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708 results for “Global dataset”
Dataset: A global synthesis of human impacts on the multifunctionality of streams and rivers
<p><span>Human impacts, particularly nutrient pollution and land-use change, have caused significant declines in the quality and quantity of freshwater resources. Most global assessments have concentrated on species diversity and composition, but effects on the multifunctionality of streams and rivers remain unclear. Here, we analyse the most comprehensive compilation of stream ecosystem functions to date to provide an overview of the responses of nutrient uptake, leaf litter decomposition, ecosystem productivity, and food web complexity to six globally pervasive human stressors. We show that human stressors inhibited ecosystem functioning for most stressor-function pairs. Nitrate uptake efficiency was most affected and was inhibited by 347% due to agriculture. However, concomitant negative and positive effects were common even within a given stressor-function pair. Some part of this variability in effect direction could be explained by the structural heterogeneity of the landscape and latitudinal position of the streams. Ranking human stressors by their absolute effects on ecosystem multifunctionality revealed significant effects for all studied stressors, with wastewater effluents (194%), agriculture (148%), and urban land use (137%) having the strongest effects. Our results demonstrate that we are at risk of losing the functional backbone of streams and rivers if human stressors persist in contemporary intensity, and that freshwaters are losing critical ecosystem services that humans rely on. We advocate for more studies on the effects of multiple stressors on ecosystem multifunctionality to improve the functional understanding of human impacts. Finally, freshwater management must shift its focus towards an ecological function-based approach and needs to develop strategies for maintaining or restoring ecosystem functioning of streams and rivers.</span></p>
A global 0.05° dataset for gross primary production of sunlit and shaded vegetation canopies (1992–2020)
<p>Distinguishing gross primary production of sunlit and shaded leaves (GPP<sub>sun</sub> and GPP<sub>shade</sub>) is crucial for improving our understanding of the underlying mechanisms regulating long-term GPP variations. Here we produce a global 0.05°, 8-day dataset for GPP, GPP<sub>shade</sub> and GPP<sub>sun</sub> over 1992-2020 using an updated two-leaf light use efficiency model (TL-LUE), which is driven by the GLOBMAP leaf area index, CRUJRA meteorology, and ESA-CCI land cover. Our products estimate the mean annual totals of global GPP, GPP<sub>sun</sub>, and GPP<sub>shade</sub> over 1992-2020 at 125.0±3.8 (mean ± std) Pg C a<sup>-1</sup>, 50.5±1.2 Pg C a<sup>-1</sup>, and 74.5±2.6 Pg C a<sup>-1</sup>, respectively, in which EBF (evergreen broadleaf forest) and CRO (crops) contribute more than half of the totals. They show clear increasing trends over time, in which the trend of GPP (also GPP<sub>sun</sub> and GPP<sub>shade</sub>) for CRO is distinctively greatest, and that for DBF (deciduous broadleaf forest) is relatively large and GPP<sub>shade</sub> overwhelmingly outweighs GPP<sub>sun</sub>. This new dataset advances our in-depth understanding of large-scale carbon cycle processes and dynamics.</p>
Dataset for "Tackling global challenges with coherent policies producing more researchers and diaspora engagement"
<p>A list of science policies that were published between 2002 and 2021 by the African Union, Southern African Development Community, and the Government of Zimbabwe.</p>
Long-term (2003-2020) hourly 0.25° global PM2.5 dataset (DeepCAMS) Part-1: 2003-2011
<p>This is part-I (2003-2011) of our DeepCAMS.</p> <p>Part-II can be found at https://doi.org/10.5281/zenodo.6969598</p> <p>Usage: The raw data -- (scaling factor: 0.1) --> the true PM2.5 concentration</p> <p>Paper title: Generating a Long-term (2003-2020) hourly 0.25° global PM2.5 dataset via spatiotemporal downscaling of CAMS with deep learning (DeepCAMS)</p> <p>Paper doi: <a href="https://doi.org/10.1016/j.scitotenv.2022.157747">https://doi.org/10.1016/j.scitotenv.2022.157747</a></p> <p>If you find our work helpful, please cite it, thank you very much!</p>
An enhanced integrated water vapour dataset from more than 10,000 global ground-based GPS stations in 2020
<p>This is a 5-min Integrated Water Vapor (IWV) product from 12,552 ground-based GPS stations worldwide in 2020. It contains 1,093,591,492 IWV estimates in total. The dataset is an enhanced version of the existing operational GPS IWV dataset from Nevada Geodetic Laboratory. The enhancement is reached by using accurate meteorological information from ERA5 for the GPS IWV retrieval with a significantly higher spatiotemporal resolution. The dataset is recommended for high-accuracy applications.</p> <p> DESCRIPTION Geodetic Institute, Karlsruhe Institute of Technology, Germany<br> OUTPUT 5-min enhanced GPS Integrated Water Vapour product<br> CONTACT Peng Yuan, pyuan@gfz-potsdam.de; Geoffrey Blewitt, gblewitt@unr.edu <br> INPUT NGL: GPS ZTD; ERA5 pressure level product: pressure and Tm</p> <p> NGL: Nevada Geodetic Laboratory, University of Nevada, http://geodesy.unr.edu<br> ERA5: the fifth generation ECMWF reanalysis, https://www.ecmwf.int<br> Authors: Peng Yuan, Geoffrey Blewitt, Corné Kreemer, William C. Hammond, <br> Donald Argus, Xungang Yin, Roeland Van Malderen, Michael Mayer,<br> Weiping Jiang, Joseph Awange, Hansjoerg Kutterer</p> <p><strong>Detailed descriptions and quality evaluations of the dataset have been published in <em>Earth System Science Data </em>(see below).</strong></p> <p><strong>If you would like to use the dataset, please cite the dataset and the associated paper as follows:</strong></p> <p>[1] Yuan, P., Blewitt, G., Kreemer, C., Hammond, W. C., Argus, D., Yin, X., Van Malderen, R., Mayer, M., Jiang, W., Awange, J., and Kutterer, H.: An enhanced integrated water vapour dataset from more than 10 000 global ground-based GPS stations in 2020, <em>Earth System Science Data</em>, 15, 723–743, <a href="https://doi.org/10.5194/essd-15-723-2023">https://doi.org/10.5194/essd-15-723-2023</a>, 2023.</p> <p>[2] Yuan, Peng, Blewitt, Geoffrey, Kreemer, Corné, Hammond, William C., Argus, Donald, Yin, Xungang, Van Malderen, Roeland, Mayer, Michael, Jiang, Weiping, Awange, Joseph, & Kutterer, Hansjörg. (2022). An enhanced integrated water vapour dataset from more than 10,000 global ground-based GPS stations in 2020 [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6973528</p> <p>>><br>File: NGL2020_12552_blgph.txt<br>Description: coordinates of the 12552 GPS stations worldwide<br>Format:<br> column #1: Site name<br> column #2: Latitude (degree)<br> column #3: Longitude (degree)<br> column #4: Geopotential altitude (geopotential meter)</p> <p>>><br>ZIP files: data saved according to the first letter of the station names<br>Description: enhanced GPS IWV data product at 12552 stations worldwide<br>Structure: saved as each day for each station, and then all the daily<br> files of each station were saved as respective ZIP file<br> which named as "site"_2020.trop.zip<br>Format: IGS TROP format</p>
Global soil moisture–atmosphere feedback and N2O emission dataset
<p><span>Soil moisture is essential to microbial nitrogen (N)-cycling networks in terrestrial ecosystems. Studies have found that soil moisture–atmosphere feedbacks dominate the changes in land carbon fluxes. </span><span>However, the influence of soil moisture–atmosphere feedbacks on the N fluxes changes, and the underlying mechanisms remain highly unsure, leading to uncertainties in climate projections. </span><span>To fill this gap, we utilized in situ observation coupled with gridded and remote sensing data to analyze N<sub>2</sub>O fluxes emissions globally. Here, we investigated the synergistic effects of temperature, hydroclimate on global N<sub>2</sub>O fluxes, as the result of soil moisture–atmosphere feedback impact on N fluxes. We found that soil moisture–temperature feedback dominates land N<sub>2</sub>O emissions by controlling the balance between nitrifier and denitrifier genes. The mechanism is that atmospheric water demand increases with temperature and thereby reduces soil moisture, which increases the dominant N<sub>2</sub>O production nitrifier (containing <em>amoA </em>AOB gene) and decreases the N<sub>2</sub>O consumption denitrifier (containing the<em> nosZ</em> gene), consequently will potential increasing N<sub>2</sub>O emissions. However, we find that the spatial variations of soil–water availability as a result of the nonlinear response of soil moisture to vapor pressure deficit caused by temperature are some of the greatest challenges in predicting future N<sub>2</sub>O emissions. Our data-driven assessment deepens the understanding of the impact of soil moisture-atmosphere interactions on the soil N cycle, which remains uncertain in earth system models. We suggest that the model needs to account for feedback between soil moisture and atmospheric temperature when estimating the response of the N<sub>2</sub>O emissions to climatic change globally, as well as when conducting field-scale investigations of the response of the ecosystem to warming.</span></p>
Forecasting 24-hour-averaged PM2.5concentration in the Aburrá Valley using tree-based ML models, global forecasts, and satellite information: Dataset
<p>Data necessary for the training and evaluating the 24-hourly-averaged PM2.5 forecast over 19 stations within the Aburrá Valley, Colombia, is included here.</p>
High resolution solar irradiance variability climatology dataset part 1: direct, diffuse, and global irradiance
<p><strong>Dataset paper</strong></p> <p>See the official dataset description paper (preprint) over at <a href="https://essd.copernicus.org/preprints/essd-2022-456/">Earth System Science Data</a>.</p> <p><strong>Dataset description</strong></p> <p>High resolution surface solar irradiance observations from the Baseline Surface Radiation Network (BSRN) of Cabauw, the Netherlands. This dataset spans 10 years, <em>from 2011-02 until 2020-12-31</em>.</p> <p>This dataset is the preprocessed 1 Hz observational record of direct, diffuse, and global horizontal irradiance, which is the basis for the official BSRN 1-minute dataset published at <a href="https://doi.pangaea.de/10.1594/PANGAEA.940531">PANGAEA</a>. Please refer to the official dataset for detailed metadata, instrument information, quality control flags, the full radiation balance and more (1 minute resolution). More information about the observational site Cabauw can be found at the <a href="https://ruisdael-observatory.nl/cabauw/">Ruisdael Observatory website</a>, and more general information about BSRN is <a href="https://essd.copernicus.org/articles/10/1491/2018/">published on ESSD</a>.</p> <p><strong>Part 1 of 2</strong></p> <p>This dataset is the basis for an analysis of surface solar irradiance variability, derived variables, supplementary meteorological data, and quicklooks, available in part 2 here: <a href="https://doi.org/10.5281/zenodo.7092058">https://doi.org/10.5281/zenodo.7092058</a></p> <p><strong>Usage disclaimer</strong></p> <p>While resampling this 1-Hz data to 1-minute, with the official BSRN quality flags, should reproduce an identical dataset to the official 1-minute BSRN dataset, this has not yet been validated for this version. When you require the most reliable version of the radiation measurements, where variability at a higher resolution than 1 minute is not of concern, please refer to the official BSRN dataset at PANGAEA.</p>
A global historical twice-daily (daytime and nighttime) land surface temperature dataset produced by AVHRR observations from 1981 to 2021 (1981–2000)
<ul> <li>Land surface temperature (LST) is a key variable for monitoring and evaluating global long-term climate change. However, existing satellite-based twice-daily LST products only date back to 2000, which makes it difficult to obtain robust long-term temperature variations. We developed the first global historical twice-daily LST dataset (GT-LST), with a spatial resolution of 0.05°, using Advanced Very High Resolution Radiometer Level-1b Global Area Coverage data from 1981 to 2021.</li> <li>Validation with in situ measurements from Surface Radiation Budget sites showed that the overall root-mean-square errors of GT-LST varied from 2.0 K to 3.9 K. Inter-comparison with a common LST product (i.e., MYD11A1) revealed that the overall root-mean-square-difference was approximately 3.2 K.</li> <li>More details of this dataset can be seen in <em>readme.pdf.</em></li> <li>This dataset provides GT-LST product from 1981 to 2000.</li> </ul>
A global historical twice-daily (daytime and nighttime) land surface temperature dataset produced by AVHRR observations from 1981 to 2021 (2001–2005)
<ul> <li>Land surface temperature (LST) is a key variable for monitoring and evaluating global long-term climate change. However, existing satellite-based twice-daily LST products only date back to 2000, which makes it difficult to obtain robust long-term temperature variations. We developed the first global historical twice-daily LST dataset (GT-LST), with a spatial resolution of 0.05°, using Advanced Very High Resolution Radiometer Level-1b Global Area Coverage data from 1981 to 2021.</li> <li>Validation with in situ measurements from Surface Radiation Budget sites showed that the overall root-mean-square errors of GT-LST varied from 2.0 K to 3.9 K. Inter-comparison with a common LST product (i.e., MYD11A1) revealed that the overall root-mean-square-difference was approximately 3.2 K.</li> <li>More details of this dataset can be seen in <em>readme.pdf.</em></li> <li>This dataset provides GT-LST product from 2001 to 2005.</li> </ul>
HR-GLDD: A globally distributed high resolution landslide dataset
<p> HR-GLDD, a high-resolution (HR) dataset for landslide mapping composed of landslide instances from ten different physiographical regions globally: South and South-East Asia, East Asia, South America, and Central America. The dataset contains five rainfall triggered and five earthquake-triggered multiple landslide events that occurred in varying geomorphological and topographical regions.</p>
SSiB5/TRIFFID/DayCent-SOM datasets for the paper's in-situ validations and global evaluations
<p>The various data used for the paper "A plant carbon-nitrogen interface coupling framework in a coupled biophysical-ecosystem-biogeochemical model: Its parameterization, implementation, and evaluation" submitted to Geoscientific Model Development for publication are shared here.</p>
Dataset used for analyzing the critical area thresholds for undergoing rapid increases of established non-native terrestrial vertebrates in global islands
<p>Biological invasions are among the threats to global biodiversity and social sustainability, especially on islands. Identifying the threshold of area at which non-native species begin to increase abruptly is crucial for the early prevention strategies. The small-island effect (SIE) was proposed to quantify the nonlinear relationship between native species richness and area but has not yet been applied to non-native species and thus to predict the key breakpoints at which established non-native species start to increase rapidly. Here, based on the extensive global dataset including 769 non-native bird, mammal, amphibian, and reptile species established on 4,277 islands across 54 archipelagos, we detected a high prevalence of SIEs across 66.7% of archipelagos, and approximately 50% of islands have reached the threshold area and thus may be undergoing a rapid increase of biological invasions. SIEs were more likely to occur in those archipelagos with more non-native species introduction events, more established historical non-native species, lower habitat diversity and larger archipelago area range. Our findings may have important implications not only for targeted surveillance of biological invasions on global islands but also for predicting the responses of both non-native and native species to ongoing habitat fragmentation under sustained land-use modification and climate change.</p>
Global Cloud Biases in Optical Satellite Remote of Rivers - Accompanying Dataset
Open the record for dataset details and reuse information.
Datasets for the paper: Lost in Translation: Using Global Fact-Checks to Measure Multilingual Misinformation Prevalence, Spread, and Evolution
<p>FullData.csv.gz: Contains links to all claims in the data-set.</p> <ul> <li>publishing_date: Date on which the fact-check was published.</li> <li>claim_date: Date that claim was made.</li> <li>verdict: Rating given by the fact-checking organisation.</li> <li>language: Language of the claim.</li> <li>cluster_{threshold}: ID of the cluster that claim belongs to at all given clusters. Entry "0" means that claim is singleton and not clustered with any other claims.</li> </ul> <p>Embeddings.npy: Contains a dictionary linking each claim to it's embedding calculated with LaBSE.</p>
Dataset for the Global Prediction Of Total Organic Carbon In Marine Sediments Using Deep Neural Networks (nn-toc)
<p>The data folder contains the raw features and labels used for training machine learning models to predict total organic carbon in marine sediments. </p> <p>The data folder has three subfolders:</p> <ol> <li>raw : contains the labels, features and other data used to train the machine learnign models</li> <li>interim : transformed data, which has to be reproduced</li> <li>output : output from the models, used for analysis and visualisation</li> </ol> <p>The data folder has to be integrated in the Git repository nn-toc, to execute the code.</p> <p> </p> <p> </p>
Global Urban Activity Changes from COVID-19 Physical Distancing Restrictions Dataset: TRacking Anomalous COVID-19 induced changEs in NightTime Lights (TRACE-NTL)
<p>We use satellite-derived (NASA Black Marble) nighttime lights to identify, quantify, and map daily changes in human activity that are atypical for each urban area, globally, from the beginning of the pandemic until two years after its onset. The dataset TRACE-NTL consists of global daily urban disruption and recovery metrics as a response to COVID-19.</p> <p>TRACE-NTL:</p> <p>├───ancillary<br>├───data<br>└───metrics<br> ├───disruption<br> │ ├───change_segment<br> │ ├───city_uncertainty<br> │ ├───daily_change<br> │ └───daily_qa_flags<br> └───recovery</p> <p><a title="Dataset description" href="https://github.com/srijac/covid-19_Nightlights">https://github.com/srijac/covid-19_Nightlights</a></p> <p>Accompanying paper: Accompanying paper: Chakraborty, S., Stokes, E.C. & Alexander, O. Global urban activity changes from COVID-19 physical distancing restrictions. <em>Sci Data</em> <strong>12</strong>, 98 (2025). https://doi.org/10.1038/s41597-025-04398-x </p>
Dataset related to the manuscript Wagner and Schepanski (submitted to JAMES, 2024): "Quantifying fire-driven dust emissions using a global aerosol model"
<p>This dataset belongs to the manuscript of Wagner and Schepanski (2024) entitled "Quantifying fire-driven dust emissions using a global aerosol model" submitted to the "Journal of Advances in Modeling Earth Systems (JAMES)".</p> <p>It contains the for the 10 year simulation period 2004-2013 the monthly, seasonal, or yearly averaged fields of the variables (variable name in brackets) that were used to prepare the plots and statements made in the manuscript. These are in detail:</p> <ol> <li>GFAS input data of FRP (frp)</li> <li>simulated AOD (tau_2d_550nm) and dust AOD (tau_comp_du_550nm)</li> <li>simulated wind-driven (emi_du_dust) and fire-driven (emi_du_fdust) dust emission fluxes</li> <li>simulated atmospheric dust concentration (du_all) including the soluble/insoluble coarse (du_ci, du_cs) and accumulation (du_ai, du_as) mode together with vertical atmospheric pressure levels (pfull)</li> </ol> <p>The simulated results are provided for both simulations, the <strong>control run</strong> without the additional fire-dust emissions and the actual <strong>firedust simulation</strong> with the new fire-dust emission parameterization.</p>
Dataset on global root zone storage capacity controls
<ul> <li>The 'master.csv' file contains all the catchment characteristics used for model training</li> <li>The p_mean_gswp3_land.nc, t_mean_gswp3_land.nc, idu_mean_land.nc, slp_land.nc files contain the 4 variables used to predict global gridded root zone storage capacity Sr</li> <li>The sr_predicted_map.nc is the global gridded sr map as direct output from the scripts in https://github.com/fvanoorschot/python_scripts_global_sr_controls</li> <li>The sr_predicted_map_with_ncinfo.nc is the same as previous file, but including the netcdf-information.</li> </ul>
Global Thermosaliniograph Dataset
<p>%%%%%%%%%%%%%%%%%%<br>Global Thermosaliniograph Dataset<br>%%%%%%%%%%%%%%%%%%<br>The dataset tsg_2020.mat is an updated version of the compiled ship thermosalinograph dataset that was originally used in Drushka et al. (2019). Details below. </p> <p><br>%%%%%%%%<br>Variable Names<br>%%%%%%%%<br>t = time in matlab datenum format<br>T = temperature<br>S = salinity<br>x = longitude<br>y = latitude<br>callsign = ship callsign, numbers correspond to call signs in call_sign.m<br>dataset = number corresponds with the names of the original dataset (see list below)</p> <p>%%%%%%%%%%%%<br>Original Dataset Names<br>%%%%%%%%%%%%<br>2: GOSUD database <br>4: SSS-OS database<br>5: PANGAEA database - R/V Polarstern<br>6: R/V Survostral<br>9: SAMOS database<br>10: PANGAEA database - R/V Poseidon<br>11: AODN database<br>12: M/V Oleander <br>15: Cruise data provided by Sophie Clayton<br>16: SAMOS database (files not QC'dby SAMOS, QC'd as described below)<br>22: JAMSTEC database<br>24: SOCAT 2020 database<br>25: LEGOS/SSSOS (update from dataset 4)<br>26: SAMOS database (update)<br>27: GOSUD database (update)</p> <p>%%%%%%%%%%%%%%%<br>Updated Version Description <br>%%%%%%%%%%%%%%%<br>The following describes what has been updated compared to the data used in Drushka et al. (2019).</p> <p>1. SOCAT: https://www.socat.info/index.php/data-access/<br>- Data up to 2019 included. Basic QC performed by comparing to Argo data and discarding outliers.<br> <br>2. SSS-OS (LEGOS): http://sss.sedoo.fr/<br>- Data up to 12/2019 included. Salinity data QC’d by LEGOS; basic QC performed on temperature by comparing to Argo data and discarding outliers. </p> <p>3. SAMOS https://samos.coaps.fsu.edu<br>- Data flatted as “good data with 0-5% flagged” " up to 12/2019 included<br> <br>4. GOSUD - http://www.gosud.org/<br>- Delayed-mode data up to 12/2019 included</p>
ScienceDex guides
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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.