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10,016 results for “Observation”
COHERENT Collaboration data release from the first observation of coherent elastic neutrino-nucleus scattering
<p>Release of COHERENT Collaboration data associated with the first observation of coherent elastic neutrino-nucleus scattering (CEvNS), as published in Science (DOI: <a href="http://dx.doi.org/10.1126/science.aao0990">10.1126/science.aao0990</a>) and also available as arXiv:1708.01294[nucl-ex].</p> <p>This data set should enable researchers to extend the study of CEvNS as desired. Future COHERENT Collaboration results will have similar data releases.</p> <p>Example code can be accessed at https://code.ornl.gov/COHERENT/codeExamples_dataRelease_april2018.<br> The full data-release package, including data, code examples, and a descriptive accompanying document can be found at http://coherent.ornl.gov/data.</p>
Swift/UVOT observation of NGC 1566 in 2012 and 2018
<p>This figure shows the brightening of the central active galactic nucleus of NGC 1566 as observed by the Neil Gehrels Swift observatory UVOT instrument between 2018-06-24 (left) and 2012-10-09 (right). A brightening in the UW2 magnitude from 15.35 in 2012 to 12.48 in 018 is measured (ucertainties of 0.01- 0.02 magnitudes). </p>
Airborne radar observation dataset of sea surface height on 8 December 2016
<p>This dataset contains the results of time-series sea surface height (SSH) observation data of flight No.1, 2, 3, 4, 7, and 8 on 8 December 2016 by airborne altimeter measurement using a frequency modulated continuous wave (FM-CW) radar. The observation flight were carried out south of Japan passed over the Kuroshio Current. The data files are written in CSV format, the columns are UTC date, time, latitude, longitude, flight altitude, observed SSH, 1 min moving averaged SSH values, geoid height, and tide height. The geoid height and the tide height are derived by the EGM 2008 model (Pavlis et al. 2012) and the Nao.99Jb model (Matsumoto et al. 2000), respectively. The original data sampling rate of 800 microseconds is resampled by 80 milliseconds in each file. The data comes from a paper under review for Geophysical Research Letter.</p>
Dataset for "Sources and sinks of carbonyl sulfide inferred from atmospheric observations at the Lutjewad tower"
<p>Measurements that are used in "Sources and sinks of carbonyl sulfide inferred from atmospheric observations at the Lutjewad tower”. The dataset includes mole fraction measurements of COS, CO<sub>2</sub>, CO and H<sub>2</sub>O made in Lutjewad (the Netherlands) and Hyytälä (Finland) between 2014 and 2018, and measurements of <sup>222</sup>Rn, SF<sub>6</sub> and meteorological parameters in Lutjewad.</p>
The SPARC water vapour assessment II: Comparison of annual, semi-annual and quasi-biennial variations in stratospheric and lower mesospheric water vapour observed from satellites
<p>Here we provide a NetCDF data set that contains the amplitudes and phases for the annual, semi-annual and quasi-biennial variations in stratospheric and lower mesospheric water vapour as observed by 30 satellite data sets. In addition, we combine the results from all data sets to provide average amplitudes and the corresponding standard deviations, among other.</p> <p>The content description of the NetCDF file looks as follows:</p> <p>netcdf results.amt-10-1111-2017 {<br> dimensions:<br> dataset = 30 ;<br> string_length = 60 ;<br> latitude = 37 ;<br> bands = 2 ;<br> altitude = 59 ;<br> variables:<br> char dataset_short(string_length, dataset) ;<br> dataset_short:standard_name = "data set" ;<br> dataset_short:long_name = "data set name" ;<br> dataset_short:description = "short label of data set" ;<br> char dataset_long(string_length, dataset) ;<br> dataset_long:standard_name = "data set" ;<br> dataset_long:long_name = "data set name" ;<br> dataset_long:description = "long label of data set" ;<br> double latitude(latitude) ;<br> latitude:standard_name = "latitude" ;<br> latitude:units = "degree_north" ;<br> latitude:minimum_value = "-90" ;<br> latitude:maximum_value = "90" ;<br> latitude:axis = "Y" ;<br> latitude:_CoordinateAxisType = "Lat" ;<br> double latitude_bands(bands, latitude) ;<br> latitude_bands:units = "degree_north" ;<br> double altitude(altitude) ;<br> altitude:standard_name = "altitude" ;<br> altitude:long_name = "pressure levels" ;<br> altitude:units = "hPa" ;<br> altitude:axis = "Z" ;<br> altitude:_CoordinateAxisType = "Alt" ;<br> double tropopause(latitude) ;<br> tropopause:standard_name = "tropopause" ;<br> tropopause:long_name = "tropopause pressure" ;<br> tropopause:description = "climatological tropopause pressure based on MERRA reanalysis data 2000 - 2014" ;<br> tropopause:units = "hPa" ;</p> <p>// global attributes:<br> :summary = "this file contains the results published in Lossow et al. (2017)" ;<br> :url = "https://www.atmos-meas-tech.net/10/1111/2017/amt-10-1111-2017.html" ;<br> :project = "second SPARC water vapour assessment (WAVAS-II)" ;<br> :creator_name = "Stefan Lossow & Farahnaz Khosrawi" ;<br> :creator_email = "stefan.lossow@kit.edu & farahnaz.khosrawi@kit.edu" ;<br> :creator_email_supplemental = "stefan.lossow@yahoo.se & f.khosrawi@gmail.com" ;<br> :value_for_nodata = "NaN" ;<br> :date_created = "20190105T112425Z" ;</p> <p>group: AO {<br> dimensions:<br> latitude = 37 ;<br> altitude = 59 ;<br> dataset = 30 ;<br> variables:<br> double amplitude(dataset, altitude, latitude) ;<br> amplitude:standard_name = "amplitude" ;<br> amplitude:long_name = "amplitude of the AO variation" ;<br> amplitude:description = "regression model is given by Eq. (1) in the manuscript; amplitude calculation based on Eq. (2)" ;<br> amplitude:units = "ppmv" ;<br> double phase(dataset, altitude, latitude) ;<br> phase:standard_name = "phase" ;<br> phase:long_name = "phase of the AO variation" ;<br> phase:description = "regression model is given by Eq. (1) in the manuscript; phase calculation based on Eq. (3)" ;<br> phase:units = "month" ;<br> double offset(dataset, altitude, latitude) ;<br> offset:standard_name = "offset" ;<br> offset:long_name = "offset component of the regression model" ;<br> offset:description = "regression model is given by Eq. (1) in the manuscript; meant for calculation of relative amplitudes" ;<br> offset:units = "ppmv" ;<br> double screening(dataset, altitude, latitude) ;<br> screening:standard_name = "screening" ;<br> screening:long_name = "screening for the amplitude and phase data" ;<br> screening:description = "screening matrix for the amplitude and phase data to calculate the standard deviations described in Sect. 3.3; 1 means screening; 0 means no screening" ;<br> screening:units = "" ;<br> double phase_difference(dataset, altitude, latitude) ;<br> phase_difference:standard_name = "phase difference" ;<br> phase_difference:long_name = "phase difference with respect to the reference data set" ;<br> phase_difference:reference_data_set_short = "MLS" ;<br> phase_difference:reference_data_set_long = "Aura/MLS v4.2" ;<br> phase_difference:description = "phase difference has been adapted so that it not exceeds the [-6,6] months interval by adding +/- 12 months; has been calculated after the screening" ;<br> phase_difference:units = "month" ;<br> double amplitude_standard_deviation(altitude, latitude) ;<br> amplitude_standard_deviation:standard_name = "standard deviation of amplitude" ;<br> amplitude_standard_deviation:long_name = "standard deviation of amplitude over all data sets" ;<br> amplitude_standard_deviation:description = "standard deviation calculation based on Eq. (6)" ;<br> amplitude_standard_deviation:units = "ppmv" ;<br> double amplitude_mean(altitude, latitude) ;<br> amplitude_mean:standard_name = "mean amplitude" ;<br> amplitude_mean:long_name = "mean amplitude over all data sets" ;<br> amplitude_mean:description = "mean calculation based on Eq. (6)" ;<br> amplitude_mean:units = "ppmv" ;<br> double amplitude_relative_standard_deviation(altitude, latitude) ;<br> amplitude_relative_standard_deviation:standard_name = "relative standard deviation of amplitude" ;<br> amplitude_relative_standard_deviation:long_name = "relatuve standard deviation of amplitude " ;<br> amplitude_relative_standard_deviation:description = "relavtive standard deviation calculation based on Eq. (6); uses \"amplitude_mean\" as reference" ;<br> amplitude_relative_standard_deviation:units = "ppmv" ;<br> double phase_difference_standard_deviation(altitude, latitude) ;<br> phase_difference_standard_deviation:standard_name = "standard deviation of phase difference" ;<br> phase_difference_standard_deviation:long_name = "standard deviation of phase difference over all data sets" ;<br> phase_difference_standard_deviation:description = "standard deviation calculation based on Eq. (7)" ;<br> phase_difference_standard_deviation:units = "month" ;<br> double phase_difference_mean(altitude, latitude) ;<br> phase_difference_mean:standard_name = "mean of phase difference" ;<br> phase_difference_mean:long_name = "mean of phase difference over all data sets" ;<br> phase_difference_mean:description = "mean calculation based on Eq. (7)" ;<br> phase_difference_mean:units = "month" ;<br> } // group AO</p> <p>group: SAO {<br> dimensions:<br> latitude = 37 ;<br> altitude = 59 ;<br> dataset = 30 ;<br> variables:<br> double amplitude(dataset, altitude, latitude) ;<br> amplitude:standard_name = "amplitude" ;<br> amplitude:long_name = "amplitude of the SAO variation" ;<br> amplitude:description = "regression model is given by Eq. (4) in the manuscript; amplitude calculation based on Eq. (2)" ;<br> amplitude:units = "ppmv" ;<br> double phase(dataset, altitude, latitude) ;<br> phase:standard_name = "phase" ;<br> phase:long_name = "phase of the SAO variation" ;<br> phase:description = "regression model is given by Eq. (4) in the manuscript; phase calculation based on Eq. (3)" ;<br> phase:units = "month" ;<br> double offset(dataset, altitude, latitude) ;<br> offset:standard_name = "offset" ;<br> offset:long_name = "offset component of the regression model" ;<br> offset:description = "regression model is given by Eq. (4) in the manuscript; meant for calculation of relative amplitudes" ;<br> offset:units = "ppmv" ;<br> double screening(dataset, altitude, latitude) ;<br> screening:standard_name = "screening" ;<br> screening:long_name = "screening for the amplitude and phase data" ;<br> screening:description = "screening matrix for the amplitude and phase data to calculate the standard deviations described in Sect. 3.3; 1 means screening; 0 means no screening" ;<br> screening:units = "" ;<br> double phase_difference(dataset, altitude, latitude) ;<br> phase_difference:standard_name = "phase difference" ;<br> phase_difference:long_name = "phase difference with respect to the reference data set" ;<br> phase_difference:reference_data_set_short = "MLS" ;<br> phase_difference:reference_data_set_long = "Aura/MLS v4.2" ;<br> phase_difference:description = "phase difference has been adapted so that it not exceeds the [-3,3] months interval by adding +/- 6 months; has been calculated after the screening" ;<br> phase_difference:units = "month" ;<br> double amplitude_standard_deviation(altitude, latitude) ;<br> amplitude_standard_deviation:standard_name = "standard deviation of amplitude" ;<br> amplitude_standard_deviation:long_name = "standard deviation of amplitude over all data sets" ;<br> amplitude_standard_deviation:description = "standard deviation calculation based on Eq. (6)" ;<br> amplitude_standard_deviation:units = "ppmv" ;<br> double amplitude_mean(altitude, latitude) ;<br> amplitude_mean:standard_name = "mean amplitude" ;<br> amplitude_mean:long_name = "mean amplitude over all data sets" ;<br> amplitude_mean:description = "mean calculation based on Eq. (6)" ;<br> amplitude_mean:units = "ppmv" ;<br> double amplitude_relative_standard_deviation(altitude, latitude) ;<br> amplitude_relative_standard_deviation:standard_name = "relative standard deviation of amplitude" ;<br> amplitude_relative_standard_deviation:long_name = "relatuve standard deviation of amplitude " ;<br> amplitude_relative_standard_deviation:description = "relavtive standard deviation calculation based on Eq. (6); uses \"amplitude_mean\" as reference" ;<br> amplitude_relative_standard_deviation:units = "ppmv" ;<br> double phase_difference_standard_deviation(altitude, latitude) ;<br> phase_difference_standard_deviation:standard_name = "standard deviation of phase difference" ;<br> phase_difference_standard_deviation:long_name = "standard deviation of phase difference over all data sets" ;<br> phase_difference_standard_deviation:description = "standard deviation calculation based on Eq. (7)" ;<br> phase_difference_standard_deviation:units = "month" ;<br> double phase_difference_mean(altitude, latitude) ;<br> phase_difference_mean:standard_name = "mean of phase difference" ;<br> phase_difference_mean:long_name = "mean of phase difference over all data sets" ;<br> phase_difference_mean:description = "mean calculation based on Eq. (7)" ;<br> phase_difference_mean:units = "month" ;<br> } // group SAO</p> <p>group: QBO {<br> dimensions:<br> latitude = 37 ;<br> altitude = 59 ;<br> dataset = 30 ;<br> variables:<br> double amplitude(dataset, altitude, latitude) ;<br> amplitude:standard_name = "amplitude" ;<br> amplitude:long_name = "amplitude of the QBO variation" ;<br> amplitude:description = "regression model is given by Eq. (5) in the manuscript; amplitude calculation based on Eq. (2)" ;<br> amplitude:units = "ppmv" ;<br> double phase(dataset, altitude, latitude) ;<br> phase:standard_name = "phase" ;<br> phase:long_name = "phase of the QBO variation" ;<br> phase:description = "regression model is given by Eq. (5) in the manuscript; phase is derived as the shift of the QBO regression fit for which the correlation with the Singapore (1N, 104E) winds at 50 hPa maximises" ;<br> phase:units = "month" ;<br> double offset(dataset, altitude, latitude) ;<br> offset:standard_name = "offset" ;<br> offset:long_name = "offset component of the regression model" ;<br> offset:description = "regression model is given by Eq. (5) in the manuscript; meant for calculation of relative amplitudes" ;<br> offset:units = "ppmv" ;<br> double screening(dataset, altitude, latitude) ;<br> screening:standard_name = "screening" ;<br> screening:long_name = "screening for the amplitude and phase data" ;<br> screening:description = "screening matrix for the amplitude and phase data to calculate the standard deviations described in Sect. 3.3; 1 means screening; 0 means no screening" ;<br> screening:units = "" ;<br> double phase_difference(dataset, altitude, latitude) ;<br> phase_difference:standard_name = "phase difference" ;<br> phase_difference:long_name = "phase difference with respect to the reference data set" ;<br> phase_difference:reference_data_set_short = "MLS" ;<br> phase_difference:reference_data_set_long = "Aura/MLS v4.2" ;<br> phase_difference:description = "phase difference has been adapted so that it not exceeds the [-14,14] months interval by adding +/- 28 months; has been calculated after the screening" ;<br> phase_difference:units = "month" ;<br> double amplitude_standard_deviation(altitude, latitude) ;<br> amplitude_standard_deviation:standard_name = "standard deviation of amplitude" ;<br> amplitude_standard_deviation:long_name = "standard deviation of amplitude over all data sets" ;<br> amplitude_standard_deviation:description = "standard deviation calculation based on Eq. (6)" ;<br> amplitude_standard_deviation:units = "ppmv" ;<br> double amplitude_mean(altitude, latitude) ;<br> amplitude_mean:standard_name = "mean amplitude" ;<br> amplitude_mean:long_name = "mean amplitude over all data sets" ;<br> amplitude_mean:description = "mean calculation based on Eq. (6)" ;<br> amplitude_mean:units = "ppmv" ;<br> double amplitude_relative_standard_deviation(altitude, latitude) ;<br> amplitude_relative_standard_deviation:standard_name = "relative standard deviation of amplitude" ;<br> amplitude_relative_standard_deviation:long_name = "relatuve standard deviation of amplitude " ;<br> amplitude_relative_standard_deviation:description = "relavtive standard deviation calculation based on Eq. (6); uses \"amplitude_mean\" as reference" ;<br> amplitude_relative_standard_deviation:units = "ppmv" ;<br> double phase_difference_standard_deviation(altitude, latitude) ;<br> phase_difference_standard_deviation:standard_name = "standard deviation of phase difference" ;<br> phase_difference_standard_deviation:long_name = "standard deviation of phase difference over all data sets" ;<br> phase_difference_standard_deviation:description = "standard deviation calculation based on Eq. (7)" ;<br> phase_difference_standard_deviation:units = "month" ;<br> double phase_difference_mean(altitude, latitude) ;<br> phase_difference_mean:standard_name = "mean of phase difference" ;<br> phase_difference_mean:long_name = "mean of phase difference over all data sets" ;<br> phase_difference_mean:description = "mean calculation based on Eq. (7)" ;<br> phase_difference_mean:units = "month" ;<br> } // group QBO<br> }</p> <p> </p>
Characterizing the gamma-ray variability of the brightest flat spectrum radio quasars observed with the Fermi LAT
<p>The FITS files contain light curves (prefix "lc"), spectral energy distributions (prefix "sed") and best-fit parameters for the whole region of interest (prefix "bestfit_roi") for the gamma-ray analyses of the six brightest flat spectrum radio quasars observed over 9.5 years with the Fermi Large Area Telescope (LAT). The file names also indicate the considered binning (weekly, daily, orbit, sub-orbital) and the considered time range in MJD.<br> The data products have been generated using the fermipy software, please see the documentation for further explanations of the columns provided in these files: <a href="https://fermipy.readthedocs.io/en/latest/">https://fermipy.readthedocs.io/en/latest/</a></p> <p>The analysis catalog are described in detail in the accompanying paper, which is submitted for publication in the Astrophysical Journal. The preprint of the submitted manuscript can be found here: <a href="https://arxiv.org/abs/1902.02291">https://arxiv.org/abs/1902.02291</a></p> <p>Additionally, the code for high level analysis including the light curves and the gamma-ray absorption in the broad line region can be found on github: <a href="https://github.com/me-manu/GaRLiC">https://github.com/me-manu/GaRLiC </a>and <a href="https://github.com/me-manu/blrabsorption">https://github.com/me-manu/blrabsorption</a></p> <p> </p>
Atmospheric, hydrodynamic and water quality observations from environmental-quality stations, water level sensors, acoustic Doppler velocimeters, and meteorological stations located at the Guadalquivir river estuary (2008 - 2010)
<p>The dataset included in this repository was obtained during the project entitled “Propuesta metodológica para diagnósticar las consecuencias de las actuaciones humanas en el estuario del Guadalquivir” funded by the Autoridad Portuaria de Sevilla (APS), by the Consejería de Innovación, Ciencia y Empresa (Junta de Andalucía), CTM2011-22580, MedEX (CTM2008-04036-E) and PR11-RNM-7722. The data were collected in real time from 2008 until 2010 with a remote monitoring system installed by the Institute of Marine Sciences of Andalusia (ICMAN-CSIC) (Navarro et al., 2011).</p> <p> </p> <p>The environmental quality station recorded turbidity, temperature, conductivity, normalized turbidity, dissolved oxygen, oxygen, oxygen saturation, percentage of oxygen saturation, fluorescence, normalized fluorescence, and salinity every thirty minutes. Current data were measured every 15 minutes by means of acoustic current profilers. The former datasets were obtained at several depths and different locations along the Guadalquivir estuary. Water level sensors recorded the position of the free water surface every 10 minutes at several locations along the Guadalquivir estuary. Wind velocity and direction and solar radiation were measured every 10 minutes in a meteorological station at the mouth of the Guadalquivir estuary.</p> <p>Brief description of dataset.</p> <ul> <li> <p>velocities.csv (in m/s)</p> </li> <li> <p>Turbidity.csv (in Volts), temperature (in Celsius), conductivity (in Siemens/m), normalized turbidity (in FNU), dissolved oxygen (mg/L), oxygen (in Volts), fluorescence (in Volts), normalized fluorescence (in Volts), oxygen saturation (mg/L), percentage of oxygen saturation (%), salinity (in PSU).</p> </li> <li> <p>qual_Salmedina.csv, R_mean (mean radiative flux in W/m²), R_max (max radiative flux in W/m²), Rel_humidity (relative humidity in %), D_mean (wind mean direction in degrees), D_max (wind maximum direction in degrees), D_sig (standard deviation of the wind direction in degrees), V_mean (mean wind velocity in m/s), V_max (maximum wind velocity in m/s), V_sig (standard deviation of the wind velocity in m/s), P_atm_mean (mean atmospheric pressure in mbar), T_mean (mean air temperature in Celsius), T_max (maximum air temperature in Celsius), T_sig (standard deviation of the air temperature in Celsius).</p> </li> <li> <p>Sealevel.csv (in meters)</p> </li> </ul> <p>A wide description of the datasets can be found in Navarro et al (2011).</p> <p>Contact person: infogdfa@ugr.es (or mcobosb@ugr.es)</p>
AI validated plant observations from social media: Flickr images from central London 2011-2019
<p>This dataset is the result of using an AI image classifer to classify images of plants on social media. We believe this is the first AI validated dataset of biological records taken from social media. This represents the dawn of AI naturalists whose domain of exploration is not the outdoor world but the digital realm. These AI naturalists will trawl streams of data from all over the globe, identifying genuine images of species, and in so doing create valuable data sets that will further our understanding of the distribution of wildlife on our planet. </p> <p>This dataset contains 31,973 classifications of images taken in central London between May 2011 and September 2019 retrieved using the search term 'flower' on Flickr.com. Some images have very low classification confidence (7910 below 0.1), while others have very high confidence (3185 over 0.9). As expected given the spatial extent of the dataset many of the observations are of planted species in gardens and parks.</p> <p>August_et_al_2019.csv provides the data while metadata.txt contains a description of the data and its generation.</p> <p>An interactive visualisation of this data can be viewed at <a href="https://tomaugust.shinyapps.io/ai_flickr_data/">https://tomaugust.shinyapps.io/ai_flickr_data/</a></p>
Turbulent kinetic energy over large wind farms observed and simulated by the mesoscale model WRF (3.8.1)
<p>This repository contains the WRF configuration files necessary to reproduce the simulations <br> as described in Siedersleben et al. 2019 (https://doi.org/10.5194/gmd-2019-100)</p> <p>The file windturbines_GMD.txt contains the locations of <br> all windturbines implemented in the simulations. The corresponding attributes of each <br> wind turbine type is described in the wind-turbine-xx.tbl. Be aware that all windturbines use the same power and thrust coefficients only the different hub heights and rotor diameters are taken into account as described in Siedersleben et al. (2019).</p> <p>The namelist.input_nameOfSimulation files necessary to run the simulations are provided in this repository as well. You may notice that <br> there are less namelist files than simulations. The simulations not using a TKE source use the same namelists as the ones with a TKE a source. However, the WRF model needs to be recompiled using the manipolated module_wind_fitch.F (you find this file in this repository). The sensitivity studies investigating the impact of the uncertainties in the power and thrust coefficients use the namelist of the control simulation CNTRb, but with manipulated wind-turbine-x_modMin/Max.tbl wind turbine files.</p> <p>The two python files get_era5*.py can be used to retrieve the ERA5 data, driving the WRF model. <br> Note that the dates and pathes have to be adjusted in the python files. <br> After downloading the surface and model level data some postprocessing <br> is necessary as described nicely here: "http://valcap74.blogspot.com/2017/10/how-to-run-wrf-model-driven-by-era5-on.html". For this<br> purpose the simple script called postProcessERA5 (based on the blog entry mentioned above) can be used.</p>
Observations of Diurnal Coastal-Trapped Waves with a Thermocline-Intensified Velocity Field
<p>Repository for the data presented in the JPO 2019 article: "Observations of Diurnal Coastal-Trapped Waves with a Thermocline-Intensified Velocity Field". Observations were compared against output from Ken Brink's model. The model is available from: <a href="https://darchive.mblwhoilibrary.org/handle/1912/10527">https://darchive.mblwhoilibrary.org/handle/1912/10527</a>.</p>
Observed and WRF-simulated air temperature and wind speed at the Czech Hydrometeorological Institute weather stations Lučina, Lysá hora and Olomouc
<p>The dataset contains two csv files with observed 2-m air temperature and 10-m wind speed data at Lučina, Lysá hora and Olomouc meteorological stations in the Czech Republic and analogical time series produced by the Weather Research and Forecasting (WRF) model. The dataset covers a period of 27 October 2010, 01:00 UTC to 01 November 2010, 00:00 UTC. WRF output is given for three model configurations:</p> <p>1) QNSE boundary layer scheme</p> <p>2) 3DTKE boundary layer scheme with Revised MM5 surface layer scheme</p> <p>3) 3DTKE boundary layer scheme with MYNN surface layer scheme</p>
Seasonal and longitudinal variability in Io's SO2 atmosphere from 22 years of IRTF/TEXES observations
<p>This dataset contains the reduced Io spectra used in the paper "Seasonal and longitudinal variability in Io's SO2 atmosphere from 22 years of IRTF/TEXES observations" (doi: 10.1016/j.icarus.2024.116151). There are 150 spectra, spanning from 2001 to 2023. These spectra are described in Table 1 of the paper.</p> <p>The spectra in the data file are listed in date order. For each spectrum, we first provide the date (YYMMDD format) and the mean Io central longitude at the time of the observation. This is then followed by the spectrum. Column 1 is the wavelength, in units of microns. Column 2 is the Io spectrum, which has been divided by a Callisto spectrum, flattened in order to correct for any residual continuum slope, and then normalized such that the continuum level is 1. </p>
CLDF dataset derived from McElhanon's "Preliminary Observations on Huon Peninsula Languages" from 1967
<p>Cite the source of the dataset as:</p> <blockquote> <p> McElhanon, K.A. 1967. Preliminary Observations on Huon Peninsula Languages. Oceanic Linguistics. 6, 1-45.</p> </blockquote>
Dataset for the publication "Superconducting gravimeter observations show that satellite-derived snow depth image improves the simulation of the snow water equivalent evolution in a high alpine site"
<p>This datasset contains data to reproduce the following figures of the paper <em>Superconducting gravimeter observations show that satellite-derived snow depth image improves the simulation of the snow water equivalent evolution in a high alpine site</em>:</p> <ul> <li> <p>Time series data of Figures 1c and 2</p> </li> <li> <p>Data (*.asc) used for plotting Figures 1d and 1e (as well as Figure S3 and S4)</p> </li> <li>Pléiades snow depth map (Figure S1)</li> <li> <p>Data used for plotting Figure S2</p> </li> </ul> <p> </p>
Hrycyna et al. 2022 - Satellite observations of NO2 indicate legacy impacts of Redlining in US Midwestern cities
<p>This dataset contains remotely sensed estimates of nitrogen dioxide (NO2, via TROPOMI accessed via Google Earth Engine) for HOLC neighborhoods in 11 US Midwestern cities, and corresponding coarse geographic and demographic data of those cities. NO2 data is reported daily for the entire calendar year of 2019, geographic and demographic variables are fixed for each city for the entire year. Each HOLC-graded neighborhood included in this dataset was filtered to be greater than 2 km2. The number of pixels used to calculate the area-weighted mean of NO2 is also reported, as is the area of the neighborhood. The dataset has also been filtered for observations that did not pass quality filters for L3 TROPOMI data. The cities included in the study are: Chicago IL, Milwaukee WI, Saint Paul MN, Minneapolis MN, Indianapolis IN, Cleveland OH, Wichita KS, Greater Kansas City KS and MO, Columbus OH, Detroit MI, and Omaha NE. HOLC neighborhood shapefiles were obtained from the Mapping Inequality project website, hosted by the University of Richmond, and resulting polygons used in analysis were created by dissolving shared boundaries in Google Earth Engine. City populations and population density were obtained from the US 2010 Census data. All data was collected and organized to assess if current day NO2 levels varied with HOLC grades in these major cities.</p> <p> </p> <p>Data was used in the study: Hrycyna et al. (2022) <em>Elementa</em> 10(1):00027 </p> <div> <div><a href="https://doi.org/10.1525/elementa.2022.00027" target="_blank" rel="noopener">https://doi.org/10.1525/elementa.2022.00027</a></div> </div> <p>Robert K. Nelson, LaDale Winling, Richard Marciano, Nathan Connolly, et al., “Mapping Inequality,” American Panorama, ed. https://dsl.richmond.edu/panorama/redlining/#loc=5/39.1/-94.58&text=downloads</p> <p><strong>Dataset for all analyses presented in Hrycyna et al. Columns described below:</strong></p> <p>HOLC_grade: A, B, C, D (neighborhood grade categories obtained from Mapping Inequality project, indicate historic HOLC designations of neighborhoods).</p> <p>HOLCAreaKm2: continuous area value in km2 of the HOLC neighborhood polygon, which may be more than one HOLC designated polygon merged from the shapefiles downloaded from Mapping Inequality.</p> <p>pixelcount: integer values of the number of TROPOMI NO2 pixels used to produce the area-weighted mean NO2 value.</p> <p>NO2_mol_m2: area-weighted mean value of TROPOMI NO2 for that HOLC neighborhood polygon in mol m-2</p> <p>system.index: designated date and time boundary of the observation collected via TROPOMI</p> <p>date: date of observation</p> <p>month: month of observation</p> <p>City: city in the US Midwest</p> <p>State: state for the city of focus</p> <p>Population: urban population obtained from 2010 census</p> <p>PopDensity: urban population density obtained from 2010 census, based on modern city boundaries (in people per square miles)</p> <p>CityArea_mi2: Area of the city of interest, in square miles.</p> <p>ln_NO2: natural log transformed NO2 values in mol m-2</p> <p>NO2_DU: NO2 value converted from mol m-2 to DU (Dobsons Units, converted by multiplying 2241.15)</p> <p>NO2_lnDU: natural log transformed NO2 values in DU<br><br></p>
Data for "Constraints on the Observability of Energetic Neutral Atoms from the Magnetosphere-Atmosphere Interactions at Callisto and Europa" by Haynes et al.
<p>Accompanying data products for publication entitled "Constraints on the Observability of Energetic Neutral Atoms from the Magnetosphere-Atmosphere Interactions at Callisto and Europa". The manuscript was submitted to JGR Space Physics shortly after upload.</p> <p>Data includes all simulation outputs that are depicted in this work, both for the AIKEF hybrid model (i.e., Figure 4) and the model used to produce synthetic ENA images (Figures 3, 6, 8, 9, 11, A1, and B1). All other figures in the work are used for illustrative purposes and were not generated with simulation output. </p> <p>Information regarding the organization and file structure can be found in H24_data_readme.txt , as well as which dataset corresponds to which figure. Any inquiries, questions, or comments may be addressed through the email associated with this data publication.</p>
Daily observed, modelled, and infilled river flows for an Irish hydrometric reference network of river flow stations
<p>Here we present a dataset of observed, modelled, and infilled daily river flow data relating to the newly updated Irish Hydrometric Reference Network (IHRN) of high-quality gauging stations located across the Republic of Ireland. Internationally applied selection criteria, analysis of historical observations and flow gauge metadata, stakeholder feedback, and trend assessments aided in the identification of the network’s 51 stations. A combination of the GR4J conceptual hydrological model and a backpropagation neural network driven by catchment specific precipitation and temperature extracted from gridded datasets was used to model flows that subsequently infilled gaps in the observational record for each of the series (from commencement of each station’s record till the end of 2022). Also included are the 2.5 and 97.5 quantile values for each station’s modelled data, which represent the upper and lower uncertainty bounds of the respective ensemble flows derived during the flow generation process. As well as providing a useful means for evaluating the impact of changing climatic conditions on Irish catchments, the IHRN data offers utility for assessing catchment based impacts for flow extremes, and the generation of both historical reconstructions and future climate projections for a range of flow regimes across the island of Ireland.</p>
Neural Network and objective analysis reconstruction of 3D Mediterranean physical fields from surface satellite and in situ observations at 1/24 deg
<p>Daily Mediterranean 3D fields of temperature, salinity and geostrophic current at 1/24° of resolution, up to 150m-depth and from 2016 to mid 2022, obtained through a 3 steps approach: (1) Temperature and salinity 3D fields have been first estimated by a machine learning approach by using mediterranean reanalysis outputs (https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R) together with satellite observations, (2) a combination of this first step with in situ observations through an Optimal interpolation to remove part of large scale biases, (3) the computation of geostrophic currents using the thermal wind equation. This work has been funded by the European Space Agency through the 4DMED-SEA project [ESA contract No. 4000141547/23/I-DT].</p>
Biogeochemical observations in adjacent mesoscale eddies of opposite polarity
<p>Datasets used for the analyses reported in the manuscript titled "Biogeochemical dynamics in adjacent mesoscale eddies of opposite polarity".</p> <p>For all files, the suffix HL4 indicates the expedition HOE-LEGACY 4, while the suffix MESOSCOPE indicates the MESO-SCOPE expedition.</p> <p>Temperature profiles measured underway across adjacent eddies are saved in files UnderwayTemperature*.csv. Station coordinates are saved in UnderwayCoordinates*.csv</p> <p>Shipboard vertical profiles of dissolved oxygen, chlorophyll fluorescence, and potential density anomaly are saved in files TransectOxygen*.csv, TransectFluorescence*.csv, and TransectSigma*.csv, respectively. Station coordinates are saved in TransectCoordinates*.csv</p> <p>Inorganic nutrient concentrations measured across adjacent eddies are saved in files EddyNutrients*.csv.</p> <p>Particulate carbon, chlorophyll a, beam attenuation, and chlorophyll fluorescence in the eddy centers are saved in file ParticlePigmentComparison_15m.csv and ParticlePigmentComparison_DCM.csv for the depth of 15 m and the depth of the DCM, respectively.</p> <p>Cell counts from flow cytometry in the eddy centers are saved in files FlowCytometry_cyclone_*.csv and FlowCytometry_anticyclone_*.csv. This files also report the depth of the DCM for each eddy center.</p> <p>Imaging FlowCytobot (IFCb) measurements during MESO-SCOPE are reported in files IFCB_Class_DCM&15m_MESOSCOPE.csv for classes and IFCB_Genera_DCM_MESOSCOPE.xlsx for genera.</p>
First In-Situ Measurements of Travelling Ionospheric Disturbances at 420 km Altitude by the Scintillation Observations and Response of The Ionosphere to Electrodynamics (SORTIE) CubeSat
<p>Companion dataset to the paper entitled "First In-Situ Measurements of Travelling Ionospheric Disturbances at 420 km Altitude by the Scintillation Observations and Response of The Ionosphere to Electrodynamics (SORTIE) CubeSat". The dataset includes the SORTIE CubeSat IVM Level 2 ion density and GPS TEC data used in the study along with the WRF simulation results.</p>
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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.