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2,610 results for “tracking”

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zenodo44/100

The Robot Tracking Benchmark (RTB)

The Robot Tracking Benchmark (RTB) is a synthetic dataset that facilitates the quantitative evaluation of 3D tracking algorithms for multi-body objects. It was created using the procedural rendering pipeline BlenderProc. The dataset contains photo-realistic sequences with HDRi lighting and physically-based materials. Perfect ground truth annotations for camera and robot trajectories are provided in the BOP format. Many physical effects, such as motion blur, rolling shutter, and camera shaking, are accurately modeled to reflect real-world conditions. For each frame, four depth qualities exist to simulate sensors with different characteristics. While the first quality provides perfect ground truth, the second considers measurements with the distance-dependent noise characteristics of the Azure Kinect time-of-flight sensor. Finally, for the third and fourth quality, two stereo RGB images with and without a pattern from a simulated dot projector were rendered. Depth images were then reconstructed using Semi-Global Matching (SGM). The benchmark features six robotic systems with different kinematics, ranging from simple open-chain and tree topologies to structures with complex closed kinematics. For each robotic system, three difficulty levels are provided: easy, medium, and hard. In all sequences, the kinematic system is in motion. While for easy sequences the camera is mostly static with respect to the robot, medium and hard sequences feature faster and shakier motions for both the robot and camera. Consequently, motion blur increases, which also reduces the quality of stereo matching. Finally, for each object, difficulty level, and depth image quality, 10 sequences with 150 frames are rendered. In total, this results in 108.000 frames that feature different kinematic structures, motion patterns, depth measurements, scenes, and lighting conditions. In summary, the Robot Tracking Benchmark allows to extensively measure, compare, and ablate the performance of multi-body tracking algorithms, which is essential for further progress in the field.

opencc-by-4.0Jan 2023View details →

zenodo44/100

40 years of continental moisture tracking using WAM-2layers

The WAM-2layers is an Eulerian moisture tracking numerical model. We used the Python version available on the GitHub repository (https://github.com/ruudvdent/WAM2layersPython). For a detailed description of the WAM-2layers, we refer the reader to van der Ent et al. (2013, 2014). Moisture tracking was conducted forward in time, focusing on the fate of evapotranspiration contributing to Terrestrial Moisture Recycling to calculate the amount of precipitation with continental origin. Our tracking experiment spans from 1979 to 2018. The model domain covers a global grid from 79.5ºN to 79.5ºS latitude. Calculations use data of specific humidity, zonal and meridional wind speeds at 24 pressure levels, surface pressure at 6-h intervals, and 3-h accumulated precipitation and evaporation, from the ERA-Interim reanalysis (Dee et al., 2011) on a 1.5º grid with 25,680 cells total (nx=240, ny=107). This set-up is based on previous studies indicating that it is adequate to track moisture spatially, both forward and backward in time and at regional and global scales.

opencc-by-4.0Jan 2023View details →

zenodo44/100

replicAnt - Plum2023 - Detection & Tracking Datasets and Trained Networks

This dataset contains all recorded and hand-annotated as well as all synthetically generated data as well as representative trained networks used for detection and tracking experiments in the replicAnt - generating annotated images of animals in complex environments using Unreal Engine manuscript. Unless stated otherwise, all 3D animal models used in the synthetically generated data have been generated with the open-source photgrammetry platform scAnt <a href="http://peerj.com/articles/11155/">peerj.com/articles/11155/</a>.  All synthetic data has been generated with the associated replicAnt project available from <a href="https://github.com/evo-biomech/replicAnt">https://github.com/evo-biomech/replicAnt</a>. Abstract: Deep learning-based computer vision methods are transforming animal behavioural research. Transfer learning has enabled work in non-model species, but still requires hand-annotation of example footage, and is only performant in well-defined conditions. To overcome these limitations, we created replicAnt, a configurable pipeline implemented in Unreal Engine 5 and Python, designed to generate large and variable training datasets on consumer-grade hardware instead. replicAnt places 3D animal models into complex, procedurally generated environments, from which automatically annotated images can be exported. We demonstrate that synthetic data generated with replicAnt can significantly reduce the hand-annotation required to achieve benchmark performance in common applications such as animal detection, tracking, pose-estimation, and semantic segmentation; and that it increases the subject-specificity and domain-invariance of the trained networks, so conferring robustness. In some applications, replicAnt may even remove the need for hand-annotation altogether. It thus represents a significant step towards porting deep learning-based computer vision tools to the field. Benchmark data Two video datasets were curated to quantify detection performance; one in laboratory and one in field conditions. The laboratory dataset consists of top-down recordings of foraging trails of Atta vollenweideri (Forel 1893) leaf-cutter ants. The colony was collected in Uruguay in 2014, and housed in a climate chamber at 25°C and 60% humidity. A recording box was built from clear acrylic, and placed between the colony nest and a box external to the climate chamber, which functioned as feeding site. Bramble leaves were placed in the feeding area prior to each recording session, and ants had access to the recording area at will. The recorded area was 104 mm wide and 200 mm long. An OAK-D camera (OpenCV AI Kit: OAK-D, Luxonis Holding Corporation) was positioned centrally 195 mm above the ground. While keeping the camera position constant, lighting, exposure, and background conditions were varied to create recordings with variable appearance: The “base” case is an evenly lit and well exposed scene with scattered leaf fragments on an otherwise plain white backdrop. A “bright” and “dark” case are characterised by systematic over- or underexposure, respectively, which introduces motion blur, colour-clipped appendages, and extensive flickering and compression artefacts. In a separate well exposed recording, the clear acrylic backdrop was substituted with a printout of a highly textured forest ground to create a “noisy” case. Last, we decreased the camera distance to 100 mm at constant focal distance, effectively doubling the magnification, and yielding a “close” case, distinguished by out-of-focus workers. All recordings were captured at 25 frames per second (fps). The field datasets consists of video recordings of Gnathamitermes sp. desert termites, filmed close to the nest entrance in the desert of Maricopa County, Arizona, using a Nikon D850 and a Nikkor 18-105 mm lens on a tripod at camera distances between 20 cm to 40 cm. All video recordings were well exposed, and captured at 23.976 fps. Each video was trimmed to the first 1000 frames, and contains between 36 and 103 individuals. In total, 5000 and 1000 frames were hand-annotated for the laboratory- and field-dataset, respectively: each visible individual was assigned a constant size bounding box, with a centre coinciding approximately with the geometric centre of the thorax in top-down view. The size of the bounding boxes was chosen such that they were large enough to completely enclose the largest individuals, and was automatically adjusted near the image borders. A custom-written Blender Add-on aided hand-annotation: the Add-on is a semi-automated multi animal tracker, which leverages blender’s internal contrast-based motion tracker, but also include track refinement options, and CSV export functionality. Comprehensive documentation of this tool and Jupyter notebooks for track visualisation and benchmarking is provided on the <a href="https://github.com/evo-biomech/replicAnt">replicAnt</a> and <a href="https://github.com/FabianPlum/blenderMotionExport">BlenderMotionExport</a> GitHub repositories. Synthetic data generation Two synthetic datasets, each with a population size of 100, were generated from 3D models of \textit{Atta vollenweideri} leaf-cutter ants. All 3D models were created with the scAnt photogrammetry workflow. A “group” population was based on three distinct 3D models of an ant minor (1.1 mg), a media (9.8 mg), and a major (50.1 mg) (see <a href="https://zenodo.org/record/7849059">10.5281/zenodo.7849059</a>)). To approximately simulate the size distribution of A. vollenweideri colonies, these models make up 20%, 60%, and 20% of the simulated population, respectively. A 33% within-class scale variation, with default hue, contrast, and brightness subject material variation, was used. A “single” population was generated using the major model only, with 90% scale variation, but equal material variation settings. A Gnathamitermes sp. synthetic dataset was generated from two hand-sculpted models; a worker and a soldier made up 80% and 20% of the simulated population of 100 individuals, respectively with default hue, contrast, and brightness subject material variation. Both 3D models were created in Blender v3.1, using reference photographs. Each of the three synthetic datasets contains 10,000 images, rendered at a resolution of 1024 by 1024 px, using the default generator settings as documented in the Generator_example level file (see documentation on <a href="https://github.com/evo-biomech/replicAnt">GitHub</a>). To assess how the training dataset size affects performance, we trained networks on 100 (“small”), 1,000 (“medium”), and 10,000 (“large”) subsets of the “group” dataset. Generating 10,000 samples at the specified resolution took approximately 10 hours per dataset on a consumer-grade laptop (6 Core 4 GHz CPU, 16 GB RAM, RTX 2070 Super). Additionally, five datasets which contain both real and synthetic images were curated. These “mixed” datasets combine image samples from the synthetic “group” dataset with image samples from the real “base” case. The ratio between real and synthetic images across the five datasets varied between 10/1 to 1/100. Funding This study received funding from Imperial College’s President’s PhD Scholarship (to Fabian Plum), and is part of a project that has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (Grant agreement No. 851705, to David Labonte). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

opencc-by-4.0Apr 2023View details →

zenodo44/100

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 →

zenodo44/100

EyeLink 1000 raw eye tracking data - Reading numbers is harder than reading words: An eye-tracking study

Reading Arabic numerals is a fundamentally different activity compared to word reading. This study aimed to investigate the eye movements of normal-reading adults when reading aloud short and long Arabic numerals (with or without a thousand separator) compared to matched-in-length words and pseudowords. This dataset contains the raw data of the article "Reading numbers is harder than reading words: An eye-tracking study" published in the journal Acta Psychologica (https://doi.org/10.1016/j.actpsy.2023.103942)

opencc-by-4.0May 2023View details →

zenodo44/100

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-4.0May 2023View details →

zenodo44/100

Simulated tracks and associated melting of 6912 small to giant Antarctic icebergs, September 1997 to December 2008

We present a dataset of Antarctic iceberg drift tracks and melting that includes small, medium-sized, and giant tabular icebergs with a realistic size distribution. An iceberg model is initialized with 6912 observed iceberg positions and sizes around Antarctica. The dataset is the result of a 2017 study "A simulation of small to giant Antarctic iceberg evolution: Differential impact on climatology estimates" published in JGR:Oceans (<a href="https://doi.org/10.1002/2016JC012513">https://doi.org/10.1002/2016JC012513</a>). We simulate drift and lateral melt using iceberg-draft averaged ocean currents, temperature, and salinity. A new basal melting scheme, originally applied in ice shelf melting studies, uses in situ temperature, salinity, and relative velocities at an iceberg's bottom. Climatology estimates of Antarctic iceberg melting based on simulations of small (≤2.2 km), “small-to-medium-sized" (≤10 km), and small-to-giant icebergs (including icebergs >10 km) exhibit differential characteristics: successive inclusion of larger icebergs leads to a reduced seasonality of the iceberg meltwater flux and a shift of the mass input to the area north of 58°S, while less meltwater is released into the coastal areas. This suggests that estimates of meltwater input solely based on the simulation of small icebergs introduce a systematic meridional bias; they underestimate the northward mass transport and are, thus, closer to the rather crude treatment of iceberg melting as coastal runoff in models without an interactive iceberg model. Future ocean simulations will benefit from the improved meridional distribution of iceberg melt, especially in climate change scenarios where the impact of iceberg melt is likely to increase due to increased calving from the Antarctic ice sheet.

opencc-by-4.0Mar 2017View details →

zenodo44/100

Dataset for "Blue-shift photoconversion of near-infrared fluorescent proteins for labeling and tracking in living cells and organisms"

Dataset that supports the observation, characterization and application of the blue-shift photoconversion of the near infrared proteins, miRFPs, reported in the manuscript: "Blue-shift photoconversion of near-infrared fluorescent proteins for labeling and tracking in living cells and organisms". The data references to the specific figures and graphs in the manuscript.

opencc-by-4.0Mar 2023View details →

zenodo44/100

Tracks of western disturbances (1950-2022) impacting South Asia

WDs are identified using the feature-tracking algorithm described in Hunt et al (2018). Relative vorticity is averaged across the 450-300 hPa layer, and then spectrally truncated to T42 to remove high-frequency noise that hinders tracking. For each region of positive vorticity, the centroid is located and labelled as a candidate WD. These centroids are connected between timesteps using a nearest-neighbour algorithm, biased to take into account the steering winds of the subtropical jet. Systems that do not on average travel eastward, last fewer than 48 hours, or do not pass through the box [20-42.5°N, 60-80°E] are rejected. Applied to ERA5, this gives over seventy years of track data (1950-2022). The method followed here is identical to Nischal et al (2022), except the northern edge of the catching box is extended from 36.5°N to 42.5°N, to ensure that all WDs that potentially impact North India are included. Column titles are: timestep: a counter indicating the number of 3-hourly timesteps that have passed since 1950-01-01 00:00 track_id: a unique identifier linking points into tracks time: string describing the date and time lon: longitude lat: latitude vort: vorticity measured at the centre of the WD averaged over the 450-300 hPa layer. Can be used for intensity filtering. eccentricity: eccentricity of the region of positive vorticity. Can be used to understand local dynamics. Hunt, K. M. R., Turner, A. G., & Shaffrey, L. C. (2018). The evolution, seasonality and impacts of western disturbances. Quarterly Journal of the Royal Meteorological Society, 144(710), 278-290. Nischal, Attada, R., & Hunt, K. M. (2022). Evaluating winter precipitation over the western Himalayas in a high-resolution Indian regional reanalysis using multisource climate datasets. Journal of Applied Meteorology and Climatology, 61(11), 1613-1633.

opencc-by-4.0Aug 2023View details →

zenodo44/100

UWB Positioning and Tracking Data Set

# UWB Positioning and Tracking Data Set UWB positioning data set contains measurements from four different indoor environments. The data set contains measurements that can be used for range-based positioning evaluation in different indoor environments.   # Measurement system The measurements were made using 9 DW1000 UWB transceivers (DWM1000 modules) connected to the networked RaspberryPi computer using in-house radio board SNPN_UWB. 8 nodes were used as positioning anchor nodes with fixed locations in individual indoor environment and one node was used as a mobile positioning tag. Each UWB node is designed arround the RaspberryPi computer and are wirelessly connected to the measurement controller (e.g. laptop) using Wi-Fi and MQTT communication technologies. All tag positions were generated beforehand to as closelly resemble the human walking path as possible. All walking path points are equally spaced to represent the equidistand samples of a walking path in a time-domain. The sampled walking path (measurement TAG positions) are included in a downloadable data set file under downloads section.   # Folder structure Folder structure is represented below this text. Folder contains four subfolders named by the indoor environments measured during the measurement campaign and a folder raw_data where raw measurement data is saved. Each environment folder has a anchors.csv file with anchor names and locations, .json file data.json with measurements, file walking_path.csv file with tag positions and subfolder floorplan with floorplan.dxf (AutoCAD format), floorplan.png and floorplan_track.jpg. Subfolder raw_data contains raw data in subfolders named by the four indor environments where the measurements were taken. Each location subfolder contains a subfolder data where data from each tag position from the walking_path.csv is collected in a separate folder. There is exactly the same number of folders in data folder as is the number of measurement points in the walking_path.csv. Each measurement subfolder contains 48 .csv files named by communication channel and anchor used for those measurements. For example: ch1_A1.csv contains all measurements at selected tag location with anchor A1 on UWB channel ch1. The location folder contains also anchors.csv and walking_path.csv files which are identical to the files mentioned previously. The last folder in the data set is the technical_validation folder, where results of technical validation of the data set are collected. They are separated into 8 subfolders: - cir_min_max_mean - los_nlos - positioning_wls - range - range_error - range_error_A6 - range_error_histograms - rss   The organization of the data set is the following: data_set + location0 - anchors.csv - data.json - walking_path.csv + floorplan - floorplan.dxf - floorplan.png - floorplan_track.jpg - walking_path.csv + location1 - ... + location2 - ... + location3 - ... + raw_data + location0 + data + 1.07_9.37_1.2 - ch1_A1.csv - ch7_A8.csv - ... + 1.37_9.34_1.2 - ... + ... + location1 + ... + location2 + ... + location3 + ... + technical validation + cir_min_max_mean + positioning_wls + range + range_error + range_error_histograms + rss - LICENSE - README   # Data format Raw measurements are saved in .csv files. Each file starts with a header, where first line represents the version of the file and the second line represents the data column names. The column names have a missing column name. Actual column names included in the .csv files are:   TAG_ID ANCHOR_ID X_TAG Y_TAG Z_TAG X_ANCHOR Y_ANCHOR Z_ANCHOR NLOS RANGE FP_INDEX RSS RSS_FP FP_POINT1 FP_POINT2 FP_POINT3 STDEV_NOISE CIR_POWER MAX_NOISE RXPACC CHANNEL_NUMBER FRAME_LENGTH PREAMBLE_LENGTH BITRATE PRFR PREAMBLE_CODE CIR (starts with this column; all columns until the end of the line represent the channel impulse response)   # Availability of CODE Code for data analysis and preprocessing of all data available in this data set is published on GitHub: https://github.com/KlemenBr/uwb_positioning.git The code is licensed under the Apache License 2.0.   # Authors and License Author of data set in this repository is Klemen Bregar, klemen.bregar@ijs.si. This work is licensed under a Creative Commons Attribution 4.0 International License.   # Funding The research leading to the data collection has been partially funded from the European Horizon 2020 Programme project eWINE under grant agreement No. 688116, the Slovenian Research Agency under Grant numbers P2-0016, J2-2507 and bilateral project with Grant number BI-ME/21-22-007.

opencc-by-4.0Oct 2021View details →

zenodo44/100

Datasets and Supporting Materials for the IPIN 2023 Competition Track 3 (Smartphone-based, off-site)

This package contains the datasets and supplementary materials used in the IPIN 2023 Competition.Contents<ul><li>Track-3_TA-2023.pdf: Technical annexe describing the competition (Version 2)</li><li>01 Logfiles: This folder contains a subfolder with the 54 training trials, a subfolder with the 4 testing trials (validation), and a subfolder with the 2 blind scoring trials (test) as provided to competitors.</li><li>02 Supplementary_Materials: This folder contains the Matlab/octave parser, the raster maps, the files for the Matlab tools and the trajectory visualization.</li><li>03 Evaluation: This folder contains the scripts we used to calculate the competition metric, the 75th percentile on the 69 evaluation points. It requires the Matlab Mapping Toolbox. We also provide the ground truth as 2 CSV files. It contains samples of reported estimations and the corresponding results.</li></ul>We provide additional information on the competition at: https://evaal.aaloa.org/2023/call-for-competitionCitation Policy Please cite the following works when using the datasets included in this package:Torres-Sospedra, J.; et al. Datasets and Supporting Materials for the IPIN 2023 Competition Track 3 (Smartphone-based, off-site), Zenodo 2023 http://dx.doi.org/10.5281/zenodo.8362205Check the updated citation policy at: http://dx.doi.org/10.5281/zenodo.8362205ContactFor any further questions about the database and this competition track, please contact: Joaquín Torres-Sospedra  Centro ALGORITMI, Universidade do Minho, Portugal info@jtorr.es - jtorres@algoritmi.uminho.pt   Antonio R. Jiménez  Centre of Automation and Robotics (CAR)-CSIC/UPM, Spain  antonio.jimenez@csic.esAntoni Pérez-Navarro Faculty of Computer Sciences, Multimedia and Telecommunication, Universitat Oberta de Catalunya, Barcelona, Spain aperezn@uoc.eduAcknowledgementsWe thank Maximilian Stahlke and Christopher Mutschler at Fraunhofer ISS, as well as Miguel Ortiz and Ziyou Li at Université Gustave Eiffel, for their invaluable support in collecting the datasets. And last but certainly not least, Antonino Crivello and Francesco Potortì for their huge effort in georeferencing the competition venue and evaluation points.We extend our appreciation to the staff at the Museum for Industrial Culture (Museum Industriekultur) for their unwavering patience and invaluable support throughout our collection days.We are also grateful to Francesco Potortì, the ISTI-CNR team (Paolo, Michele & Filippo), and the Fraunhofer IIS team (Chris, Tobi, Max, ...) for their invaluable commitment to organizing and promoting the IPIN competition.This work and competition belong to the IPIN 2023 Conference in Nuremberg (Germany). Parts of this work received the financial support received from projects and grants: <ul><li>ORIENTATE (H2020-MSCA-IF-2020, Grant Agreement 101023072)</li><li>GeoLibero (from CYTED)</li><li>INDRI (MICINN, ref. PID2021-122642OB-C42, PID2021-122642OB-C43, PID2021-122642OB-C44, MCIU/AEI/FEDER UE)</li><li>MICROCEBUS (MICINN, ref. RTI2018-095168-B-C55, MCIU/AEI/FEDER UE)</li><li>TARSIUS (TIN2015-71564-C4-2-R, MINECO/FEDER)</li><li>SmartLoc(CSIC-PIE Ref.201450E011)</li><li>LORIS (TIN2012-38080-C04-04)</li></ul>

opencc-by-4.0Sep 2023View details →

zenodo44/100

PyFLEXTRKR Global MCS Tracking Dataset using GPM MergedIR Tb and IMERG precipitation data

This is the global mesoscale convective system (MCS) tracking dataset developed by <a href="https://doi.org/10.1029/2020JD034202">Feng et al. (2021) JGR</a>. It contains the MCS track data (location, time, lifecycle evolution of MCS cloud and precipitation characteristics), monthly mean and 20-year climatological MCS statistics on 0.1 degree x 0.1 degree (lat x lon) grid. All data files are in netCDF format.The periods are from June 2000 to December 2020. The geographic coverage is 180°W-180°E, 60°S-60°N. For more detailed documentations, please refer to the README  "PyFLEXTRKR_MCS_Tracking_Data_Readme.pdf".Due to the large file size of the native 1-hourly resolution pixel-level data on the 0.1 degree x 0.1 degree grid, they are not included in this dataset. Please contact Zhe Feng (<a href="mailto:zhe.feng@pnnl.gov">zhe.feng@pnnl.gov</a>) if you are interested in obtaining the pixel-level data.

opencc-by-4.0Oct 2023View details →

edi44/100

Hubbard Brook Experimental Forest: Watershed 5 stem origin tracking data

W5 was experimentally whole-tree harvested in the winter of 1983-84. The initial post-harvest surveys were conducted by Jeffrey Hughes in 1984. The species and stem origin (i.e. stem grew from seed, sprouted from a cut stem or was an established stem post-harvest) for all stems in the 1m2 plots were recorded. This data set summarizes the apparent “winners” in both 1984 and 30 years later in 2014 (some evaluated in 2015). There were a total of 13 surveys in these plots that helped clarify the outcomes over time. This data set summarizes from the larger data set. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.

openCC (other)Oct 2021View details →

edi44/100

Continuous soil temperature, specific conductance, and volumetric water content measurements from the Water Track B Active Layer Monitoring Station (ALMS06), McMurdo Dry Valleys, Antarctica (2014-2021, ongoing)

As part of the McMurdo Dry Valleys Long Term Ecological Research (LTER) project, five Active Layer Monitoring Stations (ALMSs) were established throughout Taylor Valley, Antarctica to support new research foci around the thermal-moisture dynamics of soils that may control habitat conditions and faunal responses to seasonal and annual freezing cycles in this ecosystem. Two ALMSs were established adjacent to streams (Green Creek, Von Guerard Stream), with sensors installed through the active layer from the thalweg out to the shoreline and dry soil beyond. Two ALMSs were similarly established adjacent to water tracks (Wormherder Creek, Water Track B) that are zero-order drainages of snow and ice melt that rarely have surface flow. The remaining station was established in dry soil (F6) to serve as an ambient control. ALMSs measure soil temperature, soil moisture (as volumetric water content; VWC), and specific conductance (as electrical conductivity; EC) through the active layer (soil surface down to the frost table) at several locations from the water’s edge to dry soils. This data package contains measurements from the Active Layer Monitoring Station at Water Track B (ALMS06).

openCC (other)Jul 2022View details →

OpenNeuro40/100

Neural Differentiation Tracks Improved Recall of Competing Memories Following Interleaved Study and Retrieval Practice

Open the record for dataset details and reuse information.

openJan 2019View details →

zenodo40/100

The SPOTS Models: A Grid of Theoretical Stellar Evolution Tracks and Isochrones For Testing The Effects of Starspots on Structure and Colors

The SPOTS Models: A Grid of Theoretical Stellar Evolution Tracks and Isochrones For Testing The Effects of Starspots on Structure and Colors This repository contains the Stellar Parameters of Tracks with Starspots (SPOTS) grid of theoretical stellar evolutionary tracks and isochrones, presented in Somers, Pinsonneault, and Cao (2020, in prep). Our models were calculated with the Yale Rotating Evolution Code (e.g. van Saders & Pinsonneault, 2013, ApJ 776, 67), including updated which incorporate a treatment of surface starspots (Somers & Pinsonneault, 2015, ApJ 807, 174S). Modelling details can be found in these references. The purpose of this evolutionary suite is to provide the community with state-of-the-art predictions for the influence of starspots and magnetic activity on the structure of stars. The grid includes both isochrones and tracks. They can be downloaded individually from this repository, or in bulk by downloading the .zip files. Isochrones (.isoc): Each isochrone file contains a series of isochrones (stellar properties for a range of masses at fixed age) for ages between 1 Myrs and 4 Gyrs. Each file contains these isochrones for a different surface starspot covering fraction, given by the name of the file --  f000.isoc = 0% covering fraction, f017.isoc = 17% covering fraction, etc. Each isochrone contains several columns with different information, including, <ol> <li>Fundamental properties: mass, age, luminosity, radius, logg, Teff, convective overturn timescale (TauCZ), lithium abundance relative to initial (Li/Li0).</li> <li>Starspot properties: Covering fraction (Fspot), ratio of spot temperature to ambient temperature (Xspot), the temperatures of hot and cool regions (T_hot, T_cool).</li> <li>Two-temperature colors, including Johnson BV, Cousins RI, 2MASS JHK, WISE W1, and Gaia G, BP, RP.</li> </ol> Colors that fell outside of the calibrated range are listed as -99.0. Tracks (.track): We also include individual tracks for every combination of Fspot and Mass considered in the paper. Each .track file lists the mass and starspot covering fraction in the filename -- i.e. m055_f034.track is the model of mass 0.55Msun and with a 34% surface covering fraction. In addition to all the properties included in the isochrones, the track files also include: <ol> <li>The total moment of interia of the model (total_I) and the moment of interia of the surface convection zone (CZ_I)</li> <li>The central and surface hydrogen abundances (X_cen, X_surf) and the surface metallicity (Z/X_surf)</li> <li>The deuterium abundance relative to initial (H2/H2_0)</li> </ol>

opencc-by-4.0Dec 2019View details →

zenodo40/100

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-4.0Mar 2020View details →

zenodo40/100

anTraX: high throughput video tracking of color-tagged insects (benchmark datasets)

Datasets used to benchmark anTraX tracking software. Each dataset contains the raw videos, a configured anTraX session with all parameters required to reproduce the tracking results from the paper, as well as the tracking output for the first video in each dataset.

opencc-by-4.0Apr 2020View details →

zenodo40/100

Supplementary files for Silva et al. 2020 "Reptiles on the wrong track?": R Code, data and figures

Datasets, R code and figures pertaining to the manuscript: Silva, I., Crane, M., Marshall, B.M., & Strine, C.T. (2020). Reptiles on the wrong track? Moving beyond traditional estimators with dynamic Brownian Bridge Movement Models. Movement Ecology 8, 43 DOI: 10.1186/s40462-020-00229-3 Article available at: <a href="https://movementecologyjournal.biomedcentral.com/articles/10.1186/s40462-020-00229-3">https://movementecologyjournal.biomedcentral.com/articles/10.1186/s40462-020-00229-3</a>

opencc-by-4.0Feb 2020View details →

zenodo40/100

Dataset for the paper: TaskTracker tool: a Toolkit for Tracking of Code Snapshots and Activity Data During Solution of Programming Tasks (SIGCSE Technical Symposium 2021))

<pre>It is a dataset for the TaskTracker-tool: a Toolkit for Tracking of Code Snapshots and Activity Data During Solution of Programming Tasks paper from <a href="https://sigcse2021.sigcse.org/">SIGCSE Technical Symposium 2021</a>. The dataset consists of code snapshots, IDE actions, and demographic information gathered by <a href="https://github.com/JetBrains-Research/codetracker">this</a> tool. We had 148 participants, aged 11 to 40 (mean age is 19 years), take part in the data gathering process. </pre> <pre>During data gathering, solutions were accepted in one of four languages: Python, Java, Kotlin, or C++. However, some of the students chose not to submit tasks or solved some tasks incorrectly. At the same time, some students solved some tasks many times in multiple languages. All submitted solutions are included in the final dataset.</pre> <pre>To get more information see the README file.</pre>

opencc-by-4.0Nov 2020View details →

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