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.
708
datasets available to search
ShareScore release 0.9.0
Dataset results
708 results for “Global dataset”
A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 6
<p>This dataset is part of the LUIcube, a global dataset on land-use at 30 arcsecond spatial resolution. The LUIcube includes information on area, the change in NPP due to land conversions (HANPP<sub>luc</sub>), the harvested NPP (including losses, HANPP<sub>harv</sub>), and the NPP remaining in ecosystems after harvest (NPP<sub>eco</sub>) for 32 land-use classes in annual time-steps from 1992 to 2020. A detailed description of the LUIcube is available in the accompanying publication.</p> <p>The layers of land-use areas are provided in square kilometers (km²) per grid cell. All NPP flows are provided in tC/yr per grid cell. Adding HANPP<sub>harv</sub> to NPP<sub>eco</sub> results in the actual NPP available before harvest (NPP<sub>act</sub>=NPP<sub>eco</sub>+HANPP<sub>harv</sub>), and adding HANPP<sub>luc</sub> to NPP<sub>act</sub> results in the potential NPP available in the hypothetical absence of land use (NPP<sub>pot</sub>=NPP<sub>act</sub>+HANPP<sub>luc</sub>) for the given land-use class. Area-intensive values (in gC/m²/yr) can be calculated by dividing the NPP flows by the area of the respective land-use class per grid cell. HANPP in % of NPP<sub>pot</sub> can be calculated by summing up HANPP<sub>harv</sub> and HANPP<sub>luc</sub> and dividing it by NPP<sub>pot</sub>. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP.</p> <p>This Zenodo repository provides data on following land-use classes: cropland used for production of potatoes (CL-POTA); sweet potatoes and yams (CL-SWPY); and rest of crops (CL-REST)</p>
A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 4
<p>This dataset is part of the LUIcube, a global dataset on land-use at 30 arcsecond spatial resolution. The LUIcube includes information on area, the change in NPP due to land conversions (HANPP<sub>luc</sub>), the harvested NPP (including losses, HANPP<sub>harv</sub>), and the NPP remaining in ecosystems after harvest (NPP<sub>eco</sub>) for 32 land-use classes in annual time-steps from 1992 to 2020. A detailed description of the LUIcube is available in the accompanying publication.</p> <p>The layers of land-use areas are provided in square kilometers (km²) per grid cell. All NPP flows are provided in tC/yr per grid cell. Adding HANPP<sub>harv</sub> to NPP<sub>eco</sub> results in the actual NPP available before harvest (NPP<sub>act</sub>=NPP<sub>eco</sub>+HANPP<sub>harv</sub>), and adding HANPP<sub>luc</sub> to NPP<sub>act</sub> results in the potential NPP available in the hypothetical absence of land use (NPP<sub>pot</sub>=NPP<sub>act</sub>+HANPP<sub>luc</sub>) for the given land-use class. Area-intensive values (in gC/m²/yr) can be calculated by dividing the NPP flows by the area of the respective land-use class per grid cell. HANPP in % of NPP<sub>pot</sub> can be calculated by summing up HANPP<sub>harv</sub> and HANPP<sub>luc</sub> and dividing it by NPP<sub>pot</sub>. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP. </p> <p>This Zenodo repository provides data on following land-use classes: cropland used for production of wheat (CL-WHEA); maize (CL-MAIZ); soybean (CL-SOYB)</p>
A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 5
<p>This dataset is part of the LUIcube, a global dataset on land-use at 30 arcsecond spatial resolution. The LUIcube includes information on area, the change in NPP due to land conversions (HANPP<sub>luc</sub>), the harvested NPP (including losses, HANPP<sub>harv</sub>), and the NPP remaining in ecosystems after harvest (NPP<sub>eco</sub>) for 32 land-use classes in annual time-steps from 1992 to 2020. A detailed description of the LUIcube is available in the accompanying publication.</p> <p>The layers of land-use areas are provided in square kilometers (km²) per grid cell. All NPP flows are provided in tC/yr per grid cell. Adding HANPP<sub>harv</sub> to NPP<sub>eco</sub> results in the actual NPP available before harvest (NPP<sub>act</sub>=NPP<sub>eco</sub>+HANPP<sub>harv</sub>), and adding HANPP<sub>luc</sub> to NPP<sub>act</sub> results in the potential NPP available in the hypothetical absence of land use (NPP<sub>pot</sub>=NPP<sub>act</sub>+HANPP<sub>luc</sub>) for the given land-use class. Area-intensive values (in gC/m²/yr) can be calculated by dividing the NPP flows by the area of the respective land-use class per grid cell. HANPP in % of NPP<sub>pot</sub> can be calculated by summing up HANPP<sub>harv</sub> and HANPP<sub>luc</sub> and dividing it by NPP<sub>pot</sub>. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP.</p> <p>This Zenodo repository provides data on following land-use classes: cropland used for production of millet (CL-MILL); barley (CL-BARL); sorghum (CL-SORG); rice (CL-RICE)</p>
A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 7
<p>This dataset is part of the LUIcube, a global dataset on land-use at 30 arcsecond spatial resolution. The LUIcube includes information on area, the change in NPP due to land conversions (HANPP<sub>luc</sub>), the harvested NPP (including losses, HANPP<sub>harv</sub>), and the NPP remaining in ecosystems after harvest (NPP<sub>eco</sub>) for 32 land-use classes in annual time-steps from 1992 to 2020. A detailed description of the LUIcube is available in the accompanying publication.</p> <p>The layers of land-use areas are provided in square kilometers (km²) per grid cell. All NPP flows are provided in tC/yr per grid cell. Adding HANPP<sub>harv</sub> to NPP<sub>eco</sub> results in the actual NPP available before harvest (NPP<sub>act</sub>=NPP<sub>eco</sub>+HANPP<sub>harv</sub>), and adding HANPP<sub>luc</sub> to NPP<sub>act</sub> results in the potential NPP available in the hypothetical absence of land use (NPP<sub>pot</sub>=NPP<sub>act</sub>+HANPP<sub>luc</sub>) for the given land-use class. Area-intensive values (in gC/m²/yr) can be calculated by dividing the NPP flows by the area of the respective land-use class per grid cell. HANPP in % of NPP<sub>pot</sub> can be calculated by summing up HANPP<sub>harv</sub> and HANPP<sub>luc</sub> and dividing it by NPP<sub>pot</sub>. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP.</p> <p>This Zenodo repository provides data on following land-use classes: cropland used for production of cassava (CL-CASS); sugarcane (CL-SUGC); sugarbeet (CL-SUGB); cotton (CL-COTT); fruits and vegetables (CL-VEFR)</p>
A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 8
<p>This dataset is part of the LUIcube, a global dataset on land-use at 30 arcsecond spatial resolution. The LUIcube includes information on area, the change in NPP due to land conversions (HANPP<sub>luc</sub>), the harvested NPP (including losses, HANPP<sub>harv</sub>), and the NPP remaining in ecosystems after harvest (NPP<sub>eco</sub>) for 32 land-use classes in annual time-steps from 1992 to 2020. A detailed description of the LUIcube is available in the accompanying publication.</p> <p>The layers of land-use areas are provided in square kilometers (km²) per grid cell. All NPP flows are provided in tC/yr per grid cell. Adding HANPP<sub>harv</sub> to NPP<sub>eco</sub> results in the actual NPP available before harvest (NPP<sub>act</sub>=NPP<sub>eco</sub>+HANPP<sub>harv</sub>), and adding HANPP<sub>luc</sub> to NPP<sub>act</sub> results in the potential NPP available in the hypothetical absence of land use (NPP<sub>pot</sub>=NPP<sub>act</sub>+HANPP<sub>luc</sub>) for the given land-use class. Area-intensive values (in gC/m²/yr) can be calculated by dividing the NPP flows by the area of the respective land-use class per grid cell. HANPP in % of NPP<sub>pot</sub> can be calculated by summing up HANPP<sub>harv</sub> and HANPP<sub>luc</sub> and dividing it by NPP<sub>pot</sub>. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP.</p> <p>This Zenodo repository provides data on following land-use classes: cropland used for production of beans (CL-BEAN); other pulses (CL-OPUL); groundnuts (CL-GROU); bananas and plantains (CL-BANP)</p>
A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 9
<p>This dataset is part of the LUIcube, a global dataset on land-use at 30 arcsecond spatial resolution. The LUIcube includes information on area, the change in NPP due to land conversions (HANPP<sub>luc</sub>), the harvested NPP (including losses, HANPP<sub>harv</sub>), and the NPP remaining in ecosystems after harvest (NPP<sub>eco</sub>) for 32 land-use classes in annual time-steps from 1992 to 2020. A detailed description of the LUIcube is available in the accompanying publication.</p> <p>The layers of land-use areas are provided in square kilometers (km²) per grid cell. All NPP flows are provided in tC/yr per grid cell. Adding HANPP<sub>harv</sub> to NPP<sub>eco</sub> results in the actual NPP available before harvest (NPP<sub>act</sub>=NPP<sub>eco</sub>+HANPP<sub>harv</sub>), and adding HANPP<sub>luc</sub> to NPP<sub>act</sub> results in the potential NPP available in the hypothetical absence of land use (NPP<sub>pot</sub>=NPP<sub>act</sub>+HANPP<sub>luc</sub>) for the given land-use class. Area-intensive values (in gC/m²/yr) can be calculated by dividing the NPP flows by the area of the respective land-use class per grid cell. HANPP in % of NPP<sub>pot</sub> can be calculated by summing up HANPP<sub>harv</sub> and HANPP<sub>luc</sub> and dividing it by NPP<sub>pot</sub>. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP.</p> <p>This Zenodo repository provides data on following land-use classes: cropland used for production of other oilcrops (CL-OOIL); coffee (CL-COFF); fodder crops (CL-FODD)</p>
RRING Global Interviews Dataset (WP3)
<p>The RRING Work Package 3 (WP3) objective was to clarify how Research Funding Organisations (RFOs) and Research Performing Organisations (RPOs) operated within region-specific research and innovation environments. It explored how they navigated the governance and regulatory frameworks for Responsible Research and Innovation (RRI), as well as offering their perspectives on the entities responsible for RRI-related policy and action in their locales.</p> <p>This dataset specifically covers the global interviews research part, which was designed to investigate bottom-up perspectives and experiences of researchers and innovators. The focus here is on collecting data through and from researchers and innovators themselves (i.e. ascertaining bottom-up views). We prioritised how and why research and innovation are supplied from those who are actually supplying it. In doing this, we also paid particular attention to: key RRI-related platforms, spaces and players operating in this region; interactions between different stakeholder types; domain-specific lessons related to Digital (ICT), Energy, Bioeconomy and Waste Management; as well as region-specific insights on what is shaping day-to-day research and innovation practice.</p> <p>Our sampling focused on the following geographical regions: African states; Arab states; Asian and Pacific states; European and North American States; and Latin American and Caribbean states. Of the 130+ interviews conducted, 29 participants consented to their transcripts being shared publicly</p>
Global dataset of co-incident TLS-derived and harvested tree biomass
<p>This dataset was used to producee the figures and statistics of the publication "Estimating forest aboveground biomass with terrestrial laser scanning: current status and future directions".</p> <p>This dataset contains 391 entries. Each entry is a tree that was terrestrial laser scanned and consecutively harvested to assess its aboveground biomass (AGB). AGB was also obtained from allometric scaling equations. Several ancillary tree properties such as stem diameter, foliage conditions,... and scan metadata (type of scanner, pattern) are included. We refer to the tab 'headers' for an explanation and units of the respective columns. Elaborate method descriptions can be found in the publication or in the following original publications:</p> <ul> <li>Burt, A., Boni Vicari, M., da Costa, A. C. L., Coughlin, I., Meir, P., Rowland, L., et al. (2021). New insights into large tropical tree mass and structure from direct harvest and terrestrial lidar. Royal Society Open Science 8, 201458. doi:10.1098/rsos.201458</li> <li>Calders, K., Newnham, G., Burt, A., Murphy, S., Raumonen, P., Herold, M., et al. (2015). Nondestructive estimates of above-ground biomass using terrestrial laser scanning. Methods in Ecology and Evolution 6, 198–208. doi:10.1111/2041-210X.12301</li> <li>Demol, M., Calders, K., Krishna Moorthy, S. M., Van den Bulcke, J., Verbeeck, H., and Gielen, B. (2021). Consequences of vertical basic wood density variation on the estimation of aboveground biomass with terrestrial laser scanning. Trees 35, 671–684. doi:10.1007/s00468-020-02067-7</li> <li>Gonzalez de Tanago, J., Lau, A., Bartholomeus, H., Herold, M., Avitabile, V., Raumonen, P., et al. (2018). Estimation of above-ground biomass of large tropical trees with terrestrial LiDAR. Methods in Ecology and Evolution 9, 223–234. doi:10.1111/2041-210X.12904</li> <li>Hackenberg, J., Wassenberg, M., Spiecker, H., and Sun, D. (2015). Non destructive method for biomass prediction combining TLS derived tree volume and wood density. Forests 6, 1274–1300. doi:10.3390/ f6041274</li> <li>Kükenbrink, D., Gardi, O., Morsdorf, F., Thürig, E., Schellenberger, A., and Mathys, L. (2021). Aboveground biomass references for urban trees from terrestrial laser scanning data. Annals of Botany, 1–16doi:10.1093/aob/mcab002</li> <li>Lau, A., Calders, K., Bartholomeus, H., Martius, C., Raumonen, P., Herold, M., et al. (2019). Tree Biomass Equations from Terrestrial LiDAR: A Case Study in Guyana. Forests 10, 527. doi:10.3390/f10060527</li> <li>Momo Takoudjou, S., Ploton, P., Sonke, B., Hackenberg, J., Griffon, S., Coligny, F., et al. (2018). Using terrestrial laser scanning data to estimate large tropical trees biomass and calibrate allometric models: A comparison with traditional destructive approach. Methods in Ecology and Evolution 9, 905–916.<br> doi:10.1111/2041-210X.12933</li> <li>Stovall, A. E., Vorster, A. G., Anderson, R. S., Evangelista, P. H., and Shugart, H. H. (2017). Non-destructive aboveground biomass estimation of coniferous trees using terrestrial LiDAR. Remote Sensing of Environment 200, 31–42. doi:10.1016/j.rse.2017.08.013<br> </li> </ul> <p><br> </p>
KGClim historical: A 1-km global dataset of historical (1979-2013) Köppen-Geiger climate classification and bioclimatic variables
<p>We presented a new dataset of 1-km Köppen-Geiger climate classification maps and 12 bioclimatic variables for the historical periods (1979-2008, 1980-2009, 1981-2010, 1982-2011, 1983-2012, 1984-2013).</p>
Global dataset of Sentinel-1 azimuth shift values and derived horizontal displacements from COMET LiCSAR system
<p>For this dataset, we were exploiting spatio-temporal behaviour of side-products of coregistration of Sentinel-1 radar images, that were generated in last couple of years, within the COMET LiCSAR system for computing interferograms. These side-products are sub-pixel (azimuth) offsets, allowing for a very precise match of the images (up to a 0.0005 pixels). We have shown that by a proper approach, these offsets can be used as measurements of large-scale horizontal motion - in our case, using 250x250 km resolution cells, we aimed to measure motion of tectonic plates. Our measurements are fitting well to the latest ITRF2014 plate motion model, although the E component contains an extra overall shift, not explained at the moment of sharing the outputs.</p> <p>The sub-pixel offsets (result of intensity cross correlation and spectral diversity estimation, w.r.t. precise orbit ephemerides) were corrected for solid Earth tides and ionospheric phase advance, using external models. The code shared within the dataset includes the correction functions. The data contains both original and corrected values. Also, the data contains ITRF2014 plate motion values.</p> <p>The dataset contains outputs from both COMET LiCSAR frame units and their decomposition into N, E motion vectors, as well as some metadata on the frames that are necessary for the reprocessing if needed.</p>
Global Vkontakte User Dataset
<p>Online Social Networks enable individuals to present a version of themselves to their immediate social circle and beyond. Those presentations express cultural factors such as an individuals gender, location, political, philosophical and religions values. Obtaining such data; however, is often challenging on the aggregate level as it typically involves negotiations with private entities and ownership restrictions. This study presents a dataset of 563,615,517 user accounts from the platform Vkontakte, an online social network collected in June of 2020. Vkontakte is a social media platform similar in nature to Facebook that allows individuals to connect with other users, communicate with them through public and private messages, as well as create public personas of themselves. This dataset can be used to perform cross-national and cross-cultural analyses of online culture from a large proportion of the world.</p>
Development of a global dataset of Wetland Area and Dynamics for Methane Modeling (WAD2M)
<p>Seasonal and interannual variations in global wetland area is a strong driver of fluctuations in global methane (CH<sub>4</sub>) emissions. Current maps of global wetland extent vary with wetland definition, causing substantial disagreement and large uncertainty in estimates of wetland methane emissions. To reconcile these differences for large-scale wetland CH<sub>4</sub> modeling, we developed a global Wetland Area and Dynamics for Methane Modeling (WAD2M) dataset at ~25 km resolution at equator (0.25 arc-degree) at monthly time-step for 2000-2018. WAD2M combines a time series of surface inundation based on active and passive microwave remote sensing at coarse resolution (~25 km) with six static datasets that discriminate inland waters, agriculture, shoreline, and non-inundated wetlands. We exclude all permanent water bodies (e.g. lakes, ponds, rivers, and reservoirs), coastal wetlands (e.g., mangroves and seagrasses), and rice paddies to only represent spatiotemporal patterns of inundated and non-inundated vegetated wetlands. Globally, WAD2M estimates the long-term maximum wetland area at 13.0 million km<sup>2</sup> (Mkm<sup>2</sup>), which can be separated into three categories: mean annual minimum of inundated and non-inundated wetlands at 3.5 Mkm<sup>2</sup>, seasonally inundated wetlands at 4.0 Mkm<sup>2</sup> (mean annual maximum minus mean annual minimum), and intermittently inundated wetlands at 5.5 Mkm<sup>2</sup> (long-term maximum minus mean annual maximum). WAD2M has good spatial agreements with independent wetland inventories for major wetland complexes, i.e., the Amazon Lowland Basin and West Siberian Lowlands, with high Cohen’s kappa coefficient of 0.54 and 0.70 respectively among multiple wetlands products. By evaluating the temporal variation of WAD2M against modeled prognostic inundation (i.e., TOPMODEL) and satellite observations of inundation and soil moisture, we show that it adequately represents interannual variation as well as the effect of El Niño-Southern Oscillation on global wetland extent. This wetland extent dataset will improve estimates of wetland CH<sub>4</sub> fluxes for global-scale land surface modeling. </p> <p> </p> <p>Update: Oct.08.2021</p> <p>Documentation for WAD2M Version 2.0 can be found at <a href="https://drive.google.com/file/d/1adoAnuqu6uBWnTYKI8u_S4OAQSdgOtd6/view?usp=sharing">WAD2M_V2_update</a></p>
A Global Social Network Values Dataset
<p>Online social networks enable individuals to present a version of themselves to their immediate social circle and beyond. Those presentations express cultural factors such as an individual's gender, location, political, philosophical, and religious values. However, obtaining such data is often challenging on the aggregate level as it typically involves negotiations with private entities and ownership restrictions. This study presents a dataset of 244,629,979 user accounts from the platform Vkontakte, an online social network collected in June of 2020. Vkontakte is a social media platform similar to Facebook that allows individuals to connect with other users, communicate with them through public and private messages, and create public personas. This dataset can perform cross-national and cross-cultural analyses of online values from a large portion of the world.</p>
Dataset of globally rigid graphs
<p>This data set collectes all globally rigid graphs for dimension d<=25 with at most d+k vertices, where k might be different.<br> The graphs are collected in a zip file with respect to the dimension. Graphs are stored in graph6 data format.</p>
DATASET: Digital humanities at global scale
<p>Dataset to analyse the scientific production of the Digital humanities at global scale. Databases consulted: App Dimensions, Web of Science (WoS), Scopus.</p> <p>Dataset to analyze the scientific production with reference: </p> <p>Bocanegra Barbecho, L., Ros Muñoz, S., González-Blanco García, E., & Toscano, M. (2023). Digital humanities at global scale. Interdisciplinary Science Reviews, 48(3), 446–459. https://doi.org/10.1080/03080188.2023.2193799</p>
A Global Epidemics Dataset (1500-2020)
<p>This is a Global Epidemics Dataset analyzed in: Marani, M. , G. Katul, W.H. Pan, A. Parolari, Intensity and frequency of extreme novel epidemics, 2021</p> <p>The upload includes a revised code that corrects an error and produces revised estimates of the probability of occurrence of global epidemics in the present and in the future.</p>
Dataset associated with Senf et al. (2023): "How the extreme 2019-2020 Australian wildfire affected global circulation and adjustments"
<p>This contains data aggregates derived from global ECHAM-HAM simulation for the study for effects due to the extreme Australian wildfire event 2019/2020. This data build the basis for analysis and figures in Senf et al. (2023) submitted to ACP.</p> <p> </p> <p>Simulation Data are</p> <ul> <li>available for freely running ensembles (36 member) and nudged simulations</li> <li>conducted for fire emissions artificially scaled with factors 0, 1, 2, 3, 5.</li> <li>stored for Jan - Mar 2020</li> </ul>
ReaLSAT, a global dataset of reservoir and lake surface area variations
<p>Reservoir and Lake Surface Area Timeseries (ReaLSAT) dataset provides an unprecedented reconstruction of surface area variations of lakes and reservoirs at a global scale using Earth Observation (EO) data and novel machine learning techniques. The dataset provides monthly scale surface area variations (1984 to 2020) of 681,137 water bodies below 50°N and sizes greater than 0.1 square kilometers.</p> <p> The dataset contains the following files:</p> <p>1) ReaLSAT.zip: A shapefile that contains the reference shape of waterbodies in the dataset.</p> <p>2) monthly_timeseries.zip: contains one CSV file for each water body. The CSV file provides monthly surface area variation values. The CSV files are stored in a subfolder corresponding to each 10 degree by 10 degree cell. For example, monthly_timeseries_60_-50 folders contain CSV files of lakes that lie between 60 E and 70 E longitude, and 50S and 40 S. </p> <p>3) monthly_shapes_<bottom_left_lon>_<bottom_left_lat>.zip: contains a geotiff for each water body that lie within the 10 degree by 10 degree cell. Please refer to the visualization notebook on how to use these geotiffs. </p> <p>4) evaluation_data.zip: contains the random subsets of the dataset used for evaluation. The zip file contains a README file that describes the evaluation data.</p> <p>6) generate_realsat_timeseries.ipynb: a Google Colab notebook that provides the code to generate timerseries and surface extent maps for any waterbody.</p> <p>Please refer to the following papers to learn more about the processing pipeline used to create ReaLSAT dataset:</p> <p>[1] Khandelwal, Ankush, Anuj Karpatne, Praveen Ravirathinam, Rahul Ghosh, Zhihao Wei, Hilary A. Dugan, Paul C. Hanson, and Vipin Kumar. "ReaLSAT, a global dataset of reservoir and lake surface area variations." <em>Scientific data</em> 9, no. 1 (2022): 1-12.</p> <p>[2] Khandelwal, Ankush. "ORBIT (Ordering Based Information Transfer): A Physics Guided Machine Learning Framework to Monitor the Dynamics of Water Bodies at a Global Scale." (2019).</p> <p> </p> <p><strong>Version Updates</strong></p> <p>Version 2.0:</p> <p>- extends the datasets to 2020.</p> <p>- provides geotiffs instead of shapefiles for individual lakes to reduce dataset size.</p> <p>- provides a notebook to visualize the updated dataset. </p> <p>Version 1.4: added 1120 large lakes to the dataset and removed partial lakes that overlapped with these large lakes.</p> <p>Version 1.3: fixed visualization related bug in generate_realsat_timeseries.ipynb</p> <p>Version 1.2: added a Google Colab notebook that provides the code to generate timerseries and surface extent maps for any waterbody in ReaLSAT database.</p>
An Urban Scheme for the ECMWF Integrated Forecasting System: Global Forecasts and Residential CO2 Emissions - Dataset
<p>These data support the journal article : An Urban Scheme for the ECMWF Integrated Forecasting System: Global Forecasts and Residential CO2 Emissions (Journal of Advances in Modeling Earth Systems).</p> <p>The files provided are as follows:</p> <p>SITE_RMSE* - These files provided the computed RMSE values for SYNOP site evaluation using the control IFS and the urban IFS. Results are given for different forecast lead times, different seasons and for both 2 m and 10 m wind speed. </p> <p>DIURNAL* - These files provide the diurnal 2 m temperature output from the model and the comparison of those with observations.</p> <p>For more information please contact or access to alternative data related to the publication please contact: joe.mcnorton@ecmwf.int</p> <p> </p>
Global Wetlands: Luderick Seagrass Dataset - Test Set Image Patches
<p>This dataset is a test dataset of image patches created from the 'novel-test' split of the Global Wetlands Luderick-Seagrass dataset. The original images were divided as a grid into 50 image patches. The image patches were manually labeled into 'Background', 'Fish' and 'Seagrass' sets. The images were otherwise unaltered. </p> <p>We contribute this test dataset of underwater image patches to facilitate evaluation of coarse segmentation seagrass methods.</p> <p>Original dataset description: "This dataset comprises of annotated footage of Girella tricuspidata in two estuary systems in South East Queensland, Australia. This data is suitable for a range of classification and object detection research in unconstrained underwater environments."</p> <p>Original dataset citation: Ditria, Ellen M; Connolly, Rod M; Jinks, Eric L; Lopez-Marcano, Sebastian (2021)<strong>:</strong> Annotated video footage for automated identification and counting of fish in unconstrained marine environments. <em>PANGAEA</em>, <a href="https://doi.org/10.1594/PANGAEA.926930">https://doi.org/10.1594/PANGAEA.926930</a>.</p> <p>The original dataset is available at: <br> https://github.com/globalwetlands/luderick-seagrass<br> https://download.pangaea.de/dataset/926930/files/Fish_automated_identification_and_counting.zip<br> https://globalwetlands.blob.core.windows.net/globalwetlands-public/datasets/luderick-seagrass/luderick-seagrass.zip</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.