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43 results for “Space Weathering”
Space Weather ElectroMagnetic Database for Ireland (SWEMDI)
<p>This is a database containing electromagnetic (EM) data that can contribute to better understand and quantify the electric fields caused by space weather events at the Earth's surface, and the physical properties of Ireland’s lithosphere. The database is named Space Weather Electromagnetic Database for Ireland (SWEMDI).</p> <p>It contains measured electromagnetic time series using magnetotelluric equipment, electromagnetic tensor relationships, 3D electrical resistivity model of Ireland's lithosphere, modelled electric and magnetic time series for Ireland between 1991 and 2018, documents and publications that used parts of this database, and a series of scripts that were used to generate the database.</p>
Supporting Data Sets for "New Constraints on the Lunar Optical Space Weathering Rate"
<p>Data Sets supporting "New Constraints on the Lunar Optical Space Weathering Rate" submitted to Geophysical Research Letter on 12/18/2020. See Supporting Information (link TBD).</p>
Dataset for "Reflectance spectra of seven lunar swirls examined by statistical methods: A space weathering study"
<p>This archive corresponds to the source code, raw data, and results described in the article "Reflectance spectra of seven lunar swirls examined by statistical methods: A space weathering study" by Chrbolková et al. (2019) published in Icarus journal. See AA_README.txt for more information.</p>
Dataset for "Beating 1 Sievert: Optimal Radiation Shielding of Astronauts on a Mission to Mars" publication in Space Weather journal
<p>Datasets in .fig Matlab format and figures in .jpg format published in Space Weather journal</p> <p>effectiveDoseRF.mat contains the effective dose "response functions" and an example (how2useDoseResponceFunctions.m) of how to use them to assess GCR dose.</p>
Dataset of Machine Learning forecasted VTEC from paper: Uncertainty Quantification for Machine Learning-based Ionosphere and Space Weather Forecasting
<p>The *csv files contain forecasted one-day-ahead Vertical Total Electron Content (VTEC), consisting of the mean/median VTEC values and the upper and lower VTEC bounds of the 95% confidence intervals of 4 models based on machine learning for test data.</p> <p>The first part of the *csv file name corresponds to the type of model: SE stands for the super-ensemble VTEC model, QGB stands for the quantile gradient boosting VTEC model, BNN1 stands for the Bayesian neural network VTEC model, and BNN2 stands for the Bayesian neural network with negative log-likelihood (NLL) loss VTEC model. The second part of the file name refers to the geographic location of the VTEC points for which the forecast is performed, i.e., 10E70N for 10 degree of longitude and 70 degree of latitude, 10E40N for 10 degree of longitude and 40 degree of latitude, and 10E10N for 10 degree of longitude and 10 degree of latitude. The last part of the file name corresponds to the test year, i.e., year 2017.</p> <p>The SE_*_2017.csv file consists of 14 columns. The index column ("Date-time") is expressed in Coordinated Universal Time (UTC) as YYYY-MM-DD. Columns 1-3 contain the VTEC forecast results of Random Forest (RF) trained on three data subsets; columns 4-6 contain the VTEC forecast results of Adaptive Boosting (AB) trained on three data subsets; columns 7-9 contain the VTEC forecast results of Gradient Boosting (XGBoost) trained on three data subsets. Column 10 ("Mean") represents the mean of columns 1-9, i.e., the ensemble mean; column 11 ("Std") represents the standard deviation of columns 1-9, i.e., the ensemble spread; columns 12 ("UB") and 13 ("LB") contain the upper and lower bounds of the 95% confidence interval of VTEC, respectively; and column 14 contains the Global Ionosphere Maps (GIM) values of CODE, i.e., the ground-truth in this study.</p> <p>The QGB_*_2017.csv file consists of 4 columns. The index column ("Date-time") is expressed in UTC as YYYY-MM-DD. Column 1 ("Median") contains the median VTEC forecast, column 2 ("LB") contains the lower VTEC bound of the 95% confidence interval, column 3 ("UB") contains the upper VTEC bound of the 95% confidence interval, and column 4 contains the GIM values of CODE, i.e., the ground-truth in this study.</p> <p>The BNN*_2017.csv file consists of 5 columns. The index column ("Date-time") is expressed in UTC as YYYY-MM-DD. Column 1 ("Mean") contains the mean VTEC forecast, column 2 ("Std") contains the standard deviation, column 3 contains GIM values of CODE, i.e., ground-truth in this study; column 4 ("UB") contains the upper VTEC bound of the 95% confidence interval, and column 5 ("LB") contains the lower VTEC bound of the 95% confidence interval.</p> <p>----------------------------------------------------------------------------------------------------------------------------------------</p> <p>Contact</p> <p>----------------------------------------------------------------------------------------------------------------------------------------</p> <p>If you have any questions regarding these data, please contact:</p> <p>Randa Natras</p> <p>Deutsches Geodätisches Forschungsinstitut (DGFI-TUM)</p> <p>Technical University of Munich</p> <p>Arcisstraße 21</p> <p>80333 München</p> <p>randa.natras@tum.de</p>
Space weather disrupts nocturnal bird migration
<p>Our paper tests for the effects of space weather-induced geomagnetic disturbances on radar-detected nocturnal bird migration. We find evidence for a ~10% decrease of migration intensity after controlling for weather variables and spatiotemporal autocorrelation, and also for a decrease in the effort birds spent flying against the wind in the fall, especially under overcast conditions. This repository provides the data and the code used to arrive at these conclusions and plot the main results. Weather radar data was processed from the NOAA NEXRAD network, weather data was accessed from the North American Regional Reanalysis, and magnetometer data was accessed from the SuperMAG inventory. </p>
Participant Notes from Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes
<p>Compilation of electronic meeting notes made by attendees at the Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes.</p> <p>Files are provided for Days 1-3 of the meeting. Day 4 inputs are included in Discussion notes under a separate doi.</p> <p>The Chapman Conference was supported by NSF Award AGS 1848885 and NASA grants 936723.02.01.09.14 and 936723.02.01.11.21</p>
Data from the paper "RFI flagging in solar and space weather low frequency radio observations' by Zhang et al. 2023
<p>Data from the paper "RFI flagging in solar and space weather low frequency radio observations' by Zhang et al. 2023</p>
Data related to NAHAYO et al (2022), to appear in AGU Space Weather Journal (2022SW003092)
<p>Geomagnetic data related to the publication by Nahayo, et al. (2022).</p> <p>The file ending in "event1.csv" is for the first event (October 2003) and the second file, filename ending "event2.csv" for the second event discussed in the paper (March 2015).</p> <p> </p>
Space Weather Environment During the SpaceX Starlink Satellite Loss in February 2022
<p>All data used in support of the journal article of the same name, including:</p> <ul> <li>operational NCEP/SWPC Whole Atmosphere Model (WAM), NRLMSISE-00, NRLMSIS 2.0, and DTM2020 fixed-height output of neutral atmospheric density in NetCDF format at ten-minute cadence</li> <li>observed and forecasted solar wind and geomagnetic space weather drivers in XML format</li> <li>NOAA Space Weather Prediction Center text advisory of geomagnetic activity</li> <li>select post-launch tracks (minimum of lat/lon/alt in one-minute cadence) of Starlink satellites for three launches: Group 4-4, December 2021; Group 4-5, January 2021; and Group 4-7, February 2021</li> </ul>
ScintPi: A low-cost, easy-to-build GPS ionospheric scintillation monitor for DASI studies of space weather, education, and citizen science initiatives
<p>These data sets contain ionospheric scintillation (GPS L1) observations (S4 indices) collected by a ScintPi prototype during 2018 at a low magnetic latitude station (Presidente Prudente). ScintPi is a low-cost, easy-to-build GPS ionospheric scintillation monitor for DASI studies of space weather, education, and citizen science initiatives.</p> <p>The file named "ScintPi_PPR_S4_2018.mat" contains S4 values (s4mat) for 2018 as a function of universal time (utmat), day-of-year (doymat), GPS satellite identifier number (prnmat), GPS satellite elevation (elmat) and azimuth (azmat) angles.</p> <p>The file named "ScintPi_PPR_20180211.mat" contains example raw (10 Hz) measurements made by ScintPi on February 11, 2018. The file contains values of receiver's altitude, latitude and longitude (variables alt,lat, and lon), GPS satellite azimuth and elevation (variables el and az), GPS identifier number (prn), signal-to-noise ratio (snr), and day-of-year (doy).</p>
Dataset for "Three-dimensional modeling of the ground electric field in Fennoscandia during the Halloween geomagnetic storm", Marshalko et al. (2023), Space Weather
<p>Results of 3-D modeling of the ground electric field in Fennoscandia during the Halloween geomagnetic storm in 2003 (29-31 October).</p> <p>Electric_field_YYYYMMDDHHMMSS_YYYYMMDDHHMMSS.h5 files (in hdf5 format) contain horizontal electric field components Ex and Ey (in mV/km) corresponding to 6 h of data and latitude and longitude grids corresponding to Ex and Ey arrays. Ex and Ey are 2160x567x543 arrays (temporal resolution is 10 s, thus, 2160 time steps). Latitude and Longitude are 567x543 arrays. All values are in single-precision floating-point format. Electric field values were obtained with the use of the conductivity-based inducing source following Marshalko et al. (2023).</p> <p>Files Electric_field_CB_20031029000000_20031031235950.dat, Electric_field_MT_20031029000000_20031031235950.dat, and Electric_field_SECS_20031029000000_20031031235950.dat contain the ground electric field time series (in mV/km) modeled during the Halloween geomagnetic storm in 2003 (29-31 October) at IMAGE magnetometers' locations, Mäntsälä Finnish natural gas pipeline GIC recording point (MAN), and Point X located 0.5 degrees north of MAN. The files are in plain-text (column-based) format. Electric field values in Electric_field_CB_20031029000000_20031031235950.dat, Electric_field_MT_20031029000000_20031031235950.dat, and Electric_field_SECS_20031029000000_20031031235950.dat were obtained with the use of the conductivity-based inducing source, MT intersite impedance method, and Spherical Elementary Current Systems (SECS) based approach, correspondingly, following Marshalko et al. (2023).</p>
Analysis of the Ground Level Enhancement GLE 60 on April 15, 2001, and its Space Weather Effects: Comparison with Dosimetric Measurements - Data
<p>Computed data that was used within the "Analysis of the Ground Level Enhancement GLE 60 on April 15, 2001, and its Space Weather Effects: Comparison with Dosimetric Measurements" paper. Computations of cones were done by OTSO using TSY89 + IGRF13 magnetic field parameters. Contains the atmospheric yield functions used for radiation computation as well as the global radiation map at 35kft for GLE60. Data is provided in .csv format.</p>
Supporting data for "Space weather in the popular media, and the opportunities the upcoming solar maximum brings"
<p>This contains the supporting data for the Space Weather Editorial "Space weather in the popular media, and the opportunities the upcoming solar maximum brings". It contains the Google Trends data (multiTimeline.csv) and the F10.7 and Kp data (omniweb.txt). </p>
Data from: Detecting sub-micron space weathering effects in lunar grains with synchrotron infrared nanospectroscopy
<p>Space weathering processes induce changes to the physical, chemical, and optical properties of space-exposed soil grains. For the Moon, space weathering causes reddening, darkening, and diminished contrast in reflectance spectra over visible and near-infrared wavelengths. The physical and chemical changes responsible for these optical effects occur on scales below the diffraction limit of traditional far-field spectroscopic techniques. Recently developed super-resolution spectroscopic techniques provide an opportunity to understand better the optical effects of space weathering on the sub-micrometer length scale. This paper uses synchrotron infrared nanospectroscopy to examine depth-profile samples from two mature lunar soils in the mid-infrared, 1500–700 cm<sup>-1</sup> (6.7–14.3 µm). Our findings are broadly consistent with prior bulk observations and theoretical models of space weathered spectra of lunar materials. These results provide a direct spatial link between the physical/chemical changes in space-exposed grain surfaces and spectral changes of space-weathered bodies.</p>
Pre-generated network files for "Intersecting near-optimal spaces: European power systems with more resilience to weather variability"
<p>These are network files that can be used to investigate the impacts of weather variability on the European power system using PyPSA-Eur as in <a href="https://github.com/aleks-g/intersecting-near-opt-spaces/tree/v1.0">https://github.com/aleks-g/intersecting-near-opt-spaces/tree/v1.0</a>. Find more information about the approach in the README of that repository.</p> <p>These network files are a shortcut to reproduce the results and use a fixed configuration ("v1.0"). For other configurations, it may be necessary to download ERA5 reanalysis cutouts (more on this in the git repository).</p> <p>Instructions can be found in the git repository.</p>
Radiation Effects on Satellites during Extreme Space Weather Events (pre-publication dataset)
<p>Data for submitted paper entitled "Radiation Effects on Satellites during Extreme Space Weather Events".</p> <p>Submitted to AGU Space Weather.</p> <p> </p>
Artificial space weathering to mimic solar wind enhances the toxicity of lunar dust simulants in human lung cells
<p>During NASA's Apollo missions, inhalation of dust particles from lunar regolith was identified as a potential occupational hazard for astronauts. These fine particles adhered tightly to spacesuits and were unavoidably brought into the living areas of the spacecraft. Apollo astronauts reported that exposure to the dust caused intense respiratory and ocular irritation. This problem is a potential challenge for the Artemis Program, which aims to return humans to the Moon for extended stays in this decade. Since lunar dust is "weathered" by space radiation, solar wind, and the incessant bombardment of micrometeorites, we investigated whether treatment of lunar regolith simulants to mimic space weathering enhanced their toxicity. Two such simulants were employed in this research, Lunar Mare Simulant-1 (LMS-1), and Lunar Highlands Simulant-1 (LHS-1), which were added to cultures of human lung epithelial cells (A549) to simulate lung exposure to the dusts. In addition to pulverization, previously shown to increase dust toxicity sharply, the simulants were exposed to hydrogen gas at high temperature as a proxy for solar wind exposure. This treatment further increased the toxicity of both simulants, as measured by the disruption of mitochondrial function, and damage to DNA both in mitochondria and in the nucleus. By testing the effects of supplementing the cells with an antioxidant (N-acetylcysteine), we showed that a substantial component of this toxicity arises from free radicals. It remains to be determined to what extent the radicals arise from the dust itself, as opposed to their active generation by inflammatory processes in the treated cells.</p>
Artificial space weathering to mimic solar wind enhances the toxicity of lunar dust simulants in human lung cells
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Data from: Detecting sub-micron space weathering effects in lunar grains with synchrotron infrared nanospectroscopy
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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)
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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.