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Forest-wide bird survey at 183 sample sites the Andrews Experimental Forest from 2009 to present (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-and/4781/5. The abstract below was extracted from the Level 0 data package and is included for context: 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. The H.J. Andrews Experimental Forest is a living laboratory that provides unparalleled opportunities for the study of forest and stream ecosystems in the central Cascade Range of Oregon. Since 1980, as a part of the National Science Foundation Long Term Ecological Research (NSF-LTER) program, the Andrews Experimental Forest has become a leader in the analysis of forest and stream ecosystem dynamics. Long-term field experiments and measurement programs have focused on climate dynamics, streamflow, water quality, and vegetation succession. Currently researchers are working to develop concepts and tools needed to predict effects of natural disturba
Long-term composited and land cover-adjusted Enhanced Normalized Difference Impervious Surface Index (ENDISI) for the greater Phoenix, Arizona, USA, metropolitan area and the surrounding Sonoran desert derived from annual and seasonal Landsat imagery, 1998 to 2020
This data package consists of multiple decades of Enhanced Normalized Difference Impervious Surface Index (ENDISI) raster data across the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) study area within metropolitan Phoenix, Arizona, USA, temporally aggregated by year and by four meteorological seasons (winter, spring, summer, fall). To serve as a proxy measurement of impervious surface and urbanization across years and seasons, we derived values of ENDISI – following the methods of Chen et al. 2019 from annual and seasonal composites of 30-m resolution Landsat 5-9 Level-2 Surface Reflectance imagery. Next, we corrected the underestimated ENDISI values of dark impervious surface cover and the overestimated ENDISI values of bright bare soils based on visible Landsat bands and 2020 land cover (Sabu et al. 2023). Finally, we exported images as individual GeoTIFF raster files, each with five bands corresponding values summarized annually (band 1) and seasonally (bands 2-5). All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season and the calculation of the spectral index, can be found in the data package metadata (see 'Methods and Protocols') and accompanying Javascript code. ### citations - Gorelick N, Hancher M, Dixon M, et al. (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18–27. https://doi.org/10.1016/j.rse.2017.06.031 - Sabu, S., Frazier, A., & Rashid, B. (2023). Land use and land cover (LULC) classification of the CAP LTER study area (central Arizona, USA) using Landsat imagery: 2015 and 2020 [Dataset]. Environmental Data Initiative. https://doi.org/10.6073/PASTA/BF18E5856215BD2D4DAB3B024BA87A7E
Long-term composited Enhanced Normalized Difference Impervious Surface Index (ENDISI) for the greater Phoenix, Arizona, USA, metropolitan area and the surrounding Sonoran desert derived from annual and seasonal Landsat imagery, 1998 to 2023
This data package consists of multiple decades of Enhanced Normalized Difference Impervious Surface Index (ENDISI) raster data across the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) study area within metropolitan Phoenix, Arizona, USA, temporally aggregated by year and by four meteorological seasons (winter, spring, summer, fall). To serve as a proxy measurement of impervious surface and urbanization across years and seasons, we derived values of ENDISI – following the methods of Chen et al. 2019 – from annual and seasonal composites of 30-m resolution Landsat 5-9 Level-2 Surface Reflectance imagery. Finally, we exported images as individual GeoTIFF raster files, each with five bands corresponding values summarized annually (band 1) and seasonally (bands 2-5). All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season and the calculation of the spectral index, can be found in the data package metadata (see 'Methods and Protocols') and accompanying Javascript code. ### citations - Gorelick N, Hancher M, Dixon M, et al. (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18–27. https://doi.org/10.1016/j.rse.2017.06.031
Recruitment data from 1997 to 2021 for mussels, barnacles and rockweeds from an LTREB project in the Gulf of Maine, USA
Experimental clearings in macroalgal (Ascophyllum nodosum) stands were made in 1996 to determine if mussel beds and macroalgal stands on protected intertidal shores in New England represent alternative community states. Uncleared control plots and four sizes of circular clearings (1m, 2m, 4m and 8m in diameter), which mimicked ice scour events, were established in A. nodosum stands at 12 sites on Swan’s Island, Maine, USA. The purpose of these datasets is to provide access to data on recruitment of mussels, barnacles and fucoid seaweeds in the 60 experimental plots from 1997 to 2021. Earlier versions of the data prior to 2013 can be found in Ecological Archives (E090-039 and E096-274). This EDI version includes corrections of errors in the versions in Ecological Archives. Research was funded by NSF's LTREB program.
GRIME AI Water Segmentation Model for the USGS Lake Serene at Edgewood Camera Monitoring Site, MD, 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). All imagery was acquired from the USGS Hydrologic Imagery Visualization and Information System (HIVIS; see https://apps.usgs.gov/hivis/camera/MD_Lake_Serene_at_Edgewood 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) software (Version: Beta 16). Model performance plots were automatically generated during this process. This project was conducted in 2023-2025 by collaborators at the University of Nebraska-Lincoln, Uni
San Francisco Estuary Institute Phytoplankton eDNA and Toxin Monitoring, San Francisco Bay, CA, 2014-2023
From late 2014-present, San Francisco Estuary Institute has conducted molecular monitoring of phytoplankton in partnership with the US Geological Survey from several stations stretching from Rio Vista in the North Delta to the Lower South Bay, past the Dumbarton Bridge. These samples have been analyzed for 18S eDNA data in partnership with Timothy Otten at Bend Genetics, targeting the V7 region. The included dataset covers the 2014-2023 period, including data up to 2022, conducted with an Illumina MISEQ platform, and from 2022-2023 using a NEXTSEQ platform, which enabled much greater sampling depth. Two sets of taxonomic assignments are included in this data release, one using the Silva database, and the other using the PR2 database, two of the leading sources of phytoplankton taxonomic data aiding in assignments of OTUs to taxa. Raw sequence data will also be uploaded to NCBI for comparison. Data collection continues and this release will be updated as new time periods are added.
Survey of Pacific Northwest public land managers science and values project, 2023
This dataset records survey data about public land managers who work in Oregon and Washington (Forest Service, Bureau of Land Management, Fish and Wildlife Service, National Park Service, Oregon Department of Forestry, Washington Department of Natural Resources). Data was collected in 2023 via the online survey platform Qualtrics. Data collection is complete. The dataset includes measures of managers beliefs about 1) variable density thinning of mature growth forests, 2) salvage logging of burned areas, 3) translocation of plant species from hotter and drier seed zones to adapt to climate change. It includes how managers evaluate the usefulness of scientific evidence and the soundness of action prescriptions for each of the three management issues Respondents were randomly assigned to either receive long-term or short-term studies, and positive or negative results. The dataset includes measures of sense of belonging (how much managers believe they belong at their workplace) and measures of public support/public threat (how much they believe the public understands and supports the actions they take on the landscape). The dataset includes respondent agency.
Water column chlorophyll concentrations from lagoon, river, and 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 from multiple depths are collected during full ice cover (April), ice break-up (mid-June to early July), and open water (late July and August) periods for quantification of chlorophyll-a concentrations. Concentrations are reported in micrograms of chlorophyll per liter of filtered seawater (μg/L).
Colored dissolved organic matter (CDOM) absorbance from lagoon, ocean, and river sites along the Alaska Beaufort Sea coast, 2021-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 from multiple depths are collected during full ice cover (April), ice break-up (mid-June to early July), and open water (late July and August) periods, filtered, and analyzed for light absorption spectra within 24 hours of collection. Wavelength-specific light absorption coefficients are reported between 250 and 600 nanometers. The data is organized in "long" or "tidy" format, with columns of station, date, wavelength, and absorption. Please see included MakeColumnsAsWavelengths.R, MakeColumnsAsDates.R, and ReshapeDataInExcel.txt for some common ways to reorganize the table for further CDOM analysis. In 2022, data from 2019 were removed due to quality issues (please see revision 1 of this dataset for 2019 CDOM values). For users who may have used 2019 data from this dataset, there are additional files to inform decisions going forward. "BLE_LTER_CDOM_2019_sample_flags.csv" lists which 2019 samples are entirely unreliable, versus usable with caution. "BLE_LTER_CDOM_2021_blank_mean_sd_absorptions.csv" lists mean and standard deviations at each wavelength from all blanks taken in 2021; this is meant to give info on the instruments used and will not be updated further. Please see the methods section for more information on 2019 data.
Spot and continuous seasonal pCO2 sensor measurements from lagoon and river sites along the Alaska Beaufort Sea coast, 2019-ongoing
pCO2 (partial pressure of carbon dioxide) was measured in multiple seasons and depths in coastal ecosystems as part of the Beaufort Lagoons Ecosystems LTER core sampling program. Sites include several Beaufort Lagoons from across the North Slope of Alaska and several river sites near the lagoons. Data includes averages of sensor data for each site and depth deployment of pCO2 (uatm), CO2 (ppm), water temperature (°C), and absolute pressure (kPa). Standard deviation is included for pCO2 (uatm). Surface measurements were taken either 30 cm below the water surface, or bottom of the ice in ice-cover season, and bottom measurements were taken 30 cm from the seafloor of the lagoons. For lagoon sites, data were logged every one or five minutes; for river sites data were recorded every five minutes. Each site and depth was recorded for at least 30 minutes and up to four hours and taken as spot measurement.
Carbon and nitrogen content and stable isotope compositions from biota samples from lagoon sites along the Alaska Beaufort Sea coast, 2018-ongoing
Stable isotopic composition can be used to differentiate between predominantly marine and terrestrial food sources in nearshore marine food webs. The Beaufort Lagoon Ecosystems LTER (BLE LTER) Core Program employs spatial and temporal sampling regimes to track shifts in diet, both seasonally (ice cover, ice break-up, and open water), and spatially among lagoons along the Alaskan Beaufort Sea coast. Ice coring, net tows, ponar grabs, and trawls are employed to collect organisms associated with the sea-ice interface, the water column, and the benthos respectively. Tissues are analyzed for stable isotopic composition as well as organic carbon and nitrogen content.
Ice core, auger hole, conductivity, and shapefile data to determine bottomfast sea ice extent from lagoon sites along the Beaufort Sea Coast, Alaska, 2017-2021
The shapefile represents bottomfast sea ice (BSI) extent in lagoons along the Alaska Beaufort Sea coast during winter and spring, 2017-2021. It was created by digitizing extents from interferograms from the Alaska Satellite Facility Vertex portal. The result is used to identify BSI lateral extent in Arctic lagoons during the growth cycle seasonally. Comparing to future interferograms will identify the trend of BSI within Arctic lagoons. Each feature is attributed with applicable date range and area. Accurate data for the initial growth and maximum extent of BSI could only be collected for the winter and spring months. After the last collection in the spring, there is likely still BSI; however, the surface processes that take place after this point prevent further readings. For early winter time periods, if there are interferograms available (2017 and 2018 data had gaps in interferogram collection as Sentinel-1 was still new), the first date collected can be considered the onset of BSI formation. Ice cores are collected using a Snow, Ice, and Permafrost Research Establishment (SIPRE) corer and measured for salinity. The data is logged in Excel format following Seasonal Ice Zone Observing Network (SIZONet) practices, making it compatible with the PySIC Python toolkit for analysis. The auger data identifies key measurements collected from in-situ observations. Data are collected along five surveys and saved as a single CSV file. The data represent a 1-D representation of each auger hole. The data are used to verify satellite interpretations of BSI extent. The apparent conductivity data includes values at three frequencies (1000 Hz, 4000 Hz, 16000 Hz) recorded during the spring of 2021 in Western Elson Lagoon. Data are saved as an EMI file, which is a CSV format with specific column names and header information. MATLAB scripts to read and interpret data are included in this data package. The apparent conductivity values are used to identify the boundary between floating
Physiochemical water column parameters and hydrographic time series from river, lagoon, and open ocean sites along the Alaska Beaufort Sea coast, 2018-ongoing
To understand circulation and seasonality as part of the Beaufort Lagoon Ecosystem Long Term Ecological Research program, temperature, conductivity, salinity, pressure, depth, and current velocity are recorded hourly in situ, starting August 2018 in lagoons across the Beaufort Sea coast (Elson Lagoon, Kaktovik Lagoon, and Jago Lagoon). Moorings include combinations of 1) RBR Concerto CTDs with temperature, conductivity, and pressure sensors; 2) StarOddi CTs with temperature and conductivity sensors; and 3) Lowell TCM-1 Tilt Current meters with MAT-1 Data Loggers for velocity and bearing. In addition, during BLE LTER's annual sampling, water column physiochemical parameters (chlorophyll a, dissolved oxygen, phycoerythrin concentration, pH, temperature, conductivity, salinity) are measured by hand with a YSI data sonde at river, lagoon, and open ocean sites along the Beaufort Sea coast. Here we provide both quality controlled in situ mooring data and all YSI sonde data.
Total suspended solids from river, lagoon, and open ocean sites along the Alaska Beaufort Sea coast, 2022-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 from multiple depths are collected during full ice cover (April), ice break-up (mid-June to early July), and open water (late July and August) periods for quantification of total suspended solids and total organic suspended solids. Concentrations are reported in milligrams per liter of filtered seawater (mg/L).
Long-term monitoring of ground-dwelling arthropods in the greater Phoenix metropolitan area of central Arizona, USA (1998-2025)
The Central Arizona–Phoenix Long-Term Ecological Research (CAP LTER) program has been monitoring ground-dwelling arthropods (e.g., insects, ararchnids) at locations throughout the greater Phoenix metropolitan area (GPMA) and surrounding Sonoran desert region since 1998. Monitoring locations span a diversity of habitat types, including mesic and xeric residential yards, commercial areas, agricultural fields, desert locations within the GPMA (desert remnant), and undisturbed desert locations. Organisms are collected quarterly using unbaited pitfall traps, typically ten per location but with some variation, exposed for approximately seventy-two hours. Organisms are identified to the lowest practical taxonomic level and enumerated. Many of the sampling locations established at the beginning of the monitoring project were relocated in 2001-2002 to overlap with the CAP LTER's Ecological Survey of Central Arizona (ESCA; formerly named Survey200) long-term monitoring sites, although within the same general landscape categories.
Desert Fertilization Experiment: investigation of Sonoran desert ecosystem response to atmospheric deposition and experimental nutrient addition, ongoing since 2006
Launched in 2006 with support from the National Science Foundation (NSF) and leveraged by the CAP LTER, the Carbon and Nitrogen deposition (CNdep) project sought to answer the fundamental question of whether elemental cycles in urban ecosystems are qualitatively different from those in non-urban ecosystems. Ecosystem scientists, atmospheric chemists, and biogeochemists tested the hypothesis that distinct biogeochemical pathways result from elevated inorganic nitrogen and organic carbon deposition from the atmosphere to the land. To test the hypothesis, scientists examined the responsiveness of Sonoran desert ecosystems to nutrient enrichment by capitalizing on a gradient of atmospheric deposition in and around the greater Phoenix metropolitan area. Fifteen desert study sites were established, with five locations each west and east of the urban core, and in the urban core in desert preserves. In addition to the gradient of atmospheric deposition in and around the urban core, select study plots at each of the fifteen desert locations receive amendments of nitrogen, phosphorus, or nitrogen + phosphorus fertilizer. Measured variables include soil properties, perennial and annual plant growth, and atmospheric deposition of nitrogen. At the close of the initial grant period, the CAP LTER assumed responsibility for the project, renamed the Desert Fertilization Experiment, which provides a remarkable platform to study the long-term effects of nutrient enrichment on desert ecosystem properties.
Long-term composited Normalized Difference Vegetation Index (NDVI) for the greater Phoenix, Arizona, USA, metropolitan area and the surrounding Sonoran desert derived from annual and seasonal Landsat imagery, 1998 to 2023
### overview This data package consists of multiple decades of normalized difference vegetation index (NDVI) raster data across the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) study area within metropolitan Phoenix, Arizona (USA), temporally aggregated by year and by four meteorological seasons (Winter, Spring, Summer, Fall). To serve as a proxy measurement of vegetation greenness and productivity across years and seasons, NDVI was derived from annual and seasonal composites of 30-m resolution Landsat 5-9 Level-2 Surface Reflectance imagery. All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season and the calculation of the spectral index, can be found in the data package metadata (see 'Methods and Protocols') and accompanying Javascript code. ### citations - Gorelick N, Hancher M, Dixon M, et al. (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18–27. https://doi.org/10.1016/j.rse.2017.06.031
Yearly survey of barnacle settlement near creekbank plots at GCE LTER study sites in October 2013
To characterize spatial variation in barnacle recruitment at the creekbank, and across a gradient in salinity and distance to ocean, we deployed PVC poles to passive sample barnacle settlement. Eight poles were deployed between 4-5m apart adjacent to the creekbank vegetation monitoring plots at each GCE LTER permanent monitoring site each Fall beginning in 2012. These poles were then collected the following Fall and all barnacle that settled on the poles were identified and counted on 50cm-long sections of the 8, 3/4" diameter PVC poles. Although a few other barnacle species are occur within this estuary, only Chthamalus fragilis settled on the poles.
Long-term mid-marsh grasshopper abundance and species diversity at eight GCE-LTER sampling sites
Grasshopper abundance and species diversity were investigated at eight sampling sites within the Georgia Coastal Ecosystems (GCE) LTER study area during July or August from 2000 to 2023. Visual surveys were conducted along 8-10 2m by 10m transects randomly allocated within the mid-marsh zone at each site. All grasshoppers observed within each transect were counted and identified to species, if possible. These surveys were conducted as part of the GCE invertebrate monitoring program, and are performed annually to assess long-term changes in relative species abundances across the GCE study area.
Porewater chemistry measurements from the GCE-LTER Seawater Addition Long-Term Experiment (SALTEx)
The Georgia Coastal Ecosystems LTER Seawater Addition Long-Term Experiment (SALTEx) is a large-scale 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. Response measurements include porewater chemistry, specifically concentrations of chloride, sulfate, sulfide, dissolved organic carbon (DOC), ammonium-N, nitrate/nitrite-N, dissolved reactive phosphorus, total phosphorus, total nitrogen, organic nitrogen, carbon:nitrogen (C:N) ratio, organic-carbon:organic-nitrogen ratio, and pH. Samples of source water were taken after collection (seawater, river water) or mixing (mixed seawater and river water in tanks) and analyzed for concentrations of dissolved reactive phosphorus, total phosphorus, ammonium-N, nitrate/nitrite-N, total nitrogen, organic nitrogen. Source water samples from 2016 and beyond also included measurements of dissolved organic carbon (DOC), carbon:nitrogen ratio, organic-carbon:organic-nitrogen ratio, chloride and sulfate.
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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.