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559 results for “Track data”

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zenodo40/100

Data for - Tracking one-in-a-million: Large-scale benchmark for microbial single-cell tracking with experiment-aware robustness metrics

Large-scale Corynebacterium glutamicum data set with Segmentation and Tracking Annotation We provide five time-lapse sequences with manually corrected segmentation and tracking annotations of growing C. glutamicum cultivations. The dataset contains more than 1.4 million cell observations in 29k cell tracks and 14k cell divisions. We provide videos of the annotations (videos.zip) and the dataset in <a href="http://celltrackingchallenge.net/datasets/">Cell Tracking Challenge</a> format (ctc_format.zip). In the videos, cell contours are rendered in yellow, cell links between frames are colored red and cell divisions, and their links are colored in blue. Data Acquisition Corynebacterium glutamicum ATCC 13032 was cultivated in BHI-medium at 30°C in this study. From and overnight preculture, the main culture was inoculated the next day with a starting OD600 of 0.05 and grown at 120 rpm to a OD600 of 0.25. A chip was fabricated, according to <a href="https://doi.org/10.1039/D0LC00711K">(Täuber et al., 2020)</a>, and fixed to the microscope’s holder. The main culture cells were transferred to monolayer growth chambers (height = 720 nm) on the microfluidic chip. Flow through the microfluidic device was mediated by pressure driven pumps with a pressure of 100 mbar on the medium reservoir. The time-lapse phase contrast images of five monolayer growth chambers were taken every minute using an inverted microscope (Nikon Eclipse Ti2) with a 100x oil emersion objective and a DS-QI2 camera (Nikon) at 15 % relative DIA-illumination intensity and 100 ms exposure time. The spatial image resolution is 0.072 μm/px.

opencc-by-4.0Oct 2022View details →

zenodo40/100

Supporting Information (software and data) for: Client-side energy and GHGs assessment of advertising and tracking in the news websites

This is the open data and free/libre and open source software repository for the article "Client-side energy and GHGs assessment of advertising and tracking in the news websites" by Fabio Pesari, Giovanni Lagioia, Annarita Paiano.

opencc-by-4.0Nov 2022View details →

zenodo40/100

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-4.0Dec 2022View details →

zenodo40/100

Data for: Tracking quantum coherence in polariton condensates with time-resolved tomography

Data for publication C. Lüders, M. Pukrop, F. Barkhausen, E. Rozas, C. Schneider, S. Höfling, J. Sperling, S. Schumacher, and M. Aßmann, Tracking quantum coherence in polariton condensates with time-resolved tomography, submitted. arXiv preprint arXiv:2209.07129 (2022) Long-term quantum coherence constitutes one of the main challenges when engineering quantum devices. However, easily accessible means to quantify complex decoherence mechanisms are not readily available, nor are sufficiently stable systems. We harness novel phase-space methods - expressed through non-Gaussian convolutions of highly singular Glauber-Sudarshan quasiprobabilities - to dynamically monitor quantum coherence in polariton condensates with significantly enhanced coherence times. Via intensity- and time-resolved reconstructions of such phase-space functions from homodyne detection data, we probe the systems's resourcefulness for quantum information processing up to the nanosecond regime. Our experimental findings are confirmed through numerical simulations for which we develop an approach that renders established algorithms compatible with our methodology. In contrast to commonly applied phase-space functions, our distributions can be directly sampled from measured data, including uncertainties, and yield a simple operational measure of quantum coherence via the distribution's variance in phase. Therefore, we present a broadly applicable framework and a platform to explore time-dependent quantum phenomena and resources. Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – SFB-Geschäftszeichen TRR142/3-2022 – Projektnummer 231447078, Projects A04 and C10. A grant for computing time at the Paderborn Center for Parallel Computing (PC2) is gratefully acknowledged.     You can use 7-zip for extracting the .7z file. (https://www.7-zip.org/)

opencc-zeroJan 2023View details →

zenodo40/100

Research data supporting: "TimeSOAP: Tracking high-dimensional fluctuations in complex molecular systems via time variations of SOAP spectra"

This repository contains the set of data shown in the paper "TimeSOAP: Tracking high-dimensional fluctuations in complex molecular systems via time variations of SOAP spectra", published on The Journal of Chemical Physics (DOI: 10.1063/5.0147025).

opencc-by-4.0May 2023View details →

zenodo40/100

Imaging data from "Live-cell 3D single-molecule tracking reveals modulation of enhancer dynamics by NuRD"

3D 20ms, 3D 500ms and 2D dCas9 raw videos, localisation, tracking and trajectory analysis data From 'Live-cell 3D single-molecule tracking reveals modulation of enhancer dynamics by NuRD" (2021). Biorxiv. https://doi.org/10.1101/2020.04.03.003178

opencc-by-4.0May 2023View details →

zenodo40/100

REDVID Collision Event Data – Linear Tracks and Hits

An example, representative data set is generated using the REDuced VIrtual Detector (REDVID) simulation framework and contains complexity-reduced subatomic particle collision event data. Particle trajectory information and hit coordinates from interactions with reduced-order virtual detector models is included. The data is generated in 3D domain and follows the cylindrical coordinate system for hit point coordinates in space and trajectory function parameters. The included five tarballs each belong to a different data generation recipe. While all recipes include 10000 collision events, the number of tracks included in events varies from 1 track per event to 10000 tracks per event. This is noticeable from the tarball names. The data set is intended to be used as synthesised input for research involving ML-assisted pipeline design exploration, as well as ML model design exploration, e.g., Neural Architecture Search (NAS). To understand the data and its generation in detail, refer to the provided README file, as well as the related publication.

opencc-by-4.0Sep 2023View details →

dryad40/100