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29 results for “global wetlands”
Scripts and datas for "Climate-driven projections of future global wetlands extent"
<p>Computations scripts (1, 2), associated input dataset (3), and output datasets for wetland fractions (4, 5) used and presented in the study:</p> <p><em><strong>L. Hardouin, B. Decharme, J. Colin, C. Delire: </strong>Climate-driven projections of future global wetlands extent.</em></p> <p>The calculation and input scripts include:<br><em>1_var_comput </em>: Calculation of the main variables used to diagnose wetlands: depth of the "active" layer d_wtl, liquid water content w_l, ice content and maximum content in the layer d_wtl.</p> <p><em>2_TOPMODEL </em>: The scripts used to diagnose the wetland fraction and to calibrate the models using the TOPMODEL approach. In this folder, the mean, maximum, minimum, standard deviation and skewness datasets of the topographic indices at the grid-cell level are also included.</p> <p><em>3_alpha_and_beta </em>: Calibrated alpha and beta parameters used to obtain the historical and projected wetland fractions with the calibrated version.</p> <p>The outputs datasets contain:</p> <p><em>4_fwtl_model_period </em>: The fraction of wetlands computed from each model in the calibrated version, for the historical period and the 4 SSPs scenarios presented in the submitted work.</p> <p><em>5_not_calibrated_fwtl_model_period </em>: The fraction of wetlands computed from each model in the uncalibrated version with alpha=0.65, for the historical period and the 4 SSPs scenarios, where only the historical period is used in the submitted work.</p> <p> </p> <p>Additional data not created by the authors are needed to reproduce the study (see the Open research section in the submitted article). Feel free to contact the authors (lucas.hardouin@meteo.fr) for any help or questions.</p>
Global wetland loss reconstruction over 1700-2020
<p>This repository contains three datasets resulting from the reconstruction of global wetland loss over 1700-2020. The three datasets are listed here and described in more detail below:</p> <ul> <li><strong>A.</strong> National and subnational statistics of drained or converted areas</li> <li><strong>B.</strong> Regional wetland percentage loss estimates and geospatial polygons</li> <li><strong>C.</strong> Gridded reconstruction</li> </ul> <p>The scripts used to process input data, model and calibrate the wetland loss reconstruction, and produce the figures are publicly available at https://github.com/etiennefluetchouinard/wetland-loss-reconstruction.</p> <p> </p> <p><strong>A. National and subnational statistics of drained or converted areas</strong></p> <p>This tabular database containing national and subnational statistics of wetland area drained and peat mass extracted. The database includes four land use types: cropland, forestry, peat extraction and wetland cultivation. These data are used as input to the mapped wetland loss reconstruction. Column descriptions of drainage_db_v10.csv:</p> <ol> <li><strong>unit:</strong> Scale of administrative unit ("national" or "subnational").</li> <li><strong>type:</strong> Land use type ("Cropland", "Forestry", "Peat Extraction" or "Wetland Cultivation")</li> <li><strong>iso_a3:</strong> 3-letter code of each country.</li> <li><strong>region:</strong> Name of subnational unit. Blank if data is national scale.</li> <li><strong>HASC_1:</strong> Hierarchical Administrative Subdivisions Codes for the subnational units. Blank if data is national scale.</li> <li><strong>year:</strong> Year of data.</li> <li><strong>drained_area_1000ha:</strong> Cumulative area drained by the year specified, in thousands of hectares.</li> <li><strong>drained_weight_1000tonsyr:</strong> Annual peat extraction rate for the year, in thousand tons per year.</li> <li><strong>peatland_only:</strong> Label indicating whether the drained area applies to all wetlands or peatlands specifically ("Peatland only" or blank).</li> <li><strong>Comment:</strong> Additional description from original data source, or unit conversion, or data corrections.</li> <li><strong>Source:</strong> Reference of data source and/or compilers.</li> </ol> <p> </p> <p><strong>B. Regional wetland percentage loss estimates and geospatial polygons</strong></p> <p>A shapefile of 151 polygons projected in WGS84. Columns description for polygon shapefile of the regional wetland loss percentage: regional_loss_poly.shp:</p> <ol> <li><strong>id:</strong> Numerical identifier.</li> <li><strong>name:</strong> Name of administrative unit, region or water feature the polygon area covers.</li> <li><strong>country:</strong> Name of country.</li> <li><strong>continent:</strong> Name of continent.</li> <li><strong>wet_categ:</strong> Broad category of wetlands included in the estimate (“<em>Peatlands</em>”, “<em>Inland natural wetlands</em>”, “<em>Coastal natural wetlands</em>”, “<em>Unspecified natural type(s)</em>” or “<em>All wetlands</em>”).</li> <li><strong>yr_start:</strong> Start year of the period over which wetland loss is estimated.</li> <li><strong>yr_end:</strong> End year of the period over which wetland loss is estimated.</li> <li><strong>area_mkm2:</strong> Surface area of the polygon, in million square kilometers (Mkm<sup>2</sup>).</li> <li><strong>perc_loss:</strong> Numerical value of percentage wetland loss (positive value represent loss of wetland area between start and end year.</li> <li><strong>comment:</strong> Additional description of estimate used or estimation method.</li> <li><strong>source:</strong> Citation of original data source.</li> <li><strong>compiler: </strong>Citation of intermediary data compiler.</li> </ol> <p> </p> <p><strong>C. Gridded reconstruction</strong></p> <p>Gridded outputs are stored in a separate NetCDF file for each of the 12 reconstructions of simulated wetland and present-day wetland maps. An ensemble average was also computed from the 12 reconstructions (only individual reconstructions were discussed in the manuscript). These data consist of global maps generated from the drainage reconstruction methodology for 33 decadal intervals (1700-2020 inclusive) for 9 variables:</p> <p>The filenames of ensemble members are labelled to with the name of the input present-day and simulated wetland maps:</p> <p> “<em>wetland_loss_1700-2020_</em>” + simulated input + “_” + present-day input + “<em>_v10.nc</em>”</p> <p>The 4 simulated wetland map inputs are: LPJwsl, SDGVM, ORCHIDEE, DLEM. The 3 present-day wetland map inputs are: GIEMSv2, GLWD3, WAD2M.</p> <p>Description of the 9 variables in each NCDF file:</p> <ol> <li><strong>wetland_loss:</strong> Cumulative wetland area lost (km<sup>2</sup> per grid cell). This variable is equivalent to the sum of area drained for the seven land uses drained</li> <li><strong>nat_wetland:</strong> Remaining natural wetland area (km<sup>2</sup> per grid cell)</li> <li><strong>cropland:</strong> Cropland area drained (km<sup>2</sup> per grid cell) leading to wetland loss</li> <li><strong>forestry:</strong> Forestry area drained (km<sup>2</sup> per grid cell) leading to wetland loss</li> <li><strong>peatextr:</strong> Peat harvest area drained (km<sup>2</sup> per grid cell) leading to wetland loss</li> <li><strong>wetcultiv:</strong> Wetland cultivation area (km<sup>2</sup> per grid cell) leading to wetland loss</li> <li><strong>ir_rice:</strong> Irrigated rice area leading to wetland loss (km<sup>2</sup> per grid cell)</li> <li><strong>pasture:</strong> Pasture area drained leading to wetland loss (km<sup>2</sup> per grid cell)</li> <li><strong>urban:</strong> Urban area drained leading to wetland loss (km<sup>2</sup> per grid cell)</li> </ol> <p>All layers were capped below the land pixel area grid (from HYDE 3.2, excl. open water).</p> <p><strong>Time:</strong> 33 slices; numerical years spread at decadal intervals, ranging between 1700-2020 (inclusive)</p> <p><strong>Extent:</strong> Longitude: -180° to 180°. Latitude: -56° to 84°.</p> <p> </p> <p>See the README file for a more detailed description of this dataset. Anyone wishing to use this dataset should cite <em>Fluet-Chouinard et al. 2023</em>. Please contact Etienne Fluet-Chouinard at <a href="mailto:etienne.fluet@gmail.com">etienne.fluet@gmail.com</a> with any questions or comments with regards to the best usage of our dataset.</p> <p>Fluet-Chouinard E., Stocker B., Zhang Z., Malhotra A., Melton J.R., Poulter B., Kaplan J., Goldewijk K.K., Siebert S., Minayeva T., Hugelius G., Prigent C., Aires F., Hoyt A., Davidson N., Finlayson C.M., Lehner B., Jackson R.B., McIntyre P.B. <em>Nature</em>. Extensive global wetland loss over the last three centuries</p>
Cothran, R. D., F. Radarian, and R. A. Relyea. 2011. Altering aquatic food webs with a global insecticide: Arthropod-amphibian links in mesocosms that simulate wetland communities. Journal of the North American Benthological Society 30:893-912.
Pesticides play a critical role in maximizing yields of economically important crops and minimizing the human health threats of disease-carrying pests, but they often have collateral effects on nontarget species. We used a mesocosm study to address how the most commonly used insecticide in the USA, malathion, applied at low, ecologically relevant concentrations (20 and 110 mg/L) affects species interactions in aquatic communities. Unlike many community ecotoxicology studies, our study assessed how malathion affects both consumptive and nonconsumptive effects of predators. We also considered how the vertical distribution of predator cues and malathion (caused by potential stratification) affects species interactions. We found no evidence for vertical stratification of malathion, a result suggesting that exposure to the pesticide was uniform throughout the water column. Malathion was lethal to some primary consumers (cladocerans) at both concentrations and to top predators (dragonflies) at the highest concentration (110 mg/L). These lethal effects initiated density-mediated indirect effects in both cases. Malathion also may have decreased dragonfly foraging efficiency, resulting in increased tadpole survival (trait-mediated indirect effect), which decreased the resources used by tadpoles (periphyton). Collectively, our results show that malathion alters species interactions. However, we suggest that the degree to which pesticides affect aquatic communities will depend strongly on the species composition of communities. Therefore, the community-level consequences of pesticide exposure are likely to vary across the ecological landscape.
Dynamics of global wetlands by TOPMODEL
<p>Dynamics of global wetlands are closely linked to biodiversity conservation, hydrology, and greenhouse gas emissions. However, long-term time series of global wetland products are still lacking. Using a TOPMODEL-based diagnostic model, we produced an ensemble of 28 gridded maps of monthly global/regional wetland extent products at 0.25° × 0.25° spatial resolution based on four observation-based wetland products and seven reanalysis soil moisture datasets for the period 1980–2020. The parameters of the model are calibrated on grid-scale against four observation-based wetland products. Overall, our products can capture the spatial distributions, seasonal cycles, and interannual variabilities of observed wetland extent well, and also show a good agreement with satellite-based terrestrial water storage estimates. The resulting mean annual maximum global wetland area fluctuates within a range of 3.3–5.6 Mkm<sup>2</sup>. The long temporal coverage beyond the era of satellite datasets, the global coverage and the opportunity to provide real-time update from ongoing SM data make these products helpful for various applications such as analyzing the wetland-related methane emission.</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>
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>
Global wetland CH4 emissions estimated by LPJ-wsl model for 1980-2021
<p>This dataset contains two files that summarize the regional wetland methane emissions simulated by LPJ-wsl model for the period of 2000-2021. two estimates based on two climate forcing datasets, ground-based CRU and reanalysis-based MERRA2. The ground-based input dataset is a monthly climatic observation based on meteorological stations developed by Climatic Research Unit, University of East Anglia. The reanalysis-based climate dataset is a daily climatic dataset from 1 hourly reanalysis Modern-Era Retrospective analysis for Research and Applications Version 2. For more details about LPJ-wsl model, see Zhang et al., (2017, 2018).</p> <p>References:</p> <p>Zhang, Z., Zimmermann, N.E., Stenke, A., Li, X., Hodson, E.L., Zhu, G., Huang, C., Poulter, B., 2017. Emerging role of wetland methane emissions in driving 21st century climate change. Proceedings of the National Academy of Sciences 114, 9647–9652. <a href="https://doi.org/10.1073/pnas.1618765114">https://doi.org/10.1073/pnas.1618765114</a></p> <p>Zhang, Z., Zimmermann, N.E., Calle, L., Hurtt, G., Chatterjee, A., Poulter, B., 2018. Enhanced response of global wetland methane emissions to the 2015–2016 El Niño-Southern Oscillation event. Environmental Research Letters 13, 074009. <a href="https://doi.org/10.1088/1748-9326/aac939">https://doi.org/10.1088/1748-9326/aac939</a></p>
Dispersal, habitat filtering, and eco-evolutionary dynamics as drivers of local and global wetland viral biogeography
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Global patterns of the leaf economics spectrum in wetlands
<p></p><p>The leaf economics spectrum (LES) describes consistent correlations among a variety of leaf traits that reflect a gradient from conservative to acquisitive plant strategies. So far, whether the LES holds in wetland plants at a global scale has been unclear. Using data on 365 wetland species from 151 studies, we find that wetland plants in general show a shift within trait space along the same common slope as observed in non-wetland plants, with lower leaf mass per area, higher leaf nitrogen and phosphorus, faster photosynthetic rates, and shorter leaf life span compared to non-wetland plants. We conclude that wetland plants tend to cluster at the acquisitive end of the LES. The presented global quantifications of the LES in wetland plants enhance our understanding of wetland plant strategies in terms of resources acquisition and allocation, and provide a stepping-stone to developing trait-based approaches for wetland ecology.</p><p></p>
Supporting data for Relative increases in CH4 and CO2 emissions from wetlands under global warming dependent on soil carbon substrates
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Time-series global 30 m wetland maps from 2000 to 2022
<p><strong>Basic information</strong></p><p>A novel global 30 m wetland annual dataset with fine classification system is generated, covering the period of 2000-2022 and containing 8 wetland subcategories (permanent water, swamp, marsh, flooded flats, saline, mangrove forest, salt marshes, and tidal flats). </p><p><strong>Notes:</strong></p><p>The GWL_FCS30 annual maps are divided into 961 5°×5° geographical tiles, and each tile contains 23 bands which denotes the tidal flat maps in 2000, 2001, 2002,..., 2021, 2022.</p><p><strong>Usage Policy:</strong></p><p>If you plan to use our data in <strong>a scientific analysis paper</strong>, we strongly recommend contacting us in advance to seek opinions, and consider our contributions in the acknowledgments or as co-authors.</p><p><strong>Citations:</strong></p><p>Zhang, X., Liu, L., Zhao, T., Chen, X., Lin, S., Wang, J., Mi, J., and Liu, W.: GWL_FCS30: a global 30 m wetland map with a fine classification system using multi-sourced and time-series remote sensing imagery in 2020, Earth Syst. Sci. Data, 15, 265–293, <a href="https://doi.org/10.5194/essd-15-265-2023">https://doi.org/10.5194/essd-15-265-2023</a>, 2023</p>
Global patterns in leaf stoichiometry across coastal wetlands
<p><b>Aim: </b>Coastal wetlands provide critical ecosystem functions and services, e.g. coastal protection, nutrient retention and C sequestration. Despite the important roles in global C, N and P cycling, the global variation in leaf stoichiometry across coastal wetlands remains unclear.</p> <p><b>Location: </b>Global.</p> <p><b>Time period: </b>1980-2018.</p> <p><b>Major taxa studied: </b>Vascular plants.</p> <p><b>Methods: </b>By compiling a global dataset of 698 data records in 205 sites, we systematically analysed the world-wide trends and their determinants in leaf element contents and ratios of plants across coastal wetlands.</p> <p><b>Results: </b>Leaf N and P contents significantly increased, but C:N, C:P and N:P ratios decreased with increasing latitude in coastal wetlands. Mean annual temperature was the predominant driver of leaf N, P and C:N, while soil N:P was a good predict of leaf C:P and N:P ratios. Furthermore, N increased faster with P in plant leaves of coastal wetlands compared to terrestrial ecosystems. Within coastal wetlands, herb-dominated salt marshes had significantly higher leaf P content, lower leaf N:P ratio and lower scaling exponent of leaf N to P than tree-dominated mangroves.</p> <p><b>Main conclusions: </b>The similar latitudinal patterns of leaf stoichiometry in coastal wetlands as terrestrial ecosystems reflected the similar influences of temperature. However, different slopes of leaf P and N:P ratios and N and P scaling relationships between these two ecosystems suggested that different salinity and tidal inundation levels result in different N and P use strategies in coastal wetland plants. These differences in leaf stoichiometry between ecosystems and between different types of coastal wetlands may need to be emphasized in future biogeochemical modelling due to their different roles in global nutrient and carbon cycling.</p>
Data and model code for study: Mechanistic modelling of marsh seedling establishment provides a positive outlook for coastal wetland restoration under global climate change
<p>This folder will include data and model code for study: Mechanistic modelling of marsh seedling establishment provides a positive outlook for coastal wetland restoration under global climate change.</p>
Data of global wetland carbon sinks
<p>A database of the annual net ecosystem production (NEP) across global wetlands. In total, the database comprised 2,385 observations of annual NEP data across global wetlands.</p>
Global patterns in leaf stoichiometry across coastal wetlands
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Data from: The hydrological legacy of deforestation on global wetlands
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Data from: Amphibian responses to livestock use of wetlands: new empirical data and a global review
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Global patterns of the leaf economics spectrum in wetlands
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LPJ-EOSIM L2 Global Simulated Monthly Wetland Methane Flux V001
The Lund-Potsdam-Jena Earth Observation SIMulator (LPJ-EOSIM) model estimates global wetland methane (CH4) emissions using simulated wetland extent and characteristics including soil moisture, temperature, and carbon content. For this dataset, wetlands are defined as land areas that are either permanently or seasonally saturated, excluding small ponds, lakes, and coastal wetlands. These wetland CH4 flux data will be used to support the United States Greenhouse Gas Center ([GHGC](https://earth.gov/ghgcenter)) and its mission to study natural GHG fluxes. The model will also be used to facilitate improved rapid detection and attribution of climate-carbon feedback and in strategic placement of measurement campaigns and monitoring systems as they relate to predicted biogeochemical hotspots. The LPJ-EOSIM L2 Global Simulated Monthly Wetland Methane Flux (LPJ_EOSIM_L2_MCH4E) Version 1 data product provides simulated monthly wetland CH<sub>4</sub> flux globally at a spatial resolution of 0.5 degrees. The monthly simulation data contains aggregate versions of the daily LPJ-EOSIM L2 Global Simulated Daily Wetland Methane Flux ([LPJ_EOSIM_L2_DCH4E](https://doi.org/10.5067/Community/LPJ-EOSIM/LPJ_EOSIM_L2_DCH4E.001)) Version 1 data. The monthly data are presented in four Cloud Optimized GeoTIFF (COG) files: two based on the aggregated daily forcing datasets Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) and European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA5), and two containing the mean and standard deviation values calculated from the monthly aggregate data. Due to the latency of global carbon dioxide (CO<sub>2</sub>) concentration estimates required for computation of LPJ-EOSIM simulated monthly CH4 flux data products, low latency ([LPJ_EOSIM_L2_MCH4E_LL](https://doi.org/10.5067/Community/LPJ-EOSIM/LPJ_EOSIM_L2_MCH4E_LL.001)) and high latency (LPJ_EOSIM_L2_MCH4E) collections are available. High latency data in this collection will be delivered around May of each year when National Oceanic and Atmospheric Administration’s (NOAA) Global Monitoring Laboratory (GML) publishes the previous year’s CO2 concentration and will have a lag of at least 5 months (January-May), and at most 17 months (January of the current year to May of the next year). Please refer to Section 2.0.1 of the User Guide for a more detailed explanation of estimated CO2 inputs and timing for scheduled updates to the collections.
Global Wetland Methane Emissions derived from FLUXNET and the UpCH4 Model, 2001-2018
This dataset provides monthly globally gridded freshwater wetland methane emissions from 2001-2018 in nmol CH4 m-2 s-1, g C-CH4 m-2 d-1, and TgCH4 grid cell-1 month-1. The data were derived from a six-predictor random forest upscaling model (UpCH4) trained on 119 site-years of eddy covariance CH4 flux data from 43 freshwater wetland sites covering bog (8), fen (8), marsh (10), swamp (6), and wet tundra (11) wetland classes and distributed across Arctic-boreal (20), temperate (16), and (sub)tropical (7) climate zones. Weekly mean CH4 fluxes were computed from half-hourly FLUXNET-CH4 Version 1.0 fluxes. Each grid cell CH4 flux prediction was weighted by fractional grid cell wetland extent to estimate CH4 emissions using the primary global dataset of Wetland Area and Dynamics for Methane Modeling (WAD2M) product and an alternate Global Inundation Estimate from Multiple Satellites GIEMS version 2 global wetland map. Both WAD2M and GIEMS-2 maps were modified with several correction data layers to represent the monthly area covered by vegetated wetlands, excluding open water and coastal wetlands. The data products are: mean daily fluxes with no adjustment for wetland area (i.e., flux densities assuming hypothetical 100% wetland cover); mean daily fluxes adjusting for WAD2M or GIEMS-2 wetland area; and by-pixel monthly sum of freshwater wetland methane emissions adjusting for WAD2M or GIEMS-2 wetland area. The data are provided in NetCDF4 format.
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.