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Mollusc population abundance monitoring: Fall 2016 mid-marsh and creekbank infaunal and epifaunal mollusc abundance based on collections from GCE marsh, monitoring sites 1-10
This data set is the Fall 2016 estimate of infaunal and epifaunal mollusc abundance at the GCE-LTER marsh sites used for population monitoring. Species abundance was determined by hand-collecting all the infaunal and epifaunal molluscs from within quadrats of known area in mid-marsh and creekbank zones (n = 4 quadrats per zone) at all sites. The molluscs were returned to the lab, fixed in ethanol, transferred to and preserved in ethanol, counted and measured (size data is reported separately). The counts were converted to number per square meter. Gastropod species are listed first, followed by bivalve species. Size distribution data for these collections may be found in the GCE-LTER data set INV-GCEM-1707a.
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.
Fall 2021 crab population monitoring: mid-marsh and creek bank abundance based on crab hole counts at GCE marsh, monitoring sites 1-10
This data set is the Fall 2021 estimate of crab densities at the GCE-LTER marsh sites used for population monitoring. Crab abundance was determined by counting the number of crab holes within a 625 cm^2 quadrat and converting the counts to number per square meter. Counts were made in the mid-marsh and creek bank zones (n = 4 per zone) at GCE sites 1 through 10. Note that this census method does not differentiate which species made a particular hole and therefore only estimates total burrowing crab abundance, potentially including species Uca pugnax, Uca minax, Uca pugilator, Armases cinereum, Eurytium limosum and Sesarma reticulatum. Crab holes that are not actively maintained are quickly covered by tidal activity and other sediment disturbances, therefore plugged holes were assumed to be unoccupied and excluded from the counts.
Mollusc population abundance monitoring: Fall 2021 mid-marsh and creekbank infaunal and epifaunal mollusc abundance based on collections from GCE marsh, monitoring sites 1-10
This data set is the Fall 2021 estimate of infaunal and epifaunal mollusc abundance at the GCE-LTER marsh sites used for population monitoring. Species abundance was determined by hand-collecting all the infaunal and epifaunal molluscs from within quadrats of known area in mid-marsh and creekbank zones (n = 4 quadrats per zone) at all sites. The molluscs were returned to the lab, fixed in ethanol, transferred to and preserved in ethanol, counted and measured (size data is reported separately). The counts were converted to number per square meter. Gastropod species are listed first, followed by bivalve species. Size distribution data for these collections may be found in the GCE-LTER data set INV-GCEM-2207a.
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.
Fall 2022 crab population monitoring: mid-marsh and creek bank abundance based on crab hole counts at GCE marsh, monitoring sites 1-10
This data set is the Fall 2022 estimate of crab densities at the GCE-LTER marsh sites used for population monitoring. Crab abundance was determined by counting the number of crab holes within a 625 cm^2 quadrat and converting the counts to number per square meter. Counts were made in the mid-marsh and creek bank zones (n = 4 per zone) at GCE sites 1 through 10. Note that this census method does not differentiate which species made a particular hole and therefore only estimates total burrowing crab abundance, potentially including species Uca pugnax, Uca minax, Uca pugilator, Armases cinereum, Eurytium limosum and Sesarma reticulatum. Crab holes that are not actively maintained are quickly covered by tidal activity and other sediment disturbances, therefore plugged holes were assumed to be unoccupied and excluded from the counts.
Mollusc population abundance monitoring: Fall 2022 mid-marsh and creekbank infaunal and epifaunal mollusc abundance based on collections from GCE marsh, monitoring sites 1-10
This data set is the Fall 2022 estimate of infaunal and epifaunal mollusc abundance at the GCE-LTER marsh sites used for population monitoring. Species abundance was determined by hand-collecting all the infaunal and epifaunal molluscs from within quadrats of known area in mid-marsh and creekbank zones (n = 4 quadrats per zone) at all sites. The molluscs were returned to the lab, fixed in ethanol, transferred to and preserved in ethanol, counted and measured (size data is reported separately). The counts were converted to number per square meter. Gastropod species are listed first, followed by bivalve species. Size distribution data for these collections may be found in the GCE-LTER data set INV-GCEM-2307a.
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.
Fall 2023 crab population monitoring: mid-marsh and creek bank abundance based on crab hole counts at GCE marsh, monitoring sites 1-10
This data set is the Fall 2023 estimate of crab densities at the GCE-LTER marsh sites used for population monitoring. Crab abundance was determined by counting the number of crab holes within a 625 cm^2 quadrat and converting the counts to number per square meter. Counts were made in the mid-marsh and creek bank zones (n = 4 per zone) at GCE sites 1 through 10. Note that this census method does not differentiate which species made a particular hole and therefore only estimates total burrowing crab abundance, potentially including species Uca pugnax, Uca minax, Uca pugilator, Armases cinereum, Eurytium limosum and Sesarma reticulatum. Crab holes that are not actively maintained are quickly covered by tidal activity and other sediment disturbances, therefore plugged holes were assumed to be unoccupied and excluded from the counts.
Harmonic Baseline Experiments for Landsat-Based Forest Condition Monitoring in Southern New England 2017
This dataset was developed as part of a study of harmonic baseline model parameterization for forest condition monitoring using Landsat time series. We implemented a previously published harmonic modeling approach for forest condition monitoring in Google Earth Engine and systematically assessed the relative ability of condition change products generated using various model parameterizations for predicting pest abundances and defoliation during the 2016-2018 Lymantria dispar outbreak in southern New England. We ran a series of 32 experiments that considered a variety of parameter choices for establishing multi-year “baseline” models representing relatively stable forest conditions for each Landsat pixel in our study area. We tested a full set of factors including (a) spectral vegetation index used for model fitting, (b) baseline-modeling period, (c) frequencies of harmonic regression terms, and (d) differences in Landsat time series input imagery. We generated average condition score estimates for each of these 32 baseline parameterizations for a May 1 to September 30, 2017 monitoring period, then used Generalized Linear Mixed Models to test the relationships between ground-based observations of defoliation and defoliator abundance (larva and egg masses). This archived dataset includes the full set of experimental raster results, as well as a “reanalysis” product from a previous implementation of our condition monitoring workflow. More information on model parameterization rankings can be found in the associated publication (Pasquarella et al. 2021).
Wrack classification data based on UAV imagery from Dean Creek on Sapelo Island, GA
We used a DJI Matrice 210 UAV with a MicaSense Altum to collect a total of 20 images from January 2020 - December 2021 in a the Dean Creek marsh on Sapelo Island, GA. Wrack was classified using a principal component analysis. Wrack patches under 1 m2 were excluded from analyses. Wrack classifications were converted to polygon and point data where each point represents a 5 cm x 5 cm pixel. Those files were then used to analyze wrack characteristics, their relation to environmental drivers, and landscape based patterns. For both polygon and point data, we used the National Elevation Dataset (https://gdg.sc.egov.usda.gov/Catalog/ProductDescription/NED.html) to determine the elevation of each wrack patch. Creeks and shorelines were digitized and used to determine each wrack patches' distance to water. We calculated the frequency of wrack deposition at each point by adding together the number of images where that pixel was classified as wrack over the course of the study. Polygon data were related to tide height from a NOAA tidal station data product (Ft. Pulaski, Station 8670870; https://tidesandcurrents.noaa.gov) and wind speed and wind direction from the Marsh Landing weather station (downloaded data for the SAPMLMET met station from: https://cdmo.baruch.sc.edu/) to evaluate the relationship of wrack to environmental drivers.
Physical soil characteristics, microbial community composition, extracellular enzymatic activity, biologically based phosphorus (BBP) pools, and available phosphorus from two soil depths, four microhabitats, and four landforms at the Jornada Experimental Range, 2021.
This dataset contains physical soil characteristics, PLFA based microbial community composition, extracellular enzymatic activity, nitrate and ammonium activity, and phosphorus availability in various phosphorus pools (Biologically Based Phosphorus, potassium sulfate, Olsen-P). Soils were collected from two depths (0-2cm, 2-30 cm), four microhabitats (grass, shrub, biocrust, interspace), and four landforms (alluvial flat, alluvial fan remnant, erosional scarplet, fan piedmont – see coordinates) within the Jornada Experimental Range in July 2021 to answer questions about how these variables change across these spatial scales in drylands. This project was a collaboration between researchers at New Mexico State University and The University of Texas at El Paso as part of the Drylands Critical Zone Thematic Cluster within the Critical Zone Network. This dataset is complete.
Data from: Satellite-based Lagrangian model reveals how upwelling and oceanic circulation shape krill hotspots in the California Current System [updated]
<p><strong>Abstract</strong></p> <p>In the California Current System, wind-driven nutrient supply and primary production, computed from satellite data, provide a synoptic view of how phytoplankton production is coupled to upwelling. In contrast, linking upwelling to zooplankton populations is difficult due to relatively scarce observations and the inherent patchiness of zooplankton. While phytoplankton respond quickly to environmental forcing, zooplankton grow slower and tend to aggregate into mesoscale “hotspot” regions spatially decoupled from upwelling centers. To better understand mechanisms controlling the formation of zooplankton hotspots, we use a satellite-based Lagrangian method where variables from a plankton model, forced by wind-driven nutrient supply, are advected by near-surface currents following upwelling events. Modeled zooplankton distribution reproduces published accounts of euphausiid (krill) hotspots, including the location of major hotspots and their interannual variability. This satellite-based modeling tool is used to analyze the variability and drivers of krill hotspots in the California Current System, and to investigate how water masses of different origin and history converge to form predictable biological hotspots. The Lagrangian framework suggests that two conditions are necessary for a hotspot to form: a convergence of coastal water masses, and above average nutrient supply where these water masses originated from. The results highlight the role of upwelling, oceanic circulation, and plankton temporal dynamics in shaping krill mesoscale distribution, seasonal northward propagation, and interannual variability.</p> <p><strong>Data set description</strong></p> <p>This data set includes 2 files:</p> <ul> <li>a satellite-based 1993-2023 monthly retrospective of krill concentrations (Zbig) modeled using the growth-advection method in the California Current upwelling system. Inputs include the nitrate supply product described below and GlobCurrent 15 m oceanic currents. This dataset is updated monthly (using NRT data) at https://www.mbari.org/science/upper-ocean-systems/biological-oceanography/krill-hotspots-in-the-california-current/.</li> <li>a satellite-based 1993-2023 monthly retrospective of wind-driven nitrate supply estimated in a 150 km coastal band at 0.125° latitudinal resolution. Nitrate supply was calculated based primarily on CCMP v3.1 winds, AVISO geostrophic currents, and a climatology of in situ nitrate at 60m. This dataset is updated monthly (using NRT data) at https://www.mbari.org/science/upper-ocean-systems/biological-oceanography/nitrate-supply-estimates-in-upwelling-systems/.</li> </ul> <p>See details regarding data sources and calculations in <a href="https://doi.org/10.3389/fmars.2022.835813">Messié et al. (2022)</a>.</p> <p>[IMPORTANT NOTE:] There is an error in the Ekman pumping fields (trans_pump, Nsupply_pump, Nsupply_total) that will be corrected soon (those fields are not used in publications where only coastal transport was considered). Please contact me if you need Ekman pumping fields before this is fixed.</p>
Monthly precipitation in mm at 1 km resolution (multisource average) based on SM2RAIN-ASCAT 2007-2021, CHELSA Climate and WorldClim
<p>Monthly precipitation in mm at 1 km resolution based on SM2RAIN-ASCAT 2007-2021 (<a href="https://doi.org/10.5281/zenodo.2615278">https://doi.org/10.5281/zenodo.2615278</a>). <a href="https://github.com/Envirometrix/LandGISmaps/tree/master/input_layers/clim1km">Downscaled to 1 km resolution using gdalwarp</a> (cubic splines) and combined with WorldClim (<a href="https://worldclim.org/data/worldclim21.html">https://worldclim.org/data/worldclim21.html</a>) and CHELSA Climate (<a href="https://chelsa-climate.org/downloads/">https://chelsa-climate.org/downloads/</a>) monthly values. Final values are estimated as a simple average between the three precipitation data sources; a more objective approach would be to use training points e.g. meteo-station monthly values, then train an ensemble model using the 3 data sources as independent variables. Another global data source of precipitation images is the <a href="https://gpm.nasa.gov/data/imerg">monthly IMERGE dataset</a>, however this requires transformation and is available only for limited span of years.</p> <p>Processing steps are available <a href="https://gitlab.com/openlandmap/global-layers/-/tree/master/input_layers/SM2RAIN"><strong>here</strong></a>. Antarctica is not included. Standard deviation (sd) indicates a difference between the 3 data sources. To access and visualize maps use: <a href="https://openlandmap.org"><strong>https://openlandmap.org</strong></a>.<strong> </strong>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>clm = theme: climate,</li> <li>precipitation = variable: precipitation,</li> <li>wc.v2.1.chelsa.v2.1.sm2rain.oct = determination method: long-term average values for October,</li> <li>m = mean value,</li> <li>1km = spatial resolution / block support: 1 km,</li> <li>s0..0cm = vertical reference: land surface,</li> <li>1980..2020 = time reference: from 1980 to 2020,</li> <li>v0.3 = version number: 0.3,</li> </ul>
Daily runoff and nutrient loads for the North Sea and the Baltic Sea based on modelling and observations for the period 1961 to 2019 and adapted to NEMO-SCOBI
<p>This dataset consists of daily values of runoff and reconstructed nutrient loads for the period 1961 to 2019 for the North Sea-Baltic Sea system. Both runoff and nutrient loads were obtained from a model simulation performed with the European application of the Hydrological Predictions for the Environment model v.3.1.8 (E-HYPE). This dataset includes a more realistic number of river outlets than those from observational-based datasets, as not all rivers are monitored, and captures well the interannual variability of all parameters. However, the E-HYPE v.3.1.8 was calibrated to represent 2010 and thus cannot simulate all historical changes related to land management (i.e., the increase of fertilizers in the 1960s). Consequently, the observed rise of nutrients from land due to increased fertilizers and the consequent reduction due to nutrient regulation policy in the 1980s is not captured in the outputs from E-HYPE directly. In the North Sea and the Baltic Sea, this is of primary importance for management policy in eutrophication and deoxygenation. Therefore, we have adapted the E-HYPE nutrient loads based on yearly estimates of historical loads that use riverine concentrations, so that the high tempo-spacial resolution is kept, but with a decadal variability that is closer to reality. This dataset is mainly intended as river forcing for biogeochemical-ocean models (i.e. NEMO-SCOBI), but can also provide information on rivers that are not included in monitoring programs. Information on the dataset and the methods used to create it is given as a downloadable PDF file (E-HYPE DecVar documentation.pdf) together with two datasets and the mesh grid file (area_NEMO-Nordic.nc). The datasets are yearly netCDF files one containing daily runoff and nutrient loads for phosphate, nitrate, ammonium, organic nitrogen and organic phosphorus (zip_ehypeDecVar.zip) and the other one provides monthly silica loads (zip_silica.zip). </p>
Dataset of reports about MOF-based SERS substrates since 2011 until March 2023. Structure, characteristics, analytes, and performances.
<p>This dataset was generated to aid the creation of a review article addressing the use of Metal-Organic Frameworks (MOF)-based Surface Enhanced Raman Spectroscopy (SERS) platforms for the detection of Volatile Organic Compounds (VOCs).</p> <p>This dataset was generated employing the Web of Science database, encompassing manuscripts published up to March 2023. A literature search was initially conducted using a combination of keywords, including "MOF," "Metal-Organic Framework," "SERS," "Surface Enhanced Raman Spectroscopy," and "Surface Enhanced Raman Scattering." This search spanned the "Topic" category, enabling exploration across title, abstract, author keywords, and keyword-plus fields.</p> <p>From the initial pool of 238 documents, review articles and duplicates were systematically excluded, resulting in a refined collection of 182 articles. Subsequently, articles not concurrently addressing MOF and SERS or those utilizing MOF as sacrificial templates were further excluded, resulting in a final subset of 72 articles. From this curated set, relevant parameters were extracted, resulting in 229 entries for the dataset. </p> <p>Characteristics about the structure (in terms of MOF type and configuration; Plasmonic element type and configuration), target analyte (including type, phase, and incubation time), measurement specifications (in terms of laser, laser power, exposure time), and performance of the MOF-based SERS substrates were collected.</p> <p>Listed references 1-72 correspond with the manuscript number in the dataset.</p> <p>Listed references 73-80 correspond with references for selected examples of MOF pore diameters.</p>
Bases de datos de encuestas docentes no universitarios en España 2021-2022, sobre diversidad, interculturalidad, estereotipos y aculturación
<p>Bases de datos de encuestas docentes no universitarios en España 2021-2022, recabado en el marco del proyecto Diverprof (https://diverprof.es/). Se incluye informe de variables iniciales y ad-hoc elaboradas para analizar las actitudes hacia la aculturación y la diversidad cultural en el profesorado a nivel de educación obligatoria en España.</p> <p>Proyecto (PP-2022-06), financiado por la Universidad Internacional de La Rioja.</p>
Circumpolar mid-winter thaw and refreeze based on fusion of Metop ASCAT and SMOS, 2011/2012 - 2021/2022
<p>Rain-on-Snow (ROS) events occur across many regions of the terrestrial Arctic in mid-winter. Snow pack properties are changing and in extreme cases ice layers form which affect wildlife, vegetation and soils beyond the duration of the event.</p> <p>Active and passive microwave data have been combined to identify events over land North of 65°N (Bartsch et al. 2023). In a first step Metop ASCAT (C-Band radar) was used to identify potential sudden snow structure change. In a second step, results have been masked for coincident observation of wet snow within +- 3 days based on SMOS (derived from Centre Aval de Traitement des Données SMOS (CATDS) level 3 product). Note that the SMOS retrievals can have data gaps due to radio frequency interferences (RFI) what leads to gaps in the event detection.</p> <p>The dataset is structured by centre points of the hexagonal grid of the used Metop ASCAT product (EUMETSAT, approximately 12.5 km nominal resolution). Attributes include point ID (GPI), latitude, longitude and</p> <ul> <li>aggregated number of events for the months November to February, 2011/12 to 2021/22, and their sum per winter (referred to as annual), or</li> <li>in case of daily results (date in file name) the magnitude of ASCAT backscatter change in dB (DSigma0; no data value is '0.0').</li> </ul> <p>The dataset extents Seawinds QuikScat (Ku-band) based results for 2000-2009 (Bartsch 2010, Freund and Bartsch 2020).</p>
Prevalence and determinants of cardiovascular risk factors in Lesotho: a population-based survey
<p>These are pseudo-anonymised data from the ComBaCaL survey and belong to the manuscript "Prevalence and determinants of cardiovascular risk factors in Lesotho: a population-based survey" which can be found at <a href="https://doi.org/10.1093/inthealth/ihad058">https://doi.org/10.1093/inthealth/ihad058</a>. </p> <p>The data dictionary explains the critical data available in the dataset. Between November 2021 and August 2022 , 6061 participants over 18 years old were visited in their households in two districts of Lesotho. </p>
Global dataset of nitrogen fixation rates across inland and coastal waters based on a coordinated synthesis effort
Biological nitrogen fixation converts inert di-nitrogen gas into bioavailable nitrogen and can be an important source of bioavailable nitrogen to organisms. This dataset synthesizes the aquatic nitrogen fixation rate measurements across inland and coastal waters. Data were derived from papers and datasets published by April 2022 and include rates measured using the acetylene reduction assay (ARA), 15N2 labeling, or the N2/Ar technique. The dataset is comprised of 4793 nitrogen fixation rates measurements from 267 studies, and is structured into four tables: 1) a reference table with sources from which data were extracted, 2) a rates table with nitrogen fixation rates that includes habitat, substrate, geographic coordinates, and method of measuring N2 fixation rates, 3) a table with supporting environmental and chemical data for a subset of the rate measurements when data were available, and 4) a data dictionary with definitions for each variable in each data table. This dataset was compiled and curated by the NSF-funded Aquatic Nitrogen Fixation Research Coordination Network (award number 2015825).
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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.