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1,079 results for “source data”
XMM-Newton Serendipitous Source Catalog from Stacked Observations: Obs. Data
The stacked catalog 4XMM-DR14s (<a href="/W3Browse/xmm-newton/xmmstack.html">XMMSTACK</a>) has been compiled from 1,751 groups, comprising 10,336 overlapping XMM-Newton observations. They were selected from the public observations taken between 2000 February 1 and 2023 November 16 which overlap by at least one arcminute in radius. It contains 427,524 unique sources, 329,972 of them multiply observed, with positions and source parameters like fluxes in the XMM-Newton standard energy bands, hardness ratios, quality estimate, and information on inter-observation variability. The parameters are directly derived from the simultaneous fit, and, wherever applicable, additionally calculated for each contributing observation. Exposures that do not qualify for source detection, for example because of a high background level, are used for subsequent PSF photometry: source fluxes and flux-related parameters are derived for them at the source position and extent found during source detection. 4XMM-DR14s lists 1,807,316 individual flux measurements of the 427,524 unique sources. Stacked source detection aims at exploring the multiply observed sky regions and exploit their survey potential, in particular to study the long-term behavior of X-ray emitting sources. It thus makes use of the long(er) effective exposure time per sky area and offers the opportunity to investigate flux variability directly through the source detection process. The main catalog properties are summarized in the table below, the data processing and the stacked source detection are described in the processing summary. To ensure detection quality, background levels are assessed, and event-based astrometric corrections are applied before running source detection. After source detections, problematic detections and detection parameters are flagged by an automated algorithm. All detections are screened visually, and obviously spurious sources are flagged manually. This table contains the source parameters from the individual observations in the stacked catalog, <a href="/W3Browse/xmm-newton/xmmstack.html">4XMM-DR14s</a>. The parameters are derived from the simultaneous source-detection fit to all stacked observations at the common source position for each observation that covers a source, amounting to 1,807,316 measurements. The mean source parameters from stacked source detection are provided in the associated main table <a href="/W3Browse/xmm-newton/xmmstack.html">4XMM-DR14s</a>, referred to as XMMSTACK. The authors referred to the EPIC instruments with the following designations: PN, M1 (MOS1), and M2 (MOS2). The energy bands used in the 4XMM processing were the same as for the 3XMM catalog. The following are the basic energy bands: <pre> 1: 0.2-0.5 keV 2: 0.5-1.0 keV 3: 1.0-2.0 keV 4: 2.0-4.5 keV 5: 4.5-12.0 keV </pre> All-EPIC values cover the energy range 0.2-12.0 keV. The full catalog documentation can be found at <a href="https://xmmssc.aip.de/">https://xmmssc.aip.de/</a>. The following table gives an overview of the statistics of this catalog in comparison with the previous stacked catalogs, 4XMM-DR14s through 3XMM-DR7s: <pre> 4XMM-DR14s 4XMM-DR13s 4XMM-DR12s 4XMM-DR11s 4XMM-DR10s 4XMM-DR9s 3XMM-DR7s Number of stacks 1,751 1,688 1,620 1,475 1,396 1,329 434 Number of observations 10,336 9,796 9,355 8,292 7,803 6,604 789 Time span first to last observation Feb 01, 2000 Feb 01, 2000 Feb 01, 2000 Feb 03, 2000 Feb 03, 2000 Feb 03, 2000 Feb 20, 2000 -- Nov 16,2023 -- Nov 29, 2022 -- Dec 04, 2021 -- Dec 17, 2020 -- Dec 14, 2019 -- Nov 13, 2018 -- Apr 02, 2016 Approximate sky coverage (sq. deg.) 685 650 625 560 540 485 150 Approximate multiply observed sky area(sq. deg) 440 420 400 350 335 300 100 Total number of sources 427,524 401,596 386,043 358,809 335,812 288,191 71,951 Sources with several contributing observations 329,972 310,478 298,626 275,440 256,213 218,283 57,665 Multiply observed sources with flag 0 or 1 276,058 262,842 252,445 233,542 216,999 191,497 55,450 Multiply observed with a total detection 266,129 251,555 241,880 224,178 208,921 181,132 49,935 likelihood of at least six Multiply observed with a total detection 226,219 213,812 205,394 189,556 176,680 153,487 42,077 likelihood of at least ten Total measurements 1,807,316 1,683,264 1,592,263 1,421,966 1,322,299 1,033,264 216,393 Maximum exposures per source 173 170 155 140 140 103 69 Maximum observations per source 77 77 70 65 65 40 23 Maximum on-time per source 2.8 Ms 2.8 Ms 2.8 Ms 2.8 Ms 2.8 Ms 1.9 Ms 1.3 Ms </pre> This database table was last updated by the HEASARC in July 2024. It contains the 4XMM-DR14s observations catalog, released by ESA on 2024-07-09 and obtained from the XMM-Newton Survey Science Center Consortium at <a href="https://xmmssc.aip.de/cms/catalogues/4xmm-dr14s/">https://xmmssc.aip.de/cms/catalogues/4xmm-dr14s/</a>. It is <a href="https://xmmssc.aip.de/data/xmmstack_v3.2_4xmmdr
Comparison of whole-genome bisulfite sequencing library preparation strategies identifies sources of biases affecting DNA methylation data
GEO Series GSE77961. Mus musculus. 16 samples. Type: Methylation profiling by high throughput sequencing.
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>
Source code and intermediate data for the tradeSeqDTU project
<p>Development: working folder for reproducing the results of the mock and performance benchmarks.</p> <p>Pancreas_case: intermediate data and code for the pancreas case study. The raw data is not yet included as this is still a project in progress, and the upload would be very large (15Gb)</p> <p>iPSC_case: intermediate data and code for the pancreas case study. The raw data is not yet included as this is still a project in progress, and the upload would be very large (32Gb)</p>
Enhancing reservoir water level time series in the Mekong river basin by improving area-elevation models and integrating multi-source satellite data
<p>This is a reservoir water surface area and water levels dataset including 32 major reservoirs in Mekong River basin. For all the 32 reservoirs, water levels were inverted by using improved DEM-derived A-E model (combined with actual reservoir parameters limitation), improved DEM-derived A-E model (combined with actual reservoir parameters limitation) or satellite-derived A-E model based on the Landsat-derived surface area. An initial time series was constructed based on the optimal improved A-E model according to their own altimetry data availability. Then all the altimetry water levels (if available) were merged into the initial time series to construct the final time series water levels.Altimetry water level was preferred when the date of two datasets was identical.</p>
Near Surface Air Temperature Dataset for China with high temporal and spatial resolution generated using random forest and multi-source data (2013-2014)
<div> <p>The dataset presents the daily near surface air temperature of China with 1km spatial resolution, including daily average air temperature, maximum temperature and minimum air temperature. The dataset was generated using machine learning and multiple variables, the accuracy was:T<sub>ave</sub>, R<sup>2</sup> = 0.97, RMSE = 1.61℃ and rRMSE = 13.24%; T<sub>max</sub>, R<sup>2</sup> = 0.94, RMSE = 2.35℃ and rRMSE = 13.02%; T<sub>min</sub>, R<sup>2</sup> = 0.95, RMSE = 2.04℃ and rRMSE = 27.09%).</p> </div>
Near Surface Air Temperature Dataset for China with high temporal and spatial resolution generated using random forest and multi-source data (2017-2018)
<p>The dataset presents the daily near surface air temperature of China with 1km spatial resolution, including daily average air temperature, maximum temperature and minimum air temperature. The dataset was generated using machine learning and multiple variables, the accuracy was:T<sub>ave</sub>, R<sup>2</sup> = 0.97, RMSE = 1.61℃ and rRMSE = 13.24%; T<sub>max</sub>, R<sup>2</sup> = 0.94, RMSE = 2.35℃ and rRMSE = 13.02%; T<sub>min</sub>, R<sup>2</sup> = 0.95, RMSE = 2.04℃ and rRMSE = 27.09%).</p>
Near Surface Air Temperature Dataset for China with high temporal and spatial resolution generated using random forest and multi-source data (2011-2012)
<p>The dataset presents the daily near surface air temperature of China with 1km spatial resolution, including daily average air temperature, maximum temperature and minimum air temperature. The dataset was generated using machine learning and multiple variables, the accuracy was:T<sub>ave</sub>, R<sup>2</sup> = 0.97, RMSE = 1.61℃ and rRMSE = 13.24%; T<sub>max</sub>, R<sup>2</sup> = 0.94, RMSE = 2.35℃ and rRMSE = 13.02%; T<sub>min</sub>, R<sup>2</sup> = 0.95, RMSE = 2.04℃ and rRMSE = 27.09%).</p>
Near Surface Air Temperature Dataset for China with high temporal and spatial resolution generated using random forest and multi-source data (2009-2010)
<p>The dataset presents the daily near surface air temperature of China with 1km spatial resolution, including daily average air temperature, maximum temperature and minimum air temperature. The dataset was generated using machine learning and multiple variables, the accuracy was:T<sub>ave</sub>, R<sup>2</sup> = 0.97, RMSE = 1.61℃ and rRMSE = 13.24%; T<sub>max</sub>, R<sup>2</sup> = 0.94, RMSE = 2.35℃ and rRMSE = 13.02%; T<sub>min</sub>, R<sup>2</sup> = 0.95, RMSE = 2.04℃ and rRMSE = 27.09%).</p>
Near Surface Air Temperature Dataset for China with high temporal and spatial resolution generated using random forest and multi-source data (2007-2008)
<p>The dataset presents the daily near surface air temperature of China with 1km spatial resolution, including daily average air temperature, maximum temperature and minimum air temperature. The dataset was generated using machine learning and multiple variables, the accuracy was:T<sub>ave</sub>, R<sup>2</sup> = 0.97, RMSE = 1.61℃ and rRMSE = 13.24%; T<sub>max</sub>, R<sup>2</sup> = 0.94, RMSE = 2.35℃ and rRMSE = 13.02%; T<sub>min</sub>, R<sup>2</sup> = 0.95, RMSE = 2.04℃ and rRMSE = 27.09%).</p>
Near Surface Air Temperature Dataset for China with high temporal and spatial resolution generated using random forest and multi-source data (2005-2006)
<p>The dataset presents the daily near surface air temperature of China with 1km spatial resolution, including daily average air temperature, maximum temperature and minimum air temperature. The dataset was generated using machine learning and multiple variables, the accuracy was:T<sub>ave</sub>, R<sup>2</sup> = 0.97, RMSE = 1.61℃ and rRMSE = 13.24%; T<sub>max</sub>, R<sup>2</sup> = 0.94, RMSE = 2.35℃ and rRMSE = 13.02%; T<sub>min</sub>, R<sup>2</sup> = 0.95, RMSE = 2.04℃ and rRMSE = 27.09%).</p>
Near Surface Air Temperature Dataset for China with high temporal and spatial resolution generated using random forest and multi-source data (2019-2020)
<p>The dataset presents the daily near surface air temperature of China with 1km spatial resolution, including daily average air temperature, maximum temperature and minimum air temperature. The dataset was generated using machine learning and multiple variables, the accuracy was:T<sub>ave</sub>, R<sup>2</sup> = 0.97, RMSE = 1.61℃ and rRMSE = 13.24%; T<sub>max</sub>, R<sup>2</sup> = 0.94, RMSE = 2.35℃ and rRMSE = 13.02%; T<sub>min</sub>, R<sup>2</sup> = 0.95, RMSE = 2.04℃ and rRMSE = 27.09%).</p>
Near Surface Air Temperature Dataset for China with high temporal and spatial resolution generated using random forest and multi-source data (2003-2004)
<p>The dataset presents the daily near surface air temperature of China with 1km spatial resolution, including daily average air temperature, maximum temperature and minimum air temperature. The dataset was generated using machine learning and multiple variables, the accuracy was:T<sub>ave</sub>, R<sup>2</sup> = 0.97, RMSE = 1.61℃ and rRMSE = 13.24%; T<sub>max</sub>, R<sup>2</sup> = 0.94, RMSE = 2.35℃ and rRMSE = 13.02%; T<sub>min</sub>, R<sup>2</sup> = 0.95, RMSE = 2.04℃ and rRMSE = 27.09%).</p>
Near Surface Air Temperature Dataset for China with high temporal and spatial resolution generated using random forest and multi-source data (Samples)
<p>The dataset presents the daily near surface air temperature of China with 1km spatial resolution, including daily average air temperature, maximum temperature and minimum air temperature. The dataset was generated using machine learning and multiple variables, the accuracy was:T<sub>ave</sub>, R<sup>2</sup> = 0.97, RMSE = 1.61℃ and rRMSE = 13.24%; T<sub>max</sub>, R<sup>2</sup> = 0.94, RMSE = 2.35℃ and rRMSE = 13.02%; T<sub>min</sub>, R<sup>2</sup> = 0.95, RMSE = 2.04℃ and rRMSE = 27.09%).</p>
Near Surface Air Temperature Dataset for China with high temporal and spatial resolution generated using random forest and multi-source data (2015-2016)
<p>The dataset presents the daily near surface air temperature of China with 1km spatial resolution, including daily average air temperature, maximum temperature and minimum air temperature. The dataset was generated using machine learning and multiple variables, the accuracy was:T<sub>ave</sub>, R<sup>2</sup> = 0.97, RMSE = 1.61℃ and rRMSE = 13.24%; T<sub>max</sub>, R<sup>2</sup> = 0.94, RMSE = 2.35℃ and rRMSE = 13.02%; T<sub>min</sub>, R<sup>2</sup> = 0.95, RMSE = 2.04℃ and rRMSE = 27.09%).</p>
ROAD CONGESTION PREVENTION MODELS BASED ON DATA FROM DIFFERENT SOURCES.
<p><em><span>This article analyzes data from various sources on road congestion prevention models. Approaches to reduce congestion include modeling traffic flow, using intelligent transportation systems, improving transportation infrastructure, developing public transportation, and providing information to drivers. The article explains how these models can be used together to improve the efficiency of modern transport systems and ensure road safety. As a result, it identifies the key factors needed to develop innovative solutions and strategies in traffic prevention.</span></em></p>
Expression data from liver of young and old rats fed on different dietary fat sources, namely virgin olive, sunflower or fish oils
GEO Series GSE56166. Rattus norvegicus. 18 samples. Type: Expression profiling by array.
Data for "Human footprint dominates the distribution, sources, and ecological risk of microplastics in lakes across China"
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outdated open source data
<p>These are the <span>outdated </span>open-source data. Pls wait.</p>
Epigenetic programming by H3K23ac defines lineage fate of Meg3+ hematopoietic stem cells and drives immune aging (Source Data)
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ScienceDex guides
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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.