Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
54
datasets available to search
ShareScore release 0.9.0
Dataset results
54 results for “particle tracking”
Advances in volumetric super-resolution microscopy and single-particle tracking (associated codes and datasets)
<h2>Overview</h2> <p>This Zenodo repository contains datasets and code relating to the thesis entitled "Advances in volumetric super-resolution microscopy and single-particle tracking" by <a href="https://www.ch.cam.ac.uk/person/sgd46">Sam G. Daly</a> (Yusuf Hamied Department of Chemistry, University of Cambridge).</p> <p>Managed/updated versions my be avalible at <a href="https://github.com/TheLeeLab">https://github.com/TheLeeLab</a>.</p> <p>The Excel Workbook 'MicrolensRelayCalculator' is designed to help in the design of MLAs for SMLFM.</p> <h2>Available Datasets</h2> <h3>Chapter 4</h3> <ol> <li>Simulated localisation data for various PSFs: standard, astigmatism, double helix, SMLFM, and tetrapod; 4000 detected photons, 20 emitters per frame, 200 frames.</li> <li>Microtubule imaging in a fixed HeLa cell (dSTORM); 30 ms exposure, 640 nm excitation, 200 frames.</li> </ol> <h3>Chapter 5</h3> <ol> <li>B cell receptor imaging on a fixed B cell (dSTORM);<em> 30 ms exposure, 640 nm excitation, 200 frames, fiducial: nanodiamonds.</em></li> <li>SPT of the B cell receptor on a live B cell (PALM); <em>20 ms exposure, 640 nm excitation, 200 frames, fiducial: nanodiamonds.</em></li> <li>Membrane imaging on a fixed Jurkat T cell embedded in agarose (resPAINT); <em>20 ms exposure, 640 nm excitation, 200 frames, fiducial: nanodiamonds.</em></li> <li>PD-1 imaging on a fixed T cell (dSTORM);<em> 30 ms exposure, 640 nm excitation, 200 frames, fiducial: nanodiamonds.</em></li> <li>Membrane imaging on a fixed T cell (resPAINT); <em>20 ms exposure, 640 nm excitation, 200 frames, fiducial: nanodiamonds.</em></li> </ol> <h3>Chapter 6</h3> <ol> <li>SPT of ACBD3 in a live HeLa cell (PALM); <em>20 ms exposure, 640 and 405 nm excitation, 200 frames.</em></li> <li>SPT of TMD mutant (length: 27) in a live HeLa cell (PALM); <em>20 ms exposure, 640 and 405 nm excitation, 200 frames.</em></li> </ol> <h2>Available Code</h2> <ol> <li><strong>Autofocus (BeanShell):</strong> Counteracts axial drift in SMLFM experiments.</li> <li><strong>Calibration (BeanShell):</strong> Controls the piezo scanner for axial calibrations in 3D-SMLM.</li> <li><strong>3D Reconstruction (Matlab):</strong> Reconstructs 2D-localised SMLFM data in 3D. Maintained version available on GitHub.</li> <li><strong>Fiducial correction (Matlab):</strong> Removes focal drift artifacts from 3D localisation data.</li> <li><strong>Temporal grouping (Python):</strong> Removes multiple single-molecule blinking events.</li> <li><strong>3D tracking (Matlab):</strong> Converts 3D localisations into tracks and calculates diffusion quantities.</li> <li><strong>Matching (Matlab):</strong> Determines PPV, sensitivity, and Jaccard index from localisation data.</li> <li><strong>Membrane curvature (Python):</strong> Determines the frequency of 3D localisations at a given membrane curvature.</li> </ol> <h3><em>Supported by The Royal Society (RGF\EA\181021) </em></h3>
Single particle tracking data for "Histidine-rich domain of kinases induce phase separation to hyperphosphorylate Pol II CTD"
<p><strong>Experimental single-particle tracking (SPT) data supporting "Histidine-rich domain of kinases induce phase separation to hyperphosphorylate Pol II CTD"</strong></p> <p>This dataset contains all the raw SPT data reported in "Histidine-rich domain of kinases induce phase separation to hyperphosphorylate Pol II CTD" in the form of SPT trajectories. The SPT trajectories are provided in two different formats for convenience: a CSV format and a Matlab format. Both formats are readable by Spot-On: https://spoton.berkeley.edu/</p> <p>The SPT data contains "fast tracking" spaSPT data (Figure 2d) and this data was analyzed using the Matlab version of Spot-On which can be found and downloaded at: https://gitlab.com/tjian-darzacq-lab/spot-on-matlab</p> <p>The SPT data also contains "slow tracking" SPT data (Figure 2e).</p> <p>Full details about the Matlab and CSV formats are provided in the ReadMe files in the associated zip files.</p> <p>Please see the associated manuscript for a detailed description of how the data was acquired and analyzed. For questions about the data please contact Anders Sejr Hansen at anders.sejr.hansen {at} berkeley {dot} edu.</p>
Single-particle tracking data for "CTCF sites display cell cycle dependent dynamics in factor binding and nucleosome positioning"
<p>This dataset contains all the raw SPT data reported in "­­­­CTCF sites display cell cycle dependent dynamics in factor binding and nucleosome positioning" in the form of SPT trajectories. The SPT trajectories are provided in two different formats for convenience: a CSV format and a Matlab format. Both formats are readable by Spot-On: https://spoton.berkeley.edu/</p> <p>The SPT data contains "fast tracking" spaSPT data and this data was analyzed using the Matlab version of Spot-On which can be found and downloaded at: https://gitlab.com/tjian-darzacq-lab/spot-on-matlab</p> <p> </p> <p>Full details about the Matlab and CSV formats are provided in the ReadMe files in the associated zip files.</p> <p>Please see the associated manuscript for a detailed description of how the data was acquired and analyzed.</p>
Three dimensional localization refinement and motion model parameter estimation for confined single particle tracking under low-light conditions: Simulation datasets
<p><span>The datasets store both motion and observation information of a single fluorescent sub-diffraction limit-sized particle moving in a three-dimensional confined environment. The confined motion is following a nonlinear model driven by non-Gaussian noise, the observation is formed by engineered Double-helix (DH) point spread function (PSF) and captured by scientific complementary metal-oxide semiconductor (sCMOS) camera. Based on our prior computationally efficient application of Sequential Monte Carlo - Expectation Maximization (SMC-EM), we extended it to handle the DH-PSF for encoding the three-dimensional position of the particle in two-dimensional image plane of the camera. We focus on studying the datasets at low signal and low signal-to-background ratio (SBR). Based on the datasets across different SBR and confinement lengths, a quantitative comparison is conducted to show that in the low signal regime, the SMC-EM approach outperforms the other methods while at higher signal-to-background levels, SMC-EM and the MLE-based methods perform equally well and both are significantly better than fitting to the MSD. In addition, our results indicate that at smaller confinement lengths where the nonlinearities dominate the motion model, the SMC-EM approach is superior to the alternative approaches. </span></p>
Dataset for publication: ExTrack characterizes transition kinetics and diffusion in noisy single-particle tracks
<p>Dataset related to article </p> <p>Simon, F., J.-Y. Tinevez, S. van Teeffelen (2023) ExTrack characterizes transition kinetics and diffusion in noisy single-particle tracks. Journal of Cell Biology</p> <p>Movies: example movies for PBP1b and RodZ. G0 = 370% expression level, G1 = 130% expression level, G2 = 30% expression level.</p> <p>Tracks: resulting tracks from the replicates, each replicate is composed of several movies (one xml file per movie). G0 = 370% expression level, G1 = 130% expression level, G2 = 30% expression level.</p> <p>See the material and method section for more details on the experimental conditions and the analysis pipeline.<br> </p>
PEPT Data - The effect of retrofit design modifications on the macro-turbulence of a three-phase flotation tank – Flow characterisation using positron emission particle tracking (PEPT).
<p>Supporting Information: for the paper "The effect of retrofit design modifications on the macro-turbulence of a three-phase flotation tank – Flow characterisation using positron emission particle tracking (PEPT)."</p> <p>The file contains the trajectory data and graph data for azimuthal slices obtained with PEPT.</p> <p> </p>
Dataset of paper "Evaluation of new photochemical systems for water disinfection by the integration of particle tracking into kinetic models for microbial inactivation"
<p>Dataset of paper "Evaluation of new photochemical systems for water disinfection by the integration of particle tracking into kinetic models for microbial inactivation":</p> <ul> <li>Results for the CFD model of the residence time distribution in the tubular reactor and E(t) curve for an ideal laminar.</li> <li>UV dosage histograms at the outlet surface for the tubular reactor illuminated with the four different LED configurations.</li> <li>Microbial inactivation histograms at the outlet surface for the tubular reactor illuminated with the four different LED configurations.</li> <li>Results for the CFD model of the residence time distribution in the annular reactor for different flow rates.</li> <li>UV dosage histograms at the outlet surface for the annular reactor for different flow rates.</li> <li>Microbial inactivation histograms at the outlet surface in the annular reactor for different flow rates.</li> </ul>
Three dimensional localization refinement and motion model parameter estimation for confined single particle tracking under low-light conditions: Simulation datasets
Open the record for dataset details and reuse information.
Revealing elasmobranch distributions in turbid coastal waters: insights from environmental DNA and particle tracking
Open the record for dataset details and reuse information.
Simulated data for "Spot-On: robust model-based analysis of single-particle tracking experiments" (MATLAB format)
<p>See 10.5281/zenodo.834787 for a more complete description.</p>
Particle tracking simulations of marine macro-litter in Rostock during extreme events
<p>The simulations show the passive near-surface transport of macro-litter during extreme events (HanseSail in Rostock, Northern Germany). The simulations are based on a high resolution 3D flow model of the Warnow estuary.</p>
YAP single particle tracking data accompanying the paper "YAP condensates are highly organized hubs".
<p><span><span>·<span> </span></span></span><span>Actual_masks: data files “DataAnalyzed” 1-28 (excluding 20) containing the original YAP masks in the variable “MaskIn”.</span></p> <p><span><span>·<span> </span></span></span><span>original_tracks: contains all the trajectories for each cell.</span></p> <p><span><span>·<span> </span></span></span><span>initial_final_nuclearMasks: contains initial and final manually segmented nuclear masks used to extrapolate nuclear masks by “interpolateNuclearMask”.</span></p> <p><span><span>·<span> </span></span></span><span>tracks: tracks pre-segmented manually to only include nuclear tracks (created by “trackDivider”).</span></p> <p> </p>
u-track 3D: measuring and interrogating dense particle dynamics in three dimensions
<p>This datasets is set up to help users test the u-track 3D software. The tutorial scripts automatically download and process the following images:</p> <p><strong>Endocytosis dataset</strong>: Breast cancer cells expressing eGFP-labelled alpha subunit of the AP-2 complex imaged with diaSLM by K. Dean (Dean et. al. 2016). The raw data has been cropped and limited to 50 time point (540MB).</p> <p><strong>Mitosis dataset:</strong> HeLa cells undergoing mitosis and expressing eGFP-labeled EB3 and mCherry-labeled CENPA imaged in dual-channel lattice light-sheet microscopy by W. Legant (David et. al. 2019). The raw data has been cropped, the entire sequence has been made available.</p>
Forward in time particle-tracking simulation in the Kuroshio Extension re-circulation gyre in 2019 using GLORYS12
<p>Tracers were released at the targeted mesoscale eddy in September 03 2019, and their surface transport was modeled for the the forward two months. 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. The particle number 50 indicates that the number of particles in a grid is 50 or more.</p>
Forward in-time particle-tracking simulation in the Kuroshio Extension re-circulation gyre in 2019 using GLORYS12
<p>In total 15 million tracers were uniformly released every 1/72° in the geographical region of 20-39°N / 120-155°E and their surface transport was modeled for 26 days until 3 September 2019 when the physical microplastic sampling was conducted in the target eddy. Units are expressed as the normalized number of tracer particles in each grid of 1/12° horizontal resolution. <span>The particle number 10 indicates that the number of particles in a grid is 10 or more. Contours below 1.0 m are indicated with dashed lines. </span></p>
E2E-differentiable Charged Particle Tracking Data
<div> <div>In this data repository, we extend an earlier dataset (https://zenodo.org/records/7426388) generated for the Bergen pCT DTC by various additional Monte Carlo (MC) simulations (generated using the Gate 9.2 simulation toolkit [1, 2] built upon Geant4 [3,4,5]) with different setups and phantom materials with particular focus on an E2E-differentiable tracking algorithm provided on <a href="https://github.com/SIVERT-pCT/e2e-tracking">GitHub</a>. The data repository is further extended by trained model checkpoints and results of addtional analysis required for running the code, without re-training and re-evaluation.</div> <br> <h3><strong>Training Files</strong></h3> <strong>Files</strong>: 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 single beam spots for water phantoms of various thicknesses (100, 150 and 200 mm): <ul> <li>water_{100,150,200}_5k.npz (validation data)</li> </ul> <div> <ul> <li>water_{100,150,200}_10k.npz (test data, taken from https://zenodo.org/records/7426388)</li> </ul> </div> <div> <ul> <li>water_{100,150,200}_100k.npz (training data)</li> </ul> </div> <br> <div><strong>Columns</strong>: 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:</div> <ul> <li><strong>posX, posY, posZ</strong>: Measured x, y, z position (in millimeter) of the particle hit relative to the simulation origin defined by the center of the phantom.</li> </ul> <div> <ul> <li><strong>edep</strong>: Amount of energy (in MeV) deposited by a particle while interacting with the sensitive area of the detector.</li> </ul> </div> <div> <ul> <li><strong>eventID</strong>: 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> </ul> </div> <div> <ul> <li><strong>trackID</strong>: 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> </ul> </div> <div> <ul> <li><strong>parentID</strong>: 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> </ul> </div> <div> <ul> <li><strong>volumeID[2]</strong>: 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> </ul> </div> <div> <ul> <li><strong>volumeID[3]</strong>: 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> </div> <br> <h3><strong>Model Checkpoints and Analysis Data</strong></h3> <strong>Files</strong>: Additionaly to the simulated track data, we provide the trained model checkpoints and additional analysis data, generated using the source code for end-to-end differentiable charged particle tracking published on <a href="https://github.com/SIVERT-pCT/e2e-tracking">GitHub</a>. The compressed directory contains the following directories:<br> <div> <ul> <li><strong>cka</strong>: Calculated CKA similarities [6] estimated for all trained model combinations .</li> </ul> </div> <div> <ul> <li><strong>mode</strong>: Minimum energy connecting curves (mode connectivity) [7] of Bezier splines optimized for all trained model combinations.</li> </ul> </div> <div> <ul> <li><strong>pat_lambda_{25,50,75}</strong>: All trained end-to-end differentiable tracking networks.</li> </ul> </div> <div> <ul> <li><strong>ptt</strong>: All trained two-step tracking networks.</li> </ul> </div> <br><br> <div>[1] 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.</div> <br> <div>[2] 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.</div> <br> <div>[3] 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.</div> <br> <div>[4] 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.</div> <br> <div>[5] 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</div> <br> <div>[6] Kornblith, S., Norouzi, M., Lee, H., & Hinton, G. (2019). Similarity of Neural Network Representations Revisited. 36th International Conference on Machine Learning, pp 6156–6175, 2019.</div> <br> <div>[7] Garipov, T., Izmailov, P., Podoprikhin, D., Vetrov, D., & Wilson, A. G. (2018). Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs. Advances in Neural Information Processing Systems, pp. 8789–8798, 2018.</div> </div>
3D Real-Time Single Particle Tracking using two-photon fluorescence from bright dye-based organic nanoparticles
<p>Data set, and attached ReadMe files related to the production of the figures in the article "3D Real-Time Single Particle Tracking using two-photon fluorescence from bright dye-based organic nanoparticles" by Emperauger et al.</p> <p> </p> <p>The ReadMe files explaining how each data file is organised, the data are in txt format, organised by figure and sub-figures and compressed. </p>
Particle tracking simulations of marine macro-litter in Rostock during extreme events
<p>The simulations show the passive near-bottom transport of macro-litter during extreme events (HanseSail in Rostock, Northern Germany). The simulations are based on a high resolution 3D flow model of the Warnow estuary.</p>
Tracking Inflammatory Cells Using Superparamagnetic Particles of Iron Oxide (SPIO) and Magnetic Resonance Imaging (MRI)
ClinicalTrials.gov study NCT01169935. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Cell Tracking Using Superparamagnetic Particles of Iron Oxide (SPIO) and Magnetic Resonance Imaging (MRI) - A Pilot Study
ClinicalTrials.gov study NCT00972946. IPD Sharing: Not stated. Countries: 1. Publications: 1.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.