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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
Forest-wide bird survey at 183 sample sites the Andrews Experimental Forest from 2009 to present
Bird occurrence data collected at 183 sample locations within the H. J. Andrews Experimental Forest (HJA) from 2009-present. We used a stratified, systematic, random design to select sample locations. We stratified across elevation, distance to road, and habitat type (plantation or mature/old-growth forest). We conduct point counts on six separate occasions from May – July, which corresponded to spring arrival and subsequent breeding period for the majority of bird species at HJA. Surveys occur between 05:15h and 10:30h and each consists of a 10-min point count where we record all birds seen or heard. The species of all birds seen and heard are recorded as well as all individual squirrels, chipmunks and pikas seen and heard. Survey-level information is also collected at each point count and includes: weather and wind conditions, stream noise, snow cover on the ground, phenology of vine maple and rhododendron. Data collection is ongoing.
Air temperature at core phenology sites and additional bird monitoring sites in the Andrews Experimental Forest, 2009 to present
The H.J Andrews phenology study air temperature network includes 16 core phenology sites, 40 core bird sites and 128 auxiliary bird sites. This study examines air temperatures at multiple sites within the Andrews Experimental Forest. Air temperatures were recorded 1.5 m above ground at 184 sites distributed on an 800-m incomplete grid throughout much of the Andrews Forest. Data were collected using automated sensors starting in June of 2009 at 56 sites and in June 2011 128 additional sensors were added. These data document the complex spatial and temporal patterns of air temperature variation within the Andrews Forest, which is governed by multiple processes including inversions, regional air mixing, cold air drainage and pooling, and the effects of vegetation on temperature extremes. The data entities provided indicate various methods of data quality checking over time.
Above ground plant biomass and leaf area of moist acidic tussock tundra 1981 experimental site (MAT81), Arctic LTER, Toolik Lake, Alaska.1995.
Above ground plant biomass and leaf area were measured in a tussock tundra experimental site. The plots were set up in 1981 and have been harvested in previous years (See Shaver and Chapin Ecological Monographs, 61, 1991 pp.1-31).
Numbers of Eriophorum vaginatum inflorescences, both unclipped and clipped by small mammals, were counted in experimental small mammal exclosure plots, Arct LTER moist acidic tussock site, Toolik Field Station, Alaska, 1997 to 2015
Numbers of Eriophorum vaginatum inflorescences, both unclipped and clipped by small mammals, were counted in experimental plots. The plots are setup in moist acidic tussock tundra near Toolik Field Station, Alaska ((8 degrees 37' 27" N, 149 degrees 36' 27"W) and include fenced exclosures in both fertilized and unfertilized tundra.
Carbon and nitrogen content and stable isotope compositions from particulate organic matter samples from lagoon, river, and open ocean sites along the Alaska Beaufort Sea coast, 2018-ongoing
Multiple water types (river, lagoon, ocean) from the North Slope of Alaska and nearshore Beaufort Sea are sampled seasonally by the Beaufort Lagoon Ecosystems LTER (BLE LTER) Core Program to investigate biogeochemical linkages between terrestrial, lagoon, and open ocean ecosystems. Water samples are collected during full ice cover (April), ice break-up (mid-June to early July), and open water (late July and August) periods, and analyzed for particulate organic carbon (POC) and particulate organic nitrogen (PON) content and stable isotopic composition.
Sediment pigment concentrations from lagoon sites along the Alaska Beaufort Sea coast, 2018-ongoing
The Beaufort Lagoon Ecosystems Long Term Ecological Research (BLE LTER) project seasonally collects undisturbed surface sediments during full ice cover (April), ice break-up (mid-June to early July), and open water (late July and August) periods from lagoon sites along the Beaufort Sea (Elson, Simpson, Jago, and Kaktovik lagoons, plus Stefansson Sound) to quantify algal pigment concentrations. Pigments reported are chlorophyll a, pheophorbide, pheophytin, chlorophyllide, fucoxanthin, zeaxanthin, alloxanthin, and peridinin. Pigment concentrations are measured using high-precision liquid chromatography (HPLC). Concentrations are represented both as an areal basis (mg/m2 of surface sediment) and a mass basis (μg/g of dry sediment). In 2022 we found that a post-analysis mathematical error generated incorrect concentration values for pigments. This error was identified in subsequent QA/QC and immediately corrected since the raw data were not in error. The entire dataset to date (2018-2021) was revised in spring 2022 with the corrected data (revision knb-lter-ble.12.2).
Water column and sediment porewater nutrient concentrations from lagoon, river, and ocean sites along the Alaska Beaufort Sea coast, 2018-ongoing
Several water types (lagoon, river, ocean) and surface sediment porewater samples from the coastal Beaufort Sea system were sampled seasonally to investigate temporal and spatial shifts in nutrient dynamics. Surface and bottom water samples were collected in April, June, July, and August and analyzed for ammonium, nitrate + nitrite, orthophosphate, and silica.
Carbon and nitrogen content and stable isotope composition from sediment organic matter from lagoon sites along the Alaska Beaufort Sea coast, 2018-ongoing
Multiple sediment samples from lagoons along the nearshore Beaufort Sea are sampled seasonally by the Beaufort Lagoon Ecosystems LTER (BLE LTER) Core Program to investigate biogeochemical linkages between terrestrial, lagoon, and open ocean ecosystems. Sediment samples are collected during full ice cover (April), ice break-up (mid-June to early July), and open water (late July and August) periods, and analyzed for carbon and nitrogen content and stable isotopic composition.
Dissolved organic carbon (DOC) and total dissolved nitrogen (TDN) from river, lagoon, and open ocean sites along the Alaska Beaufort Sea coast, 2018-ongoing
Multiple water types (river, lagoon, ocean) from the North Slope of Alaska and nearshore Beaufort Sea are sampled seasonally by the Beaufort Lagoon Ecosystems LTER (BLE LTER) Core Program to investigate biogeochemical linkages between terrestrial, lagoon, and open ocean ecosystems. Water samples are collected during full ice cover (April), ice break-up (mid-June to early July), and open water (late July and August) periods and analyzed for dissolved organic carbon and total dissolved nitrogen content.
Sea ice thickness, snow depth, and sea ice freeboard in lagoon sites along the Alaska Beaufort Sea coast, 2019-ongoing
Physical parameters related to snow and sea ice have implications for lagoon circulation, sea-air heat exchange, and underwater light regimes. To understand these relationships and their greater effect on ecosystem function, the Beaufort Lagoon Ecosystem LTER (BLE LTER) uses in situ methods to assess snow depth, ice freeboard, and ice thickness in select water bodies across the Beaufort Sea coast (Elson Lagoon, Simpson Lagoon, Kaktovik Lagoon, Jago Lagoon, and Stefansson Sound). Sea ice thickness is the distance from sea ice bottom to top, not including snow. Freeboard, determined in the same drilled hole, is the distance from the surface of the water to the top of the ice, not including snow cover. These measurements are made annually, close to maximum ice thickness (typically April).
Free water metabolism (FWM) derived net ecosystem production (NEP) from lagoon sites along the Beaufort Sea Coast, Alaska, 2019-ongoing
Continuous measurements of dissolved oxygen (DO) in order to calculate daily values of free water metabolism (FWM) derived net ecosystem production (NEP) at several core Beaufort Lagoon Ecosystems LTER (BLE-LTER) stations. These deployments were done in addition to year-round core program BLE-LTER mooring deployments during the under ice sampling at surface and bottom depths (April/May), and during open water season in bottom depths (July/August). Gross primary production (GPP), ecosystem respiration (ER), and the sum of the two: net ecosystem production (NEP) are reported in mol C m-2 day-1.
Daily summer riverine and groundwater pCO2, dissolved oxygen, and ecosystem metabolism from sites along the Alaska Beaufort Sea coast, 2019-2022
Three riverine systems near Utqiaġvik, Alaska, are sampled regularly by the Beaufort Lagoon Ecosystems LTER (BLE LTER) Core Program in order to investigate biogeochemical linkages between terrestrial, lagoon, and open ocean ecosystems. Sampling includes one intermediate river, Avak Creek, and two smaller streams: the Mayoeak River (thermokarst stream) and a beaded stream. In 2019, 2021, and 2022, we performed additional sampling at these core program sites, with continuous monitoring of the streams between late June and August. Continuous measurements included riverine discharge, pCO₂, dissolved oxygen (DO), and ecosystem production, along with periodic grab samples of dissolved organic carbon (DOC). In 2022, there was also monitoring of groundwater wells at the two streams, capturing continuous hydraulic head and groundwater pCO₂, in addition to grab samples for DOC. Daily averages of continuous data and DOC laboratory results are presented for surface water and groundwater. This dataset is not part of the BLE Core Program and is not currently planned as an ongoing effort.
Stable oxygen isotope ratios of water (H2O-d18O) from river, lagoon, and open ocean sites along the Alaska Beaufort Sea coast, 2019-ongoing
Multiple water types (river, lagoon, ocean) from the North Slope of Alaska and nearshore Beaufort Sea are sampled seasonally by the Beaufort Lagoon Ecosystems LTER (BLE LTER) Core Program to investigate biogeochemical linkages between terrestrial, lagoon, and open ocean ecosystems. Water samples are collected during full ice cover (April), ice break-up (mid-June to early July), and open water (late July and August) periods, and analyzed for delta 18O (ratio of oxygen-18 to oxygen-16) to use in mixing models for source water contribution.
Circulation dynamics: currents, waves, temperature measurements from moorings in lagoon sites along the Alaska Beaufort Sea coast, 2018-ongoing
Starting August 2018, five moorings deployed on the seafloor of multiple lagoons in the Beaufort Sea will record currents, waves, temperature, and pressure. Moorings are retrieved and re-deployed each August. This data is being collected to better understand the multi-seasonal circulation dynamics between the Beaufort Sea and coastal lagoons. Two moorings are deployed in Elson Lagoon, one in Stefansson Sound, one in Jago Lagoon, and one in Kaktovik Lagoon. Each mooring contains two data loggers: RBRduo3 T.D wave loggers and Lowell Instruments LLC TCM-1 tilt current meters. The RBR instruments measure temperature, pressure, and derived wave energy, average wave period, average wave height, maximum wave period, maximum wave height, 1/10 wave period, 1/10 wave height, significant wave period, significant wave height, tidal slope, depth, and sea pressure. The Lowell LLC TML-1 tilt current meters measure water velocity, heading, and temperature.
Disturbance to permanent monitoring plots at GCE sites 1-10, from 2001 to 2024.
We established permanent vegetation monitoring plots in creekbank and midmarsh at GCE sites 1-10 in 2000. A dedicated Juncus zone was later added at sites 10 and 9. Starting in 2001, when we recorded plant sizes, we also noted any disturbance to the plots. Plots were scored as normal (no visible disturbance), disturbed by wrack (wrack present in plots and stems dead or broken), disturbed by snails (>100 Littoraria per square meter and plant biomass low), disturbed by pigs (animal trail through the plot, this mostly happened at site 8 mid-marsh), initial slump (plot at the creekbank sliding into the creek based on movement of pvc poles or formation of a crevasse), and terminal slump (plot had slid far enough down that vegetation had drowned). Plots that experienced terminal slump or could not be found for any reason were scored as lost. Lost plots were replaced with a new plot in the same general area with the plot code incremented by 10. For example if plot 3 was lost, it was replaced by 13, and then in turn by 23.
Sediment elevation measurements from the GCE LTER Seawater Addition Long-Term Experiment (SALTEx) sampling sites from July 2013 to August 2022
SALTEx (Seawater Addition Long-Term Experiment) is a field experiment designed to simulate saltwater intrusion in a tidal freshwater wetland to predict how chronic (Press) and acute (Pulse) salinization will affect this and other tidal freshwater ecosystems. The SALTEx experiment was initiated in 2012 and consists of 31 field plots, each 2.5 m on a side. There are three treatments (Press, Pulse, and Fresh) and two types of controls (with and without sides), each consisting of six replicates. The Press treatment plots receive regular (4 times each week) additions of a mixture of seawater and fresh river water. Pulse plots receive the same mixture of seawater and river water during September and October, which is historically a time of low flow in the river when natural saltwater intrusion occurs. The Fresh treatment plots receive regular additions of fresh river water. Treatment water is added during low tide to facilitate its infiltration into the soil, and all plots are inundated by astronomical tides at high tide. We are measuring soil surface elevation tables (SETs) in the plots as one of the response variables for the SALTEx project.
Abundance of the planthopper Prokelisia at GCE LTER sampling sites in October, 2003-2019
The abundance of the planthopper Prokelisia spp was estimated each year during the fall monitoring at each zone of each GCE site that was dominated by Spartina alterniflora. N Observations were made in the vicinity of the permanent vegetation plots and Prokelisia abundance was scored on a 5 point scale.
Annual monitoring of mid-marsh grazing scar at eight GCE LTER sampling sites from 2017 - 2025
Damage by chewing herbivores (grazing scars) was measured at eight sampling sites within the Georgia Coastal Ecosystems (GCE) LTER study area annually in July. Visual surveys were conducted along 8 2m by 10m transects randomly allocated within the mid-marsh zone at each site. Herbivore damage was measured by visually estimating the percent area of leaves missing (grazing scars) to haphazardly chosen leaves along the transect. This survey was conducted as part of the GCE invertebrate monitoring program, and will be performed annually to assess long-term changes in relative species abundances across the GCE study area. In 2024 only 3 sites were sampled due to time and weather constraints.
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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.