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65 results for “solar system”
96 Long Term Solar System Integrations
<p>We share 96 long term Solar System integrations. The simulations are in the REBOUND SimulationArchive format. Please see the companion paper for details.</p>
Dataset for publication: Compound parabolic collector solar disinfection system for the treatment of harvested rainwater, Strauss et al. (2018). DOI:10.1039/c8ew00152a.
<p>Datasets used for the publication: Strauss A, Reyneke B, Waso M and Khan W (2018) Compound parabolic collector solar disinfection system for the treatment of harvested rainwater. Environ Sci: Water Res. Technol. DOI: 10.1039/c8ew00152a. Please cite the article when using the datasets.</p> <p>Available datasets:</p> <ul> <li>WATERSPOUTT_688928_US_Environmental Conditions_01_1.0.0: Dataset describing the environmental conditions on sampling days while assessing a SODIS-CPC reactor for the treatment of roof-harvested rainwater.</li> <li>WATERSPOUTT_688928_US_SODIS-CPC Schematics_01_1.0.0: Schematic diagrams showing the design of the SODIS-CPC reactor.</li> <li>WATERSPOUTT_688928_US_SODIS-CPC-Microbiology_01_1.0.0: Dataset describing the results obtained while monitoring the microbiological quality of the roof-harvested rainwater before and after treatment with the SODIS-CPC reactor.</li> <li>WATERSPOUTT_688928_US_SODIS-CPC-Physicochemical_01_1.0.0: Dataset describing the physicochemical quality of the roof-harvested rainwater before and after treatment with the SODIS-CPC reactor.</li> <li>WATERSPOUTT_688928_US_UV-transmittance_01_1.0.0: Dataset describing the UV transmittance of polymethyl methacrylate and borosilicate glass.</li> </ul>
Flank vents locations on six shield volcanoes from the Solar System.
<p>The dataset contains six .txt files with the location of flank vents from six shield volcanoes located on Venus, Earth, and the Moon. The shield volcanoes are the following:<br> -Mauna Kea, Earth, with a total of 204 vents (GCS_WGS_1984).<br> -Pinacate, Earth, with a total of 433 vents (GCS_WGS_1984).<br> -Marius Hills, Moon, with a total of 212 vents (GCS_Moon_2000).<br> -Mons Rümker, Moon, with a total of 24 vents (GCS_Moon_2000).<br> -Kunapipi Mons, Venus, with a total of 125 vents (GCS_Venus_1985).<br> -Var Mons, Venus, with a total of 88 vents (GCS_Venus_1985).<br> The files contain latitude and longitude coordinates.</p>
solar_home_system_data_log: Initial release of data sets and script
<p>In this first release, this repository includes three sets of data (date/time, temperature, current, and voltage) of over 6 months of electricity consumption of three households in an off-grid area in the state of Jharkhand, India. The goal of this data set is to be made open so that the community working in off-grid electricity access can get a sense of electricity consumption patterns and apply various analytical and visualization techniques.</p>
The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for solar power in current and future electricity systems
<p><span><span>Here, we </span><span>present a</span><span> database </span><span>which collates </span><span>historical, </span><span>current</span><span>,</span><span> and future </span><span>cost and performance</span><span> data</span> <span>and </span><span>assumption</span><span>s</span> <span>for </span></span><span><span>solar</span><span> power </span><span>generation</span></span><span> <span>from</span><span> the open literature. </span></span><span><span>Solar energy supplies 5% of global electricity, and production has grown ten-fold in the decade to 2022</span><span>.</span></span><span> <span>The data are </span><span>global in scope but with </span><span>regional and national</span> <span>specificity</span><span>, </span><span>cover</span><span>s</span><span> the years 2015 t</span><span>hrough </span><span>to 2050, </span><span>and </span><span>span</span> </span><span><span>753</span></span><span><span> datapoints from </span></span><span><span>31</span></span><span><span> sources</span><span>.</span> </span></p> <p><span><span>The database </span><span>enables modellers to select and justify</span> <span>model input data and </span><span>provides </span><span>a </span><span>benchmark for comparing assumptions and projections to </span><span>other source</span><span>s</span><span> across the literature</span> <span>to </span><span>validate</span><span> model inputs and outputs</span><span>.</span><span> It is </span><span>designed to be easily updated with </span><span>new sources of</span><span> data, ensuring its utility</span><span>, comprehensiveness,</span><span> and broad applicability over time.</span></span><span> </span>Technoeconomic data on utility-scale solar PV was collected from websites, reports, academic articles and databases of national and international organisations.</p>
Simulation data for Response of the Thermosphere-Ionosphere System to an X-Class Solar Flare: March 30, 2022 Case Study
<p>GITM simulation results for the research article titled "Response of the Thermosphere-Ionosphere System to an X-Class Solar Flare: March 30, 2022 Case Study" submitted to JGR: Space Weather </p>
Main characteristics of Solar System planets
<pre><span>Main characteristics of Solar System planets. Data are included in the table, which includes non-standard EPN-TAP parameters. Data are retrieved from Archinal et al 2018 (IAU report 2015, 2018CeMDA.130...22A) [radii] and Cox et al 2000 (Allen's astrophysical quantities, 2000asqu.book.....C) [masses, heliocentric distances, and rotation periods]. </meta></span> </pre>
General relativistic precession and the long-term stability of the solar system: SimulationArchive dataset
<p>We share the data for 1280 long-term solar system simulations used in <a href="https://doi.org/10.1093/mnras/stad719">Brown & Rein (2023)</a>. The simulations are zipped together consecutively in groups of 16 and saved in the <code>REBOUND</code> (3.18.1) SimulationArchive format. Please see the companion paper and <a href="https://doi.org/10.5281/zenodo.7753656">code</a> for details.</p> <p><strong>Abstract</strong></p> <p>The long-term evolution of the solar system is chaotic. In some cases, chaotic diffusion caused by an overlap of secular resonances can increase the eccentricity of planets when they enter into a linear secular resonance, driving the system to instability. Previous work has shown that including general relativistic contributions to the planets' precession frequency is crucial when modelling the solar system. It reduces the probability that the solar system destabilizes within 5 Gyr by a factor of 60. We run 1280 additional <em>N</em>-body simulations of the solar system spanning 12.5 Gyr where we allow the general relativistic precession rate to vary with time. We develop a simple, unified, Fokker-Planck advection-diffusion model that can reproduce the instability time of Mercury with, without, and with time-varying general relativistic precession. We show that while ignoring general relativistic precession does move Mercury's precession frequency closer to a resonance with Jupiter, this alone does not explain the increased instability rate. It is necessary that there is also a significant increase in the rate of diffusion. We find that the system responds smoothly to a change in the precession frequency: There is no critical general relativistic precession frequency below which the solar system becomes significantly more unstable. Our results show that the long-term evolution of the solar system is well described with an advection-diffusion model. </p>
Optical Navigation Dataset for Solar System Small Bodies
<p>This dataset has been curated for the purpose of training and evaluating a variety of local feature extractors intended for optical navigation in the proximity of Solar System small bodies (SSSBs). It aims to serve as a resource for researchers in the field and it is referenced in the related article titled "CNN-based local features for navigation near an asteroid" [1]. Additionally, the associated Python code for this dataset can be found in [2].</p> <p>The dataset is a compilation of images obtained from four distinct space missions focused on SSSBs, specifically NEAR Shoemaker (Eros) [3], Hayabusa (Itokawa) [4], Rosetta (67P/Churyumov-Gerasimenko) [5, 6], and OSIRIS-REx (Bennu) [7]. It also incorporates synthetic data generated through the utilization of a Bennu shape model [8] and OpenGL-based rendering software [9, 10]. Access to mission-specific images is available through the NASA Planetary Data System (PDS), and for the Rosetta mission, via the ESA Planetary Science Archive [11].</p> <p>The prefix <code>rot-</code> has been applied to subsets in which images have been pre-rotated to orient the SSSB's rotation axis upwards within the image frame. These subsets are primarily intended for training purposes and encompass image pairs with pixel correspondences that can be found in the <code>aflow</code> directory. Pixel correspondences are stored as 16-bit PNG images, where the G- and B-channels respectively represent the x and y image coordinates. To facilitate data compression and storage, a fixed scaling coefficient of 8 has been employed to convert the pixel correspondence float array into a 16-bit integer array to be used by the PNG compression. These pixel correspondence files can be loaded using the <code>navex.datasets.tools.load_aflow</code> function from [2].</p> <p>On the other hand, subsets designated with a <code>-d</code> postfix include depth information (<code>*.d</code> files) and are exclusively employed during the evaluation of the proposed feature extractors. The depth data is stored as scaled grayscale 16-bit integer arrays using PNG compression. A custom additional header accompanies these images, providing two 32-bit float values, namely the subtracted offset <em>v<sub>0</sub> </em>and the scale multiplier <em>s</em> utilized in the calculation of image pixel values as <em>v</em>' = (<em>v</em> - <em>v<sub>0</sub></em>)·<em>s</em>. To access the depth data as a 32-bit float array, researchers can utilize the <code>navex.datasets.tools.load_mono</code> function from [2].</p> <p>Please note that the file paths in e.g. <code>rot-cg67p-osinac.tar</code> and <code>cg67p-osinac-d.tar</code> archives are the same, so you need to either rename the extracted folder, extract them to different folders, or only extract the archive that you need.</p> <p>For clarity, it should be noted that subsets lacking the aforementioned pre- or postfixes do not contain paired images and consequently lack pixel correspondences. These subsets were exclusively used for feature extractor training in [1].</p> <p>The dataset also includes <code>*.ckpt</code> files, which are the trained feature extractor models referred to in [1]. More details about how to use them can be found in [2].</p>
Data from: Assessing uncertainties and approximations in solar heating of the climate system
Open the record for dataset details and reuse information.
Solar System Integrations with REBOUND
<p>This dataset contains 144 high accuracy integrations of the eight major planets in the Solar System. Each simulation is integrated 10Gyrs into the future. Each file is a REBOUND Simulation Archive (Rein & Tamayo 2017). The Simulation Archives contains all information necessary to reproduce the integrations bit-by-bit. A detailed description of the dataset will be provided in an upcoming paper. </p>
Bit-wise reversible integrations of the Solar System with JANUS
<p>This dataset contains 24 integrations of the Solar System. The integrator is JANUS, a bit-wise reversible algorithm that uses a mix of integer and floating-point arithmetic. The files are Simulation Archives, which can be opened with the REBOUND code. </p>
Ionosphere-Thermosphere Data Published in "Responses of the Thermosphere and Ionosphere System to Concurrent Solar Flares and Geomagnetic Storms"
<p>This dataset supports the Journal of Geophysical Research publication "Responses of the Thermosphere and Ionosphere System to Concurrent Solar Flares and Geomagnetic Storms" by Qian et al., 2019. The data files are selected output and related analyses from the thermosphere-ionosphere-electrodynamics general circulation model (TIEGCM). The format of the data files are either IDL save files or NetCDF files or ASCII.</p>
Surface science and liquid phase investigations of oxanorbornadiene/oxaquadricyclane ester derivatives as molecular solar thermal energy storage systems on Pt(111) [doi: 10.1063/5.0158124]
<p>Primary data, meta data, and corresponding lists of figures & tables are included. [doi: 10.1063/5.0158124]</p>
ARISE-SAI-1.5: Assessing Responses and Impacts of Solar climate intervention on the Earth system with Stratospheric Aerosol Injection, with cooling to 1.5C
<p>Assessing Responses and Impacts of Solar climate intervention on the Earth system with Stratospheric Aerosol Injection (ARISE-SAI) is a set of simulations carried out with the Community Earth System Model, version 2 with the Whole Atmosphere Community Climate Model, version 6 (CESM2(WACCM6)) that aims at simulating a plausible deployment of solar climate intervention of stratospheric aerosol injection to enable community assessment of responses of the Earth system. This first set of simulations introduce stratospheric aerosol injection at ~ 21 km in simulated year 2035, called ARISE-SAI-1.5, utilize the middle-of-the-road SSP2-4.5 emission scenario,, and keep global mean surface air temperature near 1.5°C above the pre-industrial value. Sulfur dioxide injections in the ARISE-SAI-1.5 simulations are placed at four injection locations (15°S, 15°N, 30°S, 30°N) into one grid box at 180° longitude, and midpoint altitude of 21.6 km. The injection amount at each latitude is specified annually by a “controller” algorithm. This strategy ensures that the global mean surface temperature (T0), north-south temperature gradient (T1), and equator-to-pole temperature gradient (T2) remain close to ~ 1.5°C above the pre-industrial value throughout the simulation.</p> <p> </p> <p>The files contained here contain output of surface temperature (TREFHT), total precipitation (PRECT), SO4, and controller log files with amounts of SO2 injection. </p>
Process simulation-based inventory data for the perovskite single-junction, Silicon (PERC) and four-terminal perovskite/silicon tandem solar photovoltaic system life cycles
<p>Process simulation-based inventory data (mass and energy balances) for the perovskite single-junction, silicon (PERC architecture), and four-terminal perovskite/silicon tandem solar photovoltaic system life cycles. The file "0 Overview of simulation flowsheets.xlsx" contains images of the 11 flowsheets that constitute the perovskite/silicon tandem simulation model, which encompasses the perovskite single-junction and silicon (PERC) simulation models. For each unit process shown in each of the flowsheet images, the corresponding Excel file in this repository (with the same name) contains the detailed mass and energy balances, as well as full compositions and thermochemical properties of all streams and the compounds in them. That is, streams are not assumed to consist of pure elements simply moving through the system together, but rather taking into account that streams consist of compounds in solution, which have different thermochemical properties than simple mixtures of the elements involved.</p> <p>Nine additional data files, the names of which start with "Inventory - " contain summarized inventory data for the production of 1000 perovskite single-junction, silicon (PERC), and silicon/perovskite tandem PV modules, each with no Si recycling (i.e. zero circularity), 50% Si recycling, and 100% Si recycling (i.e. full Si circularity).</p>
Nowcast of Aerospace Ionizing Radiation System (NAIRAS) simulation of the effect of the 2024-05-11 coronal mass ejection and solar particle event on Earth's atmosphere
<p>The effect of the CME on the cutoff rigidity and the dose at different altitude as computed by NAIRAS.</p> <p>The neutron monitor data from OULU and the DSCOVR data for solar wind density and speed are put as a reference for when the Forbush decrease happens and when the CME arrives.</p> <p> </p> <p>The version 2 added files with shorter lead time before the CME arrival and bigger labels</p>
Yields from paper: ALUMINIUM-26 FROM MASSIVE BINARY STARS II. ROTATING SINGLE STARS UP TO CORE-COLLAPSE AND THEIR IMPACT ON THE EARLY SOLAR SYSTEM
<p>Title: Aluminium-26 From Massive Binary Stars II: ROTATING SINGLE STARS UP TO CORE-COLLAPSE AND THEIR IMPACT ON THE EARLY SOLAR SYSTEM<br> Authors: Brinkman H.E., den Hartogh J. W., Doherty C.L., Pignatari M., Lugaro M.<br> ================================================================================<br> Description of contents: A .tar.gz package containing three files with the complete set<br> of yields from the models presented in this paper. YieldsNR.txt contains the yields for the<br> non-rotating models and Yields150.txt and Yields300.txt the yields for the models<br> rotating at an initial velocity of 150 and 300 km/s, respectively.</p> <p>================================================================================</p>
Yields from paper: ALUMINIUM-26 FROM MASSIVE BINARY STARS III. BINARY STARS UP TO CORE-COLLAPSE AND THEIR IMPACT ON THE EARLY SOLAR SYSTEM
<p>================================================================================<br> Title: Aluminium-26 From Massive Binary Stars III: BINARY STARS UP TO CORE-COLLAPSE AND THEIR IMPACT ON THE EARLY SOLAR SYSTEM<br> Authors: Brinkman H.E., Doherty C.L., Pignatari M., Pols, O. R., Lugaro M.<br> ================================================================================<br> Description of contents: A .tar.gz package containing 12 files with the complete set<br> of yields from the models presented in this paper. Each file contains one primary mass,<br> e.g., Yields10Msun contains the yields for the binary systems with a 10Msun primary.<br> The second line in each file has the periods.<br> ================================================================================</p>
Table 8 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>Graphs of the data distributions, with the R<sup>2</sup> regression values of σFR and the corresponding functions.</strong></p> <p>In case of display problems or missing data, the file <a href="https://zenodo.org/api/files/305630bd-70bd-4347-99eb-bfd6fb3af726/Table%208_SFR_distributions_curves.xlsx">Table 8_SFR_distributions_curves.xlsx</a> on Drive available for consultation is this one: <a href="https://docs.google.com/spreadsheets/d/17UGHZlvZ2N-g6TOzgqpIe248_IitBmka8DbGWsv-lKQ/edit?usp=sharing">https://docs.google.com/spreadsheets/d/17UGHZlvZ2N-g6TOzgqpIe248_IitBmka8DbGWsv-lKQ/edit?usp=sharing</a> </p> <p>URL of the plots of the data distributions, with the <strong>R<sup>2</sup></strong> regression values of σFR at the time of the triggering of the 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.</strong></p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.