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FIGURE 2 in Massif Speciation Events in New Caledonian Lizards: Diversification in the Genus Marmorosphax (Scincidae) Tracks Isolation on the Island's Ultramafic Surfaces

FIGURE 2. Persistent high elevation cloud formation on northwest Massif Katépahié (adjacent to the Massif de Koniambo) during the dry season.

FIGURE 5 in Massif Speciation Events in New Caledonian Lizards: Diversification in the Genus Marmorosphax (Scincidae) Tracks Isolation on the Island's Ultramafic Surfaces

FIGURE 5. Phylogenetic relationships amongst the species and populations of Marmorosphax. Likelihood topology with UFboot and BPP Bayesian posterior probabilities shown respectively. UFboot equal to 100 and BPP equal to 1.0 are represented at nodes with black circles. The numbers in squares represent the four major groups of Marmorosphax and numbers present in circles represent groups for pairwise comparisons. – indicate nodes not recovered by both phylogenetic analyses.

opencc-by-4.0Jul 2016View details →

FIGURE 5. Event timeline. 1 in Unveiling trampling history through trackway interferences and track preservational features: a case study from the Bletterbach gorge (Redagno, Western Dolomites, Italy)

FIGURE 5. Event timeline. 1, Ripple marks and Gl1 tracks formation; 2, Gl2 and Gl3 tracks formation; 3; Gl4 trackway formation; 4, Jb trackway formation; 5, Gl5 and Ct1 trackways formation; 6, Ct2 trackway formation; 7, Gl6 and Gl7 trackways formation; 8, Pd trackway formation; 9, Final interpretative drawing of the slab MPUR NS 34/28; 10, Trackmakers advancement directions. rip, ripple marks; Pd, Pachypes dolomiticus trackway; Ct1-Ct2, Chelichnus tazelwürmi trackways; Gl1-Gl7, Ganasauripus ladinus trackways; Jb, Janusichnus bifrons trackway; dcr,? desiccation cracks. Scale bar is 50 cm.

opencc-by-4.0Sep 2024View details →

ALICE Pb-Pb Run 2 event display with red/blue tracks: animation

<div> This animated event display shows tracks in a Pb-Pb event recorded during Run 2 of the LHC. Individual tracks are shown following a red/blue colour code according to their charge, and tracks expand outwards with a velocity calculated with their measured momenta and using the pion mass hypothesis. Outer detectors are not shown for simplicity.  </div>

opencc-by-4.0Sep 2023View details →

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.

zenodo36/100

Tracking and classifying Amazon fire events in near-real time

Summary Time-series (2018-2024) of the Amazon dashboard, including minor updates to the methods. The Amazon dashboard data product tracks individual fire events across most of South America (10N - 25S, 85W - 30W) in near-real time. The model classifies fires into four key fire types (deforestation, forest, small clearing and agricultural, and savanna and grassland fires) and provides estimates of individual fire carbon emissions. Methods are described in Andela et al. (2022). Near-real time estimates are provided at https://amzfire.servirglobal.net/ and here we archive historic time-series. Methods The data archived here (v1.1) include several small updates. Two updates relate to the use of VIIRS active fire detections. First, VIIRS active fire detections have been updated from collection 1 to collection 2. Second, any full day of missing data from either the VIIRS instrument onboard NOAA-20 or Suomi NPP is now replaced by data of the other instrument. This "gap" filling helps reduce the impact of periods with instrument outage, like those of Suomi NPP VIIRS during the 2024 burning season.  The other two updates relate to the emissions calculations. First, to convert dry matter burned to carbon emissions, we have introduced fire type specific emissions factors instead of the earlier assumption of 50% carbon content for all fire types. Second, as part of the Sense4Fire project (https://sense4fire.eu/), we provide daily gridded emissions estimates of Dry Matter (DM), C, CO2, CO, and NOx at 0.1 degree resolution. We used emissions factors provided by Andrea (2019) for savanna and grassland fires as well as small clearing and agricultural fires while for forest and deforestation fires we reviewed the literature to select the most relevant emissions factors (Table 1).  Table 1: Emissions factors (gram species per kg dry matter burned) used to calculate C, CO2, CO, and NOx emissions.  <table> <tbody> <tr> <td>Fire type / trace gas emissions</td> <td>C</td> <td>CO2</td> <td>CO</td> <td>NOx</td> </tr> <tr> <td>Savanna and grassland</td> <td>480</td> <td>1656</td> <td>69.2</td> <td>2.5</td> </tr> <tr> <td>Small clearing and agricultural</td> <td>430</td> <td>1431</td> <td>76.2</td> <td>2.4</td> </tr> <tr> <td>Forest</td> <td>480</td> <td>1561</td> <td>104.0</td> <td>2.0</td> </tr> <tr> <td>Deforestation</td> <td>490</td> <td>1641</td> <td>95.5</td> <td>1.7</td> </tr> </tbody> </table>   Dataset description For full detail, please see Andela et al. (2022). The tables below (Tables 2 - 4) describe the content of the fire event (polygon) and active fire detections (point) shapefiles as well as the gridded emissions product. The active fire detections and associated estimates of dry matter burned can be combined with emissions factors (Table 1) to derive daily trace gas emissions time series for species and areas of interest. Table 2: Explanation of fire event shapefile attribute table. <table> <tbody> <tr> <td>Attribute class</td> <td>Attribute</td> <td>Explanation / units</td> </tr> <tr> <td>Fire type classification</td> <td>Fire type</td> <td>(1) savanna and grassland, (2) small clearing and agriculture, (3) forest, and (4) deforestation fires</td> </tr> <tr> <td> </td> <td>Confidence</td> <td>(1) low, (2) moderate, and (3) high</td> </tr> <tr> <td>Fire Atlas</td> <td>Size</td> <td>Fire size in km2</td> </tr> <tr> <td> </td> <td>Start day</td> <td>Day of new fire start as day of year (1-366)</td> </tr> <tr> <td> </td> <td>Duration</td> <td>Fire duration in days</td> </tr> <tr> <td> </td> <td>C Emissions</td> <td>Fire carbon emissions (ton C)</td> </tr> <tr> <td>Fire characterization</td> <td>Tree cover</td> <td>Average tree cover fraction within perimeter (%)</td> </tr> <tr> <td> </td> <td>Biomass</td> <td>Average biomass within fire perimeter (ton ha-1)</td> </tr> <tr> <td> </td> <td>Deforestation </td> <td>Fraction of 550 m grid cells with historic deforestation (five years prior to fire) within fire perimeter (%)</td> </tr> <tr> <td> </td> <td>FRP</td> <td>Average fire radiative power (FRP) for all fire detections within fire perimeter (MW)</td> </tr> <tr> <td> </td> <td>Persistence</td> <td>Average fire persistence across 550 m grid cells within fire perimeter (days)</td> </tr> <tr> <td> </td> <td>Progression</td> <td>Average fire progression fraction across 550 m grid cells within perimeter (%)</td> </tr> <tr> <td> </td> <td>Daytime</td> <td>Fraction of 1:30 pm detections (%) for all fire detections within fire perimeter</td> </tr> <tr> <td> </td> <td>Detections</td> <td>Total active fire detections within fire perimeter</td> </tr> </tbody> </table>   Table 3: Explanation of active fire detection shapefile attribute table. <table> <tbody> <tr> <td>Attribute class</td> <td>Attribute</td> <td>Explanation / units</td> </tr> <tr> <td>VIIRS active fire detections</td> <td>FRP</td> <td>Fire radiative power (MW)</td> </tr> <tr> <td> </td> <td>DOY</td> <td>Day of year (1-366)</td> </tr> <tr> <td>Fire type classification</td> <td>Fire type</td> <td>(1) savanna and grassland, (2) small clearing and agriculture, (3) forest, and (4) deforestation fires</td> </tr> <tr> <td> </td> <td>Confidence</td> <td>(1) low, (2) moderate, and (3) high</td> </tr> <tr> <td>Emissions</td> <td>C Emissions</td> <td>Fire carbon emissions (ton C) associated with each active fire detection</td> </tr> <tr> <td> </td> <td>DM Emissions</td> <td>Dry matter burned (ton) associated with each active fire detection</td> </tr> </tbody> </table>   Table 4: Content of daily gridded (0.1 degree resolution) emissions netcdf files. The daily emissions product provides emissions estimates of dry matter, C, CO2, CO, and NOx. For DM and CO partitioned emissions are also provided by fire type, for other species these can be derived by multiplying the dry matter burned (DM) estimates with trace gas specific emissions factors (Table 1). Values of each grid cell can be multiplied by the number of seconds per day and grid cell area to calculate total emissions (convert "kg species m-2 s-1" to "kg species day-1 per grid cell"). <table> <tbody> <tr> <td>/ancill</td> <td>grid_cell_area</td> </tr> <tr> <td>/partitioned_DM_emissions</td> <td>Deforestation emissions</td> </tr> <tr> <td> </td> <td>Forest emissions</td> </tr> <tr> <td> </td> <td>Savanna and grassland emissions</td> </tr> <tr> <td> </td> <td>Small clearing and agricultural emissions</td> </tr> <tr> <td>/partitioned_CO_emissions</td> <td>Deforestation emissions</td> </tr> <tr> <td> </td> <td>Forest emissions</td> </tr> <tr> <td> </td> <td>Savanna and grassland emissions</td> </tr> <tr> <td> </td> <td>Small clearing and agricultural emissions</td> </tr> <tr> <td>/total_emissions</td> <td>DM emissions</td> </tr> <tr> <td> </td> <td>C emissions</td> </tr> <tr> <td> </td> <td>CO2 emissions</td> </tr> <tr> <td> </td> <td>CO emissions</td> </tr> <tr> <td> </td> <td>NOx emissions</td> </tr> </tbody> </table>   Results Despite the various small improvements to the dataset, the data are largely consistent with the original dataset published for 2019-2020 (Table 5).  Table 5: Comparison of model versions (original from Andela et al., 2022 and v1.1 published here) for April-December 2019 (equator-25S, 85W - 30W). Note that the current version (v1.1) is complete for 2019, but the original dataset had missing data due to incomplete active fire detections from NOAA-20 VIIRS at that time. <table> <tbody> <tr> <td>Dataset</td> <td>Fire type</td> <td>Fire detections (x1,000)</td> <td>Mean fire radiative power (MW)</td> <td>Number of events (x1,000)</td> <td>Emissions (Tg C)</td> </tr> <tr> <td>Original</td> <td>Deforestation</td> <td>756.65</td> <td>15.15</td> <td>24.24</td> <td>99.18</td> </tr> <tr> <td>Original</td> <td>Forest</td> <td>637.58</td> <td>12.73</td> <td>5.28</td> <td>85.46</td> </tr> <tr> <td>Original</td> <td>Small clearing and agricultural</td> <td>348.49</td> <td>10.91</td> <td>154.68</td> <td>10.55</td> </tr> <tr> <td>Original</td> <td>Savanna and grassland</td> <td>1935.06</td> <td>12.11</td> <td>296.42</td> <td>71.75</td> </tr> <tr> <td>v1.1</td> <td>Deforestation</td> <td>742.64</td> <td>14.81</td> <td>24.02</td> <td>97.18</td> </tr> <tr> <td>v1.1</td> <td>Forest</td> <td>626.92</td> <td>12.06</td> <td>5.16</td> <td>77.84</td> </tr> <tr> <td>v1.1</td> <td>Small clearing and agricultural</td> <td>350.7</td> <td>10.89</td> <td>155.56</td> <td>9.27</td> </tr> <tr> <td>v1.1</td> <td>Savanna and grassland</td> <td>1877.16</td> <td>11.89</td> <td>299.17</td> <td>70.55</td> </tr> </tbody> </table>   Acknowledgements The Sense4Fire project is funded by ESA under ESA Contract Number: 4000134840/21/I-NB.  References Andela, N., Morton, D.C., Schroeder, W., Chen, Y., Brando, P.M. and Randerson, J.T., 2022. Tracking and classifying Amazon fire events in near real time. Science advances, 8, eabd2713. https://doi.org/10.1126/sciadv.abd2713. Andreae, M.O., 2019. Emission of trace gases and aerosols from biomass burning–an updated assessment. Atmospheric Chemistry and Physics, 19, 8523-8546. https://doi.org/10.5194/acp-19-8523-2019.

opencc-by-4.0Jun 2022View details →

ClinicalTrials.gov36/100

PACESETTER: Program to Avoid Cerebrovascular Events Through Systematic Electronic Tracking and Tailoring of an Eminent Risk-factor

ClinicalTrials.gov study NCT03401489. IPD Sharing: NO. Countries: 1. Publications: 1.

closedIPD-NOFeb 2026View details →

opencc-by-4.0Jul 2020View details →

Asynchronous event-based clustering and tracking for intrusion monitoring in UAS

This dataset describes a collection of rosbag files for event-based intruder monitoring using UAS. A DAVIS346 camera was mounted over a DJI Flamewheel F550 Drone, and an onboard computer recorded the sensor information from the event camera. Each dataset includes events, frames, and IMU measurements. The monitoring scenes were recorded outdoors at the School of Engineering of the University of Seville. In each dataset, an intruder moves and hides from the field of view of the camera simulating a scape-intrusion situation. A total of four monitoring setting were recorded: Daylight monitoring: A daylight scene for intruder monitoring. An intruder runs and hides behind the objects of the scene to evade the camera field of view. Night light monitoring: A monitoring scene during the night without the presence of any artificial light. The low light condition increases the difficulty of monitoring task due to the increment of noisy events. Multi-target: An experiment with a suspect and a chaser drone moving in the monitoring area. The drone follows the suspect by simulating a pursuit operation. Monitoring under illumination changes: A night scene where the lighting conditions changes by the movement of artificial lights in the scene.

opencc-by-4.0Oct 2023View details →

Particle tracking simulations of marine macro-litter in Rostock during extreme events

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.

dryad32/100

Label-free three-photon imaging of intact human cerebral organoids: Tracking early events in brain development and deficits in Rett Syndrome

Human cerebral organoids are unique in their development of progenitor-rich zones akin to ventricular zones from which neuronal progenitors differentiate and migrate radially. Analyses of cerebral organoids thus far have been performed in sectioned tissue or in superficial layers due to their high scattering properties. Here, we demonstrate label-free three-photon imaging of whole, uncleared intact organoids (~2 mm depth) to assess early events of early human brain development. Optimizing a custom-made three-photon microscope to image intact cerebral organoids generated from Rett Syndrome patients, we show defects in the ventricular zone volumetric structure of mutant organoids compared to isogenic control organoids. Long-term imaging of live organoids reveals that shorter migration distances and slower migration speeds of mutant radially migrating neurons are associated with more tortuous trajectories. Our label-free imaging system constitutes a particularly useful platform for tracking normal and abnormal development in individual organoids, as well as for screening therapeutic molecules via intact organoid imaging.

opencc-zeroJun 2022View details →

opencc-by-4.0Jan 2022View details →

Large Radius Tracking Events in pp collisions with a Generic detector

This dataset is generated with the <a href="https://github.com/acts-project/acts">ACTS framework</a>, using a generic detector description (similar to <a href="https://www.kaggle.com/c/trackml-particle-identification">the one in the TrackML challenge</a>) and a constant magnetic field of 2 Tesla in the beam direction (i.e. z-axis). The event simulation was done via the Fastras and the digitization was done via smearing functions, which is configured with the <a href="https://github.com/acts-project/acts/blob/main/Examples/Algorithms/Digitization/share/default-smearing-config-generic.json">default configuration file in ACTS</a>. We did not use an exact release when producing the dataset, but we believe release v15.0.0 should reproduce the dataset. We processed the original outputs from ACTS so that the dataset is machine-learning friendly. Four different physics processes are generated with the Pythia8 generator at 13 TeV of proton-proton collisions without the presence of addition collision (i.e. no pileup events). <ul> <li>Heavy Neural Lepton with the neutral lepton mass of 15 GeV and a lifetime of 100 mm</li> <li>Heavy Neural Lepton with the neutral lepton mass of 10 GeV and a lifetime of 100 mm.</li> <li>Heavy Neural Lepton with the neutral lepton mass of 15 GeV and a lifetime of 200 mm.</li> <li>ttbar events</li> </ul> The heavy neural lepton generated along with a neutrino through a virtual W boson is forced to decay to two muons and a muon neutrino. The dataset is saved as the torch dataset. One can access it via the following code snippet. <pre><code class="language-python">>>> import numpy as np >>> arrays = np.load("ttbar_PileUp000_10K/100.npz") >>> [print(key,) for key in arrays.keys()] hid layerless_true_edges layers pid x particles</code></pre> <ul> <li>hid is the spacepoint id</li> <li>layerless_true_edges is a table of the true-level edges where edges connect spacepoints coming from the same track.</li> <li>layers is the layer id where the spacepoint is recorded</li> <li>pid is a table of particle IDs each spacepoint associated with. hid is a table of spacepoint IDs,</li> <li>x is a table of spacepoints with dimension of [number of spacepoints, number of spacepoint features], the spacepoint features are their 3D global positions.</li> <li>particles is a table for particle information. Columns are: ['particle_id', 'particle_type', 'process', 'vx', 'vy', 'vz', 'vt', 'px', 'py', 'pz', 'm', 'q', 'parent_pid', 'pt', 'radius', 'eta']</li> </ul> The first Version 1.0 saved the dataset in torch DataSet format which requires torch 1.7.1 and PyG 1.7.1 in order to read those files. The version 1.1 saved the dataset in numpy file format. The version 2.0 adds particle information.

opencc-by-4.0Mar 2024View details →

EDOPT: Event-camera 6-DoF Dynamic Object Pose Tracking

The dataset can be used to test your event-based 6-DoF pose tracking algorithm. If you use any of this data, please cite the following publication: @inproceedings{glover2024,   title={EDOPT: Event-camera 6-DoF Dynamic Object Pose Tracking },   author={Glover, Arren and Gava, Luna and Li, Zhichao and Bartolozzi, Chiara},   booktitle={2024 IEEE International Conference on Robotics and Automation (ICRA)},   year={2024} } The dataset includes event-driven data and ground truth of 5 objects: dragon, jell-o, mustard, soup can, and spam. For each object, six different motions on independent axes are recorded.  To import .log files containing events, we suggest <a href="https://github.com/event-driven-robotics/bimvee">bimvee</a> Python library. Specifically, use the functions to import .log files: data = importIitYarp(filePathOrName=input_path) Ground-truth .csv files have 8 columns, each one corresponding to a different measure:  timestamp | x | y | z | qx | qy | qz | qw x, y, z refer to the object position in the camera reference frame, while qx, qy, qz and qw refer to the object orientation expressed in quaternions.

opencc-by-4.0Oct 2023View details →

Particle tracking simulations of marine macro-litter in Rostock during extreme events

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.

ClinicalTrials.gov32/100

French National Observatory Tracking Viral Myocarditis: Mortality, Cardiovascular Events, Sequels on (Magnetic Resonance Imaging) MRI

ClinicalTrials.gov study NCT02717143. IPD Sharing: NO. Countries: 1. Publications: 16.

closedIPD-NOFeb 2026View details →

ClinicalTrials.gov32/100

Frequency of Vascular Events With Short-term Thromboprophylaxis in Fast-track Hip and Knee-arthroplasty.

ClinicalTrials.gov study NCT01557725. IPD Sharing: Not stated. Countries: 1. Publications: 6.

restrictedIPD-UNDECIDEDFeb 2026View details →

dryad32/100

Label-free three-photon imaging of intact human cerebral organoids: Tracking early events in brain development and deficits in Rett Syndrome

Open the record for dataset details and reuse information.

publicJul 2022View details →

zenodo28/100

FIGURE 6 in Massif Speciation Events in New Caledonian Lizards: Diversification in the Genus Marmorosphax (Scincidae) Tracks Isolation on the Island's Ultramafic Surfaces

FIGURE 6. The Mt. Kaala Massif in the distance from the summit of the Massif d'Ouazangou-Taom, separated by the low-lying Iouanga River valley.

zenodo28/100

FIGURE 3 in Massif Speciation Events in New Caledonian Lizards: Diversification in the Genus Marmorosphax (Scincidae) Tracks Isolation on the Island's Ultramafic Surfaces

FIGURE 3. High elevation forest habitat on the Massif d'Ouazangou-Taom (A) and exposed cuirasse rock cap variably distributed across the summit area and ridges of massifs in the central-west and northwest ultramafic region (B).