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571 results for “Brightness”
SMAP L1B Brightness Temperatures Arctic
<p>This is a data set of polarised brightness temperatures (TBs) from the L-band (1.4 GHz) passive microwave sensor flying onboard the Soil Moisture Active Passive (SMAP) satellite. The data set was produced to enable a consistent combination of TBs from SMAP with those measured by the SMOS (Soil Moisture and Ocean Salinity) mission.</p> <p>It is based on the version 3 SMAP L1B brightness temperatures (Piepmeier et al., 2016), which are not corrected with respect to solar and cosmic radiation or atmospheric effects. The data are aggregated daily and gridded to a north polar EASE-grid 2.0 (Brodzik et al. 2012) with a grid size of 12.5 km. The data were produced within the framework of the EU Horizon2020 project SPICES and therefore only covers the period from the first available SMAP data to the end of the project (1 April 2015 to 31 May 2018).</p> <p>Within SPICES, SMAP and SMOS data were combined to a homogenized data set, which was then used to estimate sea ice thickness. For details see Schmitt and Kaleschke (2018) and the related data sets of SMOS TBs and SMOS/SMAP sea ice thickness.</p> <p>The files contain the following data fields:<br> <strong>Tbv</strong> - brightness temperatures at vertical polarisation<br> <strong>Tbh</strong> - brightness temperatures at horizontal polarisation<br> <strong>Tbv_std</strong> - weighted standard error of Tbv<br> <strong>Tbh_std</strong> - weighted standard error of Tbh<br> <strong>nmp</strong> - effective number of measurements used for averaging</p> <p>The grid coordinates are provided as a separate file <em>Latlon_e12.5.nc</em></p>
SMOS Brightness Temperatures at 40° incidence angle Arctic
<p>This is a data set of polarised brightness temperatures (TBs) at 40° incidence angle from the L-band (1.4 GHz) passive microwave sensor flying onboard the Soil Moisture and Ocean Salinity (SMOS) mission. The data set was produced to enable a consistent combination of TBs from SMOS with those measured by the SMAP (Soil Moisture Active Passive) satellite.</p> <p>It is based on the version v620 SMOS L1C brightness temperatures, which are not corrected with respect to solar and cosmic radiation or atmospheric effects. A fitting function is applied to the daily multi-angular SMOS data (see Zhao et al., 2015 and Schmitt and Kaleschke, 2018) to obtain brightness temperature values at the SMAP incidence angle of 40°. The data are gridded to a north polar EASE-grid 2.0 (Brodzik et al. 2012) with a grid size of 12.5 km. The data were produced within the framework of the EU Horizon2020 project SPICES and therefore only covers the period from the first available SMAP data to the end of the project (1 April 2015 to 31 May 2018).</p> <p>Within SPICES, SMOS and SMAP data were combined to a homogenized data set, which was then used to estimate sea ice thickness. For details see Schmitt and Kaleschke (2018) and the related data sets of SMAP TBs and SMOS/SMAP sea ice thickness.</p> <p>The files contain the following data fields:<br> <strong>Tbv</strong> - brightness temperatures at vertical polarisation at 40° incidence angle<br> <strong>Tbh</strong> - brightness temperatures at horizontal polarisation at 40° incidence angle<br> <strong>RMSE_v</strong> - root-mean-squared-error of fitting function for horizontal polarisation<br> <strong>RMSE_h</strong> - root-mean-squared-error of fitting function for horizontal polarisation<br> <strong>nmp</strong> - number of incidence angles used for the fit<br> <strong>dataloss</strong> - fraction of discarded data</p> <p>The grid coordinates are provided as a separate file <em>Latlon_e12.5.nc</em></p>
PEATCLSM(Tb): A land surface data assimilation product for peatlands using PEATCLSM and brightness temperature (Tb) satellite observations (Northern Hemisphere output)
<p>The datasets archived here include simulation results shown in the paper, “Improved Groundwater Table and L-band Brightness Temperature Estimates for Northern Hemisphere Peatlands Using New Model Physics and SMOS Observations in a Global Data Assimilation Framework”, published in Remote Sensing of Environment Journal (Bechtold et al., 2020). The output was produced by combining peatland-specific land surface modeling (Bechtold et al., 2019b) embedded in the NASA Catchment Land Surface Model (CLSM) with L-band brightness temperature (Tb) observations (SMOS), applying the data assimilation framework of the SMAP Level‐4 Soil Moisture product (Reichle et al., 2019). We provide netcdf files (9-km resolution EASEv2 grid, period Jan 2010 – Nov 2019, and between 45°N and 70°N, NE Asia excluded) of the four experiments of the manuscript: model-only (open-loop, OL) and data assimilation (DA) for each land model version, that is CLSM without and with the use of the PEATCLSM modules. The highest accuracy is provided by the DA product using PEATCLSM and Tb observations. When referring to the latter product use the name ‘PEATCLSM(Tb)’. We provide three types of netcdf files:<br> • daily_images_*.nc: Daily land states and fluxes (Table 1), provided as netCDF image-chunked image stack<br> • ObsFcstAna_images_*.nc: Brightness temperature observations, forecasts and analysis (Table 2), provided as netCDF image-chunked image stack<br> • incr_timeseries_*.nc: Data assimilation increments (Table 3), provided as netCDF timeseries-chunked image stack</p> <p>The file content is described in the file PEATCLSM_Tb_Documentation_20200505.pdf</p> <p>Please contact Michel Bechtold (michel.bechtold@kuleuven.be) for any questions.</p> <p>Data usage statement:<br> This work is licensed under a Creative Commons Attribution 4.0 International License: https://creativecommons.org/licenses/by/4.0/<br> If you decide to work with this data, we kindly ask to be informed at the outset of the nature of this work. If the data are essential to the work, or if an important result or conclusion depends on the PEATCLSM(Tb) data product, we would appreciate that you discuss these findings with us to ensure correct use and interpretation of the PEATCLSM(Tb) product. Furthermore, we are continuously improving the data assimilation product, a discussion of your work at an early stage may (i) help us to improve our product, and (ii) allow us to provide you with a newer version. Thanks!</p> <p>References:</p> <p>Bechtold, M., De Lannoy, G. J. M., Reichle, R. H., & Koster, R. D. (2019a). PEAT-CLSM simulation output (Northern Peatlands) version 1. https://doi.org/10.17605/OSF.IO/E58YM</p> <p>Bechtold, M. et al. (2019b). PEAT‐CLSM: A Specific Treatment of Peatland Hydrology in the NASA Catchment Land Surface Model. <em>Journal of Advances in Modeling Earth Systems</em>, <em>11</em>(7), 2130–2162. https://doi.org/10.1029/2018MS001574</p> <p>Bechtold, M., De Lannoy, G. J. M., Reichle, R. H., Roose, D., Balliston, N., Burdun, I., Devito, K., Kurbatova, J., Strack, M., & Zarov, E. A. (2020). Improved Groundwater Table and L-band Brightness Temperature Estimates for Northern Hemisphere Peatlands Using New Model Physics and SMOS Observations in a Global Data Assimilation Framework. <em>Remote Sensing of Environment</em>. https://doi.org/10.1016/j.rse.2020.111805</p> <p>Reichle, R. H., Liu, Q., Koster, R. D., Crow, W. T., De Lannoy, G. J. M., Kimball, J. S., Ardizzone, J. V., Bosch, D., Colliander, A., Cosh, M., Kolassa, J., Mahanama, S. P., Prueger, J., Starks, P., & Walker, J. P. (2019). Version 4 of the SMAP Level-4 Soil Moisture Algorithm and Data Product. <em>Journal of Advances in Modeling Earth Systems</em>, <em>11</em>(10), 3106–3130. https://doi.org/10.1029/2019MS001729</p>
Pre-trained models for segmentation and tracking of Coronal Bright Fronts from SDO AIA Base Difference images
<p>Here we present pretrained U-NET-based models followed by SDO AIA Base Difference(BD) validation set after intensity tresholding [-50;150] with predicted feature masks samples. <br>We provide a command-line Python utility for image segmentation using our CNNs designed to process images of solar eruptive phenomena. The https://gitlab.com/iahelio/helios_cnn repository includes regularly updated and newly published models. </p> <p>First model we present is designed to predict the likelihood of each pixel belonging to a certain class or feature in the solar image. A probabilistic output allows for a more nuanced interpretation of ambiguous region. The output can be converted into binary masks through thresholding. The range of values also gives insights into the model's confidence</p> <p>We also present sample segmentation results and the second model designed to produce binary masks.</p>
275 Candidates and 149 Validated Planets Orbiting Bright Stars in K2 Campaigns 0-10
<p>This dataset contains transit model posterior distributions and validation analyses for the 275 exoplanet candidates (in 233 systems) analyzed in Mayo et al. (2018), titled "275 Candidates and 149 Validated Planets Orbiting Bright Stars in K2 Campaigns 0-10".</p> <p>The dataset takes the form of 233 compressed directories each corresponding to an exoplanet system and titled after its EPIC ID. Within a given directory there are two numpy pickles named EPICXXXXXXXXX_chains.npy and EPICXXXXXXXXX_lnlikes.npy (where XXXXXXXXX is the 9 digit EPIC number) as well as n subdirectories, where n is the number of planet candidates in the system.</p> <p>The EPICXXXXXXXXX_chains.npy pickle is a representative sample of the posterior distribution of the transit model for a given exoplanet system. The pickle is a numpy array of size (j,k,l), where j is the number of walkers in the Markov chain Monte Carlo ensemble simulation that sampled the posterior distribution (note: we chose to fix j = 2*l), k is the number of walker steps reported in this dataset (the full posteriors were thinned down to between 750 and 10,000 steps), and l is the number of parameters in the transit model for the exoplanet system. The EPICXXXXXXXXX_lnlikes.npy pickle contains the associated ln(likelihood) values for each walker step in the previously described pickle. This pickle is a numpy array of size (j,k) where j and k are defined as above.</p> <p>The number of parameters will always be of the form 4 + 5*n, where n is again the number of planets in the systems. The first four parameters in the pickle are a baseline offset parameter for the normalized flux, a noise parameter to take the place of flux error bars, and two quadratic limb darkening parameters q<sub>1</sub> and q<sub>2</sub> based on Kipping et al. (2013). The next five parameters (and each subsequent set of five parameters in multi-candidate systems) refer to the reference epoch (a mid-transit time in BJD - 2454833), the period (in days), log<sub>10</sub>(R<sub>p</sub>/R<sub>*</sub>), the transit duration (T<sub>IV</sub>-T<sub>I</sub> in days), and the impact parameter. It should be noted that there is no consistent ordering of the planets in the posterior samples (for example, in a three planet system parameters 5-9 may refer to planet b, planet c, or planet d). Therefore, planetary periods should be used as reference to identify candidates. All parameters and the nature of the transit model are described in detail in Mayo et al. (2018).</p> <p>Each subdirectory contains the input and output of the validation analysis conducted via the VESPA validation package (Morton 2012, 2015). For additional details please refer to the relevant citations or the <a href="https://github.com/timothydmorton/VESPA">VESPA github repository</a>. Each subdirectory is named after the appropriate candidate listed in Mayo et al. (2018; specifically Tables 5 and 7).</p>
Microwave brightness temperature of lunar south polar region
<p>These are Brightness temperature data of lunar south polar region (≤-80°) obtained by CE-2 MRM</p>
Data products of the surface brightness modelling of the confirmed and candidates lenses in Borsato et al. 2023.
<p>This repository contains the data products for the surface brightness analysis of strongly lensed candidates presented in Borsato et al. 2023. This repository is organized as follows.</p><p>The folder `cutouts' contains the cutouts (20x20 arcsec) of all the HST snapshots.</p><p>The folder `SB_models' includes the A and B folders containing the confirmed lenses and candidate lenses (A and B classes in the paper). Each of these folders contains a set of subfolders including the image cutouts, surface brightness models, residuals, noise maps, and PSFs of the confirmed lenses. </p>
Extracted Source Properties Catalog for "Monitoring the X-ray Variability of Bright X-ray Sources in M33"
<p>Supplemental data to the article "Monitoring the X-ray Variability of Bright X-ray Sources in M33" accepted for publication in ApJ. Contains all extracted source properties for the 56-source final catalog, including single-ObsID extractions and merged values. See ReadMe for column descriptions and additional comments.</p>
Review of redshift values of bright AGNs with hard spectra in 4LAC catalog v3
<p>Review of redshift values of bright AGNs with hard spectra in 4LAC catalog</p> <p>FITS table and description file (pdf)</p>
Surface brightness temperatures measured by the HATPRO microwave radiometer onboard the RV Polarstern during the ATWAICE expedition PS144 to the Arctic in summer 2022
<p>The data set contains daily files of raw microwave radiation measurements by the HATPRO microwave radiometer (see Rose et al., 2015) onboard about 22 m height at the top deck (starboard) of RV Polarstern during cruise PS131 (ATWAICE expedition, see Kanzow, 2023). Via a mirror construction the radiometers were observing the surface at a viewing angle of about 53° off-nadir for 15 min each hour. The actual viewing angle could vary by a few degree because of ship motion. The data covers the range July 11, 2022 to August 11, 2022. The radiation measurements are given as brightness temperatures in seven K band channels (22.24 - 31.4 GHz), vertical polarization, and seven V band (51.26 - 58 GHz) channels, horizontal polarization. </p> <p>Version 2 of the uploaded data is quality-controlled (see the flag variable).</p>
Surface brightness temperatures measured by the MiRAC-P microwave radiometer onboard the RV Polarstern during the ATWAICE expedition PS144 to the Arctic in summer 2022
<p>The data set contains daily files of raw microwave radiation measurements by the MiRAC-P (or LHUMPRO-243-340) microwave radiometer (see Mech et al., 2019) onboard about 22 m height at the top deck (starboard) of RV Polarstern during cruise PS131 (ATWAICE expedition). Via a mirror construction the radiometers were observing the surface at a viewing angle of about 53° off-nadir for 15 min each hour. The actual viewing angle could vary by a few degree because of ship motion. The data covers the range July 11, 2022 to August 11, 2022. The radiation measurements are given as brightness temperatures in six double side band averaged G band (183.31 +/- 0.6 to 183.31 +/- 7.5 GHz), vertical polarization, and one higher frequency (243 GHz) channel, horizontal polarization. The 340 GHz channel was malfunctioning. </p> <p>Version 2 of the uploaded data is quality-controlled (see the flag variable).</p>
Brightness Temperature Variances from On-Planet Views in the A1–A3 Channels by the Mars Climate Sounder
<p>Heavens, Nicholas (2022), “Brightness Temperature Variances from On-Planet Views in the A1–A3 Channels by the Mars Climate Sounder”, Zenodo, V1, doi: 10.5281</p> <p>Title: Brightness Temperature Variances from On-Planet Views in the A1–A3 Channels by the Mars Climate Sounder</p> <p>Author: Nicholas G. Heavens, Space Science Institute, Boulder, CO, USA and London, UK (nheavens@spacescience.org)</p> <p>Date: 25 March 2022 </p> <p>Overview: This dataset contains an improvement and extension of significant data analysis products related to: </p> <p>Heavens, N.G., A. Pankine, J.M. Battalio, C. Wright, D.M. Kass, A. Kleinböhl, S. Piqueux, J.T. Schofield, 2022, Mars Climate Sounder Observations of Gravity-Wave Activity throughout Mars' Lower Atmosphere, Plan. Sci. J., 3, 57, doi: 10.3847/PSJ/ac51ce. </p> <p>These fall into three broad categories: diagnoses of detrended brightness temperature variance (GW) at 595–615 cm-1 (A1), 615–645 cm-1 (A2), and 635-665 cm-1 (A3) in individual views in the nadir or off-nadir by Mars Climate Sounder on board Mars Reconnaissance Orbiter; averages and other statistics of those diagnoses in space and time; and estimated gravity wave visibility functions for nadir, off-nadir, and nadir views with baselines like off-nadir views. This document presumes the manuscript is available to the dataset user.</p> <p>The purpose of archiving this dataset is to allow for comparison with a forthcoming analysis of gravity wave activity in limb observations by Mars Climate Sounder.</p> <p>The original dataset was published as:</p> <p>Heavens, Nicholas (2022), “Brightness Temperature Variances from On-Planet Views in the A1–A3 Channels by the Mars Climate Sounder”, Mendeley Data, V2, doi: 10.17632/5k6nybdy92.2</p> <p>The extension of the dataset consists of extension of the analysis time period to the end of January 2022 (MY 36, Ls=166.87).</p> <p>The improvement consists of a flag to indicate when an on-planet observations is likely to intersect a loop structure observed in the limb, and thus be contaminated by a high altitude cloud, which results in overestimate of gravity wave activity in the tropics at night during the clear season. Averages are now included that filter out flagged observations, as well as the original averages that include the flagged observations. </p> <p>If you are using this dataset and are feeling confused or wish there were some additional information from the article in this dataset, please contact me. A complete accounts of the contents and a restatement of this description is included as <em>MCS_OP_A13_GW_Analysis_Dataset_Documentation.pdf.</em></p> <p>Acknowledgments: The archiving of this dataset is supported by NASA’s Mars Data Analysis Program (80NSSC19K1215).</p>
Simulated microwave brightness temperatures based on two radiosoundings performed during the MOSAiC expedition
<p>This data set contains simulated brightness temperatures in the microwave spectrum for a summer case and a winter case based on radiosoundings performed during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition <strong>[1]</strong>. The simulations cover the frequencies 1-400 GHz and have been performed with the Passive and Active Microwave radiative TRAnsfer model (PAMTRA, <strong>[2]</strong>). Dimensions 'grid_x', 'grid_y', 'outlevel' can be truncated as they have the length 1. To get simulated brightness tempeartures (TBs) of a zenith-looking microwave radiometer, chose the last index of the dimension 'angles' (which is zenith angle 0°) and average over the 'passive_polarization' dimension. The data has been used to generate Fig. 1 of <strong>[3]</strong>.</p> <p> </p> <p><strong>[1]:</strong> Maturilli, M., Holdridge, D. J., Dahlke, S., Graeser, J., Sommerfeld, A., Jaiser, R., Deckelmann, H., Schulz, A.: Initial radiosonde data from 2019-10 to 2020-09 during project MOSAiC [dataset publication series]. Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, PANGAEA, https://doi.org/10.1594/PANGAEA.928656, 2021.</p> <p><strong>[2]:</strong> Mech, M., Maahn, M., Kneifel, S., Ori, D., Orlandi, E., Kollias, P., Schemann, V., and Crewell, S.: PAMTRA 1.0: the Passive and Active Microwave radiative TRAnsfer tool for simulating radiometer and radar measurements of the cloudy atmosphere, Geoscientific Model Development, 13, 4229–4251, https://doi.org/10.5194/gmd-13-4229-2020, 2020.</p> <p><strong>[3]:</strong> Walbröl, A., Griesche, H. J., Mech, M., Crewell, S., and Ebell, K.: Combining low- and high-frequency microwave radiometer measurements from the MOSAiC expedition for enhanced water vapour products, Atmospheric Measurement Techniques, 17, 6223-6245, https://doi.org/10.5194/amt-17-6223-2024, 2024.</p>
Walter Bright Jefferson (j0645)
<b>-- <a href="https://doi.org/10.5281/zenodo.11582199">Documentation</a> --</b><br><br><u>Name</u>: Walter Bright Jefferson<br><u>musiXplora-ID</u>: j0645<br><u>musiXplora-URI</u>: <a href="https://musixplora.de/mxp/j0645">https://musixplora.de/mxp/j0645</a><br><u>Gender</u>: m<br><u>First Mentioned</u>: 13 July 1912<br><u>Sectors</u>: Instrumentenbau<br><u>Professions (Historical)</u>: Patentinhaber<br><u>Other Places of Activity</u>: Philadelphia<br><br><br><br><u>Changelog</u>:<br> - v0.0.1: Initial Upload.<br>
Bright Southern Variable Stars in the bRing Survey
<p>The corresponding data and plots for the 353 variables in the comprehensive survey of bright stars from the bRing telescopes. The paper has been accepted to the Astrophysical Journal Supplemental Series (July 27, 2019). An arXiv pre-print article is now available.</p> <p>If these data are to be used in future works, we ask that a short list of the bRing team be included as co-authors. Please contact Samuel Mellon (smellon@ur.rochester.edu) or Matthew Kenworthy (kenworthy@strw.leidenuniv.nl) for details.</p> <p>Paper Abstract:</p> <p>Besides monitoring the bright star <em>β</em> Pic during the near transit event for its giant exoplanet, the <em>β</em> Pictoris b Ring (bRing) observatories at Siding Springs Observatory, Australia and Sutherland, South Africa have monitored the brightnesses of bright stars (<em>V</em> ≃ 4--8 mag) centered on the south celestial pole (<em>δ</em> ≤ -30∘) for approximately two years. Here we present a comprehensive study of the bRing time series photometry for bright southern stars monitored between 2017 June and 2019 January. Of the 16762 stars monitored by bRing, 353 of them were found to be variable. Of the variable stars, 80% had previously known variability and 20% were new variables. Each of the new variables was classified, including 3 new eclipsing binaries (HD 77669, HD 142049, HD 155781), 26 <em>δ</em> Scutis, 4 slowly pulsating B stars, and others. This survey also reclassified four stars based on their period of pulsation, light curve, spectral classification, and color-magnitude information. The survey data were searched for new examples of transiting circumsecondary disk systems, but no candidates were found.</p>
Bird plumage brightness scores and blood parasite prevalence values of North American passerine species
<p>Dataset with bird plumage brightness scores and blood parasite prevalence values for 114 North American passerine host species. One file contains the data table. One file contains a table with descriptions of the columns in the data table.</p> <p>Note: These data were reconstructed from files used in Read & Harvey 1989 (<a href="https://doi.org/10.1038/339618a0">https://doi.org/10.1038/339618a0</a>) with column headings inferred with the help of Read 1991 (<a href="https://doi.org/10.1086/285225">https://doi.org/10.1086/285225</a>).</p>
Satellite-based measurements of brightness temperatures (AMSR2 sensor) colocated to MOSAiC ground measurements
<p>The file contains measurements of brightness temperatures of satellite overpasses of the research vessel Polarstern during the MOSAiC expedition from October 26, 2019 - May 26, 2020 as well as co-located measurements of different parameters. For every overpass of Polarstern, the satellite measurement closest to the hourly position of Polarstern is taken.</p> <p>The satellite sensor is AMSR2 (six frequencies between 6.9 and 89 GHz and both polarizations) and we use the Level 1R (<em>Madea et al., 2016)</em> product available at JAXA <a href="https://gportal.jaxa.jp/gpr/">https://gportal.jaxa.jp/gpr/</a></p> <p>The co-located parameters are liquid water path, total water vapor, sea ice concentration, multi-year ice fraction, snow depth, snow-air interface temperature, snow-ice interface temperature, wind speed and sea surface temperature. In addition to the co-located parameters as ground truth, the dataset also contains their “uncertainties” given as temporal and/or spatial variability.</p> <p>Note: The dataset contains <strong>only</strong> satellite overpasses where co-located data is available.</p> <p>More information on the parameters are found below and they are described in more detail in <em>Rückert et al., 2023</em><em> </em>and the references given therein.</p> <ul> <li> <p><strong>scantime</strong>: time of satellite observation as included in the satellite data from JAXA</p> </li> <li> <p><strong>lon</strong>: longitude in decimal degrees (DD) of satellite observations as included in the satellite data from JAXA</p> </li> <li> <p><strong>lat</strong>: latitude in decimal degrees (DD) of satellite observations as included in the satellite data from JAXA</p> </li> <li> <p><strong>distance</strong>: distance to the hourly Polarstern position</p> </li> <li> <p><strong>TB6.9V, TB6.9H, TB10.7V, TB10.7H, TB18.7V, TB18.7H, TB23.8V, </strong><strong>T</strong><strong>B23.8H, TB36.5V, TB36.5H, TB89V, TB89H</strong>: Brightness temperatures (TB) measured by AMSRE2, the name includes the frequency in GHz and the polarization (either H for horizontal or V for vertical polarization), e.g, TB6.9V is the brightness temperature at 6.9 GHz and vertical polarization</p> </li> <li> <p><strong>LWP</strong>: liquid water path in kg/m² measured by a radiometer onboard the ship (<em>Walbröl et al., 2022</em>), averaged within +/- 10 minutes of the satellite observations</p> </li> <li> <p><strong>sigma_LWP</strong>: temporal variability of liquid water path (see previous point) given as standard deviation within +/- 10 minutes of the satellite observation time</p> </li> <li> <p><strong>TWV</strong>: total water vapor (integrated water vapor) in kg/m² measured by a radiometer onboard the ship (<em>Walbröl et al. (2022)</em>), averaged within +/- 10 minutes satellite observation time</p> </li> <li> <p><strong>sigma_TWV</strong>: temporal variability of total water vapor (see previous point) given as standard deviation within +/- 10 minutes of the satellite observation time</p> </li> <li> <p><strong>WSP</strong>: wind speed in m/s from the vessel’s meteorological observatory (<em>Schmithüsen et al., 2021</em>), averaged within +/- 10 minutes of the satellite observation time</p> </li> <li> <p><strong>sigma_WSP</strong>: temporal variability of wind speed (see previous point) given as standard deviation within +/- 10 minutes of the satellite observation time</p> </li> <li> <p><strong>SST</strong>: sea water temperature in K from the vessel’s meteorological observatory (<em>Schmithüsen et al., 2021</em>), averaged within +/- 10 minutes of the satellite observation time</p> </li> <li> <p><strong>sigma_SST</strong>: temporal variability of sea water temperature (see previous point) given as standard deviation within +/- 10 minutes of the satellite observation time</p> </li> <li> <p><strong>SND</strong>: snow depth in m obtained from the median of daily snow depth from available Snow and Ice Mass Balance Apparatus (SIMBA) buoys (<em>Lei et al., 2021</em><em>a</em>) in the proximity of Polarstern.</p> </li> <li> <p><strong>sigma_SND</strong>: spatial variability of snow depth (see previous point) given as standard deviation of all buoys available on that day.</p> </li> <li> <p><strong>Tsi:</strong> Snow-ice interface temperature in K obtained from the median of daily measurements from available Snow and Ice Mass Balance Apparatus (SIMBA) buoys (e.g. <em>Lei et al., 2021b</em>, for references of all buoys the reader is referred to the references given in <em>Rückert et al., 2023</em>) in the proximity of Polarstern.</p> </li> <li> <p><strong>sigma_Tsi:</strong> spatial variability of snow-ice interface temperature (see previous point) given as standard deviation of all buoys available on that day.</p> </li> <li> <p><strong>MYIF:</strong> multi-year ice fraction (from 0 to 1) based on classified TerraSAR-X scenes (<em>Guo et al., 2023</em>) in the proximity of Polarstern, interpolated to daily values.</p> </li> <li> <p><strong>sigma_MYIF:</strong> estimated (constant) uncertainty of multi-year ice fraction (see previous point).</p> </li> <li> <p><strong>SIC</strong>: sea ice concentration (from 0 to 1) based on classified TerraSAR-X scenes (<em>Guo et al., 2023</em>) in the proximity of Polarstern, interpolated to daily values.</p> </li> <li> <p><strong>sigma_SIC:</strong> estimated (constant) uncertainty of sea ice concentration (see previous point).</p> </li> <li> <p><strong>Tsa</strong>: Snow-air interface temperature in K based on infrared thermometer data (<em>Cox et al., 2023 a)-d)</em>) installed at four positions in the proximity of Polarstern, averaged within +/- 20 minutes of the satellite observation time.</p> </li> <li> <p><strong>sigma_Tsa:</strong> spatial variability of snow-ice interface temperature (see previous point), given as spatial (4 sites) and temporal (within +/- 20 minutes of the satellite observation time) standard deviation.</p> </li> </ul>
Dataset and Simulation Files for article "Bright and Vivid Diffractive-Plasmonic Reflective Filters for Color Generation"
<p>This work was supported by Ministério da Ciência Tecnologia, Inovações e Comunicações, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Brasil, Finance, Code 001, National Counsel of Technological and Scientific Development, and São Paulo Research Foundation (Fapesp) through grants 2018/15580-6, 2018/15577-5, 2016/18308-0, 2012/ 17610-3, and 2012/17765-7. Part of the results presented in this work were obtained through Project 4716-11, funded by Samsung Eletrônica da Amazônia Ltda., under the Brazilian Informatics Law 8.248/91. The authors thank the Center for Semiconductor Components and Nanotechnologies for the nanofabrication infrastructure.</p>
Data set on the main text of "A bright and fast source of coherent single photons"
<p>The data set that is presented in the main text is uploaded to the repository. Please note that all the data is scaled according to the axis on the paper, that means if the axis has a multiplication by 1e3 then the data is divided by 1e3.</p> <p>Each file is named after the corresponding subfigure.</p> <p>The preprint version of the article can be found in: https://arxiv.org/abs/2007.12654</p>
CytoNuke Dataset: Towards reliable whole-cell segmentation in bright-field histological images
<p>This is the dataset from the preprint "Cyto R-CNN and CytoNuke Dataset: Towards reliable whole-cell segmentation in bright-field histological images" by Raufeisen et al. (2024). It contains 6,683 annotations (3,991 nuclei and 2,607 whole cells) of head and neck squamous cell carcinoma cells in hematoxylin and eosin stained histological images. The annotations are in COCO format and distributed over 83 PNG images. Cyto R-CNN was trained on this dataset and compared with other state-of-the-art methods. The CytoNuke dataset is released under the CC BY 4.0 license.</p> <p>The histological images are from the CPTAC dataset:<br>National Cancer Institute Clinical Proteomic Tumor Analysis Consortium (CPTAC). (2018). The Clinical Proteomic Tumor Analysis Consortium Head and Neck Squamous Cell Carcinoma Collection (CPTAC-HNSCC) (Version 15) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2018.UW45NH81</p> <p>Funding: Behrus Puladi was funded by the Medical Faculty of RWTH Aachen University as part of the Clinician Scientist Program. We acknowledge FWF enFaced 2.0 [KLI 1044, https://enfaced2.ikim.nrw/] and KITE (Plattform für KI-Translation Essen) from the REACT-EU initiative [https://kite.ikim.nrw/, EFRE-0801977]. Fabian Hörst, Jianning Li, Jens Kleesiek and Jan Egger received funding from the Cancer Research Center Cologne Essen (CCCE).</p>
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.