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10,016 results for “Observation”
ARMOR and NALMA data corresponding to "Observations of anomalous charge structures in supercell thunderstorms in the Southeastern United States"
<p>Dataset includes dual-polarization C-band University of Alabama in Huntsville (UAH) Advanced Radar for Meteorological and Operational Research (ARMOR) data in Raw and quality-controlled Universal Format (UF) from a selected period on 10 April 2009 as well as the National Aeronautics and Space Administration (NASA) Marshall Space Flight Center (MSFC) North Alabama Lightning Mapping Array (NALMA) data in American Standard Code for Information Interchange (ASCII) format from selected period on 10 April 2009. </p> <p>The ARMOR is located at the Huntsville International Airport in Huntsville, Alabama at 34.64597, -86.77131, 200 m MSL. A set of 15 radar sampling volumes between 1712 UTC and 1821 UTC on 10 April 2009 are included in the dataset. Each of the raw and corrected UF files contains horizontal reflectivity (dBZ), differential reflectivity (dB), Doppler velocity (m s<sup>-1</sup>), spectrum width (m s<sup>-1</sup>), differential phase (°), and total power (dBZ) data. The corrected UF files additionally contain horizontal reflectivity and differential reflectivity data corrected for attenuation and differential attenuation following the methods of Bringi et al. (2001). The corrected files also contain estimated differential propagation phase (°) and computed specific differential phase (° km<sup>-1</sup>) data (Hubbert and Bringi 1995). </p> <p> </p> <p>ARMOR file naming conventions are as follows: </p> <p> </p> <p>RAW_NA_000_125_20090410171216.gz</p> <p>RAW: file format</p> <p>125: can scan type, where 125 indicates a full or sector volume plan position indicator </p> <p>20090410171216: date and time in the order of year, month, day, hour, minute, and second</p> <p> </p> <p>ARMOR_20090410171216_qc1.uf.gz</p> <p>ARMOR: radar name</p> <p>20090410171216: date and time in the order of year (YYYY), month (MM), day (DD), hour (HH), minute (MM), and second (SS)</p> <p>qc1: denotes ARMOR processed data</p> <p>uf: denotes the file format </p> <p> </p> <p>NALMA data consist of undecimated VHF source-level lightning measurements in hourly files. The center of the network is located at 34.72461, -86.64533. The network consisted of 11 sensors distributed throughout north Alabama and south-central Tennessee. Information about contributing stations is available in the header of each hourly file, including the station location, status, and the number of sources detected by each station. Further network-specific information documented by Koshak et al. (2004) while Rison et al. (1999) discuss LMA characteristics.</p> <p>Source data include information about the time the source was detected (UTC seconds of the day), latitude and longitude (decimal degrees), altitude (m), reduced chi<sup>2</sup> value associated with post-processing (unitless), power (dBW), and a network mask indicating the detecting stations (unitless). The format is (f15.9 f10.6 f11.6f 7.1 f5.2 f5.1 4x). </p> <p> </p> <p>Hourly file naming conventions are as follows:</p> <p> </p> <p>LYLOUT_090410_160000_3600.dat.gz</p> <p>LYLOUT: LMA file designator</p> <p>090410: date in order of last two digits of year (YY), month (MM), and day (DD)</p> <p>160000: time in order of hour (HH), minute (MM), and second (SS)</p> <p>3600: length of period covered in file in seconds (3600 s = 1 hour)</p> <p> </p> <p>Acknowledgments: </p> <p>Data were collected with support from NASA MSFC Award NNM05AA22A.</p> <p> </p> <p>References:</p> <p>Bringi, V. N., Keenan, T. D., & Chandrasekar, V. (2001). Correcting C-band radar reflectivity and differential reflectivity data for rain attenuation: A self-consistent method with constraints. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, <em>39</em>(9), 1906–1915. https://doi.org/10.1109/36.951081</p> <p>Hubbert, J., and V. N. Bringi, 1995: An iterative filtering technique for the analysis of copolar differential phase and dual-frequency radar measurements. <em>Journal of Atmospheric and Oceanic Technology</em>, <strong>12</strong>, 643–648. </p> <p>Koshak, W. J., Solakiewicz, R. J., Blakeslee, R. J., Goodman, S. J., Christian, H. J., Hall, J. M., … Cecil, D. J. (2004). North Alabama Lightning Mapping Array (LMA): VHF source retrieval algorithm and error analyses. <em>Journal of Atmospheric and Oceanic Technology</em>, <em>21</em>(4), 543–558. https://doi.org/10.1175/1520-0426(2004)021<0543:NALMAL>2.0.CO;2</p> <p>Rison, W., Thomas, R. J., Krehbiel, P. R., Hamlin, T., & Harlin, J. (1999). A GPS-based three-dimensional lightning mapping system: Initial observations in Central New Mexico. <em>Geophysical Research Letters</em>, <em>26</em>(23), 3573–3576.</p>
Optimal neutron-star mass ranges to constrain the equation of state of nuclear matter with electromagnetic and gravitational-wave observations: EOS library
<p>This repository includes a library of equations of state (EOS) and stellar models presented in the publications Weih et al. (2019) (see also the related identifier) and Most et al. (2018). The library includes ~ 3 Million physically plausible EOSs that fulfill a number of astrophysical and nuclear constraints. See the README for more information. </p>
Black Swift Technologies S1 Unmanned Aircraft System Observations from LAPSE-RATE
<p>This dataset contains meteorological data collected by Black Swift Technologies' S1 unmanned aircraft system during the 2018 LAPSE-RATE (Lower Atmospheric Profiling Studies at Elevation - a Remotely-piloted Aircraft Team Experiment) field campaign. Questions about the dataset should be addressed to Jack Elston (elstonj@bst.aero).</p>
Data files for: Comparison of high-speed optical observations of a lightning flash from space and the ground
<p>This dataset accompanies the paper in the AGU open acces journal <em>Earth and Space Science</em>, special section "A New Era of Lightning Observations From Space"<em> </em>and contains data of the lightning flash in Colombia detected by:</p> <ul> <li>Geostationary Lightning Mapper (GLM) </li> <li>Lightning Imaging Sensor on the International Space Station (ISS-LIS) </li> <li>Atmosphere-Space Interactions Monitor - Modular Multi-spectral Imaging Array (ASIM MMIA)</li> <li>High-speed intensified Phantom V7.3 camera fielded in Cartagena, Colombia.</li> </ul> <p>The high-speed video .cine files can be read by (free) CineViewer and PCC software of Vision Research Inc. which can convert to avi files. For any questions, contact the first author.</p>
University of Colorado MURC Observations from LAPSE-RATE
<p>This dataset contains meteorological data collected by the University of Colorado's Mobile UAS Research Collaboratory (MURC) vehicle during the 2018 LAPSE-RATE (Lower Atmospheric Profiling Studies at Elevation - a Remotely-piloted Aircraft Team Experiment) field campaign. Questions about the dataset should be addressed to Gijs de Boer (gijs.deboer@colorado.edu).</p>
Data and R-Scripts for "Quality and timing of crowd-based water level class observations"
<p>This are the data and the R-scripts used for the manuscript "Quality and timing of crowd-based water level class observations" accepted for publication in the journal Hydrological Processes in July 2020 as a Scientific Briefing. To run the code, just run the R-script with the name "RunThisForResults.R". Results will be written to the "Figures" and the "Results" folder.</p>
ULTRACAM Observations of PSR J2039-5617
<p>Optical observations of PSR J2039-5617 taken by the high-speed triple-beam CCD camera ULTRACAM on ESO's New Technology Telescope. Observations were taken on 2017 June 18, 2018 June 02 and 2019 July 07.<br> <br> Files with names like <instrument>_fluxes_<band>.dat contain the reduced light curves in ASCII format.<br> Columns are:<br> 1) MJD time (barycentered TDB),<br> 2) orbital phase (in rotations, with phase = 0 corresponding to pulsar's ascending node),<br> 3) flux density (in erg cm<sup>-2</sup> s<sup>-1</sup> Hz<sup>-1</sup>)<br> 4) flux density uncertainty (in erg cm<sup>-2</sup> s<sup>-1</sup> Hz<sup>-1</sup>)</p> <p>"ultracam_rawdata.tar.gz" contains all of the raw ULTRACAM data (bias frames, flat frames, calibration observations of standard stars and science frames) from which the reduced light curves were produced. These files are in the ULTRACAM data pipeline format.</p> <p>"J2039-5617.par" is an ephemeris file that is compatible with a recent version of tempo2 in which the "ORBIFUNC" parameters describing an interpolating function for the orbital phase variations have been implemented.</p>
An interactive figure of the 2016 and 2020 X-ray light curves of LMC 1968 as observed by the XRT instrument on Swift
<p>This repository contains all the files necessary to create the interactive figure in the Research Note ov Schwarz, Page, Kuin, & Darnley 2020. The figure was created using the <a href="https://aas-timeseries.readthedocs.io/en/latest/">aas-timeseries</a> package of the <a href="https://www.astropy.org">astropy</a> project. The file lmc68.py is the underlying python code while the two lmcrel*.csv are the input files for the 2016 and 2020 eruptions of the recurrent nova LMC 1968 as observed by the XRT instrument on board the Neil Gehrels Swift observatory. A Jupyter notebook is required to preview the interactive figure. The output from the code is saved in the interactive.tar.gz package. It consists of four files:</p> <ul> <li>index.html</li> <li>figure.json</li> <li>data_75e74aca-09f1-4846-966e-9e33c7acc8d3.csv</li> <li>data_5402e718-01cf-4ad7-92a5-7679d4076ed5.csv</li> </ul> <p>The first file, index.html, is the html framework that houses the interactive figure. figure.json contains the interactive figure commands while the two data*csv files are the underlying data. The interactive figure can be viewed if this package is opened on a web server. A copy of this interactive figure is available <a href="https://authortools.aas.org/LMC1968/">here</a> so you can try it out.</p>
JUMP - Data collection - Part II: Zonal jets using three different approaches, laboratory - Global Climate Models - observations.
<p>The formation of large scale structures in three-dimensional (3D) turbulent flows. How small-scale dynamics organize in turbulent flows to grow large scale coherent circulation? is at the heart of fundamental studies in fluid dynamics. It appears to be equally important for our understanding of atmospheric dynamics, oceanography, meteorology and more generally geophysical fluid dynamics. Here, we deliver a data collection that <strong>(1)</strong> gathers measurements of 3D turbulent flows that emulate planetary atmospheres of the gas giants. Turbulent flows are explored using three different approaches, laboratory experiments, numerical simulations and direct planetary observations. All data set are computed in order to easily extract flow properties, i.e. high resolution maps of the different velocity components and flow vorticity (useful for further diagnostic). The data collected are fully discribed in Cabanes et al GRL (2020) "Revealing the intensity of turbulent energy transfer in planetary atmospheres" and can be used to compute <strong>(2)</strong> theoretical diagnostics with the numerical codes that allow to reveal the physical meaning of flow measurements. Numerical codes are available on https://github.com/scabanes</p> <p>We deliver (1) data collection and (2) numerical codes in the following files attached:</p> <p>(1) Data collection:</p> <ul> <li>A PDF file named <strong>JUMP-zonal-jets-data-collection-GRL.pdf</strong> that describes the following data files and nomenclature.</li> <li>A zip File of the velocity fields in the lab, interpolated on Polar and Cartesian grids <ul> <li><strong>JUMP-JetsInTheLab.zip</strong></li> </ul> </li> <li>A netcdf file of velocity fields of our Saturn reference simulation <ul> <li><strong>uvData-SRS-istep-312000-nstep-50-niz-12.nc</strong></li> </ul> </li> <li>Two netcdf files of velocity fields from Cassini observations of Jupiter<strong> </strong> <ul> <li><strong>uvData-JupObs-istep-0-nstep-4-niz-1.nc</strong></li> <li><strong>StatisticalData-JupObs.nc</strong></li> </ul> </li> <li>A zip file of potential vorticity profiles for Saturn and Jupiter observations <ul> <li><strong>IPV-QGPV-Jupiter-Saturn.zip</strong></li> </ul> </li> </ul> <p>(2) Numerical codes:</p> <ul> <li>Codes for statistical analysis in spherical geometry on Github. --> <a href="https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fscabanes%2FPOST&sa=D&sntz=1&usg=AFQjCNFuDU0eij4XGxQfReO92CHfJz6PBA">https://github.com/scabanes/POST</a></li> <li>Codes for statistical analysis in cylindrical geometry on Github. --> <a href="https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fscabanes%2FJUMP&sa=D&sntz=1&usg=AFQjCNGUQ1YIFhSxBAg4Hl_5gOLB_4LxLA">https://github.com/scabanes/JUMP</a></li> <li>Codes for statistical analysis in cartesian geometry on Github. --> <a href="https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fscabanes%2FJUMP&sa=D&sntz=1&usg=AFQjCNGUQ1YIFhSxBAg4Hl_5gOLB_4LxLA">https://github.com/scabanes/JUMP</a></li> </ul> <p> </p> <p>The purpose of this data collection is to reveal statistical properties of planetary flows. By computing the same analysis on different data sets the researcher allows direct confrontation of planetary observations with idealized laboratory and numerical models. Idealized models are specially designed to sweep on a large array of parameters in order to understand what parameters control planetary global circulation. The data collected and generated by the researcher deliver <strong>(1)</strong> velocity measurements of 3D turbulent flows using the different approaches (observations-laboratory-numerics) and <strong>(2)</strong> guidelines to compute the appropriate statistical analysis through the PTST. Here, the ground-breaking novelty is that the researcher deliver the possibility to compute statistical diagnostics adapted to the different geometries: the spherical geometry of planetary flows, i.e. 2D latitude-longitude maps, the cylindrical geometry of laboratory experiments, i.e. 2D flows in a rotating cylindrical tank, and the Cartesian geometry of idealized numerical simulations. Indeed, the math behind each statistical diagnostics must account for the different geometrical configurations in order to properly confront the different approaches. The PTST is also designed to be easily re-used by different communities such as experimentalists, numericists and atmosphericists that deal with 3D or 2D turbulent flows.</p> <p> </p> <p><strong>Acknowledgments</strong></p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement N° 797012.</p>
Quantifying hail and lightning risk factors using long-term observations around Australia - Hail and Lightning Datasets
<p>These data accompany a paper referenced as doi: 10.1029/2020JD033101. Two spreadsheets are provided as used to derive the hourly and monthly occurrence of hail or lightning events within the domains of the ten radars sites (Melbourne, Wollongong, Gympie, Grafton, Canberra, Marburg, Adelaide, Namoi, Perth, Hobart). Lightning events as defined in this paper (derived from post-processed lightning information) were provided for 2005-2018 and hail data from 1997-2018 (noting that the start date of hail is dependent on when the respective radar site was established). A value of 1 indicates a lightning/hail event occurred during that hour (in UTC +0). Information on processing methods and data are provided in the published paper for the doi listed above.</p>
DEIMOS Spectroscopic observations of MS0451-03
<p>The tar file contains extracted spectroscopic observations of galaxies in the field of MS 0451-03 galaxy cluster. The observations were obtained with the DEIMOS instrument on the Keck Observatory. Details of the observations are given in Crawford et al., 2011, ApJ, 741, 98, <a href="https://ui.adsabs.harvard.edu/link_gateway/2011ApJ...741...98C/doi:10.1088/0004-637X/741/2/98">DOI: 10.1088/0004-637X/741/2/98</a>. The catalogs associated with that paper also have further information about these objects.</p> <p>The following files are included:</p> <p>1. w05.m*-crawford.fits: A catalog for each of the masks. This catalog includes the information about the object in each slit as well as the measurement of the redshift. The</p> <p>2. iraf.w05.m*.*.*.fits. These are 1-D extracted files in an iraf compatible format. The names of the files correspond to the mask, slit, and original id in the catalog used for selection. Unfortunately, due to an bug in the original extraction software, the primary target appears usually appears in the file named serendip1 and not the file with the object id.</p>
Air-Sea Ammonia Fluxes Calculated from High-Resolution Summertime Observations Across the Atlantic Southern Ocean
<p>This data set includes ocean ammonium concentrations, atmospheric ammonia gas concentrations, and calculated air-sea ammonia fluxes from the Atlantic sector of the Southern Ocean during summer. Associated with the folloiwng paper: </p> <p> https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020GL091963</p>
University of Nebraska-Lincoln Unmanned Aerial System observations from LAPSE-RATE
<p>This dataset contains thermodynamic (pressure, temperature, humidity) measurements from the University of Nebraska-Lincoln unmanned aerial system multirotors during the LAPSE-RATE campaign from 14--19 July 2018. This dataset contains 171 files from two multirotors. File names are coded as per the LAPSE-RATE community standard naming agreement. See README_v1.txt for more information about the naming convention and other details.</p>
Data relating to Chiedozie et al. How many medications do doctors in primary care use? An observational study of the DU90% indicator in primary care in England.
<p>Data in .csv format relating to the paper Chiedozie et al. 2020 "How many medications do doctors in primary care use? An observational study of the DU90% indicator in primary care in England." Also contains eTables 5-8 in Excel format, and Stata do file for deriving the DU90% indicator.</p>
Observed runoff time series from a green roof field campaign in Hannover-Herrenhausen
<p>This dataset includes a csv file comprising observed runoff time series from a green roof field campaign, which was conducted by Prof. Dr.-Ing. Hans-Joachim Liesecke. The csv file provides runoff from 11 green roof variants (10 was excluded, since it has a different design). Rows include daily runoff totals (collected each morning, excluding weekends). Please refer to this article, which describes the dataset in more detail: </p> <p><strong>Iffland, R., Förster, K., Westerholt, D., Pesci, M. H., & Lösken, G. Robust vegetation parameterization for green roofs in EPA SWMM. Hydrology. </strong><a href="https://doi.org/10.3390/hydrology8010012">https://doi.org/10.3390/hydrology8010012</a></p> <p>The field campaign involved a total of 11 superstructures in triple repetition. In the csv file, each column represents average values computed out of three independent measurements (in mm*d<sup>-1</sup>)</p> <p>The individual test plots were 2 m x 2 m with a slope of 2 % and a drainage opening in the middle of the lowest point of the slope. The outflowing water was collected in non-weighable lysimeters (rain barrels) that were read and emptied at 8 A.M. every day. On weekends, readings were taken the following workday.</p> <p> </p>
Marquette, Michigan Ground Observations and Atmospheric River Dataset
<p>This dataset contains ground-based meteorological observations and accompanying atmospheric river data used in "The influence of atmospheric rivers on cold-season precipitation in the Upper Great Lakes region", Mateling, Pettersen, Kulie, Mattingly, Henderson, and L'Ecuyer, submitted to GRL, in review.</p> <p>The ground-based data contains meteorological data including temperature, wind speed and direction, radar reflectivity and Doppler velocity from a Micro-Rain Radar2 (MRR) and precipitation data from a Precipitation Imaging Package (PIP) hosted at the National Weather Service in Marquette, Michigan (Pettersen, Kulie, et al., 2020; Pettersen, Bliven, et al., 2020; Kulie et al., 2021).</p> <p>The atmospheric river (AR) data contains a flag to identify when an AR is within 100 km of Marquette during a deep cold-season precipitation event. Additionally, the associated integrated water vapor transport (IVT) and motion vectors are within this file. The AR database was created and analyzed in Mattingly et al. (2018). </p>
Meteorological and ground observations in the Qinghai-Tibet Engineering Corridor
<p>Observation of meteorological factors was conducted at two permanent meteorological stations (Golmud and Wudaoliang) and one field meteorological station (Xidatan) with daily meteorological records. All three meteorological stations contain ground observations.</p>
Observation logs for APOGEE-1
<p>These files are the observation logs for the SDSS-III/APOGEE survey. The provide information on when different locations on the sky were observed and for how long. They are to be used with the code at https://github.com/jobovy/apogee for determining the APOGEE selection function (the fraction of potential targets observed as in different parts of the sky).</p>
Still images from the KuramBio expedition 2012 (Stations 3-6, 8-11) obtained with the Ocean Floor Observation System
<p>Repository with still images obtained from the videos of the KuramBio 2012 expedition in 5 second intervals. Still images belong to deep-sea stations where the Ocean Floor Observation System (OFOS) was deployed and enough video survey was obtained (i.e., Stations 3-6, 8-11). The Station 7 survey length was enough but has no HD video. Thus, images were not retrieved since the quality does not allow accurate lebensspuren or benthic fauna identification. </p><p>The OFOS was lowered into the water at the CTD position. The first 300 meters lowering was conducted with 0.5 m/sec, and then the speed was increased to 0.8 m/sec while the ship was kept in position. At 500 meters above ground the speed was reduced to 0.5 m/sec, and further reduced to 0.3 m/sec at 200 meters above ground. As soon as visual contact with the bottom was established, the winch was stopped. The ship started moving with 0.5 knots in the appropriate direction, which was chosen depending on current and wind situation at the according station. The OFOS was kept in an appropriate distance to the seafloor, enabling the scientists to watch the macrofaunal organisms. The approximate size of the observed animals could be calculated with the help of two laser pointers having a distance of 10 cm between each other (see Fig. 1-3). Generally, the survey lasted slightly over one hour, then the ship was stopped and heaving of the OFOS started. This was first conducted at 0.5 m/sec and accelerated to 1.0 m/sec. For more information see the cruise report (RV Sonne cruise SO223; doi:10.1016/j.dsr2.2014.11.001).</p>
MAARSY-Optical Coincident Meteor Observations
<p>Meteor observations from the MAARSY radar and a two-camera network deployed by the University of Western Ontario. The data are organized into individual directories for each event.</p><p>Each event directory contains a subset of the following files:</p><ul><li>*_echo.txt: MAARSY data for the event, including time, position, SNR, RCS, and velocity. Each file is title with the timestamp of the event</li><li>data*W.dat: optical camera data for the event, including time, position, and magnitude. Each file is titled with the observing camera: site 01 or site 02, wide-field (W) camera</li><li>Camera images of the event</li><li>Range-time plots of the radar and optical data</li></ul>
ScienceDex guides
Understand access before you commit
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.