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65 results for “solar system”
Table 4 for the Study: "Observation of correlation between earthquake triggering of M>4.3 and specific Sun-Moon-Planets positions in the Solar System, from 1600 in Italy."
<p><strong>Video explaining the determination of the Resulting Gravitational Force sigma for the 2009 L'Aquila earthquake.</strong></p> <p>For subtitles in English: <a href="https://youtu.be/-OVk2r8U8QA?t=563">https://youtu.be/-OVk2r8U8QA?t=563</a> --> URL of the video explaining the determination of the Resulting Gravitational Force sigma for the 2009 L'Aquila earthquake, for the article "Correlation observation between the triggering of M>4.3 earthquakes and specific Sun-Moon-Planet positions in the Solar System since 1600 in Italy" , SUBTITLES IN EN, paragraph 2.16.</p>
Photometry of outer Solar System objects from the Dark Energy Survey I: photometric methods, light curve distributions and trans-Neptunian binaries - data release
<p>This repository contains the full data release for the 814 outer Solar System objects found in the Dark Energy Survey.</p> <p>A full description of the object search is described in <a href="http://(https://ui.adsabs.harvard.edu/abs/2022ApJS..258...41B/abstract">Bernardinelli et al (2022)</a>, and a full description of the photometric processing is described in<a href="https://ui.adsabs.harvard.edu/abs/2023arXiv230403017B/abstract"> Bernardinelli et al (2023)</a>. If you use these files, we ask you to cite the corresponding papers.</p> <p>The FITS table `y6_des_tnos_color.fits` contains both the orbital elements and the colors for each object. The full description of the orbital element information is given in Table 3 of <a href="https://ui.adsabs.harvard.edu/abs/2022ApJS..258...41B/abstract">Bernardinelli et al (2022</a>). In addition to these, the table also includes the mean absolute magnitudes in each band, as well as mean <span class="math-tex">\((g-r, r-i, r-z)\)</span> colors and their corresponding covariance matrix, and the 68% confidence interval for the lightcurve amplitude.</p> <p>Inside the `fluxes` directory, the complete photometric record for each object is included in a `hdf5` file (for each object), and the MCMC chains for their fluxes and LCAs. The three Jupyter Notebooks included in the `notebooks` directory explains the columns and how to use these files to reproduce the results of the paper. Inside this directory there are also additional files needed to reproduce the code.</p> <p>The `binary` directory has the MCMC chains for their mutual orbits (in `.npy` files), as well as the astrometric and photometric record for the binary measurements. Another `README` file is included in that directory with a detailed explanation.</p>
Planning resource adequacy of wind- and solar-based electricity systems: Input data and results files
<p>This record contains the input data and raw results files for the study titled "<a href="https://www.sciencedirect.com/science/article/pii/S2666792424000234">Planning reliable wind- and solar-based electricity systems</a>."</p> <p>Tyler H. Ruggles, Edgar Virgüez, Natasha Reich, Jacqueline Dowling, Hannah Bloomfield, Enrico G.A. Antonini, Steven J. Davis, Nathan S. Lewis, Ken Caldeira, "Planning reliable wind- and solar-based electricity systems," Advances in Applied Energy, 2024, https://doi.org/10.1016/j.adapen.2024.100185.</p> <p>Additionally, csv files are provided to recreate the associated figures in the paper in the "Figures_files.zip" file.</p> <p>The input data contains wind and solar generation availability profiles and electricity demand profiles for the contiguous US. The profiles cover the years 1950-2022 and are calculated from the ERA5 dataset. The study only used the satellite era data from the year 1979 onward. Input profiles are presented at 4 resolutions: hourly, 2-hour, 3-hour, and 4-hour resolution.</p> <p>The results files contain some keys indicating the modeling scenario used: "SWB" = "Solar+Wind+Battery"; "SWBNG" = "Solar+Wind+Battery+Natural Gas generation"; and "SWBPGP" = "Solar+Wind+Battery+Power-to-H2-to-Power Loop". The files can be grouped into multiple categories:</p> <ol> <li>The main analysis including the initial energy system optimization results and the secondary system performance testing results. <ol> <li>Initial optimization results are found in zip files titled "Initial_Optimization_Jan29v1_*.zip"</li> <li>The testing of the optimized systems are found in the zip file "Lost_Load_Decade_Testing_Jan29v1.zip"</li> </ol> </li> <li>A secondary analysis compared systems optimized on a single year of data and tested on a single other year of data. Those results are in "Matrix_Figure_NYrs1_Aug04v1.zip"</li> <li>A supplementary analysis compared the modeled results using input data with the 4 different time resolutions. These results can be found in the zip files titled "DeltaT_Test_July08v1dt*.zip"</li> </ol>
Ventilation Pre-heating Effectiveness of a PCM Solar Air Collector with Ventilated Window System
<p>The dataset includes the published data in the article Ventilation Pre-heating Effectiveness of a PCM Solar Air Collector with Ventilated Window System. Dataset including experimental data and data for numerical validation. For the details of the data description please refer to the paper.</p>
Extreme power shortage events of wind-solar supply systems for individual countries
<p><span>Raw data of extreme power shortage events in wind-solar supply system in the paper entitled “Climate change impacts on the power shortage events of wind-solar supply systems worldwide during 1980–2022” on Nature Communications.</span></p>
PaSTS An Operational Dataset for Domestic Solar Thermal Systems
<p>Solar thermal systems play an important role in the decarbonization of the domestic heating sector, yet there exist no publicly available datasets of such systems. Therefore, this paper presents the PaSTS dataset, a unique collection of operational data from domestic Solar Thermal Systems (STS) manufactured by Ritter Energie and marketed under the Paradigma brand. Unlike previous research that primarily relied on simulated or unpublished experimental data, this dataset is derived from the service team at Ritter Energie, offering a realistic reflection of the challenges commonly faced in the field. This paper provides a comprehensive dataset overview, emphasizing its application in anomaly and fault detection tasks within STS and establishing it as the first dataset of its kind.</p> <p>Given the inherent complexities of fault detection in STS, we elaborate on the expert system-based fault detection mechanism currently and use and advocate for applying semi-supervised or unsupervised anomaly detection techniques tailored to the dataset's characteristics. </p>
Unveiling Surface and Near-Surface Properties of Inner Solar System Bodies Through Impact Cratering: Datasets
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Evidence for asteroid scattering and distal solar system solids from meteorite paleomagnetism
<p>Alternating field demagentisation data for the Tagish Lake meteorite, published in 'Evidence for asteroid scatting and distal solars system solids from meteorite paleomagnetism' (2020) <em>The Astrophysical Journal</em>, <strong>892</strong>, 126</p>
Dataset for optimal Wind+Hydrogen+Other+Battery+Solar (WHOBS) electricity systems for European countries
<p>Data outputs from optimisation of Wind+Hydrogen+Other+Battery+Solar (WHOBS) electricity systems for European countries.</p> <p>Input data and code can be found here:</p> <p>https://github.com/PyPSA/WHOBS</p> <p>The summary of the results with metadata can be found here:</p> <p>https://github.com/PyPSA/WHOBS/tree/master/results-181002</p> <p>if you don't want to download 6 GB.</p> <p>To load the data, install PyPSA and do e.g. for Germany (DE) in 2030:</p> <p>network = pypsa.Network("DE-2030.nc")</p> <p>Note that in these files, in some hours batteries discharge and charge at the same time. This is because the model wants to dump energy and this behaviour has the same cost and effect as curtailing wind and solar. In the latest version of the github code, wind and solar have tiny marginal costs (0.02 and 0.01 EUR/MWh respectively) to prevent this behaviour. With these costs, curtailment is preferred.</p>
Excel sheet Overview Exoplanets plus Solar system from NASA
<p> <strong>This Excel sheet is produced based on the data from the NASA Exoplanet Archive. It is Reference [14] in Seeking Evidence for the Cosmic Influx Theory (CIT) Collaborating with ChatGPT <a href="../records/12683899">https://zenodo.org/records/12683899</a> </strong> <br><br>You find the calculations for the Preferred Distances of star systems in column N from cell 7 down. The largest planets are likely found at that preferred distance, based on NASA data. Open the Excel sheet and scroll down to row 195 to view the original data from NASA.<br><a href="http://exoplanetarchive.ipac.caltech.edu">http://exoplanetarchive.ipac.caltech.edu</a> </p> <p>In <em>'Seeking Evidence for the Cosmic Influx Theory (CIT)</em> <em>Unveiling a Universal Ether-like Energy Field spanning the vast scales of exoplanets down to the minute details of dew and rime)</em>', we find surprising confirmations from ChatGPT that many natural phenomena exemplify an influx of energy, converting the Big Bang (BB) concept into Continuous Creation (CC) This perspective represents a significant shift in how we view familiar phenomena—from rain to stardust, and from volcanoes to the spreading ocean floor.<br>Citation: Loeffen, R. (2024). Seeking Evidence for the Cosmic Influx Theory (CIT) Collaborating with ChatGPT<br><a href="../records/12683899">https://zenodo.org/records/12683899</a> </p>
Mitigating Sn Loss via Anion Substitution in the Cu2+-Sn2+ precursor system for Cu2ZnSn(S, Se)4 solar cells
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Dataset of "Electron holography details the Tagish Lake parent body and implies early planetary dynamics of the Solar System"
<p>Input files for iSALE-2D of the paper "Electron holography details the Tagish Lake parent body and implies early planetary dynamics of the Solar System" by Y. Kimura et al.<br> <br> Please note that usage of the iSALE-2D is not fully open source; it is distributed on a case-by-case basis to academic users in the impact community, strictly for noncommercial use. Scientists interested in using or developing iSALE code may apply at the iSALE Web page (http://www.isale-code.de). </p>
(old version) Table 7 for the Study: "Observation of correlation between earthquake triggering of M>4.3 and specific Sun-Moon-Planets positions in the Solar System, from 1600 in Italy."
<p>Table 7 for the Study: "Observation of correlation between earthquake triggering of M>4.3 and specific Sun-Moon-Planets positions in the Solar System, from 1600 in Italy."</p> <p><strong>There is a statistical correlation between the position of the planets, Moon and Sun and the Magnitude of the earthquakes.</strong></p>
Table 7 for the Study: "Observation of correlation between earthquake triggering of M>4.3 and specific Sun-Moon-Planets positions in the Solar System, from 1600 in Italy."
<p>Table 7 for the Study: "Observation of correlation between earthquake triggering of M>4.3 and specific Sun-Moon-Planets positions in the Solar System, from 1600 in Italy", paragraph 3.01.06.</p> <p><strong>Summary Excel tables of the 200 earthquakes, results of the first hypothesis.</strong></p>
Table 9 for the Study: "Observation of correlation between earthquake triggering of M>4.3 and specific Sun-Moon-Planets positions in the Solar System, from 1600 in Italy." Calandra Stefano
<p><strong>Distribution and probability of σFR indices of the 2nd hypotesis for all 200 earthquakes.</strong></p> <p>Distribution and probability of σFR indices of the 2nd hypotesis, and the time affected in 1 month by the same σFR index present at the time of seismic triggering, for all 3 lines of experimental analysis, for all 200 earthquakes analyzed in the paper <em>"Observation of correlation between earthquake triggering of M>4.3 and specific Sun-Moon-Planets positions in the Solar System, from 1600 in Italy", paragraph </em><strong>3.02.03.</strong></p> <p>Table 9 shows that the values and σFR indices calculated for the 200 earthquakes are concentrated in very small values. With this result, it is shown that by using these highly concentrated σFR values and indices, <em>75% of the time of the reference month of 65% earthquakes >M4.3 can be excluded as seismic hazard. </em></p>
Datasets for "Irradiance and cloud optical properties from solar photovoltaic systems" (final version)
<p>This dataset contains all the relevant data for the algorithms described in the paper "<a href="https://amt.copernicus.org/articles/16/4975/2023/">Irradiance and cloud optical properties from solar photovoltaic systems</a>", which were developed within the framework of the <a href="https://www.h-brs.de/de/satelliten-und-meteorologie-unterstuetzte-vorhersage-der-energieerzeugung-von-pv-anlagen-auf">MetPVNet</a> project.</p> <p><strong>Input data:</strong></p> <ol> <li><a href="http://www.cosmo-model.org/">COSMO</a> weather model data (DWD) as NetCDF files (cosmo_d2_2018(9).tar.gz) <ol> <li>COSMO atmospheres for <a href="http://www.libradtran.org/">libRadtran</a> (cosmo_atmosphere_libradtran_input.tar.gz)</li> <li>COSMO surface data for calibration (cosmo_pvcal_output.tar.gz)</li> </ol> </li> <li><a href="https://aeronet.gsfc.nasa.gov/">Aeronet</a> data as text files (MetPVNet_Aeronet_Input_Data.zip)</li> <li>Measured data from the <a href="https://www.h-brs.de/de/satelliten-und-meteorologie-unterstuetzte-vorhersage-der-energieerzeugung-von-pv-anlagen-auf">MetPVNet</a> measurement campaigns as text files (MetPVNet_Messkampagne_2018(9).tar.gz) <ol> <li>PV power data</li> <li>Horizontal and tilted irradiance from pyranometers</li> <li>Longwave irradiance from pyrgeometer</li> </ol> </li> <li>MYSTIC-based lookup table for translated tilted to horizontal irradiance (gti2ghi_lut_v1.nc)</li> </ol> <p><strong>Output data:</strong></p> <ol> <li>Global tilted irradiance (GTI) inferred from PV power plants (with calibration parameters in comments) <ol> <li>Linear temperature model: MetPVNet_gti_cf_inversion_results_linear.tar.gz</li> <li>Faiman non-linear temperature model: MetPVNet_gti_cf_inversion_results_faiman.tar.gz</li> </ol> </li> <li>Global horizontal irradiance (GHI) inferred from PV power plants <ol> <li>Linear temperature model: MetPVNet_ghi_inversion_results_linear.tar.gz</li> <li>Faiman non-linear temperature model: MetPVNet_ghi_inversion_results_faiman.tar.gz</li> </ol> </li> <li>Combined GHI averaged to 60 minutes and compared with COSMO data <ol> <li>Linear temperature model: MetPVNet_ghi_inversion_combo_60min_results_linear.tar.gz</li> <li>Faiman non-linear temperature model: MetPVNet_ghi_inversion_combo_60min_results_faiman.tar.gz</li> </ol> </li> <li>Cloud optical depth inferred from PV power plants <ol> <li>Linear temperature model: MetPVNet_cod_cf_inversion_results_linear.tar.gz</li> <li>Faiman non-linear temperature model: MetPVNet_cod_cf_inversion_results_faiman.tar.gz</li> </ol> </li> <li>Combined COD averaged to 60 minutes and compared with COSMO and APOLLO_NG data <ol> <li>Linear temperature model: MetPVNet_cod_inversion_combo_60min_results_linear.tar.gz</li> <li>Faiman non-linear temperature model: MetPVNet_cod_inversion_combo_60min_results_faiman.tar.gz</li> </ol> </li> </ol> <p><strong>Validation data:</strong></p> <ol> <li>COSMO cloud optical depth (cosmo_cod_output.tar.gz)</li> <li>APOLLO_NG cloud optical depth (MetPVNet_apng_extract_all_stations_2018(9).tar.gz)</li> <li>COSMO irradiance data for validation (cosmo_irradiance_output.tar.gz)</li> <li><a href="https://www.soda-pro.com/web-services/radiation/cams-radiation-service">CAMS</a> irradiance data for validation (CAMS_irradiation_detailed_MetPVNet_MK_2018(9).zip)</li> </ol> <p><strong>How to import results:</strong></p> <p>The results files are stored as text files ".dat", using Python multi-index columns. In order to import the data into a Pandas dataframe, use the following lines of code (replace [filename] with the relevant file name):</p> <p>import pandas as pd<br>data = pd.read_csv("[filename].dat",comment='#',header=[0,1],delimiter=';',index_col=0,parse_dates=True)</p> <p>This gives a multi-index Dataframe with the index column the timestamp, the first column label corresponds to the measured variable and the second column to the relevant sensor</p> <p><strong>Note:</strong></p> <p>The output data has been updated to match the latest version of the paper, whereas the input and validation data remains the same as in Version 1.0.0</p>
Au catalyzed energy release in a molecular solar thermal (MOST) system: A combined liquid-phase and surface science study
<p>Raw Data, evaluated files and a list of experiments is provided.</p>
Data from: Solar-powered flow-through system for aquatic field studies
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A Catalog of Spectra, Albedos, and Colors of Solar System Bodies
<p>This dataset contains the geometric albedo, spectra, and colors of 19 different Solar System objects for use as exoplanet references. </p> <p><strong>Journal article for data:</strong> Madden & Kaltenegger (2018) A Catalog of Spectra, Albedos, and Colors of Solar System Bodies for Exoplanet Comparison. Astrobiology (<a href="https://doi.org/10.1089/ast.2017.1763">https://doi.org/10.1089/ast.2017.1763</a>)</p> <p><strong><em>Contents</em></strong></p> <p><em>Albedos</em> - Contains the geometric albedos with wavelength (microns)</p> <p><em>Spectra</em> - Contains the spectra (W/m<sup>2</sup>Å) with wavelength (microns) in a native resolution and an R=8 resolution for objects as if orbiting an M9V, M0V, K0V, G0V, F0V star, and the Sun. </p> <p><em>Magnitudes</em> - Contains the V, R, I, J, H, K, and Ks colors for each object at the native and R=8 resolution. </p> <p><em>Filters</em> - Contains the throughput for each filter used. </p>
CALCULATION OF THERMAL-TECHNICAL PARAMETERS OF THE SOLAR POND HEATING SYSTEM OF INDOOR SWIMMING POOLS
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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.