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559 results for “Track data”
MAGELLAN RAW RADIO TRACKING DATA V1.0
Archival Tracking Data Files (ATDFs, also sometimes known as TRK-2-25 data) from the MAGELLAN mission to Venus (1989-1994) and Orbit Data Files (ODFs, also known as TRK-2-18 data) from the final 2-3 years of the MAGELLAN mission (1992-1994). These data include range and Doppler measurements from the radio tracking system of the NASA Deep Space Network (DSN). The measurements can be used to derive a spherical harmonic model of the Venus gravity field.
Discovery of Abnormal Flight Patterns in Flight Track Data
The National Airspace System (NAS) is an ever changing and complex engineering system. As the Next Generation Air Transportation System (NextGen) is developed, there will be an increased emphasis on safety and operational and environmental efficiency. Current operations in the NAS are monitored using a variety of data sources, including data from flight recorders, radar track data, weather data, and other massive data collection systems. Although numerous technologies exist to monitor the frequency of known but undesirable behaviors in the NAS, there are currently few methods that can analyze the large repositories to discover new and previously unknown events in the NAS. Having a tool to discover events that have implications for safety or incidents of operational importance, increases the awareness of such scenarios in the community and helps to broaden the overall safety of the NAS, whereas only monitoring the frequency of known events can only provide mitigations for already established problems. This paper discusses a novel approach for discovering operationally significant events in the NAS that are currently not monitored and have potential safety and/or efficiency implications using radar-track data. This paper will discuss the discovery algorithm and describe in detail some flights of interest with comments from subject matter experts who are familiar with the operations in the airspace that was studied.
DXC'11 Industrial Track Competition Data
Competition data, including nominal and faulty scenarios, for Diagnostic Problem I of the Third International Diagnostic Competition. Three file formats are provided, tab-delimited .txt files, Matlab .mat files, and tab-delimited .scn files. The scenario (.scn) files are read by the DXC framework. See the DXC'11 Industrial Track Sample Data resource page for additional documentation, including system catalogs and schematics. There were no DA entries for Diagnostic Problem II so we are withholding the data for use in a future Diagnostic Competition.
ULY JUP SCE RAW ARCHIVAL TRACKING DATA FILES V1.0
This data set contains raw radio tracking data from the Ulysses (ULY) Solar Corona Experiment (SCE) obtained during the ULY Jupiter flyby. The data target the Io Plasma Torus (IPT) and cover the period 1992-02-04 20:28 to 1992-02-09 15:44.
ATLAS/ICESat-2 L3A Along Track Inland Surface Water Data V006
This data set (ATL13) contains along-track surface water products for inland water bodies. Inland water bodies include lakes, reservoirs, rivers, bays, estuaries and a 7km near-shore buffer. Principal data products include the along-track water surface height and standard deviation, subsurface signal (532 nm) attenuation, significant wave height, wind speed, and coarse depth to bottom topography (where data permit).
DXC'10 Industrial Track Competition Data
Competition data, including nominal and faulty scenarios, for Diagnostic Problems I and II of the Second International Diagnostic Competition. Three file formats are provided, tab-delimited .txt files, Matlab .mat files, and tab-delimited .scn files. The scenario (.scn) files are read by the DXC framework. See the DXC'10 Industrial Track Sample Data resource page for additional documentation, including system catalogs and schematics.
DXC'09 Industrial Track Competition Data
Competition data, including nominal and faulty scenarios, for Industrial Track Tier 1 and Tier 2 of the First International Diagnostic Competition. Three file formats are provided, tab-delimited .txt files, Matlab .mat files, and tab-delimited .scn files. The scenario (.scn) files are read by the DXC framework. See the DXC'09 Industrial Track Sample Data resource page for additional documentation, including system catalogs and schematics. Note that a "rematch" took place after the competition at the DX-09 Workshop. The rematch data consisted of the data for the original competition plus new data sets taken specifically for the rematch.
LRO MOON RADIO SCIENCE RAW TRACKING DATA V1.0
This data set contains archival raw, partially processed, and ancillary/supporting radio science data acquired during the Lunar Reconnaissance Orbiter (LRO) mission. The radio observations were carried out using the LRO spacecraft and Earth-based stations of the commercial Universal Space Network (USN), the Ka-band ground station at White Sands, New Mexico (WS1), and the Next Generation Satellite Laser Ranging station in Greenbelt, Maryland (NGSLR). Additional data were provided by the NASA Deep Space Network (DSN) during commissioning. Ongoing participation by the International Laser Ranging Service (ILRS) network of stations provides further LR data that are processed by the Lunar Orbiter Laser Altimeter (LOLA) Science Operations Center (SOC). Of most interest are likely to be the Tracking Data Files in the TRK directory.
DXC'13 Industrial Track Sample Data
The sample scenarios provided here are competition scenarios from previous DXC competitions. They are identical to the competition data associated with previous years' projects, but also listed here for convenience. The zip files have a spreadsheet that lists the scenarios and relevant fault information. Each scenario is provided in three formats: tab-delimited .txt file, Matlab .mat file, and tab-delimited .scn file. The different formats are provided for your convenience; they have the same data. The scenario (.scn) files are the format read by the DXC framework. See the system catalogs and schematics for additional information.
DXC'11 Industrial Track Sample Data
The sample scenarios provided here are competition scenarios from Diagnostic Problems I and II of DXC'10. The zip file has a spreadsheet (and pdf) that lists the scenarios and relevant fault information. Each scenario is provided in three formats: tab-delimited .txt file, Matlab .mat file, and tab-delimited .scn file. The different formats are provided for your convenience; they have the same data. The scenario (.scn) files are the format read by the DXC framework. See the system catalogs and schematics for additional information. For those involved in last year's competition, note that DXC'10 Diagnostic Problem I competition data posted last year (dated 20101105) had some problems which were fixed in the DPI sample data set posted here. The battery "AbruptParasiticLoad" scenario (Exp. 1311) was removed as it was found to have different behavior prior to fault injection than nominal scenarios. Exp 1229_015f was too short (224 seconds) and was removed. Several runs caused an issue when a value was expected to be a real but was interpreted as an integer in the framework (1251, 1285, 1316, 1323, and 1330); this has been fixed. There are no "AbruptParasiticLoad" faults in the DXC11 DPI competition data.
DXC'10 Industrial Track Sample Data
Sample data, including nominal and faulty scenarios, for Diagnostic Problems I and II of the Second International Diagnostic Competition. Three file formats are provided, tab-delimited .txt files, Matlab .mat files, and tab-delimited .scn files. The scenario (.scn) files are read by the DXC framework. See the Second International Diagnostic Competition project page for more information.
ATLAS/ICESat-2 L3A Along Track Inland Surface Water Data Quick Look V006
ATL13QL is the quick look version of ATL13. Once final ATL13 files are available the corresponding ATL13QL files will be removed. ATL13 contains along-track surface water products for inland water bodies. Inland water bodies include lakes, reservoirs, rivers, bays, estuaries and a 7 km near-shore buffer. Principal data products include the along-track water surface height and standard deviation, subsurface signal (532 nm) attenuation, significant wave height, wind speed, and coarse depth to bottom topography (where data permit).
ATLAS/ICESat-2 L3A Along Track Inland Surface Water Data V006
This data set (ATL13) contains along-track surface water products for inland water bodies. Inland water bodies include lakes, reservoirs, rivers, bays, estuaries and a 7km near-shore buffer. Principal data products include the along-track water surface height and standard deviation, subsurface signal (532 nm) attenuation, significant wave height, wind speed, and coarse depth to bottom topography (where data permit).
DXC'09 Industrial Track Sample Data
Sample data, including nominal and faulty scenarios, for Tier 1 and Tier 2 of the First International Diagnostic Competition. Three file formats are provided, tab-delimited .txt files, Matlab .mat files, and tab-delimited .scn files. The scenario (.scn) files are read by the DXC framework. See the Support/Documentation section below and the First International Diagnostic Competition project page for more information.
Data Corpus for the IEEE-AASP Challenge on Acoustic Source Localization and Tracking (LOCATA)
<p>This repository contains the final release of the development and evaluation datasets for the LOCATA Challenge.</p> <p>The challenge of sound source localization in realistic environments has attracted widespread attention in the Audio and Acoustic Signal Processing (AASP) community in recent years. Source localization approaches in the literature address the estimation of positional information about acoustic sources using a pair of microphones, microphone arrays, or networks with distributed acoustic sensors. The IEEE AASP Challenge on <strong>acoustic source LOCalization And TrAcking (LOCATA)</strong> aimed at providing researchers in source localization and tracking with a framework to objectively benchmark results against competing algorithms using a common, publicly released data corpus that encompasses a range of realistic scenarios in an enclosed acoustic environment.</p> <p>Four different microphone arrays were used for the recordings, namely:</p> <ul> <li>Planar array with 15 channels (DICIT array) containing uniform linear sub-arrays</li> <li>Spherical array with 32 channels (Eigenmike)</li> <li>Pseudo-spherical array with 12-channels (robot head)</li> <li>Hearing aid dummies on a dummy head (2-channel per hearing aid).</li> </ul> <p>An optical tracking system (OptiTrack) was used to record the positions and orientations of talker, loudspeakers and microphone arrays. Moreover, the emitted source signals were recorded to determine voice activity periods in the recorded signals for each source separately. The ground truth values are compared to the estimated values submitted by the participants using several criteria to evaluate the accuracy of the estimated directions of arrival and track-to-source association. </p> <p>The datasets encompass the following six, increasingly challenging, scenarios:</p> <ul> <li><strong>Task 1:</strong> Localization of a single, static loudspeaker using static microphones arrays</li> <li><strong>Task 2:</strong> Multi-source localization of static loudspeakers using static microphone arrays</li> <li><strong>Task 3:</strong> Localization of a single, moving talker using static microphone arrays</li> <li><strong>Task 4:</strong> Localization of multiple, moving talkers using static microphone arrays</li> <li><strong>Task 5:</strong> Localization of a single, moving talker using moving microphone arrays</li> <li><strong>Task 6:</strong> Multi-source localization of moving talkers using moving microphone arrays.</li> </ul> <p>The development and evaluation datasets in this repository contain the following data:</p> <ul> <li>Close-talking speech signals for human talkers, recorded use DPA microphones</li> <li>Distant-talking recordings using four microphone arrays: <ul> <li>Spherical Eigenmike (32 channels)</li> <li>Pseudo-spherical prototype NAO robot (12 channels)</li> <li>Planar DICIT array (15 channels)</li> <li>Hearing aids installed in a head-torso simulator (4 channels)</li> </ul> </li> <li>Ground-truth annotations of all source and microphone positions, obtained using an OptiTrack system of infrared cameras. The ground-truth positions are provided at the frame rate of the optical tracking system</li> </ul> <p>The following software is provided with the data:</p> <ul> <li>Matlab code to read the datasets: <a href="https://github.com/cevers/sap_locata_io">github.com/cevers/sap_locata_io</a></li> <li>Matlab code for performance evaluation of localization and tracking algorithms: <a href="https://github.com/cevers/sap_locata_eval">github.com/cevers/sap_locata_eval</a></li> </ul> <p>For further information, see:</p> <ul> <li>C. Evers, H. W. Löllmann, H. Mellmann, A. Schmidt, H. Barfuss, P. A. Naylor, W. Kellermann<br> <em>"</em>The LOCATA Challenge: Acoustic Source Localization and Tracking<em>," </em>in <em>IEEE/ACM Transactions on Audio, Speech, and Language Processing</em>, vol. 28, pp. 1620-1643, 2020, doi: <a href="https://doi.org/10.1109/TASLP.2020.2990485">10.1109/TASLP.2020.2990485</a></li> <li>Documentation: <a href="https://www.locata.lms.tf.fau.de/files/2020/01/Documentation_LOCATA_final_release_V1.pdf">https://www.locata.lms.tf.fau.de/files/2020/01/Documentation_LOCATA_final_release_V1.pdf</a></li> </ul>
DXC'09 Synthetic Track Sample Data
Sample data for the DXC'09 Synthetic Track.
DXC'09 Synthetic Track Competition Data
Synthetic Track Competition Data
Track normalization and averaging of H3K4me3 ChIP-seq data across various cell and tissue types from Mouse ENCODE.
GEO Series GSE73834. Mus musculus. 1 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Third-party reanalysis.
Track normalization and averaging of bislufite-seq DNA methylation data across various cell and tissue types
GEO Series GSE64577. Homo sapiens. 8 samples. Type: Methylation profiling by high throughput sequencing; Third-party reanalysis.
Kopaitic Island Chinstrap penguin GPS tracking data 2018-2019
<p>These represent the speed filtered GPS datasets for individual Chinstrap penguins instrumented between November 2018 and February 2019 at Kopiaitic Island approximately 500m offshore from the Chilean Bernardo O'Higgins Antarctic Base on the western Antarctic Peninsula. They are stored as trip objects within standard .RData Workspaces #NOTE# the tags were set to record data at VERY fine temporal scales (every few seconds) thus the files are large and unwieldy in R . The objects are unprojected, but adhere to the CRS projection " +proj=lonlat +ellps=WGS84 +datum=WGS84" Individuals were also instrumented with dive loggers and accelerometer / magnetometer tags, and a subset were also instrumented with HD video cameras. These additional datasets will be published that are linked by unique animal #ID and deployment round number.</p>
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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.