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43 results for “Space weather”
Discussion Survey from Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes
<p>These files include summaries of pre-meeting survey of important topics to discuss at the Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes</p> <p>The Chapman Conference was supported by</p> <p>NASA Grants: 936723.02.01.09.14 and 936723.02.01.11.21 and by NSF Award AGS 1848885</p> <p> </p>
Questions for Presenters at the Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes
<p>This file lists the questions asked of Chapman Conference presenters by the Chapman Conference attendees.</p> <p>-Questions, along with presenter names and presentation titles are included</p> <p>-The document contains</p> <p> 1) an explanation sheet </p> <p> 2) a sheet with all questions sorted by day and presenter</p> <p> 3) a sheet with questions that were asked in a general setting.</p> <p>The materials have been lightly edited to spell out acronyms, correct spelling and clarify non specific references when possible.</p> <p>The Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes was supported by NASA Grants 936723.02.01.09.14 and 936723.02.01.11.21 and by NSF Award AGS 1848885</p> <p> </p>
Priorities Survey from the Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes
<p>Mid-meeting survey results on Priorities from the Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes</p> <p>2 Files are provided: An Excel Spreadsheet and a summary pdf of highest priorities.</p> <p>Statistics compiled by Tomoko Matsuo</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>
Discussion Notes from Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes
<p>Discussion notes from the Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes</p> <p>Notes from Days 1, 3 and 4 are provided. Conference Day 2 was a 'Poster Day'</p> <p>These notes were aggregated from meeting scribes and conveners</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>
Table of Contents for Meeting Artifacts from Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes
<p>Table of Contents of Meeting Artifacts and Output from the Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes, 11-15 February 2019, Pasadena, CA, USA</p> <p>Each entry in the table of contents provides a short description and/or artifact title, along with the number of associated files and a weblink showing the associated DOI or permanent URL.</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>
Distinguishing Between Shock Darkening and Space Weathering Trends in Ordinary Chondrite Reflectance Spectra
<p>The data file contains raw spectra of Chelyabinsk meteorite IM, SD, and SW series as described in the linked journal publication.</p>
Chemical compositions data for "Space weathering of the Chang'e-5 lunar sample from a mid-high latitude region on the Moon"
<p>Data for “Space weathering of the Chang’e-5 lunar sample from a mid-high latitude region on the Moon”</p>
Data Archives - LAFORET et al - Silicate-sulfide interaction within quenched melts of space weathered Ryugu grains
<p>Contains STEM-EDXS raw data used in the paper Laforet et al. submitted in Meteoritics and Planetary Science</p> <p> </p>
Space Weather Modeling Framework ensemble simulations
<p><strong>Space Weather Modeling Framework ensemble simulations</strong></p> <p>This archive contains folders for 41 simulations with the operational geospace configuration of the University of Michigan's Space Weather Modeling Framework[1]. The operational configuration uses the University of Michigan's BATS-R-US magnetohydrodynamics code[2], the Ridley ionospheric electrodynamics solver[3], and the Rice Convection Model[4] inner magnetosphere model. More details of the operational geospace configuration are given by [5].</p> <p>These simulations were performed for, and used in, the paper</p> <p>> "Perturbed Input Ensemble Modeling with the Space Weather Modeling Framework",<br> > S.K. Morley, D.T. Welling and J.R. Woodroffe,<br> > Space Weather, 2018. doi: <a href="https://doi.org/10.1029/2018SW002000">10.1029/2018SW002000</a></p> <p><em>Directory Structure</em><br> All numbered directories are members of a perturbed input ensemble. The directory labeled "orig" is the reference (unperturbed) simulation. Each directory is structured identically.</p> <p>Each run directory contains `PARAM.in`, `LAYOUT.in` and `magin_GEM.dat` files. This set of files consistutes the required inputs for each run that are invariant. That is, these files are identical between runs and control the setup of the model and the types of outputs generated. Each run directory also contains an `IMF.dat` file that sets the upstream boundary condition. This file differs between each simulation. The values in each ensemble member have been perturbed from the values given in the reference simulation using a block resampling of measurement errors between an L1 solar wind monitor and a near-Earth monitor.</p> <p>Each run directory also contains `GM` and `GM\IO2` subdirectories. The `GM\IO2` subdirectory contains simulation output from the global magnetosphere module. Three files are present for each simulation: `geoindex_e20100404-190000.log`, `magnetometers_e20100404-190000.mag`, and `log_e20100404-190000.log`. These are standard SWMF log files that can be parsed and analyzed using, for example, the `pybats` module in the SpacePy[6] software package[7]. The simulation ouput includes ground magnetic perturbations at a set of magnetic observatory locations, local K indices, an estimated Kp index, a 1 minute resolution Sym-H/Dst index equivalent and simulated auroral electrojet indices.</p> <p><br> <em>Basic Analysis</em><br> To derive the time derivative of the horizontal ground magnetic perturbation (dB/dt) the magnetometer log file can be loaded using SpacePy<br> </p> <pre><code class="language-python">>>> import spacepy.pybats.bats >>> magdata = spacepy.pybats.bats.MagFile('run_001/GM/IO2/magnetometers_e20100404-190000.mag') >>> magdata.calc_h() #calculates horizontal from North and East components >>> magdata.calc_dbdt() #calculates time derivatives</code></pre> <p>To then calculate binned maxima in the dB/dt time series, e.g., for the Yellowknife (YKC) station<br> </p> <pre><code class="language-python">>>> import datetime as dt >>> import numpy as np >>> import spacepy.toolbox as tb >>> dBdt_max20, bintimes = tb.windowMean(magdata['YKC'], time=subset['time'], winsize=dt.timedelta(minutes=20), overlap=dt.timedelta(0), st_time=dt.datetime(2010,4,5), op=np.max)</code></pre> <p><br> and to turn this into a binary event series indicating a threshold crossing<br> </p> <pre><code class="language-python">>>> threshold = 1.1 #nT/s >>> predicted_event = np.asarray(dBdt_max20) >= threshold</code></pre> <p><br> Assuming that the observational data are obtained from NASA's CCMC and similarly processed, the event validation statistics can be calculated and displayed using the PyForecastTools package[8].<br> </p> <pre><code class="language-python">>>> import verify >>> c_table = verify.Contingency2x2.fromBoolean(predicted_event, observed_event) >>> ctable.summary(ci='bootstrap', verbose=True)</code></pre> <p> </p> <p><em>Footnotes</em><br> [1] Tóth, G., I. V. Sokolov, T. I. Gombosi, D. R. Chesney, C. R. Clauer, D. L. D. Zeeuw, K. C. Hansen, K. J. Kane, W. B. Manchester, R. C. Oehmke, K. G. Powell, A. J. Ridley, I. I. Roussev, Q. F. Stout, O. Volberg, R. A. Wolf, S. Sazykin, A. Chan, B. Yu, and J. KÃşta (2005), Space weather modeling framework: A new tool for the space science community, Journal of Geophysical Research: Space Physics, 110(A12), doi:10.1029/2005JA011126.</p> <p>[2] de Zeeuw, D. L., T. I. Gombosi, C. P. T. Groth, K. G. Powell, and Q. F. Stout (2000), An adaptive MHD method for global space weather simulations, IEEE Transactions on Plasma Science, 28(6), 1956–1965, doi:10.1109/27.902224.</p> <p>[3] Ridley, A. J., T. I. Gombosi, and D. L. DeZeeuw (2004), Ionospheric control of the magnetosphere: conductance, Annales Geophysicae, 22(2), 567–584, doi:10.5194/angeo-22-567-2004.</p> <p>[4] Toffoletto, F., S. Sazykin, R. Spiro, and R. Wolf (2003), Inner magnetospheric modeling with the Rice convection model, Space Science Reviews, 107(1), 175–196, doi: 10.1023/A:1025532008047.</p> <p>[5] Haiducek, J. D., D. T. Welling, N. Y. Ganushkina, S. K. Morley, and D. S. Ozturk (2017), SWMF global magnetosphere simulations of January 2005: Geomagnetic indices and cross-polar cap potential, Space Weather, 15(12), 1567–1587, doi: 10.1002/2017SW001695.</p> <p>[6] Morley, S. K., J. Koller, D. T. Welling, B. A. Larsen, M. G. Henderson, and J. T. Niehof (2011), Spacepy - a Python-based library of tools for the space sciences, in Proceedings of the 9th Python in science conference (SciPy 2010), Austin, TX.</p> <p>[7] SpacePy is packaged on PyPI, with the official git repository on SourceForge and an unofficial mirror on github.</p> <p>[8] PyForecastTools is packaged on PyPI and the repository is on github. The latest release is archived on Zenodo with doi: 10.5281/zenodo.1256921. The citation for v1.0.1 is Steve Morley. (2018, June 28). drsteve/PyForecastTools: PyForecastTools: Version 1.0.1 (Version v1.0.1). Zenodo. http://doi.org/10.5281/zenodo.1299389</p> <p> </p>
Space Weathering and Compositional Stratigraphy of Apollo 17 Double Drive Tube 73001/2
<p>This dataset contains OMAT, FeO and TiO2 content and depth of Apollo 17 double drive tube 73001/2.</p>
Raman spectral data for ''The evaluation of space weathering effects on lunar samples from a Raman spectroscopic perspective''
Open the record for dataset details and reuse information.
The star-planet interaction by combining asteroseismic and space weather techniques
<p>The characterization of exoplanets conditions cannot be based solely on the knowlegde of the planetary main parameters, since the properties and the activity level of the host star, as well as the effects of extreme space weather phenomena, need also to be considered. Here we propose a synergic strategy based both on an asteroseismic approach and a space weather/space climate analysis. By studying the oscillation spectra it is possible to derive the host star fundamental parameters, including a precise determination of the age. Combining this informations with those coming from observations in the UV spectrum of the star (Ca II H & K lines), and by using relations which we have already calibrated on the Sun (Reda et al. 2021 submitted), we are able to estimate the mean stellar wind acting on the exoplanets, enabling to estimate the erosion of their atmospheres. The best targets for this approach consists of terrestrial planets orbiting around solar-like stars, which are exactly the primary target of the PLATO mission.</p>
Data used for global TEC forecasting for space weather application based on deep learning techniques: a comparative study and considerations for real-time implementation
<p>This dataset contains the measurements of TEC from Global Ionospheric Maps (GIMs), provided by the International GNSS Service (IGS), as the target parameter and the global geomagnetic Kp index as the external input. This data set is composed of samples from 2005 to 2017. The datasets have been curated to obtain the same resolution (2 hs) of the two parameters.</p>
Recording of the STEREO-A space weather beacon with the Allen Telescope Array
<p>This dataset contains an IQ recording of the X-band 8443.5 MHz space weather beacon from STEREO-A, done with one of the antennas of the Allen Telescope Array. The recording was made as part of a demo for some GNU Radio tutorials for the Breakthrough Listen REU program.</p> <p>The recording was done with the spacecraft at around 9 million km from Earth, so the SNR is very good. Only one linear polarization (X, or horizontal) was recorded, using antenna 4g from the array.</p> <p>The hardware configuration was as follows: the antenna was connected to RFCB LO d, which was tuned to a frequency of 8443.54 MHz and used an output IF of 512 MHz. A USRP N321 digitized the X polarization. The USRP used an external 10 MHz reference and PPS coming from the observatory distribution system. The IQ sample rate of the USRP was 480 ksps, and a GNU Radio flowgraph was used to record IQ data to disk. The GNU Radio flowgraph stored the IQ data for each channel as 16-bin integers in the <a href="https://wiki.gnuradio.org/index.php/Metadata_Information">GNU Radio metatata file format</a>, with detached headers.</p> <p>After recording, the data was formatted using <a href="https://github.com/gnuradio/SigMF">SigMF</a>.</p>
Four great space weather events observed by SOHO and WIND
<p>The dataset is a supplemental material for the paper: Extreme Space Weather Events of the Past 30 Years: Analysis and Implications for Vigil’s L5 Observations.</p> <p>It consists of data from 4 most extreme space weather events that produced great geomagnetic storms on date:</p> <ul> <li>2001-03-31</li> <li>2003-10-30</li> <li>2003-11-20</li> <li>2004-11-08</li> </ul> <p>The dataset spans a period of 10 days before and 10 days after the date when the most extreme Dst index value was reached. Images and in-situ measurements are from the following space-based instruments in the highest available cadency.</p> <ul> <li>SOHO / EIT - images in 195 filter (https://umbra.nascom.nasa.gov/eit/)</li> <li>SOHO / MDI - magnetograph images (http://soi.stanford.edu/)</li> <li>SOHO / LASCO C2 and C3 - coronagraphs images (https://lasco-www.nrl.navy.mil/)</li> <li>SOHO / CELIAS - proton speed and density measurements (http://www2.physik.uni-kiel.de/SOHO-CELIAS/)</li> <li>WIND / MFI - magnetic field measurements (https://wind.nasa.gov/mfi/)</li> </ul> <p>The data were obtained by the pipeline presented at https://github.com/space-lab-sk/vigil-like-data. They provide a limited vizualization of how the ESA Vigil mission, which is currently under development, would have seen the most extreme events that were ever captured. </p>
Dataset of "Gravity waves – Toward connecting space weather and atmosphere-ionosphere coupling"
<p>The first file is the Ap file, describing the variation of the geomagnetic index used in the model simulations. The data files starting with sw are the model simulation files for the different model experiments. Each file is on a 2x15 (lat x lon) horizontal grid and on 66 pressure levels. There are 24 longitude and 91 latitudes. The files describe the simulations from 7th to 9th August 2018 in two hour time steps. The output variables in each file are zonal wind, geopotential height, and zonal gravity wave drag, in this given order. The files are formatted in the standard binary format for GraDS. </p>
MIDAS GPS TEC data for "On the Annual Asymmetry of High-Latitude Sporadic-F" (Space Weather)
<p>GPS-based TEC data related to high-latitude ionospheric variability in 2014</p>
Datasets Space Weather
<p>Datasets space weather satellites, ground monitors, muon detector.</p>
Advancing Mars space weather forecasting: multi-point validation during a major solar storm
Open the record for dataset details and reuse information.
Space-weather HMI Active Region Patch (SHARP)
SHARP stands for Space-weather HMI Active Region Patch. A SHARP is a DRMS series that contains (1) various space-weather quantities calculated from the photospheric vector magnetogram data and stored as FITS header keywords, and (2) 31 data segments (described in detail below), including each component of the vector magnetic field, the line-of-sight magnetic field, continuum intensity, doppler velocity, error maps and bitmaps. The data segments are not full-disk; rather, they are partial-disk, automatically-identified active region patches. SHARPs are calculated every 12 minutes. Often, there is more than one active region on the solar disk at any given time. Thus, SHARPs are indexed by two prime keys: time, T_REC, and HMI Active Region Patch Number, HARPNUM.
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.