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304 results for “Planets”
Magnetic excitation moments for large moons of the giant planets
<p>Text files containing the spatially uniform (degree-1) magnetic oscillations experienced by each large moon of the giant planets as a function of frequency, also known as the excitation moments. All moments are in complex notation relative to the J2000 epoch. Used for determining the strength of induced magnetic fields from the moons from an interior conductivity structure. This dataset is compatible for use with the <a href="https://github.com/itsmoosh/MoonMag" target="_blank" rel="noopener">MoonMag</a> and <a href="https://github.com/vancesteven/PlanetProfile" target="_blank" rel="noopener">PlanetProfile frameworks</a> for calculating induced magnetic fields of target moons. Refer to the publication linked below for more information.</p> <p>All vector components are in IAU coordinates, such that at the body center, +<em>z</em> is directed along the body spin axis, +<em>x</em> is directed approximately toward the parent planet in the plane of an IAU-defined meridian feature, and +<em>y</em> is directed approximately opposite to the orbital velocity to complete the right-handed set. For the uranian moons and Triton, the IAU +<em>z</em> axes are opposite the spin axes because of their angles relative to the solar system invariable plane; the +<em>y</em> axes for these bodies are therefore directed approximately along the orbital velocity vector.</p> <p>Excitation fields are determined by evaluation of SPICE kernels over a time series at the location of the body center. Position information is inserted into a magnetospheric model for the parent planet and complex Fourier coefficients are inverted from the time series using linear least squares optimization. The magnetic field models we use for each planet are:</p> <ul> <li><strong>Jupiter</strong> - JRM33 + C2020 current sheet (Connerney et al., 2022, 2020) except Callisto, for which we use VIP4+K (Connerney et al., 1998; Khurana, 1997)</li> <li><strong>Saturn</strong> - Cassini 11+ (Cao et al., 2020)</li> <li><strong>Uranus</strong> - AH<sub>5</sub> (Herbert, 2009)</li> <li><strong>Neptune</strong> - O8 (Connerney et al., 1991)</li> </ul> <p>ASCII text files are included for all major moons of these planets.</p>
275 Candidates and 149 Validated Planets Orbiting Bright Stars in K2 Campaigns 0-10
<p>This dataset contains transit model posterior distributions and validation analyses for the 275 exoplanet candidates (in 233 systems) analyzed in Mayo et al. (2018), titled "275 Candidates and 149 Validated Planets Orbiting Bright Stars in K2 Campaigns 0-10".</p> <p>The dataset takes the form of 233 compressed directories each corresponding to an exoplanet system and titled after its EPIC ID. Within a given directory there are two numpy pickles named EPICXXXXXXXXX_chains.npy and EPICXXXXXXXXX_lnlikes.npy (where XXXXXXXXX is the 9 digit EPIC number) as well as n subdirectories, where n is the number of planet candidates in the system.</p> <p>The EPICXXXXXXXXX_chains.npy pickle is a representative sample of the posterior distribution of the transit model for a given exoplanet system. The pickle is a numpy array of size (j,k,l), where j is the number of walkers in the Markov chain Monte Carlo ensemble simulation that sampled the posterior distribution (note: we chose to fix j = 2*l), k is the number of walker steps reported in this dataset (the full posteriors were thinned down to between 750 and 10,000 steps), and l is the number of parameters in the transit model for the exoplanet system. The EPICXXXXXXXXX_lnlikes.npy pickle contains the associated ln(likelihood) values for each walker step in the previously described pickle. This pickle is a numpy array of size (j,k) where j and k are defined as above.</p> <p>The number of parameters will always be of the form 4 + 5*n, where n is again the number of planets in the systems. The first four parameters in the pickle are a baseline offset parameter for the normalized flux, a noise parameter to take the place of flux error bars, and two quadratic limb darkening parameters q<sub>1</sub> and q<sub>2</sub> based on Kipping et al. (2013). The next five parameters (and each subsequent set of five parameters in multi-candidate systems) refer to the reference epoch (a mid-transit time in BJD - 2454833), the period (in days), log<sub>10</sub>(R<sub>p</sub>/R<sub>*</sub>), the transit duration (T<sub>IV</sub>-T<sub>I</sub> in days), and the impact parameter. It should be noted that there is no consistent ordering of the planets in the posterior samples (for example, in a three planet system parameters 5-9 may refer to planet b, planet c, or planet d). Therefore, planetary periods should be used as reference to identify candidates. All parameters and the nature of the transit model are described in detail in Mayo et al. (2018).</p> <p>Each subdirectory contains the input and output of the validation analysis conducted via the VESPA validation package (Morton 2012, 2015). For additional details please refer to the relevant citations or the <a href="https://github.com/timothydmorton/VESPA">VESPA github repository</a>. Each subdirectory is named after the appropriate candidate listed in Mayo et al. (2018; specifically Tables 5 and 7).</p>
Reflection Spectra Repository for Cool Giant Planets
<p>Supplementary material for <a href="http://iopscience.iop.org/article/10.3847/1538-4357/aabb05"><em>Exploring H2O Prominence in Reflection Spectra of Cool Giant Planets</em></a> - ApJ 858, 69 (2018).</p> <p>This repository contains 65520 model reflection spectra of cool giant planets. The grid explores the influence of metallicity, gravity, effective temperature, and sedimentation efficiency on H<sub>2</sub>O absorption signatures in giant planet atmospheres. We also include two animations to visualise how the prominence of H<sub>2</sub>O absorption evolves over this parameter space. The included models range over:</p> <p>*m => 1-100 x solar (log(m) @ 0.0, 0.5, 1.0, 1.5, 1.7, 2.0 dex) <-- log(m) = 1.7 new for V2 of the database.<br> *g => 1-100 m/s<sup>2</sup> (evenly over log(g) in steps of 0.1 dex)<br> *T<sub>eff</sub> => 150-400 K (linearly in steps of 10 K)<br> *f<sub>sed</sub> => 1-10 (linearly in steps of 1)</p> <p>(V 1.0, March 30th 2018):</p> <blockquote> <p>Initial release of the reflection spectra repository. </p> </blockquote> <p>(V 2.0, Oct 1st 2019): </p> <blockquote> <p>The cool giant reflection spectra grid has been re-computed using the latest version of the PICASO albedo code (doi: <a href="https://arxiv.org/ct?url=https%3A%2F%2Fdx.doi.org%2F10.3847%2F1538-4357%2Fab1b51&v=77076c4a">10.3847/1538-4357/ab1b51</a>). This fixes a few bugs and adds new model features (e.g. Raman scattering, see Batalha+2019).</p> <p>The new grid is packaged as a HDF5 file with an accompanying python script 'Open_Albedo_Database.py'. The python script is provided to show how to open the albedo database, plot the spectra, and save spectra as a .txt file. The user need only change 4 lines (specifying log(m), log(g), T<sub>eff</sub>, f<sub>sed</sub>) and run the python script to produce a plot of the albedo spectra (both with and without H<sub>2</sub>O absorption).</p> </blockquote> <p><strong>NEW</strong>: (V 2.1, Oct 3rd 2019): </p> <blockquote> <p>Fixed a bug causing models with log(g) = 3.4 or 3.9 to not display cloud opacity.</p> </blockquote>
Composite X-EUV + optical model spectrum of the planet-hosting star HIP 67522 (HD 120411)
<p>Composite spectrum of HIP 67522 obtained by joining a Phoenix photospheric spectrum with the X-EUV spectrum synthesized from the reconstructed plasma Emission Measure Distribution (EMD) vs. temperature in chromosphere, transition region, and corona. The FITS file contains 3 extensions with the spectrum, the EMD, and the plasma chemical abundances, derived from the analysis of X-ray and FUV high-resolution spectra, obtained with simultaneous observations with XMM-Newton and HST.</p> <p>In the attached figure, the upper panel shows the specific flux at Earth, while the bottom panel is the photon flux at a distance of 1 AU. In green the Phoenix spectrum resampled to a wavelength resolution of 1 Angstrom, down to 1700 A; the XUV spectrum in the range 1-1700 A instead has a resolution of 0.01 A. The green and blue segments in the upper panel, at about 200 nm, mark the Phoenix model flux and the observed flux integrated over the OM UVM2 band.</p>
Protected planet (protected areas), forests and intact forest landscapes at 100 m, 250 m to 1 km resolution
<p><a href="https://www.protectedplanet.net/en/thematic-areas/wdpa?tab=WDPA">Protected planet</a> (protected areas; version Oct 2024) and <a href="https://intactforests.org/data.ifl.html">intact forest landscapes</a> (2000, 2013, 2016 and 2020) rasterized to 100 m, 250 m and 1 km resolutions. The aggregated map contains all pixels that are either protected or intacts. To use these resources please refer to original data producers:</p> <ul> <li>Defourny, P., Lamarche, C., Bontemps, S., De Maet, T., Van Bogaert, E., Moreau, I., Brockmann, C., Boettcher, M., Kirches, G., Wevers, J., Santoro, M., Ramoino, F., & Arino, O. (2017). Land Cover Climate Change Initiative - Product User Guide v2. Issue 2.0. <a href="http://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf">http://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf</a></li> <li>Olsson, E., Albrecht, R., & Golden Kroner, R.E. (2021). PADDDtracker Data Release Version 2.1: Technical Notes. Conservation International, Arlington, VA. DOI: 10.5281/zenodo.4749615.</li> <li>Potapov, P., Hansen, M. C., Laestadius L., Turubanova S., Yaroshenko A., Thies C., Smith W., Zhuravleva I., Komarova A., Minnemeyer S., Esipova E. The last frontiers of wilderness: Tracking loss of intact forest landscapes from 2000 to 2013. <a href="http://advances.sciencemag.org/content/3/1/e1600821">Science Advances, 2017; 3:e1600821</a></li> <li>UNEP-WCMC and IUCN (2024), Protected Planet: The World Database on Protected Areas (WDPA) [Online], October 2024, Cambridge, UK: UNEP-WCMC and IUCN. Available at: <a title="Visit Protected Planet" href="http://protectedplanet.net/" target="_blank" rel="noopener">www.protectedplanet.net</a>.</li> </ul> <p>The time-series of forest areas (<strong>forest.areas_esa.cci_p</strong>) are based on the <a href="https://climate.esa.int/en/odp/#/project/land-cover">ESA CCI Land Cover time-series</a> (2000–2022) 300-m resolution data; also available at 1-km resolution based on "average" resampling. Two maps (<strong>forest.cover.sum_esa.cci_p_250m</strong> and <strong>forest.cover.diff_esa.cci_p_250m</strong>) show long term cumulative forest cover and difference in forest cover for 2022 vs 2000.</p> <p>The protected planet areas and intact forest landscapes were rasterized using:</p> <pre><code>## https://www.protectedplanet.net/en/thematic-areas/wdpa?tab=WDPA for(j in 0:2){ system(paste0('gdal_rasterize -ot Byte -a_nodata 0 -burn 100 -where "IUCN_CAT LIKE \'I%\'" /data/CCI_LandCover/WDPA_Oct2024_Public_shp_', j, '/WDPA_Oct2024_Public_shp-polygons.shp WDPA_Oct2024_Public_shp_', j, '_1km.tif -tr 0.008333333 0.008333333 -te -180 -65.00208 180 87.37 -co COMPRESS=DEFLATE -a_srs EPSG:4326')) } s = sds(rast("WDPA_Oct2024_Public_shp_ALL_0_1km.tif"), rast("WDPA_Oct2024_Public_shp_ALL_1_1km.tif"), rast("WDPA_Oct2024_Public_shp_ALL_2_1km.tif")) dg.x = app(s, fun=max, na.rm=TRUE, cores = 32) dg.x0 = terra::ifel(is.na(dg.x), 0, dg.x, filename="protected.areas_wdpa.all_p_1km_s_2023_2024_go_epsg4326_v20241025.tif", wopt=list(gdal=c("COMPRESS=DEFLATE"), datatype='INT2S'), overwrite=TRUE) ## https://intactforests.org/data.ifl.html for(j in c(2000,2013,2016,2020)){ system(paste0('gdal_rasterize -ot Byte -a_nodata 0 -burn 100 -l \"ifl_', j, '\" /mnt/lacus/raw/protectedplanet/ifl_', j, '.shp intact.forest_gfw_p_1km_s_', j, '0101_', j, '1231_go_epsg4326_v20241025.tif -tr 0.008333333 0.008333333 -te -180 -65.00208 180 87.37 -co COMPRESS=DEFLATE -a_srs EPSG:4326')) } ## Combination IFL & WPDA b = sds(rast("protected.areas_wdpa.all_p_1km_s_2023_2024_go_epsg4326_v20241025.tif"), rast("intact.forest_gfw_p_1km_s_20200101_20201231_go_epsg4326_v20241025.tif")) bg.x = app(b, fun=max, na.rm=TRUE, cores = 32) bg.x0 = terra::ifel(is.na(bg.x), 0, bg.x, filename="protected.intact.areas_wdpa.ifl_p_1km_s_2020_2024_go_epsg4326_v20241025.tif", wopt=list(gdal=c("COMPRESS=DEFLATE"), datatype='INT2S'), overwrite=TRUE)</code></pre>
Color Classification of Extrasolar Giant Planets: Prospects and Cautions
<p>This dataset contains the reflected light models used in the analysis of<a href="http://adsabs.harvard.edu/abs/2018AJ....156..158B"> Batalha et al. 2018 (Color Classification of Extrasolar Giant Planets: Prospects and Cautions) </a>. The paper explains the full calculation of the models and the parameter space. </p> <p>The public repository <a href="https://github.com/natashabatalha/colorcolor">colorcolor</a> contains <a href="https://github.com/natashabatalha/colorcolor/tree/master/notebooks">notebooks</a> that explain how extract spectra from the database. To show it's simplicity, we post a small code snippet below. </p> <pre><code class="language-python">import colorcolor as c planet_dict = {'cloud': 0.03, 'distance': 0.85, 'gravity': 25, 'metallicity': 0.0, 'phase': 100.0, 'temp': 150} planet = c.select_model(planet_dict) wave, albedo = planet['WAVELN'], planet['GEOMALB']</code></pre> <p> </p> <p>Further explanation can be found at the GitHub repository and within the paper. </p> <p> </p> <p> </p>
Planet Four Data Catalog
<p>This is the data catalog for the paper:</p> <p><a href="https://www.sciencedirect.com/science/article/abs/pii/S0019103518301039">Planet Four: Probing springtime winds on Mars by mapping the southern polar CO2 jet deposits</a></p> <p>The catalog can be automatically retrieved from here using the Python package <a href="https://pypi.org/project/p4tools/">p4tools</a></p>
Water in the terrestrial planet-forming zone of the PDS 70 disk
<p>This release includes the portion of the JWST-MIRI MRS spectrum of the PDS 70 disk analysed in the paper by Perotti et al. (2023). The original observational data are part of the Guaranteed Time Observation (GTO) program 1282 (PI: Th. Henning) with observation number 66 and will become public on 2 August, 2023 on the MAST database (https://archive.stsci.edu/). This release contains:<br> <br> 1) the full rebinned (4.9-22.5 μm) JWST-MIRI MRS spectrum of PDS 70 presented in Fig. 2 of Perotti et al. (2023);<br> 2) the JWST-MIRI MRS spectrum of PDS 70 in the 6.78-7.36 μm region used for the water line analysis shown in Fig. 3 of Perotti et al. (2023);<br> 3) the Spitzer-IRS low-resolution spectrum observed as part of the Spitzer-IRS GTO program 40679 (PI: G. Rieke) shown in Fig.1 of Perotti et al. (2023). </p> <p>The first dataset consists of one .csv file (1_PDS70_fig2_MIRI_Perotti23.csv) which contains the the 4.9-22.5 μm JWST-MIRI MRS spectrum of PDS 70 presented in Fig. 2 of Perotti et al. (2023). The spectrum is rebinned by averaging 15 spectral points and assign errors σ to the rebinned spectral points assuming a normal error distribution with equal weights for each individual spectral element.<br> <br> The second dataset consists of one .dat file and one python script. One .dat file contains the JWST-MIRI spectrum of PDS 70 in the 6.78-7.36 μm region shown in Fig. 3 of Perotti et al. (2023) where the brightest water emission lines are observed (2_PDS70_fig3_MIRI_Perotti23.dat). The JWST-MIRI continuum-subtracted spectrum and the best-fit water LTE slab model are included. The Python script used to reproduce Figure 3 of Perotti et al. (2023) is also provided (2_script_plot_fig3.py). <br> <br> The third dataset consists of one .dat file (3_PDS70_fig1_IRS_Perotti23.dat) which represents the Spitzer-IRS low-resolution spectrum of PDS 70 shown in Figure 1 of Perotti et al. (2023).</p>
Brightness Temperature Variances from On-Planet Views in the A1–A3 Channels by the Mars Climate Sounder
<p>Heavens, Nicholas (2022), “Brightness Temperature Variances from On-Planet Views in the A1–A3 Channels by the Mars Climate Sounder”, Zenodo, V1, doi: 10.5281</p> <p>Title: Brightness Temperature Variances from On-Planet Views in the A1–A3 Channels by the Mars Climate Sounder</p> <p>Author: Nicholas G. Heavens, Space Science Institute, Boulder, CO, USA and London, UK (nheavens@spacescience.org)</p> <p>Date: 25 March 2022 </p> <p>Overview: This dataset contains an improvement and extension of significant data analysis products related to: </p> <p>Heavens, N.G., A. Pankine, J.M. Battalio, C. Wright, D.M. Kass, A. Kleinböhl, S. Piqueux, J.T. Schofield, 2022, Mars Climate Sounder Observations of Gravity-Wave Activity throughout Mars' Lower Atmosphere, Plan. Sci. J., 3, 57, doi: 10.3847/PSJ/ac51ce. </p> <p>These fall into three broad categories: diagnoses of detrended brightness temperature variance (GW) at 595–615 cm-1 (A1), 615–645 cm-1 (A2), and 635-665 cm-1 (A3) in individual views in the nadir or off-nadir by Mars Climate Sounder on board Mars Reconnaissance Orbiter; averages and other statistics of those diagnoses in space and time; and estimated gravity wave visibility functions for nadir, off-nadir, and nadir views with baselines like off-nadir views. This document presumes the manuscript is available to the dataset user.</p> <p>The purpose of archiving this dataset is to allow for comparison with a forthcoming analysis of gravity wave activity in limb observations by Mars Climate Sounder.</p> <p>The original dataset was published as:</p> <p>Heavens, Nicholas (2022), “Brightness Temperature Variances from On-Planet Views in the A1–A3 Channels by the Mars Climate Sounder”, Mendeley Data, V2, doi: 10.17632/5k6nybdy92.2</p> <p>The extension of the dataset consists of extension of the analysis time period to the end of January 2022 (MY 36, Ls=166.87).</p> <p>The improvement consists of a flag to indicate when an on-planet observations is likely to intersect a loop structure observed in the limb, and thus be contaminated by a high altitude cloud, which results in overestimate of gravity wave activity in the tropics at night during the clear season. Averages are now included that filter out flagged observations, as well as the original averages that include the flagged observations. </p> <p>If you are using this dataset and are feeling confused or wish there were some additional information from the article in this dataset, please contact me. A complete accounts of the contents and a restatement of this description is included as <em>MCS_OP_A13_GW_Analysis_Dataset_Documentation.pdf.</em></p> <p>Acknowledgments: The archiving of this dataset is supported by NASA’s Mars Data Analysis Program (80NSSC19K1215).</p>
Temperature measurements from the SMS Gazelle, Valdivia, and SMS Planet in the Indian Ocean
<p>This dataset contains digitized temperature records from the SMS Gazelle (1874–1876), Valdivia (1898–1899), and SMS Planet (1906–1907) observations in the Indian Ocean. The data is described in:</p> <p>Wenegrat, J.O., E. Bonanno, U. Rack, and G. Gebbie, 2022: A century of observed temperature change in the Indian Ocean. <em>Geophys. Res. Letters.</em> doi:10.1029/2022GL098217.</p> <p>Data was digitized from the original cruise reports using independent double-entry, and checked for consistency. A number of observations were discarded due to data problems, as described in Wenegrat et al. 2022 (see also associated code repository doi:10.5281/zenodo.6646645).</p>
Assessment of the condition of winter crops before winter dormancy on the basis of Planet data; season 2018
<p>NDVI determined on the basis of images of Planets from the dates 07.09.2018 and 14.10.2018, were used to study the assessment of the winter crop before winter dormancy. Available data from the September and October dates were used to assess the degree of development and density of plants.</p>
Assessment of the condition of winter crops on the basis of Planet data; season 2017/2018
<p>NDVI determined on the basis of images of Planets from the dates 17.10.2017 and 13.04.2018, were used to study the assessment of wintering of crops. Acquisition of data before and after winter rest allows to assess the condition of winter crops. Available data come from the research area of the Kujawsko-Pomorskie voivodeship.</p>
Climate change impact and mitigation cost data - The economically optimal warming limit of the planet
<p>This climate change impact data (future scenarios on temperature-induced GDP losses) and climate change mitigation cost data (REMIND model scenarios) is published under doi: 10.5281/zenodo.3541809 and used in this paper:</p> <p>Ueckerdt F, Frieler K, Lange S, Wenz L, Luderer G, Levermann A (2018) The economically optimal warming limit of the planet. Earth System Dynamics. <a href="https://doi.org/10.5194/esd-10-741-2019">https://doi.org/10.5194/esd-10-741-2019</a></p> <p>Below the individual file contents are explained. For further questions feel free to write to Falko Ueckerdt (ueckerdt@pik-potsdam.de).</p> <p> </p> <p><strong>Climate change impact data</strong></p> <p>File 1: Data_rel-GDPpercapita-changes_withCC_per-country_all-RCP_all-SSP_4GCM.csv</p> <p>Content: Data of relative change in absolute GDP/CAP levels (compared to the baseline path of the respective SSP in the SSP database) for each country, RCP (and a zero-emissions scenario), SSP and 4 GCMs (spanning a broad range of climate sensitivity). Negative (positive) values indicate losses (gains) due to climate change. For figure 1a of the paper, this data was aggregated for all countries.</p> <p> </p> <p>File 2: Data_rel-GDPpercapita-changes_withCC_per-country_all-SSP_4GCM_interpolated-for-REMIND-scenarios.csv</p> <p>Content: Data of relative change in absolute GDP/CAP levels (compared to the baseline path of the respective SSP in the SSP database) for each country, SSP and 4 GCMs (spanning a broad range of climate sensitivity). The RCP (and a zero-emissions scenario) are interpolated to the temperature pathways of the ten REMIND model scenarios used for climate change mitigation costs. Hereby the set of scenarios for climate impacts and climate change mitigation are consistent and can be combined to total costs of climate change (for a broad range of mitigation action).</p> <p> </p> <p>File 3: Data_rel-GDPpercapita-changes_withCC_per-country_SSP2_12GCM_interpolated-for-REMIND-scenarios.csv</p> <p>Content: Same as file 2, but only for the SSP2 (chosen default scenario for the study) and for all 12 GCMs. Data of relative change in absolute GDP/CAP levels (compared to the baseline path of the respective SSP in the SSP database) for each country, SSP-2 and 12 GCMs (spanning a broad range of climate sensitivity). The RCP (and a zero-emissions scenario) are interpolated to the temperature pathways of the ten REMIND model scenarios used for climate change mitigation costs. Hereby the set of scenarios for climate impacts and climate change mitigation are consistent and can be combined to total costs of climate change (for a broad range of mitigation action).</p> <p><br> In addition, reference GDP and population data (without climate change) for each country until 2100 was downloaded from the SSP database, release Version 1.0 (March 2013, <a href="https://tntcat.iiasa.ac.at/SspDb/">https://tntcat.iiasa.ac.at/SspDb/</a>, last accessed 15Nov 2019).</p> <p> </p> <p><strong>Climate change mitigation cost data</strong></p> <p>The scenario design and runs used in this paper have first been conducted in [1] and later also used in [2].</p> <p>File 4: REMIND_scenario_results_economic_data.csv</p> <p>File 5: REMIND_scenarios_climate_data.csv</p> <p>Content: A broad range of climate change mitigation scenarios of the REMIND model. File 4 contains the economic data of e.g. GDP and macro-economic consumption for each of the countries and world regions, as well as GHG emissions from various economic sectors. File 5 contains the global climate-related data, e.g. forcing, concentration, temperature.</p> <p>In the scenario description “FFrunxxx” (column 2), the code “xxx” specifies the scenario as follows. See [1] for a detailed discussion of the scenarios.</p> <p>The first dimension specifies the climate policy regime (delayed action, baseline scenarios):</p> <p>1xx: climate action from 2010<br> 5xx: climate action from 2015<br> 2xx climate action from 2020 (used in this study)<br> 3xx climate action from 2030<br> 4x1 weak policy baseline (before Paris agreement)</p> <p>The second dimension specifies the technology portfolio and assumptions:</p> <p>x1x Full technology portfolio (used in this study)<br> x2x noCCS: unavailability of CCS<br> x3x lowEI: lower energy intensity, with final energy demand per economic output decreasing faster than historically observed<br> x4x NucPO: phase out of investments into nuclear energy<br> x5x Limited SW: penetration of solar and wind power limited<br> x6x Limited Bio: reduced bioenergy potential p.a. (100 EJ compared to 300 EJ in all other cases)<br> x6x noBECCS: unavailability of CCS in combination with bioenergy</p> <p>The third dimension specifies the climate change mitigation ambition level, i.e. the height of a global CO2 tax in 2020 (which increases with 5% p.a.).</p> <p>xx1 0$/tCO2 (baseline)<br> xx2 10$/tCO2<br> xx3 30$/tCO2<br> xx4 50$/tCO2 <br> xx5 100$/tCO2<br> xx6 200$/tCO2<br> xx7 500$/tCO2<br> xx8 40$/tCO2<br> xx9 20$/tCO2<br> xx0 5$/tCO2</p> <p>For figure 1b of the paper, this data was aggregated for all countries and regions. Relative changes of GDP are calculated relative to the baseline (4x1 with zero carbon price).</p> <p> </p> <p>[1] Luderer, G., Pietzcker, R. C., Bertram, C., Kriegler, E., Meinshausen, M. and Edenhofer, O.: Economic mitigation challenges: how further delay closes the door for achieving climate targets, Environmental Research Letters, 8(3), 034033, doi:10.1088/1748-9326/8/3/034033, 2013a.</p> <p>[2] Rogelj, J., Luderer, G., Pietzcker, R. C., Kriegler, E., Schaeffer, M., Krey, V. and Riahi, K.: Energy system transformations for limiting end-of-century warming to below 1.5 °C, Nature Climate Change, 5(6), 519–527, doi:10.1038/nclimate2572, 2015.</p>
Planet Microbe Functional and Taxonomic annotation of Illumina WGS Prokaryotic Fraction for Semantic Web Analysis
<p>Functional and Taxonomic annotations computed from a subset of Illumina Whole-Genome Sequencing samples from the prokaryotic fraction of the <a href="https://www.planetmicrobe.org/">Planet Microbe</a> database. Data was computed using the pipeline available from https://github.com/hurwitzlab/planet-microbe-functional-annotation/, and post processing scripts from https://github.com/hurwitzlab/planet-microbe-semantic-web-analysis. Files contain total annotation counts of Interpro, GO and NCBITaxon annotations, as well as additional sample metadata. See readme.txt file for more information.</p>
Clouds and Seasonality on Terrestrial Planets with Varying Rotation Rates
<p>Base Isca namelists used to construct experiment grid and figure plotting notebooks. Includes .zip files that contain post-processed data used to produce the figures—note that .ipynb notebooks have not had file structure changed to match files in this directory, which will need to be changed if the script is re-run (plots are viewable in notebooks).</p>
Mapping the planet's critical areas for biodiversity and people
<p>Data associated with "Mapping the planet's critical areas for biodiversity and people"</p> <p>Abstract: Meeting global commitments to conservation, climate, and sustainable development requires consideration of synergies and tradeoffs among targets. We evaluate the spatial congruence of ecosystems providing globally high levels of nature’s contributions to people, biodiversity, and areas with high development potential across several sectors. We find that conserving approximately half of global land area through protection or sustainable management could provide 90% of the current levels of ten of nature’s contributions to people and meet minimum representation targets for 26,709 terrestrial vertebrate species. This finding supports recent commitments by national governments under the Global Biodiversity Framework to conserve at least 30% of global lands and waters, and proposals to conserve “half Earth”. More than one-third of areas required for conserving nature’s contributions to people and species are also highly suitable for agriculture, renewable energy, oil and gas, mining, or urban expansion. This indicates potential conflicts among conservation, climate and development goals.</p> <p>This dataset contains code and outputs of spatial optimizations run using prioritizr (https://prioritizr.net/index.html). R code used to run the spatial optimizations is contained in a zipfile named "code.zip".</p> <p>Output data includes raster files (TIF format). Raster values are 0-1, where 1 means the grid cell was selected to achieve a particular target, 0 means the grid cell was not selected, and values between 0 and 1 indicate a grid cell was partially selected.</p> <p>Three variations of the spatial optimization were run. Each TIF or ZIP file contains the outputs from one of these variations:</p> <ol> <li>NCP (Nature's contributions to people) only <ol> <li>File name: NCP_only_2km.zip (ZIP file)</li> </ol> </li> <li>NCP and biodiversity, prioritization run at 10km then masked to natural and semi-natural habitat at 2km <ol> <li>File names: es00bio1_nathab_mask.tif, es05bio1_nathab_mask.tif, etc. (TIF files)</li> </ol> </li> <li>NCP and biodiversity, with protected areas and OECM (WDPA) locked in <ol> <li>File name: NCP_biod_WDPA_nathab.zip (ZIP file)</li> </ol> </li> </ol> <p>Within each variation, 19 different spatial optimizations were run, with NCP targets ranging from 5%-95% in 5% increments.</p> <ul> <li>Raster filenames within ZIP files indicate the NCP (ecosystem service) target (for example, es05 indicates a target of 5%)</li> <li>Whether biodiversity was included or not (for example, bio1 indicates biodiversity was included, bio0 indicates it was not)</li> </ul> <p>Two additional TIF files were included, which are the result of summing the rasters from the above scenarios. Raster values range from 0-19, where 19 indicates grid cells selected in all scenarios, 0 indicates grid cells selected in 0 scenarios. Higher values (e.g. 19) indicate cells with the highest levels of NCP globally in the least amount of area. These rasters were used to create Figure 2 in the paper.</p> <ol> <li>NCP_only_2km_sum - NCP only scenario, all rasters summed.</li> <li>NCP_biod_nathab_sum - NCP and biodiversity scenario, masked to natural habitat, all rasters summed.</li> </ol> <p>Additional files include:</p> <ol> <li>dpi.tif - Development Potential Index raster</li> <li>dpi_key.csv - legend describing the DPI raster values</li> <li>HDP_DriverCats.tif - High Development Potential areas disaggregated by sector (raster)</li> <li>HDP_DriverCats_key.csv - legend describing the HDP raster values</li> <li>es90bio1_hdp_drivers_multiply.tif - raster resulting from the combination of the prioritized areas for NCP and biodiversity combined with High Development Potential areas for each economic sector (key is the same as for HDP raster)</li> <li>nathab_2km_WGS84.tif - raster with natural and semi-natural habitat mask (based on ESA 2015 land cover) (2 km)</li> </ol> <p> </p>
Venus coronae topographic (a)symmetry classification (from Gülcher et al., 2023, JGR Planets)
<p>This is a PDF file of the coronae classification that accompanies the manuscript "<strong>Tectono-magmatic evolution of asymmetric coronae on Venus: Topographic classification and 3D thermo-mechanical modeling</strong>" by Gülcher et al. (2023) in <i>Journal of Geophysical Research: Planets</i>, 128, e2023JE007978, <a href="https://doi.org/10.1029/2023JE007978">https://doi.org/10.1029/2023JE007978</a><i> </i><br><br>This database consists of the 150 largest coronae (those with a diameter equal to or larger than 300 km) in the publicly available Venusian coronae nomenclature database (USGS Planetary Nomenclature, (<a href="https://planetarynames.wr.usgs.gov/Page/VENUS/target"><i>https://planetarynames.wr.usgs.gov/Page/VENUS/target</i></a>) and the database of Stofan et al. (1992, <i>JGR, </i><a href="https://doi.org/10.1029/92je01314">https://doi.org/10.1029/92je01314</a>) combined, and five additional smaller coronae. The (a)symmetry of these coronae is defined based on the topographic features (e.g., troughs, rims, rises) and their variability across the coronae. For further information on this classification, please see the main paper. The global distribution of this classification is illustrated in Figure 1 in the main paper and Figure S1 in the Supplementary Information SI1. </p>
Spherical harmonic model of the planet Venus: VenusTopo719
<p><strong>VenusTopo719.shape</strong> is a spherical harmonic model of the shape of the planet Venus. This model makes use of 4-pi normalized spherical harmonic functions that exclude the Condon-Shortley phase factor of (-1)<sup>m</sup>. The description of how this spherical harmonic model was constructed can be found in Wieczorek (2015).</p>
Reflectance and emission spectra of Earth-like planets orbiting red giant stars
<p>Spectra of red giant stellar hosts and reflectance/emission planetary spectra as described in Kozakis & Kaltenegger (2020). File names and content are explained in 0readme.txt. Please email theakozakis@gmail.com with any questions.</p> <p> </p>
Coarse-grained Near-global Aqua-planet Simulation with Computed Dynamical Tendencies
<p>This dataset includes the coarse-grained 3D state of the near-global CRM simulations (NG-Aqua). The simulation is run at a 4km resolution using the System for Atmospheric Modeling (SAM)</p> <p>A dataset derived from the same simulation is included at the <a href="https://dx.doi.org/10.5281/zenodo.1226370">10.5281/zenodo.1226370</a>. This current posting supplements this dataset with the dynamical tendencies for total water and liquid-ice potential temperature, respectively given by FQT and FSLI. These are computed by initializing SAM run at a 160km resolution with the coarse-grained fields from NG-Aqua; evolving the state forward for 10 30 second time steps; saving the output; and finally computing the difference with the initial condition.</p> <p>This netCDF dataset is split into several part files for more robust uploading/downloading. To download this data, download each "part" file, and combine them with the "cat" linux command:</p> <pre><code>cat noBlur.nc.part?? > noBlur.nc</code></pre> <p>If using this with the uwnet code repository, you should then move this file to "data/processed/training/noBlur.nc", creating that folder if necessary.</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.