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Figure 5 in Can humans share spaceship earth?
Figure 5. The rare Earless monitor lizard, Lanthanotus borneensis, from Borneo. Photo by Alain Compost.
Figure 3 in Can humans share spaceship earth?
Figure 3. Global warming will shorten activity times for lizards, thus reducing energy gains from feeding below minimum levels needed for reproduction, potentially leading to failed reproduction and extinction (Reprinted in modified form with permission from Huey et al. 2010, Science 328:833).
Figure 1 in Can humans share spaceship earth?
Figure 1. Biological "cake" showing the intersection of taxonbased sciences (slices) and concept-based sciences (layers)— neither is complete without the other. Rather than specialize on just one taxonomic unit, ecologists study the interactions between organisms and their environments across all taxa.
Winter Precipitation-Type Models for "Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications"
<p>This contains trained model weights, scalers, and evaluation metrics for the winter precipitation-type models trained as part of the paper "Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications". </p>
The data for Radar Circular Polarization Ratio of Near-Earth Asteroids: Links to Spectral Taxonomy and Surface Processes
<p>README</p> <p>% =========================================================================<br>% Project: "Radar Circular Polarization Ratio of Near-Earth Asteroids: Links to <br>% Spectral Taxonomy and Surface Processes"<br>% Author: Edgard G. Rivera-Valentín<br>% Institution: Johns Hopkins University Applied Physics Laboratory<br>% Email: edgard.rivera-valentin@jhuapl.edu<br>% ORCID: 0000-0002-0786-7307<br>% Date: 18 September 2024<br>% =========================================================================</p> <p>% =========================================================================<br>% Licenses:<br>% Any software provided in this repository is licenced under the MIT License, detailed below and within <br>% this archive. <br>% Any data provided in this repository is licenced under Creative Commons Attribution 4.0 International, <br>% detailed within this archive. <br>%<br>% MIT License<br>%<br>% Copyright (c) 2024 The Johns Hopkins University Applied Physics Laboratory LLC<br>%<br>% Permission is hereby granted, free of charge, to any person obtaining a copy<br>% of this software, data, and associated documentation files (the "Software"), to deal<br>% in the Software without restriction, including without limitation the rights<br>% to use, copy, modify, merge, publish, distribute, sublicense, and/or sell<br>% copies of the Software, and to permit persons to whom the Software is<br>% furnished to do so, subject to the following conditions:<br>%<br>% The above copyright notice and this permission notice shall be included in<br>% all copies or substantial portions of the Software.<br>%<br>% THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR<br>% IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,<br>% FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE<br>% AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER<br>% LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,<br>% OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN<br>% THE SOFTWARE.<br>%<br>% ADDITIONAL LICENSING INFORMATION:<br>% Any reuse of the figures and data provided in this repository must abide by <br>% the Copyright policy of the American Astronomical Society Journals:<br>% https://journals.aas.org/article-charges-and-copyright/#AAS_material<br>% =========================================================================</p> <p>% =========================================================================<br>% Description:<br>% This is a data repository for the manuscript published in the Planetary<br>% Science Journal:<br>% Title = Radar Circular Polarization Ratio of Near-Earth Asteroids: Links <br>% to Spectral Taxonomy and Surface Processes<br>% Please see the Zenodo metadata for detailed publicatoin information.<br>% This repository includes:<br>% (1) A csv file that has the table of data used in the published work. The<br>% table includes the data for each near-Earth asteroid used in the<br>% anlaysis. The header information includes: Asteroid Number, Designation<br>% or Name, Taxonomic Type (Following the Bus-DeMeo classification system),<br>% CPR (where CPR is circular polarization ratio), CPR uncertainty (where<br>% the uncertainty is the 1-sigma value), a (where a is semi-major axis in<br>% au), q (where q is the perihelion distance in au), Q (where Q is the<br>% aphelion distance in au), P (where P is the rotational period in hours),<br>% Hmag (where Hmag is the absolute magnitude), and Orbital Class. <br>% (2) A .mrt file that contains the same data as the csv file, but formatted<br>% according to the specifications of the machine-readable table format<br>% used by the AAS Journals. The .mrt version will also be published with<br>% the final PSJ article.<br>% (3) .mat files containing the output from the statistical modeling<br>% presented in the paper. These files are required to run the .m file<br>% included in this repository.<br>% (4) Make Figures.m, which is a Matlab code that remakes all the figures<br>% presented in the paper given the data in each of the .mat files. <br>% (5) The .png files for each of the figures presented in the paper. <br>% =========================================================================</p> <p>% =========================================================================<br>% File Formats:<br>% This archive includes various file formats. <br>% .csv file is a text file format that uses commas to separate values, and<br>% newlines to separate records. <br>% .mrt file is an ASCII byte-by-byte format that is documented here:<br>% https://journals.aas.org/mrt-overview/<br>% and readable by tools such as astropy, TOPCACT, etc. <br>% .m file is a simple text file used by Matlab, it can be opened by any<br>% text editor and executed by Matlab. <br>% .mat file is the file format used by MATLAB for saving data. It can be<br>% ready by other software, such as python, e.g., <br>% https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.loadmat.html<br>% .png file is short for Portable Network Graphic, which is a type of<br>% raster image file. <br>% =========================================================================</p>
SeasFire Cube: A Global Dataset for Seasonal Fire Modeling in the Earth System
<p>The <strong>SeasFire Cube</strong> is a scientific datacube for seasonal fire forecasting around the <strong>globe</strong>. Apart from seasonal fire forecasting, which is the aim of the SeasFire project, the datacube can be used for several other tasks. For example, it can be used to model teleconnections and memory effects in the earth system. Additionally, it can be used to model emissions from wildfires and the evolution of wildfire regimes.<br> <br> It has been created in the context of the <a href="https://seasfire.hua.gr/">SeasFire project</a>, which deals with "<em>Earth System Deep Learning for Seasonal Fire Forecasting</em>" and <strong>is funded by the European Space Agency (ESA) </strong> in the context of ESA Future EO-1 Science for Society Call.<br> <br> It contains <strong>21 years</strong> of data (2001-2021) in an <strong>8-days</strong> time resolution and <strong>0.25 degrees grid</strong> resolution. It has a diverse range of seasonal fire drivers. It expands from atmospheric and climatological ones to vegetation variables, socioeconomic and the target variables related to wildfires such as burned areas, fire radiative power, and wildfire-related CO2 emissions.</p> Datacube properties <table><tbody><tr> <th> <p><strong>Feature</strong></p> </th> <th> <p><strong>Value</strong></p> </th> </tr> </tbody><tbody> <tr> <td> <p>Spatial Coverage</p> </td> <td> <p>Global</p> </td> </tr> <tr> <td> <p>Temporal Coverage</p> </td> <td> <p>2001 to 2021</p> </td> </tr> <tr> <td> <p>Spatial Resolution</p> </td> <td> <p>0.25 deg x 0.25 deg</p> </td> </tr> <tr> <td> <p>Temporal Resolution</p> </td> <td> <p>8 days</p> </td> </tr> <tr> <td> <p>Number of Variables</p> </td> <td> <p>54</p> </td> </tr> <tr> <td> <p>Tutorial Link </p> </td> <td> <p><a href="https://github.com/SeasFire/seasfire-datacube">https://github.com/SeasFire/seasfire-datacube</a></p> </td> </tr> </tbody> </table> <table> <tbody><tr> <th>Full name</th> <th>DataArray name</th> <th>Unit</th> <th>Contact *</th> </tr> </tbody><tbody> <tr> <th>Dataset: <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels?tab=overview">ERA5 Meteo Reanalysis Data</a></th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Mean sea level pressure</th> <td>mslp</td> <td>Pa</td> <td>NOA</td> </tr> <tr> <th>Total precipitation</th> <td>tp</td> <td>m</td> <td>MPI</td> </tr> <tr> <th>Relative humidity</th> <td>rel_hum</td> <td>%</td> <td>MPI</td> </tr> <tr> <th>Vapor Pressure Deficit</th> <td>vpd</td> <td>hPa</td> <td>MPI</td> </tr> <tr> <th>Sea Surface Temperature</th> <td>sst</td> <td>K</td> <td>MPI</td> </tr> <tr> <th>Skin temperature</th> <td>skt</td> <td>K</td> <td>MPI</td> </tr> <tr> <th>Wind speed at 10 meters</th> <td>ws10</td> <td>m*s-2</td> <td>MPI</td> </tr> <tr> <th>Temperature at 2 meters - Mean</th> <td>t2m_mean</td> <td>K</td> <td>MPI</td> </tr> <tr> <th>Temperature at 2 meters - Min</th> <td>t2m_min</td> <td>K</td> <td>MPI</td> </tr> <tr> <th>Temperature at 2 meters - Max</th> <td>t2m_max</td> <td>K</td> <td>MPI</td> </tr> <tr> <th>Surface net solar radiation</th> <td>ssr</td> <td>MJ m-2</td> <td>MPI</td> </tr> <tr> <th>Surface solar radiation downwards</th> <td>ssrd</td> <td>MJ m-2</td> <td>MPI</td> </tr> <tr> <th>Volumetric soil water level 1</th> <td>swvl1</td> <td>m3/m3</td> <td>MPI</td> </tr> <tr> <th> <table> <tbody> <tr> <th>Volumetric soil water level 2</th> </tr> </tbody> </table> </th> <td>swvl2</td> <td>m3/m3</td> <td>MPI</td> </tr> <tr> <th>Volumetric soil water level 3</th> <td>swvl3</td> <td>m3/m3</td> <td>MPI</td> </tr> <tr> <th>Volumetric soil water level 4</th> <td>swvl4</td> <td>m3/m3</td> <td>MPI</td> </tr> <tr> <th>Land-Sea mask</th> <td>lsm</td> <td>0-1</td> <td>NOA</td> </tr> <tr> <th>Dataset: Copernicus <p><a href="http://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-fire-historical?tab=overview">CEMS</a></p> </th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Drought Code Maximum</th> <td>drought_code_max</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Drought Code Average</th> <td>drought_code_mean</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Fire Weather Index Maximum</th> <td>fwi_max</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Fire Weather Index Average</th> <td>fwi_mean</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Dataset: <a href="http://confluence.ecmwf.int/display/CKB/CAMS%3A+Global+Fire+Assimilation+System+%28GFAS%29+data+documentation">CAMS: Global Fire Assimilation System (GFAS)</a></th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Carbon dioxide emissions from wildfires</th> <td>cams_co2fire</td> <td>kg/m²</td> <td>NOA</td> </tr> <tr> <th>Fire radiative power</th> <td>cams_frpfire</td> <td>W/m²</td> <td>NOA</td> </tr> <tr> <th>Dataset: <a href="https://climate.esa.int/en/projects/fire/data/">FireCCI - European Space Agency’s Climate Change Initiative</a></th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Burned Areas from Fire Climate Change Initiative (FCCI)</th> <td>fcci_ba</td> <td>ha</td> <td>NOA</td> </tr> <tr> <th>Valid mask of FCCI burned areas</th> <td>fcci_ba_valid_mask</td> <td>0-1</td> <td>NOA</td> </tr> <tr> <th><br> Fraction of burnable area</th> <td>fcci_fraction_of_burnable_area</td> <td>%</td> <td>NOA</td> </tr> <tr> <th>Number of patches</th> <td>fcci_number_of_patches</td> <td>N</td> <td>NOA</td> </tr> <tr> <th>Fraction of observed area</th> <td>fcci_fraction_of_observed_area</td> <td>%</td> <td>NOA</td> </tr> <tr> <th>Dataset: Nasa MODIS <a href="https://lpdaac.usgs.gov/products/mod11c1v006/">MOD11C1</a>, <a href="https://lpdaac.usgs.gov/products/mod13c1v006/">MOD13C1</a>, <a href="https://lpdaac.usgs.gov/products/mcd15a2hv006/">MCD15A2</a></th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Land Surface temperature at day</th> <td>lst_day</td> <td>K</td> <td>MPI</td> </tr> <tr> <th>Leaf Area Index</th> <td>lai</td> <td>m²/m²</td> <td>MPI</td> </tr> <tr> <th>Normalized Difference Vegetation Index</th> <td>ndvi</td> <td>unitless</td> <td>MPI</td> </tr> <tr> <th>Dataset: Nasa SEDAC <a href="https://sedac.ciesin.columbia.edu/data/set/gpw-v4-population-density-adjusted-to-2015-unwpp-country-totals-rev11">Gridded Population of the World (GPW), v4</a></th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Population density</th> <td>pop_dens</td> <td>persons per square kilometers</td> <td>NOA</td> </tr> <tr> <th>Dataset: <a href="http://www.globalfiredata.org/data.html">Global Fire Emissions Database (GFED)</a></th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Burned Areas from GFED (large fires only)</th> <td>gfed_ba</td> <td>hectares (ha)</td> <td>MPI</td> </tr> <tr> <th>Valid mask of GFED burned areas</th> <td>gfed_ba_valid_mask</td> <td>0-1</td> <td>NOA</td> </tr> <tr> <th>GFED basis regions</th> <td>gfed_region</td> <td>N</td> <td>NOA</td> </tr> <tr> <th>Dataset: <a href="http://gwis.jrc.ec.europa.eu/apps/country.profile/downloads">Global Wildfire Information System (GWIS)</a></th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Burned Areas from GWIS</th> <td>gwis_ba</td> <td>ha</td> <td>NOA</td> </tr> <tr> <th>Valid mask of GWIS burned areas</th> <td>gwis_ba_valid_mask</td> <td>0-1</td> <td>NOA</td> </tr> <tr> <th>Dataset: <a href="https://psl.noaa.gov/data/climateindices/list/">NOAA Climate Indices</a></th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Arctic Oscillation Index</th> <td>oci_ao</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Western Pacific Index</th> <td>oci_wp</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Pacific North American Index</th> <td>oci_pna</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>North Atlantic Oscillation</th> <td>oci_nao</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Southern Oscillation Index</th> <td>oci_soi</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Global Mean Land/Ocean Temperature</th> <td>oci_gmsst</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Pacific Decadal Oscillation</th> <td>oci_pdo</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Eastern Asia/Western Russia</th> <td>oci_ea</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>East Pacific/North Pacific Oscillation</th> <td>oci_epo</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Nino 3.4 Anomaly</th> <td>oci_nino_34_anom</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Bivariate ENSO Timeseries</th> <td>oci_censo</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Dataset: <a href="https://www.esa-landcover-cci.org/">ESA CCI</a></th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Land Cover Class 0 - No data</th> <td>lccs_class_0</td> <td>%</td> <td>NOA</td> </tr> <tr> <th>Land Cover Class 1 - Agriculture</th> <td>lccs_class_1</td> <td>%</td> <td>NOA</td> </tr> <tr> <th>Land Cover Class 2 - Forest</th> <td>lccs_class_2</td> <td>%</td> <td>NOA</td> </tr> <tr> <th>Land Cover Class 3 - Grassland</th> <td>lccs_class_3</td> <td>%</td> <td>NOA</td> </tr> <tr> <th>Land Cover Class 4 - Wetlands</th> <td>lccs_class_4</td> <td>%</td> <td>NOA</td> </tr> <tr> <th>Land Cover Class 5 - Settlement</th> <td>lccs_class_5</td> <td>%</td> <td>NOA</td> </tr> <tr> <th>Land Cover Class 6 - Shrubland</th> <td>lccs_class_6</td> <td>%</td> <td>NOA</td> </tr> <tr> <th>Land Cover Class 7 - Sparse vegetation, bare areas, permanent snow and ice</th> <td>lccs_class_7</td> <td>%</td> <td>NOA</td> </tr> <tr> <th>Land Cover Class 8 - Water Bodies</th> <td>lccs_class_8</td> <td>%</td> <td>NOA</td> </tr> <tr> <th>Dataset: <a href="https://ecoregions.appspot.com/">Biomes</a></th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Dataset: Calculated</th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Grid Area in square meters</th> <td>area</td> <td>m²</td> <td>NOA</td> </tr> </tbody> </table> <p>*The datacube specifications (temporal, spatial resolution, chunk size) have been set up by the Max Planck Institut (MPI) team. For the variables that the contact is MPI, Lazaro Alonso (lalonso <at> bgc-jena.mpg.de) has led the efforts to collect and process them. For the variables that the contact is NOA, Ilektra Karasante (ile.karasante <at> noa.gr) has led the efforts to collect and process them.</p>
Depth-dependent anisotropy in the Earth's inner core linked to chemical stratification
Open the record for dataset details and reuse information.
Data and Processing from "Carbon-centric dynamics of Earth's marine phytoplankton"
<div><strong>Brief Summary:</strong></div> <div>This documentation is for associated data and code for: </div> <div>A. Stoer, K. Fennel, Carbon-centric dynamics of Earth's marine phytoplankton. Proceedings of the National Academy of Sciences (2024).</div> <div> </div> <div>To cite this software and data, please use:</div> <div> <div>A. Stoer, K. Fennel, Data and processing from "Carbon-centric dynamics of Earth's marine phytoplankton". Zenodo. <a href="https://doi.org/10.5281/zenodo.10949682" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10949682</a>. Deposited 1 October 2024.</div> </div> <div> </div> <div><strong>List of folders and subfolders and what they contain:</strong></div> <div> <ol> <li>raw data: Contains raw data used in the analysis. This folder does not contain the satellite imagery, which will need to be downloaded from the NASA Ocean Color website (https://oceancolor.gsfc.nasa.gov/). <ol> <li>bgc-argo float data (subfolder): Includes Argo data from its original source or put into a similar Argo format</li> <li>global region data (subfolder): Includes data used to subset the Argo profiles into each 10deg lat region and basin.</li> <li>graff et al 2015 data (subfolder): Include the data digitized from Graff et al.'s Fig. 2.</li> </ol> </li> <li>processed data: data processing by this study (Stoer and Fennel, 2024) <ol> <li>processed bgc-argo data (subfolder): A binned processed file is present for each Argo float used in the analysis. Note these files include those describe in Table S1 (these are later processed in "3_stock_bloom_calc.py")</li> <li>processed satellite data (subfolder): includes a 10-deg latitude averaged for each satellite image processed (called "chl_sat_df_merged.csv"). This is later used to calculate a satellite chlorophyll-a climatology in "3_stock_bloom_calc.py".</li> <li>processed chla-irrad data (subfolder): includes the quality-controlled light diffuse attenuation data coupled with the chlorophyll-a fluorescence data to calculate slope factor corrections (the file is called "processed chla-irrad data.csv").</li> <li>processed topography data (subfolder): includes smoothed topography data (file named "ETOPO_2022_v1_60s_N90W180_surface_mod.tiff").</li> </ol> </li> <li>software: <ol> <li>0_ftp_argo_data_download.py: This program downloads the Argo data from the Global Data Assembly Center's FTP. Running this program will provide new Argo float profiles. However, there will be new floats and profiles present if downloaded. This will not match the historical record of Argo floats used in this analysis but could be useful for replicating this analysis when more data becomes available. The historical record of BGC-Argo floats are present in "/raw data/bgc-argo float data/" path. If you wish to downloaded other float data, see Gordon et al. (2020), Hamilton and Leidos (2017) and the data from the misclab website (https://misclab.umeoce.maine.edu/floats/).</li> <li>1_argo_data_processing.py: This program quality-controls and bins the biogeochemical data into a consistent format. This includes corrections and checks, like the spike/noise test or the non-photochemical quenching correction.</li> <li>2_sat_data_processing.py: this program processes the satellite data downloaded from the NASA Ocean Color website.</li> <li>3_stock_bloom_calc.py: this is the main program used to described the results of the study. The program takes the processed Argo data and groups it into regions and calculates slope factors, phytoplankton carbon & chlorophyll-a, global stocks, and bloom metrics.</li> <li>4_stock_calc_longhurst_province.py: This program repeats the global stocks calculations performed in "3_stock_bloom_calc.py" but bases the grouping on Longhurst Biogeochemical Provinces.</li> </ol> </li> </ol> </div> <div><strong>How to Replicate this Analysis:</strong></div> <div>Each program should be run in the order listed above. Path names where the data files have been downloaded will need to be updated in the code.</div> <div> </div> <div>To use the exact same Sprof files as used in the paper, skip running "0_ftp_argo_data_download.py" and start with "1_argo_data_processing.py" instead. Use the float data from the folder "bgc-argo float data". The program "0_ftp_argo_data_download.py" downloads the latest data from Argo database, so it is useful for updating the analysis. The program "1_argo_data_processing.py" may also be skipped to save time and the processed BGC-Argo float data may be used instead (see folder named "processed bgc-argo data"). </div> <div> </div> <div>Similarly, the program "2_sat_data_processing.py" may also be skipped, which otherwise can take multiple hours to process. The raw data is available from the NASA Ocean Color website (<a href="https://oceancolor.gsfc.nasa.gov/">https://oceancolor.gsfc.nasa.gov/</a>). The processed data from "2_sat_data_processing.py" is available so this step may be skipped to save time as well.</div> <div> </div> <div>The program "3_stock_bloom_calc.py" will require running "ocean_toolbox.py" (see below) in another tab. The portion of the program that involves QC for the irradiance profiles has been commented out to save processing time, and the pre-processed data used in the study has been linked instead (see folder "processed light data"). Similarly, pre-processed topography data is present in this repository. The original Earth Topography data can be accessed at <a href="https://www.ncei.noaa.gov/products/etopo-global-relief-model">https://www.ncei.noaa.gov/products/etopo-global-relief-model.</a></div> <p> </p> <p>A version of "3_stock_bloom_calc.py" using Longhurst provinces is available for exploring alternative groupings and their effects on stock calculations. See the program named "4_stock_calc_longhurst_province.py". You will need to download the Longhurst biogeochemical provinces from <a href="https://www.marineregions.org/">https://www.marineregions.org/</a>.</p> <p>To explore the effects of different slope factors, averaging methods, bbp spectral slopes, etc, the user will likely want to make changes to "3_stock_bloom_calc.py". Please do not hesitate to contact the correponding author (Adam Stoer) for guidance or questions.</p> <p><strong>ocean_toolbox.py:</strong></p> <p>import statsmodels.formula.api as smf<br>import os<br>import matplotlib.pyplot as plt<br>import numpy as np<br>from uncertainties import unumpy as unp<br>from scipy import stats</p> <p>def file_grab(root,find,start): #grabs files by file extensions and location<br> filelst = []<br> for subdir, dirs, files in os.walk(root):<br> for file in files:<br> filepath = subdir + os.sep + file<br> if filepath.endswith(find):<br> if filepath.startswith(start):<br> filelst.append(filepath)<br> return filelst</p> <p>def sep_bbp(data, name_z, name_chla, name_bbp):<br> <br> '''<br> data: Pandas Dataframe containing the profile data<br> name_z: name of the depth variable in data<br> name_chla: name of the chlorophyll-a variable in data<br> name_bbp: name of the particle backscattering variable in data <br> <br> returns: the data variable with particle backscattering partitioned into <br> phytoplankton (bbpphy) and non-algal particle components (bbpnap).<br> '''<br> #name_chla = 'chla'<br> #name_z = 'depth'<br> #name_bbp = 'bbp470'<br> dcm = data[data.loc[:,name_chla]==data.loc[:,name_chla].max()][name_z].values[0] # Find depth of deep chla maximum<br> part_prof = data[(data.loc[:,name_bbp]<np.median(data.loc[:,name_bbp]))] # find median bbp of profile<br> <br> mod = smf.quantreg('bbp470 ~ ' + str(name_z), <br> part_prof).fit(q=0.01) # Find model to 1 percentile<br> y_pred = mod.predict(part_prof.loc[:,name_z]) # Create predicted bbp_nap<br> <br> part_prof.loc[:,'bbp_back'] = y_pred.values # Predicted bbp NAP from linear trend<br> z_lim = part_prof.loc[(part_prof.loc[:,'bbp_back'].div(part_prof.loc[:,name_bbp])>=1), name_z].min() <br> <br> # Find depth where bbp NAP and bbp intersect<br> data.loc[data[name_z]>=z_lim, 'bbp_back'] = data.loc[data[name_z]>=z_lim, name_bbp].tolist()<br> data.loc[data[name_z]<z_lim,'bbp_back'] = data.loc[data[name_z]==z_lim, name_bbp].values[0] #data.loc[data[name_z]<z_lim, name_z].mul(lr.slope).add(lr.intercept)<br> <br> <br> data.loc[:,'bbpphy'] = data.loc[:, name_bbp].sub(data.loc[:,'bbp_back']) # Subtract bbp NAP from bbp for bbp from phytoplankton<br> data.loc[(data['bbpphy']<0)|(data['depth']>z_lim),'bbpphy'] = 0 # Subtract bbp NAP from bbp for bbp from phytoplankton</p> <p> return data['bbpphy'], z_lim</p> <p>def bbp_to_cphy(bbp_data, sf):<br> <br> '''<br> data: Pandas Dataframe containing the profile data<br> name_bbp: name of the particulate backscattering variable in data<br> name_bbp_err: name of particulate backscattering error variable in data<br> <br> returns: the data variable with particle backscattering [/m] converted into<br> phytoplankton carbon [mg/m^3].<br> '''<br> <br> cphy_data = bbp_data.mul(sf) </p> <p> return cphy_data</p>
Datasets for Earth Observation Using Python: A Practical Programming Guide
<p>These are the datasets used in the <a href="https://agupubs.onlinelibrary.wiley.com/doi/book/10.1002/9781119606925">2021 Edition</a> of "Earth Observation Using Python: A Practical Programming Guide." These contain example text, csv, netCDF, and GRIB files that are used in code exercises within the book. Datasets are original files and modified public domain files from large Earth satellite data providers, such as NASA, NOAA, EUMETSAT, and ESA. These files may be used freely, but if not heavilly modified, please attribute credit to the original author for collecting the examples.</p>
Linked collectors and determiners for: UAM Earth Sciences Collection (Arctos).
Natural history specimen data linked to collectors and determiners held within, "UAM Earth Sciences Collection (Arctos)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/9a201e2f-dada-4cb9-88c7-695e391aeb2f">https://bionomia.net/dataset/9a201e2f-dada-4cb9-88c7-695e391aeb2f</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/9a201e2f-dada-4cb9-88c7-695e391aeb2f">https://gbif.org/dataset/9a201e2f-dada-4cb9-88c7-695e391aeb2f</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Non-vertebrate Paleontology, Jackson School Museum of Earth History, University of Texas at Austin.
Natural history specimen data linked to collectors and determiners held within, "Non-vertebrate Paleontology, Jackson School Museum of Earth History, University of Texas at Austin". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/1f2cfb6f-c91b-498e-80f3-8eeeec688292">https://bionomia.net/dataset/1f2cfb6f-c91b-498e-80f3-8eeeec688292</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/1f2cfb6f-c91b-498e-80f3-8eeeec688292">https://gbif.org/dataset/1f2cfb6f-c91b-498e-80f3-8eeeec688292</a>. Formatted as a Frictionless Data package.
Earth-observation based products from the CoastObs project
<p>The Earth-observation based products are in the CoastObs portal: https://coastobs.lizard.net. Login details to the portal will be provided after finalising the CoastObs training materials: https://coastobs.eu/e-training</p> <p>Products in the portal include demonstration products of the following parameters:</p> <p>Basic products:</p> <ul> <li>Chl-a</li> <li>Suspended matter</li> <li>Turbidity</li> <li>Sea surface temperature</li> </ul> <p>Innovative and higher level products:</p> <ul> <li>Sea grass percentage coverage</li> <li>Phytoplankton size classes</li> <li>Harmful algae bloom (HAB) indicators for Pseudonitzschia and Alexandrium</li> <li>(Mean) food quality for shell fish</li> <li>Mussel growth potential</li> <li>Relative growth rate (of mussels)</li> </ul> <p>There are products for the following areas:</p> <ul> <li>Loire Estuary (France)</li> <li>Bourgneuf Bay (France)</li> <li>Eastern Scheldt + Voordelta (Netherlands)</li> <li>Wadden Sea (Netherlands)</li> <li>Venice Lagoon (Italy)</li> <li>Galician coast (Spain)</li> <li>Rias Baixas (Spain)</li> </ul> <p>In the portfolio are more products, such as:</p> <ul> <li>Primary production</li> <li>WFD indicators</li> <li>HAB forecasts</li> <li>Plume morphology</li> <li>Phytoplankton bloom morphology</li> </ul>
Phosphorus Chemistry in the Earth's Upper Atmosphere
<p><strong>Phosphorus Chemistry in the Earth’s Upper Atmosphere</strong></p> <p>by</p> <p>John M.C. Plane<sup>1*</sup>, Wuhu Feng<sup>1,2</sup> and Kevin M. Douglas<sup>1</sup></p> <p><sup>1</sup> School of Chemistry, University of Leeds, UK</p> <p><sup>2</sup> National Centre for Atmospheric Science and School of Earth and Environment, University of Leeds, UK</p> <p> </p> <p>The repository contains the data used in the above paper.</p>
Data for "The interplay between seismic and aseismic slip along the Chaman fault illuminated by InSAR" submitted to JGR: Solid Earth
<p>This compressed folder contains data presented in Figures of the following paper : <strong>"The interplay between seismic and aseismic slip along the Chaman fault illuminated by InSAR" </strong>by<em> </em>M. Dalaison, R. Jolivet, E. M. van Rijsingen and S. Michel, submitted to <em>JGR: Solid Earth </em>in August 2021.</p> <p>Data are in their final processed version. Raw data used in this study are freely available online ( scihub.copernicus.eu, earthdata.nasa.gov, www.ecmwf.int, pubs.usgs.gov/of/2007/1103 )</p>
Text-fig. 2. Stratigraphic ranges of Microtoscoptini-bearing sites. Correlation of ELMA and NALMA with earth years is according to Hilgen et al. (2012). Localities of very broad stratigraphic range (e.g., the entire Turolian or Hemphillian, as for Vasilivka 1, Lobkove, Little Valley, Cambridge), do not contribute to detail the range, and are omitted in this figure. in Comments On The Age And Dispersal Of Microtoscoptini (Rodentia: Cricetidae)
Text-fig. 2. Stratigraphic ranges of Microtoscoptini-bearing sites. Correlation of ELMA and NALMA with earth years is according to Hilgen et al. (2012). Localities of very broad stratigraphic range (e.g., the entire Turolian or Hemphillian, as for Vasilivka 1, Lobkove, Little Valley, Cambridge), do not contribute to detail the range, and are omitted in this figure.
Text-fig. 2. Fossiliferous localities in the Ashawq Formation north and north-east of Thaytiniti, Dhofar Governorate, Oman (for latitude, longitude and altitude, see App. 1). The large white area either side of the north-south road is the village of Aydim (map modified from Google Earth). in Large Mammals From The Rupelian Of Oman - Recent Finds
Text-fig. 2. Fossiliferous localities in the Ashawq Formation north and north-east of Thaytiniti, Dhofar Governorate, Oman (for latitude, longitude and altitude, see App. 1). The large white area either side of the north-south road is the village of Aydim (map modified from Google Earth).
Text-fig. 1. Location of Gánovce-Hrádok Neanderthal site in northern Slovakia and the rest of the travertine mound (map source: https://commons.wikimedia.org and Google Earth; 2017). in Revised Floral And Faunal Assemblages From Late Pleistocene Deposits Of The Gánovce-Hrádok Neanderthal Site -Biostratigraphic And Palaeoecological Implications
Text-fig. 1. Location of Gánovce-Hrádok Neanderthal site in northern Slovakia and the rest of the travertine mound (map source: https://commons.wikimedia.org and Google Earth; 2017).
Geochemistry and petrography of martian meteorite Northwest Africa 11115: A rare earth element-enriched olivine-phyric shergottite closely linked to Northwest Africa 1068
<p>This is the Electronic Appendix of the manuscript "<strong>Geochemistry and petrography of martian meteorite Northwest Africa 11115: A rare earth element-enriched olivine-phyric shergottite closely linked to Northwest Africa 1068</strong>", by M. Melwani Daswani, N. Greber, J. Hu, R. C. Greenwood, and P. R. Heck, submitted to <em>Meteoritics & Planetary Science</em>.</p> <p>Corresponding author: M. Melwani Daswani (daswani@jpl.caltech.edu)</p> <p>The compressed folder contains two files:</p> <p>1) <strong>NWA11115_CT_scan_TIFF_substack.tif</strong></p> <p>This is a TIFF image stack of the CT scan of the full NWA 11115 sample. Open in a program such as ImageJ (Rasband, W.S., ImageJ, U. S. National Institutes of Health, Bethesda, Maryland, USA, <a href="https://imagej.nih.gov/ij/">https://imagej.nih.gov/ij/</a>, 1997-2018).</p> <p>2) <strong>NWA11115_CT_scan_60FPS_JPEG.avi</strong></p> <p>This is a video file of the CT scan of the same NWA 11115 sample. The TIFF stack was converted to a video file (.avi), compressed to JPEG quality, and at a rate of 60 frames per second. Open this file with software such as VLC (<a href="https://www.videolan.org/vlc/">https://www.videolan.org/vlc/</a>).</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>The authors acknowledge T. Boudreaux for donating NWA 11115 to the Field Museum, J. Greer and J. Holstein for help with sample preparation, L. Kööp and B. Strack for SEM support, S. Rastegar for preliminary SEM analysis, L. Dussubieux for LA-ICP-MS support, A. I. Neander and Z.-X. Luo for CT scanning and support, and J. Filiberto and A. Treiman for useful discussions. We thank Thomas Pettke for assistance with LA-ICP-MS analyzes of the pressed powder pellets at the University of Bern. GPS Division analytical facilities at Caltech and Chi Ma are thanked for the support on EMPA analysis. PRH acknowledges support from the Tawani Foundation. MMD’s portion of the work was done partly as a private venture and not in the author’s capacity as an employee of the Jet Propulsion Laboratory, California Institute of Technology. The authors declare no competing interests.</p> <p>EOF</p>
Data for the paper: Earth System Model Parameter Adjustment Using a Green's Functions Approach
<p>This dataset contains model codes and scripts used to generate the results of the paper submitted to Geoscientific Model Development journal</p>
Dataset for "Seismological Expression of the Iron Spin Crossover in Ferropericlase in the Earth's Lower Mantle"
<p>The folder contains data related to the publication: Shephard, Houser, Hernlund, Trønnes, Valencia-Cardona, Wentzkovitch (2021) Seismological Expression of the Iron Spin Crossover in Ferropericlase in the Earth’s Lower Mantle. Accepted for publication in Nature Communications.</p> <p>It include numerical grids, a simple plotting script, colour maps, and images (jpg and ps files). There is a README file explaining the content and uses of the these additional data files.</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.