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.
76,402,788
datasets available to search
ShareScore release 0.7.1
Dataset results
76,402,788 results
Harmonized Palmer Drought Severity Indices throughout the Contiguous United States for HydroBASINS basins
In times of a changing hydroclimate and growing human population, there is a need to assess how various climatic and demand conditions influence water availability on the landscape. Tendency for drought conditions is a prime example of a key hydroclimatic metric that is useful for understanding water retention in the surrounding landscape. However, merging drought climatological data with co-located aquatic data is challenging. To facilitate national-scale analyses of basin-level drought conditions (i.e., Palmer Drought Severity Index; PDSI) with co-located water quality data, we present aggregated PDSI data for the contiguous United States. Data are aggregated using the HydroBASINS basin shapefiles. HYBAS_ID is retained to enable merging with HydroBASINS parent datasets.
Harmonized Soil Organic Carbon and Phosphorus Data for the Contiguous United States
Soil organic carbon (SOC) and soil phosphorus can strongly influence adjacent water quality by introducing nutrients into aquatic ecosystems and also altering the light environment of those ecosystems. However, national-scale data are uncommon, and even when available, they are usually not aggregated in a manner that is expeditiously merged with basin-level data. To facilitate national-scale analyses of soil data with co-located water quality data, we present aggregated SOC and soil phosphorus data for the Contiguous United States. Data are aggregated using the HydroBASINS basin shapefiles. HYBAS_ID is retained to enable merging with HydroBASINS parent datasets.
Harmonized National Atmospheric Deposition Products for HydroBASINS basins
Water quality is largely reflective of processes occurring on the surrounding landscape. While terrestrial inputs often strongly influence water quality, atmospheric deposition can be a significant source of allochthonous constituents to aquatic ecosystems. In the Contiguous United States, the National Atmospheric Deposition Program (NADP) has been collecting in situ atmospheric deposition of several key ions for decades. However, merging these data with co-located aquatic data is challenging. To facilitate national-scale analyses of basin-level atmospheric deposition of sulfate, ammonium, nitrate, and hydrogen with co-located water quality data, we present aggregated atmospheric deposition data for the Contiguous United States. Data are aggregated using the HydroBASINS basin shapefiles. HYBAS_ID is retained to enable merging with HydroBASINS parent datasets.
Tree seedling abundance mediated by local neighborhood effects and microsite conditions
Tree recruitment across elevational gradients is shaped by myriad factors, including abundance of nearby conspecific adults and abiotic establishment conditions. We measured seedling abundance and overstory basal area for six tree species that dominate northern temperate and montane boreal forests on mountains in the northeastern United States. Data were collected on elevational transects spaced at 100m-intervals of elevation on ten mountains. Associated environmental variables, including vegetation cover, substrate, microclimate, and light environment, were also collected.
LAGOS-US HUMAN v2: Data module of human population(1990-2020), urbanization classification, and lake access in the conterminous U.S.
The LAGOS-US HUMAN v1 data package is an extension module of the LAGOS-US research platform that includes data characterizing human population (population count, race, ethnicity, socioeconomic information), urbanization, and lake access of 479,950 lakes larger than or equal to 1 ha in the conterminous U.S. (48 states plus the District of Columbia). This data module contains four data tables linked through the unique lake identifier for the LAGOS-US research platform, lagoslakeid. Human population characteristics (race, ethnicity, and socioeconomic factors) were derived from U.S. census data for 1990, 2000, 2010, and 2020. Lakes were classified as urban or not using two different classifications: one based on the ‘Developed’ land category in the National Land Cover Dataset; and another based on the 2020 Census Urban Areas category. Metrics for lake access were developed from national datasets on public boat launches, transportation, and public lands. LAGOS-US HUMAN v1 provides a link between lake data and human contexts, facilitating interdisciplinary research in limnology, urban ecology, environmental justice, and conservation. To facilitate such studies, users are encouraged to use the other three core data modules of the LAGOS-US platform: LOCUS (location, identifiers, and physical characteristics of lakes and their watersheds); GEO (geospatial ecological context at multiple spatial and temporal scales); and LIMNO (in situ lake physical, chemical, and biological measurements through time) that are each found in their own data packages.
NRCS-USFS Soil Moisture Measurements - Fernow Experimental Forest, WV, 2022-2025
This dataset consists of soil moisture (volumetric water content and water potential), temperature, and electrical conductivity measurements at multiple depths within 20 soil pedons distributed across Watersheds 4, 5, 6, and 7 at the Fernow Experimental Forest from September 2022 to June 2025. This work is a part of a larger partnership between the U.S. Forest Service (USFS) and the Natural Resources Conservation Service (NRCS) to install, monitor and generate long-term soil moisture datasets across multiple forested watersheds in the U.S. Associated data packages from both the Coweeta Hydrologic Laboratory and Hubbard Brook Experimental Forest can be found on the EDI Data Portal. Dataset contributors: Fernow site selection and project planning conducted by Ben Rau (USFS), Ann Tan (NRCS), and James Leonard (NRCS). Megan Thomas (NRCS) and Joel Gebhard (NRCS) assisted with site installation. Site visits, data downloading, and logger maintenance was by Tyler Sharretts (USFS) and Chris Cassidy (USFS). The dataset was curated by Emily Piche (USFS, ORISE) and Amanda Pennino (NRCS). Overall partnership initiation and project management was by Stephanie Connolly (USFS) and Skye Wills (NRCS).
GRIME AI Water Segmentation Model for the USGS Platte River near Grand Island, NE, 2023-2024
Ground-based observations from fixed-mount cameras have the potential to fill an important role in environmental sensing, including direct measurement of water levels and qualitative observation of ecohydrological research sites. All of this is theoretically possible for anyone who can install a trail camera. Easy acquisition of ground-based imagery has resulted in millions of environmental images stored, some of which are public data, and many of which contain information that has yet to be used for scientific purposes. The goal of this project was to develop and document key image processing and machine learning workflows, primarily related to semi-automated image labeling, to increase the use and value of existing and emerging archives of imagery that is relevant to ecohydrological processes. This data package includes imagery, annotation files, water segmentation model and model performance plots, and model test results (overlay images and masks) for the Platte River, near Grand Island, NE, 2023-2024. All imagery was acquired from the USGS Hydrologic Imagery Visualization and Information System (HIVIS; see https://apps.usgs.gov/hivis/camera/NE_Platte_River_near_Grand_Island for this specific data set) and/or the National Imagery Management System (NIMS) API. Water segmentation models were created by tuning the open-source Segment Anything Model 2 (SAM2, https://github.com/facebookresearch/sam2) using images that were annotated by team members on this project. The models were trained on the "water" annotations, but annotation files may include additional labels, such as "snow", "sky", and "unknown". Image annotation was done in Computer Vision Annotation Tool (CVAT) and exported in COCO format (.json). All model training and testing was completed in GaugeCam Remote Image Manager Educational Artificial Intelligence (GRIME AI, https://gaugecam.org/) software (Version: Beta 16). Model performance plots were automatically generated during this process. This project w
GRIME AI Water Segmentation Model for the USGS Monitoring Site Discovery Farms Waterway AO1 Near Antigo, WI, 2023-2024
Ground-based observations from fixed-mount cameras have the potential to fill an important role in environmental sensing, including direct measurement of water levels and qualitative observation of ecohydrological research sites. All of this is theoretically possible for anyone who can install a trail camera. Easy acquisition of ground-based imagery has resulted in millions of environmental images stored, some of which are public data, and many of which contain information that has yet to be used for scientific purposes. The goal of this project was to develop and document key image processing and machine learning workflows, primarily related to semi-automated image labeling, to increase the use and value of existing and emerging archives of imagery that is relevant to ecohydrological processes. This data package includes imagery, annotation files, water segmentation model and model performance plots, and model test results (overlay images and masks) for the USGS monitoring location Discovery Farms Waterway AO1 Near Antigo, WI (2023-2024). All imagery was acquired from the USGS Hydrologic Imagery Visualization and Information System (HIVIS; see https://apps.usgs.gov/hivis/camera/WI_AO1_STAFF for this specific data set) and/or the National Imagery Management System (NIMS) API. Water segmentation models were created by tuning the open-source Segment Anything Model 2 (SAM2, https://github.com/facebookresearch/sam2) using images that were annotated by team members on this project. The models were trained on the "water" annotations, but annotation files may include additional labels, such as "snow", "sky", and "unknown". Image annotation was done in Computer Vision Annotation Tool (CVAT) and exported in COCO format (.json). All model training and testing was completed in GaugeCam Remote Image Manager Educational Artificial Intelligence (GRIME AI, https://gaugecam.org/) software (Version: Beta 16). Model performance plots were automatically generated during this process.
GRIME AI Water Segmentation Model for the USGS Monitoring Site Beggars Bridge Creek Near Dawley Corners, VA, 2023-2024
Ground-based observations from fixed-mount cameras have the potential to fill an important role in environmental sensing, including direct measurement of water levels and qualitative observation of ecohydrological research sites. All of this is theoretically possible for anyone who can install a trail camera. Easy acquisition of ground-based imagery has resulted in millions of environmental images stored, some of which are public data, and many of which contain information that has yet to be used for scientific purposes. The goal of this project was to develop and document key image processing and machine learning workflows, primarily related to semi-automated image labeling, to increase the use and value of existing and emerging archives of imagery that is relevant to ecohydrological processes. This data package includes imagery, annotation files, water segmentation model and model performance plots, and model test results (overlay images and masks) for the USGS Monitoring Site Beggars Bridge Creek Near Dawley Corners, VA, 2023-2024. All imagery was acquired from the USGS Hydrologic Imagery Visualization and Information System (HIVIS; see https://apps.usgs.gov/hivis/camera/VA_Beggars_Cr_nr_Dawley_Corners_RSIE for this specific data set) and/or the National Imagery Management System (NIMS) API. Water segmentation models were created by tuning the open-source Segment Anything Model 2 (SAM2, https://github.com/facebookresearch/sam2) using images that were annotated by team members on this project. The models were trained on the "water" annotations, but annotation files may include additional labels, such as "snow", "sky", and "unknown". Image annotation was done in Computer Vision Annotation Tool (CVAT) and exported in COCO format (.json). All model training and testing was completed in GaugeCam Remote Image Manager Educational Artificial Intelligence (GRIME AI, https://gaugecam.org/) software (Version: Beta 16). Model performance plots were automatically generated du
GRIME AI Water Segmentation Model for the USGS Monitoring Site Chippewa River at Grand Ave at Eau Claire, WI, 2023-2024
Ground-based observations from fixed-mount cameras have the potential to fill an important role in environmental sensing, including direct measurement of water levels and qualitative observation of ecohydrological research sites. All of this is theoretically possible for anyone who can install a trail camera. Easy acquisition of ground-based imagery has resulted in millions of environmental images stored, some of which are public data, and many of which contain information that has yet to be used for scientific purposes. The goal of this project was to develop and document key image processing and machine learning workflows, primarily related to semi-automated image labeling, to increase the use and value of existing and emerging archives of imagery that is relevant to ecohydrological processes. This data package includes imagery, annotation files, water segmentation model and model performance plots, and model test results (overlay images and masks) for the USGS Monitoring Site Chippewa River at Grand Ave at Eau Claire, WI, 2023-2024. All imagery was acquired from the USGS Hydrologic Imagery Visualization and Information System (HIVIS; see https://apps.usgs.gov/hivis/camera/WI_Chippewa_River_at_Grand_Ave_at_Eau_Claire for this specific data set) and/or the National Imagery Management System (NIMS) API. Water segmentation models were created by tuning the open-source Segment Anything Model 2 (SAM2, https://github.com/facebookresearch/sam2) using images that were annotated by team members on this project. The models were trained on the "water" annotations, but annotation files may include additional labels, such as "snow", "sky", and "unknown". Image annotation was done in Computer Vision Annotation Tool (CVAT) and exported in COCO format (.json). All model training and testing was completed in GaugeCam Remote Image Manager Educational Artificial Intelligence (GRIME AI, https://gaugecam.org/) software (Version: Beta 16). Model performance plots were automatically gen
GRIME AI Water Segmentation Model for the USGS Monitoring Site East Branch Brandywine Creek below Downingtown, PA, 2023-2024
Ground-based observations from fixed-mount cameras have the potential to fill an important role in environmental sensing, including direct measurement of water levels and qualitative observation of ecohydrological research sites. All of this is theoretically possible for anyone who can install a trail camera. Easy acquisition of ground-based imagery has resulted in millions of environmental images stored, some of which are public data, and many of which contain information that has yet to be used for scientific purposes. The goal of this project was to develop and document key image processing and machine learning workflows, primarily related to semi-automated image labeling, to increase the use and value of existing and emerging archives of imagery that is relevant to ecohydrological processes. This data package includes imagery, annotation files, water segmentation model and model performance plots, and model test results (overlay images and masks) for USGS Monitoring Site East Branch Brandywine Creek below Downingtown, PA. All imagery was acquired from the USGS Hydrologic Imagery Visualization and Information System (HIVIS; see https://apps.usgs.gov/hivis/camera/PA_East_Branch_Brandywine_Creek_below_Downingtown for this specific data set) and/or the National Imagery Management System (NIMS) API. Water segmentation models were created by tuning the open-source Segment Anything Model 2 (SAM2, https://github.com/facebookresearch/sam2) using images that were annotated by team members on this project. The models were trained on the "water" annotations, but annotation files may include additional labels, such as "snow", "sky", and "unknown". Image annotation was done in Computer Vision Annotation Tool (CVAT) and exported in COCO format (.json). All model training and testing was completed in GaugeCam Remote Image Manager Educational Artificial Intelligence (GRIME AI, https://gaugecam.org/) software (Version: Beta 16). Model performance plots were automatically generate
GRIME AI Water Segmentation Model for the USGS Monitoring Site at East River at County Trunk HWY ZZ near Greenleaf, WI, 2023-2024
Ground-based observations from fixed-mount cameras have the potential to fill an important role in environmental sensing, including direct measurement of water levels and qualitative observation of ecohydrological research sites. All of this is theoretically possible for anyone who can install a trail camera. Easy acquisition of ground-based imagery has resulted in millions of environmental images stored, some of which are public data, and many of which contain information that has yet to be used for scientific purposes. The goal of this project was to develop and document key image processing and machine learning workflows, primarily related to semi-automated image labeling, to increase the use and value of existing and emerging archives of imagery that is relevant to ecohydrological processes. This data package includes imagery, annotation files, water segmentation model and model performance plots, and model test results (overlay images and masks) for USGS Monitoring Site at East River at County Trunk HWY ZZ near Greenleaf, WI. All imagery was acquired from the USGS Hydrologic Imagery Visualization and Information System (HIVIS; see https://apps.usgs.gov/hivis/camera/WI_East_River_at_HWY_ZZ_near_Greenleaf for this specific data set) and/or the National Imagery Management System (NIMS) API. Water segmentation models were created by tuning the open-source Segment Anything Model 2 (SAM2, https://github.com/facebookresearch/sam2) using images that were annotated by team members on this project. The models were trained on the "water" annotations, but annotation files may include additional labels, such as "snow", "sky", and "unknown". Image annotation was done in Computer Vision Annotation Tool (CVAT) and exported in COCO format (.json). All model training and testing was completed in GaugeCam Remote Image Manager Educational Artificial Intelligence (GRIME AI, https://gaugecam.org/) software (Version: Beta 16). Model performance plots were automatically generated duri
GRIME AI Water Segmentation Model for the USGS Monitoring Site at Kearney Outdoor Learning Area, NE, 2024-2024
Ground-based observations from fixed-mount cameras have the potential to fill an important role in environmental sensing, including direct measurement of water levels and qualitative observation of ecohydrological research sites. All of this is theoretically possible for anyone who can install a trail camera. Easy acquisition of ground-based imagery has resulted in millions of environmental images stored, some of which are public data, and many of which contain information that has yet to be used for scientific purposes. The goal of this project was to develop and document key image processing and machine learning workflows, primarily related to semi-automated image labeling, to increase the use and value of existing and emerging archives of imagery that is relevant to ecohydrological processes. This data package includes imagery, annotation files, water segmentation model and model performance plots, and model test results (overlay images and masks) for the USGS Monitoring Site at Kearney Outdoor Learning Area, NE, 2024-2024. All imagery was acquired from the USGS Hydrologic Imagery Visualization and Information System (HIVIS; see https://apps.usgs.gov/hivis/camera/NE_Kearney_Outdoor_Learning_Area for this specific data set) and/or the National Imagery Management System (NIMS) API. Water segmentation models were created by tuning the open-source Segment Anything Model 2 (SAM2, https://github.com/facebookresearch/sam2) using images that were annotated by team members on this project. The models were trained on the "water" annotations, but annotation files may include additional labels, such as "snow", "sky", and "unknown". Image annotation was done in Computer Vision Annotation Tool (CVAT) and exported in COCO format (.json). All model training and testing was completed in GaugeCam Remote Image Manager Educational Artificial Intelligence (GRIME AI, https://gaugecam.org/) software (Version: Beta 16). Model performance plots were automatically generated during this pr
GRIME AI Water Segmentation Model for the USGS Monitoring Site at Missouri River at Hermann, MO, 2022-2024
Ground-based observations from fixed-mount cameras have the potential to fill an important role in environmental sensing, including direct measurement of water levels and qualitative observation of ecohydrological research sites. All of this is theoretically possible for anyone who can install a trail camera. Easy acquisition of ground-based imagery has resulted in millions of environmental images stored, some of which are public data, and many of which contain information that has yet to be used for scientific purposes. The goal of this project was to develop and document key image processing and machine learning workflows, primarily related to semi-automated image labeling, to increase the use and value of existing and emerging archives of imagery that is relevant to ecohydrological processes. This data package includes imagery, annotation files, water segmentation model and model performance plots, and model test results (overlay images and masks) for the USGS Monitoring Site at Missouri River at Hermann, MO, 2022-2024. All imagery was acquired from the USGS Hydrologic Imagery Visualization and Information System (HIVIS; see https://apps.usgs.gov/hivis/camera/MO_Missouri_River_at_Hermann for this specific data set) and/or the National Imagery Management System (NIMS) API. Water segmentation models were created by tuning the open-source Segment Anything Model 2 (SAM2, https://github.com/facebookresearch/sam2) using images that were annotated by team members on this project. The models were trained on the "water" annotations, but annotation files may include additional labels, such as "snow", "sky", and "unknown".Image annotation was done in Computer Vision Annotation Tool (CVAT) and exported in COCO format (.json). All model training and testing was completed in GaugeCam Remote Image Manager Educational Artificial Intelligence (GRIME AI, https://gaugecam.org/) software (Version: Beta 16). Model performance plots were automatically generated during this process. Th
GRIME AI Water Segmentation Model for the USGS Monitoring Site at Pecos River near Acme, NM, 2022-2024
Ground-based observations from fixed-mount cameras have the potential to fill an important role in environmental sensing, including direct measurement of water levels and qualitative observation of ecohydrological research sites. All of this is theoretically possible for anyone who can install a trail camera. Easy acquisition of ground-based imagery has resulted in millions of environmental images stored, some of which are public data, and many of which contain information that has yet to be used for scientific purposes. The goal of this project was to develop and document key image processing and machine learning workflows, primarily related to semi-automated image labeling, to increase the use and value of existing and emerging archives of imagery that is relevant to ecohydrological processes. This data package includes imagery, annotation files, water segmentation model and model performance plots, and model test results (overlay images and masks) USGS Monitoring Site at Pecos River near Acme, NM, 2022-2024. All imagery was acquired from the USGS Hydrologic Imagery Visualization and Information System (HIVIS; see https://apps.usgs.gov/hivis/camera/NM_Pecos_River_near_Acme for this specific data set) and/or the National Imagery Management System (NIMS) API. Water segmentation models were created by tuning the open-source Segment Anything Model 2 (SAM2, https://github.com/facebookresearch/sam2) using images that were annotated by team members on this project. The models were trained on the "water" annotations, but annotation files may include additional labels, such as "snow", "sky", and "unknown". Image annotation was done in Computer Vision Annotation Tool (CVAT) and exported in COCO format (.json). All model training and testing was completed in GaugeCam Remote Image Manager Educational Artificial Intelligence (GRIME AI, https://gaugecam.org/) software (Version: Beta 16). Model performance plots were automatically generated during this process. This project was
GRIME AI Water Segmentation Model for the USGS Monitoring Site at Rio Grande below Elephant Butte Dam, NM, 2023-2024
Ground-based observations from fixed-mount cameras have the potential to fill an important role in environmental sensing, including direct measurement of water levels and qualitative observation of ecohydrological research sites. All of this is theoretically possible for anyone who can install a trail camera. Easy acquisition of ground-based imagery has resulted in millions of environmental images stored, some of which are public data, and many of which contain information that has yet to be used for scientific purposes. The goal of this project was to develop and document key image processing and machine learning workflows, primarily related to semi-automated image labeling, to increase the use and value of existing and emerging archives of imagery that is relevant to ecohydrological processes. This data package includes imagery, annotation files, water segmentation model and model performance plots, and model test results (overlay images and masks) for the USGS Monitoring Site at Rio Grande below Elephant Butte Dam, NM, 2023-2024. All imagery was acquired from the USGS Hydrologic Imagery Visualization and Information System (HIVIS; see https://apps.usgs.gov/hivis/camera/NM_Rio_Grande_below_Elephant_Butte_Dam for this specific data set) and/or the National Imagery Management System (NIMS) API. Water segmentation models were created by tuning the open-source Segment Anything Model 2 (SAM2, https://github.com/facebookresearch/sam2) using images that were annotated by team members on this project. The models were trained on the "water" annotations, but annotation files may include additional labels, such as "snow", "sky", and "unknown". Image annotation was done in Computer Vision Annotation Tool (CVAT) and exported in COCO format (.json). All model training and testing was completed in GaugeCam Remote Image Manager Educational Artificial Intelligence (GRIME AI, https://gaugecam.org/) software (Version: Beta 16). Model performance plots were automatically generated du
Interagency Ecological Program: Integrated Dataset of Phytoplankton Enumeration Data in the San Francisco Estuary, 1992-2024
Phytoplankton community composition is an important driver of zooplankton productivity and food supply for higher trophic levels in the San Francisco Estuary. Various monitoring surveys throughout the region collect phytoplankton enumeration data dating back to the 1990s. These include surveys from the CA Department of Water Resources (CADWR), CA Department of Fish and Wildlife (CDFW), the US Bureau of Reclamation (USBR), and the US Geological Survey (USGS). These surveys collect data via various sampling and laboratory methods which are not always directly comparable. This integrated dataset includes both enumeration counts and well-documented metadata to allow for informed decision-making in the integration of these data. It also standardizes taxonomic names between groups via a key list. Note that, in this dataset, we make conservative decisions about taxonomic resolution to ensure maximum compatibility between groups. For more detailed metadata and higher taxonomic resolution, refer to individual surveys’ publications or reach out to their primary contact.
Desert Fertilization Experiment: investigation of Sonoran desert ecosystem response to atmospheric deposition and experimental nutrient addition, ongoing since 2006 (Reformatted to the ecocomDP Design Pattern)
This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-cap/632/9. The abstract below was extracted from the Level 0 data package and is included for context:
Interagency Ecological Program Zooplankton Study
The Zooplankton Study was implemented in 1972 to assess food resources available to fish in the upper San Francisco Estuary (SFE). Mandated by the State Water Resources Control Board's Water Right Decision D-1641, the study is conducted jointly by the California Department of Fish and Wildlife and the California Department of Water Resources under the guidance and management of the Interagency Ecological Program. For over five decades, this study has monitored the zooplankton community in the region, tracking abundance trends and distributional patterns, detecting and monitoring introduced species, and documenting changes in community composition. The Zooplankton Study uses three gear types to sample macro-, meso-, and micro-zooplankton across the upper SFE on a monthly basis.
Projected Snow Cover Reductions and Mid-latitude Cyclone Responses in the North American Great Plains, 1986 - 2005
Extratropical cyclones are responsible for major weather events and trends in the mid-latitudes and preferentially develop in regions of enhanced cyclogenesis and proceed along climatological storm tracks. It has been shown that terrestrial snow cover exerts considerable influence on atmospheric baroclinicity which is largely responsible for the aforementioned cyclogeneses and storm tracks. Research about the effect which terrestrial snow cover exerts on cyclones' intensities, trajectories, and precipitation characteristics is limited but indicates a robust relationship with these factors. Many examinations of climate model projections have generally shown a poleward shift in storm tracks by the late 21st century though none have determined the degree to which the coincident poleward shift in snow extent is responsible. A method of imposing 10th, 50th, and 90th percentile values of snow retreat between the late 20th and 21st centuries as projected by 14 models of the Coupled Model Intercomparison Project Phase Five (CMIP5) is used to alter 20 historical cold season cyclones which tracked over or adjacent to the North American Great Plains. Simulations by the Advanced Research version of the Weather Research and Forecast Model (WRF-ARW) are initialized at 0 to 4 days prior to cyclogenesis. Including control and sensitivity testing wherein snow is unaltered or removed entirely, each cyclone case is simulated 25 times for a total of 500 simulations.
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.