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12,072 results for “Global”
Data for: Global political responsibility for the conservation of albatrosses and large petrels
<p>Data derivatives from analysis of seabird tracking data. These data allow one to reproduce the results of the paper "Global political responsibility for the conservation of albatrosses and large petrels by Beal et al (in press). </p>
Global monthly catch of tuna, tuna-like and shark species (1950-2023) by 1° or 5° squares (IRD level 2) - and efforts level 0 (1950-2023)
<div> </div> <p>This deposit contains various datasets describing tuna fisheries activities (currently catches and efforts) and different levels of processing on 1° or 5° spatial grids with a monthly temporal resolution.</p>
TCOM-HF : Daily global gap-free stratospheric hydrogen fluoride (HF) profile data set based on TOMCAT CTM and Occultation Measurements
<p><strong>Methodology: TOMCAT simulation is performed at T64L32 resolution for the 2000-2024 time period. Collocated hydrogen fluoride (HF) profiles are divided in five latitude bins: SH polar (90S-50S), SH mid-lat (70S-20S), tropics (40S-40N), NH mid-lat (20N-70N) and NH polar (50N-90N). Initially, model-measurement differences are calculated for each zonal bins (51 height levels, 10km to 60km). Note that if enough ACE measurements are not avaliable for a particular level then data is purely based on TOMCAT simulated output field. Separate XGBoost regression models are trained for the differences between TOMCAT and measurements at each level for a given latitude bin. XGBoost model is then used to estimate error corrections for all the TOMCAT grids. TOMCAT output sampled at 1.30 pm local time at the equator. Estimated corrections for a given model grid that are added to the original TOMCAT simulated day and night time hydrogen fluoride profiles. Height resolved data are then interpolated on 28-pressure levels (300 - 0.1hPa). For overlapping latitude bins, we use averages and then calculate daily zonal mean values. For more details see attached presentation. Previous version use both HALOE and ACE data. Here only ACE data is used.</strong></p> <p><strong>Dataset also includes two files containing daily mean zonal mean hydrogen fluoride profiles on height (10-50 km) and pressure (300-0.1 hPa) levels:</strong></p> <p><strong>zmhf_TCOM_hlev_T2Dz_2000_2024.nc – height level data (10 to 50 km)</strong></p> <p><strong>zmhf_TCOM_plev_T2Dz_2000_2024.nc – pressure level data (300 to 0.1 hPa)</strong></p> <p><strong>Daily 3D profiles on height and pressure levels would be made available on request.</strong></p>
Global Mangrove Watch: Mangrove Habitat Mask
<p>This is the habitat mask used to define the locations where mangroves can be found. It was used during the creation of the Global Mangrove Watch (GMW; <a href="https://www.globalmangrovewatch.org/?map=eyJiYXNlbWFwIjoibGlnaHQiLCJ2aWV3cG9ydCI6eyJsYXRpdHVkZSI6MjAsImxvbmdpdHVkZSI6MCwiem9vbSI6MiwiYmVhcmluZyI6MCwicGl0Y2giOjB9fQ%3D%3D">https://www.globalmangrovewatch.org</a>) extent products. Details of how this layer was originally produced are within Bunting et al., 2018 but it has subsequently been edited with further regions added as the GMW layers have been updated and improved. This is considered a living dataset which is edited, and new versions are produced when missing areas or improvements are identified. New versions will be uploaded here on zenodo.</p> <p><strong>Relevant publications:</strong></p> <p>Bunting, P., Rosenqvist, A., Lucas, R., Rebelo, L.-M., Hilarides, L., Thomas, N., Hardy, A., Itoh, T., Shimada, M., Finlayson, C., 2018. The Global Mangrove Watch—A New 2010 Global Baseline of Mangrove Extent. Remote Sens-basel 10, 1669. <a href="https://doi.org/10.3390/rs10101669" target="_blank" rel="noopener">https://doi.org/10.3390/rs10101669</a></p> <p>Bunting, Pete, Rosenqvist, Ake, Lucas , Richard, Rebelo, Lisa-Maria, Hilarides, Lammert, Thomas, Nathan, Hardy, Andy, Itoh, Takuya, Shimada, Masanobu, & Finlayson, Max. (2019). Global Mangrove Watch (1996 - 2016) Version 2.0 (2.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.5658808" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.5658808</a></p> <p>Bunting, P., Rosenqvist, A., Hilarides, L., Lucas, R.M., Thomas, N., 2022. Global Mangrove Watch: Updated 2010 Mangrove Forest Extent (v2.5). Remote Sens-basel 14, 1034. <a href="https://doi.org/10.3390/rs14041034" target="_blank" rel="noopener">https://doi.org/10.3390/rs14041034</a></p> <p>Pete Bunting, Ake Rosenqvist, Lammert Hilarides, Richard M. Lucas, & Nathan Thomas. (2022). Global Mangrove Watch 2010 Baseline (v2.5) (2.5) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.5828339" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.5828339</a></p> <p>Bunting, P., Rosenqvist, A., Hilarides, L., Lucas, R.M., Thomas, N., Tadono, T., Worthington, T.A., Spalding, M., Murray, N.J., Rebelo, L.-M., 2022. Global Mangrove Extent Change 1996–2020: Global Mangrove Watch Version 3.0. Remote Sens-basel 14, 3657. <a href="https://doi.org/10.3390/rs14153657" target="_blank" rel="noopener">https://doi.org/10.3390/rs14153657</a></p> <p>Bunting, P., Rosenqvist, A., Hilarides, L., Lucas, R., Thomas, N., Tadono, T., Worthington, T., Spalding, M., Murray, N., Rebelo, L.-M., (2022) Global Mangrove Watch (1996 - 2020) Version 3.0 Dataset. <a href="https://doi.org/10.5281/zenodo.6894273" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.6894273</a></p> <p><br><br> </p>
ML-TOMCAT V2.0: Machine-Learning-Based Satellite-Corrected Global Stratospheric Ozone Profile Dataset
<p>MLTOMCAT V2 is 46 years (1979-2024) of gap free ozone profile data sets that is created by correcting biases in a TOMCAT Chemical Transport Model (CTM) simulated ozone profiles. We use Random Forest regression model to correct model biases. </p> <p>Each file contain monthly mean zonal mean ozone profiles. There are 6 data files.</p> <p><a href="https://zenodo.org/api/files/0416f9bd-c908-4d3f-9368-47ca2e04d7bd/MLTOMCAT_1979_2020_72_ht_vmr.nc">MLTOMCAT_1979_2024_72_ht_vmr_V2.nc</a> contains ozone profiles on geometric height levels (1 to 60 km) in mixing ratio units, whereas <a href="https://zenodo.org/api/files/0416f9bd-c908-4d3f-9368-47ca2e04d7bd/MLTOMCAT_1979_2020_72_ht_vmr.nc">MLTOMCAT_1979_2024_72_ht_nd_V2.nc</a> contains ozone profile in number density units.</p> <p>Similarly, </p> <p><a href="https://zenodo.org/api/files/0416f9bd-c908-4d3f-9368-47ca2e04d7bd/MLTOMCAT_1979_2020_72_ht_vmr.nc">MLTOMCAT_1979_2024_72_plev_vmr_V2.nc</a> contains ozone profiles on 43 MLS pressure levels (1000 to 0.1 hPa) in mixing ratio units, whereas <a href="https://zenodo.org/api/files/0416f9bd-c908-4d3f-9368-47ca2e04d7bd/MLTOMCAT_1979_2020_72_ht_vmr.nc">MLTOMCAT_1979_2024_72_plev_nd_V2.nc</a> contains ozone profile in number density units.</p> <p>Please note that data below 300 hPa (~8km) and 1 hPa (~50 km) should be used with caution.</p> <p>There are two straospheric column files</p> <p>ML-TOMCAT-SCO_120ppb_boundary_V2_197901-202412.nc and</p> <p>ML-TOMCAT-SCO_150ppb_boundary_V2_197901-202412.nc</p> <p>Stratospheric column files calculated using 120 ppb and 150 ppb as a chemical ozone boundaries.</p> <p>A manuscript describing MLTOMCAT would be published in EESD (Dhomse et al., 2021).</p>
Global Naturalized Alien Flora (GloNAF). Open access data to support research on understanding global plant invasions.
<p>This dataset is a snapshot of the Global Naturalized Alien Flora (GloNAF) database, version 2.02. GloNAF is a continuously updated, curated compilation of alien naturalized vascular plant inventories for geographic regions from around the world. The dataset has 16,429 unique taxa reported as naturalized or invasive and covers 1,343 regions (including 427 islands) from 336 data sources. For each region, the status (invasive, naturalized) is provided as listed in the original source. We provide the scientific names included with the original data source, and the matching accepted name or synonym of the taxon as given in the World Checklist of Vascular Plants (WCVP) Version 12. In addition, we provide an ESRI shapefile of polygons for each region. We also provide several variables that can be used to filter the data according to quality and completeness of alien taxon lists, which vary among the combinations of regions and data sources.</p> <p>The 'glonaf_flora2.csv' file lists the IDs ('taxon_wcvp_id') of all naturalized taxa contained in GloNAF and the regions they occur in. The 'glonaf_taxon_wcvp.csv' lists the original taxon names provided in the source data along with the corresponding accepted taxon name from the WCVP (version 12) for all alien taxa in GloNAF, regardless of their naturalization status. To link taxon names with naturalization records, join the 'id' column of the 'glonaf_taxon_wcvp.csv' file to the 'taxon_wcvp_id' column in 'glonaf_flora2.csv' . Additional information regarding the original source of the data ('glonaf_reference.csv'), specific attributes of the taxon lists ('glonaf_list.csv') and the region ('glonaf_region.csv') can also be joined similarly to 'glonaf_flora2.csv '. </p> <p> </p>
DeepOWT v2.25.1: An updated and improved global offshore wind turbine dataset until 2025Q1
<p>DeepOWT (deep learning derived global offshore wind turbines) is an independent and openly accessible data set of offshore wind energy infrastructure locations and their temporal deployment dynamics on a global scale. It is derived by applying deep learning based object detection on ESA's spaceborne Sentinel-1 synthetic aperture radar (SAR) archive. DeepOWT provides OWT locations along with their quarterly deployment stages from 2016Q1 until 2025Q1. It differentiates between platforms under construction, OWTs which are readily deployed and offshore wind farm substations, such as transformer stations.<br><br>The dataset continues the work of <a href="https://essd.copernicus.org/articles/14/4251/2022/">10.5194/essd-14-4251-2022</a>.</p> <p>File metadata</p> <table> <tbody> <tr> <th>File</th> <th>Time</th> <th>Geometry</th> <th>Spatial extent</th> </tr> <tr> <td>DeepOWT.geojson (Dataset)</td> <td>2016Q1-2025Q1</td> <td>points</td> <td>Global</td> </tr> <tr> <td>gt_2021Q2_nsb.geojson (Ground Truth Location)</td> <td>2021Q2</td> <td>polygons</td> <td>North Sea Basin</td> </tr> <tr> <td>gt_2021Q2_ecs.geojson (Ground Truth Location)</td> <td>2021Q2</td> <td>polygons</td> <td>East China Sea</td> </tr> <tr> <td>gt_2021Q2_vtn.geojson (Ground Truth Location)</td> <td>2021Q2</td> <td>polygons</td> <td>Southeast Vietnamese Coast</td> </tr> <tr> <td>gt_nsb_gridded.geojson (Ground Truth Region)</td> <td>-</td> <td>polygon</td> <td>North Sea Basin</td> </tr> <tr> <td>gt_ecs_gridded.geojson (Ground Truth Region)</td> <td>-</td> <td>polygon</td> <td>East China Sea</td> </tr> <tr> <td>gt_ecs_gridded.geojson (Ground Truth Region)</td> <td>-</td> <td>polygon</td> <td>Southeast Vietnamese Coast</td> </tr> </tbody> </table> <p> </p> <table> <thead> <tr> <th>Used semantic label</th> </tr> </thead> <tbody> <tr> <td>open sea</td> </tr> <tr> <td>under construction</td> </tr> <tr> <td>offshore wind turbine</td> </tr> <tr> <td>offshore wind farm substation</td> </tr> </tbody> </table> <p> </p>
Model simulation data used in "The global impact of the transport sectors on atmospheric aerosol in 2030 – Part 2: Aviation" (Righi et al., Atmos. Chem. Phys., 2016)
<p>This dataset contains the output of the EMAC global model simulations analysed and discussed in Righi et al. (<i>Atmos. Chem. Phys.</i>, 2016). For details see the README.md file.</p>
Model simulation data used in "The global impact of the transport sectors on atmospheric aerosol in 2030 – Part 1: Land transport and shipping" (Righi et al., Atmos. Chem. Phys., 2015)
<p>This dataset contains the output of the EMAC global model simulations analysed and discussed in Righi et al. (<i>Atmos. Chem. Phys.</i>, 2015). For details see the README.md file.</p>
GRWSE-global river water surface elevation from sentinel-3
<p>This dataset includes time series of Water Surface Elevation (WSE) of large rivers at over 3000 virtual stations. The WSE time series were created using Sentinel-3A and Sentinel-3B altimetry data. </p>
Global ammonia emissions from CAMEO throughout the century for 3 scenarios (2000-2100)
<p><strong>Global ammonia emissions from the CAMEO process-based model </strong>(general model description and evaluation can be found in Beaudor et al., 2023, GMD; https://doi.org/10.5194/gmd-16-1053-2023).</p><p>Monthly files containing global NH3 emissions and Manure application rates in gN.m2.yr-1 (2.5° lon x 1.27° lat; IPSL-CM6A-LR Earth System Model resolution):</p><p>1) total agricultural emissions (TOT_AGRI; the sum of manure management and agricultural soil emissions)</p><p>2) manure management emissions (MANURE_MANAG.)</p><p>3) agricultural soil emissions (SOIL_AGRI)</p><p>4) natural soil emissions (SOIL_NAT) corrected for baresoil (excluding Sahara in this new version)</p><p>5) Fraction of continent (CONT_FRAC) from the model to use for CTM prescription or global budget calculation</p><p>6) TAN and non TAN applied to grassland from ruminants during grazing (tan_input_graz, nontan_input_graz)</p><p>7) TAN and non TAN applied to grassland from ruminants and considered as fertilizers (tan_input_manureApp_grass, nontan_input_manureApp_grass)</p><p>8) TAN and non TAN applied to cropland from all types of animal and considered as fertilizers (tan_input_manureApp_crop, nontan_input_manureApp_crop)</p><p>9) Grazing intensity (grazing_intensity, unitless)</p><p>10) Net Primary Production of grassland and grass biomass dedicated to livestock feed (NPP_grass, Cgrass_ingested in gC.m2.yr-1)</p><p>The four files correspond to a specific simulation using input4MIPs forcing files :</p><p>- Present-day simulation from 2000 to 2014 </p><p>- Future simulation from 2015 to 2100 under scenario SSP-2.45</p><p>- Future simulation from 2015 to 2100 under scenario SSP-4.34</p><p>- Future simulation from 2015 to 2100 under scenario SSP-5.85</p><p>Note that these datasets have been prepared in the scope of a publication to be submitted.</p><p><i><strong>Beaudor, M., N. Vuichard, J. Lathière, D. Hauglustaine., Historical and future ammonia emissions database (2000-2100) from the CAMEO process-based model, in preparation.</strong></i></p>
Global Violent Deaths (GVD) database 2004-2021, 2023 update, version 1.0
<p>The <a href="https://www.smallarmssurvey.org/database/global-violent-deaths-gvd">Global Violent Deaths (GVD) database</a> integrates indicators on the major causes of lethal interpersonal and communal violence—intentional and unintentional homicides, killings in legal interventions, and direct conflict deaths—and combines them in a single violent deaths indicator. These indicators are also reported in a disaggregated format by the sex of the victim and perpetration mechanism, namely firearm killings. The GVD database tracks this information across 222 countries and territories worldwide yearly from 2004 and reports both crude counts and rates per 100,000 population. The input data is retrieved from reliable sources, such as governments, national and international organizations, trusted non-governmental organizations, and verified media outlets. Missing data points are estimated using the methods described in this document.</p> <p>The GVD database is updated annually by the <a href="https://www.smallarmssurvey.org/">Small Arms Survey</a>, an associated programme of the Geneva Graduate Institute, which strengthens the capacity of governments and practitioners to reduce illicit arms flows and armed violence. This is done through three mutually reinforcing activities: the generation of policy-relevant knowledge, the development of authoritative resources and tools, and the provision of training and other services. The GVD database benefits from financial support from governments and organizations, and notably its core donors, who are publicly disclosed <a href="https://www.smallarmssurvey.org/who-we-are/funding-and-finance">online</a>. The Small Arms Survey follows rigorous procedures to ensure that the input data, the applied methods, and the results are of reasonable quality. If the user encounters apparent errors, they should contact us via email at <a href="mailto:media@smallarmssurvey.org">media@smallarmssurvey.org</a>.</p> <p>Regions, sub-regions, countries, and territories are defined based on the classification system used by the UN Statistical Division (2013 revision), except for Kosovo, England and Wales, Northern Ireland, and Scotland. The names and designations reported in the database do not imply any sort of endorsement by the Small Arms Survey.</p>
Higher orders for cosmological phase transitions: a global study in a Yukawa model
<p>Data used in the article with preprint title: <a href="https://arxiv.org/abs/2310.02308">Higher orders for cosmological phase transitions: a global study in a Yukawa model by Oliver Gould and Cheng Xie</a></p><p>Contains file: dataPublishedExport.csv, which consists of the variable used in, and evaluations from the global parameter scan. More details of the content are specified in README.txt.</p>
SM2RAIN-ASCAT (2007-2022): global daily satellite rainfall from ASCAT soil moisture
<p><strong>SM2RAIN-ASCAT is a new global scale rainfall product</strong> obtained from ASCAT satellite soil moisture data through the SM2RAIN algorithm (<em>Brocca et al., 2014; 2019</em>). The SM2RAIN-ASCAT rainfall dataset (in mm/day) is provided over a regular grid at 0.1-degree sampling (3600x1801) on a global scale. The product represents the accumulated rainfall between the 00:00 and the 23:59 UTC of the indicated day. The SM2RAIN method was applied to the ASCAT soil moisture product (<em>Wagner et al., 2013</em>) for the period from January 2007 to December 2022 (16 years), for version 2.1.2n.</p> <p>The rainfall dataset is provided in NetCDF format. A total of 16 NetCDF files, one per year, are provided. The quality flag provided with the dataset has been used to mask out low quality data, as well as the areas characterised by complex topographic, frozen soil, and presence of tropical forests. In addition to the daily accumulated rainfall value, also the rainfall noise (mm/day) is provided for every day.</p> <p><strong>Version 2.1.2 should not be used due to an error in the precipitation data. Version 2.1.2n with respect to version 2.1 is calibrated anew and extended to December 2022.</strong></p> <p>A GeoTIFF version of the dataset (v1.5) is available here: <a href="https://doi.org/10.5281/zenodo.2615278">https://doi.org/10.5281/zenodo.2615278</a></p> <p>A monthly version at 0.25- and 0.5-degree resolution (v1.4) is available here: <a href="https://doi.org/10.5281/zenodo.4570191">https://doi.org/10.5281/zenodo.4570191</a></p> <p>A sample dataset that can be used for testing SM2RAIN algorithm is available here: <a href="../record/2580285#.XLrYDugzbIU">https://zenodo.org/record/2580285</a></p> <p>Details on the dataset development and its assessment with ground and reanalysis observations are provided as:</p> <p><strong>Brocca, L.</strong>, Filippucci, P., Hahn, S., Ciabatta, L., Massari, C., Camici, S., Schüller, L., Bojkov, B., Wagner, W. (2019). SM2RAIN-ASCAT (2007-2018): global daily satellite rainfall from ASCAT soil moisture. <em>Earth System Science Data</em>, 11, 1583–1601, doi:10.5194/essd-11-1583-2019. <a href="https://doi.org/10.5194/essd-11-1583-2019">https://doi.org/10.5194/essd-11-1583-2019</a>.</p> <p><strong>Simple Python and Matlab codes for the extraction of SM2RAIN-ASCAT rainfall at one and multiple station(s)\location(s) are available at (note that reader for versions <1.3 are not usable for version >1.3): </strong><a href="https://github.com/IRPIhydrology/SM2RAIN_ASCAT_reader">https://github.com/IRPIhydrology/SM2RAIN_ASCAT_reader</a></p> <p><strong>The SM2RAIN code in Python is available here</strong>: <a href="https://github.com/IRPIhydrology/sm2rain">https://github.com/IRPIhydrology/sm2rain</a><br><strong>The SM2RAIN code in Matlab is available here</strong>: <a href="https://github.com/IRPIhydrology/SM2RAIN_Matlab">https://github.com/IRPIhydrology/SM2RAIN_Matlab</a><br><strong>The SM2RAIN code in R is available here</strong>: <a href="https://github.com/IRPIhydrology/sm2rainR">https://github.com/IRPIhydrology/sm2rainR</a></p> <p> </p> <p><strong>Acknowledgements</strong></p> <ul> <li>EUMETSAT Global SM2RAIN project (contract n° EUM/CO/17/4600001981/BBo)</li> <li>EUMETSAT "Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF)" CDOP 3 (EUM/C/85/16/DOC/15).</li> </ul>
Sustainability in Chemical Education - A Global Young Chemists Survey
<p>In 2020, young chemists of the German Young Chemists' Network (GDCh-JCF) designed a survey to get a snapshot of how their peers perceive the importance of sustainability in chemical education. With the help of the International Younger Chemists Network (IYCN) and the European Young Chemists' Network (EYCN) they were able to reach around 500 young chemists from 46 countries. Roughly half of the responses came from people that represented the German education system. Although the results need to be taken with a grain of salt as stated in the disclaimer (see .pdf document), they paint a picture of a desperate need for more sustainability topics to be covered by chemical education globally, particularly in Germany. More than 90% of young chemists globally call for more detailed coverage of sustainability topics while the current adequacy is only rated as good or better by a quarter of all respondents. A significant portion (>50% inside of Germany and >30% outside of Germany) does not feel prepared to contribute to the sustainability strategy of a company despite >80% rating the sustainability startegy of a company as an important factor for their career choice.</p>
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (SAI_2016_2020)
<p>Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change.</p> <p>This data repository is linked to <a href="../records/10815170" target="_blank" rel="noopener">https://zenodo.org/records/10815170</a></p>
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling
<p><strong>Summary</strong>: Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change.</p> <p><br><strong>Format</strong>: NetCDF.<br><strong>Institution</strong>: Atmospheric, Climate, and Earth Sciences Division, Pacific Northwest National Laboratory<br><strong>Contacts</strong>: Lingcheng Li (lingcheng.li@pnnl.gov; lingchengliwhu@gmail.com), Gautam Bisht (gautam.bisht@pnnl.gov)</p> <p><strong>Description</strong>: This dataset provides land surface parameters specifically designed for global kilometer scale earth system modeling.<br><strong>Spatial resolution</strong>: ~1 km, corresponding to 1/120 degree.<br><strong>Temporal resolution</strong>: includes yearly (2001-2020), monthly (2001-2020), and static data for different parameters.</p> <p><br><strong>Reference</strong>: <strong>Li, L., Bisht, G., Hao, D., and Leung, L.-Y. R.: Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-242, Acceptance, 2023.</strong></p> <p>It includes four categories of parameters, Please refer to the readme file for details:<br>1. LULC: land use and land cover parameters<br>2. VEGE: vegetation paramertes<br>3. SOIL: soil parameters<br>4. TOPO: topography parameters</p> <p>Due to storage limitations, the LAI and SAI files are stored in the following repositories:</p> <p>1) LAI 2001-2005: <a href="../records/10815637" target="_blank" rel="noopener">https://zenodo.org/records/10815637</a>; 2) LAI 2006-2010: <a href="../records/10815649" target="_blank" rel="noopener">https://zenodo.org/records/10815649</a>; 3) LAI 2011-2015: <a href="../records/10815658" target="_blank" rel="noopener">https://zenodo.org/records/10815658</a>; 4) LAI 2016-2020: <a href="../records/10815662" target="_blank" rel="noopener">https://zenodo.org/records/10815662</a>;</p> <p>5) SAI 2001-2005: <a href="../records/10815623" target="_blank" rel="noopener">https://zenodo.org/records/10815623</a>; 6) SAI 2006-2010: <a href="../records/10815629" target="_blank" rel="noopener">https://zenodo.org/records/10815629</a>; 7) SAI 2011-2015: <a href="../records/10790724" target="_blank" rel="noopener">https://zenodo.org/records/10790724</a>; 8) SAI 2016-2020: <a href="../records/10790758" target="_blank" rel="noopener">https://zenodo.org/records/10790758</a></p>
Global Phytoplankton Phenological Indices - 25km resolution
<p>Bloom phenology metrics calculated from OC-CCI v6.0 data at a spatial resolution of 25km. </p> <p>Metrics calculated using three approaches:</p> <ol> <li>Threshold method</li> <li>Cumulative Sum method</li> <li>Rate of Change method</li> </ol> <p> </p> <p><strong>Version 1.0:</strong></p> <ul> <li>Data from 1997 to 2022.</li> </ul> <p><strong>Version 1.1:</strong></p> <ul> <li>Data from 1997 to 2023.</li> </ul> <p> </p>
A global dataset gathering 37 field experiments involving cereal-legume intercrops and their corresponding sole crops.
<p>The overall description of the dataset is reported in the <strong>data_report.pdf</strong> file. The methodology for data curation and tidying is published in Peer Community Journal (<a href="https://doi.org/10.24072/pcjournal.389">Mahmoud2024</a>).</p> <p>This dataset gathers the results of 37 field experiments, which involved cereal-legume intercrops and their corresponding sole crops. The field experiments were carried in 5 European countries (France, Denmark, Italy, Germany and England) from 2001 to 2017. The dataset includes:</p> <ul> <li>5 legume species , <em>i.e.</em> chickpea (<em>Cicer arietinum</em> L.), faba bean (<em>Vicia faba</em> L.), lentil (<em>Lens culinaris</em> Med.), lupin (<em>Lupinus albus</em> L.) and pea (<em>Pisum sativum</em> L.),</li> <li>3 cereal species, <em>i.e.</em> barley (<em>Hordeum vulgare</em> L.), durum wheat (<em>Triticum turgidum</em> L.) and soft wheat (<em>Triticum aestivum</em> L.), </li> <li>8 resulting intercrops, <em>i.e.</em> i) barley associated with faba bean, lupin or pea, ii) durum wheat associated with chickpea, faba bean or pea, and iii) soft wheat associated with lentil or pea. </li> </ul> <p>In total, the dataset contains 299 sole crop and 308 intercrop experimental units, one given experimental unit being defined as the unique combination of {site, year, crop management}, with the crop management including species and cultivar choice as well as agricultural interventions (sowing conditions, inputs).</p> <p>The global dataset includes four tables, all sharing a common identifier (experiment_id):</p> <ul> <li>data_trials.csv: the global features describing the experimental sites,</li> <li>data_management.csv: the agricultural management actions carried out on each of the experimental sites,</li> <li>data_traits.csv: measured plant and crop characteristics,</li> <li>data_climate.csv: climate for the experimental sites, retrieved from NASA POWER API.</li> </ul> <p>Additionally, a metadata file is provided (<strong>metadata.xlsx</strong>), describing the table to which the variables belong (variable_type, i.e. trials, management, traits or climate), their name (variable_name), their significance (description) and their unit (unit). Finally, a table including the original references related to experimental files gathered (<strong>references.xlsx</strong>) is also provided.</p> <p>Data providers and field experiments: Laurent Bedoussac, Eric Justes, Etienne-Pascal Jour- net, Christophe Naudin, Henrik Hauggaard-Nielsen, Erik Steen Jensen, Elise Pelzer, Guénaëlle Corre-Hellou, Bochra Kammoun, Loic Viguier, Romain Barillot, Antoine Couëdel, Philippe Hinsinger</p> <p>Database and management: Noémie Gaudio, Rémi Mahmoud, Pierre Casadebaig</p>
A global dataset of specialty crop biomass and N2O emissions
<div> <p>We reviewed global field studies of vineyard, orchard, and vegetable cropping systems, which were also included in a meta-analysis (<a href="https://doi.org/10.1111/gcb.17233">https://doi.org/10.1111/gcb.17233</a>). We narrowed down the studies to those with field measurements of adequate variables (biomass C, N, and N<sub>2</sub>O) covering at least one growing season. As a result, cumulative N₂O emission measurements (per growing rotation, season, or year), along with biomass data of different plant organs from the same regions, were compiled for grape (<em>Vitis vinifera</em>), almond [<em>Prunus dulcis</em> (Mill.) D.A. Webb], peach (<em>Prunus persica</em> L.), walnut (<em>Juglans regia</em>), lettuce (<em>Lactuca sativa</em>), broccoli (<em>Brassica oleracea</em> var. <em>italica </em>P.), cauliflower (<em>Brassica oleracea</em> var. <em>botrytis </em>L.), and tomato (<em>Lycopersicon esculentum</em> L.) planting system. These observations were collected from fields spanning seven Koppen-Geiger climate types and five countries (the United States, Germany, Spain, France, and Australia). When only dry mass was measured, biomass C content for aboveground vegetable crops and berry fruit was assumed at 43%; nut fruit and woody organs of orchard tree at 48%. Area-weighted averages of N<sub>2</sub>O emissions were used (tree/vine row and interrow).</p> <p> </p> <p>Corresponding author: Mu Hong (mu.hong@colostate.edu)</p> </div> <p> </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.