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Long-term Mollusc Population Abundance and Size Data from the Georgia Coastal Ecosystems LTER Fall Marsh Monitoring Program
This data set includes long-term observational data on mollusc species abundance and size distribution at 10 Georgia Coastal Ecosystems marsh sites used for annual plant and invertebrate population monitoring. Infaunal and epifaunal molluscs were hand-collected from within quadrats of known area in mid-marsh and creekbank zones (n = 4 quadrats per zone) at all sites annually in October. Molluscs were also collected from an additional high marsh Juncus zone (n = 4 quadrats) at several sites beginning in 2009. The molluscs were returned to the lab, preserved in ethanol, identified and counted to determine species abundance and density in each plot. The length of each measurable individual was then determined using calipers or an ocular micrometer mounted in a stereomicroscope to determine mollusc size. Population abundance and size measurement data are reported separately by site, zone, plot and species because analyses were performed at different times, specimens were not individually identifiable, and not all individuals were measureable. This data set includes cumulative long-term observations from 2000 to 2022, and will be updated annually to include the prior year observations.
Mollusc population size distribution monitoring: Fall 2016 mid-marsh and creekbank infaunal and epifaunal mollusc size distributions based on collections from GCE marsh monitoring sites 1-10
This data set is the Fall 2016 report of infaunal and epifaunal mollusc species size distributions at the GCE-LTER marsh sites used for population monitoring. Infaunal and epifaunal molluscs were hand-collected from within quadrats of known area from mid-marsh and creekbank zones (n = 4 quadrats per zone) at all sites. The molluscs were returned to the lab, preserved in ethanol, measured and counted (count data is reported separately). Length of each measurable individual was determined using calipers or an ocular micrometer mounted in a stereomicroscope. Species abundance and density data for these collections may be found in the GCE-LTER data set INV-GCEM-1707. Numbers of individuals of each species in the abundance data file may not correspond exactly to the numbers of individuals in the size data file because some individuals may not have been measureable.
Mollusc population size distribution monitoring: Fall 2021 mid-marsh and creekbank infaunal and epifaunal mollusc size distributions based on collections from GCE marsh monitoring sites 1-10
This data set is the Fall 2021 report of infaunal and epifaunal mollusc species size distributions at the GCE-LTER marsh sites used for population monitoring. Infaunal and epifaunal molluscs were hand-collected from within quadrats of known area from mid-marsh and creekbank zones (n = 4 quadrats per zone) at all sites. The molluscs were returned to the lab, preserved in ethanol, measured and counted (count data is reported separately). Length of each measurable individual was determined using calipers or an ocular micrometer mounted in a stereomicroscope. Species abundance and density data for these collections may be found in the GCE-LTER data set INV-GCEM-2207. Numbers of individuals of each species in the abundance data file may not correspond exactly to the numbers of individuals in the size data file because some individuals may not have been measureable.
Mollusc population size distribution monitoring: Fall 2022 mid-marsh and creekbank infaunal and epifaunal mollusc size distributions based on collections from GCE marsh monitoring sites 1-10
This data set is the Fall 2022 report of infaunal and epifaunal mollusc species size distributions at the GCE-LTER marsh sites used for population monitoring. Infaunal and epifaunal molluscs were hand-collected from within quadrats of known area from mid-marsh and creekbank zones (n = 4 quadrats per zone) at all sites. The molluscs were returned to the lab, preserved in ethanol, measured and counted (count data is reported separately). Length of each measurable individual was determined using calipers or an ocular micrometer mounted in a stereomicroscope. Species abundance and density data for these collections may be found in the GCE-LTER data set INV-GCEM-2307. Numbers of individuals of each species in the abundance data file may not correspond exactly to the numbers of individuals in the size data file because some individuals may not have been measureable.
Zooplankton and macroinvertebrate size spectra, biomass, and community composition; and harvest of bigmouth buffalo and common carp in six shallow lakes in Iowa, USA (2018-2020)
This data product contains biological data collected within six shallow lakes in Iowa, USA between 2018 - 2020, where four lakes were undergoing targeted removals of common carp (Cyprinus carpio) and bigmouth buffalo (Ictiobus cyprinellus). Parts of these data were a portion of Albright et al. 2022 (https://doi.org/10.6073/pasta/1d3797fd573208bae6f78963479445a0), however the data herein include additional survey data from the Ambient Lake Monitoring network instituted through Iowa State University and the Iowa Department of Natural Resources (https://www.iowadnr.gov/environmental-protection/water-quality/water-monitoring/ambient-lake-monitoring#ambient-lake-monitoring-sampling-plan). Data are packaged and formatted specifically for size spectra analysis and compositional analysis.
Age and Size of Smith Firs at Treeline in Tibet 1700-2013
The most widespread response to global warming among alpine treeline ecotones is not an upward shift, but an increase in tree density. However, the impact of increasing density on interactions among trees at treeline is not well understood. Here, we test if treeline densification induced by climatic warming leads to increasing intraspecific competition. We mapped and measured the size and age of Smith fir trees growing in two treelines located in the southeastern Tibetan Plateau. We used spatial point-pattern and codispersion analyses to describe the spatial association and covariation among seedlings, juveniles, and adults grouped in 30-year age classes from the 1860s to the present. Effects of competition on tree height and regeneration were inferred from bivariate mark-correlations. Since the 1950s, a rapid densification occurred at both sites in response to climatic warming. Competition between adults and juveniles or seedlings at small scales intensified as density increased. Encroachment negatively affected height growth and further reduced recruitment around mature trees. We infer that tree recruitment at the study treelines was more cold-limited prior to 1950 and shifted to a less temperature-constrained regime in response to climatic warming. Therefore, the ongoing densification and encroachment of alpine treelines could alter the way climate drives their transitions towards subalpine forests.
Long-term fish size data for Wisconsin Lakes Department of Natural Resources and North Temperate Lakes LTER 1944 - 2012 (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-ntl/357/2. The abstract below was extracted from the Level 0 data package and is included for context: This dataset describes long-term (1944-2012) variations in individual fish total lengths from Wisconsin lakes. The dataset includes information on 1.9 million individual fish, representing 19 species. Data were collected by Wisconsin Department of Natural Resource fisheries biologists as part of routine lake fisheries assessments. Individual survey methodologies varied over space and time and are described in more detail by Rypel, A. et al., 2016. Seventy-Year Retrospective on Size-Structure Changes in the Recreational Fisheries of Wisconsin. Fisheries, 41, pp.230-243. Available at: http://afs.tandfonline.com/doi/abs/10.1080/03632415.2016.1160894
Lake Mendota, Wisconsin, USA, Zebra Mussel Body Size and Biomass Biometrics 2018
We sampled 98 individuals of the zebra mussel (Dreissena polymorpha) population of Lake Mendota from many littoral zone sites in 2018 to create biometric relationships between several metrics of body size and several metrics of biomass, including length, width, height, living weight, wet weight, dry weight, shell weight, shell-free dry weight, and ash-free dry weight. We selected individuals to span a wide range of body sizes and found strong relationships between most combinations of body size and biomass metrics.
Long-term fish size data for Wisconsin Lakes Department of Natural Resources and North Temperate Lakes LTER 1944 - 2012 (Reformatted to a Darwin Core Archive)
This data package is formatted as a Darwin Core Archive (DwC-A, event core). For more information on Darwin Core see https://www.tdwg.org/standards/dwc/. This Level 2 data package was derived from the Level 1 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-ntl/345/4, which was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-ntl/357/2. The abstract below was extracted from the Level 0 data package and is included for context: This dataset describes long-term (1944-2012) variations in individual fish total lengths from Wisconsin lakes. The dataset includes information on 1.9 million individual fish, representing 19 species. Data were collected by Wisconsin Department of Natural Resource fisheries biologists as part of routine lake fisheries assessments. Individual survey methodologies varied over space and time and are described in more detail by Rypel, A. et al., 2016. Seventy-Year Retrospective on Size-Structure Changes in the Recreational Fisheries of Wisconsin. Fisheries, 41, pp.230-243. Available at: http://afs.tandfonline.com/doi/abs/10.1080/03632415.2016.1160894
Size scalability of Monte Carlo simulations applied to oxidized polypyrrole systems: Data and Codes
<p>This work generalizes our recently proposed coarse grained force field (CGFF) for halogen oxidized PPy in the condensed phases and introduces a novel implementation of the Nettropolis Monte Carlo (MMC) simulation based on the CGFF that enables simulations of polymer systems with more than<br>100000 particles. The MMC implementation utilizes a combination of CPU and GPUs and exploits a numerical approximation based on polynomial piecewise interpolation for the calculation of the CGFF pairwise additive terms. Our simulations evidence that the oxidized PPy thermodynamic and structural properties are consistent as the system size is scaled up. Predicted properties include density, enthalpy, potential energy, heat capacity, coefficient of thermal expansion, caloric curve, glass transition temperature range, compressibility, bulk modulus, radial distribution functions, and polymer chain characteristics.</p>
Laboratory study on microplastic fiber size and concentration effects on leopard frog (Lithobates pipiens) tadpole survival, development, behavior, and parasite susceptibility
This dataset contains comprehensive raw data from a completed laboratory experiment conducted from May 24 to June 30, 2021 (with additional analysis performed in 2025), investigating the effects of polyester microplastic (MP) fiber exposure on northern leopard frog (Lithobates pipiens) tadpoles and their interactions with echinostome trematodes (Echinostoma sp.). Tadpole egg masses were collected from a wetland in Indiana, USA, and ramshorn snails (Helisoma trivolvis), serving as trematode hosts, were collected from Tioga County, New York, USA. The experiment was conducted under controlled laboratory conditions using a static-renewal design, exposing tadpoles to short (~0.24 mm) or long (~1.50 mm) polyester MP fibers at concentrations of 0, 10, or 40 µg L⁻¹ for 32 days, followed by controlled exposure to echinostome cercariae. The dataset includes measurements of tadpole mortality, developmental traits (mass, snout-to-vent length, Gosner stage), behavioral activity (number of moving pre- and post-parasite exposure), MP fiber ingestion, and susceptibility to trematode infection (metacercarial cyst counts in kidneys). These data provide a resource for studying the ecological and toxicological impacts of microplastics on amphibian health, and host-parasite dynamics in freshwater ecosystems, making the dataset suitable for researchers in ecotoxicology, and disease ecology. The dataset is complete, with no ongoing data collection, and is designed to support analyses of microplastic-mediated effects on aquatic organisms.
Size-Fractionated Chlorophyll a, Primary Productivity, and Photosynthetic Physiological Parameters of Phytoplankton in the Cosmonaut Sea, Southern Ocean, During Summer 2022
This dataset provides vertical distribution profiles of size-fractionated phytoplankton parameters measured in the Cosmonaut Sea, a marginal ice zone in the Southern Ocean, during the austral summer of 2022. Sampling was conducted across multiple stations spanning latitudes from approximately 33°N to 60°N and longitudes from -62°E to -67°E, focusing on surface and subsurface waters up to depths of about 40 meters. The data capture key aspects of phytoplankton physiology and productivity in this dynamic polar environment, influenced by seasonal ice melt and nutrient availability. Parameters include chlorophyll a concentrations (Chl a), primary productivity indicators such as maximum photosynthetic rates (PBm), photosynthetic efficiency (α), saturation irradiance (Ek), and integrated gross primary productivity (IGPPeu), all differentiated by size fractions: net phytoplankton (>20 μm), nano- and pico-phytoplankton (<20 μm), and total community. Additional measurements encompass photosynthetically active radiation (PAR) and mixed layer depths, providing context for light and stratification effects on phytoplankton dynamics. Data were derived from in situ incubations and fluorometric analyses, with values reported for discrete depths at each station to highlight vertical gradients in biomass and photosynthetic performance. This completed dataset is particularly valuable for studies on polar marine ecosystems, carbon cycling, and climate-driven changes in phytoplankton communities, offering insights into how size-structured assemblages respond to environmental gradients in the Southern Ocean. It does not include taxonomic details beyond general phytoplankton groupings but emphasizes physiological metrics for modeling primary production in ice-influenced regions.
Examining genome size and nutrient influence on plant damage patterns
Data was collected to examine whether and how plant genome size (GS) interacts with environmental nutrient additions to influence the amount and patterns of damage plants sustain from invertebrate herbivores and fungal pathogens. Plants were selected based on visual abundance in treatment plots in which nitrogen (N), phosphorus (P), or NP combined had been annually added (Cont. is the abbreviation we used for the control plot with no nutrients added). Additionally, plant traits of percent foliar carbon (% C), percent foliar nitrogen (% N), and specific leaf area (SLA) were measured from all the same plants that damage values were observed from. Data was collected from 847 plants (626 forb individuals, 221 grass individuals) in eight grassland sites that are part of the Nutrient Network (https://nutnet.org), a globally distributed experiment in which plots have different nutrient amendment treatments that are administered identically to allow cross-site comparisons of the effects of nutrients on biodiversity patterning. The sites chosen varied along a north-south latitude, longitude, mean annual precipitation (MAP) and mean annual temperature (MAT) gradient in the United States. All field data was collected between May 2022 and August 2022. The sites included in this study are listed below with their respective Nutrient Network site codes. churn.us= Churning Rapids in Hancock, MI spin.us= Spindletop Farm in Lexington, KY temple.us= Temple in Temple, TX kbs.us= Kellogg Biological Station in Hickory Corners, MI konz.us= Konza Prairie Biological Station in Manhattan, KS cgbg.us= Chichaqua Bottoms Greenbelt in Maxwell, IA cdcr.us= Cedar Creek in East Bethel, MN msum.us= Minnesota State University at Moorhead in Moorhead, MN
Data from “Larval and juvenile Longfin Smelt diets as a function of fish size and prey density in the San Francisco Estuary”
This publication includes the raw data from the manuscript: Lojkovic Burris, Z. P., R. D. Baxter, and C. E. Burdi. 2022. Larval and juvenile Longfin Smelt diets as a function of fish size and prey density in the San Francisco Estuary. California Fish and Wildlife Journal 108:e11. http://www.doi.org/10.51492/cfwj.108.11 Data includes the diets of larval and juvenile Longfin Smelt in the San Francisco Estuary from 2005 to 2008 in the form of diet by number, diet by weight, macroinvertebrate prey lengths, prey length-weight equations, and prey weight conversions.
Mollusc population size distribution monitoring: Fall 2020 mid-marsh and creekbank infaunal and epifaunal mollusc size distributions based on collections from GCE marsh monitoring sites 1-10
This data set is the Fall 2020 report of infaunal and epifaunal mollusc species size distributions at the GCE-LTER marsh sites used for population monitoring. Infaunal and epifaunal molluscs were hand-collected from within quadrats of known area from mid-marsh and creekbank zones (n = 4 quadrats per zone) at all sites. The molluscs were returned to the lab, preserved in ethanol, measured and counted (count data is reported separately). Length of each measurable individual was determined using calipers or an ocular micrometer mounted in a stereomicroscope. Species abundance and density data for these collections may be found in the GCE-LTER data set INV-GCEM-2107. Numbers of individuals of each species in the abundance data file may not correspond exactly to the numbers of individuals in the size data file because some individuals may not have been measureable.
Abundance and Size of Seagrass-Associated Fishes in the Virginia Coastal Lagoons, 2019-2024
These data comprise annual summer estimates of the abundance (counts) and size (length) of fishes across restored seagrass meadows of the Virginia coastal lagoons. Fish were collected using a 25-ft (7.62-m) wide beach seine hauled by hand over a 25 m linear swath of the seafloor. Seine hauls were collected in June at 31 sites (1 haul per site). All fish caught in the seine were identified to lowest practical taxonomic level, counted, measured (total length), and released. Data collection began in June 2019 and continues annually (sampling was not carried out in 2020 due to logistical interruptions associated with the COVID-19 pandemic). Data on water temperature, salinity, and conductivity were collected while sampling occurred using a YSI 30 probe. Dissolved oxygen measurements were collected using a YSI ProODO probe. In 2019, these data were collected on at the top and bottom of the water column, but in 2021 and subsequent sampling only one observation (mid-water column) was made. To reconcile this difference for the combined data set, top and bottom environmental measurements from 2019 were averaged. Each fish collection site is co-located with a nearby synoptic site where long-term measurements of seagrass, sediments, and fauna are made. The relationship between site names and coordinates are given in Synoptic_fish_sites.csv. The sites where fish sampling occurred are different and are given by the "fish_sites" column, with coordinates for these sites under the "fish_longitude" and "fish_latitude" columns. Importantly, the coordinates of where sampling occurred will differ slightly between years without a change to the name of the site. Site geographic coordinates for individual years are in the PhysicalSamples.csv file. Sites are separated by at least 300 meters. In 2023, three new sites were added to represent unvegetated areas outside of but near the seagrass meadows. These sites are HI29, SPDR-bare, and SS-bare, and are designed to serve as references for se
Update of: The Global Fire Atlas of individual fire size, duration, speed and direction
<p>This is an updated and extended record of the Global Fire Atlas introduced by Andela et al. (2019). Input data (burned area and land cover products) are updated to the MODIS Collection 6.1 (the original version featured in Andela et al. (2019) was based on collection 6.0 burned area and collection 5.1 land cover products, respectively). The timeseries is extended to cover the period 2002 to August 2024.</p> <h2><strong>Methodological Notes:</strong></h2> <p>The method employed to create the dataset precisely follows the approach described by Andela et al. (2019).</p> <p>The input burned area product is MCD64A1 Collection 6.1. It is described by Giglio et al. (2018) and available at: https://lpdaac.usgs.gov/products/mcd64a1v061/. </p> <p>The input land cover product is MCD12Q1 Collection 6.1. It is described by Sulla-Menashe et al. (2019) and available at: https://lpdaac.usgs.gov/products/mcd12q1v061/. </p> <p>Note that while the methods have remained the same compared to Andela et al. (2019), we do observe small differences between the Global Fire Atlas products originating from differences between the MCD64A1 collection 6.1 burned area data used here and the collection 6 data used in the original product. In addition, we observe more substantial differences in the dominant land cover class associated with each fire due to the differences between the MCD12Q1 collection 6.1 data used here and collection 5.1 data used in the original product. </p> <p>Please note that the year string in filenames refers to the locally-defined fire season in which the fire ignited, not the calendar year. For each MODIS tile, the fire season is defined as the twelve months centred on the month with peak burned area (see Andela et al., 2019). For example, for a MODIS tile with peak burned area in December, the 2023 fire season would be defined as the period from July 2023 to June 2024, with the current record ending in August 2024. This is particularly relevant in the Southern extratropics and the northern hemisphere subtropics, where the fire seasons often span the new year. The local definition of the fire season is based on climatological peak in burned area as described by Andela et al. (2019).</p> <p>Here we extended the time-series to include the fire season of 2002, and extended the time-series until February 2025.</p> <h2> </h2> <h2><strong>Usage Notes:</strong></h2> <h3><strong>Incomplete Observations for the Latest Fire Seasons:</strong></h3> <p>Please note that the year string in filenames refers to the locally-defined fire season in which the fire ignited, not the calendar year. As such, the time-series can be incomplete for the latest fire season (e.g. the "2024 fire season") and also for the penultimate fire season (e.g. the "2023 fire season") due to the way that fire seasons are defined (see above). For example, if the month with peak burned area for a tile is December, then full data covering the 2023 fire season in that tile are not available until midway through the 2024 calendar year. This contrasts with the original dataset from Andela et al. (2019), which only included the data for entire fire seasons between 2003 and 2016. </p> <h3><strong>Observational Outages:</strong></h3> <p>For the purpose of time-series analysis, we note that the 2002 product may have been affected by outages of Terra-MODIS (most notably, June 15 2001 - July 3 2001 and March 19 2002 - March 28 2002), which affects the burn date estimates and Global Fire Atlas product. Following the launch of Aqua-MODIS in May 2002 burn date estimates are more reliable as estimated from both MODIS sensors onboard Terra and Aqua. </p> <h3><strong>File Naming Convention:</strong></h3> <p>GFA_v<em>{time-stamp}</em>_<em>{data-type}</em>_<em>{fire_season}</em>.<em>{file_type}</em></p> <p><em>{time-stamp}</em><strong> </strong>= Date that code was run.</p> <p><em>{data-type}</em><strong> </strong>= “ignitions” or “perimeters” for vector files; “day_of_burn”, “direction”, “fire_line”, or “speed” for raster files.</p> <p><em>{fire_season} </em>= the locally-defined fire season in which the fire was ignited (see more below).</p> <p><em>{file_type} </em>= ".shp" for vector files; ".tif" for raster files. </p> <p>Please note that the year string in filenames refers to the locally-defined fire season in which the fire ignited, not the calendar year. Hence the file GFA_v20240409_perimeters_2003.shp can include fires from the 2003 fire season that ignited in the calendar years 2002 or 2004. </p> <h3>Coordinate systems (Map Projections):</h3> <p>Vector data are provided on the WGS84 projection.</p> <p>Raster data are provided on the MODIS sinusoidal projection used in NASA tiled products. The WKT string defining this projection is:</p> <pre><code>'PROJCS["unnamed",GEOGCS["Unknown datum based upon the custom spheroid",DATUM["Not_specified_based_on_custom_spheroid",SPHEROID["Custom spheroid",6371007.181,0]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]]],PROJECTION["Sinusoidal"],PARAMETER["longitude_of_center",0],PARAMETER["false_easting",0],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH]]'</code></pre> <p> </p> <h2><strong>Data Layers:</strong></h2> <p><em><strong>Table 1: Overview of the Global Fire Atlas data layers. </strong></em>The shapefiles of ignition locations (point) and fire perimeters (polygon) contain attribute tables with summary information for each individual fire, while the underlying 500 m gridded layers reflect the day-to-day behavior of the individual fires. In addition, we provide aggregated monthly summary layers at a 0.25° resolution for regional and global analyses.</p> <table> <tbody> <tr> <td>File name</td> <td>Content</td> </tr> <tr> <td>SHP_ignitions.zip</td> <td>Shapefiles of ignition locations with attribute tables (see Table 2)</td> </tr> <tr> <td>SHP_perimeters.zip</td> <td>Shapefiles of final fire perimeters with attribute tables (see Table 2)</td> </tr> <tr> <td>GeoTIFF_direction.zip</td> <td>500 m resolution daily gridded data on direction of spread (8 classes)</td> </tr> <tr> <td>GeoTIFF_day_of_burn.zip</td> <td>500 m resolution daily gridded data on day of burn (day of year; 1-366)</td> </tr> <tr> <td>GeoTIFF_speed.zip</td> <td>500 m resolution daily gridded data on speed (km/day)</td> </tr> <tr> <td>GeoTIFF_fire_line.zip</td> <td>500 m resolution daily gridded data on the fire line (day of year; 1-366)</td> </tr> <tr> <td>GeoTIFF_monthly_summaries.zip</td> <td>Aggregated 0.25° resolution monthly summary layers. These files include the sum of ignitions, average size (km2), average duration (days), average daily fire line (km), average daily fire expansion (km2/day), average speed (km/day), and dominant direction of spread (8 classes). </td> </tr> </tbody> </table> <p> </p> <p><em><strong>Table 2: Overview of the Global Fire Atlas shapefile attribute tables. </strong></em>The shapefiles of ignition locations (point) and fire perimeters (polygon) contain attribute tables with summary information for each individual fire.</p> <table> <tbody> <tr> <td>Attribute</td> <td>Explanation / units</td> </tr> <tr> <td>lat, lon</td> <td>Coordinates of ignition location (°)</td> </tr> <tr> <td>size</td> <td>Fire size (km2)</td> </tr> <tr> <td>perimeter</td> <td>Fire perimeter (km)</td> </tr> <tr> <td>start_date, start_DOY</td> <td>Start date (yyyy-mm-dd), start day of year (1-366)</td> </tr> <tr> <td>end_date, end_DOY</td> <td>End date (yyyy-mm-dd), end day of year (1-366)</td> </tr> <tr> <td>duration</td> <td>Duration (days)</td> </tr> <tr> <td>fire_line</td> <td>Average length of daily fire line (km)</td> </tr> <tr> <td>spread</td> <td>Average daily fire growth (km2/day)</td> </tr> <tr> <td>speed</td> <td>Average speed (km/day)</td> </tr> <tr> <td>direction, direc_frac</td> <td>Dominant direction of spread (N, NE, E, SE, S, SW, W, NW) and associated fraction</td> </tr> <tr> <td>MODIS_tile</td> <td>MODIS tile id</td> </tr> <tr> <td>landcover, landc_frac</td> <td>MCD12Q1 dominant land cover class and fraction (UMD classification), provided for 2002-2023</td> </tr> <tr> <td>GFED_regio</td> <td>GFED region (van der Werf et al., 2017; available at https://www.globalfiredata.org/)</td> </tr> </tbody> </table> <p> </p> <p> </p>
North Carolina Outer Banks, USA Coastal Foredune Sediment Cores - Grain Size Data & Core Log Descriptions
<p>This repository includes sediment core data collected at seven sites along the northern Outer Banks, North Carolina, USA. From north to south, the sites include Pine Island, Corolla Reserve, Duck, the US Army Corps of Engineers Field Research Facility (FRF) North, FRF South, Southern Shores (i.e., Hillcrest Beach Access), and Nags Head (Bonnett St. Beach Access).</p><p>At each site, internal dune sedimentology and stratigraphy were characterized using sediment vibracores, each 1.5–2.2 m long, collected along a cross-shore transect from the dune toe to the dune heel. Coring locations were selected based on dune morphology to capture the stratigraphy of the dune toe, stoss slope, primary dune crest, lee slope, swale, and secondary dune crest, as applicable. Sediment core locations were documented using RTK-GPS and are included in the .kmz file.</p><p>All sediment cores were split, photographed, described for sedimentary structures, texture (as compared to standards), mineralogy, and color (Munsell, 2012). Sediment cores were described using the Modified Burmister System in 10-cm intervals, with additional intervals added as needed to capture stratigraphic units with thicknesses less than 10 cm but greater than 1 cm. Sediment core log descriptions are included in the NOAA_NCDunes_Vibracore_CoreLogs.xlsx data file.</p><p>Sediment size and shape were analyzed from oven-dried samples using a CAMSIZERX2Ⓡ. These data are included in the Dune_Grain_Size_camsizer_outputs.csv data file. Metrics reported for each sample include the following: Site, Core ID, Sample Number, Depth (cm below ground surface), Elevation (m, NAVD88), D2 (mm), D5 (mm), D10 (mm), D16 (mm), D25 (mm), D50 (mm), D75 (mm), D84 (mm), D90 (mm), D95 (mm), D98 (mm), average grain symmetry, average grain sphericity, average grain aspect ratio, percent pebble, percent granule, percent very coarse sand, percent coarse sand, percent medium sand, percent fine sand, percent very fine sand, and percent silt.</p><p><strong>More details regarding these measurements can be found in the following manuscript:</strong></p><p>Davis, E.H., Hein, C.J., Cohn, N., White, A.E., Zinnert, J.C. Differences in internal sedimentologic and biotic structure between natural, managed, and constructed coastal foredunes (in review).</p>
PsPM-PubFe: Pupil size response in a delay fear conditioning procedure with auditory CS and electrical US.
<p>This dataset includes pupil size response (PSR), skin conductance response (SCR), electrocardiogram (ECG) and respiration measurements. Also included are CS and US information, keypress responses, keypress response times and key correctness for each of 22 healthy unmedicated participants (7 males and 15 females aged 26.4+/-5.2 years) participating in a classical (Pavlovian) discriminant delay fear conditioning task. CS consists of two sine tones with constant frequency (220 Hz or 440 Hz, 50-ms onset and offset ramp). US is a train of electric square pulses delivered with a constant current stimulator (Digitimer DS7A, Digitimer, Welwyn Garden City, UK) on participants' dominant forearm through a pin-cathode/ring-anode configuration. SOA betwen the CS and US is 3.5 s. The ITI is randomly determined on each trial to be 7, 9, or 11 s.</p>
Experimental data for "Deep Learning Methods for Colloidal Silver Nanoparticle Concentration and Size Distribution Determination from UV-Vis Extinction Spectra"
<p>Testing data (experimental data) for neural networks published in preprint https://doi.org/10.48550/arXiv.2404.10891</p> <p>The UV-VIS-NIR spectral data was also used in the dissertation of Nadzeya Khinevch, titled "Two-dimensional structures of nanoparticles for elements of surface-enhanced Raman scattering substrates".</p> <p>Emails of the corresponding authors:</p> <p>Tomas Klinavičius tomas.klinavicius@ktu.lt</p> <p>Tomas Tamulevičius tomas.tamulevicius@ktu.lt</p>
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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.