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77 results for “Vegetation modeling”
Spartina alterniflora marsh vegetation data along the Georgia coast used in the Belowground Ecosystem Resiliency Model version 2.0
Study plots (1-m2) were established in eight Spartina alterniflora-dominated marshes (7 on Sapelo Island, Georgia, and 1 on Skidaway Island, Georgia). At three sites, plots were sampled once each during May, July, August, September, and October of 2016. At all sites, plots were sampled once each during June, August, and November of 2021, February, May, August, and November of 2022, and February of 2023. One long-term (quarterly 2013 to 2023) GCE LTER sampling site is also included. Nine replicate plots were placed in vegetated marsh along transects that spanned 3 Landsat-8 and -9 pixel footprints, with 3 plots per pixel footprint. In each plot, measurements included plant biomass, plant species, stem density, and height. Aboveground biomass was calculated using allometric relationships between plant height and mass from plant clipping studies. During these surveys, destructive core sampling was also performed in the proximity of the plots (n = 1 per plot) to measure above and below ground biomass. Chlorophyll, foliar N, and Leaf Area Index measurements were taken in the proximity of the plots. This dataset reflects an update to the "PLT-GCED-2106" dataset (doi: 10.6073/pasta/03f4f78c6498aecca34faf4339591129). This project also utilized data from the "PLT-GCEM-1610" dataset doi: 10.6073/pasta/9746c71b35e9f8c544ea12c601c33949). Those data utilized in this project are duplicated here for completeness.
Monthly Spartina alterniflora marsh vegetation data for additional sites along the Georgia coast used in the Belowground Ecosystem Resiliency Model
Study plots (1-m2) were established in three Spartina alterniflora-dominated marshes - 2 on Sapelo Island, Georgia, and 1 on Skidaway Island, Georgia, and sampled once each during May, July, August, September, and October of 2016. Nine replicate plots were placed in vegetated marsh along transects that spanned 3 Landsat-8 pixel footprints, with 3 plots per pixel foot print. In each plot, measurements included plant biomass, plant species, stem density, and height. Aboveground biomass was calculated using allometric relationships between plant height, flowering status and mass from plant clipping studies. During these surveys, destructive core sampling was also performed in the proximity of the plots (n = 1 per plot) to measure above and below ground biomass. Chlorophyll, foliar N, and Leaf Area Index measurements were taken in the proximity of the plots.
Biophysical effects of vegetation cover change from satellite and models
<p>Vegetation cover changes associated with land use and land cover change (LULCC) can perturb the local surface energy balance, which in turn can affect the local climate. Land surface models (LSMs) can be used to simulate such land-climate interactions, but their capacity to model these biophysical effects accurately across the globe remain unclear due to the complexity of the phenomena. This dataset provides idealized simulations from four LSMs (JULES, ORCHIDEE, JSBACH and CLM) that are harmonized with estimations obtained from satellite observations, enabling the inter-comparison and benchmarking of LSM performances and which can serve to identify model limitations and prioritize efforts in model development. The dataset provides the change in latent heat flux, in combined sensible and ground heat flux and in net radiation caused by 15 specific vegetation cover transitions on a 1° by 1° grid at monthly time scale for a synthetic year based on data from 2008 until 2012. The dataset was generated from a collaborative effort lead by JRC within the FP7 LUC4C project (luc4c.eu).</p>
Saddle catchment Distributed Hydrology Soil Vegetation Model Simulation (DHSVM) surface variable outputs (SWE, snowmelt, streamflow, soil moisture), 2 meter, 2000-2019.
The Saddle Catchment of the Niwot Ridge LTER is a densely observed, high elevation site that is ideal for hydrological model simulation and calibration. The files produced are the result of a calibration of the Distributed Hydrology Soil Vegetation model (DHSVM) using observationally based states and forcings. Input state files of vegetation, soil properties, shading, and elevation were generated using ground and satellite observations, which, in the case of coarse-resolution or point scale observations, were then interpolated to match the high resolution of the model (2-meter grid cells). Temporally continuous meteorological forcings at the hourly time-step were used to force the model to produce an hourly simulation of the surface and subsurface hydrology within the Saddle catchment. DHSVM was calibrated to effectively reproduce the annual cycle (r^2) and total volume (percent bias) of observed runoff using observations of streamflow at the outflow pour point of the Saddle Catchment from 2001-2019. Calibrated parameters include the lateral conductivity of soil types, exponential decrease of soil conductivity, snow roughness, the snow melting temperature threshold, and the vertical conductivity of the soils. The resulting simulation generated spatially distributed time series of the snow water equivalent, snow melt, precipitation, total evapotranspiration, potential evapotranspiration, and a time-series of the total runoff generated at the outflow pour-point of the Saddle catchment. This data package contains the spatially distributed time series of snow water equivalent, snow melt, and runoff, as well as the model configuration file. Outputs of precipitation, total evapotranspiration, actual evapotranspiration, as well as model inputs are archived separately on the Environmental Data Initiative.
Saddle catchment Distributed Hydrology Soil Vegetation Model Simulation (DHSVM) precipitation and transpiration variable outputs (precipitation, total, potential and actual evapotranspiration), 2 meter, 2000-2019.
The Saddle Catchment of the Niwot Ridge LTER is a densely observed, high elevation site that is ideal for hydrological model simulation and calibration. The files produced are the result of a calibration of the Distributed Hydrology Soil Vegetation model (DHSVM) using observationally based states and forcings. Input state files of vegetation, soil properties, shading, and elevation were generated using ground and satellite observations, which, in the case of coarse-resolution or point scale observations, were then interpolated to match the high resolution of the model (2-meter grid cells). Temporally continuous meteorological forcings at the hourly time-step were used to force the model to produce an hourly simulation of the surface and subsurface hydrology within the Saddle catchment. DHSVM was calibrated to effectively reproduce the annual cycle (r^2) and total volume (percent bias) of observed runoff using observations of streamflow at the outflow pour point of the Saddle Catchment from 2001-2019. Calibrated parameters include the lateral conductivity of soil types, exponential decrease of soil conductivity, snow roughness, the snow melting temperature threshold, and the vertical conductivity of the soils. The resulting simulation generated spatially distributed time series of the snow water equivalent, snow melt, precipitation, total evapotranspiration, potential evapotranspiration, and a time-series of the total runoff generated at the outflow pour-point of the Saddle catchment. This data package contains the spatially distributed time series of precipitation, total evapotranspiration and actual evapotranspiration Outputs of snow water equivalent, snow melt, and runoff, as well as the model configuration file, as well as model inputs are archived separately on the Environmental Data Initiative.
Future hydrologic outputs using the Distributed Hydrology Soil Vegetation Model (DHSVM) for the Saddle Catchment, 2001 - 2100.
The Saddle Catchment of the Niwot Ridge LTER is subject to warming in a future climate and thus changes in precipitation phase, precipitation redistribution, and timing and distribution of surface water inputs (the summation of rainfall and snowmelt) as well as changes in atmospheric demand (potential evapotranspiration, PET) and the amount of evapotranspiration (ET). The input warming data were developed to first force a future climate across the Saddle Catchment and evaluate resultant hydrologic outputs using the Distributed Hydrology Soil Vegetation Model (DHSVM). Future forcing data were generated by calculating and implementing delta values between daily average historical data and those generated from end-of-current-century Weather Research Forecasting model data. The variables perturbed in the warming DHSVM simulation were: precipitation, air temperature, relative humidity and longwave radiation. Target outputs included: daily spatially distributed precipitation (historical and future), daily spatially distributed surface water inputs (historical and future), total spatially distributed PET (historical and future), and total spatially distributed ET (historical and future). The precipitation and surface water inputs products are orthorectified (UTM projection) raster products, and the forcing data and PET and ET are CSV files. The forcing data represent catchment averages, which are distributed within DHSVM, and all other files are at the 2 m resolution.
Remote sensing based species distribution modelling based on GLCM and vegetation fractions for the city of Leipzig
<p>Modelling dataset and fractional vegetation cover dataset used in the study "Earth observation based indication for avian species distribution models using the spectral trait concept and machine learning in an urban setting" Wellmann et al. 2020.</p> <p> </p> <p>Reference:</p> <p></p> <p>Wellmann, T., Lausch, A., Scheuer, S., & Haase, D. (2020). Earth observation based indication for avian species distribution models using the spectral trait concept and machine learning in an urban setting. <em>Ecological Indicators</em>, <em>111</em>(April 2020), 106029. https://doi.org/10.1016/j.ecolind.2019.106029</p> <p></p>
Simulations from the LPJmL3.5 dynamic global vegetation model for the Vegetation Carbon Turnover Intercomparison
<p>Outputs from the LPJmL3.5 dynamic global vegetation model are provided for two global simulations. One forced by CRU-NCEP v5 climate data for the period 1901-2014 and one forced by bias-corrected IPSL-CM5A-LR RCP 8.5 climate data for the period 1901-2099. Only potential natural vegetation was simulated, i.e. no human land-use. Both simulations are at 0.5 x 0.5 degree spatial resolution. Carbon turnover fluxes for live vegetation for each individual turnover-causing process in the model were outputted individually. For a full description of the simulations, please refer to the accompanying paper.</p>
Simulations from the ORCHIDEE dynamic global vegetation model for the Vegetation Carbon Turnover Intercomparison
<p>Outputs from the ORCHIDEE dynamic global vegetation model are provided for two global simulations. One forced by CRU-NCEP v5 climate data for the period 1901-2014 and one forced by bias-corrected IPSL-CM5A-LR RCP 8.5 climate data for the period 1901-2099. Only potential natural vegetation was simulated, i.e. no human land-use. Both simulations are at 0.5 x 0.5 degree spatial resolution. Carbon turnover fluxes for live vegetation for mortality, leaf phenological turnover and fine root phenological turnover were outputted individually. For a full description of the simulations, please refer to the accompanying paper.</p>
Simulations from the JULES dynamic global vegetation model for the Vegetation Carbon Turnover Intercomparison
<p>Outputs from the JULES dynamic global vegetation model are provided for two global simulations. One forced by CRU-NCEP v5 climate data for the period 1901-2014 and one forced by bias-corrected IPSL-CM5A-LR RCP 8.5 climate data for the period 1901-2099. Only potential natural vegetation was simulated, i.e. no human land-use. Both simulations are at 1.875 x 1.25 degree spatial resolution. Carbon turnover fluxes for live vegetation for each individual turnover-causing process in the model were outputted individually. For a full description of the simulations, please refer to the accompanying paper.</p>
Simulations from the LPJ-GUESS dynamic global vegetation model v3.0 for the Vegetation Carbon Turnover Intercomparison
<p>Outputs from the LPJ-GUESS dynamic global vegetation model v3.0 are provided for two global simulations. One forced by CRU-NCEP v5 climate data for the period 1901-2014 and one forced by bias-corrected IPSL-CM5A-LR RCP 8.5 climate data for the period 1901-2099. Only potential natural vegetation was simulated, i.e. no human land-use. Both simulations are at 0.5 x 0.5 degree spatial resolution. Carbon turnover fluxes for live vegetation for mortality, leaf phenological turnover and fine root phenological turnover were outputted individually. For a full description of the simulations, please refer to the accompanying paper.</p>
Model codes and simulation data for "Modeling demographic-driven vegetation dynamics and ecosystem biogeochemical cycling in NASA GISS's Earth system model (ModelE-BiomeE v.1.0)"
<p>ModelE-BiomeE v1.0 model codes and data This folder contains the simulation data and model codes that were used in the paper ‘Modeling demographic-driven vegetation dynamics and ecosystem biogeochemical cycling in NASA GISS’s Earth system model (ModelE-BiomeE v.1.0)’ (https://doi.org/10.5194/gmd-2022-72). We included the data simulated by ModelE-BiomeE v.1.0 with settings of full demography (folder FullDemography) and single cohort (folder SingleCohort), and initial settings of land grids and vegetation data (folder GlobalVegetation). The codes include the full ModelE 2.1, module BiomeE files in ModelE, and the standalone BiomeE. In the folder FullDemography, we have 4 netcdf files for global output and 25 files for single grids output. The files ‘FullDM_2588_JAN.nc’ and ‘FullDM_2588_JUL.nc’ are the original model output of January and July in the year 2588. The file ‘FullDM_2588_Annual.nc’ is the yearly summary of model simulations. The file ‘FullDM_Selected.nc’ is an annual summary of 588 years of model simulation only with selected variables. The csv files are for single grids output at the time steps of daily and yearly. The last digit 1~8 represents the sites of 'BNC','MNT','HF','OKR','KZ','SV','WGK','TPJ', respectively (Table 1). Table 1 Site ID and file number ['BNC', 'MNT', 'HF', 'OKR', 'KZ', 'SV', 'WGK', 'TPJ'] ['8991', '8992', '8993', '8994', '8995', '8996', '8997', '8998'] ['8971', '8972', '8973', '8974', '8975', '8976', '8977', '8978'] ['8961', '8962', '8963', '8974', '8965', '8966', '8977', '8968'] Please refer to Table 2 in the paper for the detail of these 8 sites. ‘DailyLAIGPP.csv’ is a summary of all ‘DailyEcosystem’ files with LAI and GPP data. We included the Python scripts that can be used to generate the figures in out paper (Plotting-BiomeE-MsTMIP.py, Plotting-Scatter-Comparison.py, PlottingBiomeEMaps.py, and PlottingGridOutput.py). For the convenience of readers (in reproducing our figures), we included the summary of reanalysis of the data from observations and MsTMIP in folder ‘Sum-Obs-Simu’. Please refer to the original sources listed in our paper for the detail of these data.</p>
Urban Vegetation Data - Canopy Height Model (Brussels Capital Region, 2021)
<p>This GIS dataset was created for the following scientific publication, as part of the EU-funded <a href="https://coolschools.eu/">Cool Schools</a> research project (under Grant Agreement No. 101003758) : Gallez, E., Canters, F., Gadeyne, S., & Baró, F. (2024). <a href="https://www.sciencedirect.com/science/article/pii/S2212041624000846?via%3Dihub">A multi-indicator distributive justice approach to assess school-related green infrastructure benefits in Brussels - ScienceDirect</a>. Ecosystem Services, 70, 101677. https://doi.org/10.1016/j.ecoser.2024.101677. </p> <p><em>Very-High Resolution Canopy Height Model (resolution : 25cm), distinguishing between 4 vegetation types (trees, high shrubs, low shrubs and grass) in the Brussels Capital Region.</em></p> <p><em>Coordinate system : Lambert_Belge_72.</em></p> <p><em>The CHM was built on </em><em>:</em></p> <ul> <li><em>VHR aerial orthophotos (visible RGB and NIR) (“UrbIS-Ortho N-S, 2021”) for the Brussels Capital Region, of 5x5cm resolution Source: Paradigm. (2021). UrbIS-Ortho N-S. Paradigm.Brussels. <a href="https://datastore.brussels/web/data/dataset/fec72767-d6b6-41b9-a767-616df2779aae#access">https://datastore.brussels/web/urbisdownload</a>. and;</em></li> <li><em>digital terrain models (DSM and DTM) of 50x50cm, captured on 22/09/2021. Paradigm.Brussels. </em><em>Source: Paradigm. (2021). DSM / DTM. Paradigm.Brussels. <a href="https://datastore.brussels/web/data/dataset/1d7bd49d-fe83-4388-af85-6f5dc8ec7909#access">https://datastore.brussels/web/urbisdownload.</a></em></li> </ul> <p><em>Both the orthophotos and digital terrain models were resampled to a 25x25cm resolution, using a bilinear interpolation method. </em></p> <p><em>The Canopy Height Model was then created by selecting NDVI values of 0.2 and higher, - a commonly used threshold value to distinguish vegetated land from built land (Hashim et al., 2019) -, </em><em>and vegetation height thresholds of < 0.5m (for grass), 0.5 - 2m (for low shrubs), 2 - 5m (for high shrubs), and > 5m (for trees) (Derkzen et al., 2015; Sankey et al., 2018). </em><em>Green roofs were excluded.The CHM raster was then converted to polygon features. </em></p> <p><em>Classification :</em></p> <ul> <li><em>From 0 to 0.5 m (nDSM value) : gridcode 1 = </em><em>grass</em></li> <li><em>From 0.5 to 2 m (nDSM value): gridcode 2 = </em><em>low shrubs</em></li> <li><em>From 2 to 5 m (nDSM value): gridcode 3 =</em><em> high shrubs</em></li> <li><em>From 5 to 113.96 m (nDSM value): gridcode 4 = </em><em>trees</em></li> </ul>
Leaf water and stem cellulose oxygen isotope ratios simulated with global dynamic vegetation model LPX-Bern
<p>Description of leaf water and stem cellulose oxygen isotope ratios simulated with LPX-Bern</p> <p>Citation of describing paper:</p> <p>Keel SG, Joos F, Spahni R, Saurer M, Weigt RB, Klesse S. 2016. Simulating oxygen isotope ratios in tree ring cellulose using a dynamic global vegetation model, Biogeosciences, 13, 3869–3886, 2016 doi:10.5194/bg-13-3869-2016</p> <p>download: www.biogeosciences.net/13/3869/2016/</p> <p>General Information: Format: NetCDF, gridded</p> <p>Model: Dynamic global vegetation model LPX-Bern Version 1.0 (Land surface Processes and eXchanges, Bern) (Spahni et al., 2013; Stocker et al., 2013)</p> <p>Resolution: 3.75° x 2.5° lat/lon global Time: Monthly from Jan 1960 to Dec 2012</p> <p>Variables:</p> <p>cellu18: monthly stem cellulose δ18O (per mil) lw18: monthly leaf water δ18O (per mil) -2 NPP: monthly net primary production (g C m ) FPC: monthly fractional plant cover</p> <p>Dimensions: i=longitude, j=latitude, l=time, k=plant functional type Codes for plant functional types (k):</p> <ol> <li> <p>1 tropical broad-leaved evergreen</p> </li> <li> <p>2 tropical broad-leaved deciduous (raingreen)</p> </li> <li> <p>3 temperate needle-leaved evergreen</p> </li> <li> <p>4 temperate broad-leaved evergreen</p> </li> <li> <p>5 temperate broad-leaved deciduous (summergreen)</p> </li> <li> <p>6 boreal needle-leaved evergreen</p> </li> <li> <p>7 boreal needle-leaved deciduous (summergreen)</p> </li> <li> <p>8 boreal broad-leaved deciduous (summergreen)</p> </li> <li> <p>9 temperate herbaceous</p> </li> <li> <p>10 tropical herbaceous</p> </li> </ol>
Updated fruits and vegetable parameters for swat models
<p>Ensuring accurate crop yield simulations in ecohydrological models such as the Soil and Water Assessment Tool (SWAT) is crucial to improve our understanding of agricultural systems and productivity. This, in turn, can facilitate the development of more sustainable agricultural practices. In this study, we focused on validating the crop growth parameters of the SWAT model for 24 table food fruits and vegetables in Iowa, located in the western Corn Belt region of the United States.</p> <p>To estimate these parameters, we used five primary sources: a) existing parameters in the SWAT crop parameter database, b) alternative parameters in the Environmental Policy Integrated Climate (EPIC) and Agricultural Policy/Environmental eXtender (APEX) crop parameter databases, c) literature, d) PHU fraction for scheduling dates, and e) expert communication among modeling team members. Among the 24 crops tested, 15 initial parameter data sets were already available in the SWAT database, and the remaining crop types were added to this plant.dat file.</p>
Model output, drivers and parameters for Ecosystem Recovery from Disturbance is Constrained by N Cycle Openness, Vegetation-Soil N Distribution, Form of N Losses, and the Balance Between Vegetation and Soil-Microbial Processes
Files used to generate the data for figures in: Rastetter, EB, Kling, GW, Shaver, GR, Crump, BC, Gough, L. Ecosystem Recovery from Disturbance Is Constrained by N Cycle Openness, Vegetation-Soil N Distribution, Form of N Losses, and the Balance between Vegetation and Soil-Microbial Processes. Ecosystems (2020). https://doi.org/10.1007/s10021-020-00542-3. This paper present a framework for assessing biogeochemical recovery of terrestrial ecosystems from disturbance. We identify three recovery phases. In Phase 1, nitrogen is redistributed from soil organic matter to vegetation, but the ecosystem continues to lose nitrogen because the recovering vegetation cannot take up nitrogen as fast as it is released from soil. In Phase 2, the ecosystem begins re-accumulating nitrogen and converges on a quasi-steady state in which vegetation and soil-microbial processes are in balance. In Phase 3, vegetation and soil-microbial processes remain in balance and the ecosystem slowly re-accumulates the remaining nitrogen.
Water Balance Modeling Project at the Sevilleta National Wildlife Refuge, New Mexico: Vegetation Plot Data (1995-1998)
The water balance vegetation plots were part of a larger water balance monitoring project at the Sevilleta LTER. The plots were designed to measure the percent cover of photosynthetic/transpiring (green) plant species at specific sites where time domain reflectometry (TDR) probes and weather stations were already installed. In 1995, there were three sites (Field Station, Deep Well and Rio Salado). A 30m x 30m plot was installed at each site, and collection of vegetation data commenced in July 1995. Percent cover (green) and species identities were recorded monthly at a representative sample of 1m square quadrats within each plot.
Simulations from the SEIB-DGVM dynamic global vegetation model for the Vegetation Carbon Turnover Intercomparison
<p>Outputs from the SEIB-DGVM dynamic global vegetation model. One forced by CRU-NCEP v5 climate data for the period 1901-2014 and one forced by bias-corrected IPSL-CM5A-LR RCP 8.5 climate data for the period 1901-2099. Only potential natural vegetation was simulated, i.e. no human land-use. Both simulations are at 0.5 x 0.5 degree spatial resolution. Carbon turnover fluxes for live vegetation for each individual turnover-causing process in the model were outputted individually. For a full description of the simulations, please refer to the accompanying paper.</p>
Model outputs for the paper "Drastic Vegetation Change in the Guajira Peninsula (Colombia) during the Neogene" submitted to Paleoceanography and Paleoclimatology
<p>We provide the netcdf files used to plot precipitation figures over South America in the paper. Four files are provided. Three files are for "preindustrial", "no Andes" and "open Central American Seaway" experiments that were carried out with the fully coupled model IPSL-CM4 (Marti et al., 2010). One file contains the outputs from a Miocene simulation carried out with NCAR CESM in Zhou et al. (2018).</p> <p>References :</p> <p>Marti, Olivier, P. Braconnot, J.-L. Dufresne, J. Bellier, R. Benshila, S. Bony, P. Brockmann, et al. 2010. « Key Features of the IPSL Ocean Atmosphere Model and Its Sensitivity to Atmospheric Resolution ». <em>Climate Dynamics</em> 34 (1): 1‑26. https://doi.org/10.1007/s00382-009-0640-6.</p> <p>Zhou, Haoran, Brent R. Helliker, Matthew Huber, Ashley Dicks, et Erol Akçay. 2018. « C4 Photosynthesis and Climate through the Lens of Optimality ». <em>Proceedings of the National Academy of Sciences</em> 115 (47): 12057‑62. https://doi.org/10.1073/pnas.1718988115.</p> <p> </p>
Dataset for: African manatee (Trichechus senegalensis) habitat suitability at Lake Ossa, Cameroon using trophic state models and predictions of submerged aquatic vegetation
<p>See research article here: https://onlinelibrary.wiley.com/doi/epdf/10.1002/ece3.8202</p> <p>Aim: The present study aims at investigating the past and current trophic status of Lake Ossa and evaluating its potential impact on African manatee health.</p> <p>Location: Lake Ossa is known as a refuge for the threatened African manatees in Cameroon. Little information exists on the water quality and health of the ecosystem as reflected by its chemical and biological characteristics.</p> <p>Methods: Aquatic biotic and abiotic parameters including water clarity, nitrogen, phosphorous and chlorophyll concentrations were measured monthly during four months at each of 18 water sampling stations evenly distributed across the lake. These parameters were then compared with historical values obtained from the literature to examine the dynamic trophic state of Lake Ossa.</p> <p>Results: Results indicate that Lake Ossa’s trophic state parameters doubled in only three decades (from 1985 to 2016), moving from a mesotrophic to a eutrophic state. The decreasing nutrient gradient moving from the mouth of the lake (in the south) to the north indicates that the flow of the adjacent Sanaga River is the primary source of nutrient input. Further analysis suggests that the poor transparency of the lake is not associated with chlorophyll concentrations but rather with the suspended sediments brought-in by the Sanaga River. Consequently, our model demonstrated that despite nutrient enrichment, less than 5% of the lake bottom surface sustained submerged aquatic vegetation. Thus, shoreline emergent vegetation is the primary food available for the local manatee population. During the dry season, water recedes drastically and disconnects from the dominant shoreline emergent vegetation, decreasing accessibility for manatees.</p> <p>Main conclusions: The current study revealed major environmental concerns (eutrophication and sedimentation) that may negatively impact habitat quality for manatees. Efficient land use and water management across the entire watershed may be necessary to mitigate such issues.</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.