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edi60/100

Long-term seasonally and annually aggregated climatic variables for the greater Phoenix, Arizona, USA, metropolitan area and the surrounding Sonoran desert, derived from single-day NASA Daymet images, 2000 to 2022

This data package consists of multiple decades of bioclimatic raster data across the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) study area within metropolitan Phoenix, Arizona, USA, temporally aggregated by year and by four meteorological seasons (winter, spring, summer, fall). We sourced each bioclimatic variable from 1-km resolution gridded estimates of daily climatic data from NASA Daymet V4, including daily mean (ppt) and total precipitation (ppt_sum), daily maximum air temperature (temp_max), daily minimum air temperature (temp_min), incident shortwave radiation flux density (srad), and daily average partial pressure of water vapor (vp). For each of these six variables, we created temporally aggregated raster images by calculating mean pixel-values of each for each season and year, as well as producing a seventh variable of seasonally and annually summed precipitation (ppt_sum). Finally, we exported images as individual GeoTIFF raster files, each with five bands corresponding values summarized annually (band 1) and seasonally (bands 2-5). All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season, can be found in the data package metadata (see 'Methods and Protocols') and accompanying Javascript code. ### citations - Gorelick N, Hancher M, Dixon M, et al. (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18–27. https://doi.org/10.1016/j.rse.2017.06.031

openCC0Feb 2025View details →

zenodo52/100

Reconstructed high-rate SEIS data recorded during HP3 hammering from the NASA InSight mission to Mars

The NASA InSight lander successfully placed a seismometer on the surface of Mars. Alongside, a hammering device was deployed that penetrated into the ground to attempt the first measurements of the planetary heat flow of Mars. The hammering of the heat probe generated repeated seismic signals that were registered by the seismometer. However, the broad frequency content of the seismic signals generated by the hammering extends beyond the Nyquist frequency governed by the seismometer's sampling rate of 100 samples per second. Here, we provide data that was reconstructed at a higher sampling rate of 2000 samples per second using a dedicated de-aliasing algorithm described in the accompanying article. This archive will be updated regularly with new data acquired on Mars.  For a detailed data description and instructions on how to cite this dataset, please refer to the README file.

opencc-by-4.0Aug 2020View details →

zenodo52/100

Spectral reflectance data of Mercury's surface collected by the Mercury Atmospheric and Surface Composition Spectrometer (MASCS) instrument during orbital observations of the NASA MESSENGER mission between 2011 and 2015 resampled to a [55399 × 396] tabular data format.

MASCS is a three sensor point spectrometer with a spectral coverage from 200 nm to 1450 nm. Single spectra are resamples in to steps to a format useful for our ML application : a datacube with ~400 spectral channel covering the whole surface of Mercury. The final dataset has dimension [N×M] where N is the number of grid cells (360 × 180 = 64, 800) and M is the number of spectral features (396). Due to the incomplete coverage and data filtering, some grid cells are empty. After removing these empty cells, the size of the dataset is [55399 × 396]. This specific product is stored as a gzip compressed json, where each element is a grid cell. We are in the process to publish a complete pipeline to produce this product from RAW data on https://github.com/epn-ml/MESSENGER-Mercury-Surface-Cassification-Unsupervised_DLR/ . Spectral reflectance data of Mercury’s surface collected by the Mercury Atmospheric and Surface Composition Spectrometer (MASCS) instrument during orbital observations of the NASA MESSENGER mission between 2011 and 2015. MASCS is a three sensor point spectrometer with a spectral coverage from 200 nm to 1450 nm. Single spectra are resamples in to steps to a format useful for our ML application : a datacube with ~400 spectral channel covering the whole surface of Mercury. The final dataset has dimension [N×M] where N is the number of grid cells (360 × 180 = 64, 800) and M is the number of spectral features (396). Due to the incomplete coverage and data filtering, some grid cells are empty. After removing these empty cells, the size of the dataset is [55399 × 396]. 0. Pre-filtering We used the most recent dataset that had large-scale photometric corrections and thus was almost free from observation geometry effects. However, extreme geometry are still present and are typically associated with high noise and some residual instrumental effects. Based on our empirical tests, we filtered out observations with an emission/incidence angle ≥80∘. We also calculated the median value per wavelength and per cell grid when constructing the global hyperspectral data cube and filtered out observations falling under the 2nd percentile and above 99.9th percentile to clean some residual geometry effects. With this approach we create an effective noise filter while retaining enough observations to be able to analyse the entirety of the surface of the planet. 1. Spectral resmpling Unprocessed MASCS spectra could have 512 or 256 channes, depending on binning. We resampled the data in the spectral dimension to a common wavelength range from 260 nm to 1052 nm with a  4 nm resolution (2 nm spectral sampling), resulting in 396 spectral channels. This approach slightly oversamples the original 4.77 nm spectral resolution and removes some points from the original 200-1050 nm range. The resulting data matrix is expressed in tabular form, with each row representing a single grid cell or pixel on the surface. The elements of each row are the spectral reflectance values from the VIS instrument at 396 (resampled) wavelengths. 2. Spatial resmpling The whole dataset of &sim; 5 million spectra is resampled to a planet-wide rectangular grid of 1×1deg in the latitudinal band between ± 80. The cell longitudinal size varies between &sim; 40 km at the equator to a minimum of &sim; 10 km at ±80∘. Thus, the area spanned by each grid cell depends on the latitude. However, the same is true for the acquisition process, where higher spatial resolution is reached near the equator and lower resolution at the poles.

opencc-by-4.0Dec 2022View details →

zenodo44/100

Impact of urban and shipping emissions on NASA-Unified Weather Research and Forecasting model results

This dataset supports Huang et al. (2019, JGR-Atmospheres): "Impact of aerosols from urban and shipping emission sources on terrestrial carbon uptake and evapotranspiration: a case study in East Asia". The file named "NUWRFout.tar.gz" contains NUWRF base and sensitivity simulation results on 31 May 2016. The file named "LIS_soil_LAI.zip" contains model grid information, soil conditions and leaf area index (LAI) at NUWRF initialization times in late May 2016.

opencc-by-4.0Dec 2019View details →

zenodo44/100

HiRISE DTMs generated using NASA's Ames Stereo Pipeline

In an effort to better understand the surface roughness of Martian lava flows, we generated over 30 HiRISE DTMs using ISIS3 and ASP and extracted their roughness (Rodriguez Sanchez-Vahamonde and Neish, 2020). We have posted these DTMs for public use here.  HiRISE stereo images typically have a spatial sampling of 25 - 50 centimeters, providing us with DTMs of 1 - 2 meters per pixel. We also converted the HiRISE stereo-pair ID for each product into its proper DTM ID using the NASA Planetary Data System product naming convention for HiRISE DTMs  (<a href="https://www.uahirise.org/dtm/about.php">https://www.uahirise.org/dtm/about.php</a>; last accessed 18.09.2019).

opencc-by-4.0Mar 2020View details →

zenodo44/100

Sensor Response Files for the Relativistic Proton Spectrometer aboard NASA's Van Allen Probes

This data set provides the NASA Van Allen Probes Relativistic Proton Spectrometer (RPS) sensor response function files. These files provide the sensor’s response to protons and electrons as a function of energy and angle of incidence.

opencc-by-4.0Oct 2021View details →

zenodo44/100

NASA's Airborne Topographic Mapper (ATM) ground calibration data for the Arctic Spring campaign 2019

opencc-by-4.0Feb 2022View details →

zenodo44/100

Water and chlorine in the Martian subsurface along the traverse of NASA's Curiosity rover: DAN measurement profiles along the traverse

This dataset contains a water map and a table of water and chlorine content in shallow Martian subsurface derived from the DAN instrument data from a landing site up to MSL sol 3333. DAN is a neutron spectrometer onboard the NASA’s Curiosity rover (see Mitrofanov, I. G., et al., (2012). Dynamic Albedo of Neutrons (DAN) experiment onboard NASA’s Mars Science Laboratory. Space Science Reviews, 170(1–4), 559–582. <a href="https://doi.org/10.1007/s11214-012-9924-y">https://doi.org/10.1007/s11214-012-9924-y</a>). The DAN instrument consists of two separate units: the DAN DE is the detector and electronics block, the DAN PNG is pulsed neutron generator. The DAN DE contains two proportional counters filled with 3He gas for recording thermal and epithermal neutrons up to energy of 100 eV (CTN detector) and epithermal neutrons from 0.4 eV up to 100 eV (CETN detector). DAN provides two types of measurements: active and passive. When DAN DE operates in the passive mode, its detectors record the local neutron background. In the active mode the DAN PNG unit generates short pulses of 14 MeV neutrons, and DAN DE record additional counts of post-pulse emission of moderated neutrons after their interactions with nuclei of shallow subsurface. The results of active and passive DAN measurements along the traverse are assigned to two independent types of pixels: Pixel with Active Data (PAD) and Pixels of Passive Data (PPD). The content of water reported as Water Equivalent Hydrogen (WEH) for both kinds of pixel. The content of absorption equivalent chlorine (AEC) reported only for PAD. Statistical errors are given in each pixel. Method of active data analysis is described in Lisov, D. I., et al., (2018). Data Processing Results for the Active Neutron Measurements by the DAN Instrument on the Curiosity Mars Rover. Astronomy Letters, 44(7), 482–489. <a href="https://doi.org/10.1134/S1063773718070034">https://doi.org/10.1134/S1063773718070034</a>. The "Method of Referencing by Active Data" (MRAD) to analyze passive data was described in Nikiforov, S. Y., et al., (2020). Assessment of water content in Martian subsurface along the traverse of the Curiosity rover based on passive measurements of the DAN instrument. Icarus, 346, 113818. <a href="https://doi.org/10.1016/j.icarus.2020.113818">https://doi.org/10.1016/j.icarus.2020.113818</a>. PPD pixels are presented as squares in the water map, PAD are presented as circles. Table of water and chlorine contains seven values for each pixel: (1) is the successive number of pixel, (2) is a mark of its type, either PAD or PPD, (3) is longitude and (4) is latitude coordinates of the center of the pixel, (5) is the associated member of the MSL stratigraphic column, and (6) is estimated WEH values (wt.%) in PAD or PPD and (7) is estimated AEC value in PAD (wt.%).

opencc-by-4.0Aug 2022View details →

zenodo44/100

Model codes and simulation data for "Modeling demographic-driven vegetation dynamics and ecosystem biogeochemical cycling in NASA GISS's Earth system model (ModelE-BiomeE v.1.0)"

ModelE-BiomeE v1.0 model codes and data This folder contains the simulation data and model codes that were used in the paper ‘Modeling demographic-driven vegetation dynamics and ecosystem biogeochemical cycling in NASA GISS’s Earth system model (ModelE-BiomeE v.1.0)’ (https://doi.org/10.5194/gmd-2022-72). We included the data simulated by ModelE-BiomeE v.1.0 with settings of full demography (folder FullDemography) and single cohort (folder SingleCohort), and initial settings of land grids and vegetation data (folder GlobalVegetation). The codes include the full ModelE 2.1, module BiomeE files in ModelE, and the standalone BiomeE. In the folder FullDemography, we have 4 netcdf files for global output and 25 files for single grids output. The files ‘FullDM_2588_JAN.nc’ and ‘FullDM_2588_JUL.nc’ are the original model output of January and July in the year 2588. The file ‘FullDM_2588_Annual.nc’ is the yearly summary of model simulations. The file ‘FullDM_Selected.nc’ is an annual summary of 588 years of model simulation only with selected variables. The csv files are for single grids output at the time steps of daily and yearly. The last digit 1~8 represents the sites of 'BNC','MNT','HF','OKR','KZ','SV','WGK','TPJ', respectively (Table 1). Table 1 Site ID and file number ['BNC',  'MNT',   'HF',  'OKR',  'KZ',   'SV',   'WGK',  'TPJ'] ['8991', '8992', '8993', '8994', '8995', '8996', '8997', '8998'] ['8971', '8972', '8973', '8974', '8975', '8976', '8977', '8978'] ['8961', '8962', '8963', '8974', '8965', '8966', '8977', '8968'] Please refer to Table 2 in the paper for the detail of these 8 sites. ‘DailyLAIGPP.csv’ is a summary of all ‘DailyEcosystem’ files with LAI and GPP data. We included the Python scripts that can be used to generate the figures in out paper (Plotting-BiomeE-MsTMIP.py, Plotting-Scatter-Comparison.py, PlottingBiomeEMaps.py, and PlottingGridOutput.py). For the convenience of readers (in reproducing our figures), we included the summary of reanalysis of the data from observations and MsTMIP in folder ‘Sum-Obs-Simu’. Please refer to the original sources listed in our paper for the detail of these data.

opencc-by-4.0Sep 2022View details →

zenodo44/100

NASA's Airborne Topographic Mapper (ATM) ground calibration data for waveform data products

The Airborne Topographic Mapper (ATM) was a scanning lidar developed and used by NASA for observing the Earth’s topography for several scientific applications, foremost of which was the measurement of changing Arctic and Antarctic ice sheets, glaciers and sea ice. ATM measured topography to an accuracy of better than 5 centimeters by incorporating measurements from GPS (global positioning system) receivers and inertial navigation system (INS) attitude sensors. The purpose of this data set is to enable users working with NASA’s ATM airborne lidar waveform data to estimate their own range calibration if using range tracking methods different from the centroid estimate included in the ATM data files (Studinger et al., 2022;  <a href="https://doi.org/10.5194/tc-16-3649-2022">https://doi.org/10.5194/tc-16-3649-2022</a>). The airborne data are freely available from the National Snow and Ice Data Center (<a href="https://nsidc.org/home">NSIDC</a>) at the links listed in the table below.  In pressurized aircraft the transmitted laser pulse travels through the aircraft’s optical window close to the scan mirror. Backscatter from both the scan mirror and the aircraft’s optical window in the fuselage are close in time to the transmitted laser pulse and partially overlap with the transmit waveform recorded by the ATM’s optical receiver. To record a “clean” transmit waveform the transmit pulse is sampled from behind a translucent beam splitter and subsequently injected into a multimode fiber-optic cable to provide a fixed optical delay that results in temporal separation between the recorded transmit pulse and contamination from backscattered photons from the scan mirror and the aircraft’s optical window. The delay due to the optical fiber and other system components introduce a laser time-of-flight range bias. The signal strength can also affect the calculated range in a way that depends on the waveform tracking algorithm. This variable influence, known as range walk, and the bias are determined from ground calibration measurements in which ATM data is collected from a stationary target at known range (true range) while varying the return intensity from signal extinction to detector saturation. The resulting signal-dependent deviation of measured range from the true range combine the system delay and range walk correction and are subtracted from the uncalibrated ranges to yield the calibrated range estimates "/laser/calrng" in the airborne waveform files (Studinger et al., 2022; Appendix B3; <a href="https://doi.org/10.5194/tc-16-3649-2022">https://doi.org/10.5194/tc-16-3649-2022</a>). This data set includes ATM ground test waveform data from the wide and narrow scanner, the true ranges, as well as the range calibration tables (caltables) used for processing the airborne data products. A MATLAB® function to read the ground test waveform data is available at: <a href="https://doi.org/10.5281/zenodo.6248436">https://doi.org/10.5281/zenodo.6248436</a>   <table> <tbody> <tr> <td> ATM Data Product ID at NSIDC </td> <td> Temporal Coverage </td> </tr> <tr> <td> <a href="https://nsidc.org/data/ilatmw1b">https://nsidc.org/data/ilatmw1b</a> </td> <td> 17 July 2017 - 20 November 2019 </td> </tr> <tr> <td> <a href="https://nsidc.org/data/ilnsaw1b">https://nsidc.org/data/ilnsaw1b</a> </td> <td> 29 October 2017 - 20 November 2019 </td> </tr> </tbody> </table> The file name of the groundtest calibration table that was used to process the airborne data is stored in the field "/ancillary_data/documentation/header_text" of each airborne data file. The corresponding groundtest waveform file has the same time tag and data set identifier as the calibration table file. E.g., the corresponding ground test waveform file for the calibration table “caltable_20170628_193814.atm6AT5.binned_data.txt” is “ILATMW1B_20170628_193814.atm6AT5.h5”. The table below lists the ground test waveform files that should be used for each campaign and instrument: <table> <tbody> <tr> <td> Year </td> <td> Campaign </td> <td> Data Set </td> <td> Groundtest Waveform Data </td> </tr> <tr> <td> 2017 </td> <td> 17-JUL-2017  25-JUL-2017 </td> <td> ILATMW1B </td> <td> ILATMW1B_20170628_193814.atm6AT5.h5 </td> </tr> <tr> <td> </td> <td> 29-OCT-2017  25-NOV-2017 </td> <td> ILATMW1B </td> <td> ILATMW1B_20171017_141954.atm6AT6.h5 </td> </tr> <tr> <td> </td> <td> 29-OCT-2017  25-NOV-2017 </td> <td> ILNSAW1B </td> <td> ILNSAW1B_20171208_122808.atm6BT7.h5 </td> </tr> <tr> <td> 2018 </td> <td> 22-MAR-2018  01-MAY-2018 </td> <td> ILATMW1B </td> <td> ILATMW1B_20180309_110944.atm6AT6.h5 </td> </tr> <tr> <td> </td> <td> 10-OCT-2018  16-NOV-2018 </td> <td> ILATMW1B </td> <td> ILATMW1B_20181002_151220.atm6AT6.h5 </td> </tr> <tr> <td> </td> <td> 22-MAR-2018  01-MAY-2018 </td> <td> ILNSAW1B </td> <td> ILNSAW1B_20180301_115659.atm6DT7.h5 </td> </tr> <tr> <td> </td> <td> 10-OCT-2018  16-NOV-2018 </td> <td> ILNSAW1B </td> <td> ILNSAW1B_20181002_160127.atm6DT7.h5 </td> </tr> <tr> <td> 2019 </td> <td> 03-APR-2019  16-MAY-2019 </td> <td> ILATMW1B </td> <td> ILATMW1B_20190321_102239.atm6AT6.h5 </td> </tr> <tr> <td> </td> <td> 03-SEP-2019  16-SEP-2019 </td> <td> ILATMW1B </td> <td> ILATMW1B_20190817_132201.atm6AT6.h5 </td> </tr> <tr> <td> </td> <td> 23-OCT-2019  20-NOV-2019 </td> <td> ILATMW1B </td> <td> ILATMW1B_20191017_121209.atm6AT6.h5 </td> </tr> <tr> <td> </td> <td> 03-APR-2019  16-MAY-2019 </td> <td> ILNSAW1B </td> <td> ILNSAW1B_20190327_105730.atm6DT7.h5 </td> </tr> <tr> <td> </td> <td> 03-SEP-2019  16-SEP-2019 </td> <td> ILNSAW1B </td> <td> ILNSAW1B_20190817_131026.atm6DT7.h5 </td> </tr> <tr> <td> </td> <td> 23-OCT-2019  20-NOV-2019 </td> <td> ILNSAW1B </td> <td> ILNSAW1B_20191017_122059.atm6DT7.h5 </td> </tr> </tbody> </table>   See also:  User guide for NASA's Airborne Topographic Mapper HDF5 Waveform Data: Products:<a href="https://doi.org/10.5281/zenodo.7246097"> https://doi.org/10.5281/zenodo.7246097</a> Collection of MATLAB® functions for working with ATM (Airborne Topographic Mapper, laser altimetry data products in HDF5 waveform format: <a href="https://github.com/mstudinger/ATM-waveform-tools">https://github.com/mstudinger/ATM-waveform-tools</a> Airborne Topographic Mapper (ATM) Bathymetry Toolkit (MATLAB® functions): <a href="https://doi.org/10.5281/zenodo.6341229">https://doi.org/10.5281/zenodo.6341229</a>

opencc-by-4.0Oct 2022View details →

zenodo44/100

NASA Eulerian Snow On Sea Ice Model Version 1.1 (NESOSIMv1.1) data: 1980 - 2024

Repository updates Update on Sep 12th 2024: The repository now includes NESOSIM v1.1 output from September 1st 2022 to April 30th 2023 and September 1st 2023 to April 30th 2024  Update on Sep 5th 2022: The repository now includes NESOSIM v1.1 output from September 1st 2021 to April 30th 2022 Update on June 7th 2022: The repository now includes NESOSIM v1.1 output from September 1st 2021 to March 31st 2022 Update on March 8th 2021: The gridded forcing files are now available in the gridded_forcings.zip file. Data are stored as Python pickles which can be easily read in by the core NESOSIM source code.  Update on March 8th 2021: The repository now includes zip files of gridded forcing (snowfall, winds, ice drift, ice concentration, initial conditions) as well as gridded Operation IceBridge snow depths.  Update on January 30th 2021: The repository now also includes a NESOSIM v1.1. daily gridded snow climatology using the mean (np.nanmean) of all data available between September 1 2010 and April 30 2020. Overview NESOSIM is a three-dimensional, two-layer (vertical), Eulerian snow on sea ice budget model developed with the primary aim of producing daily estimates of the depth and density of snow on sea ice across the polar oceans through the winter accumulation season, generally September through April (Petty et al., 2018). This repository contains model output from September 1st 1980 to April 30th 2021 [and September 1st 2021 to March 31st 2022 as of June 7th 2022] based on the NESOSIM v1.1 code release which is available on GitHub (https://github.com/akpetty/NESOSIM/tree/v1.1) and archived through Zenodo (10.5281/zenodo.4448355). More information about changes between the v1.0 and v1.1 model framework can be found in those links. A preprint is now available in The Cryosphere Discuss explaining these upgrades and their impacts on ICESat-2 winter Arctic sea ice thickness estimates (Petty et al., 2022).  Data production: Data are re-initialized at the end of summer each year (September 1st) using summer near-surface air temperature-scaled initial snow depths and run through until the end of April of the following year. The 1987-1988 winter is missing due to the lack of passive-microwave derived ice concentration data available during this period. Daily data are generated on a 100 km x 100 km North Polar Stereographic grid (EPSG: 3413) across the entire Arctic Ocean including the peripheral seas. Forcings: NB: Recent year runs often require the use of near-real-time data products, so the underlying forcings used in this v1.1 release can change in time, as noted below: <ul> <li>Snowfall: European Center for Medium Range Weather Forecasts (ECMWF) ERA5 (https://cds. climate.copernicus.eu, September 1 1980 onwards2) + CloudSat scaling (Cabaj et al., 2020).</li> <li>Near-surface winds: ECMWF ERA5 (https://cds. climate.copernicus.eu, September 1 1980 onwards).</li> <li>Sea ice drift: NSIDC Polar Pathfinder v4 (https://nsidc.org/data/nsidc-0116, September 1 1980 to April 30 2019), OSI SAF merged (https://osi-saf.eumetsat.int/products/osi-405-c, September 1 2019 onwards).</li> <li>Sea ice concentration: Final v3 NSIDC Climate Data Record (https://nsidc.org/data/g02202/versions/3/, September 1 1980 to December 31 2020), and near-real-time v2 NSIDC Climate Data Record (https://nsidc.org/data/g10016, January 1 2021 onwards).</li> <li>Near-surface air temperature (to derive temperature-scaled initial conditions): ECMWF ERA5 (https://cds. climate.copernicus.eu, September 1 1980 onwards).</li> </ul> The forcings used to generate each winter dataset are described in a new 'forcings' variable in each NetCDF file.  Operation IceBridge snow depths: The repository now also includes the gridded Operation IceBridge snow depths we used for calibration purposes, as described in Petty et al., (2022). The data contained within gridded_oib_snowdepths.zip includes the daily gridded data on the NESOSIM v1.1 100 km domain, ordered by day of collection. Data are stored as Python pickles and text files and include estimates derived from the following snow depth algorithms: SRLD (2009-2015): snow radar layer detection, JPL (2009-2015): Jet Propulsion Laboratory, GSFC (2009-2015): Goddard Space Flight Center, NSIDC (2009-2012): archived NASA GSFC data on the NSIDC, QL (2013-2019): NSIDC quick-look data based on the GSFC algorithm. MEDIAN (2010-2015): consensus snow depth from median of GSFC, JPL and SRLD.  References: Cabaj, A., P. J. Kushner, C. G. Fletcher, S. Howell, A. Petty (2020), Constraining reanalysis snowfall over the Arctic Ocean using CloudSat observations, Geophysical Research Letters, 47, doi:10.1029/2019GL086426. Petty, A. A., M. Webster, L. N. Boisvert, T. Markus (2018), The NASA Eulerian Snow on Sea Ice Model (NESOSIM) v1.0: Initial model development and analysis, Geosci. Model Dev., doi: 10.5194/gmd-11-4577-2018. Petty A. A., N. Keeney, A. Cabaj, P. Kushner, M. Bagnardi (2023), Winter Arctic sea ice thickness from ICESat-2: upgrades to freeboard and snow loading estimates and an assessment of the first three winters of data collection, The Cryosphere, 17, 127–156, doi: 10.5194/tc-17-127-2023.

opencc-by-4.0Sep 2024View details →

zenodo44/100

sohamphanseiitb/BIG_Data_5MSEC: BIG Data Analysis of NASA's 5 Millennium Solar Eclipse Database

Solar eclipses are a topic of interest among astronomers, astrologers and the general public as well. There were and will be about 11898 eclipses in the 5 millennia from 2000 BC to 3000 AD. Data visualization and regression techniques offer a deep insight into how various parameters of a solar eclipse are related to each other. Physical models can be verified and can be updated based on the insights gained from the analysis. The study covers the major aspects of data analysis including data cleaning, pre-processing, EDA, distribution fitting, regression and machine learning based data analytics. We provide a cleaned and usable database ready for EDA and statistical analysis.

openother-openDec 2021View details →

zenodo44/100

NASA GLOBE Cloud GAZE Test Dataset

NASA GLOBE Community science project Leveraging Online and User Data through GLOBE And Zooniverse Engagement, or CLOUD GAZE is a NASA funded pilot project aimed to help NASA better understand the effect clouds are having on Earth’s climate. The CLOUD GAZE project is a collaboration between two giants of citizen science:<a href="https://www.globe.gov/"> The GLOBE Program</a> and the<a href="https://www.zooniverse.org/"> Zooniverse</a> online platform and is funded through NASA’s Citizen Science for Earth Systems Program. The CLOUD GAZE citizen science project characterizes cloud properties from sky photographs sent in through GLOBE Clouds ground observations. The GLOBE Clouds/CLOUD GAZE team at NASA Langley Research Center extracts cloud properties from sky photographs submitted to the GLOBE Program using the Zooniverse online platform. The team produces datasets from three sources: ground-cloud observations from The GLOBE Program collocated with NASA/NOAA satellite data and the CLOUD GAZE cloud cover and cloud type characterizations. The datasets are for cloud type worldwide investigations and serve as training sets for machine learning. This data is provided as CSV files.   <a href="https://www.globe.gov/documents/16792331/0/Summary+Data+Variables+CLOUD+GAZE_2.0.docx/388b8c8f-e869-148f-31c2-78f2d005f38d?t=1654531372682">NASA GLOBE CLOUD GAZE Data Description</a> The data obtained from the Zooniverse, NASA Langley Research Center (NASA LaRC), and The GLOBE Program are free of charge for use in research, publications, and commercial applications. When data from The Zooniverse, The GLOBE Program, and NASA LaRC are used in a publication, we request this acknowledgment be included, "These data were obtained from the Zooniverse online platform, the GLOBE Program and NASA Langley Research Center." Please include such statements, either where the use of the data or other resource is described, or within the Acknowledgements section of the publication.

opencc-by-4.0Jun 2022View details →

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Climate Forced Hydropower Simulations Using NASA NEX-GDDP

* This update includes the corrected values for all Peruvian Hydropower Plants included in the original dataset.  This dataset includes the results of simulations of future hydropower usable capacity for power plants in Brazil, Colombia, and Peru. These simulations have been forced using NASA's Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset, which includes maximum temperature, minimum temperature, and precipitation simulations from 21 Global Climate Models (GCM) and three scenarios. The scenarios include a retrospective run (1950-2005) and two projection runs for Representative Concentration Pathways (RCP) 4.5 and 8.5. There is a folder for each country that includes a power plant characteristics file, with a list of all the power plants and the characteristics used for the analysis (installed capacity, effective height, reservoir specifications, etc.). Additionally, there is a folder including the dates for the usable capacity files. Each file inside the usable capacity folder is labeled "power_", followed by the power plant name (e.g. "tres_irmaos"), and the scenario (e.g. "rcp45_2006_2099").  This work is based on the future publication: Caceres, A.L., Jaramillo, P., Matthews, H.S., Samaras, C. & Nijssen B.  "Hydropower under climate uncertainty: characterizing the usable capacity of Brazilian, Colombian and Peruvian power plants under climate scenarios".

opencc-by-4.0Oct 2020View details →

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NASA Pandora PSF Models

These fits files contain the expected PSF of the Pandora mission as a function of location on the detector, wavelength, and temperature. These are required inputs to build simulations of the Pandora dataset using the pandora-sat open source software package. These files were built by Lawrence Livermore National Labs.   These can be read by pandora-sat v0.9.0 https://github.com/PandoraMission/pandora-sat/tree/main

opencc-by-4.0Jan 2023View details →

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Data for figures in Kemp, E M, J W Wegiel, S V Kumar, J V Geiger, D M Mocko, J P Jacob, and C D Peters-Lidard, 2021: A NASA-Air Force precipitation analysis for near-real-time operations. Submitted to _J Hydrometeor_

Tar files containing gridded metrics, domain-wide metric means and confidence intervals, and rain-gauge reports used to generate figures in Kemp et al (2021). Citation:   Kemp, E M, J W Wegiel, S V Kumar, J V Geiger, D M Mocko, J P Jacob, and C D Peters-Lidard, 2021: A NASA-Air Force precipitation analysis for near-real-time operations. Submitted to _J Hydrometeor_.

opencc-by-4.0Nov 2021View details →

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NASA's Voyager 1 Refined Magnetic Field Data (1-10 au)

This dataset contains NASA's Voyager 1 spacecraft in situ measurements of the solar wind for the Cartesian RTN magnetic field (nT), heliocentric distance (au), proton density (cm^-3), and bulk flow speed (km/s), with time resolution 1.92 seconds. The original source for this refined dataset can be found via the Goddard Space Flight Center Space Physics Data Facility (SPDF), for 2 second MAG data and hourly averaged plasma data. The range of heliocentric distance covered in this dataset is from 1-10 au and each file contains a time series of magnetic field measurements over two days. Each file is labeled with the start and end day covered by the magnetic field time series. Information regarding heliocentric distance, proton density, and bulk flow speed in each file are represented by averages over the corresponding two-day interval. These averages were taken from the hourly averaged data, provided by SPDF, over the appropriate time covered by the magnetic field time series. The CDF files have global attributes: 'CREATOR', 'AFFILIATION', 'S/C NAME', and 'DATE CREATED'. Variable keys are 'SSEPOCH' (time), 'BR' (radial magnetic field), 'BT' (tangential magnetic field), 'BN' (normal magnetic field), 'R' (heliocentric distance), 'N' (proton density), and 'VSW' (bulk flow speed), with variable attribute 'UNITS'. The 'SSEPOCH' variable are timed measurements in units of seconds since Epoch (January 1, 1970). The CDF files were generated in Python using the spacepy.pycdf library. For more specifics regarding the magnetic field cleaning methods utilized to produce this dataset, please refer to the Master's Thesis "Voyager 1: Cleaning Magnetic Field Data for Turbulence Studies" and contact the author for any questions related to the thesis or access. Please send any questions/concerns to Manuel Enrique Cuesta at mecuesta@udel.edu for any problems related to downloading/accessing the data from the CDF files.

opencc-by-4.0Nov 2021View details →

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Model simulations of NASA GISS ModelE-BiomeE v.1.0

This dataset contains the data that were used in the manuscript ‘Modeling demographic-driven vegetation dynamics and ecosystem biogeochemical cycling in NASA GISS’s Earth system model (ModelE-BiomeE v.1.0)’ (in review by GMDD, https://doi.org/10.5194/gmd-2022-72). We included the data simulated by ModelE-BiomeE v.1.0 with settings of full demography (folder FullDemography) and single cohort (folder SingleCohort), and initial settings of land grids and vegetation data (folder GlobalVegetation). In the folder FullDemography, we have 4 netcdf files for global output and 25 files for single grids output. The files ‘FullDM_2588_JAN.nc’ and ‘FullDM_2588_JUL.nc’ are the original model output of January and July in the year 2588. The file ‘FullDM_2588_Annual.nc’ is the yearly summary of model simulations. The file ‘FullDM_Selected.nc’ is an annual summary of 588 years of model simulation only with selected variables. The csv files are for single grids output at the time steps of daily and yearly. The last digit 1~8 represents the sites of 'BNC','MNT','HF','OKR','KZ','SV','WGK','TPJ', respectively (Table 1). Table 1 Site ID and file number ['BNC',  'MNT',   'HF',  'OKR',  'KZ',   'SV',   'WGK',  'TPJ'] ['8991', '8992', '8993', '8994', '8995', '8996', '8997', '8998'] ['8971', '8972', '8973', '8974', '8975', '8976', '8977', '8978'] ['8961', '8962', '8963', '8974', '8965', '8966', '8977', '8968'] Please refer to Table 2 in the paper for the detail of these 8 sites. ‘DailyLAIGPP.csv’ is a summary of all ‘DailyEcosystem’ files with LAI and GPP data. We included the Python scripts that can be used to generate the figures in out paper (Plotting-BiomeE-MsTMIP.py, Plotting-Scatter-Comparison.py, PlottingBiomeEMaps.py, and PlottingGridOutput.py). For the convenience of readers (in reproducing our figures), we included the summary of reanalysis of the data from observations and MsTMIP in folder ‘Sum-Obs-Simu’. Please refer to the original sources listed in our paper for the detail of these data.

opencc-by-4.0Apr 2022View details →

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Archive of NASA-Unified WRF model daily forecasting simulations for DOE TRACER IOP

<pre># Copyright 2022 NASA GSFC All rights reserved. # Creative commons attribution 4.0 international license NASA-Unified WRF model daily simulations for DOE TRACER IOP Document updated: 22 June 2022 Point of contact: Takamichi Iguchi (ESSIC UMD, Code612 NASA GSFC), takamichi.iguchi@nasa.gov Toshi Matsui (ESSIC UMD, Code612 NASA GSFC), toshihisa.matsui-1@nasa.gov Contents: ./READMEtracer.txt # this file ./namelist.wps.tracer_iop_31.template # namelist.wps file to configure WRF Pre-Processing System (WPS) ./namelist.input.real.tracer_iop_31.template # namelist.input file for NU-WRF model real.exe ./namelist.input.wrf.tracer_iop_31.template # namelist.input file for NU-WRF model wrf.exe ./${YYYY}${MM}${DD} # these directories contain files produced from 48-hours NU-WRF forecasting from 00UTC on ${YYYY}${MM}${DD}: pyplot_${YYYY}-${MM}-${DD}_${HH}${MN}${SC}.png # Plot for Composite radar reflectivity (dBZ) # PBL height (m) + 10-m horizontal wind (850hPa-level wind in plots before 06/02/2022), # OLR TOA (W m-2), and 5-mins-accumulated IC+CG lighting flash extent density (flash km-2) # Note that this composite dBZ is calculated from NSSL 2-moment microphysics for S-band, # not from POLARRIS radar simulator pyplot.gif # Gif annimation file combining the png plot files for 1~48 hours in the forecasting accprecip_${YYYY}-${MM}-${DD}_${HH}${MN}${SC}.png # Plot for 1, 3, 6-hours, and total accumulated surface precipitation (mm) accprecip.gif # Gif annimation file combining the png plot files for 1~48 hours in the forecasting polarris_zh_zdr_rh_vr_${YYYY}_${MM}${DD}_${HH}${MN}${SC}.png # Plot from POLARRIS radar simulator in NU-WRF for QCed Reflectivity (dBZ), # differential reflectivity (dB), cross-polar correlation (-), and # radial velocity (m s-1) at 0.5 degree elevation angle polarris_zh_zdr_rh_vr.gif # Gif annimation file combining the png plot files roughly every hour # for 1~48 hours in the forecasting polarris_zh_4sweeps_${YYYY}_${MM}${DD}_${HH}${MN}${SC}.png # Plot from POLARRIS radar simulator in NU-WRF for QCed Reflectivity (dBZ) # at 0.5, 1.8, 4.0 and 8.0 degree elevation angles polarris_zh_4sweeps.gif # Gif annimation file combining the png plot files roughly every hour # for 1~48 hours in the forecasting # following files are produced 3 days late # day1 represent the first 0-24hr forecast, day2 represents the 24-48hr forecast. CFAD_con_day?.png # Convective part of Contoured Frequency of Altitude Diagrams CFAD_str_day?.png # Stratiform part of Contoured Frequency of Altitude Diagrams QVP_con_day?.png # Convective part of QVP-like domain-mean radar profiles QVP_str_day?.png # Stratiform part of QVP-like domain-mean radar profiles RadarFrac_day?.png # Composite Radar Horizontal Fraction (0-1) by different minimum reflectivity thresholds</pre>

opencc-by-4.0Oct 2022View details →

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NASA GES-DISC Knowledge Graph for Link Prediction

This dataset includes a knowledge graph of NASA GES-DISC collections, featuring interconnected nodes for datasets, data center, projects, platforms, instruments, science keywords, and publications. Designed for link prediction tasks, it aids machine learning research in satellite observation, remote sensing, and climate change. The dataset is in CSV format, ready for graph databases and ML frameworks.

opencc-by-4.0Jun 2024View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record