Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,477
datasets available to search
ShareScore release 0.9.0
Dataset results
1,477 results for “earth”
Data and code associated with "The Observed Availability of Data and Code in Earth Science and Artificial Intelligence"
<p>Data and code associated with "The Observed Availability of Data and Code in Earth Science <br>and Artificial Intelligence" by Erin A. Jones, Brandon McClung, Hadi Fawad, and Amy McGovern.</p> <p>Instructions: To reproduce figures, download all associated Python and CSV files and place<br> in a single directory.<br> Run BAMS_plot.py as you would run Python code on your system.</p> <p>Code:<br>BAMS_plot.py: Python code for categorizing data availability statements based on given data<br> documented below and creating figures 1-3. </p> <p> Code was originally developed for Python 3.11.7 and run in the Spyder <br> (version 5.4.3) IDE.<br> <br> Libraries utilized:<br> numpy (version 1.26.4) <br> pandas (version 2.1.4)<br> matplotlib (version 3.8.0)<br> <br> For additional documentation, please see code file.</p> <p>Data:<br>ASDC_AIES.csv: CSV file containing relevant availability statement data for Artificial <br> Intelligence for the Earth Systems (AIES)<br>ASDC_AI_in_Geo.csv: CSV file containing relevant availability statement data for Artificial <br> Intelligence in Geosciences (AI in Geo.)<br>ASDC_AIJ.csv: CSV file containing relevant availability statement data for Artificial <br> Intelligence (AIJ)<br>ASDC_MWR.csv: CSV file containing relevant availability statement data for Monthly <br> Weather Review (MWR)<br><br></p> <p><br>Data documentation:<br>All CSV files contain the same format of information for each journal. The CSV files above are <br>needed for the BAMS_plot.py code attached.</p> <p>Records were analyzed based on the criteria below.</p> <p> Records:<br> 1) Title of paper<br> The title of the examined journal article.<br> 2) Article DOI (or URL)<br> A link to the examined journal article. For AIES, AI in Geo., MWR, the DOI is <br> generally given. For AIJ, the URL is given.<br> 3) Journal name<br> The name of the journal where the examined article is published. Either a full<br> journal name (e.g., Monthly Weather Review), or the acronym used in the <br> associated paper (e.g., AIES) is used.<br> 4) Year of publication<br> The year the article was posted online/in print.<br> 5) Is there an ASDC?<br> If the article contains an availability statement in any form, "yes" is <br> recorded. Otherwise, "no" is recorded.<br> 6) Justification for non-open data?<br> If an availability statement contains some justification for why data is not <br> openly available, the justification is summarized and recorded as one of the <br> following options: 1) Dataset too large, 2) Licensing/Proprietary, 3) Can be <br> obtained from other entities, 4) Sensitive information, 5) Available at later <br> date. If the statement indicates any data is not openly available and no <br> justification is provided, or if no statement is provided is provided "None" <br> is recorded. If the statement indicates openly available data or no data <br> produced, "N/A" is recorded.<br> 7) All data available<br> If there is an availability statement and data is produced, "y" is recorded <br> if means to access data associated with the article are given and there is no <br> indication that any data is not openly available; "n" is recorded if no means <br> to access data are given or there is some indication that some or all data is <br> not openly available. If there is no availability statement or no data is <br> produced, the record is left blank.<br> 8) At least some data available<br> If there is an availability statement and data is produced, "y" is recorded <br> if any means to access data associated with the article are given; "n" is <br> recorded if no means to access data are given. If there is no availability <br> statement or no data is produced, the record is left blank.<br> 9) All code available<br> If there is an availability statement and data is produced, "y" is recorded <br> if means to access code associated with the article are given and there is no <br> indication that any code is not openly available; "n" is recorded if no means <br> to access code are given or there is some indication that some or all code is <br> not openly available. If there is no availability statement or no data is <br> produced, the record is left blank.<br> 10) At least some code available<br> If there is an availability statement and data is produced, "y" is recorded <br> if any means to access code associated with the article are given; "n" is <br> recorded if no means to access code are given. If there is no availability <br> statement or no data is produced, the record is left blank.<br> 11) All data available upon request<br> If there is an availability statement indicating data is produced and no data <br> is openly available, "y" is recorded if any data is available upon request to <br> the authors of the examined journal article (not a request to any other <br> entity); "n" is recorded if no data is available upon request to the authors <br> of the examined journal article. If there is no availability statement, any <br> data is openly available, or no data is produced, the record is left blank.<br> 12) At least some data available upon request<br> If there is an availability statement indicating data is produced and not all <br> data is openly available, "y" is recorded if all data is available upon <br> request to the authors of the examined journal article (not a request to any <br> other entity); "n" is recorded if not all data is available upon request to <br> the authors of the examined journal article. If there is no availability <br> statement, all data is openly available, or no data is produced, the record<br> is left blank.<br> 13) no data produced<br> If there is an availability statement that indicates that no data was<br> produced for the examined journal article, "y" is recorded. Otherwise, the<br> record is left blank.<br> 14) links work<br> If the availability statement contains one or more links to a data or code <br> repository, "y" is recorded if all links work; "n" is recorded if one or more <br> links do not work. If there is no availability statement or the statement <br> does not contain any links to a data or code repository, the record is left <br> blank. </p>
Dataset for Earth Sciences at Freie Universität Berlin: Open Access, Licenses and Persistent Identifiers Monitoring
<p>In <em>Version 4</em>, <strong>publishers </strong>and <strong>journals</strong> names has been extended.</p> <p>In <em>Version 3</em>, new entries have been added for both <strong>journal </strong>and <strong>non-journal article outputs</strong>, specifically including data from the year <strong>2023</strong>. Minor adjustments were also made to URLs and open access (OA) statuses.</p> <p><em>Note</em>: Data for journal and non-journal article outputs from the year 2023 were unavailable at the time of preparing the <strong>short paper</strong> presenting the results, findable under <a href="https://doi.org/10.5281/zenodo.14170751" target="_blank" rel="noopener">10.5281/zenodo.14170751</a> [1]).</p> <p><br>Started in 2021, Berlin University Alliance (BUA) Open Science Dashboards, followed by the BUA Open Science Magnifiers projects, seek to investigate Open Science (OS) practices across different research domains and communities. A primary focus of these initiatives lies in the development of OS indicators, tailored to discipline specific ones, alongside their visualisation for monitoring.</p> <p>Collaborating closely with the Department of Earth Sciences at Freie Universität Berlin (FU), one of the project's key objectives is the implementation of an Open Science Dashboard for Earth Sciences FU. The visualisation of the first OS metrics is already available under <a href="https://quest-open-earthsciences.charite.de/">https://quest-open-earthsciences.charite.de/</a>.</p> <p>The datasets utilized include the outputs from the Department of Earth Sciences at FU, i.a. on Open Access (OA) categorisations and statuses, persistent identifiers (PIDs) and Open Licences (Creative Commons) availability, published between 2016-2023. These datasets consist of (i) <strong>"journal_articles_v3.csv"</strong> and (ii) <strong>"non_journal_articles_outputs_v3.csv"</strong>, the latter including “book”, “book chapter”, “conference paper”, “conference abstract”, and “other research outputs” (e.g. book reviews, project reports, book chapters in school books, or electronic supplementary material).</p> <p>Data for the dashboard was obtained from the FU university bibliography (<a href="https://frub-berlin.primo.exlibrisgroup.com/">https://frub-berlin.primo.exlibrisgroup.com/</a>), but coverage of PID information was incomplete, OA category information was incomplete and often erroneous, and copyright/open licence information was missing in this data set. Therefore, the data set was <strong>enriched with manually researched information</strong>. Data enrichment was different for journal articles and for non-journal-article publications. For <strong><em>journal articles</em></strong>, <em>copyright/open licence</em> information was added, and <em>open access category</em> information was checked and added or corrected. For <strong><em>non-journal-article outputs</em></strong>, missing <em>PIDs</em> were added and <em>open access category</em> information was checked and added or corrected. </p> <p>The "<em>data_dictionary_earth_sciences_v3.csv"</em> table documents all variables of each data file containing here.</p> <p>Both for the dashboard, and in our following publications, we categorized <strong>"bronze"</strong> OA outputs as closed access. Although such publications are openly available on the publisher's websites, they lack licence information and thus cannot be openly reused, and presumably even change its openness status at any time. Following the methodology of Charité Dashboard on Responsible Research (<a href="https://quest-dashboard.charite.de/#tabStart">https://quest-dashboard.charite.de/#tabStart</a>) we only include "gold", "hybrid" and "green" OA as true OA. Further details about the enrichment process conducted on these datasets can be found under <a href="https://doi.org/10.5281/zenodo.1099821" target="_blank" rel="noopener">10.5281/zenodo.1099821</a>9 [2]</p> <p> </p> <p>[1] Duine, M., Iarkaeva, A., & Hübner, A. (2024, November 15). Initiating discipline-specific Open Science Monitoring with the Open Science Dashboard for Earth Sciences. 28th International Conference on Science, Technology and Innovation Indicators (STI2024), Berlin, Germany. <a href="https://doi.org/10.5281/zenodo.14170751" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.14170751</a><br>[2] Duine, M., Hübner, A., & Iarkaeva, A. (2024). Enrichment of university bibliography data for open science monitoring. Zenodo. <a href="https://doi.org/10.5281/zenodo.10998219">https://doi.org/10.5281/zenodo.10998219</a></p>
Data for publication "Statistical characteristics of extreme daily precipitation during 1501 BCE - 1849 CE in the Community Earth System Model".
<p>Here, the data used in Kim, W. M., Blender, R., Sigl, M., Messmer, M., & Raible, C. C. (2021). "Statistical characteristics of extreme daily precipitation during 1501 BCE–1849 CE in the Community Earth System Model" in <em>Climate of the Past </em>(<a href="https://doi.org/10.5194/cp-2021-61">https://doi.org/10.5194/cp-2021-6</a>) are provided.</p> <p>Two simulations covering the period 1501 BCE - 2008 CE are performed with CESM 1.2.2: the orbital-only and the full-forcing simulations. The full-forcing transient simulation includes the new long record of volcanic eruptions (<a href="https://doi.org/10.1594/PANGAEA.928646">https://doi.org/10.1594/PANGAEA.928646</a>) that covers the last 3500 years. The output from the simulations is used to examine the long-term variability and characteristics of daily extreme precipitation during 1501BCE-1849 CE.</p> <p>The following files are provided:</p> <ul> <li> <strong>CESM122.transient.PRECT.anom.above99th.1501BCE-1849CE_I and II</strong>: Daily precipitation anomalies above the 99th percentiles relative to 1501BCE-1849CE from the full forcing simulation. The file is split into two parts, with the first file containing the first 50% of extremes (I) and the second file containing the rest 50% (II).</li> <li><strong>CESM122.orbital.PRECT.anom.above99th.1501BCE-1849CE I and II:</strong> Daily precipitation anomalies above the 99th percentiles relative to 1501BCE-1849CE from the orbital-only simulation.</li> <li> <strong>CESM122.transient.variables.mon.1979-2008CE:</strong> monthly precipitation, temperature, and geopotential height at 500 hPa for 1979-2008CE from the full-forcing simulation.</li> <li><strong>CESM122.trans.variable_names.years:</strong> Monthly variables from the full-forcing simulation. The simulation starts from the model year 1, which corresponds to the actual year 1501BCE. The variables are solar insolation (SOLIN), clear-sky net surface shortwave radiation (FSNSC), geopotential height at 500hPa (Z500), and surface temperature (TS).</li> <li><strong>CESM122.orbital.variable_names.years:</strong> Monthly variables from the orbital-only simulation. The simulation starts from the model year 1, which corresponds to the actual year 1501BCE.</li> <li><strong>CESM122.*.log-likelihood-GPDmodel-ExtForcing</strong>: Negative log-likelihood for the stationary and non-stationary Generalized Pareto Distribution models for external forcings.</li> <li> <strong>CESM122.*.log-likelihood-GPDmodel-ModesVar</strong>: Negative log-likelihood for the stationary and non-stationary Generalized Pareto Distribution models for modes of variability.</li> <li><strong>Evolk_EVA_distribution_1501BCE-2015CE</strong>: Distribution of volcanic aerosol for CAM5, produced based on Kim et al. (2021).</li> </ul> <p>If you use this dataset, please cite:</p> <p><em>Kim, W. M., Blender, R., Sigl, M., Messmer, M., & Raible, C. C. (2021). Statistical characteristics of extreme daily precipitation during 1501 BCE–1849 CE in the Community Earth System Model. Climate of the Past Discussions, 1-38. <a href="https://doi.org/10.5194/cp-2021-61">https://doi.org/10.5194/cp-2021-61</a></em></p>
Radiocarbon in the land and ocean components of the Community Earth System Model: data to prepare figures
<p>The files contain the data to plot the graphics displayed in the publication by Frischknecht, T., Ekici, A., Joos, F. Radiocarbon in the land and ocean components of the Community Earth System Model, Global Biogeochemical Cycles, 2022, in press.</p>
Global Environmental and Weather data for PyPSA-Earth: An Open Optimisation Model of the Earth Energy System.
<p><strong>PyPSA-Earth </strong>is an open model dataset of the global power system at different network levels that cover our Earth. The African model can be built using the code provided at <a href="https://github.com/pypsa-meets-africa/pypsa-africa">https://github.com/pypsa-meets-africa/pypsa-africa</a>. Other regions follow soon under the same code base.</p> <p>Since the GitHub codebase is not suited for handling large changing files, we provide here separate <strong>data bundles and cutouts</strong> to be downloaded and extracted as noted in the <a href="https://pypsa-meets-africa.readthedocs.io/en/latest/index.html">documentation</a></p> <p>The below-provided <strong>cutouts </strong>are spatiotemporal subsets of the Earth weather data from the <a href="https://software.ecmwf.int/wiki/display/CKB/ERA5+data+documentation">ECMWF ERA5</a> reanalysis dataset and the <a href="https://wui.cmsaf.eu/safira/action/viewDoiDetails?acronym=SARAH_V002">CMSAF SARAH-2</a> solar surface radiation dataset for the <strong>year 2013</strong>. They have been prepared by and are for use with the <a href="https://github.com/PyPSA/atlite">atlite</a> tool (<a href="https://atlite.readthedocs.io/">https://atlite.readthedocs.io/</a>). They can be reproduced or extended for other weather years (approx. 40-50 years) around the world by using the <a href="https://github.com/pypsa-meets-africa/pypsa-africa/blob/main/scripts/build_cutout.py">build.cutout.py</a></p> <p><strong>ECMWF ERA5</strong></p> <ul> <li><strong>Source: </strong><a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview">https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview</a></li> <li><strong>Terms of Use: </strong><a href="https://cds.climate.copernicus.eu/api/v2/terms/static/20180314_Copernicus_License_V1.1.pdf">https://cds.climate.copernicus.eu/api/v2/terms/static/20180314_Copernicus_License_V1.1.pdf</a></li> </ul>
Detrital Carbonate Minerals in Earth's Element Cycles (Data & Scripts)
<p>Earth surface conditions, including climate and sea level, are largely controlled by the cycling of carbon and biogeochemically coupled elements. However, most elemental budgets cannot be consentaneously balanced for the present state. Here, we investigate the possible role of riverine carbonate minerals in biogeochemical cycles. We derive individual river basin export fluxes, the global export flux to the ocean and its reduction by human influence, utilizing state-of-the-art regression techniques and published global-scale datasets. Results point to a significance of riverine detrital carbonates for the global mass balances of carbon, calcium, alkalinity and strontium, which might help solving this long-standing problem. </p> <p>[Plain Language summary from: Müller et al. 2022, Detrital Carbonates in Earth's Element Cycles, GBC, <a href="https://doi.org/10.1002/essoar.10508409.1">https://doi.org/10.1002/essoar.10508409.1</a> ].</p> <p>Here data and scripts on which these investigations are based can be accessed.</p> <p> </p> <p>Funding:<br> This work was carried out under the umbrella of the Netherlands Earth System Science Centre (NESSC). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie, grant agreement No 847504. Funding was also provided by BMBF-project PALMOD (Ref 01LP1506C) through the German Federal Ministry of Education and Research (BMBF) as Research for Sustainability inititative (FONA). AS thanks the European Research Council for Consolidator Grant 771497.</p>
Data and code used in "Satellite magnetic data reveal interannual waves in Earth's core"
<p>Eigen mode solutions and code to obtain them for the results presented in <a href="https://doi.org/10.1073/pnas.2115258119">Satellite magnetic data reveal interannual waves in Earth's core</a>. The package uses the freely available code <a href="https://github.com/fgerick/Mire.jl">Mire.jl</a>.</p> <p><strong>Prerequisites</strong></p> <p>Installed python3 with matplotlib ≥v2.1, cmocean and cartopy. A working Julia ≥v1.7.</p> <p><strong>Run</strong></p> <p>In the project folder run</p> <pre><code>julia --project=.</code></pre> <p><br> Then, from within the Julia REPL run</p> <pre><code>]instantiate</code></pre> <p>at first time, to install all dependencies.</p> <p>After that, to compute all plots, run</p> <pre><code>using QGMCSat allfigs()</code></pre> <p>They're automatically saved in the "figs" subfolder of the repository.</p> <p>If loading QGMCSat fails, due to a missing cartopy or cmocean in the python version. Run (within Julia)<br> </p> <pre><code>ENV["PYTHON"] = "python" #this should point to the python version that has cartopy installed ]build PyCall</code></pre> <p><br> To calculate all data, run</p> <pre><code>using QGMCSat calculate_data()</code></pre> <p>This will take several hours/days depending on the machine (needs enough memory).</p> <p>Individual data can be accessed directly through the .jld2 files from Julia. You can check out the individual figure functions to get an idea where which data is stored.</p> <p>If there are any issues or questions, please don't hesitate to get in touch!</p>
Data/ codes used in the the Natural Hazards and Earth System Sciences (NHESS) publication titled "Wind-Wave Characteristics and extremes along the Emilia-Romagna coast" by Pranavam Ayyappan Pillai et al. (2022)
<p>The archive contains datasets and codes used in the manuscript titled "Wind-Wave Characteristics and extremes along the Emilia-Romagna coast", and published in the journal <em>Natural Hazards and Earth System Sciences</em> (<em>NHESS</em>) by Pranavam Ayyappan Pillai et al., 2022.</p> <p>Pranavam Ayyappan Pillai, U., Pinardi, N., Federico, I., Causio, S., Trotta, F., Unguendoli, S., and Valentini, A.: Wind-Wave Characteristics and extremes along the Emilia-Romagna coast, Nat. Hazards Earth Syst. Sci. Discuss. https://doi.org/10.5194/nhess-2022-103, 2022.</p>
Stochastic accretion of the Earth
<p>Raw output data associated with the publication 'Stochastic accretion of the Earth' in Nature Astronomy. </p>
Data for Li et al., Coupling remote sensing and particle tracking to estimate trajectories in large water bodies, International Journal of Applied Earth Observation and Geoinformation, 2022
<p>This data set contains four parts:</p> <p>1) compressed folder with input parameters and results for the hydrodynamic model</p> <p>2) compressed folder with input parameters and results for the particle tracking</p> <p>3) compressed folder with satellite data </p> <p>4) code used in the article for hydrodynamic model, particle tracking and image processing</p> <p>Each folder contains a readme file,</p>
ecochange: An R-package to derive ecosystem change indicators from freely available earth observation products
<p>This release includes the R code necessary to reproduce Figures 2-4 in the Application paper entitled: "ecochange: An R-package to derive ecosystem change indicators from freely available Earth Observation products."</p>
Waveform data for centroid moment tensor solutions presented in publication "Bayesian seismic source inversion with a 3-D Earth model of the Japanese islands"
<p>The dataset includes waveform data for centroid moment tensor solutions inferred using Hamiltonian Monte Carlo and a 3-D Earth model in the Japanese islands. The data are provided as Green's strains at the maximum-likelihood location (indicated in the title of each text file) for all study events inverted at different periods. Inversion period is also indicated in the title. All the data are filtered between 15 s and 80 s. Additionally we provide a Python code to obtain displacement from strains given a moment tensor.</p>
Model codes and simulation data for "Modeling demographic-driven vegetation dynamics and ecosystem biogeochemical cycling in NASA GISS's Earth system model (ModelE-BiomeE v.1.0)"
<p>ModelE-BiomeE v1.0 model codes and data This folder contains the simulation data and model codes that were used in the paper ‘Modeling demographic-driven vegetation dynamics and ecosystem biogeochemical cycling in NASA GISS’s Earth system model (ModelE-BiomeE v.1.0)’ (https://doi.org/10.5194/gmd-2022-72). We included the data simulated by ModelE-BiomeE v.1.0 with settings of full demography (folder FullDemography) and single cohort (folder SingleCohort), and initial settings of land grids and vegetation data (folder GlobalVegetation). The codes include the full ModelE 2.1, module BiomeE files in ModelE, and the standalone BiomeE. In the folder FullDemography, we have 4 netcdf files for global output and 25 files for single grids output. The files ‘FullDM_2588_JAN.nc’ and ‘FullDM_2588_JUL.nc’ are the original model output of January and July in the year 2588. The file ‘FullDM_2588_Annual.nc’ is the yearly summary of model simulations. The file ‘FullDM_Selected.nc’ is an annual summary of 588 years of model simulation only with selected variables. The csv files are for single grids output at the time steps of daily and yearly. The last digit 1~8 represents the sites of 'BNC','MNT','HF','OKR','KZ','SV','WGK','TPJ', respectively (Table 1). Table 1 Site ID and file number ['BNC', 'MNT', 'HF', 'OKR', 'KZ', 'SV', 'WGK', 'TPJ'] ['8991', '8992', '8993', '8994', '8995', '8996', '8997', '8998'] ['8971', '8972', '8973', '8974', '8975', '8976', '8977', '8978'] ['8961', '8962', '8963', '8974', '8965', '8966', '8977', '8968'] Please refer to Table 2 in the paper for the detail of these 8 sites. ‘DailyLAIGPP.csv’ is a summary of all ‘DailyEcosystem’ files with LAI and GPP data. We included the Python scripts that can be used to generate the figures in out paper (Plotting-BiomeE-MsTMIP.py, Plotting-Scatter-Comparison.py, PlottingBiomeEMaps.py, and PlottingGridOutput.py). For the convenience of readers (in reproducing our figures), we included the summary of reanalysis of the data from observations and MsTMIP in folder ‘Sum-Obs-Simu’. Please refer to the original sources listed in our paper for the detail of these data.</p>
Assessing tropospheric turbulence impact on VGOS telescope placement in the Indian subcontinent for the estimation of Earth Orientation Parameters
<p>The dataset accompanying this study is composed of three distinct files, each integral to the research conducted. The file, named 'Simulated Data', includes the results derived from the simulations performed within this investigation. This file serves as a repository of the computed outcomes. The file, named 'Data and Code', includes the MATLAB scripts utilized to compute the Cn value from the Zenith Wet Delay (ZWD). Additionally, this file encompasses the relevant datasets for both wind speed and ZWD at various station locations. The third file contains the geographical coordinates of the Indian stations that were used in the study. Together, these files constitute a complete dataset that supports the study’s objectives and verification of the findings. </p>
Earth's atmosphere protects the biosphere from nearby supernovae
<p>Dataset for manuscript: "Earth’s atmosphere protects the biosphere from nearby supernovae".</p> <p>Communications Earth & Environment</p> <p>DOI: <a href="https://doi.org/10.1038/s43247-024-01490-9" target="_blank" rel="noopener noreferrer">10.1038/s43247-024-01490-9</a></p> <div><span>CONTRIBUTORS: </span>Theodoros Christoudias; Jasper Kirkby; Dominik Stolzenburg; Andrea Pozzer; Eva Sommer; Guy P. Brasseur; Markku Kulmala; Jos Lelieveld</div>
The Earth BioGenome Project Phase II: Illuminating the Eukaryotic Tree of Life. Data file underpinning Figure 2A and Figure 2B
<div>These datasheets accompany the article "The Earth BioGenome Project Phase II: Illuminating the Eukaryotic Tree of Life" in Frontiers in Science</div> <div>This file contains data processed from Catalog of Life on 31 December 2023. The catalog was downloaded and post-processed to</div> <div>remove prokaryotic taxa</div> <div>remove extinct and fossil taxa</div> <div>remove taxon names that were listed as junior synonyms</div> <div>remove taxon names listed as "invalid"</div> <div>Total living, valid eukaryotic genera 167,085</div> <div>The taxa were sorted by the nomenclatorial Code under which they were declared (to avoid namespace clashes)</div> <div>International Code for Algae, Fungi and Plants https://www.iapt-taxon.org/nomen/main.php</div> <div>Algal, Fungal, Plant code genera 31,076</div> <div>International Code of Zoological Nomenclature https://www.iczn.org/the-code/the-code-online/</div> <div>Zoological code genera 136,009</div> <div>The Code-sorted taxa were aggregated by the generic portion of their names, and two plots were generated:</div> <div>a plot aggregating the cumulative number of species in genera sorted by species number (Figure 2A)</div> <div>a plot illustrating the distribution of the size of genera (Figure 2B)</div> <div>This data file gives access to these processed data for</div> <div>Figure 2 A Data</div> <div>Figure 2 B Data</div> <div>The original data including the intermediate calculations of values, and the plotted graphs, are available as a GoogleDoc at https://docs.google.com/spreadsheets/d/1V-bTtWjIRasC3AgID0jGlyToKqI-H9h1aPeSxUpNrjk/edit?usp=sharing</div>
Data of publication All-optical control of long-lived nuclear spins in rare-earth doped nanoparticles
<p>Data corresponding to the figures of the publication "All-optical control of long-lived nuclear spins in rare-earth doped nanoparticles" by D. Serrano et al. (https://www.nature.com/articles/s41467-018-04509-w). A text file describes data in each compressed folder, please refer to the publication for more details. </p>
Supporting Information for "An empirical modification of the force field approach to describe the modulation of galactic cosmic rays close to Earth in a broad range of rigidities"
<p>This supporting information provides the Data Set S1 used to produce Fig. 6 in <strong>"An empirical modification of the force field approach to describe the modulation of galactic cosmic rays close to Earth in a broad range of rigidities"</strong> (Gieseler et al., 2017). It can be used to calculate the rigidity-dependent solar modulation potential <span class="math-tex">\(\phi(P)\)</span> for monthly intervals from 1973-2017 following Eq. 10 in Gieseler et al. (2017).</p> <p>If you use this data, please refer to and cite <strong>BOTH</strong> following publications:</p> <ul> <li>Gieseler, J., B. Heber, and K. Herbst, <em>An empirical modification of the force field approach to describe the modulation of galactic cosmic rays close to Earth in a broad range of rigidities</em>, J. Geophys. Res., 2017 (doi:10.1002/2017JA024763).</li> <li>Usoskin, I. G., G. A. Bazilevskaya, and G. A. Kovaltsov, <em>Solar modulation parameter for cosmic rays since 1936 reconstructed from ground-based neutron monitors and ionization chambers</em>, J. Geophys. Res., 2011 (doi:10.1029/2010JA016105).</li> </ul> <p>This data set contains the solar modulation potential values in MV for monthly intervals from 1973-2017 derived from the proton proxies IMP-8 He and ACE/CRIS C (Phi_pp), and from Usoskin et al. (2011) as provided by http://cosmicrays.oulu.fi/phi/phi.html (Phi_Uso11). The uncertainties of Phi_pp are given in column 4, those of Phi_Uso11 are 26 MV for the observed period. The LIS used to calculate the modulation potentials is that from Burger et al. (2000) as given by Usoskin et al. (2005).</p> <p>Column 1: Fractional year (start of interval)<br> Column 2: Month<br> Column 3: Phi_pp /MV<br> Column 4: Uncertainty of Phi_pp /MV<br> Column 5: Phi_Uso11 /MV</p> <p>Data also available at http://www.ieap.uni-kiel.de/et/ag-heber/cosmicrays</p>
Data of publication Controlled size reduction of rare earth doped nanoparticles for optical quantum technologies
<p>Data corresponding to the figures of the publication " Controlled size reduction of rare earth doped nanoparticles for optical quantum technologies" by S. Liu et al. (https://pubs.rsc.org/en/content/articlelanding/2018/ra/c8ra07246a#!divAbstract). A text file describes data in each compressed folder, please refer to the publication for more details. </p>
EMAG2: Earth Magnetic Anomaly Grid (2-arc-minute resolution) compressed for NumPy
<p>A compressed NumPy version of the <a href="https://www.ngdc.noaa.gov/geomag/emag2.html">EMAG2 (v3)</a> global Earth Magnetic anomaly grid compiled from satellite, ship, and airborne magnetic measurements. The original CSV data was imported, transformed, and saved to a compressed NumPy archive as follows:</p> <pre><code class="language-python">import numpy as np mag_data = np.loadtxt('EMAG2_V3_20170530.csv', delimiter=',', usecols=(2,3,4,5,7)) lon_mask = mag_data[:,0] > 180.0 mag_data[lon_mask,0] -= 360.0 np.savez_compressed('EMAG2_V3_20170530.npz', data=mag_data.astype(np.float32))</code></pre> <p>The NumPy archive (contained in this repository) can be efficiently loaded in Python workflows. It contains the following columns:</p> <ol> <li>Longitude - geographic longitudinal coordinates in decimal degrees (WGS84)</li> <li>Latitude - geographic latitudinal coordinates in decimal degrees (WGS84)</li> <li>SeaLevel - magnetic anomaly value at sea level (nT)</li> <li>UpCont - magnetic anomaly value at continuous 4km altitude (nT)</li> <li>Error - Error estimate (nT)</li> </ol> <p>Code 888 is assigned in certain cells on grid edges where the data source is ambiguous and assigned an error of -888 nT.<br> Code 999 is assigned in cells where no data is reported with the anomaly value assigned 99999 nT and an error of -999 nT.</p> <p><strong>Reference</strong></p> <p>Brian Meyer, Richard Saltus, Arnaud Chulliat (2017): EMAG2: Earth Magnetic Anomaly Grid (2-arc-minute resolution) Version 3. National Centers for Environmental Information, NOAA. Model. doi:10.7289/V5H70CVX</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.