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2,009 results for “Global data”
Supplementary Data: Cosmological constraints on decaying axion-like particles: a global analysis
<p><strong>Supplementary Data</strong></p> <p><em>Cosmological constraints on decaying axion-like particles: a global analysis</em></p> <p>This record contains the supplemetary data for the GAMBIT article, "Cosmological constraints on decaying axion-like particles: a global analysis". </p>
GPM_API - Global Hourly Soil Moisture from GPM IMERG Data - 2019
<p><strong># GPM_API 2019</strong></p> <p>GPM_API data root: <a href="https://zenodo.org/record/6489998">https://zenodo.org/record/6489998</a></p> <p><strong># Related article:</strong></p> <p>Ramsauer, T., & Marzahn, P. (2023). Global Soil Moisture Estimation based on GPM IMERG Data using a Site Specific Adjusted Antecedent Precipitation Index. <em>International Journal of Remote Sensing</em>, 44(2), 542-566.</p> <p>Article: <a href="https://doi.org/10.1080/01431161.2022.2162351">https://doi.org/10.1080/01431161.2022.2162351</a></p> <p>Free PDF: <a href="https://www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351">www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351</a></p>
GPM_API - Global Hourly Soil Moisture from GPM IMERG Data - 2016
<p><strong># GPM_API 2016</strong></p> <p>GPM_API data root: <a href="https://zenodo.org/record/6489998">https://zenodo.org/record/6489998</a></p> <p><strong># Related article:</strong></p> <p>Ramsauer, T., & Marzahn, P. (2023). Global Soil Moisture Estimation based on GPM IMERG Data using a Site Specific Adjusted Antecedent Precipitation Index. <em>International Journal of Remote Sensing</em>, 44(2), 542-566.</p> <p>Article: <a href="https://doi.org/10.1080/01431161.2022.2162351">https://doi.org/10.1080/01431161.2022.2162351</a></p> <p>Free PDF: <a href="https://www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351">www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351</a></p>
GPM_API - Global Hourly Soil Moisture from GPM IMERG Data - 2018
<p><strong># GPM_API 2018</strong></p> <p>GPM_API data root: <a href="https://zenodo.org/record/6489998">https://zenodo.org/record/6489998</a></p> <p><strong># Related article:</strong></p> <p>Ramsauer, T., & Marzahn, P. (2023). Global Soil Moisture Estimation based on GPM IMERG Data using a Site Specific Adjusted Antecedent Precipitation Index. <em>International Journal of Remote Sensing</em>, 44(2), 542-566.</p> <p>Article: <a href="https://doi.org/10.1080/01431161.2022.2162351">https://doi.org/10.1080/01431161.2022.2162351</a></p> <p>Free PDF: <a href="https://www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351">www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351</a></p>
GPM_API - Global Hourly Soil Moisture from GPM IMERG Data - 2017
<p><strong># GPM_API 2017</strong></p> <p>GPM_API data root: <a href="https://zenodo.org/record/6489998">https://zenodo.org/record/6489998</a></p> <p><strong># Related article:</strong></p> <p>Ramsauer, T., & Marzahn, P. (2023). Global Soil Moisture Estimation based on GPM IMERG Data using a Site Specific Adjusted Antecedent Precipitation Index. <em>International Journal of Remote Sensing</em>, 44(2), 542-566.</p> <p>Article: <a href="https://doi.org/10.1080/01431161.2022.2162351">https://doi.org/10.1080/01431161.2022.2162351</a></p> <p>Free PDF: <a href="https://www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351">www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351</a></p>
GPM_API - Global Hourly Soil Moisture from GPM IMERG Data - 2020
<p><strong># GPM_API 2020</strong></p> <p>GPM_API data root: <a href="https://zenodo.org/record/6489998">https://zenodo.org/record/6489998</a></p> <p><strong># Related article:</strong></p> <p>Ramsauer, T., & Marzahn, P. (2023). Global Soil Moisture Estimation based on GPM IMERG Data using a Site Specific Adjusted Antecedent Precipitation Index. <em>International Journal of Remote Sensing</em>, 44(2), 542-566.</p> <p>Article: <a href="https://doi.org/10.1080/01431161.2022.2162351">https://doi.org/10.1080/01431161.2022.2162351</a></p> <p>Free PDF: <a href="https://www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351">www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351</a></p>
Brewer Global Iradiance (GI) total ozone data at two Norwegian sites (2000 to 2020)
<p>Total column ozone (TCO) derived from Global Irradiance (GI) measurements from the Brewer instruments B042 in Oslo (Norway) and B104 in Andøya (Norway) from 01-01-2000 to 31-12-2020.</p> <p>The data consist of daily values averaged +/-2 hours around local noon.</p> <p>GI calibrations where performed with a clear sky direct sun (DS) measurements in 06-2001, 08-2014, 08-2016, 08-2018, and 08-2019 at Andøya, and in 08-2005, 06-2019, and 08-2019 at Oslo. The data has been calibrated with standard lamp measurements and have been homogenized with DS measurements as a function of clouds and solar zenith angle.</p> <p>The method, calibration, and homogenization is described by Bernet et al. (2022) (Appendix A).</p> <p>Responsible institute: NILU - Norwegian Institute for Air Research</p> <p>Funded by the Swiss National Science Foundation and the Norwegian Environment Agency</p> <p>Bernet, L., Svendby, T., Hansen, G., Orsolini, Y., Dahlback, A., Goutail, F., Pazmiño, A., Petkov, B., and Kylling, A., Total ozone trends at three northern high-latitude stations, 2022.</p>
Global agricultural ammonia emissions simulated with the ORCHIDEE land surface model : Model Ouput Data
<p>This upload includes data associated with the manuscript "Global agricultural ammonia emissions simulated with the ORCHIDEE land surface model : )" submitted to Geoscientific Model Development. The dataset includes an output file with the simulated ammonia emissions for the agricultural sector.</p> <p>The emissions (manure management and soil), manure production and soil ammonium concentrations are monthly fields from the simulation for 2007-2015.</p> <p>Additional information is given in the readme file</p>
Data files for figures in "Deep learning extreme precipitation of the past, present, and under 1.5°C and 2.0°C global warming" by Bird et al. 2022
<p>The data files for figures in <em>Deep learning extreme precipitation of the past, present, and under 1.5°C and 2.0°C global warming</em> by Bird, Bodeker and Clem. The data files are provided either as self-describing netCDF files, or .csv files with column descriptors.</p>
Harmonized data and code for "Plankton response to global warming is characterized by non-uniform shifts in assemblage composition since the last ice age"
<p>Harmonized data and R code for "Plankton response to global warming is characterized by non-uniform shifts in assemblage composition since the last ice age" by Tonke Strack, Lukas Jonkers, Marina C. Rillo, Helmut Hillebrand and Michal Kucera (in <em>Nature Ecology & Evolution</em>, 2022, https://doi.org/10.1038/s41559-022-01888-8).</p> <p>Analyse planktonic foraminifera species assemblages from the North Atlantic Ocean over the past 24,000 years.</p> <p>Scripts written by Tonke Strack</p> <p>DATA SOURCES<br>* WOA18: Locarnini, R. A. et al. World Ocean Atlas 2018, Volume 1: Temperature. A. Mishonov, Technical Editor. NOAA Atlas NESDIS 81, 52 (2019).<br>* LGMR: Osman, M. B. et al. Globally resolved surface temperatures since the Last Glacial Maximum. Nature 599, 239-244, doi:10.1038/s41586-021-03984-4 (2021).<br>* MARGO: Kucera, M., Rosell-Melé, A., Schneider, R., Waelbroeck, C. & Weinelt, M. Multiproxy approach for the reconstruction of the glacial ocean surface (MARGO). Quat. Sci. Rev. 24, 813-819, doi:10.1016/j.quascirev.2004.07.017 (2005). Kucera, M. et al. Reconstruction of sea-surface temperatures from assemblages of planktonic foraminifera: multi-technique approach based on geographically constrained calibration data sets and its application to glacial Atlantic and Pacific Oceans. Quat. Sci. Rev. 24, 951-998, doi:10.1016/j.quascirev.2004.07.014 (2005).<br>* planktonic foraminifera assemblage data: individual citations provided in CoreList_PlanktonicForaminifera.csv</p> <p>DATA<br>1. Harmonized assemblage data*: FullDataTable_PF_harmonized.txt<br>2. Core list with additional information to time series: CoreList_PlanktonicForaminifera.csv<br>3. Reference list for PF names: ReferenceList_PlanktonicForaminifera.csv</p> <p>CODE<br>1. 01_DataAnalysis_PCA.R: principal component analysis on assemblage data of individual time series as well as on whole dissimilarity matrix (results shown in Fig. 1 and 2)<br>2. 02_DataAnalysis_LocalBiodiversityChange.R: local biodiversity change analysis of individual time series (results shown in Fig. 3 and Extended Data Fig. 1); also recalculates resolution of time-series<br>3. 03_DataAnalysis_NoAnalogueAssemblages.R: calculates compositional dissimilarity to the nearest LGM sample to analyse existence of no-analogues (results shown in Fig. 4, as well as Extended Data Fig. 3 and 4)<br>4. 04_DataAnalysis_LDG_LGMresiduals.R: visualises latitudinal diversity gradient through time and the difference between richness and Shannon diversity to their respective LGM mean values (results shown in Fig. 5)</p> <p>*Assemblage data of individual time series were manually downloaded, checked and harmonized following the taxonomy of Siccha and Kucera (2017) and combined into one data file. Species not reported in the time series data were assumed to be absent (i.e., zero abundance). We merged <em>Globigerinoides ruber ruber</em> and <em>Globigerinoides ruber albus</em>, because some studies only reported them together as <em>Globigerinoides ruber</em>. Also, P/D intergrades (an informal category of morphological intermediates between <em>Neogloboquadrina incompta</em> and <em>Neogloboquadrina dutertrei</em>) were merged with <em>Neogloboquadrina incompta</em>. In total, 41 species of planktonic foraminifera were included in this study.</p> <p>Siccha, M. & Kucera, M. ForCenS, a curated database of planktonic foraminifera census counts in marine surface sediment samples. <em>Sci. Data</em> 4, 170109, doi:10.1038/sdata.2017.109 (2017).</p>
The SPIN covid19 RMRIO dataset: Global trade network data for the years 2016-2026 reflecting macroeconomic effects of the covid19 pandemic - B. Data for 2020 - 2026 - Covid scenario
<p>The SPIN covid19 RMRIO dataset is a time series of MRIO tables covering years from 2016-2026 on a yearly basis. The dataset covers 163 sectors in 155 countries.</p> <p>This repository includes data for years from 2020 to 2026 (<em>covid</em> scenario).<br> Code, method material and data for years 2016-2019 are stored in the following repository: <a href="http://doi.org/10.5281/zenodo.5713811">10.5281/zenodo.5713811</a><br> Data for the <em>counterfactual</em> scenario are stored in the following repository: <a href="https://doi.org/10.5281/zenodo.5713839">10.5281/zenodo.5713839</a></p> <p>Tables are generated using the <a href="https://github.com/TBeaufils/SPIN">SPIN method</a>, based on the <a href="https://doi.org/10.5281/ZENODO.3993659">RMRIO tables</a> for the year 2015, GDP, imports and exports data from the <a href="https://data.imf.org/?sk=4c514d48-b6ba-49ed-8ab9-52b0c1a0179b">International Financial Statistics</a> (IFS) and the World Economic Outlooks (WEO) of <a href="https://www.imf.org/en/Publications/WEO/weo-database/2019/October">October 2019</a> and <a href="https://www.imf.org/en/Publications/WEO/weo-database/2021/April">April 2021</a>.</p> <p>The <em>covid</em> scenario is in line with April 2021 WEO's data and includes the macroeconomic effects of Covid 19.</p> <p>All tables are labelled in 2015 US$ and valued in basic prices.</p>
The SPIN covid19 RMRIO dataset: Global trade network data for the years 2016-2026 reflecting macroeconomic effects of the covid19 pandemic - A. Code and data for 2016-2019
<p>The SPIN covid19 RMRIO dataset is a time series of MRIO tables covering years from 2016-2026 on a yearly basis. The dataset covers 163 sectors in 155 countries.</p> <p>This repository includes data for years from 2016 to 2019 (<em>hist</em> scenario) and the corresponding labels.<br> Data for years 2020 to 2026 are stored in the corresponding repositories:</p> <ul> <li><em>covid</em>: <a href="https://doi.org/10.5281/zenodo.5713825">10.5281/zenodo.5713825</a></li> <li><em>counterfactual: </em><a href="https://doi.org/10.5281/zenodo.5713839">10.5281/zenodo.5713839</a></li> </ul> <p>Tables are generated using the <a href="https://github.com/TBeaufils/SPIN">SPIN method</a>, based on the <a href="https://doi.org/10.5281/ZENODO.3993659">RMRIO tables</a> for the year 2015, GDP, imports and exports data from the <a href="https://data.imf.org/?sk=4c514d48-b6ba-49ed-8ab9-52b0c1a0179b">International Financial Statistics</a> (IFS) and the World Economic Outlooks (WEO) of <a href="https://www.imf.org/en/Publications/WEO/weo-database/2019/October">October 2019</a> and <a href="https://www.imf.org/en/Publications/WEO/weo-database/2021/April">April 2021</a>.</p> <p>From 2020 to 2026, the dataset includes two diverging scenarios. The <em>covid</em> scenario is in line with April 2021 WEO's data and includes the macroeconomic effects of Covid 19. The<em> counterfactual</em> scenario is in line with October 2019 WEO's data and simulates the global economy without Covid 19. Tables from 2016 to 2019 are labelled as <em>hist</em>.</p> <p>The <em>Projections</em> folder includes the generated tables for years from 2016 to 2019 (<em>hist</em> scenario) and the corresponding labels.<br> The <em>Sources </em>folder contains the data records from the IFS and WEO databases. The <em>Method data</em> contains the data files used to generate the tables with the SPIN method and the following Python scripts:</p> <ul> <li><em>SPIN_covid19_MRIO_files_preparation.py</em> generates the data files from the source data.</li> <li><em>SPIN_covid19_RMRIO runs.py</em> is the command to run the SPIN method and generate the dataset.</li> <li><em>figures.py</em> is a script to produce figures reflecting the consistency of the projected tables and the evolution of macroeconomic figures in the 2016-2026 period for a selection of countries.</li> </ul> <p>All tables are labelled in 2015 US$ and valued in basic prices.</p>
The SPIN covid19 RMRIO dataset: Global trade network data for the years 2016-2026 reflecting macroeconomic effects of the covid19 pandemic - C. Data for 2020 - 2026 - Counterfactual scenario
<p>The SPIN covid19 RMRIO dataset is a time series of MRIO tables covering years from 2016-2026 on a yearly basis. The dataset covers 163 sectors in 155 countries.</p> <p>This repository includes data for years from 2020 to 2026 (<em>counterfactual</em> scenario).<br> Code, method material and data for years 2016-2019 are stored in the following repository: <a href="http://doi.org/10.5281/zenodo.5713811">10.5281/zenodo.5713811</a><br> Data for the <em>covid</em> scenario are stored in the following repository: <a href="https://doi.org/10.5281/zenodo.5713825">10.5281/zenodo.5713825</a></p> <p>Tables are generated using the <a href="https://github.com/TBeaufils/SPIN">SPIN method</a>, based on the <a href="https://doi.org/10.5281/ZENODO.3993659">RMRIO tables</a> for the year 2015, GDP, imports and exports data from the <a href="https://data.imf.org/?sk=4c514d48-b6ba-49ed-8ab9-52b0c1a0179b">International Financial Statistics</a> (IFS) and the World Economic Outlooks (WEO) of <a href="https://www.imf.org/en/Publications/WEO/weo-database/2019/October">October 2019</a> and <a href="https://www.imf.org/en/Publications/WEO/weo-database/2021/April">April 2021</a>.</p> <p>The<em> counterfactual</em> scenario is in line with October 2019 WEO's data and simulates the global economy without Covid 19.</p> <p>All tables are labelled in 2015 US$ and valued in basic prices.</p>
Data supplement for 'Global Dataset of Thermohaline Staircases obtained from Argo Floats and Ice-Tethered Profilers'
<p>This is the data supplement for 'Global Dataset of Thermohaline Staircases obtained from Argo Floats and Ice-Tethered Profilers'. Both algorithm and dataset described in this publication can be found in this folder.</p> <p>Please cite 'Global dataset of thermohaline staircases obtained from Argo floats and Ice-Tethered Profilers' when using this data set (doi: 10.5194/essd-2020-197).</p> <p>The newest/most updated version of the code can be found on GitHub: https://github.com/cvanderboog/Staircase-detection-algorithm.</p> <p> </p> <p> </p> <p> </p>
Dataset linking to the paper "Exploring characteristics of national forest inventories for integration with global space-based forest biomass data"
<p>The dataset links to the study titled “Exploring characteristics of national forest inventories for integration with global space-based forest biomass data”. This study is published in the journal “Science of the Total Environment” and the publication can be found at <a href="https://doi.org/10.1016/j.scitotenv.2022.157788">https://doi.org/10.1016/j.scitotenv.2022.157788</a>. The dataset contains four csv files that were used to produce the results and other figures in the paper. The description of the individual data files contained in the dataset is given below.</p> <p><strong>NFI availability and characteristics data: </strong>The data file “NFI_availability_characteristics.csv” contains data on the total number of NFIs, the NFI extent, and the year of the most recent NFI in countries with NFI as reported in FRA 2020 country reports. The respective data variables in the data file are termed as Number_of_NFI, Latest_NFI_extent_FRA2020, and Latest_NFI_year_FRA2020 (NFI years generally refer to the years of data collection). In addition, the data file contains data on the region and tropical domain per country. The tropical and subtropical countries were considered tropical in the analysis and interpretation of the results. These data were used to produce Figure 2 of the study. ArcMap 10.7.1 was used for this purpose. </p> <p><strong>National biomass intercomparison data: </strong>The data file “national_biomass_intercomparison.csv” contains national forest AGB data for the year 2018 from FRA 2020 and CCI Biomass product that were used in the national biomass intercomparison analysis. The total (tons) and average space-based AGB (tons/ha) are extracted directly from the CCI Biomass Map 2018 for each country included in the study. The processing is done in Python and R environments. The spatial resolution of the map is 100 m. The average FRA AGB data in tons per ha was compiled from FRA 2020 country reports. The total FRA AGB data (tons) was estimated by multiplying each country's average FRA AGB data with FRA forest area data (in ha).</p> <p>The data unit for total AGB was converted from tons to gigaton (Gt) in intercomparison analysis. The total CCI Map AGB estimates used in the analysis are termed as CCI_MAP_AGB_Gt in the data file and the average as CCI_Map_AGB_tons.ha. Similarly, the total FRA AGB data are termed as FRA_AGB_Gt and the average as FRA_AGB_ton.ha. The NFI availability and temporality were also used in intercomparison analysis and this data is termed as Latest_NFI_year_FRA2020 in the data file. The data were used to produce Figure 3 of the study in the R environment.</p> <p><strong>NFI plot design characteristics: </strong>The data file named “NFI_plot_design_characteristics.csv” contains data on variables that were used in the analysis of NFI plot designs in 46 tropical countries. This data file mainly contains the data that was used to produce Figure 4 and Figure 6 in the R environment. The value “uniform” in the sampling_stratification variable means no stratification was used in the sampling design. The variable name “psu” stands for primary sampling unit (both cluster and single plots), “psu_distance_km” for the distance between primary sampling units in km, “cluster_plotdis_m” for the distance between plots in meter in the cluster, “plotsize_ha” for plot (single and cluster plots ) size in ha, “plotshape” for plot shapes (single and cluster plots), “ILUA” for Integrated Land Use Assessment. The data were compiled from the latest NFI design manuals and NFI reports.</p> <p><strong>NFI years: </strong>The data file “NFI_years_tropical_countries_data.csv” contains data on NFI years of the latest NFI in 46 tropical countries that were used to produce Figure 1 using ArcMap 10.7.1. The years generally refer to the last years of data collection. Data were compiled from the latest country NFI design manual or NFI report. This included both ongoing and completed NFI.</p>
GSDM-WBT: Global station-based daily maximum wet-bulb temperature data for 1981-2020
<p>The wet-bulb temperature integrates the temperature and humidity to comprehensively describe the thermal environment and the energy regulation of human bodies. Daily maximum wet-bulb temperature is an important indicator to be used for research on extreme humid heat. GSDM-WBT is a new dataset of global station-based daily maximum wet-bulb temperature, which was produced through calculating wet-bulb temperature, data quality control, infilling missing values and homogenisation based on the HadISD station-based observations and the NCEP-DOE reanalysis data. GSDM-WBT covers the complete daily series of 1834 stations around the world from 1981 to 2020. We provide the NetCDF files of GSDM-WBT for each station and one compressed file containing all data.</p>
A data set of monthly global ocean vertical velocity from 1950-2014
<p>This data set provides monthly global ocean vertical velocity from 1950-2014. It was constructed from 41 CMIP6 models (historical experiment). It may be used for investigating the large-scale upwelling and downwelling.</p> <p>Note that this data set has not been widely tested. Please feel free to contact the author if you had any questions or concerns.</p> <p>It will be greatly appreciated if you could send the author an email when you used this data set, so that the author can better improve this data set, and more importantly, provide you with updated data sets or any modifications.</p> <p> </p> <p> </p>
Development of a global inundation map at high spatial resolution from topographic downscaling of coarse-scale remote sensing data
<p><strong>Overview:</strong> The Global Inundation Extent from Multi-Satellites (GIEMS; Prigent et al. 2007, Papa et al. 2010) downscaled at 15 arc-second (GIEMS-D15; Fluet-Chouinard et al. 2015) was produced through the downscaling of the GIEMS database (natively at 0.25°). The downscaling procedure predicts the location of surface water cover with an inundation ranking surface generated by bagged decision trees. The decision trees were trained on binary presence/absence of wetland in the GLC2000 global land cover map (Bartholomé & Belward 2005) and used 13 topographic and hydrographic predictors derived from the SRTM-derived HydroSHEDS database (Lehner, Verdin & Jarvis 2008). The downscaling technique to three temporal aggregation of the GIEMS dataset representing three states of land surface inundation extents: mean annual minimum (MA<sub>Min</sub>; total area, 6.5 × 106 km<sup>2</sup>), mean annual maximum (MA<sub>Max</sub>; 12.1 × 106 km<sup>2</sup>), and long-term maximum (LT<sub>Max</sub>; 17.3 × 106 km<sup>2</sup>). The area of MAMin and MAMax from GIEMS were supplemented with the minimum area value from lakes, river and reservoirs from GLWD (Lehner & Döll 2004; classes 1,2,3). LTMax was corrected as the mean area from 3-year rolling maximum from GIEMS and the total wetland area from GLWD (classes 1-12). The accuracy of GIEMS-D15 reflects distribution errors introduced by the downscaling process as well as errors from the original satellite estimates. Yet, a comparison against independent regional wetland maps showed adequate agreement over large floodplains and wetlands. GIEMS-D15 offers a higher resolution delineation of inundated areas than originally offered by GIEMS, allowing for the assessment of global freshwater resources and the study of large floodplain and wetland ecosystems.</p> <p><strong>Projection:</strong> WGS84 (EPSG:4326)</p> <p><strong>Geographic extent:</strong></p> <ul> <li>Longitude: -180° to 180°</li> <li>Latitude: -56° to 84°</li> </ul> <p><strong>Spatial resolution: </strong>15 arc-second (500m at equator)</p> <p><strong>Legend</strong> (for discrete pixel values):</p> <ul> <li>0 = Upland</li> <li>1 = Mean Annual Minimum (MA<sub>Min</sub>)</li> <li>2 = Mean Annual Maximum (MA<sub>Max</sub>)</li> <li>3 = Long Term Maximum (LT<sub>Max</sub>)</li> </ul>
COALMOD-World 2.0 data, results, figures for: Stranded assets and early closures in global coal mining under 1.5°C
<p>This dataset contains all COALMOD-World 2.0 data for Hauenstein (2023): Stranded assets and early closures in global coal mining under 1.5°C (doi.org/10.1088/1748-9326/acb0e5) </p> <p>With the input data files and the GAMS scenario file the model (https://doi.org/10.5281/zenodo.7077678) can be run to reproduce the model results.</p> <p>Furthermore, the output.zip folder contains the results file, the R code to compile the figures, and PDFs of the figures.</p>
Global ice drilling and archive location data for select ice cores
<p>This document includes ice drill site information and ice core repository information for select ice cores retrieved between 1958 and 2022. Included data are not representative of all ice cores drilled during this time period, nor are they representative of all ice core samples collected and maintained by all of the contributing programs and facilities. Data are presented as they were provided by contributing facilities in 2022, when they were used to generate a figure for an article in Past Global Changes Magazine (doi.org/10.22498/pages.30.2.98).</p> <p>The data describe ice core drilling sites (latitude, longitude, elevation, site name), ice core samples (bottom depth, bottom age, core diameter, core completion date, corresponding publications), and ice core storage facilities (latitude, longitude, name).</p> <p>Contributing facilities include the following: Alfred Wegener Institute (Germany), Australian Antarctic Division (Australia), Australian Antarctic Program Partnership (Australia), Byrd Polar Center - University of Ohio (United States of America), Canadian Ice Core Lab (Canada), Chiba University (Japan), Commonwealth Scientific and Industrial Research Organization (Australia), Institute of Environmental Geosciences - University of Grenoble (France), Institute of Low Temperature Science - University of Hokkaido (Japan), Institute of Polar Science and Engineering - Jilin University (China), Karakoram International University (Pakistan), Lanzhou Institute of Glaciology and Geocryology (China), Nagoya University (Japan), National Institute of Polar Research (Japan), National Science Foundation Ice Core Facility (United States of America), New Zealand National Ice Core Facility (New Zealand, Physics of Ice Climate and Earth - University of Copenhagen (Denmark), Polar Research Institute of China (China), Research Institute for Humanity and Nature (Japan), and Tibet University. </p> <p>We are grateful to each of these facilities for contributing details of their ice core collections for this work. </p> <p> </p> <p> </p> <p>Electronic data accessibility and sample request procedures for a few of these facilities of which the authors are aware are listed below.</p> <p>Australia: data can be obtained from the Australian Antarctic Data Centre (<a href="https://urldefense.com/v3/__https://data.aad.gov.au/__;!!K-Hz7m0Vt54!k4oxTmHZ_w1LKmpFwH8LzlfLDG73TEDLZwozl9Q6dL-wfS_EQG7S75R9T3faMQA7BHyK5mv3Br0-kyWRnumedvhR$">https://data.aad.gov.au</a>); access to ice from the Australian Antarctic Program is via application (see <a href="https://urldefense.com/v3/__https://www.antarctica.gov.au/science/information-for-scientists/__;!!K-Hz7m0Vt54!k4oxTmHZ_w1LKmpFwH8LzlfLDG73TEDLZwozl9Q6dL-wfS_EQG7S75R9T3faMQA7BHyK5mv3Br0-kyWRnosG8VPm$">https://www.antarctica.gov.au/science/information-for-scientists/)</a></p> <p>Denmark: data can be obtained from <a href="https://www.iceandclimate.nbi.ku.dk/data/">www.iceandclimate.nbi.ku.dk/data</a>; the ice sampling request procedure is listed here: <a href="https://www.iceandclimate.nbi.ku.dk/data/samplingprocedure/">https://www.iceandclimate.nbi.ku.dk/data/samplingprocedure/</a> </p> <p>United States: many ice core datasets can be found at the NOAA World Data Center (<a href="https://www.ncei.noaa.gov/products/paleoclimatology/ice-core">https://www.ncei.noaa.gov/products/paleoclimatology/ice-core</a>); the allocation policy for ice core samples can be found here: <a href="https://icecores.org/policy">https://icecores.org/policy</a>.</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.