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1,477 results for “Earth”
Analytical Framework for Precise Relative Motion in Low Earth Orbits
<p>The data sets provided here can be used to recreate the plots of the paper “Analytical Framework for Precise Relative Motion in Low Earth Orbits” available at this <a href="https://arc.aiaa.org/doi/10.2514/1.G004716">link</a>.</p> <p>That paper presents a practical and efficient analytical framework for the precise modelling of the relative motion in low Earth orbits.</p>
Nabro volcano event catalogue from Lapins et al., 2021, JGR Solid Earth
<p>Catalogue of seismic events from Nabro volcano (Sep 2011 - Oct 2012). Data format is a csv file.</p> <p>Events were detected by U-GPD phase arrival picking model. See following paper for details on event detection and location procedure: <em>A Little Data Goes A Long Way Way: Automating Seismic Phase Arrival Picking at Nabro Volcano With Transfer Learning</em> by Lapins et al., 2021, <a href="https://doi.org/10.1029/2021JB021910">https://doi.org/10.1029/2021JB021910</a>).</p> <p>Original seismic waveforms are from the Nabro Urgency Array (Hammond et al., 2011; <a href="https://doi.org/10.7914/SN/4H_2011">https://doi.org/10.7914/SN/4H_2011</a>), which is publicly available through IRIS Data Services (<a href="http://service.iris.edu/fdsnws/dataselect/1/">http://service.iris.edu/fdsnws/dataselect/1/</a>). See Hammond et al. (<a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021JB021910#jgrb55017-bib-0025">2011</a>) for further details on waveform data access and availability.</p> <p>Full code to reproduce our U-GPD transfer learning model, perform model training, run the U-GPD model over continuous sections of data and use model picks to locate events in NonLinLoc (Lomax et al., <a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021JB021910#jgrb55017-bib-0044">2000</a>) are available at <a href="https://github.com/sachalapins/U-GPD">https://github.com/sachalapins/U-GPD</a>, with the release (v1.0.0) associated with this study also archived and available through Zenodo (Lapins, <a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021JB021910#jgrb55017-bib-0036">2021</a>; <a href="https://doi.org/10.5281/zenodo.4558121">https://doi.org/10.5281/zenodo.4558121</a>).</p> <p> </p> <p>Dataset column key:</p> <p>time = Origin time of seismic event (UTC)</p> <p>lat = Hypocentre latitude in decimal degrees</p> <p>lon = Hypocentre longitude in decimal degrees</p> <p>depth = Hypocentre depth in km</p> <p>rms = RMS error for phase arrival picks and hypocentre (sec)</p> <p>erh = Estimate of horizontal Gaussian error (km)</p> <p>erz = Estimate of vertical Gaussian error (km)</p> <p>azgap = Azimuthal gap (maximum angle separating two adjacent seismic stations, measured from earthquake epicentre)</p> <p>cluster = HDBSCAN cluster number (see Chapter 6 of Lapins, 2021 doctoral thesis: <em>Detecting and characterising seismicity associated with volcanic and magmatic processes through deep learning and the continuous wavelet transform</em>. Persistent URL: <a href="https://hdl.handle.net/1983/ea90148c-a1b2-47ae-afad-5dd0a8b5ebbd">https://hdl.handle.net/1983/ea90148c-a1b2-47ae-afad-5dd0a8b5ebbd</a>)</p> <p>nab*_p_time = P-wave arrival time for station NAB* (UTC)</p> <p>nab*_p_prob = Maximum detection 'probability' around P-wave phase arrival from U-GPD model (between 0 and 1)</p> <p>nab*_s_time = S-wave arrival time for station NAB* (UTC)</p> <p>nab*_s_prob = Maximum detection 'probability' around S-wave phase arrival from U-GPD model (between 0 and 1)</p> <p> </p> <p>Station csv column key:</p> <p>Network = Seismic network name</p> <p>Station = Seismic station name</p> <p>Latitude = Latitude in decimal degrees</p> <p>Longitude = Longitude in decimal degrees</p> <p>Elevation_asl_km = Station elevation in km above sea level</p>
Database of local seismicity registered on ocean bottom seismometers (OBS). Database related to Bornstein et al. (accepted in Earth and Space Science), PICKBLUE
<p>We assembled a database of Ocean Bottom Seismometer (OBS) waveforms and manual P and S picks from local seismicity, on which we trained PickBlue, a deep-learning picker, using the seismometer data and the hydrophone channel. The dataset belongs to Bornstein et al. (accepted 2023 in Earth and Space Science). The picker and database are available in the SeisBench platform, allowing easy and direct application to OBS traces and hydrophone records.</p><p>The complete database is also accessible with SEISBENCH: <br><a href="https://seisbench.readthedocs.io">https://seisbench.readthedocs.io</a><br>SEISBENCH on github:<br><a href="https://github.com/seisbench">https://github.com/seisbench</a></p><p>Related paper:</p><p>Bornstein, T., Lange, D., Münchmeyer, J., Woollam, J., Rietbrock., A., Barcheck, G., Grevemeyer, I., Tilmann, F. (accepted 2023 in Earth and Space Science). PickBlue: Seismic phase picking for ocean bottom seismometers with deep learning, Earth and Space Science. </p>
Dataset for paper "Target selection for Near-Earth Asteroids in-orbit sample collection missions"
<p>This dataset can be used to reproduce the results of the paper titled "Target selection for Near-Earth Asteroids in-orbit sample collection missions."</p> <p>The "results" folder contains the data to reproduce the maps and the rankings of the target asteroids.</p> <p>The "trajectories" folder contains the propagation of the sample trajectories used to obtain the grids.</p>
NEON soil inorganic nitrogen measurements 2017-2020, derived data and code for Earth's Future manuscript
Nitrogen (N) is a key limiting nutrient in terrestrial ecosystems, but there remain critical gaps in our ability to predict and model controls on soil N cycling. This may be in part due to lack of standardized sampling across broad spatial-temporal scales. In a paper submitted for publication in Earth's future, we introduce a continentally distributed, publicly available dataset collected by the National Ecological Observatory Network (NEON) that can help fill these gaps. To overcome methodological challenges and generate a standardized dataset, we produced a derived data version of soil inorganic N pools and net N transformation rate tables, which accounts for nitrite contamination in blanks. This derived dataset is then used to evaluate sources of variation within the NEON sampling design with mixed effects models, and we also compare measured net N mineralization to simulated fluxes from the Community Earth System Model 2 (CESM2).
iSDAsoil: soil fine-earth bulk density for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths
<p>iSDAsoil dataset soil fine-earth bulk density in 10×kg/m3 predicted at 30 m resolution for 0–20 and 20–50 cm depth intervals. Data has been projected in WGS84 coordinate system and compiled as <a href="https://gdal.org/drivers/raster/cog.html">COG</a>. Predictions have been generated using multi-scale Ensemble Machine Learning with 250 m (MODIS, PROBA-V, climatic variables and similar) and 30 m (DTM derivatives, Landsat, Sentinel-2 and similar) resolution covariates. For model training we use a pan-African compilations of soil samples and profiles (<a href="https://www.isda-africa.com/national-soil-services/">iSDA points</a>, <a href="https://www.isric.org/projects/africa-soil-profiles-database-afsp">AfSPDB</a>, <a href="https://landpotential.org/data-portal/">LandPKS</a>, and other national and regional soil datasets). Cite as:</p> <p>Hengl, T., Miller, M.A.E., Križan, J. <em>et al.</em> African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. <em>Sci Rep</em> <strong>11, </strong>6130 (2021). <a href="https://doi.org/10.1038/s41598-021-85639-y">https://doi.org/10.1038/s41598-021-85639-y</a></p> <p>To open the maps in QGIS and/or directly compute with them, please use the <a href="https://gitlab.com/openlandmap/africa-soil-and-agronomy-data-cube"><strong>Cloud-Optimized GeoTIFF version</strong></a>.</p> <p>Layer description:</p> <ul> <li>sol_db_od_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil bulk density mean value,</li> <li>sol_db_od_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil bulk density model (prediction) errors,</li> </ul> <p>Model errors were derived using bootstrapping: md is derived as standard deviation of individual learners from 5-fold cross-validation (using spatial blocking). The model 5-fold cross-validation (<a href="https://mlr.mlr-org.com/reference/makeStackedLearner.html">mlr::makeStackedLearner</a>) for this variable indicates:</p> <pre><code>Variable: db_od R-square: 0.819 Fitted values sd: 0.269 RMSE: 0.126 Random forest model: Call: stats::lm(formula = f, data = d) Residuals: Min 1Q Median 3Q Max -1.06778 -0.06450 0.00215 0.06585 0.90016 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.05538 0.04860 -1.140 0.25451 regr.ranger 0.86305 0.01577 54.733 < 2e-16 *** regr.xgboost 0.15383 0.01651 9.315 < 2e-16 *** regr.cubist 0.02039 0.01113 1.832 0.06695 . regr.nnet 0.03465 0.03710 0.934 0.35036 regr.cvglmnet -0.03021 0.01032 -2.927 0.00343 ** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.1263 on 13565 degrees of freedom Multiple R-squared: 0.8194, Adjusted R-squared: 0.8193 F-statistic: 1.231e+04 on 5 and 13565 DF, p-value: < 2.2e-16</code></pre> <p>To back-transform values (y) to kg/m-cubic use:</p> <pre><code>kg/m3 = y * 10</code></pre> <p>To submit an issue or request support please visit <a href="https://isda-africa.com/isdasoil"><strong>https://isda-africa.com/isdasoil</strong></a></p>
Quantifying Both Socioeconomic and Climate Uncertainty in Coupled Human-Earth Systems Analysis
<p>This data repository is associated with the paper:</p> <p>Morris,J., A. Sokolov, J. Reilly, A. Libardoni, C. Forest, S. Paltsev, A Schlosser, R. Prinn and H. Jacoby (2025). Quantifying Both Socioeconomic and Climate Uncertainty in Coupled Human-Earth Systems Analysis. <em>Nature Communications </em><strong>16</strong>, 2703. https://doi.org/10.1038/s41467-025-57897-1</p> <p>This paper quantifies key socio-economic and climate uncertainties using the MIT Integrated Global System Model. </p>
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (SAI_2016_2020)
<p>Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change.</p> <p>This data repository is linked to <a href="../records/10815170" target="_blank" rel="noopener">https://zenodo.org/records/10815170</a></p>
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling
<p><strong>Summary</strong>: Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change.</p> <p><br><strong>Format</strong>: NetCDF.<br><strong>Institution</strong>: Atmospheric, Climate, and Earth Sciences Division, Pacific Northwest National Laboratory<br><strong>Contacts</strong>: Lingcheng Li (lingcheng.li@pnnl.gov; lingchengliwhu@gmail.com), Gautam Bisht (gautam.bisht@pnnl.gov)</p> <p><strong>Description</strong>: This dataset provides land surface parameters specifically designed for global kilometer scale earth system modeling.<br><strong>Spatial resolution</strong>: ~1 km, corresponding to 1/120 degree.<br><strong>Temporal resolution</strong>: includes yearly (2001-2020), monthly (2001-2020), and static data for different parameters.</p> <p><br><strong>Reference</strong>: <strong>Li, L., Bisht, G., Hao, D., and Leung, L.-Y. R.: Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-242, Acceptance, 2023.</strong></p> <p>It includes four categories of parameters, Please refer to the readme file for details:<br>1. LULC: land use and land cover parameters<br>2. VEGE: vegetation paramertes<br>3. SOIL: soil parameters<br>4. TOPO: topography parameters</p> <p>Due to storage limitations, the LAI and SAI files are stored in the following repositories:</p> <p>1) LAI 2001-2005: <a href="../records/10815637" target="_blank" rel="noopener">https://zenodo.org/records/10815637</a>; 2) LAI 2006-2010: <a href="../records/10815649" target="_blank" rel="noopener">https://zenodo.org/records/10815649</a>; 3) LAI 2011-2015: <a href="../records/10815658" target="_blank" rel="noopener">https://zenodo.org/records/10815658</a>; 4) LAI 2016-2020: <a href="../records/10815662" target="_blank" rel="noopener">https://zenodo.org/records/10815662</a>;</p> <p>5) SAI 2001-2005: <a href="../records/10815623" target="_blank" rel="noopener">https://zenodo.org/records/10815623</a>; 6) SAI 2006-2010: <a href="../records/10815629" target="_blank" rel="noopener">https://zenodo.org/records/10815629</a>; 7) SAI 2011-2015: <a href="../records/10790724" target="_blank" rel="noopener">https://zenodo.org/records/10790724</a>; 8) SAI 2016-2020: <a href="../records/10790758" target="_blank" rel="noopener">https://zenodo.org/records/10790758</a></p>
Data of publication Ultra-narrow Optical Linewidths in Rare-Earth Molecular Crystals
<p>Data corresponding to main text Figures, Extended Data figures, and Supplementary Figures in publication 'Ultra-narrow Optical Linewidths in Rare-Earth Molecular Crystals, by D. Serrano, S. Kumar Kuppusamy, B. Heinrich, O. Fuhr, M. Ruben and P. Goldner.</p>
Martian crater ages and crater counting - Does the impact flux of small and large asteroids varied through time on Mars, the Earth and the Moon?
<ul> <li>The SM_mars_crater_dating.xlsx table contains all the information used to date the 49 martian impact craters considered in this study (< 600 Ma). </li> </ul> <ol> <li>CRATER ID </li> <li>CRATER NAME</li> <li>DIAM KM </li> <li>LAT </li> <li>LONG </li> <li>DEPTH RIM KM </li> <li>DEPTH SURF KM </li> <li>DEPTH FLOOR KM </li> <li>NUMBER LAYER</li> <li>MORPHO EJECTA </li> <li>PRESERVATION </li> <li>COUNT AREA KM2: counting area from ejecta banket mapping </li> <li>COUNT AREA ASCI* KM2: counting area after removal of surfaces contaminated by secondary craters </li> <li>THRESHOLD AREA KM2: minimum size of Voronoi polygon area below which all associated detected craters are considered of secondary origin</li> <li>NB SEC: number of secondary craters dentified by ASCI </li> <li>PERCENT SEC</li> <li>NB CRAT 100M: total number of craters > 100 m detected by the CDA** on the CTX global mosaic*** over the counting area</li> <li>NB PRIM 100M: number of craters identified as primaries by ASCI</li> <li>TURNOFF DIAM KM: minimum crater diameter used to fit the crater-size frequency distribution (CSFD) with an isochron</li> <li>NB CRAT FIT: number of craters used to fit the CSFD with an isochron</li> <li>AGE GA: model age based on Hartmann (2005) chronology model**** and Michael et al. (2016) fitting technique*****</li> <li>AGE MAX GA</li> <li>AGE MIN GA</li> <li>N(1): equivalent number of accumulated craters >1km per km2</li> <li>N(1) MAX</li> <li>N(1) MIN</li> </ol> <p>*ASCI: Automatic Secondary Crater Identification: A. Lagain, K. Servis, G. K. Benedix, C. Norman, S. Anderson, P. A. Bland, Model Age Derivation of Large Martian Impact Craters, Using Automatic Crater Counting Methods, Earth and Space Science 8 (2) (2021). doi:10.1029/2020EA001598.</p> <p>**CDA: Crater Detection Algorithm: G. K. Benedix, A. Lagain, K. Chai, S. Meka, S. Anderson, C. Norman, P. A. Bland, J. Paxman, M. C. Towner, T. Tan, Deriving Surface Ages on Mars Using Automated Crater Counting, Earth and Space Science 7 (3) (2020). doi:10.1029/2019EA001005.</p> <p>*** CTX global mosaic: Context Camera global mosaic: J. L. Dickson, L. A. Kerber, C. I. Fassett, B. L. Ehlmann, A Global, Blended CTX Mosaic of Mars with Vectorized Seam Mapping: A New Mosaicking Pipeline Using Principles of Non-Destructive Image Editing, in: Lunar and Planetary Science Conference (2018), p. 2480.</p> <p>**** W. K. Hartmann, Martian cratering 8: Isochron refinement and the chronology of Mars, Icarus 174 (2) (2005) 294–320. doi:10.1016/j.icarus.2004.11.023.</p> <p>***** G. G. Michael, T. Kneissl, A. Neesemann, Planetary surface dating from crater size-frequency distribution measurements: Poisson timing analysis, Icarus 277 (2016) 279–285. doi:10.1016/j.icarus.2016.05.019.</p> <ul> <li>The crater_counting.csv table contains the location and size of impact craters used to derive the ages of the 49 craters younger than 600 Ma old presented in this study. </li> </ul>
ANE Site Placemarks for Google Earth
<p>ANE.kmz is a set of site placemarks for Google Earth of a selection of the most important archaeological sites in the Ancient Near East. ANE.kmz works with Google Earth Pro, which first has to be downloaded for free. When opened inside Google Earth Pro, ANE.kmz gives, to the left, an alphabetic list of ancient sites and, to the right, on the satellite images the same sites marked. For the moment, there are some 2500 sites with modern names; among them some 400 have ancient names. Additions of more sites are planned. Ancient names are written without parenthesis. Modern names are within parenthesis. Most sites have been identified on the satellite images.</p> <p>ANE Waters.kmz is an experimental set of provisional water placemarks for Google Earth covering Mesopotamia up to modern time.</p> <p>ANE Picture.jpg is just illustrating the appearence of ANE.kmz before zooming in and is not for use.</p>
EXTREMA: Ballistic capture sets at Mars over an Earth–Mars synodic period from January 1, 2030, to February 20, 2032
<p>EXTREMA (short for Engineering Extremely Rare Events in Astrodynamics for Deep-Space Missions in Autonomy) enables self-driving spacecraft, challenging the current paradigm under which spacecraft are piloted in the interplanetary space. Deep-space guidance, navigation, and control applied in a complex scenario is the subject of EXTREMA, which wants to engineer ballistic capture in a totally autonomous fashion. EXTREMA is erected on three pillars. Pillar 1 is on autonomous navigation. Pillar 2 involves autonomous guidance and control. Pillar 3 deals with autonomous ballistic capture, the focus of this work. The project has been awarded a European Research Council (ERC) Consolidator Grant in 2019.</p> <p>In Pillar 3 it is investigated how a spacecraft can attain ballistic capture in autonomy. Ballistic capture is an event that occurs in extremely-rare occasions, and requires acquiring a proper state (position, velocity) far away from the target planet [1]. Massive numerical simulations are required to find the specific conditions that support capture [2]. On average, 1 out of 10,000 conditions explored by the algorithm grants capture [3]. The union of these points defines the capture set, which in turn is used to find the capture corridors: these are streams of orbits that can be targeted far away from the planet and that guarantee ballistic capture.</p> <p>The data set contains the initial conditions of weakly-stable, unstable, crash, moon-crash, and capture sets at Mars with initial epochs uniformly distributed from 01 JAN 2030 12:00:00.000 (UTC) to 20 FEB 2032 10:32:39.144 (UTC), covering a complete Earth–Mars synodic period of approximately 780 days. The grid of initial conditions is built to maximize the capture ratio for Mars (see Figure 10 in [3]). Initial conditions are propagated in high-fidelity. The equations of motion of the restricted n-body problem are considered. The gravitational attractions of the Sun, Mercury, Venus, Earth (B*), Mars (central body), Jupiter (B), Saturn (B), Uranus (B), and Neptune (B) are taken into account. Additionally, solar radiation pressure, Mars’ non-spherical gravity, and relativistic corrections [4] (Schwarzschild solution, geodesic precession, and Lense-Thirring precession) are also included in the model.</p> <p>For additional information about the EXTREMA project visit the page <a href="http://extrema.polimi.it">extrema.polimi.it</a>.</p> <p><strong>References</strong><br> [1] F. Topputo and E. Belbruno,'Earth–Mars transfers with ballistic capture', Celestial Mechanics and Dynamical Astronomy, Vol. 121, No. 4, 2015, pp. 329–346. DOI: <a href="http://doi.org/10.1007/s10569-015-9605-8">10.1007/s10569-015-9605-8</a>.<br> [2] F. Topputo and E. Belbruno, 'Computation of weak stability boundaries: Sun–Jupiter system', Celestial Mechanics and Dynamical Astronomy, Vol. 105, No. 1-3, 2009, pp. 3–17. DOI: <a href="http://doi.org/10.1007/s10569-009-9222-5">10.1007/s10569-009-9222-5</a><br> [3] Z.-F. Luo and F. Topputo, 'Analysis of ballistic capture in Sun–planet models', Advances in Space Research, Vol. 56, No. 6, 2015, pp. 1030–1041. DOI: <a href="http://doi.org/10.1016/j.asr.2015.05.042">10.1016/j.asr.2015.05.042</a><br> [4] C. Huang, J. C. Ries, B. D. Tapley, and M. M.Watkins, 'Relativistic effects for near-earth satellite orbit determination', Celestial Mechanics and Dynamical Astronomy, Vol. 48, No. 2, 1990, pp. 167–185. DOI: <a href="http://doi.org/10.1007/BF00049512">10.1007/BF00049512</a></p> <p>* Here B stands for barycenter.</p>
Soil bulk density (fine earth) 10 x kg / m-cubic at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution
<p>Soil bulk density (fine earth) 10 x kg / m<sup>3</sup> at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. Processing steps are described in detail <strong><a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil">here</a></strong>. Antarctica is not included.</p> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>sol = theme: soil,</li> <li>bulkdens.fineearth = variable: soil bulk density,</li> <li>usda.4a1h = determination method: laboratory method code,</li> <li>m = mean value,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b10..10cm = vertical reference: 10 cm depth below surface,</li> <li>1950..2017 = time reference: period 1950-2017,</li> <li>v0.2 = version number: 0.2,</li> </ul>
ERT Datasets for the paper of Nickschick et al. (2019) in Solid Earth
<p>Underlying data for the paper</p> <p>Nickschick, T., Flechsig, C., Mrlina, J., Oppermann, F., Löbig, F. & Günther, T. (2019): Large-scale electrical resistivity tomography in the Cheb Basin (Eger Rift) at an ICDP monitoring drill site to image fluid-related structures. Solid Earth. https://doi.org/10.5194/se-2019-38.</p> <p>The paper contains two types of data:</p> <p>DC resistivity (geoelectrics) ERT data in four profiles<br> -------------------------------------------------------<br> P1, P2 and P3 represent classical multi-electrode ERT data with a unit electrode spacing of a=5m using the Wenner array. They are a representative selection of in total 8 profiles measured in the frame of the (German) MSc work of F. Loebig (there called P2, P5 and P7) where also the lithological section (Fig. 2) was developed:<br> 1. P1 between Lesinka and Hnevin, 620m long<br> 2. P2 around the Hartousov mofette, 700m long<br> 3. P3 between Hartousov and Kacerov, 700m long</p> <p>The inversion results are shown in Fig. 6a,b,c.</p> <p>The large-scale dataset represents data from a dipole-dipole experiment that is in detail described in the paper (Figs. 3-5) with the inversion result given in Fig. 7a along with borehole data.<br> For location see Fig. 1.</p> <p>For all four profiles we provide<br> - measured data with electrode positions on top and the electrode array (abmn) along with the resistance below, topography at the bottom<br> - the configuration file for the inversion software BERT (see https://gitlab.com/resistivity-net/bert), we used version 2.2.9 from January 2019<br> - kml/gpx files denoting the positions of the electrode chains (P1-P3) or an Excel file containing the positions in UTM33N</p> <p>Note that for P2 and the large-scale profile the topography needs to be taken into account whereas it is not necessary <br> Users should be able to reproduce the results by calling<br> bert cfgfile all show</p> <p>Gravity data<br> ------------<br> The data represent a two-column file:<br> 1. position along the ERT profile (projected) in metres<br> 2. Bouguer anomaly in mGal</p> <p>See also special README file in the gravity folder.</p>
Natural Earth data in Goode's Homolosine projection
<p>Produced from NaturalEarth <a href="https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/50m/cultural/ne_50m_admin_0_map_subunits.zip">1:50m Admin0 - Details map sub units cultural vector</a>data (version 5.1.1) and with <a href="https://zenodo.org/record/1841337">Vectors for Goode's Homolosine projection</a></p> <p> </p> <p>Created with QGIS 3.20.3</p>
Dataset for "Nicolas & Buffett (2023) - Excitation of high-latitude MAC waves in Earth's core, GJI"
<p>Data from the geodynamo model 'Calypso', used as forcings for MAC waves in Earth's core (see Nicolas & Buffett 2023 - Excitation of high-latitude MAC waves in Earth's core, GJI). Code to analyze this data is published at <a href="https://zenodo.org/badge/latestdoi/296985370">zenodo.org/badge/latestdoi/296985370</a>.</p> <p>All files use the netCDF4 format, a format that allows to represent labeled arrays.</p>
Supervised land cover classification using Google Earth Engine in Córdoba, Argentina, 2018-2020
Land cover information is critical to scientific, economic, and public policy-making. There is a high demand for accurate and timely land cover information that affects the accuracy of all subsequent applications. The availability of Google Earth Engine (GEE), which derives temporal aggregation methods from time-series images (i.e., the use of metrics such as mean or median), has also enabled optimization of computation time, such as managing large amounts of data to obtain more accurate results. Our objective was to obtain a land cover map for the northwest of the province of Córdoba, Argentina. The study was carried out in rural communities that belong to the departments of Cruz del Eje and Ischilín, northwest of Córdoba, and have different degrees of intervention in the land cover. Sentinel 2 Level 2A images were acquired for the study area. Images available from January 1, 2018, to December 31, 2020, were sampled. To create a thematic map, the median value was calculated for the sample of images from the selected time interval. Finally, the Normalized Difference Vegetation Index (NDVI) was calculated and added to the total bands of the median image. Training polygons were placed there considering the visual features in the median image. The Random Forest algorithm was used as the classification method. To verify the quality of the classified map, a list of 97,753 verification pixels was obtained. In addition, a confusion matrix was created to collect the conflicts that arise between categories, and the precision and kappa coefficient was calculated to define the quality of the map obtained. Image acquisition, preprocessing, and analysis were performed on the Google Earth Engine platform. Thematic maps with eight classes were obtained, with a total area of 719880 ha. The confusion matrix showed an overall precision of 99.26% and a corrected kappa index of 0.99, the classes were correctly classified by the algorithm.
Earth - Venus Low-Thrust Optimal Transfers / Database A
<p>A database of mass optimal trajectories of a low thrust spacecraft from Earth to Venus' orbit starting on the date 7th of May 2005 and arriving at Venus' orbit. </p> <p>This database was generated with a perturbation size of 0.2 and contains 429,316 trajectories with 100 samples along each trajectory.</p> <p>The database is in the HDF5 format with 4 dataframes included. These are the 'nominal', 'train', 'val' and 'test' dataframes each of which contains rows of entries in the following format:</p> <pre>['t', 'p', 'f', 'g', 'h', 'k', 'L', 'm', 'lp', 'lf', 'lg', 'lh', 'lk', 'lL', 'lm', 'T', 'ux', 'uy', 'uz', 'traj_id', 'sampl_id', 'vf']</pre> <p>which are the time, equinoctial elements (6 of them), the mass, the costates of the Optimal Control Problem Hamiltonian (7 of them), the thrust magnitude, the thrust directions (ux, uy, uz correspond to fr, ft, fn), the unique trajectory id, the sample id (nth sample from the start), and the value function.</p> <p>We are in the process of writing a paper titled "Real-Time Optimal Guidance for Interplanetary Transfers Using Deep Networks" that uses this dataset for the training of a neural network. The details on how we generated this data can be found in the paper, but it is essentially done using the (famous) "Backward Generation of Optimal Samples" method.</p>
Earth - Venus Low-Thrust Optimal Transfers / Database F
<p>A database of mass optimal trajectories of a low thrust spacecraft from Earth to Venus' orbit starting on the date 7th of May 2005 and arriving at Venus' orbit. </p> <p>This database was generated with a perturbation size of (5.0, 1.0, 1.0, 0.0, 0.0, 0.01) and contains 557,395 trajectories with 100 samples along each trajectory.</p> <p>The database is in the HDF5 format with 4 dataframes included. These are the 'nominal', 'train', 'val' and 'test' dataframes each of which contains rows of entries in the following format:</p> <pre>['t', 'p', 'f', 'g', 'h', 'k', 'L', 'm', 'lp', 'lf', 'lg', 'lh', 'lk', 'lL', 'lm', 'T', 'ux', 'uy', 'uz', 'traj_id', 'sampl_id', 'vf']</pre> <p>which are the time, equinoctial elements (6 of them), the mass, the costates of the Optimal Control Problem Hamiltonian (7 of them), the thrust magnitude, the thrust directions (ux, uy, uz correspond to fr, ft, fn), the unique trajectory id, the sample id (nth sample from the start), and the value function.</p> <p>We are in the process of writing a paper titled "Real-Time Optimal Guidance for Interplanetary Transfers Using Deep Networks" that uses this dataset for the training of a neural network. The details on how we generated this data can be found in the paper, but it is essentially done using the (famous) "Backward Generation of Optimal Samples" method.</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.