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155 results for “Great Lakes”
Water transparency in the Laurentian Great Lakes
The Laurentian Great Lakes in the mid-east region of North America is one of the largest freshwater ecosystems in the world. This data set contains limnological data, focusing on underwater transparency in this ecosystem and the surrounding region from 1999-2024. Current water transparency data sets highlight the increase in offshore transparency measured by a deepening of average Secchi Disk depth since the invasion of the Dreissenid mussels. However, limited data exists on the Great Lakes to provide insight to the underwater optical environment of the offshore, nearshore, and surrounding water bodies of these lakes. These data illustrate the vertical underwater light environment with data from wavelengths of sunlight including photosynthetically active radiation (400 – 700 nm), UV-A radiation (320 - 400 nm) and UV-B radiation (290 - 320 nm). Understanding the depths to which these specific wavelengths of sunlight reach within a lake, as well as the substances within the lake that may influence the attenuation of sunlight can provide a picture of how habitats within the lake system have been impacted. This data set is a part of a continuous data collaboration among multiple research groups, and will be updated periodically as more data are made available. As such, this data set provides a snapshot of water transparency in the Great Lakes. The data here are contained in four files, including WaterTransparencyData_GreatLakes.csv, SiteInformation_GreatLakes.csv, Methods_GreatLakes.csv, and Variables_GreatLakes. The main data are in WaterTransparencyData_GreatLakes.csv. SiteInformation_GreatLakes.csv, Methods_GreatLakes.csv, and Variables_GreatLakes.csv support the main data file with descriptions of the sampling sites, methods by which samples were processed, and descriptions of variables, respectively.
Upper Midwest Great Lakes Region Citizen Secchi Data 1938 - 2012
Upper MidwestorGreat Lakes Region Citiizen Secchi Data includes 239,741 citizen Secchi monitoring records (1938 – 2012) from Illinois Volunteer Lake Monitoring Program, Indiana Clean Lakes Program, Iowa Secchi Dip-In Project, Michigan Clean Water Corps, Lakes of Missouri Volunteer Program, Minnesota Citizen Lake Monitoring Program, Ohio Citizen Lake Awareness Program, and Wisconsin Citizen Lake Monitoring Records. Data were obtained from above monitoring groups and merged with the high resolution National Hydrography Dataset (www.nhd.usgs.gov) based on citizen proved latitudeorlongitude coordinates to verify the location of individual lakes and size of lake (hectare). Code used to estimate annual average Secchi depth (m) provided in metadata. These citizen-collected, publically available Secchi depth measurements were collected to answer two questions: (1) what are the long-term trends in lake water quality across a broad geographic region?; (2) how do trends differ as a function of spatial location, size of lake monitored, and when Secchi records were collected. Data collection and analysis were funded by the National Science Foundation (MSB- 1065786, EF-1065818, EF-1065649), NTL-LTER (DEB-0822700), STRIVE grant 2011-W-FS-7 from the Environmental Protection Agency. GLERL contribution number (1703).
Data Package for the 2022 Great Lakes Winter Grab
WARNINGS: 1. For Ice Thickness data, please use data in "WinterGrab_snow_ice_properties" file instead of data in Table 2 of the manuscript published in Limnology and Oceanography Letters! 2: In "WinterGrab_phytoplankton_abundance_McKay", EC1 has two sets of data records because it was sampled both on 2/28 and 3/10, both records are included in this data. --- The data package contains the results from a multi-institutional winter limnology sampling campaign on the Laurentian Great Lakes. Researchers from 19 institutions sampled 49 locations in all five of the Great Lakes over a period of 24 days in February-March 2022. This dataset contains information on diverse physical, chemical, and biological parameters. Great Lakes Winter Grab ArcGIS Storymap showing all locations of sampling sites and select photos: https://storymaps.arcgis.com/stories/8ff1c332dd944ba9a744dc0e0fc18906
Output files corresponding to "Direct groundwater discharge and vulnerability to hidden nutrient loads along the Great Lakes coast of the United States"
<p>This dataset corresponds to the output files that were produced for the study reported in:</p> <p>Knights, Deon, Kevin C. Parks, Audrey H. Sawyer, Cédric H. David, Trevor N. Browning, Kelsey M. Danner, and Corey D. Wallace, (2017), Direct groundwater discharge and vulnerability to hidden nutrient loads along the Great Lakes coast of the United States, <em>Journal of Hydrology,</em> 554, 331-341</p> <p><strong>Data sources</strong></p> <p>The following sources were used to produce files in this dataset:</p> <ul> <li>The National Hydrography Dataset Plus (NHDPlus) Version 2, obtained from http://www.horizon-systems.com/nhdplus/NHDplusV2_data.php. Region used is: Great Lakes (04)</li> <li>The second phase of the North American Land Data Assimilation System (NLDAS2), obtained from ftp://hydro1.sci.gsfc.nasa.gov/data/s4pa/NLDAS. Model outputs used are: NLDAS_MOS0125_MC.002, NLDAS_NOAH0125_MC.002, and NLDAS_VIC0125_MC.002.</li> <li>The United States 2011 National Land Cover Database (NLCD 2011), obtained from: http://www.mrlc.gov/nlcd2011.php.</li> </ul> <p> </p> <p><strong>Description of files</strong></p> <p>The files in this dataset contain are described below:</p> <ul> <li><em>Flowlines</em>: This folder contains a shapefile (<em>GL_coastcatchment_NHDflowline</em>) with the coastline of the Contiguous United States as described by NHDPlus V2, and was merged from a subsample of all river reaches available in the region used. </li> <li><em>Catchment</em>: This folder contains a shapefile (GL_coastcatchment_polygon) with the contributing catchments of NHDPlus V2 corresponding to the above coastline, and was merged from a subsample of all catchments available in the region used. </li> <li><em>Centroid</em>: This folder contains a shapefile (GL_coastcatchment_centroid) with the centroids of the above catchments. </li> <li><em>DischargeVulnerabilities.csv</em>. This .csv file contains the following data (units are in parentheses): <ul> <li>COMID: Unique feature identifier in NHDPlusV2 ().</li> <li>Length_km: Length of coastline feature (km).</li> <li>Area_sqkm: Area of coastal catchment feature (km<sup>2</sup>).</li> <li>Infiltration_kgsqm: Average annual infiltrating runoff for REACHCODE (kg/m<sup>2</sup>)</li> <li>REACHCODE: Reach identifier in NHDPlusV2; reaches can include multiple features; Submarine Groundwater Discharge (SGD) is computed by reach, not feature ().</li> <li>RLength_km: Total length of coastline accumulated by REACHCODE (km).</li> <li>RArea_sqkm: Total area of coastal catchment accumulated by REACHCODE (km<sup>2</sup>).</li> <li>RInfiltration_kgsqm: Average annual infiltrating runoff for REACHCODE (kg/m<sup>2</sup>)</li> <li>DGWD: Average annual direct groundwater discharge for REACHCODE (m<sup>2</sup>/y).</li> <li>Vulnerable_percent: Percentage of reach area with developed or agricultural land use in 2011 (%).</li> <li>Vulnerable: Vulnerability to coastal contamination (0- not vulnerable; 1-vulnerable)</li> </ul> </li> </ul> <p> </p>
Great Lakes WRF-FVCOM model ensemble outputs: Summer 2018 daily LST and T2m
<p>Postprocessed model data for the paper: "Coupled Lake-Atmosphere-Land Physics Uncertainties in a Great Lakes Regional Climate Model"</p> <p>Perturbed Physics Ensemble outputs from a coupled lake-atmosphere-land Great Lakes regional model: <br>- Time period: May, June, July of 2018 <br>- Computational domain: Great Lakes region as contained within <a href="../api/records/10806629/draft/files/wrf_grid.nc/content" target="_blank" rel="noopener noreferrer">wrf_grid.nc</a> (atmosphere-land) and <a href="../api/records/10806629/draft/files/fvcom_grid.nc/content" target="_blank" rel="noopener noreferrer">fvcom_grid.nc</a> (lake).<br>- Quantities of interest: lake surface temperature and 2-m near-surface air temperature<br>- Training set: "<a href="../api/records/10806629/draft/files/wfv_global_daily_temperature_training_set.pkl/content" target="_blank" rel="noopener noreferrer">wfv_global_daily_temperature_training_set.pkl</a>" [18 members]. Associated with "<a href="../api/records/10806629/draft/files/perturbation_matrix_9variables_korobov18.nc/content" target="_blank" rel="noopener noreferrer">perturbation_matrix_9variables_korobov18.nc</a>" input model configuration matrix.<br>- Test set: "<a href="https://zenodo.org/api/records/13863491/draft/files/wfv_global_daily_temperature_test_set.pkl/content" target="_blank" rel="noopener noreferrer">wfv_global_daily_temperature_test_set.pkl</a>" [9 members]. Associated with "<a href="https://zenodo.org/api/records/13863491/draft/files/perturbation_matrix_9variables_latin_hypercube9.nc/content" target="_blank" rel="noopener noreferrer">perturbation_matrix_9variables_latin_hypercube9.nc</a>" input model configuration matrix.</p>
GEM-Hydro gridded simulations for the Great Lakes Runoff Inter-comparison Project for Lake Erie (GRIP-E)
<p>This dataset provides gridded model simulations in NetCDF format over the Lake Erie using the GEM-Hydro model done within the Great Lakes Runoff Inter-comparison Project for Lake Erie (GRIP-E). The data are produced with SPS (GEM-Surf + SVS, the surface component of GEM-Hydro) open-loop runs with the SVS calibrated parameters obtained during GRIP-E project. For more information on the model and on calibration methodology, see GEM-Hydro section in Mai et al. 2020 (in prep.).</p> <p>The original model outputs had all variables accumulated for each day. During post-processing all variables have been de-accumulated by subtracting the accumulation of the previous hour from the accumulation of the current hour. Two variables (ALAT and O1) are also only valid over the land tile of each grid cell. Two additional variables (ALAT_full and O1_full) valid now over the whole grid cell have been added for convenience of the users.</p> <p><strong>Domain boundaries (WGS84 system): </strong><br> - lon_min = -85.5, lon_max = -77.94<br> - lat_min = 40.3, lat_max = 44.26</p> <p><strong>Resolution of model variables provided:</strong><br> - spatial: ~10km x 10km <br> - temporal: hourly </p> <p><strong>Simulation period:</strong><br> - 01 Jan 2011 - 31 Dec 2014 <br> - 01 Jan 2010 - 31 Dec 2010 (warm-up)</p> <p><strong>Meteorological input data:</strong><br> - RDRS-v1; see Mai et al. 2020 (in prep)</p> <p><strong>Variables available:</strong><br> float <strong>PR_0</strong>(time, rlat, rlon) ;<br> PR_0:units = "m" ;<br> PR_0:long_name = "Quantity of precipitation (valid over whole grid cell)" ;<br> float <strong>AHFL_0</strong>(time, rlat, rlon) ;<br> AHFL_0:units = "mm" ;<br> AHFL_0:long_name = "Surface evaporation (valid over whole grid cell)" ;<br> float <strong>TRAF_60268832</strong>(time, rlat, rlon) ;<br> TRAF_60268832:units = "mm" ;<br> TRAF_60268832:long_name = "Surface runoff (valid over whole grid cell)" ;<br> float <strong>ALAT_0</strong>(time, rlat, rlon) ;<br> ALAT_0:units = "mm" ;<br> ALAT_0:long_name = "Accumulation of total soil lateral flow (valid over land tile of grid cell)" ;<br> float <strong>ALAT_0_full</strong>(time, rlat, rlon) ;<br> ALAT_0_full:units = "mm" ;<br> ALAT_0_full:long_name = "Accumulation of total soil lateral flow (valid over whole grid cell)" ;<br> float <strong>O1_0</strong>(time, rlat, rlon) ;<br> O1_0:units = "mm" ;<br> O1_0:long_name = "Accumulation of base drainage (valid over land tile of grid cell)" ;<br> float <strong>O1_0_full</strong>(time, rlat, rlon) ;<br> O1_0_full:units = "mm" ;<br> O1_0_full:long_name = "Accumulation of base drainage (valid over whole grid cell)" ;<br> float <strong>WT_59868832</strong>(time, rlat, rlon) ;<br> WT_59868832:units = "1" ;<br> WT_59868832:long_name = "Fraction of grid cell covered with land" ;</p> <p>===============================================================</p> <p>These data and model runs have been performed under the Great Lakes Runoff Inter-comparison Project for Lake Erie (GRIP-E) led by Juliane Mai and Bryan Tolson (both University of Waterloo) and funded under the Integrated Modelling Program for Canada (IMPC) within the Global Water Futures program. </p>
Population-adjusted pseudo global warming simulations over the Great Lakes Region
<p>Summaries of pseudo global warming simulations over the Great Lakes Region. Specifics below:</p> <p> </p> <p>Each CSV file provides regional summaries from PGW simulations, either over land, for urban grids, or rural grids. This includes cumulative hours above critical heat stress threholds as well as their population-adjusted values.</p> <p>The 'Future_sensitivities_land.csv' file calculates heat stress in the future by keeping all variables except one the same value as the historical run.</p> <p>Among the geotiffs, _perc_land files include percentage of hours in summer above the National Weather Service heat index and wet bulb globe temperature thresholds, _pop_land incorporate population adjusted heat stress above those thresholds, the TEMP_contribution files estimate changes in heat stress if only air temperature changed and all variables remained the same as historical values and the HI_ and WBGT_pop rasters include summer average heat stress and their corresponding populations. </p> <p>See Chakraborty et al. Under Review (will be updated on paper publication) for more details.</p>
Datasets of Radwin 2023 Great Salt Lake Remote Sensing Historical Assessment
<p>Included are the culminated datasets for an article in review to be published with the Utah Geological Association. This data helps an investigator reproduce or utilize the data. There are datasets for both Landsat and Sentinel, for both the North and South arms of the Great Salt Lake. Additionally, there are datasets documenting the NDWI threshold used for each Landsat image, the outlier images not used in analyses, NDWI error assessment, and calculation of stats/facts.</p> <p>Paper abstract:</p> <p>The Great Salt Lake has been rapidly shrinking since the highstand of the mid-1980s, creating cause for concern in recent decades as the lake has reached historic lows. Many investigators have assessed the evolution of lake elevation, geochemistry, anthropogenic impacts, and links to climate and atmospheric processes; however, the use of remote sensing to study the evolution of the lake has been significantly limited. Harnessing recent advancements in cloud-processing, specifically Google Earth Engine cloud computing, this study utilizes over 600 Landsat TM/OLI and Sentinel MSI satellite images from 1984-2023 to present time-series analyses of remotely sensed Great Salt Lake water area, exposed lakebed area, surface cover types, and chlorophyll-a analyses paired with modelled estimates for water and exposed lakebed area. Results show that since the highstand of 1986-1987, the water area has declined by 45% (~3,000 km<sup>2</sup>) and the exposed lakebed area has increased to ~3,500 km<sup>2</sup> from ~500 km<sup>2</sup>. The area of unconsolidated sediments not protected by vegetation or halite crusts has risen to ~2,400 km<sup>2</sup>. Significant halite crusts are observed in the North Arm, having a max extent of ~150 km<sup>2</sup> between 2002 and 2003, while only small extents of halite crusts are observed for the South Arm. Vegetation is more prevalent in the Bear River Bay and South Arm, with surface area increases over 400% since 1990. Gypsum is widely observed independent of halite crusts. The results highlight multiple instances of land-use/water-management that led to observable changes in water/exposed lakebed area and halite crust extent. This study demonstrates the important benefits of maintaining a lake elevation above ~4,194 ft to maximize lake and halite crust area, which would help mitigate possible dust events and maintain broad lake extent.</p> <p> </p> <p>Files should be self-explanatory based on filename, where BRB means Bear River Bay. Note there are two video files animating the evolution of the North and South Arms of the Great Salt Lake using satellite imagery from 1984 to 2023. </p> <p> </p> <p>Visit https://github.com/radwinskis for details on code used for this study.</p> <p>Please contact me at markradwin@gmail.com with any questions.</p> <p> </p>
FIGURE 1. Moraria hudsoni n in A new species of Moraria (Crustacea: Copepoda: Harpacticoida) from the Laurentian Great Lakes
FIGURE 1. Moraria hudsoni n. sp., female. A, habitus, dorsal view; B, posterior urosomites and caudal rami, dorsal view.
Great Lakes Coordinated Monthly Precipitation Data
<p>Coordinated Monthly Precipitation Data Information (1900-2023)</p> <p>This dataset consists of the coordinated amount of monthly precipitation that has fallen over the basin areas of the different Great Lakes. The data are presented in both mm and inches. Most of the processing is done by the US Army Corps of Engineers Detroit Office using models developed by that agency and the Great Lakes Environmental Research Lab (GLERL) of NOAA. The data is then verified by Environment and Climate Change Canada.</p> <p>Different methods used to calculate monthly precipitation for certain periods in record period. For more information on data sources reference this paper: *<a href="http://www.glerl.noaa.gov/pubs/fulltext/2015/20150006.pdf">https://www.glerl.noaa.gov/pubs/fulltext/2015/20150006.pdf</a></p> <p>An advancement made in most recent period, beginning in 1948. Data from 1948 to present calculated through AHPS model with GLERL DTP method. GLERL DTP method uses a group of meteorological stations surrounding the Great Lakes basin to calculate precipitation. In a recent study, it was found that some of the stations in the group were reporting erroneous data in recent years. Therefore, station list updated.</p> <p>Also, AHPS model replaced with GLSHFS model. GLSHFS uses same method for calculating precipitation. With the model change to GLSHFS and the method's (GLERL DTP) improved station list, precipitation data updated back to 1948. </p> <p><strong>Period Current Method** Previous Method**</strong> </p> <p>1900-1930 USACE-AWD USACE-AWD</p> <p>1931-1947 GLERL-MTP GLERL-MTP</p> <p>1948-2017 GLERL-DTP (GLSHFS) GLERL-DTP (AHPS)</p> <p>2018-YYYY GLERL-DTP (GLSHFS) </p> <p>*Citation:</p> <p>Hunter, T. S., Clites, A. H., Campbell, K. B., & Gronewold, A. D. (2015). Development and application of a North American Great Lakes hydrometeorological database—Part I: Precipitation, evaporation, runoff, and air temperature. Journal of Great Lakes Research, 41(1), 65-77.</p> <p>**Acronyms</p> <p>AHPS: Advanced Hydrologic Prediction System</p> <p>AWD: Areally Weighted District</p> <p>DTP: Daily Thiessen Program</p> <p>GLERL: Great Lakes Environmental Research Laboratory</p> <p>GLSHFS: Great Lakes Seasonal Hydrological Forecasting System</p> <p>MTP: Monthly Thiessen Polygon</p> <p>USACE: United States Army Corps of Engineers</p>
Increasing marsh bird abundance in coastal wetlands of the Great Lakes (2011–2021) likely caused by increasing water levels
<p class="MsoNoSpacing"><span>Wetlands of the Laurentian Great Lakes of North America, i.e., lakes Superior, Michigan, Huron, Erie, and Ontario, provide critical habitat for marsh birds. We used 11 years (2011–2021) of data collected by the Great Lakes Coastal Wetland Monitoring Program at 1,962 point count locations in 792 wetlands to quantify the first-ever annual abundance indices and trends of 18 marsh-breeding bird species in coastal wetlands throughout the entire Great Lakes. Nine species (50%) increased by 8–37% per year across all of the Great Lakes combined, whereas none decreased. Twelve species (67%) increased by 5–50% per year in at least 1 of the 5 Great Lakes, whereas only 3 species (17%) decreased by 2–10% per year in at least 1 of the lakes. There were more positive trends among lakes and species (<em>n </em>= 34, 48%) than negative trends (<em>n </em>= 5, 7%). </span><span>These large increases are welcomed because most of the species are of conservation concern in the Great Lakes. <span>Trends were likely caused by long-term, cyclical fluctuations in Great Lakes water levels. Lake levels increased over most of the study, which inundated vegetation and increased open water-vegetation interspersion and open water extent, all of which are known to positively influence abundance of most of the increasing species and negatively influence abundance of all of the </span>decreasing species. Coastal wetlands may be more important for marsh birds than once thought if they provide <span>high-lake-level-induced population pulses for species of conservation concern. Coastal wetland protection and restoration are of utmost importance to safeguard this process. Future climate projections show </span>increases in lake levels over the coming decades, which will cause "coastal squeeze" of many wetlands if they are unable to migrate landward fast enough to keep pace. If this happens, less habitat will be available to support periodic pulses in marsh bird abundance, which appear to be important for regional population dynamics. Actions that allow landward migration of coastal wetlands during increasing water levels <span>by removing or preventing barriers to movement, </span>such as shoreline hardening, will be useful for maintaining marsh bird breeding habitat in the Great Lakes.</span></p>
Newly identified nematodes from the Great Salt Lake are associated with microbialites and specially adapted to hypersaline conditions
<p>Extreme environments enable the study of simplified food-webs and serve as models for evolutionary bottlenecks and early Earth ecology. We investigated the biodiversity of invertebrate meiofauna in the benthic zone of the Great Salt Lake (GSL), UT, one of the most hypersaline lake systems in the world. The hypersaline bays within the GSL are currently thought to support only two multicellular animals: brine fly larvae and brine shrimp. Here, we report the presence, habitat, and microbial interactions of novel free-living nematodes. Nematode diversity drops dramatically along a salinity gradient from a freshwater river into the south arm of the lake. In Gilbert Bay, nematodes primarily inhabit reef-like organosedimentary structures built by bacteria called microbialites. These structures likely provide a protective barrier to UV and aridity, and bacterial associations within them may support life in hypersaline environments. Notably, sampling from Owens Lake, another terminal lake in the Great Basin that lacks microbialites, did not recover nematodes from similar salinities. Phylogenetic divergence suggests that GSL nematodes represent previously undescribed members of the family Monhysteridae – one of the dominant fauna of the abyssal zone and deep-sea hydrothermal vents. These findings update our understanding of halophile ecosystems and the habitable limit of animals.</p>
Data for the manuscript: Planning for climate migration in Great Lake Legacy Cities
<p>Our analysis for the manuscript, "Planning for climate migration in Great Lake Legacy Cities" uses county level spatial data from the FEMA National Risk Index (USFEMA, 2021) and the CDC SVI ranking system (ATSDR, 2018) in the form of shapefiles(.shp). To create the geovisualization, we used boundaries of the Great Lakes that are published here https://www.glc.org/greatlakesgis. All analysis was conducted using R (2020), with code that can be found here: https://derekvanberkel.github.io/Planning-for-climate-migration-in-Great-Lake-Legacy-Cities/ </p> <p>ATSDR. (2018). Cdc/atsdr social vulnerability index. https://www.atsdr.cdc.gov/placeandhealth/svi/fact sheet/fact sheet.html.</p> <p>USGCRP. (2018). Impacts, risks, and adaptation in the united states: Fourth national climate assessment. US Global Change Research Program.</p> <p> </p>
Figure 2 in Towards a framework for invasive aquatic plant survey design in Great Lakes coastal areas
Figure 2. Sample units surveyed by year (points indicate sample unit centroids). Milwaukee, 2017–2019 (A–C), Cleveland 2017, 2019 (D, E), St Joseph River 2017 (F), Detroit River 2018, 2019 (G, H), Saginaw River 2018 (I).
Figure 3 in Towards a framework for invasive aquatic plant survey design in Great Lakes coastal areas
Figure 3. Species-effort curves for each site, showing number of observed species (Sobs) and average species-effort curve (solid line) with 95% confidence interval (CI, dashed lines). The level of effort required to sample the estimated species total (Sasym; error bars show 95% CI) and 95% (S95%) of Sasym are indicated. Milwaukee (A–C, 2017–2019; D, combined), Cleveland (E, combined data), Detroit River (F, combined data), Saginaw River (G), St Joseph River (H).
Dataset: Great Lakes Dredge & Dock Corporation (GLDD) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Fig. 3 in Field sampling methods for Asiatic garden beetle (Coleoptera: Scarabaeidae) adult movement and flight in the Great Lakes region
Fig. 3. Mean number of Maladera formosae adults captured in milkjug traps containing either water, ethylene glycol antifreeze, or propylene glycol antifreeze, from 14 to 21 Jul 2021 (a) and from 21 to 28 Jul 2021 (b) in soybean at the Fulton3 site near Wauseon, Ohio. Uppercase letter(s) above each bar indicates significant differences at the P ≤ 0.05 level from pairwise comparison analyses.
Fig. 2 in Field sampling methods for Asiatic garden beetle (Coleoptera: Scarabaeidae) adult movement and flight in the Great Lakes region
Fig. 2. Mean number of Maladera formosae adults captured in pitfall traps during individual evaluation periods at Fulton1 (a) and Fulton2 (b) sites, in northwest Ohio. Uppercase letter(s) above each bar indicates significant differences at the P ≤ 0.05 level from pairwise comparison analyses. Traps were deployed on 28 Jun 2017, 1 wk prior to the sampling data indicated in each figure.
Fig. 1 in Field sampling methods for Asiatic garden beetle (Coleoptera: Scarabaeidae) adult movement and flight in the Great Lakes region
Fig. 1. Sampling methods for Maladera formosae adults: within field – pitfall trap (a) and yellow sticky card (b); at field edge – blacklight trap (c), and antifreeze milkjug trap (d). Photo credit: Adrian Pekarcik, The Ohio State University, Wooster, Ohio, USA.
Great Lakes monthly water balance components from the Large Lakes Statistical Water Balance Model (L2SWBM)
<p>**Note that an updated version of the data (v3.0) was uploaded on October 2, 2024, which supersedes earlier versions**</p> <p>These data sets are the results of leveraging bi-national data and the Large Lakes Statistical Water Balance Model (L2SWBM) specifically tailored for the Laurentian Great Lakes to produce value-added time series of water supply components, including expressions of uncertainty, that ultimately close the water balance across the interconnected Great Lakes system. </p> <p>The model serves as a new cornerstone for bi-national coordination of hydrologic data throughout this international transboundary basin, providing an improved means of capturing data patterns, revealing seasonal variabilities, as well as short-term and long-term trends.</p> <p>This repository includes monthly output from the L2SWBM. Output datasets include over-lake precipitation, over-lake evaporation, lateral tributary inflow (runoff), connecting channel flow (cms and also included in mm normalized to lake area), diversion flow (cms and also included in mm normalized to lake area), and component net basin supply. Data is included for lakes Superior, Michigan-Huron, Erie, and Ontario.</p> <p>This version contains data from 1950 to 2022.</p>
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.