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42 results for “GEDI”
Dataset for assessing amazon rainforest regrowth with GEDI and ICESat-2 data
<p>This dataset includes GEDI data, ICESat-2 data, auxiliary data, and intermediate results necessary to reproduce results in <a href="https://doi.org/10.1016/j.srs.2022.100051">Milenkovic et al. 2022</a>. The code required to process the data is on: <a href="https://github.com/MilutinMM/SecFor-Regrowth.git">https://github.com/MilutinMM/SecFor-Regrowth.git</a>.</p> <p>Short descriptions of files:</p> <ul> <li><strong>ATL08_gdf.json</strong> - ICESat-2 ATL08 segments in Rondonia </li> <li><strong>ATL08_gdf_Para_MG.json</strong> - ICESat-2 ATL08 segments intersecting the two calibration sites</li> <li><strong>ATL08_h5_fileNames_Rondonia.txt</strong> - A list of ICESat-2 orbits (ATL08 h5 files) intersecting Rondonia (primary input)</li> <li><strong>calibartionModels.zip</strong> - GEDI and ICESat-2 calibration models and statistics (xlsx files)</li> <li><strong>deforested_poligons_2018_2019.zip</strong> - SPH file of a deforested polygon in the calibration site</li> <li><strong>gedi_L2A_allTime_gdf_Para_MG.json</strong> - GEDI shots intersecting the two calibration sites</li> <li><strong>gedi_L2A_allTime_MG_all.csv</strong> - GEDI shots within the FN calibration site</li> <li><strong>gedi_L2A_allTime_Para_all.csv</strong> - GEDI shots within the TNF calibration site</li> <li><strong>GEDI_L2A_fileNames_Rondonia.txt </strong> - A list of GEDI orbits (L2A h5 files) intersecting Rondonia (primary input)</li> <li><strong>gedi_L2A_gdf_Para_MG_sens_a2.json</strong> - GEDI shots intersecting the two calibration sites with sensitivities derived from the algorithm setting group 2</li> <li><strong>gedi_L2A_gdf_sens_a2.json</strong> - GEDI shots in Rondonia</li> <li><strong>gedi_L2A_MG_all_sens_a2.csv </strong>- GEDI shots within the FN calibration site (sensitivity from the alg. set. group 2)</li> <li><strong>gedi_L2A_Para_all_sens_a2.csv</strong> - GEDI shots within the TFN calibration site (sensitivity from the alg. set. group 2)</li> <li><strong>gedi_L2A_Rondonia_all_sens_a2.csv</strong> - GEDI shots in Rondonia </li> <li><strong>MG_ATL08_h5_fileNames.txt</strong> - A list of ICESat-2 orbits (ATL08 h5 files) intersecting the FN calibration site</li> <li><strong>Para_ATL08_h5_fileNames.txt</strong> - A list of ICESat-2 orbits (ATL08 h5 files) intersecting the TFN calibration site</li> <li><strong>svbr-rondonia-2018.tif</strong> - Forest age map for Rondonia (Silva Junior et al. 2020)</li> <li><strong>svbr-rondonia-2018_bw_eroded.tif</strong> - a secondary forest extent mask with removed border pixels</li> </ul> <p>References:</p> <p>Milenković, M., Reiche, J., Armston, J., Neuenschwander, A., De Keersmaecker, W., Herold, M., Verbesselt, J., Assessing amazon rainforest regrowth with GEDI and ICESat-2 data, <em>Science of Remote Sensing</em>, 2022, 100051, ISSN 2666-0172, <a href="https://doi.org/10.1016/j.srs.2022.100051">https://doi.org/10.1016/j.srs.2022.100051</a>.</p> <p>Silva Junior, C.H.L., Heinrich, V.H.A., Freire, A.T.G. <em>et al.</em> Benchmark maps of 33 years of secondary forest age for Brazil. <em>Sci Data</em> <strong>7, </strong>269 (2020). <a href="https://doi.org/10.1038/s41597-020-00600-4">https://doi.org/10.1038/s41597-020-00600-4</a></p>
Filtered canopy top height estimates from GEDI LIDAR waveforms for 2019 and 2020
<p>Canopy top height (RH98) is estimated from GEDI L1B waveforms globally between 51.6° N & S from L1B Version 1 data for April-July 2019 and 2020. We refer to the original research article below for further information. The footprint data were filtered with respect to predictive uncertainty and MODIS non-vegetated probability.</p><p>The unfiltered data organized in hdf5 files corresponding to the orbit files of the GEDI L1B Version 1 data is available here:</p><p>April-July 2019: <a href="https://doi.org/10.5281/zenodo.5704852">https://doi.org/10.5281/zenodo.5704852</a></p><p>April-July 2020: <a href="https://doi.org/10.5281/zenodo.7737869">https://doi.org/10.5281/zenodo.7737869</a></p><p><strong>GEDI mission website</strong>: <a href="https://gedi.umd.edu/">https://gedi.umd.edu/</a>.</p><p><strong>Citation:</strong></p><p>Use of these data require citation of this dataset:</p><p>Lang, Nico, Kalischek, Nikolai, Armston, John, Schindler, Konrad, Dubayah, Ralph, & Wegner, Jan Dirk. (2021). Filtered canopy top height estimates from GEDI LIDAR waveforms for 2019 and 2020 (1.0) [Dataset]. Zenodo. <a href="https://doi.org/10.5281/zenodo.7737946">https://doi.org/10.5281/zenodo.7737946</a></p><p>Original research article:</p><p>Lang, N., Kalischek, N., Armston, J., Schindler, K., Dubayah, R., & Wegner, J. D. (2022). Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles. <i>Remote Sensing of Environment</i>, <i>268</i>, 112760.</p><p>This filtered dataset (2019 and 2020) was used to develop the global canopy height model fusing Sentinel-2 and GEDI that is presented in:</p><p>Lang, N., Jetz, W., Schindler, K., & Wegner, J. D. (2023). A high-resolution canopy height model of the Earth. Nature Ecology & Evolution, 1-12, <a href="https://doi.org/10.1038/s41559-023-02206-6">https://doi.org/10.1038/s41559-023-02206-6</a></p>
FORMS: Forest Multiple Source height, wood volume, and biomass maps in France at 10 to 30 m resolution based on Sentinel-1, Sentinel-2, and GEDI data with a deep learning approach.
<p>The products can be vizualized at <a href="https://martinschwartz0.users.earthengine.app/view/forms-height-biomass-volume-viewer">https://martinschwartz0.users.earthengine.app/view/forms-height-biomass-volume-viewer</a></p> <p>- FORMS-H: Canopy height map of France at 10 m resolution. The units are in centimeter (10^-2 m).</p> <p>- FORMS-B: Above-ground biomass density map of France at 30 m resolution. The units are in Mg ha-1</p> <p>- FORMS-V: Wood volume density map of France at 30 m resolution. The units are in m3 ha-1</p> <p>Please refer to the paper <a href="https://doi.org/10.5194/essd-15-4927-2023">https://doi.org/10.5194/essd-15-4927-2023</a> for further details.</p>
Estimation of forest height and biomass from open-access multi-sensor satellite imagery and GEDI Lidar data: high-resolution maps of metropolitan France
<p>Maps of forest height, aboveground biomass (AGB)* and volume (VOL)* at 10 m spatial resolution for the year 2020 on France. </p> <p>* AGB and Volume maps are available on request.</p> <p>The methodology and validation of the maps are presented here: https://hal.science/hal-04249151</p> <p>Please cite :</p> <p>David Morin, Milena Planells, Stéphane Mermoz, Florian Mouret. Estimation of forest height and biomass from open-access multi-sensor satellite imagery and GEDI Lidar data: high-resolution maps of metropolitan France. 2023. hal-04249151</p>
Mapping sugarcane globally at 10 m resolution using GEDI and Sentinel-2
<p><strong>Dataset Abstract:</strong><br>Sugarcane is an important source of food, biofuel, and farmer income in many countries. At the same time, sugarcane is implicated in many social and environmental challenges, including water scarcity and nutrient pollution. Currently, few of the top sugar-producing countries generate reliable maps of where sugarcane is cultivated. To fill this gap, we introduce a dataset of detailed sugarcane maps for the top 13 producing countries in the world, comprising nearly 90% of global production. Maps were generated for the 2019-2022 period by combining data from the Global Ecosystem Dynamics Investigation (GEDI) and Sentinel-2 (S2). GEDI data were used to provide training data on where tall and short crops were growing each month, while S2 features were used to map tall crops for all cropland pixels each month. Sugarcane was then identified by leveraging the fact that sugar is typically the only tall crop growing for a substantial fraction of time during the study period. Comparisons with field data, pre-existing maps, and official government statistics all indicated high precision and recall of our maps. Agreement with field data at the pixel level exceeded 80% in most countries, and sub-national sugarcane areas from our maps were consistent with government statistics. Exceptions appeared mainly due to problems in underlying cropland masks, or to under-reporting of sugarcane area by governments. <br>The final maps should be useful in studying the various impacts of sugarcane cultivation and producing maps of related outcomes such as sugarcane yields.</p> <p><strong>USAGE: Users must mask the provided sugarcane map with the most appropriate crop mask from the ones provided. If none of the provided crop masks are suitable, users can use an external crop mask instead.</strong></p> <p>Validation results for the sugarcane maps are detailed in Section 4.3 of the paper. For Indonesia and Guatemala, no field-level data or raster datasets were available for validation of our sugarcane maps.</p> <p><br><strong>Dataset:</strong> <br>5 bands<br>b1: Number of tall months<br>b2: Sugarcane Map: 0 = non-sugarcane, 1 = sugarcane<br>b3: ESA crop mask: 0 = non-cropland, 1 = cropland<br>b4: ESRI crop mask: 0 = non-cropland, 1 = cropland<br>b5: GLAD crop mask: 0 = non-cropland, 1 = cropland</p> <p> </p> <p>The dataset can be accessed on Google Earth Engine (GEE) at <br><strong><a href="https://code.earthengine.google.com/?asset=projects/lobell-lab/gedi_sugarcane/maps/imgColl_10m_ESAESRIGLAD">https://code.earthengine.google.com/?asset=projects/lobell-lab/gedi_sugarcane/maps/imgColl_10m_ESAESRIGLAD</a><br></strong><br>Example GEE script for visualizing and masking the sugarcane maps by country available at:<br><strong><a href="https://code.earthengine.google.com/545a87ce9bc29f2b5ad180955d974f8c?asset=projects%2Flobell-lab%2Fgedi_sugarcane%2Fmaps%2FimgColl_10m_ESAESRIGLAD">https://code.earthengine.google.com/545a87ce9bc29f2b5ad180955d974f8c?asset=projects%2fl Bell-lab%2Fgedi_sugarcane%2 Maps%2FimgColl_10m_ESAESRIGLAD</a></strong></p>
High-Resolution Canopy Fuel Maps Based on GEDI: A Foundation for Wildfire Modeling in Germany
<p>Open access publication under review.</p> <p>Visit <a href="https://ee-forestfuels-ger.projects.earthengine.app/view/gedi-fuels"><strong>this Earth Engine app</strong></a> to explore the data interactively.</p> <p> </p> <p>Abstract:</p> <p>Forest fuels are essential for wildfire behavior modeling and risk assessments but difficult to quantify accurately. An increase in fire frequency in recent years, particularly in regions traditionally not prone to fire, such as central Europe, has increased demands for large-scale remote sensing fuel information. This study develops a methodology for mapping canopy fuels over large areas (Germany) at high spatial resolution, exclusively relying on open remote sensing data.</p> <p><br>We propose a two-step approach where we first use measurements from NASA’s GEDI instrument to estimate canopy fuel variables at the footprint level, before predicting high-resolution raster maps. Instead of using field measurements, we generate (GEDI-) footprint-level estimates for Canopy (Base) Height (CH, CBH),<br>Cover (CC), Bulk Density (CBD), and Fuel Load (CFL) by segmenting airborne LiDAR point clouds and processing tree-level metrics with allometric crown biomass<br>models. To predict footprint-level canopy fuels we fit and tune Random Forest models, which are cross-validated using k-fold Nearest Neighbor Distance Matching.<br>Predictions at >1.6 M GEDI footprints and biophysical raster covariates are combined with a Universal Kriging method to produce countrywide maps at 20-meter resolution.</p> <p><br>Agreement (RMSE/R²) with validation data (from the same population) was strong for footprint-level predictions and moderate for map predictions. A validation<br>with estimates based on National Forest Inventory data revealed low to modest agreement. Better accuracy was achieved for variables related to height (CH, CBH)<br>rather than to cover or biomass (CBD, CFL). Error analysis pointed towards a mixture of biases in model predictions and validation data, as well as underestimation of<br>model prediction standard errors. Contributing factors may be simplification through allometric equations and spatial and temporal mismatch of data inputs.<br>The proposed workflow has the potential to support regions where wildfire is an emerging issue, and fuel and field information is scarce or unavailable.</p> <p> </p> <p>Data:</p> <p>This repository contains modeling data, model objects (R), and predicted maps. The TIFF-files each have six bands, which includes (1) the final Universal Kriging result, (2) the linear model prediction (3) the prediction of residual Kriging, (4) the Kriging variance, (5) the linear model prediction standard error, and (6) Universal Kriging standard error.</p> <p> </p> <p>Disclaimer:<br>Maps in this repository are predicted using canopy fuel estimates from GEDI measurements. These are limited the region between 51.6° North and South. Map predictions exceeding this range should be considered an extrapolation of the model to an unknown biophysical domain. Error maps (6) can aid in utilizing our canopy fuel maps.</p>
Global canopy top height estimates from GEDI LIDAR waveforms for 2020
<p>Canopy top height (RH98) is estimated from GEDI L1B waveforms globally between 51.6° N & S from L1B Version 1 data from April-July 2020. The footprint level RH98 predictions are stored in hdf5 files corresponding to the orbit files of the GEDI L1B Version 1 data.</p> <p>See also the repository for the data from April-July 2019: <a href="https://doi.org/10.5281/zenodo.5704852">https://doi.org/10.5281/zenodo.5704852</a>. This repository also contains the file <a href="https://zenodo.org/api/files/0a9300b5-2dea-4791-a019-319ed6209713/load_pred_RH98_files.py?versionId=6af41185-f13b-44aa-9042-a59efd4abb82">load_pred_RH98_files.py </a>with more information on how to parse and load the prediction orbit files.</p> <p><strong>GEDI mission website</strong>: <a href="https://gedi.umd.edu/">https://gedi.umd.edu/</a>.</p> <p><strong>Citation: </strong></p> <p>Use of these data require citation of this dataset:</p> <p>Lang, Nico, Kalischek, Nikolai, Armston, John, Schindler, Konrad, Dubayah, Ralph, & Wegner, Jan Dirk. (2021). Global canopy top height estimates from GEDI LIDAR waveforms for 2020 (1.0) [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.7737869</p> <p>Original research article:</p> <p>Lang, N., Kalischek, N., Armston, J., Schindler, K., Dubayah, R., & Wegner, J. D. (2022). Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles. <em>Remote Sensing of Environment</em>, <em>268</em>, 112760.</p>
Large-scale Forest Stand Height mapping for the northeast of U.S. and China using L-band spaceborne repeat-pass InSAR and GEDI
<p><span>The forest height mosaic for the northeastern parts of China and U.S are generated based on a global-to-local inversion approach proposed in </span><span><span>(Yu et al., 2023)</span></span><span> by making use of Spaceborne repeat-pass InSAR and spaceborne GEDI data. The sparsely but extensively distributed LiDAR samples provided by NASA’s GEDI mission are used to parametrize the semi-empirical repeat-pass InSAR scattering model(Lei et al., 2017) and to obtain forest height estimates. Compared to our previous efforts </span><span><span>(Lei et al., 2018, Lei and Siqueira, 2022)</span></span><span>, this paper further removes the assumptions that were made given the limited availability of calibration samples at that time, and developed a new inversion approach based on a global-to-local two-stage inversion scheme. This approach allows a better use of local GEDI samples to achieve finer characterization of temporal decorrelation pattern and thus higher accuracy of forest height inversion.<span> </span>This approach is further fully automated to enable a large-scale forest mapping capability. Two forest height mosaic maps were generated for the entire northeastern regions of U.S. and China with total area of 18 million hectares and 112 million hectares, respectively. The validation of the forest height estimates demonstrates much improved accuracy achieved by the proposed approach compared to the previous efforts i.e., reducing from RMSE of 3-4 m on the order of 3-6-hectare aggregated pixel size to RMSE 3-4 m on the order of 0.81-hectare pixel size. The proposed fusion approach not only addresses the sparse spatial sampling problem inherent to the GEDI mission, but also improve the accuracy of forest height estimates compared to the GEDI-interpolated maps by a factor of 20% at 30-m resolution. The extensive evaluation of forest height inversion against LVIS LiDAR data indicates an accuracy 3-4 m on the order of 0.81 hectare over smooth areas and 4-5 m over hilly areas in U.S., whereas the forest height estimates over northeastern China are best compared with small footprint LiDAR validation data even at an accuracy of even below 3.5 m with R2 mostly above 0.6. Such a forest height inversion accuracy at sub-hectare pixel size provides promising values towards the existing and future spaceborne LiDAR (</span><span>JAXA’s MOLI, NASA’s GEDI, China’s TECIS</span><span>) and InSAR missions (</span><span>NASA-ISRO’s NISAR, JAXA’s ALOS-4 and China’s LuTan-1</span><span>). This fusion prototype can work as a cost-effective solution for public users to obtain a wall-to-wall forest height mapping at large scale when only spaceborne repeat-pass InSAR data are available and freely accessible.</span></p>
Data from: Investigating the Association of Seasonal Dynamics in GEDI Canopy Cover Profiles and Sentinel-1 Backscatter in Temperate Forests
<p>This dataset supports the analysis about <em>Investigating the Association of Seasonal Dynamics in GEDI Canopy Cover Profiles and Sentinel-1 Backscatter in Temperate Forests</em></p>
GEDI and ALS LiDAR data for the Upper Austrian national park "Kalkalpen"
<p>This dataset contains GEDI L1B,L2B,L2A data for the Upper Austrian national park "Kalkalpen".<br> Also included are the obtained ALS return pulses for the Kalkalpen.</p> <p>The GEDI waveforms are averaged to 1m vertical height layers. Afterwards, the mean noise level stored in GEDI L1B dataset is subtracted from each waveform</p> <p>The ALS pulses are collocated to the GEDI waveforms (x/y-shift = +- 10m).</p>
Global canopy top height estimates from GEDI LIDAR waveforms for 2019
<p>Canopy top height (RH98) is estimated from GEDI L1B waveforms globally between 51.6° N & S. The map is based on the first four months of L1B Version 1 data (April-July 2019). The sparse footprint level predictions are averaged at 0.5 degree resolution (approx. 55 km raster cells at the equator) to obtain a dense map. We refer to the original research article below for further information, especially on how the predictions were filtered before the aggregation.</p> <p>The footprint level RH98 predictions are stored in hdf5 files corresponding to the orbit files of the GEDI L1B Version 1 data. The file <a href="https://zenodo.org/api/files/0a9300b5-2dea-4791-a019-319ed6209713/load_pred_RH98_files.py?versionId=6af41185-f13b-44aa-9042-a59efd4abb82">load_pred_RH98_files.py </a>contains more information on how to parse and load the prediction orbit files.</p> <p><strong>GEDI mission website</strong>: <a href="https://gedi.umd.edu/">https://gedi.umd.edu/</a>.</p> <p><strong>Citation: </strong>Use of these data require citation of this dataset and the original research article. These citations are as follows:</p> <p>Lang, N., Kalischek, N., Armston, J., Schindler, K., Dubayah, R., & Wegner, J. D. (2022). Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles. <em>Remote Sensing of Environment</em>, <em>268</em>, 112760.</p> <p>Lang, Nico, Kalischek, Nikolai, Armston, John, Schindler, Konrad, Dubayah, Ralph, & Wegner, Jan Dirk. (2021). Global canopy top height estimates from GEDI LIDAR waveforms for 2019 (1.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5704852</p> <p> </p>
Prospective Multicenter Registry of Gender, Diversity and Inclusion (GEDI) of Women With Acute Coronary Syndrome
ClinicalTrials.gov study NCT06441942. IPD Sharing: UNDECIDED. Countries: 1. Publications: 15.
Data from: From lidar waveforms to vegetation products: 7380 km2 of high-resolution airborne and simulated GEDI data over Sierra Nevada, California
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GEDI L4A Footprint Level Aboveground Biomass Density, Version 1
This dataset contains Global Ecosystem Dynamics Investigation (GEDI) Level 4A (L4A) predictions of the aboveground biomass density (AGBD; in Mg/ha) and estimates of the prediction standard error within each sampled geolocated laser footprint. The footprints are located within the global latitude band observed by the International Space Station (ISS), nominally 51.6 degrees N and S and reported for the period 2019-04-18 to 2020-09-02. The GEDI instrument consists of three lasers producing a total of eight beam ground transects, which instantaneously sample eight ~25 m footprints spaced approximately every 60 m along-track. The GEDI beam transects are spaced approximately 600 m apart on the Earth's surface in the cross-track direction, for an across-track width of ~4.2 km. Footprint AGBD was derived from parametric models that relate simulated GEDI Level 2A (L2A) waveform relative height (RH) metrics to field plot estimates of AGBD. Height metrics from simulated waveforms associated with field estimates of AGBD from multiple regions and plant functional types (PFT) were compiled to generate a calibration dataset for models representing the combinations of world regions and PFTs (i.e., deciduous broadleaf trees, evergreen broadleaf trees, evergreen needleleaf trees, deciduous needleleaf trees, and the combination of grasslands, shrubs, and woodlands).
GEDI L4A Footprint Level Aboveground Biomass Density, Version 2
This dataset contains Global Ecosystem Dynamics Investigation (GEDI) Level 4A (L4A) Version 2 predictions of the aboveground biomass density (AGBD; in Mg/ha) and estimates of the prediction standard error within each sampled geolocated laser footprint. In this version, the granules are in sub-orbits. The algorithm setting group selection used for GEDI02_A Version 2 has been modified for Evergreen Broadleaf Trees in South America to reduce false positive errors resulting from the selection of waveform modes above ground elevation as the lowest mode. The footprints are located within the global latitude band observed by the International Space Station (ISS), nominally 51.6 degrees N and S and reported for the period 2019-04-18 to 2021-08-05. The GEDI instrument consists of three lasers producing a total of eight beam ground transects, which instantaneously sample eight ~25 m footprints spaced approximately every 60 m along-track. The GEDI beam transects are spaced approximately 600 m apart on the Earth's surface in the cross-track direction, for an across-track width of ~4.2 km. Footprint AGBD was derived from parametric models that relate simulated GEDI Level 2A (L2A) waveform relative height (RH) metrics to field plot estimates of AGBD. Height metrics from simulated waveforms associated with field estimates of AGBD from multiple regions and plant functional types (PFTs) were compiled to generate a calibration dataset for models representing the combinations of world regions and PFTs (i.e., deciduous broadleaf trees, evergreen broadleaf trees, evergreen needleleaf trees, deciduous needleleaf trees, and the combination of grasslands, shrubs, and woodlands). For each of the eight beams, additional data are reported with the AGBD estimates, including the associated uncertainty metrics, quality flags, model inputs, and other information about the GEDI L2A waveform for this selected algorithm setting group. Model inputs include the scaled and transformed GEDI L2A RH metrics, footprint geolocation variables and land cover input data including PFTs and the world region identifiers. Additional model outputs include the AGBD predictions for each of the six GEDI L2A algorithm setting groups with AGBD in natural and transformed units and associated prediction uncertainty for each GEDI L2A algorithm setting group. Providing these ancillary data products will allow users to evaluate and select alternative algorithm setting groups. Also provided are outputs of parameters and variables from the L4A models used to generate AGBD predictions that are required as input to the GEDI04_B algorithm to generate 1-km gridded products. Note that there are 351 granules in this release affected by duplicate GEDI shots for selected days (2020-297 to 2020-300, 2020-365, and 2021-106).
Evaluating the Accuracy of Different Number of GEDI Footprints for Interpolation and Aboveground Biomass Estimation: A Case Study from Shangri-La
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Source data for GEDI - part 2
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Source data for GEDI - part 1
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Source data for GEDI - part 4
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Source data for GEDI - part 3
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.