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38 results for “Planetary data”
Raw planetary images and boulder labels data (as shapefiles) collected during the BOULDERING Marie Skłodowska-Curie Global fellowship
<p>This database contains 64 large images of craters on the lunar and martian surfaces and 3 images of boulder fields on Earth (see manuscript <a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023JE008013">https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023JE008013</a> for more information on those terrestrial locations). The data was collected during the BOULDERING Marie Skłodowska-Curie Global fellowship between October 2021 and 2024.</p> <p>For each image, the boulder outlines within specific tiles within the image were carefully mapped in QGIS. More information about the labelling procedure can be found in the following manuscript (<a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023JE008013">https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023JE008013</a>). This dataset differs from the previous dataset included along with the manuscript <a href="https://zenodo.org/records/8171052">https://zenodo.org/records/8171052</a>, as it contains more mapped images, especially of boulder populations around young impact structures on the Moon (cold spots). </p> <p>For each location, you will find a raster with a .tif format, and three shapefiles:</p> <ul> <li> <p>a boulder-mapping file, which is the manually digitized outline of boulders.</p> </li> <li> <p>a tiles-completely-mapped file, which depicts the patches/tiles/windows on which the boulder mapping has been conducted.</p> </li> <li> <p>a global-tiles file, which shows all of the image patches/tiles/windows (pick the term you are the most familiar with) within a raster.</p> </li> </ul> <p>In addition you will find .pkl (which stands for pickle), which contains some information about the patches/tiles/windows if you would need to clip those windows out from the original raster. You can find more information in the way we process this raw data into a format which can be ingested in a deep learning model (see <a href="https://zenodo.org/records/14250874" target="_blank" rel="noopener">https://zenodo.org/records/14250874</a>) in the two following github repositories (<a href="https://github.com/astroNils/YOLOv8-BeyondEarth" target="_blank" rel="noopener">https://github.com/astroNils/YOLOv8-BeyondEarth</a> and <a href="https://github.com/astroNils/MLtools/tree/main" target="_blank" rel="noopener">https://github.com/astroNils/MLtools</a>). If you don't plan in adding more training data, you can directly used the pre-processed database (see <a href="https://zenodo.org/records/14250874" target="_blank" rel="noopener">https://zenodo.org/records/14250874</a>).</p> <p>There are multiple locations/images per planetary body. Cold spots are located on the Moon, but they are saved in a folder of their own. </p> <p>Note that the cold spots boulder mapping shapefiles are partially manually mapped, and partially originating from predictions made from a deep learning model (which explains the outline of boulders are predicted within one pixel).</p> <p><strong>How to cite:</strong></p> <p>Please refer to the "how to cite" section of the readme file of <a href="https://github.com/astroNils/YOLOv8-BeyondEarth" target="_blank" rel="noopener">https://github.com/astroNils/YOLOv8-BeyondEarth.</a></p> <p><strong>Structure:</strong></p> <pre><code>. └── raw_data/ ├── coldspots/ │ └── image_name/ │ ├── shp/ │ │ ├── <image_name>-tiles-completely-mapped.shp │ │ ├── <image_name>-boulder-mapping.shp │ │ └── <image_name>-global-tiles.shp │ └── raster/ │ └── <image_name>.tif ├── earth/ │ └── image_name/ │ ├── shp/ │ │ ├── <image_name>-tiles-completely-mapped.shp │ │ ├── <image_name>-boulder-mapping.shp │ │ └── <image_name>-global-tiles.shp │ └── raster/ │ └── <image_name>.tif ├── mars/ │ └── image_name/ │ ├── shp/ │ │ ├── <image_name>-tiles-completely-mapped.shp │ │ ├── <image_name>-boulder-mapping.shp │ │ └── <image_name>-global-tiles.shp │ └── raster/ │ └── <image_name>.tif └── moon/ └── image_name/ ├── shp/ │ │ ├── <image_name>-tiles-completely-mapped.shp │ │ ├── <image_name>-boulder-mapping.shp │ │ └── <image_name>-global-tiles.shp └── raster/ └── <image_name>.tif</code></pre>
Drone-based photogrammetric survey raw data from ESA PANGAEA-X 2017 planetary analogue campaign - Data collected on 2017-11-19
<p>Drone-based photogrammetric survey data from ESA PANGAEA-X 2017 planetary analogue campaign. Data were collected in the framework of the ESA PANGAEA-X testing campaign held in November 2017: We acknowledge ESA for organising the campaign and providing scientific and logistic assistance on site. The authors would like also to thank the Geopark of Lanzarote, the touristic center of Cueva de Los Verdes, the Cabildo of Lanzarote, the National Park of Timanfaya and the IGEO-CSIC-UCM for providing the necessary permits. Data collected on 2017-11-19 during an aerial survey with a DJI Phantom 4 - data from AGPA experiments (AGPA-D) see http://www.agpa-project.eu</p>
O(3P)+CO2 scattering cross sections at superthermal collision energies for planetary aeronomy: Raw data release
<p>Raw data and codes used in M. Gacesa, R. J. Lillis, and K. J. Zahnle, "O(3P)+CO2 scattering cross sections at superthermal collision energies for planetary aeronomy", MNRAS 491, 5650-5659 (2020).</p> <ul> <li>v1.1 includes <strong>differential cross section</strong> data for inelastic scattering: O(3P)+CO2(v=0,j=ji) -> O(3P)+CO2(v=0,jf) and energy transfer to the internal degrees of freedom calculated as in Gacesa & Kharchenko, Geophys. Res. Lett. 39, L10203 (2012).</li> </ul> <p>These files are distributed under GNU General Public License v3.0 and include NO liability or warranty of any kind. No support is provided by the authors. We cannot promise to answer any questions related to this dataset nor to prepare different products for you.</p> <p>Please cite this work as: Marko Gacesa, Lillis, Robert J., & Zahnle, Kevin J. (2019). O(3P)+CO_2 scattering cross sections at superthermal collision energies for planetary aeronomy: Raw data pre-release (Version v0.9-beta) [Data set]. Zenodo. <a href="http://doi.org/10.5281/zenodo.3256699">http://doi.org/10.5281/zenodo.3256699</a></p>
Spectral data associated to the publication: "Near-infrared reflectance spectroscopy of sublimating salty ice analogues. Implications for icy moons" by R. Cerubini et al. (Planetary and Space Science 211, 2022)
<p>This is the complete set of experimental NIR reflectance data collected by R. Cerubini and co-authors for the article "Near-infrared reflectance spectroscopy of sublimating salty ice analogues. Implications for icy moons" published in Planetary and Space Science 211 (2022). doi: https://doi.org/10.1016/j.pss.2021.105391.</p> <p>The article itself is published in open-access and provides the methodology for the spectral aquisitions, discussion of the errors and uncertainties, analysis of the spectra and implications for the composition of Solar System surfaces.</p> <p>The data are contained in ASCII files (columns separated by comma). The first column is the wavelength (in micrometers) and the other columns contain the reflectance data (in unit of reflectance factor). The different compositions are indicated in the filenames and correspond directly to the figures in the published paper.</p> <p> </p> <p> </p>
Input and output data (images + boulder labels, model setup, model weights and more) for the manuscript "Automatic characterization of boulders on planetary surfaces from high-resolution satellite images"
<p><strong>File 1:</strong> raw_data_BOULDERING.zip</p> <p><strong>Size:</strong> 8.8 GB</p> <p><strong>Summary: </strong>It contains all of the rasters (planetary images) and labeled boulders (raw data):</p> <ul> <li> <p>a boulder-mapping file, which is the manually digitized outline of boulders.</p> </li> <li> <p>a ROM file (stands for Region of Mapping), which depicts the image patches on which the boulder mapping has been conducted.</p> </li> <li> <p>a global-tiles file, which shows all of the image patches within a raster.</p> </li> </ul> <p>There are multiple locations/images per planetary body.</p> <p><strong>Structure:</strong></p> <pre>. └── raw_data/ ├── earth/ │ └── image_name/ │ ├── shp/ │ │ ├── <image_name>-ROM.shp │ │ ├── <image_name>-boulder-mapping.shp │ │ └── <image_name>-global-tiles.shp │ └── raster/ │ └── <image_name>.tif ├── mars/ │ └── image_name/ │ ├── shp/ │ │ ├── <image_name>-ROM.shp │ │ ├── <image_name>-boulder-mapping.shp │ │ └── <image_name>-global-tiles.shp │ └── raster/ │ └── <image_name>.tif └── moon/ └── image_name/ ├── shp/ │ ├── <image_name>-ROM.shp │ ├── <image_name>-boulder-mapping.shp │ └── <image_name>-global-tiles.shp └── raster/ └── <image_name>.tif</pre> <p> </p> <p><strong>File 2:</strong> best_model.zip</p> <p><strong>Size:</strong> 624.7 MB</p> <p><strong>Summary:</strong></p> <p>This zip file contains all of the inputs and outputs required/obtained from the training of the BoulderNet Mask R-CNN model (model setup, augmentation pipeline, model weights, log during training, logged metrics):</p> <ul> <li> <p>augmentation_pipeline.json (required as inputs for the training of the algorithm to apply augmentations). See <a href="https://github.com/astroNils">https://github.com/astroNils</a> and the MLtools repository for more information.</p> </li> </ul> <ul> <li> <p>Base-RCNN-FPN.yaml (base model setup file).</p> </li> <li> <p>config.yaml (complete model setup file, merge of the base and Mars-Moon-Earth setup file).</p> </li> <li> <p>Mars-MoonEarth-v050...yaml (model setup file).</p> </li> <li> <p>log.txt (log during training of the algorithm).</p> </li> <li> <p>model_0055999.pth (model weights at second last saving step)</p> </li> <li> <p>model_0063999.pth (model weights at last saving step)</p> </li> </ul> <p>We advice the use of model weights model_0055999.pth (to avoid slight overfitting).</p> <p><strong>File 3:</strong> Apr2023-Mars-Moon-Earth-mask-5px.zip (pre-processed input images)</p> <p><strong>Size:</strong> 252.8 MB</p> <p><strong>Summary:</strong></p> <p>This zip files contains the input data (images and boulder outlines) for the train, validation and test datasets. See <a href="https://github.com/astroNils">https://github.com/astroNils</a> and the MLtools repository for more information in how-to-use the different files.</p> <ul> <li> <p>The json folder contains json files that can be given as input (as a custom dataset) to the Detectron2 platform. The only differences between the two files is how the bounding boxes around masks have been generated. We advised to use "Apr2023-Mars-Moon-Earth-mask-5px.json".</p> </li> <li> <p>The pkl folder and pickle file includes some informations about the 950 image patches in our boulder dataset.</p> </li> <li> <p>The pre-processing folder contains all of the training, validation and test image patches and corresponding shapefiles.</p> </li> <li> <p>The shapefile folder is actually empty (it should not be there!).</p> </li> </ul> <p><strong>Structure:</strong></p> <pre>. └── preprocessed_inputs/ ├── json ├── pkl ├── preprocessing/ │ ├── train/ │ │ ├── images │ │ └── labels │ ├── validation/ │ │ ├── images │ │ └── labels │ └── test/ │ ├── images │ └── labels └── shp</pre> <p> </p>
Data Supplement for ApJ Paper: Stumbling over planetary building blocks: AU Microscopii as an exampleof the challenge of retrieving debris-disk dust properties
<p>Computational data set underlying a manuscript analyzing visible to near infrared wavelength Hubble-STIS spectrographic coronagraph observations of the AU Microscopii debis disk. To interpret this multi-wavelength data set, we calculated light scattering properties of three different debris disk dust grain shape models: spheres, porous spheres, and more realistic irregular agglomerated debris particles. Calculations of scattering efficiency and phase function were done over a range of dust grain sizes and material refractive indices.</p> <p>This bundle contains three .hdf table files. Each table file contains a size parameter array (1D), complex refractive index array (1D), scattering efficiency array (1D), and scattering phase function array (2D). The 1st dimension of each array is the same size. The 2nd dimension of the scattering phase function is 60, the number of scattering angles calculated at an angular resolution of 3 degrees.</p>
Data accompanying article: A planetary boundary for green water
<p>This deposit contains the LPJmL model simulation outputs of mean monthly root-zone soil moisture ("LPJmL_rzsm_hist.zip") and the data underlying the plot in Fig. 3 ("Fig_3_plotdata.xlsx") in the article: Wang-Erlandsson, L., Tobian, A., van der Ent, R. J., Fetzer, I., te Wierik, S., Porkka, M., Staal, A., Jaramillo, F., Dahlmann, H., Singh, C., Greve, P., Gerten, D., Keys, P.W., Gleeson, T, Cornell, S. E., Steffen, W., Bai, X., Rockström, J., (2022): A<em> planetary boundary for green water</em>. Nature Reviews Earth & Environment. For method description, please refer to the article.</p> <p>Files in the folder "LPJmL_rzsm_hist.zip" are named as NN_rzsm_hist_M.tif, in which:</p> <ul> <li>NN refers to the name of an Earth system model, of which the outputs were used as forcing in the LPJmL runs</li> <li>rzsm refers to "root zone soil moisture"</li> <li>hist refers to historical period 1850-2014</li> <li>M refers to name of month (jan for January etc.)</li> </ul> <p> </p> <p> </p>
Data Archive for "Speckle Noise Reduction via Linewidth Broadening for Planetary Laser Reflectance Spectrometers"
<p>This archive contains the raw speckle images and experimental notes for the data contained in the journal article: "Speckle Noise Reduction via Linewidth Broadening for Planetary Laser Reflectance Spectrometers".</p> <p>The data available are:</p> <p>Raw speckle images for each of 5 illumination sources:</p> <ol> <li>Single-Frequence diode (files are named "IPS")</li> <li>Dual Mode pump diode (files are named "II-IV")</li> <li>Fabry Perot didoe (files are names "FP")</li> <li>Superluminescent Diode (files are named "SLD")</li> <li>Whilte light halogen source (files are named "WL")</li> </ol> <p>For each of theses source there are 5 images for each reflectance target, four speckle patterns and one background image with the laser source off. The naming convention is:</p> <p>LaserName_TargetReflectance_Target Rotation State or BKG.tiff</p> <p>For example, IPS_50_2 is the single-frequency laser using the 50% reflectance target and second rotation state of the target.</p> <p>The integration time for each image is give in the file "Experiment Paramters.csv" and was manually change to keep the maximum intensity at roughly 75% saturation.</p> <p>The spectra of the three laser sources presented in the manuscript are also included as .csv files</p>
Data related to the paper: Surface Heating Steers Planetary-Scale Ocean Circulation
<p>Preprocessed outputs to reproduce figures in the paper: <a href="https://doi.org/10.1175/JPO-D-23-0016.1">https://doi.org/10.1175/JPO-D-23-0016.1</a>.</p>
Data from: Survival limits of yeasts and other extremophilic microorganisms with relevance for planetary protection of the icy moons
Open the record for dataset details and reuse information.
Data for One Earth publication "Breaching planetary boundaries: Over half of global land area suffers critical losses in functional biosphere integrity"
<p>This repository contains data and processing scripts for the study: Stenzel et al. 2025, One Earth, "Breaching planetary boundaries: Over half of global land area suffers critical losses in functional biosphere integrity".</p>
A letter to the Lancet Planetary Health: Assessment from our world in data of overall gain in mortality after COVID pharmaceutical intervention
<p>Straightforward comparison of COVID mortality before and after the pharmaceutical intervention as recovered from the site https://ourworldindata.org/explorers/coronavirus-data-explorer</p> <p>Submitted as a letter to Lancet Planetary Health</p>
Data for: `Limits on planetary-mass primordial black holes from the OGLE high-cadence survey of the Magellanic Clouds'
<p>This repository contains data presented in the paper "Limits on planetary-mass primordial black holes from the OGLE high-cadence survey of the Magellanic Clouds".</p>
Data for: Connecting hemispheric asymmetries of planetary albedo and surface temperature
<p>Satellite measurements show that the Northern and Southern hemispheres reflect equal amounts of short-wave radiation ("albedo symmetry"), but no theory exists on if, how, and why the symmetry is established and maintained. Ambiguously, climate models are strongly biased in albedo symmetry but agree in the sign of the response to CO<sub>2</sub>. We find that mean-state biases in albedo symmetry and surface temperature asymmetry correlate negatively. Similarly, the response of albedo asymmetry to CO<sub>2</sub> forcing correlates negatively with the magnitude of the asymmetry in surface warming. This is true across many and within single climate model simulations: a too warm or stronger warming hemisphere is darker or darkens more than its counterpart. In the 21 years of observations we find the same tendency and hypothesize a) albedo symmetry is a function of the current climate state and b) we will observe an evolution towards albedo asymmetry in coming decades.</p>
Figure data for paper "The first ground level enhancement seen on three planetary surfaces: Earth, Moon and Mars"
<p>Here we provide the data used for generating Figures 1-4 as published in the paper "The first ground level enhancement seen on three planetary surfaces: Earth, Moon and Mars" </p>
Data and Code for "A planetary health innovation for disease, food, and water challenges in Africa"
<p>Data and R code for both economic and non-economic analysis for the paper titled, "A planetary health innovation for disease, food, and water challenges in Africa"</p>
Data for: Connecting hemispheric asymmetries of planetary albedo and surface temperature
Open the record for dataset details and reuse information.
Rapid Imaging Planetary Spectrograph: 2018 Mercury Exosphere Data
<p>Containing raw FITS files associated with the Schmidt et al. article: "The Rapid Imaging Planetary Spectrograph: Observations of Mercury’s Sodium Exosphere in Twilight." Submitted to The Planetary Science Journal in 2019. IDL analysis code is available at <a href="https://github.com/CarlSchmidt/RIPS-Pipeline">https://github.com/CarlSchmidt/RIPS-Pipeline</a></p>
Data from: Study on the optimization of the deposition rate of planetary GaN-MOCVD films based on CFD simulation and the corresponding surface model
Metal-organic chemical vapour deposition (MOCVD) is a key technique for fabricating GaN thin film structures for light-emitting and semiconductor laser diodes. Film uniformity is an important index to measure equipment performance and chip processes. This paper introduces a method to improve the quality of thin films by optimizing the rotation speed of different substrates of a model consisting of a planetary with seven 6-inch wafers for the planetary GaN-MOCVD. A numerical solution to the transient state at low pressure is obtained using computational fluid dynamics. To evaluate the role of the different zone speeds on the growth uniformity, single factor analysis is introduced. The results show that the growth rate and uniformity are strongly related to the rotational speed. Next, a response surface model was constructed by using the variables and the corresponding simulation results. The optimized combination of the matching of different speeds is also proposed as a useful reference for applications in industry, obtained by a response surface model and genetic algorithm with a balance between the growth rate and the growth uniformity. This method can save time, and the optimization can obtain the most uniform and highest thin film quality.
Data for "Double Superionicity in Icy Compounds at Planetary Interior Conditions"
<p>Data for "Double Superionicity in Icy Compounds at Planetary Interior Conditions" by authors Kyla de Villa, Felipe Gonzalez-Cataldo, and Burkhard Militzer (DOI: 10.1038/s41467-023-42958-0). </p><p>To keep the data volume reasonable, only the files from the first checkpoint of DFT-MD and ML-MD simulations are provided. Further simulation data are available from the corresponding author upon reasonable request. </p>
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