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10,016 results for “Observation”
PENELLOPE - X-Shooter spectra of targets in the Lupus and Chamaeleon regions observed in 2021
<p>Spectra of the targets in the Lupus, Chamaeleon I and eta-Cha regions observed with the VLT/X-Shooter spectrograph in May-August 2021. These spectra are part of the PENELLOPE Large Program and are obtained close in time to the HST/ULLYSES spectra of the same targets. </p> <p>Here one can find the 1D absolute flux-calibrated and telluric corrected spectra. Each object is observed simultaneously in three arms, UVB, VIS, and NIR, and the spectrum of each arm is saved in one file. The units of the spectra are: Flux [erg/s/cm2/nm]; Wavelength [nm]. In some cases, multiple observations for one target have been taken.</p> <p>Data are public, we ask to ask to the authors of this release, who contributed to the observation preparation and data reduction, whether they want to be included in any publication using these data. The data reduction procedure is presented in Manara et al. (2021). Please quote this paper in any related publication. Please contact Carlo Manara for estimates of the stellar and accretion properties of these targets based on this data. </p> <p>For questions or comments please contact Carlo Manara, cmanara@eso.org</p>
Data for "Impact of bulk-edge coupling on observation of anyonic braiding statistics in quantum Hall interferometers"
<p>This contains the data sets for the work "Impact of bulk-edge coupling on observation of anyonic braiding statistics in quantum Hall interferometers". Data is in CVS format. </p>
Triple collocations of SST observations from ATSR-2/AATSR, drifting buoys and TMI/AMSR-E.
<p>Triple collocations of SST observations from ATSR-2/AATSR, drifting buoys and TMI/AMSR-E.</p> <p>The ATSR-2/AATSR observations are from the respective prouct of CCI SST Phase 1 (Merchant, C.J.; Embury, O.; Roberts-Jones, J.; Fiedler, E.K.; Bulgin, C.E.; Corlett, G.K.; Good, S.A.; McLaren, A.; Rayner, N.A.; Donlon, C. (2014): ESA Sea Surface Temperature Climate Change Initiative (ESA SST CCI): Along-Track Scanning Radiometer (ATSR) level 3 uncollated data (L3U) long-term product version 1.1. NERC Earth Observation Data Centre, 29 May 2014. doi:10.5285/79229cee-71ab-48b6-b7d6-2fceccead938. http://dx.doi.org/10.5285/79229cee-71ab-48b6-b7d6-2fceccead938).</p> <p>The observations of TMI/AMSR-E are from version 7 produced by Remote Sensing Systems. TMI data were produced by Remote Sensing Systems and sponsored by the NASA Earth Sciences Program. Data are available at www.remss.com/missions/tmi. AMSR data are produced by Remote Sensing Systems and were sponsored by the NASA AMSR-E Science Team and the NASA Earth Science MEaSUREs Program. Data are available at www.remss.com.</p>
Triple collocations of SST observations from ATSR-2/AATSR, drifting buoys and TMI/AMSR-E.
<p>Triple collocations of SST observations from ATSR-2/AATSR, drifting buoys and TMI/AMSR-E.</p> <p>The ATSR-2/AATSR observations are from the respective prouct of CCI SST Phase 1 (Merchant, C.J.; Embury, O.; Roberts-Jones, J.; Fiedler, E.K.; Bulgin, C.E.; Corlett, G.K.; Good, S.A.; McLaren, A.; Rayner, N.A.; Donlon, C. (2014): ESA Sea Surface Temperature Climate Change Initiative (ESA SST CCI): Along-Track Scanning Radiometer (ATSR) level 3 uncollated data (L3U) long-term product version 1.1. NERC Earth Observation Data Centre, 29 May 2014. doi:10.5285/79229cee-71ab-48b6-b7d6-2fceccead938. http://dx.doi.org/10.5285/79229cee-71ab-48b6-b7d6-2fceccead938).</p> <p>The observations of TMI/AMSR-E are from version 7 produced by Remote Sensing Systems. TMI data were produced by Remote Sensing Systems and sponsored by the NASA Earth Sciences Program. Data are available at www.remss.com/missions/tmi. AMSR data are produced by Remote Sensing Systems and were sponsored by the NASA AMSR-E Science Team and the NASA Earth Science MEaSUREs Program. Data are available at www.remss.com.</p>
PENELLOPE - ESPRESSO spectra of targets in the Lupus and Chamaeleon I regions observed in 2021
<p>Spectra of the targets in the Lupus and Chamaeleon I regions observed with the VLT/ESPRESSO spectrograph in May-August 2021. These spectra are part of the PENELLOPE Large Program and are obtained close in time to the HST/ULLYSES spectra of the same targets. </p> <p>Here one can find the 1D flux-calibrated and telluric corrected spectra. The units of the spectra are: Flux [erg/s/cm2/AA]; Wavelength [AA]. Each object is typically observed 3 times, the spectra are thus labelled as ep1, ep2, ep3. </p> <p>Data are public, we ask to ask to the authors of this release, who contributed to the observation preparation and data reduction, whether they want to be included in any publication using these data. The data reduction procedure is presented in Manara et al. (2021). Please quote this paper in any related publication. Please contact Carlo Manara for estimates of the stellar and accretion properties of these targets based on the corresponding X-Shooter data, and Antonio Frasca for stellar properties based on this data. </p> <p>For questions or comments please contact Carlo Manara, cmanara@eso.org</p>
Wikidata Dump Observation towers
<p> RDF dump of wikidata produced with <a href="https://tools.wmflabs.org/wdumps/">wdumps</a>. </p> <p> <br> <a href="https://tools.wmflabs.org/wdumps/dump/1990">View on wdumper</a> </p> <p> <b>entity count</b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0 </p>
Automatic Detection of Photovoltaic from Sentinel-2 observations by an Enhanced U-Net method - DataSet
<p>Data Availability for <strong>Enhanced U-Net(E-UNET)</strong></p> <p>Thank you for your interest in our dataset.<br> <strong>Repository contents:</strong><br> <em>Sentinel-2 L2A product</em>: The tiles that we selected containing photovoltaic.<br> <em>ImageFusion_Result</em>: The image fusion results of ROI including photovoltaic which we intercepted from the Sentinel-2 data. It contains 10m resolution, 10m and 20m resolution, 10m, 20m and 60m resolution fusion results, and RGB 3-channels fusion results.<br> <em>Image</em>: The RGB images for the ROI containing photovoltaic.<br> <em>Label</em>: The manual annotations for the ROI containing photovoltaic.</p>
Data for Theory, Observations, and Simulations of Kinetic Entropy in a Magnetotail Electron Diffusion Region
<p>This .rar file contains the simulation data used in the paper titled "Theory, Observations, and Simulations of Kinetic Entropy in a Magnetotail ElectronDiffusion Region" along with needed instructions for reproducing the simulation plots shown in the paper. </p>
Observed Kapuas River water level in 2018
<p>This is supplement data for our publication in Biogeosciences (https://doi.org/10.5194/bg-2021-273). To use this data, please give us credit by citing our article.</p> <p><strong>How to cite</strong><strong>: </strong>Sampurno, J., Vallaeys, V., Ardianto, R., and Hanert, E.: Modeling interactions between tides, storm surges, and river discharges in the Kapuas River delta, Biogeosciences Discuss. [preprint], https://doi.org/10.5194/bg-2021-273, in review, 2021.</p>
Parental behavior influence in pediatric dentistry: a randomized observational study
<p>This work examines the influence of parental attitudes on their child's behavior in pediatric dentistry.</p> <p>This prospective randomized observational study was conducted at the pediatric dentistry consultation. All sessions were filmed. Two experts collected data using the modified Venham scale for children and a hetero-rating scale for parents. 20 observers scored with the same tools a randomized selection of recordings of 5 children per group. Data were analyzed using SPSS software.</p> <p>60 children divided into 3 groups of 20: 4 years and under, 5 to 6 years and over 7 years. Internal validation showed a very high correlation (Cronbach's a> 0.920). There is a correlation between parental behavior and that of children in the dental office in the 3 age groups for observers (Cronbach's a> 0.700). </p>
Year-round observations of stable carbon isotopic composition of diacids and related compounds in fine aerosols at Tianjin, North China – Data set
<p>To better understand the origins and photochemical processing of water-soluble organic aerosols in the Tianjin region, North China, we studied the stable isotopic composition (δ<sup>13</sup>C) of diacids, oxoacids, α-dicarbonyls, citric acid and fatty acids in fine aerosols (PM<sub>2.5</sub>) collected at an urban (Nankai District, ND; <em>n</em> = 121) and a suburban (Haihe Education Park, HEP; <em>n</em> = 40) sites in Tianjin from 5 July 2018 to 4 July 2019. The δ<sup>13</sup>C of diacids and related compounds were measured using a capillary gas chromatography (Agilent 7890B) coupled with isotope ratio mass spectrometry (GC-irMS) system. Based on seasonal variations in δ<sup>13</sup>C of selected species and linear relations with their concentrations and mass ratios, we found that the diacids and related compounds were mainly derived from fossil fuel including coal combustion and biomass burning and photochemical processing, preferably in gaseous- and aqueous-phases in warm (March-September) and cold (October-February) periods, respectively, in Tianjin, North China.</p>
Tropospheric delays and precipitable water vapor retrieved from global radiosonde observations from 2014 to 2019
<p>The data consists of a set of meteorological quantities including tropospheric delays (zenith wet delay, zenith hydrostatic delay, and zenith total delay), precipitable water vapor, and surface temperature and pressure. The data is retrieved from the observations of 414 globally distributed radiosonde stations from 2014 to 2019. In addition to the geographic information of radiosonde stations, the profiles of tropospheric delays and precipitable water vapor are contained in the data file. This data has a wide range of applications, e.g., validating the tropospheric delays and precipitable water vapor derived from other techniques, investigating the spatial-temporal variations of water vapor, and acting as training data of machine learning to build tropospheric delay models.</p> <p>In the manuscript "Machine Learning-based Model for Real-time GNSS Precipitable Water Vapor Sensing", this data is used to train a machine learning model to map the zenith total delays to precipitable water vapor. The data is split into training data and test data, where the data from 2014 to 2018 are employed for model training, and the data of 2019 are used for testing. The developed models and the results for the manuscript are saved in the directories of Models and Results, respectively.</p>
Observations and Modeling of a Buoyant Plume Exiting into a Tidal Cross-flow and Exhibiting Along-front Instabilities
<p>This post-processed dataset contains the sUAS imagery and numerical modeling output used in the paper "Observations and Modeling of a Buoyant Plume Exiting into a Tidal Cross-flow and Exhibiting Along-front Instabilities". The provided code details the frontal processing routine used for both the observations and modeling results. </p>
Figures 8–9. Stenelytrana splendens. 8 in New larval host plants and ecological observations on North American Cerambycidae (Coleoptera)
Figures 8–9. Stenelytrana splendens. 8) Female. 9) Male.
Direct observation of a dynamical glass transition in a nanomagnetic artificial Hopfield network
<p>Source files used to produce the publication "Direct observation of a dynamical glass transition in a nanomagnetic artificial Hopfield network."</p>
Conjugate observation data between DMSP satellites and all-sky imager of Chinese Yellow River station at Ny-Ålesund, Svalbard from January 2005 to December 2009
<p>Chinese Arctic Yellow River Station (YRS) locates at Ny-Ålesund, Svalbard with the geographic coordinates (78.92°N, 11.93°E) and the corrected geomagnetic latitude 76.24°. The relation between local time and universal time is MLT≈UT+3hr. In November 2003, a set of monochromatic auroral observation system was installed at YRS, which is consisted of three identical all-sky imageries (ASIs) with the filters at 427.8nm, 557.7nm and 630.0nm, respectively. DMSP (Defense Meteorological Satellite Program) satellites are a collection of polar-orbit weather satellites launched by the U.S. department of defense. This series of satellites is sun synchronous satellite, which takes about 101 minutes to orbit the earth, has an altitude of about 835-850km, an inclination of about 96°, and crosses the equatorial plane daily from south to north (ascending segment) and from north to south (descending segment) at a fixed time (DMSP F13 is at 05:45LT and 17:45LT, and F15 is at 09:30LT and 21:30LT). DMSP satellite is equipped with Special Sensor for Particle Flux (SSJ/4), which can measure the fluxes of downgoing electrons and ions with energies between from 30eV to 30keV in 19 energy steps (34, 49, 71, 101, 150, 218, 320, 460, 670, 960 eV, and 1.4, 2.1, 3.0, 4.4, 6.5, 9.5, 14.0, 20.5, 29.5 keV), with a time resolution of 1 second. According to the orbit of the DMSP satellites and the observation of the ASIs of YRS, we obtained the conjugate observation periods of the satellite flying over the ASI.</p> <p>During the period from January 2005 to December 2009, a total of 136 conjugate observation events were obtained. Moreover, according to the morphological characteristics of discrete aurora in the all-sky image, 136 events are classified according to four typical forms of dayside discrete auroras, namely 27 events of drapery dayside corona (DDC), 24 events of radial dayside corona (RDC), 37 events of hot-spot aurora (HSA), and 48 events of arc aurora (ARC). The event list of 136 events is recorded in the “asi&dmsp@YRS03-09-v2.xlsx” file. The EPS file gives the trajectory of the DMSP satellite crossing the ASI in each conjugate observation event, while the JPG files are the corresponding all-sky images.</p>
Accurate observation of black and brown carbon in atmospheric fine particles via a versatile aerosol concentration enrichment system (VACES)
<p>The processed data used to prepare the paper.</p>
Flowing days Derived from CubeSat Imagery and Ground Observations in Hassayampa River (HR), Arizona from 2019-2021 (Water Years)
<p>Flowing days derived from CubeSat imagery and ground observations in Hassayampa River (HR), Arizona from October 2018 to September 2021. This database supports the following paper:</p> <div> <div>Wang, Z., & Vivoni, E. R. (2022). Detecting streamflow in dryland rivers using CubeSats. <em>Geophysical Research Letters</em>, 49, e2022GL098729. <a href="https://doi.org/10.1029/2022GL098729">https://doi.org/10.1029/2022GL098729</a></div> </div> <div> <div> </div> </div> <p>This database includes three folders.</p> <ol> <li>GroundObservations: Ground observations along the HR retrieved from USGS website (https://waterdata.usgs.gov/nwis) and Flood Control District of Maricopa County using Single Sensor Data Reports tool (https://alert.fcd.maricopa.gov/showrpts_mc.html): <ol> <li><strong>Precip</strong>: Records of daily precipitation amount.</li> <li><strong>Stream</strong>: Records of daily streamflow amount.</li> </ol> </li> <li>GIS: GIS layers used in deriving flowing days. <ol> <li><strong>Subreach</strong>: Shapefiles of nine sub reaches along the HR.</li> <li><strong>Buffer segment</strong>: Buffer zones perpendicular to HR reaches at 90 m resolution. Column ID is sorted in ascending order along the HR.</li> <li><strong>Channel masks: </strong>Channel masks of HR derived from Planet data.</li> </ol> </li> <li>Results <ol> <li><strong>BufferSegment_Flowdays.xlsx:</strong> Days with flow determined using the NIR difference threshold for water years (WY) 2019 to 2021 for each 90 m buffer areas. The relative location of each buffer areas can be found in the Buffer Segment GIS layer. ‘NaN’ suggests no data.</li> </ol> </li> </ol> <div> <div> </div> </div>
The Global Navigation Satellite System (GNSS) data came from the Crustal Movement Observation Network of China
<p>The dataset reports the estimated vertical Total Electron Content (TEC) from 52 GPS receivers came from the Crustal Movement Observation Network of China on 4 March 2014. Every receiver's data is saved in a TXT file, whose time resolution is thirty seconds.</p>
Magnetic Field Disorder May Explain Energetic Proton Diffusion through Heliosheath Plasma: Evidence from Voyager 2 Observations
<p>The spreadsheets of monthly data used for Figure 2 in the paper " <strong>Magnetic Field Disorder May Explain Energetic Proton Diffusion through Heliosheath Plasma: Evidence from Voyager 2 Observations</strong>" submitted to Geophysical Research Letters by the authors, and an explanatory text file named "Readme".</p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.