories were based on randomly dropping virtual particles in the pore space, and tracking their movement through a CFD-based velocity field (see below). The positions were calculated for the time corresponding to each radiograph of a micro-CT experiment. The folder "GroundTruthData" contains the locations of all particles at the central time of each micro-CT scan, as well as their radii. Check the associated readme file to read the data file. - The main data is contained in the directory "TimeFrames", containing the reconstructed 3D images at 7 time steps (70 seconds interval), with a voxel size of 11.8 µm, in 3D .tif format. This can be opened in for example Fiji/ImageJ. - The directory "clearFrame" contains an image of the pore space without particles, matching with the time frame images, in the same format and with the same voxel size as the time frame images. - The directory "SegmentedImage" contains two binary 3D images (same format as images before) which was created by segmenting the clearFrame image. There are two versions: the original segmentation, and a version where pores were eroded. The eroded segmentation was used to mask the pore space during particle detection (this avoids spurious detections near pore walls, caused by minor mis-alignments of the clearImage). - The original segmentation was used as input to simulate the velocity fields in the directory "simulatedVelocityFields", which contains 3D .tif images that represent the three components of the velocity vector field (the X-direction was the axis of the sample, equaling the flow direction). There is also an input text file and an output text file. The simulation was performed with the code from single-phase OpenFOAM implementation from Ali Raeini and others at Imperial College London: http://www.imperial.ac.uk/earth-science/research/research-groups/perm/research/pore-scale-modelling/ - The trackingOutput folder contains the experimentally determined velocity points (.csv, only particles that could be tracked at least 6 time frames) and the experimentally determined velocity magnitude field (.tif, voxel size 23.6 µm)

opencc-by-4.0Feb 2022View details →

X-ray micro-computed tomography based X-ray particle tracking velocimetry dataset in a porous glass filter

Authors: Tom Bultreys, Stefanie Van Offenwert, Wannes Goethals, Matthieu N. Boone, Jan Aelterman and Veerle Cnudde; Ghent University (Belgium) Date: 8th February 2022 For any usage, please cite the accompanying publication: T. Bultreys, S. Van Offenwert, W. Goethals, M. N. Boone, J. Aelterman and V. Cnudde, "X-ray Tomographic Micro-Particle Velocimetry in Porous Media", Physics of Fluids, 34, 042008 (2022). https://doi.org/10.1063/5.0088000 ----------------------------- Dataset of a micro-computed tomography based particle tracking velocimetry experiment performed on a glass filter (ROBU P0; sample size 4 mm diameter by 1 cm). - The main data is contained in the directory "TimeFrames", containing the reconstructed 3D images at 59 time steps (35 seconds interval), with a voxel size of 11.8 µm, in 3D .tif format. This can be opened in for example Fiji/ImageJ. - The directory "clearFrame" contains a high-quality pre-scan taken before the main experiment, which was registered and resampled to the time frame images, in the same format and with the same voxel size as the time frame images. - The directory "SegmentedImage" contains two binary 3D images (same format as images before) which was created by segmenting the clearFrame image. There are two versions: the original segmentation, and a version where pores were eroded. The eroded segmentation was used to mask the pore space during particle detection (this avoids spurious detections near pore walls, caused by minor mis-alignments of the clearImage). - The original segmentation was used as input to simulate the velocity fields in the directory "simulatedVelocityFields", which contains 3D .tif images that represent the three components of the velocity vector field (the X-direction was the axis of the sample, equaling the flow direction). There is also an input text file and an output text file. The simulation was performed with the code from single-phase OpenFOAM implementation from Ali Raeini and others at Imperial College London: http://www.imperial.ac.uk/earth-science/research/research-groups/perm/research/pore-scale-modelling/ - The trackingOutput folder contains the experimentally determined velocity points (.csv, only particles that could be tracked at least 20 time frames) and the experimentally determined velocity magnitude field (.tif, voxel size 23.6 µm)

opencc-by-4.0Feb 2022View details →

X-ray micro-computed tomography based particle tracking velocimetry dataset in a sandpack

Authors: Tom Bultreys, Stefanie Van Offenwert, Wannes Goethals, Matthieu N. Boone, Jan Aelterman and Veerle Cnudde; Ghent University (Belgium) Date: 8th February 2022 For any usage, please cite the accompanying publication: T. Bultreys, S. Van Offenwert, W. Goethals, M. N. Boone, J. Aelterman and V. Cnudde, "X-ray Tomographic Micro-Particle Velocimetry in Porous Media", Physics of Fluids, 34, 042008 (2022). https://doi.org/10.1063/5.0088000 ----------------------------- Dataset of a micro-computed tomography based particle tracking velocimetry experiment performed on a sand pack (grainsize 500-710 µm; sample size 4 mm diameter by 2 cm). - The main data is contained in the directory "TimeFrames", containing the reconstructed 3D images at 79 time steps (35 seconds interval), with a voxel size of 11.8 µm, in 3D .tif format. This can be opened in for example Fiji/ImageJ. - The directory "clearFrame" contains a high-quality pre-scan taken before the main experiment, which was registered and resampled to the time frame images, in the same format and with the same voxel size as the time frame images. - The directory "SegmentedImage" contains two binary 3D images (same format as images before) which was created by segmenting the clearFrame image. There are two versions: the original segmentation, and a version where pores were eroded. The eroded segmentation was used to mask the pore space during particle detection (this avoids spurious detections near pore walls, caused by minor mis-alignments of the clearImage). - The original segmentation was used as input to simulate the velocity fields in the directory "simulatedVelocityFields", which contains 3D .tif images that represent the three components of the velocity vector field (the X-direction was the axis of the sample, equaling the flow direction). There is also an input text file and an output text file. The simulation was performed with the code from single-phase OpenFOAM implementation from Ali Raeini and others at Imperial College London: http://www.imperial.ac.uk/earth-science/research/research-groups/perm/research/pore-scale-modelling/ - The trackingOutput folder contains the experimentally determined velocity points (.csv, only particles that could be tracked at least 20 time frames) and the experimentally determined velocity magnitude field (.tif, voxel size 23.6 µm)

opencc-by-4.0May 2022View details →

Data for Li et al., Coupling remote sensing and particle tracking to estimate trajectories in large water bodies, International Journal of Applied Earth Observation and Geoinformation, 2022

This data set contains four parts: 1) compressed folder with input parameters and results for the hydrodynamic model 2) compressed folder with input parameters and results for the particle tracking 3) compressed folder with satellite data  4) code used in the article for hydrodynamic model, particle tracking and image processing Each folder contains a readme file,

opencc-by-4.0Oct 2022View details →

Multiple Particle Tracking Data from Neonatal Organotypic Rat Brain Slices

The data includes statistical features generated from raw multiple particle tracking data from videos collected during three independent experiments: (1) 5 different brain regions, (2) 3 different treatment conditions in the brain, and (3) 5 different brain ages.    <table> <thead> <tr> <th scope="col">Feature</th> <th scope="col">Model Abbreviation</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>alpha</td> <td>alpha</td> <td>Exponent of the anomalous diffusion equation.</td> </tr> <tr> <td>Effective diffusion coefficient</td> <td>D_fit</td> <td>Coefficient of the anomalous diffusion equation</td> </tr> <tr> <td>Kurtosis</td> <td>kurtosis</td> <td>The fourth moment of the projected positions on the dominant eigenvector of the radius gyration tensor (T).</td> </tr> <tr> <td>Asymmetry1</td> <td>asymmetry1</td> <td>Characterizes the asymmetry of the trajectory. Asymmetry1 equals 0 for circularly symmetric trajectories and 1 for linear trajectories.</td> </tr> <tr> <td>Asymmetry2</td> <td>asymmetry2</td> <td>The ratio of the smaller to larger principal radius of gyration.</td> </tr> <tr> <td>Asymmetry3</td> <td>asymmetry3</td> <td>An asymmetry feature that accounts for non-cylindrically symmetric point distributions.</td> </tr> <tr> <td>Aspect ratio</td> <td>AR</td> <td>The ratio of the kong and short side of the trajectory's minimum bounding rectangle. Perfectly symmetric trajectories have an aspect ratio of 1, and aspect ratio increases as trajectories become more elongated. </td> </tr> <tr> <td>Elongation</td> <td>elongation</td> <td>An estimation of amount of extension of the trajectory from its centroid. </td> </tr> <tr> <td>Boundedness</td> <td>boundedness</td> <td>Boundedness quantifies how much a particle with diffusion coefficient Deff is restricted by a circular confinement of radius r when diffusing for a period of time $N\Delta t $</td> </tr> <tr> <td>Fractal Dimension</td> <td>fractal_dim</td> <td>Fractal dimension is a measure of how "complicated" a self similar figure is. </td> </tr> <tr> <td>Trappedness</td> <td>trappedness</td> <td>The probability ($\textit{P}_{\textit{t}} $) that a particle with duffusion coefficient Deff is trapped in a region (r0) for a period of time $N\Delta t $. </td> </tr> <tr> <td>Efficiency</td> <td>efficiency</td> <td>The ratio of the squared net displacement to the sum of step lengths. </td> </tr> <tr> <td>Straightness</td> <td>straightness</td> <td>The ratio of the net displacement to the sum of step lengths. </td> </tr> <tr> <td>MSD Ratio</td> <td>MSD_ratio</td> <td>MSD ratio characterizes the shape of the MSD curve. For Brownian motion, it is 0; For restricted motion it is < 0; For directed motion it is > 0. </td> </tr> <tr> <td>Frames</td> <td>frames</td> <td>The total number of frames the trajectory spans. </td> </tr> <tr> <td>Effective Diffusion Coefficient 1</td> <td>Deff1</td> <td>Effective diffusion coefficient at 0.33 s.</td> </tr> <tr> <td>Effective Diffusion Coefficient 2</td> <td>Deff2</td> <td>Effective diffusion coefficient at 3.3s. </td> </tr> </tbody> </table> Mean values were calculated based on surrounding datapoints for alpha, D_fit, kurtosis, asymmetry1, asymmetry2, asymmetry3, AR, elongation, boundedness, fractal_dim, trappedness, efficiency, straightness, MSD_ratio, Deff2, and Deff2.

opencc-by-4.0Aug 2024View details →

PEPT data for Understanding the effect of fluid viscosity in Vertical Stirred Mills using the Positron Emission Particle Tracking (PEPT) approach

The raw PEPT data collected for the paper "Understanding the effect of fluid viscosity in vertical stirred mills using the positron emission particle tracking (PEPT) approach." The paper is the first to use the PEPT technique to investigate the effect of fluid viscosity on the efficiency of the grinding process. This data can be post-processed using the PEPT-ML library and used in isolation or it can be used to calibrate an equivalent simulation. The simulation template is available on GitHub and the link to this is under the Software tab. Each file is labelled by the fluid viscosity and attritor speed used in the experiment, The data for a single run is often split across files but can be combined by the PEPT-ML library.

opencc-by-4.0May 2023View details →

Single particle tracking data for Sittewelle & Royle (2023)

A project to analyse intracellular vesicle mobility. A preprint of the manuscript is available at <a href="https://doi.org/10.1101/2023.05.10.540182">https://doi.org/10.1101/2023.05.10.540182</a> Code associated with the manuscript can be found at <a href="https://github.com/quantixed/p063p036">https://github.com/quantixed/p063p036</a>  TrackMate XML files for: <ul> <li>ATG9A, Clathrin, EB3, LAMP1, ML1N, Rab5, Rab11, Rab30, Rab35, SCAMP1, SCAMP3, TPD54</li> <li>TPD54 (pre and post bleach)</li> </ul> are available here together with some of the larger outputs from the code. These TrackMate XML files can be processed using <a href="https://github.com/quantixed/TrackMateR">TrackMateR</a> as described <a href="https://quantixed.github.io/TrackMateR/">here</a> or using the code associated with the manuscript. The folders contain calibration csv files which will correct the scaling where required during processing with TrackMateR.

opencc-by-4.0May 2023View details →

Simulated data for particle tracking and use in TrackMateR package

The purpose of this dataset is to provide some example data for users of TrackMateR to use to become familiar with the package. The dataset contains three elements: <ol> <li>Code to simulate some images (movies) of particle motions in 2D in Fiji. `particleSimulator.ijm` will generate images and ground truth positions of particles moving in six different modes (see below). An example output is given in `particleSimulatorOutput/`</li> <li>Code to automate the tracking of these images using TrackMate in Fiji. An example output is give in `TrackMateOutput/` These XML files can be used as the input in TrackMateR package.</li> <li> Outputs from TrackMate v 0.3.5 in `TrackMateROutput/`</li> </ol> The simulated data is: <ul> <li>Simulation A - particles moving in linear direction, variable but constant direction, high speed</li> <li>Simulation B - particles moving in linear direction, variable but constant direction, slow speed</li> <li>Simulation C - random motion high D (diffusion coefficient)</li> <li>Simulation D - random motion low D</li> <li>Simulation E - random motion, 50:50 mix of high and low D particles</li> <li>Simulation F - random motion, subdiffusive</li> </ul> These TrackMate XML files can be processed using <a href="https://github.com/quantixed/TrackMateR">TrackMateR</a> as described <a href="https://quantixed.github.io/TrackMateR/">here</a>.

opencc-by-4.0Mar 2020View details →

Particle tracking dataset for: Exceptional 20th century ocean circulation in the Northeast Atlantic

Particle tracking data for: "Exceptional 20th century ocean circulation in the Northeast Atlantic" Peter T. Spooner, David J. R. Thornalley, Delia W. Oppo, Alan Fox, Svetlana Radionovskaya, Neil L. Rose, Robbie Mallett, Emma Cooper, J. Murray Roberts VIKING20 (is a 1/20th degree ocean model, forced by a hindcast simulation of the atmosphere: CORE2 (Griffies et al., 2009). The reverse tracks of 113200 particles per year for 50 years, (1959-2009) were simulated with the ARIANE software (Döös, 1995) modified to include independent vertical motion of particles. Particles were seeded at the seabed in 10 km x 10 km boxes centered on MC16-A/17-5P and RAPID-21-3K (representing the settling location). The reverse tracks 'rose' (sinking) at 100 m/day (Takahashi & Be, 1984) and were then allowed to drift freely within the upper 100 m of the water column for six months (i.e. spanning the reasonable lifespan for many species of planktic foraminifera). Track data for the full 50 years are stored in a single netcdf file (output of ncdump -h <filename> given below). The 3D particle positions are in variables traj_lon, traj_lat and traj_depth with the Viking20 model along-track temperature, salinity and density in traj_temp, temp_sal and traj_dens, respectively. The main complication is the obscure storage of time (see also ARIANE software documentation). Variable init_t gives particle start time, counting in 5-day periods from 12:00 pm on 29 December 1957. Viking20 uses a fixed 365 day year so the year can be found for track 'traj' according to:            year    =     1958 + ( (init_t(traj)-1) \ 73 )     where '\' represents integer division, discarding the remainder. All particle tracks 'begin' (actually the end of the track in time as these are tracked backwards) at the start of July (12:00 pm July 1 in model). Particles are ordered by release time, so trajectories 1-113200 are 1959; 113201-226400 are 1960; etc. Positions are stored every 5 days, counting backwards. Further details are available from the authors.   References Döös, K. (1995). Interocean exchange of water masses. Journal of Geophysical Research, 100(C7), 13499. <a href="https://doi.org/10.1029/95JC00337">https://doi.org/10.1029/95JC00337</a> Griffies, S. M., Biastoch, A., Böning, C., Bryan, F., Danabasoglu, G., Chassignet, E. P., et al. (2009). Coordinated Ocean-ice Reference Experiments (COREs). Ocean Modelling, 26(1–2), 1–46. <a href="https://doi.org/10.1016/J.OCEMOD.2008.08.007">https://doi.org/10.1016/J.OCEMOD.2008.08.007</a> Takahashi, K., & Be, A. W. H. (1984). Planktonic foraminifera: factors controlling sinking speeds. Deep Sea Research Part A. Oceanographic Research Papers, 31(12), 1477–1500. <a href="https://doi.org/10.1016/0198-0149(84)90083-9">https://doi.org/10.1016/0198-0149(84)90083-9</a>   $ ncdump -h ariane_trajectories_qualitative.nc netcdf ariane_trajectories_qualitative { dimensions: ntraj = 5660000 ; nb_output = UNLIMITED ; // (74 currently) variables: double init_x(ntraj) ; init_x:title = "What is init_x ?" ; init_x:longname = "Initial position in i" ; init_x:units = "No dimension" ; init_x:missing_value = 1.e+20 ; double init_y(ntraj) ; init_y:title = "What is init_y ?" ; init_y:longname = "Initial position in j" ; init_y:units = "No dimension" ; init_y:missing_value = 1.e+20 ; double init_z(ntraj) ; init_z:title = "What is init_z ?" ; init_z:longname = "Initial position in k" ; init_z:units = "No dimension" ; init_z:missing_value = 1.e+20 ; double init_t(ntraj) ; init_t:title = "What is init_t ?" ; init_t:longname = "Initial position in l (time)" ; init_t:units = "See global attributes..." ; init_t:missing_value = 1.e+20 ; double init_age(ntraj) ; init_age:title = "What is init_age ?" ; init_age:longname = "Initial age (time)" ; init_age:units = "seconds" ; init_age:missing_value = 1.e+20 ; double init_transp(ntraj) ; init_transp:title = "What is init_transp ?" ; init_transp:longname = "Initial transport" ; init_transp:units = "m3/s" ; init_transp:missing_value = 1.e+20 ; double l_matureage(ntraj) ; l_matureage:title = "What is l_matureage ?" ; l_matureage:longname = "Larval age of maturity" ; l_matureage:units = "days" ; l_matureage:missing_value = 1.e+20 ; double l_descendage(ntraj) ; l_descendage:title = "What is l_descendage ?" ; l_descendage:longname = "Larval age of competency" ; l_descendage:units = "days" ; l_descendage:missing_value = 1.e+20 ; double l_maxspeedup(ntraj) ; l_maxspeedup:title = "What is l_maxspeedup ?" ; l_maxspeedup:longname = "Max upward larval swim speed" ; l_maxspeedup:units = "mm s-1" ; l_maxspeedup:missing_value = 1.e+20 ; double l_maxspeeddown(ntraj) ; l_maxspeeddown:title = "What is l_maxspeeddown ?" ; l_maxspeeddown:longname = "Max downward larval swim speed" ; l_maxspeeddown:units = "mm s-1" ; l_maxspeeddown:missing_value = 1.e+20 ; int l_targetdepth(ntraj) ; l_targetdepth:title = "What is l_targetdepth ?" ; l_targetdepth:longname = "Target shallow depth" ; l_targetdepth:units = "No dimension" ; l_targetdepth:missing_value = -1. ; double final_x(ntraj) ; final_x:title = "What is final_x ?" ; final_x:longname = "Final position in x (or i)" ; final_x:units = "No dimension" ; final_x:missing_value = 1.e+20 ; double final_y(ntraj) ; final_y:title = "What is final_y ?" ; final_y:longname = "Final position in y (or j)" ; final_y:units = "No dimension" ; final_y:missing_value = 1.e+20 ; double final_z(ntraj) ; final_z:title = "What is final_z ?" ; final_z:longname = "Final position in z (or k)" ; final_z:units = "No dimension" ; final_z:missing_value = 1.e+20 ; double final_t(ntraj) ; final_t:title = "What is final_t ?" ; final_t:longname = "Final position in t (time)" ; final_t:units = "See global attributes..." ; final_t:missing_value = 1.e+20 ; double final_age(ntraj) ; final_age:title = "What is fial_age ?" ; final_age:longname = "Final Age." ; final_age:units = "seconds" ; final_age:missing_value = 1.e+20 ; double final_transp(ntraj) ; final_transp:title = "What is final_transp ?" ; final_transp:longname = "Final transport" ; final_transp:units = "m3/s" ; final_transp:missing_value = 1.e+20 ; float traj_lon(nb_output, ntraj) ; traj_lon:title = "What is traj_lon ?" ; traj_lon:longname = "Trajectory: x positions" ; traj_lon:units = "No dimension" ; traj_lon:missing_value = 1.e+20 ; float traj_lat(nb_output, ntraj) ; traj_lat:title = "What is traj_lat ?" ; traj_lat:longname = "Trajectory: y positions" ; traj_lat:units = "No dimension" ; traj_lat:missing_value = 1.e+20 ; float traj_depth(nb_output, ntraj) ; traj_depth:title = "What is traj_depth ?" ; traj_depth:longname = "Trajectory: z positions" ; traj_depth:units = "No dimension" ; traj_depth:missing_value = 1.e+20 ; float traj_time(nb_output, ntraj) ; traj_time:title = "What is traj_time ?" ; traj_time:longname = "Trajectory: time positions" ; traj_time:units = "See global attributes" ; traj_time:missing_value = 1.e+20 ; float traj_iU(nb_output, ntraj) ; traj_iU:title = "ind i on grid U" ; traj_iU:longname = "Trajectory: i on grid U" ; traj_iU:units = "No dimension" ; traj_iU:missing_value = 1.e+20 ; float traj_jV(nb_output, ntraj) ; traj_jV:title = "ind j on grid V" ; traj_jV:longname = "Trajectory: j on grid V" ; traj_jV:units = "No dimension" ; traj_jV:missing_value = 1.e+20 ; float traj_kW(nb_output, ntraj) ; traj_kW:title = "ind k on grid W" ; traj_kW:longname = "Trajectory: k on grid W" ; traj_kW:units = "No dimension" ; traj_kW:missing_value = 1.e+20 ; float traj_temp(nb_output, ntraj) ; traj_temp:title = "What is traj_temp ?" ; traj_temp:longname = "Trajectory: temperatures" ; traj_temp:units = "degres" ; traj_temp:missing_value = 1.e+20 ; float traj_salt(nb_output, ntraj) ; traj_salt:title = "What is traj_salt ?" ; traj_salt:longname = "Trajectory: salinities" ; traj_salt:units = "psu" ; traj_salt:missing_value = 1.e+20 ; float traj_dens(nb_output, ntraj) ; traj_dens:title = "What is traj_dens ?" ; traj_dens:longname = "Trajectory: densities" ; traj_dens:units = "..." ; traj_dens:missing_value = 1.e+20 ;   // global attributes: :key_roms = ".FALSE." ; :key_symphonie = ".FALSE." ; :key_B2C_grid = ".FALSE." ; :key_sequential = ".TRUE." ; :key_alltracers = ".TRUE." ; :key_ascii_outputs = ".FALSE." ; :key_iU_jV_kW = ".TRUE." ; :key_read_age = ".FALSE." ; :mode = "qualitative" ; :forback = "backward" ; :bin = "nobin" ; :init_final = "NONE" ; :nmax = 10000000 ; :tunit = 86400. ; :ntfic = 5 ; :tcyc = 1639872000. ; :key_approximatesigma = ".FALSE." ; :key_computesigma = ".TRUE." ; :zsigma = 1000. ; :memory_log = ".TRUE." ; :output_netcdf_large_file = ".FALSE." ; :key_interp_temporal = ".TRUE." ; :maxcycles = 50 ; :delta_t = 86400. ; :frequency = 5 ; :nb_output = 73 ; :mask = ".TRUE." ; :key_region = ".FALSE." ; :key_larvae = ".TRUE." ; :imt = 1784 ; :jmt = 1719 ; :kmt = 46 ; :lmt = 3796 ; :key_computew = ".TRUE." ; :w_surf_option = "" ; :key_partialsteps = ".TRUE." ; :key_jfold = ".FALSE." ; :pivot = "T" ; :key_periodic = ".FALSE." ; :dir_mesh = "./GRID" ; :fn_mesh = "1_mesh_mask.nc" ; :nc_var_xx_tt = "glamt" ; :nc_var_xx_uu = "glamu" ; :nc_var_zz_ww = "gdepw_0" ; :nc_var_e2u = "e2u" ; :nc_var_e1v = "e1v" ; :nc_var_e1t = "e1t" ; :nc_var_e2t = "e2t" ; :nc_var_e3t = "e3t" ; :nc_var_tmask = "tmask" ; :nc_mask_val = 0. ; :c_dir_zo = "./DATA" ; :c_prefix_zo = "V20_nest_5d_" ; :ind0_zo = 1958 ; :indn_zo = 2009 ; :maxsize_zo = 4 ; :c_suffix_zo = "_U.nc" ; :nc_var_zo = "vozocrtx" ; :nc_var_eivu = "NONE" ; :nc_att_mask_zo = "missing_value" ; :c_dir_me = "./DATA" ; :c_prefix_me = "V20_nest_5d_" ; :ind0_me = 1958 ; :indn_me = 2009 ; :maxsize_me = 4 ; :c_suffix_me = "_V.nc" ; :nc_var_me = "vomecrty" ; :nc_var_eivv = "NONE" ; :nc_att_mask_me = "missing_value" ; :c_dir_te = "./DATA" ; :c_prefix_te = "V20_nest_5d_" ; :ind0_te = 1958 ; :indn_te = 2009 ; :maxsize_te = 4 ; :c_suffix_te = "_T.nc" ; :nc_var_te = "votemper" ; :nc_att_mask_te = "missing_value" ; :c_dir_sa = "./DATA" ; :c_prefix_sa = "V20_nest_5d_" ; :ind0_sa = 1958 ; :indn_sa = 2009 ; :maxsize_sa = 4 ; :c_suffix_sa = "_T.nc" ; :nc_var_sa = "vosaline" ; :nc_att_mask_sa = "missing_value" ; }

opencc-by-4.0Jul 2024View details →

Optical Particle Tracking in the Pneumatic Conveying of Metal Powders through a Thin Capillary Pipe

An experimental setup utilizing high-speed cameras and specialized optics was constructed to collect the conveying flow characteristics. The data here presented is pre-processed using ImageJ/Fiji, and uses the TrackMate package (see https://github.com/trackmate-sc/TrackMate/pull/296). The videos can be loaded to Fiji using the FFMPG package.

opencc-by-4.0Dec 2022View details →

Particle Tracking Data: Bergen DTC Prototype

Dataset Description: Proton computing tomography is an imaging modality promising improved treatment planning for proton therapy. For this application, the <a href="https://www.uib.no/en/ift/142356/medical-physics-bergen-pct-project">Bergen pCT collaboration</a> is developing a high granularity Digital Tracking Calorimeter (DTC), capable of measuring high multiplicities of particles in parallel [1]. In this dataset, we include various Monte Carlo (MC) simulations (generated using the Gate 9.2 simulation toolkit [2, 3] built upon Geant4 [4,5,6]) with different setups and phantom materials for evaluating and comparing particle reconstruction algorithms on the Bergen DTC. Files: We provide multiple simulations for different phantom geometries and simulation setups, each generated with a mono-energetic pencil beam (230 MeV, 2 sigma). The supplied files include a spot scanning dataset generated for a pediatric head phantom [7], spot scanning on water phantoms (100, 150 and 200 mm) as well as single beam spots for water phantoms of various thicknesses (0, 100, 150 and 200 mm): <ul> <li>head_filtered_2k_spot.npz</li> <li>water_{100,150,200}_2k_spot.npz</li> <li>water_{100,150,200}_10k.npz</li> <li>no_phantom_10k.npz</li> </ul> Columns: All above-mentioned simulation files contain MC simulated data of a single simulation run in tabular form, where each row represents a single particle hit inside the detector. Furthermore, each particle hit is parametrized by the following columns: <ul> <li>posX, posY, posZ: Measured x, y, z position (in millimeter) of the particle hit relative to the simulation origin defined by the center of the phantom.</li> <li>edep: Amount of energy (in MeV) deposited by a particle while interacting with the sensitive area of the detector.</li> <li>eventID: Each primary is simulated in its own isolated "event" and gets an incremental ID. Everything that happens during the simulation of said primary is grouped under the same eventID. Events are simulated independent of each other. trackIDs are only unique within their respective event.</li> <li>trackID: A track describes a single particle throughout its entire lifetime in the simulation. In any given event, the first track (trackID = 1) is always associated with the primary particle. Every subsequently produced secondary particle has an incremental trackID.</li> <li>parentID: The parentID specifies the trackID in the current event that caused this track to exist. If the parentID is 0, the particle is a primary, i.e., generated by the particle beam. Otherwise, the row describes a secondary which was generated through interactions of a primary with the traversed matter.</li> <li>volumeID[2]: Incremental numerical identifier of layer containing particle hit inside GATE volume 2 defined within the detector geometry. 0 for tracking layers, 1 for calorimeter layers.</li> <li>volumeID[3]: Incremental numerical identifier of layer containing particle hit inside GATE volume 3 defined within the detector geometry.  Unique identifiers (starting from zero) for tracking layer (0, 1) and calorimeter layer (0, 1, …, 40).</li> </ul>   References [1] J. Alme, G. G. Barnafoldi, R. Barthel et al., “A High-Granularity Digital Tracking Calorimeter Optimized for Proton CT, ”Frontiers in Physics, vol. 8, no. October, pp. 1–20, 2020. [2] S. Jan, G. Santin, D. Strul et al., “GATE -Geant4 Application for Tomographic Emission: a simulation toolkit for PET and SPECT,”Phys Med Biol. Phys Med Biol, vol. 49, no. 19, pp. 4543–4561, 2004. [3] S. Jan, D. Benoit, E. Becheva et al., “GATE V6: A major enhancement of the GATE simulation platform enabling modelling of CT and radiotherapy, ”Physics in Medicine and Biology, vol. 56, no. 4,pp. 881–901, 2011. [4] S. Agostinelli, J. Allison, K. Amako et al., “GEANT4 - A simulation toolkit, ”Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, vol. 506, no. 3, pp. 250–303, 2003. [5] J. Allison, K. Amako, J. Apostolakis et al., “Geant4 developments and applications, ”IEEE Transactions on Nuclear Science, vol. 53, no. 1, pp. 270–278, 2006. [6] J. Allison, K. Amako, J. Apostolakis et al., “Recent developments in geant4”, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, vol. 835, pp. 186–225, 201 [7] V. Giacometti, S. Guatelli, M. Bazalova-Carter et al., “Development of a high resolution voxelised head phantom for medical physics applications. ”Physica Medica, vol. 33, pp. 182–188, 2017.

opencc-by-4.0Jul 2023View details →

Backward and forward in time particle-tracking simulation in the Kuroshio Extension re-circulation gyre in 2019

Tracers were released at the targeted mesoscale eddy in September 01 2019, and their surface transport was modeled for the previous 26 days and the forward 72 days. Units are expressed as the number of tracer particles in each glid of 1/12° horizontal resolution. The color shades indicate the number of particles.

opencc-by-4.0Jul 2017View details →

Experimental data for "Spot-On: robust model-based analysis of single-particle tracking experiments"

Overview of experimental spaSPT data To comprehensively test Spot-On over many different conditions, we conducted 1064 spaSPT experiments. The raw data is freely available and the purpose of this ReadMe file is to describe the organization, acquisition parameters and format of the data. The data is for 4 different cell lines imaged over 15 different conditions yielding a total of 60 different conditions. The four cell lines were: <ul> <li> U2OS C32 Halo-CTCF </li> <li> U2OS H2B-Halo-SNAP </li> <li> U2OS Halo-3xNLS </li> <li> mESC (JM8.N4) C3 Halo-Sox2 </li> </ul> The cell lines were constructed in different ways. U2OS C32 Halo-CTCF was made by homozygous endogenous N-terminal tagging of CTCF in human osteosarcoma U2OS cells using CRISPR/Cas9-mediated genome-editing as described (C32 refers to clone number 32)1. We note the CTCF is an essential gene and that N-terminal tagging did not appear to affect CTCF function or expression level according to a series of control experiments1. Moreover, C32 Halo-CTCF has been authenticated using Short Tandem Repeat (STR) profiling (performed by Dr. Alison N. Killilea at the UC Berkeley Cell Culture Facility) against the following loci: THO1, D5S818, D13S317, D7S820, D16S539, CSF1PO, AMEL, vWA and TPOX. The C32 Halo-CTCF cell line showed a 100% match with U2OS. U2OS H2B-Halo-SNAP was made through random integration of a H2B-HaloTag-SNAP-Tag transgene expressed using the EF1a promoter with an IRES-NeoR gene for drug selection. After transfection, cells were selected using G418 until a pure cell population was obtained. This cell line has also been described previously1. The wild-type U2OS cell line used to make this cell line was also authenticated using STR profiling against the same loci as C32 and also showed a 100% match with U2OS. U2OS Halo-3xNLS was made through random integration of a FLAG-Halo-3xNLS (3x SV40 NLS: PKKKRKV) transgene expressed using the EF1a promoter. NeoR for drug selection was separately expressed using an SV40 promoter. After transfection, cells were selected using G418 until a pure cell population was obtained. This cell line has also been described previously1. The wild-type U2OS cell line used to make this cell line was also authenticated using STR profiling against the same loci as C32 and also showed a 100% match with U2OS. mESC C3 Halo-Sox2 was made through homozygous N-terminal tagging of Sox2 in JM8.N42 mouse embryonic stem cells using CRISPR/Cas9-mediated genome editing as previously described (C3 refers to clone number 3)3. The functionality of the C3 Halo-Sox2 knock-in was validated through control experiments and pluripotency through teratoma assays as described previously3. Each file contains single-molecule trajectories from a single cell imaged over 30,000 frames. Localization and tracking was performed using a custom-written Matlab implementation of the MTT-algorithm4 and the following settings: Localization error: 10-6.25; deflation loops: 0; Blinking (frames): 1; max competitors: 3; max D (m2/s): 20. The same 15 conditions were used for each of the 4 cell lines. ExpA PA-JF549 The purpose of this experiment was to test the effect of “motion-blurring” on the Spot-On estimated DFREE and FBOUND. 5 different experimental conditions were considered. Full details are given in the Methods section. Briefly, cells were grown overnight on plasma-cleaned 25 mm circular coverslips either directly (U2OS) and MatriGel coated as described1. Cell were labeled with 5-50 nM PA-JF5495 for around 15-30 min, washed twice and medium exchanged to phenol-red free medium. 30,000 frames were collected at a camera exposure time (Andor iXon Ultra 897; frame-transfer mode; vertical shift speed: 0.9 μs; -70C) of 9.5 ms which together with a ~447 μs camera integration time gave a frame rate of ~100 Hz. PA-JF549 dyes were photo-activated during the ~447 μs camera integration time using 405 nm pulses and the 405 nm pulse intensity optimized to achieve a mean density of 1 molecule per frame per nucleus. The JF549 dye was excited using a 561 nm laser and the total number of excitation photons kept constant but either delivered during a 1 ms pulse, a 2 ms pulse, a 4 ms pulse, a 7 ms pulse or with constant illumination. For each cell line and condition, 4 replicates were performed. We count a replicate as an independent experiment performed on a different day. For each replicate around 5 cells were imaged. Occasionally, fewer than 5 cells are available. To avoid tracking errors, we removed cells with too high a localization density from the analysis. All of this information is available in the file name. For example, “U2OS_C32_Halo-CTCF_PA-JF549_1ms-561nm_100Hz_rep2_cell03” refers to the third cell imaged in the second replicate of U2OS C32 Halo-CTCF using a 1 ms excitation pulse of 561 nm laser at a frame rate of 100 Hz. Similarly, “U2OS_C32_Halo-CTCF_PA-JF549_cont-561nm_100Hz_rep4_cell01” refers to the first cell imaged in the fourth replicate of U2OS C32 Halo-CTCF using constant 561 nm laser at a frame rate of 100 Hz. The five ExpA_PAJF549 conditions are separated by cell line such that each cell line is provided in a separate directory. E.g. the directory “U2OS_H2B_ExpA_PAJF549” contains all data for the U2OS H2B-Halo-SNAP cell line. ExpA PA-JF646 This experiment was exactly identical to the “ExpA_PA-JF549” experiment except cell were labeled with PA-JF6465 and excited using a 633 nm laser. The file names and data organization was otherwise the same and the same five excitation conditions were considered. ExpB PA-JF646 The purpose of this experiment was to test if the Spot-On estimated DFREE and FBOUND values would depend on the frame rate. In particular, all four proteins exhibit some levels of apparent anomalous diffusion, which could cause a dependence on the frame rate. Cells were labeled with PA-JF646 and grown and imaged as described above. Photo-activation took place during the ~447 μs camera integration time and JF646 dyes were excited using 1 ms stroboscopic 633 nm excitation pulses. To change the frame rate, the camera exposure time was set to 4.5 ms (~201 Hz), 5.5 ms (~167 Hz), 7 ms (~134 Hz), 13 ms (~74 Hz) and 19.5 ms (~50 Hz) when also counting the ~447 μs camera integration time. All of this information is available in the file name. For example, “U2OS_Halo-3xNLS_PA-JF646_1ms-633nm_74Hz_rep2_cell04” refers to the fourth cell imaged in the second replicate of U2OS Halo-3xNLS using a 1 ms excitation pulse of 633 nm laser at a frame rate of 74 Hz. Similarly, “mESC_C3_Halo-Sox2_PA-JF646_1ms-633nm_201Hz_rep1_cell03” refers to the third cell imaged in the first replicate of mESC Halo-Sox2 using a 1 ms excitation pulse of 633 nm laser at a frame rate of 201 Hz. Data format All data is available in two different formats: CSV-files and Matlab MAT-files. Both file formats are readable by the web-version of Spot-On. The Matlab version of Spot-On is only able to read the MAT-files. The CSV format consists of comma-separated values and contains headers. If opened with Microsoft Excel, it should appear as shown: Here the “frame” column contains the frame number in which the molecule was detected. The “t” column contains the timestamp. The “trajectory” column contains the trajectory number. For example, trajectory number 1 was only detected in frame 13 after which it disappeared. In contrast, trajectory number 4 was detected in frames 20, 21 22, 23 and 24. Finally, the “x” and “y” columns contain the x,y coordinates of the localization in units of micrometers (μm). The MAT-files contain a structure array named “trackedPar”. trackedPar contains three variables: <ul> <li> trackedPar.xy: “xy” is a matrix with 2 columns and a number of rows corresponding to the number of localizations in that trajectory. The first column is the x-coordinate and the second column is the y-coordinate. The units are micrometers (μm). </li> <li> trackedPar.Frame: “Frame” is a column vector where each element is the frame where the particle was localized. </li> <li> trackedPar.TimeStamp: “TimeStamp” is a column vector where each element is the timepoint where the particle was localized. </li> </ul> Each element in the structure array “trackedPar” correspond to a different trajectory.

opencc-by-4.0Jul 2017View details →

Simulated data for "Spot-On: robust model-based analysis of single-particle tracking experiments"

Generation of simulated data To systematically evaluate the performance of Spot-On as well as other common analysis tools such as MSDi and vbSPT, we considered a comprehensive set of 3480 realistic SPT simulations spanning the range of plausible dynamics. The simulations were performed using simSPT, which is freely available at GitLab: https://gitlab.com/tjian-darzacq-lab/simSPT. The simulation methods are described in detail at GitLab. A full description of the parameters which allows exact reproduction of the simulations is available together with the data (see Data Availability section). Briefly, we parameterized simSPT to consider that particles diffuse inside a sphere (the nucleus) of 8 µm diameter illuminated using HiLo illumination (assuming a HiLo beam width of 4 µm), with an axial detection range of ~700 nm, centered at the middle of the HiLo beam. Molecules are assumed to have a half-life of 4 frames (when inside the HiLo beam) and of 40 frames when outside the HiLo beam. The localization error was set to 25 nm and the simulation was run until 100000 in-focus trajectories were recorded. More specifically, the effect of the exposure time (1 ms, 4 ms, 7 ms, 13 ms, 20 ms), the free diffusion constant (from 0.5 µm²/s to 14.5 µm²/s in 0.5 µm²/s increments) and the fraction bound (from 0 % to 95 % in 5 % increments) were investigated, yielding a dataset consisting of 3480 simulations. The advantage of simulations is that the ground truth is known. This allows a quantitative assessment of which method works the best. Content of the archives: <ol> <li>170718_simSPT_simulations.zip the code and instructions to reproduce the simulations</li> <li>4um.tar.bz2 simulated data inside a 4 µm nucleus</li> <li>20um.tar.bz2 simulated data inside a 20 µm nucleus, in which virtually no confinement occurs.</li> <li>subsampled.tar.bz2 is a set of subsampled datasets, containing either 99999, 30000, 10000, 3000, 1000, 300, 100 or 30 trajectories. Each subsampling was done 50 times, yielding 50 files per subsmpling.</li> </ol> Formats: The data is provided both in CSV and .mat formats. .mat files are provided in the following dataset: 10.5281/zenodo.835541

opencc-by-4.0Nov 2023View details →

Particle tracking data in idealized and realistic estuary models

Particle tracking data in the realistic North River estuary model, Delaware estuary model, and idealized estuary models with different channel dimensions.

opencc-by-4.0Mar 2022View details →

Particle tracks of 3D flow field during the initiation of a fluvial particle (case 6S)

The dataset belongs to experimental work described in the paper "Role of low-order proper orthogonal decomposition modes and large-scale coherent structures on sediment particle entrainment" published in the Journal of Hydraulics Research. The paper has been published as open access: https://doi.org/10.1080/00221686.2020.1869604 The dataset consists of particle tracks of the three-dimensional flow field during the entrainment of a fluvial particle of test case 6S.

opencc-by-4.0Mar 2022View details →

Dataset- Barking up the right tree: Using tree bark to track airborne particles in school environment and link science to society

Experimental data regarding magnetic properties measured in bio-sensors and air filters. Bio-sensors include tree bark samples, either collected in situ (courtyard tree bark) and exposed indoors-outdoors (bio-sensors) in the schools. Bio-sensors had their magnetic properties measured.  PM1, EC and OC concentrations and SIRM measured on air filters.