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Saint Louis River Estuary Water Chemistry, Wisconsin, Minnesota, USA 2012 - 2013
These data pertain to water and sediments collected from the Saint Louis River Estuary (SLRE) and its nearby water sources by Luke Loken and collaborators for his Masters thesis and additional publications. In brief, we sampled SLRE surface waters and sediments for a variety of physical, chemical, and biological attributes. Ten estuary stations were sampled approximately monthly from April 2012 through September 2013. On four of the sampling campaigns, water was collected from an additional 20 sites. Sites were selected to represent a gradient from the Saint Louis River to Lake Superior and included several tributaries that drain directly into the estuary. This design aimed to understand the spatial and temporal mixing pattern of the estuary as it receives water from several rivers, 2 waste water treatment plant, and Lake Superior. We sampled the estuary to assess the magnitude and timing of source water contributions to the estuary and establish a baseline of chemical and physical measurements to aid in future limnological research. Additionally, we performed nitrogen and carbon cycling rate experiments to determine the estuary-wide influence on nitrate, ammonium, and dissolved organic carbon. This included 8 sediment denitrification, 1 nitrification, and 2 breakdown dissolved organic carbon (BDOD) surveys. This work was funded by the Minnesota and Wisconsin Sea Grant and in coordination with the establishment of the Lake Superior National Estuary Research Reserve (LSNERR).
Geographically paired lake-reservoir dataset derived from the 2007 USA EPA National Lakes Assessment
Climate change poses a significant threat to lake and reservoir ecosystems, though the exact nature of these threats may differ between lakes and reservoirs. To assess differences between lakes and reservoirs that may influence their response to climate change, we compared catchment and waterbody attributes of 132 geographically paired lakes and reservoirs from the 2007 United States Environmental Protection Agencys National Lakes Assessment (NLA) dataset. The data include the NLA IDs of each waterbody and their elevation, catchment area, surface area, perimeter, maximum depth, residence time, Secchi disk depth, surface temperature, and bottom temperature. Residence time data was collected from estimates generated by Brooks, J.R., J.J. Gibson, S.J. Birks, M.H. Weber, K.D. Rodecap, J.L. Stoddard. 2014. Stable isotope estimates of evaporation: inflow and water residence time for lakes across the United States as a tool for national lake water quality assessments. Limnology and Oceanography 59(6):2150-2165.
Long-term trends and synchrony in dissolved organic matter characteristics in Wisconsin, USA lakes: quality, not quantity, is highly sensitive to climate
Dissolved organic matter (DOM) is a fundamental driver of many lake processes. In the past several decades, many lakes have exhibited a substantial increase in DOM quantity, measured as dissolved organic carbon (DOC) concentration. While increasing DOC is now widely recognized, fewer studies have sought to understand how characteristics of DOM (DOM quality) change over time. Quality can be measured in several ways, including the optical characteristics spectral slope (S275-295), spectral ratio (SR), absorbance at 254 nm (a254), and DOC-specific absorbance (SUVA; a254:DOC). However, long-term measurements of quality are not nearly as common as long-term measurements of DOC concentration. We used 24 years of DOC and absorbance data for seven lakes in the North Temperate Lakes Long Term Ecological Research site in northern Wisconsin, USA to examine temporal trends and synchrony in both DOC concentration and quality. We predicted lower SR and S275-295 and higher a254 and SUVA trends, consistent with increasing DOC and greater allochthony. DOC concentration exhibited both significant positive and negative trends among lakes. In contrast, DOC quality exhibited trends suggesting reduced allochthony or increased degradation, with significant long-term increases in SR in three lakes. Patterns and synchrony of DOM quality parameters suggest they are more responsive to climatic variations than DOC concentration. SUVA in particular tended to increase with greater moisture and decrease with drier conditions. These results demonstrate that DOC quantity and quality can exhibit different complex long-term trends and responses to climate components, with important implications for aquatic ecosystems.
Dissolved Organic Carbon Concentration, Dissolved Organic Matter Optical Properties, and Water Quality Indicators in the Plum Island Estuary (PIE), Massachusetts, USA (2018-2023)
This is a data set of paired in situ measurements of water quality parameters, total suspended solids concentration, and concentration and optical properties (absorption coefficient spectra and fluorescence indices) of dissolved organic matter (DOM) collected between 2018 and 2023 in the Plum Island Estuary and nearshore waters. In situ water quality measurements (salinity, temperature, optical dissolved oxygen saturation, turbidity, and dissolved organic matter fluorescence) were collected with a water quality sonde from the surface (top 1 m of water column), along with corresponding samples that were processed and analyzed in the lab for dissolved organic carbon (DOC) concentration, chromophoric DOM (CDOM), absorption coefficient spectra, DOM excitation-emission matrix (EEM) fluorescence, and total suspended sediment (TSS) concentration. The data were used in multiple studies (see manuscripts listed below) focusing on the dynamics of DOC and CDOM in the Plum Island Estuary.
SBC LTER: Reference: Sea-surface water temperature, Santa Barbara Harbor, Santa Barbara, CA, USA, 1955 to present, ongoing
The SBC-LTER has access to data on seawater temperature collected at Santa Barbara Harbor, Santa Barbara, CA, USA through the Scripps Institution of Oceanography Manual Shore Stations program. The SIO Manual Shore Stations program provides data and information about this shore station. For further information, please visit the SIO Manual Shore Stations website at https://library.ucsd.edu/dc/object/bb07606686. Please note: manual shore station data is updated periodically, not continuously. Funding for the Shore Stations Program provided by the California Department of Parks and Recreation, Natural Resources Division, Award# C22820005. Contact shorestation@ucsd.edu if you have questions
Mean Seagrass Meadow Metabolism by Time of Day in Virginia, USA
The study examined benthic O2 fluxes over a seagrass meadow using the aquatic eddy covariance technique, while measuring variables that have been shown to affect metabolism (PAR, temperature, O2 concentration, current velocity). Accurate daily metabolic estimates of respiration, gross primary production, and net ecosystem metabolism are widely used to assess ecosystem health and blue carbon sequestration and storage in vegetated coastal ecosystems. Aquatic eddy covariance allows direct measurements under naturally varying in situ conditions of oxygen (O2) fluxes between a benthic substrate and the water above. Here, we used hourly O2 fluxes measured with this approach to examine how respiration for a Zostera marina seagrass meadow varies through night and day, and how this affects commonly performed metabolic estimates. Fitting our database of 2,115 hourly benthic O2 fluxes for a seagrass meadow revealed that respiration decreased linearly by 29% through the night. We primarily attribute this to consumption of highly labile compounds that are formed by photosynthesis and accumulate during daytime. Furthermore, a corresponding linear increase in respiration through the day coupled with photosynthetic production described by a standard photosynthesis irradiance curve provided an accurate prediction of measured O2 fluxes (R2 = 0.993). These results document that night- and daytime respiration vary significantly in a seagrass meadow, and that both can be described accurately by a piecewise linear relationship. Many studies have questioned the widely used assumption in metabolic estimates that night- and daytime respiration are constant and equal. However, if night- and daytime respiration can be approximated as we found here by piecewise linear relationships, these standard means for calculating daily metabolic numbers remain valid. It is, however, important that such estimates are based on full 24-h records of benthic flux data.
Data for "The effects of weather and mobility on respiratory viruses dynamics before and during the COVID-19 pandemic in the USA and Canada".
<p>Epidemiological and mobility data analysed in the paper "The effects of weather and mobility on respiratory viruses dynamics before and during the COVID-19 pandemic in the USA and Canada".</p>
Labeled high-resolution orthoimagery time-series of an alluvial river corridor; Elwha River, Washington, USA.
<h2>Labeled high-resolution orthoimagery time-series of an alluvial river corridor; Elwha River, Washington, USA.</h2><h4>Daniel Buscombe, Marda Science LLC</h4><p>There are two datasets in this data release:</p><p>1. <strong>Model training dataset</strong>. A manually (or semi-manually) labeled image dataset that was used to train and evaluate a machine (deep) learning model designed to identify subaerial accumulations of large wood, alluvial sediment, water, and vegetation in orthoimagery of alluvial river corridors in forested catchments. </p><p>2. <strong>Model output dataset</strong>. A labeled image dataset that uses the aforementioned model to estimate subaerial accumulations of large wood, alluvial sediment, water, and vegetation in a larger orthoimagery dataset of alluvial river corridors in forested catchments. </p><p>All of these label data are derived from raw gridded data that originate from the U.S. Geological Survey (<i>Ritchie et al., 2018</i>). That dataset consists of 14 orthoimages of the Middle Reach (MR, in between the former Aldwell and Mills reservoirs) and 14 corresponding Lower Reach (LR, downstream of the former Mills reservoir) of the Elwha River, Washington, collected between the period 2012-04-07 and 2017-09-22. That orthoimagery was generated using SfM photogrammetry (following <i>Over et al., 2021</i>) using a photographic camera mounted to an aircraft wing. The imagery capture channel change as it evolved under a ~20 Mt sediment pulse initiated by the removal of the two dams. The two reaches are the ~8 km long Middle Reach (MR) and the lower-gradient ~7 km long Lower Reach (LR). </p><p>The orthoimagery have been labeled (pixelwise, either manually or by an automated process) according to the following classes (inter class in the label data in parentheses):</p><p>1. vegetation / other (0)</p><p>2. water (1)</p><p>3. sediment (2)</p><p>4. large wood (3)</p><h3>1. Model training dataset.</h3><p>Imagery was labeled using a combination of the open-source software Doodler (<i>Buscombe et al., 2021</i>; <a href="https://github.com/Doodleverse/dash_doodler">https://github.com/Doodleverse/dash_doodler</a>) and hand-digitization using QGIS at 1:300 scale, rasterizeing the polygons, and gridded and clipped in the same way as all other gridded data. Doodler facilitates relatively labor-free dense multiclass labeling of natural imagery, enabling relatively rapid training dataset creation. The final training dataset consists of 4382 images and corresponding labels, each 1024 x 1024 pixels and representing just over 5% of the total data set. The training data are sampled approximately equally in time and in space among both reaches. All training and validation samples purposefully included all four label classes, to avoid model training and evaluation problems associated with class imbalance (<i>Buscombe and Goldstein, 2022</i>). </p><p>Data are provided in geoTIFF format. The imagery and label grids (imagery) are reprojected to be co-located in the NAD83(2011) / UTM zone 10N projection, and to consist of 0.125 x 0.125m pixels.</p><p>Pixel-wise labels measurements such as these facilitate development and evaluation of image segmentation, image classification, object-based image-analysis (OBIA), and object-in-image detection models, and numerous potential other machine learning models for the general purposes of river corridor classification, description, enumeration, inventory, and process or state quantification. For example this dataset may serve in transfer learning contexts for application in different river or coastal environments or for different tasks or class ontologies.</p><h4>Files:</h4><p>1. Labels_used_for_model_training_Buscombe_Labeled_high_resolution_orthoimagery_time_series_of_an_alluvial_river_corridor_Elwha_River_Washington_USA.zip, 63 MB, label tiffs</p><p>2. Model_<i>training_</i> images1of4.zip, 1.5 GB, imagery tiffs</p><p>3. Model_<i>training_</i> images2of4.zip, 1.5 GB, imagery tiffs</p><p>4. Model_<i>training_</i> images3of4.zip, 1.7 GB, imagery tiffs</p><p>5. Model_<i>training_</i> images4of4.zip, 1.6 GB, imagery tiffs</p><h3>2. Model output dataset.</h3><p>Imagery was labeled using a deep-learning based semantic segmentation model (<i>Buscombe, 2023</i>) trained specifically for the task using the Segmentation Gym (<i>Buscombe and Goldstein, 2022</i>) modeling suite. We use the software package Segmentation Gym (<i>Buscombe and Goldstein, 2022</i>) to fine-tune a Segformer (<i>Xie et al., 2021</i>) deep learning model for semantic image segmentation. We take the instance (i.e. model architecture and trained weights) of the model of <i>Xie et al. (2021)</i>, itself fine-tuned on ADE20k dataset (<i>Zhou et al., 2019</i>) at resolution 512x512 pixels, and fine-tune it on our 1024x1024 pixel training data consisting of 4-class label images.</p><p>The spatial extent of the imagery in the MR is [455157.2494695878122002,5316532.9804129302501678 : 457076.1244695878122002,5323771.7304129302501678] (NAD83(2011) / UTM zone 10N). Imagery width is 15351 pixels and imagery height is 57910 pixels. The spatial extent of the imagery in the LR is [457704.9227139975992031,5326631.3750646486878395 : 459241.6727139975992031,5333311.0000646486878395] (NAD83(2011) / UTM zone 10N). Imagery width is 12294 pixels and imagery height is 53437 pixels. Data are provided in Cloud-Optimzed geoTIFF (COG) format. The imagery and label grids (imagery) are reprojected to be co-located in the NAD83(2011) / UTM zone 10N projection, and to consist of 0.125 x 0.125m pixels. All grids have been clipped to the union of extents of active channel margins during the period of interest.</p><p>Reach-wide pixel-wise measurements such as these facilitate comparison of wood and sediment storage at any scale or location. These data may be useful for studying the morphodynamics of wood-sediment interactions in other geomorphically complex channels, wood storage in channels, the role of wood in ecosystems and conservation or restoration efforts. </p><h4>Files:</h4><p>1. Elwha_MR_labels_Buscombe_Labeled_high_resolution_orthoimagery_time_series_of_an_alluvial_river_corridor_Elwha_River_Washington_USA.zip, 9.67 MB, label COGs from Elwha River Middle Reach (MR)</p><p>2. Elwha<i>MR_ imagery_ part1_ of</i>_<i> </i>2.zip, 566 MB, imagery COGs from Elwha River Middle Reach (MR)</p><p>3. Elwha<i>MR_ imagery_ part2_ of</i>_<i> </i>2.zip, 618 MB, imagery COGs from Elwha River Middle Reach (MR)</p><p>3. Elwha_LR_labels_Buscombe_Labeled_high_resolution_orthoimagery_time_series_of_an_alluvial_river_corridor_Elwha_River_Washington_USA.zip, 10.96 MB, label COGs from Elwha River Lower Reach (LR)</p><p>4. ElwhaL<i>R_ imagery_ part1_ of</i>_<i> </i>2.zip, 622 MB, imagery COGs from Elwha River Middle Reach (MR)</p><p>5. ElwhaL<i>R_ imagery_ part2_ of</i>_<i> </i>2.zip, 617 MB, imagery COGs from Elwha River Middle Reach (MR)<br> </p><p>This dataset was created using open-source tools of the Doodleverse, a software ecosystem for geoscientific image segmentation, by Daniel Buscombe (<a href="https://github.com/dbuscombe-usgs">https://github.com/dbuscombe-usgs</a>) and Evan Goldstein (<a href="https://github.com/ebgoldstein">https://github.com/ebgoldstein</a>). Thanks to the contributors of the Doodleverse!. Thanks especially Sharon Fitzpatrick (<a href="https://github.com/2320sharon">https://github.com/2320sharon</a>) and Jaycee Favela for contributing labels. </p><h3>References</h3><p>• Buscombe, D. (2023). <strong>Doodleverse/Segmentation Gym SegFormer models for 4-class (other, water, sediment, wood) segmentation of RGB aerial orthomosaic imagery (v1.0)</strong> [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.8172858">https://doi.org/10.5281/zenodo.8172858</a></p><p>• Buscombe, D., Goldstein, E. B., Sherwood, C. R., Bodine, C., Brown, J. A., Favela, J., et al. (2021).<strong> Human-in-the-loop segmentation of Earth surface imagery</strong>. Earth and Space Science, 9, e2021EA002085. <a href="https://doi.org/10.1029/2021EA002085">https://doi.org/10.1029/2021EA002085</a></p><p>• Buscombe, D., & Goldstein, E. B. (2022). <strong>A reproducible and reusable pipeline for segmentation of geoscientific imagery.</strong> Earth and Space Science, 9, e2022EA002332. <a href="https://doi.org/10.1029/2022EA002332">https://doi.org/10.1029/2022EA002332</a> See: <a href="https://github.com/Doodleverse/segmentation_gym">https://github.com/Doodleverse/segmentation_gym</a></p><p>• Over, J.R., Ritchie, A.C., Kranenburg, C.J., Brown, J.A., Buscombe, D., Noble, T., Sherwood, C.R., Warrick, J.A., and Wernette, P.A., 2021, <strong>Processing coastal imagery with Agisoft Metashape Professional Edition, version 1.6—Structure from motion workflow documentation</strong>: U.S. Geological Survey Open-File Report 2021–1039, 46 p., <a href="https://doi.org/10.3133/ofr20211039">https://doi.org/10.3133/ofr20211039</a>.</p><p>• Ritchie, A.C., Curran, C.A., Magirl, C.S., Bountry, J.A., Hilldale, R.C., Randle, T.J., and Duda, J.J., 2018, <strong>Data in support of 5-year sediment budget and morphodynamic analysis of Elwha River following dam removals</strong>: U.S. Geological Survey data release, <a href="https://doi.org/10.5066/F7PG1QWC">https://doi.org/10.5066/F7PG1QWC</a>.</p><p>• Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M. and Luo, P., 2021. <strong>SegFormer: Simple and efficient design for semantic segmentation with transformers</strong>. Advances in Neural Information Processing Systems, 34, pp.12077-12090.</p><p>• Zhou, B., Zhao, H., Puig, X., Xiao, T., Fidler, S., Barriuso, A. and Torralba, A., 2019. <strong>Semantic understanding of scenes through the ade20k dataset</strong>. International Journal of Computer Vision, 127, pp.302-321.</p><p><br> </p>
Urban Riparian Wetland Hydrology Dataset_Stormwater Capture in Beaver-mediated Wetlands along Walnut Creek, Raleigh, North Carolina, USA
<p>This is the initial release of a <strong>hydrology</strong> dataset pertaining to the <strong>riparian floodplain wetlands</strong> alongside Walnut Creek in Raleigh, North Carolina USA. Walnut Creek is the main drainage channel in an <strong>urbanized watershed</strong> (HUC-12: 030202011101) in central North Carolina. There are several riparian floodplain wetlands along the creek which are largely supplied by <strong>urban stormwater</strong> runoff including directed <strong>storm sewer flows</strong> and regular <strong>overbank flooding</strong> events. In many of these wetlands local water retention and residence time in the surface ponds is mediated by the damming activity of <strong>North American beavers (<em>Castor canadensis</em>)</strong>. This dataset contains data specific to the hydrology of Walnut Creek, and the surface ponds and groundwater at the <strong>Walnut Creek Wetland Park</strong> which is actively influenced by resident beavers. The period of this dataset is from <strong>January 22, 2023 through January 30, 2024</strong>. </p> <p>The core of the dataset is water stage measured in five surface pond sites and six groundwater monitoring wells within Walnut Creek Wetland Park. This data was collected using synchronized Solinst Levelogger pressure transducer sensors at 15-minute intervals, compensated with corrections for barometric pressure measured locally using a Solinst Barologger sensor. In addition to this data collected by the authors, this dataset also includes publicly available stream stage and precipitation data obtained from the <strong>US Geological Survey,</strong> and weather and soils data from the <strong>North Carolina State Climate Office</strong>. In total, this dataset aims to provide a comprehensive view of surface and subsurface hydrology in the studied wetlands as it connects with precipitation events, antecedent moisture conditions, directed stormwater flows and overbank flood events. </p> <p>This hydrology dataset is intended to accompany the <u>separate</u> <strong>water quality dataset</strong> published on Zenodo at URL: <a href="https://doi.org/10.5281/zenodo.10888463">https://doi.org/10.5281/zenodo.10888463</a>. Together, these datasets are meant to support an improved understanding of the water availability and water quality found in connection with beaver-mediated stormwater capture in an urbanized watershed in the North Carolina Piedmont.</p> <p>This dataset resulted from research supported with a Graduate Student Research Grant awarded by the <strong>North Carolina Water Resources Research Institute (WRRI)</strong>, under Project Number 23-10-W: "Stormwater Diversion, Storage, and Treatment by Beaver-enhanced Floodplain Wetlands in Piedmont Urban Watersheds". </p> <p>This material is based upon work supported by the <strong>National Science Foundation (NSF)</strong> Graduate Research Fellowship Program (GRFP) under Grant No. (DGE 2137100). Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors(s) and do not necessarily reflect the views of the National Science Foundation.</p> <p>Special thanks to <strong>Raleigh Parks</strong> and <strong>Walnut Creek Wetland Park</strong> for making this work possible.</p>
PM_152252_USA_Houston
<u>File Name</u>: PM_152252_USA_Houston.jpg <br><u>Sublocation</u>: Museum of Fine Arts <br><u>Location</u>: Houston <br><u>Province</u>: Texas <br><u>Country</u>: USA <br><u>Header</u>: Painting, Vera Icon, atelier van Dierick Bouts <br><u>Description</u>: Painting, Vera Icon, workshop of Dierick Bouts <br><u>Keywords</u>: Cultural heritage, Museum/private collection, Painting, Techniques, Texas, USA&Canada <br><br><u>Author</u>: Photo: Paul M.R. Maeyaert <br><u>Copyright</u>: Paul M.R. Maeyaert <br>
PM_152253_USA_Houston
<u>File Name</u>: PM_152253_USA_Houston.jpg <br><u>Sublocation</u>: Museum of Fine Arts <br><u>Location</u>: Houston <br><u>Province</u>: Texas <br><u>Country</u>: USA <br><u>Header</u>: Painting, Vera Icon, atelier van Dierick Bouts <br><u>Description</u>: Painting, Vera Icon, workshop of Dierick Bouts <br><u>Keywords</u>: Cultural heritage, Museum/private collection, Painting, Techniques, Texas, USA&Canada <br><br><u>Author</u>: Photo: Paul M.R. Maeyaert <br><u>Copyright</u>: Paul M.R. Maeyaert <br>
PM_152254_USA_San_Francisco
<u>File Name</u>: PM_152254_USA_San_Francisco.jpg <br><u>Sublocation</u>: Fine arts Museum of San Francisco/Roscoe and Margareth Oakes Collection <br><u>Location</u>: San Francisco <br><u>Province</u>: California <br><u>Country</u>: USA <br><u>Header</u>: Schilderij, Maria met Kind, atelier van Dierick Bouts, ca 1400 <br><u>Description</u>: Painting Virgin and child After Dierick Bouts Ca 1400 / <br><u>Keywords</u>: California, Cultural heritage, Museum/private collection, Painting, San Francisco, Techniques, USA&Canada <br><br><u>Author</u>: Dieric Bouts (c. 1410/1415-1475) <br><u>Copyright</u>: Paul M.R. Maeyaert <br>
PM_152255_USA_San_Francisco
<u>File Name</u>: PM_152255_USA_San_Francisco.jpg <br><u>Sublocation</u>: Fine arts Museum of San Francisco/Roscoe and Margareth Oakes Collection <br><u>Location</u>: San Francisco <br><u>Province</u>: California <br><u>Country</u>: USA <br><u>Header</u>: Schilderij, Maria met Kind, atelier van Dierick Bouts, ca 1400 <br><u>Description</u>: Painting Virgin and child After Dierick Bouts Ca 1400 / <br><u>Keywords</u>: California, Cultural heritage, Museum/private collection, Painting, San Francisco, Techniques, USA&Canada <br><br><u>Author</u>: Dieric Bouts (c. 1410/1415-1475) <br><u>Copyright</u>: Paul M.R. Maeyaert <br>
Supplementary data to Dating the timbers from the 'Sparrow-Hawk', a shipwreck from Cape Cod, USA. Journal of Archaeological Science: Reports 103374
<p>This record gives access to all supplementary data that forms the background to the paper: Daly, A., Hocker, F. & Mires, C., 2022. Dating the timbers from the ‘Sparrow-Hawk’, a shipwreck from Cape Cod, USA. Journal of Archaeological Science: Reports https://doi.org/10.1016/j.jasrep.2022.103374</p> <p>In 1626, a vessel making its way to Virginia was forced off course and damaged in a storm, which drove the ship onto the eastern shore of the Cape Cod peninsula, Massachusetts. Onboard were two English merchants and some servants and farmers, many of whom were Irish. In 1863, a storm exposed the weathered remains of a vessel at Old Ship Harbor. At the time, it was hailed as the same ship that had brought the Virginia-bound passengers to Plymouth in 1626. Recent wiggle-match C14 dating and dendrochronology suggests that this is indeed a ship from the early seventeenth century.</p>
jhu-usa
<p>COVID-19 data for United States of America from 2020-01-22 to 2023-03-09, including tot_confirmed, tot_deaths.</p> <p>Files:</p> <ul> <li>".pkl" Cache file backup</li> <li>Dataframe exported "PyCoa-DF.csv"</li> <li>Original database backup </li> </ul>
Climatology of rainfall from Atlantic hurricanes in the USA from radar data
<p>Atlantic Tropical Cyclone Rainfall Climatology in the USA<br> Data sources (see references): NEXRAD level III data, hourly precipitation; IBtracs best track data; University of Colorado extended best track data<br> Available as NetCDF files and Matlab structure</p> <p>Classification as TC precipitation criteria: within radius of outermost closed isobar of a TC at a given time</p> <p>Scope: 100km radius around corresponding radar station</p> <p>Dealing with radar outages: up to 2h gap - interpolation of precipitation, larger gaps - rescaling of frequency with fraction of available data (see formulas)</p> <p>Available variables per radar station:</p> <ul> <li>Location: name [ ], coordinates [°N, °W]</li> <li>Grid: lat [°N], lon [°E]</li> <li>Frequency rescaling: re_freq [ ]</li> </ul> <p>Available variables per event:</p> <ul> <li>Storm identifiers: name [ ], year [a]</li> <li>Storm total precipitation <ul> <li>Area distribution: Ptot [kg/m<sup>2</sup>], gridded (0.1x0.1°)</li> <li>Area average: Ptot_av [kg/m<sup>2</sup>]</li> <li>Area maximum within 0.5x0.5°: Ptot_max [kg/m<sup>2</sup>]</li> </ul> </li> <li>Annual exceedance frequency: f(Ptot_max) [a^-1]</li> </ul> <p>Relevant formulas:</p> <p>re_freq = total duration of storm exposure / duration of viable measurements<br> f (Ptot_max) = (number of events exceeding Ptot_max / length of observation) * re_freq</p> <p>Matlab structure:</p> <ul> <li>Level 1: TCP_climatology</li> <li>Level 2: station variables -> station_event_data leads to event variables</li> <li>Level 3: event variables -> Ptot leads to spatially gridded precipitation</li> <li>Level 4: Ptot-grid</li> </ul>
Molecular and Taxonomic Reevaluation of the Digitaria filiformis Complex (Poaceae) including a Globally Extinct, Single Site Endemic from New Hampshire, USA, and a New Species from Mexico
<p>We examine the <em>Digitaria filiformis </em>complex, to determine the proper taxonomic rank and rarity of each taxon. The taxonomy of the <em>D. filiformis </em>complex is highly debated and includes two widespread species, <em>D. filiformis </em>and <em>D. villosa</em>; a possibly extinct species endemic to a single-site in New Hampshire, <em>D. laeviglumis</em>; and a rare species of southern Florida and the West Indies, <em>D.</em><em> dolichophylla. </em>We conducted morphologic comparisons and molecular analysis of the four members of the <em>D. filiformis</em> complex, together with specimens from Mexico and Venezuela purportedly identified as <em>D. laeviglumis</em> (morphology only). Based on results of phylogenetic analyses of plastid and nuclear ITS sequences and morphologic comparisons, we recognize five species in the <em>D. filiformis </em>complex, including a newly described Mexican endemic <em>D. glabrifloris. </em>After field investigation we have moved the global rank of <em>D. laeviglumis </em>from globally historical (GH) to extinct (GX), as there is virtually no likelihood of rediscovery. <em>Digitaria</em><em> dolichophylla </em>is much rarer than previously recognized, moving from secure (T5) to imperiled with extinction (G2).</p>
Water chemistry and aquatic vegetation data from Les Cheneaux Islands, Northern Lake Huron, Michigan, USA, 2016-2018
Remote sensing approaches that could identify species of submerged aquatic vegetation (SAV) and measure their extent in lake littoral zones would greatly enhance their study and management, especially if they can provide faster or more accurate results than traditional field methods. Remote sensing with multispectral sensors can provide this capability, but SAV identification with this technology must address the challenges of light extinction in aquatic environments where chlorophyll, dissolved organic carbon, and suspended minerals can affect water clarity and the strength of the sensed light signal. Here, we present environmental data collected to support a study using an unmanned aerial system (UAS)-enabled methodology to identify the extent of the invasive SAV species Myriophyllum spicatum (Eurasian watermilfoil, or EWM) in the Les Cheneaux Islands area of northwestern Lake Huron, Michigan, USA. Data collected includes water chemistry (nitrogen, phosphorus, carbon, suspended solids, chlorophyll a), light profiles, and submerged aquatic vegetation characteristics including cover, species dominance using aquatic vegetation survey methods (AVAS), and biomass.
Spatiotemporal variation in internal phosphorus loading, sediment characteristics, water column chemistry, and thermal mixing in a hypereutrophic reservoir in southwest Iowa, USA (2019-2020)
The primary aim of the data product is to quantify seasonal and spatial variation in sediment phosphorus fluxes in a temperate reservoir and evaluate mechanisms responsible for instances of elevated sediment phosphorus release. We studied Green Valley Lake, a hypereutrophic reservoir in southwest Iowa, USA, from 2019 to 2020. We measured sediment phosphorus flux rates and potential explanatory variables at three sites along the longitudinal gradient of the reservoir over six sampling events during winter and summer stratification as well as mixing events in the spring, summer, and fall. Ex situ sediment core incubations were used to measure sediment P release rates under ambient temperature and dissolved oxygen conditions. Explanatory variables measured included sediment phosphorus chemistry, sediment physical characteristics, epilimnetic and hypolimnetic nutrient concentrations, and thermal stratification patterns. These data will be used to identify mechanisms driving hot spots and hot moments of sediment phosphorus release, which will contribute to our understanding of how areas of lakebed and times of the year can disproportionately influence whole-lake water chemistry.
Common Raven (Corvus corax) Occupancy Survey and Habitat Selection Data in Cliff Habitat of the Central Appalachian Region, USA, 2009-2010
We identified 24 cliff sites across four states of the Central Appalachian Region of the eastern USA (Kentucky, North Carolina, Virginia, and West Virginia) with known raven occupancy at which to perform occupancy surveys for estimating detection probability and the effects of covariates. We surveyed each cliff site 2-4 times in either 2009 or 2010 and recorded time-to-first detection and time to confirmed cliff occupancy during a two-hour survey. Daily surveys were completed between 06:00 and local solar noon. During each survey, we recorded covariates, including air temperature at survey start time, cloud cover, wind speed, and day of year. We also calculated the distance of the observation point from the cliff being surveyed and the forest cover around the cliff. We also collected data thought to be pertinent for habitat selection by ravens on 26 cliffs occupied by ravens and 26 cliffs deemed unoccupied by ravens in 2010. For each cliff, we measured cliff physiographic characteristics, such as cliff length, cliff height, and occlusion by vegetation, and landscape characteristics, including percent forest and urban cover around the cliff and distances from the cliff to the nearest road and human habitation.
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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.