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5,424 results for “USA”
A meteorology and snow dataset from adjacent forested and meadow sites at Crested Butte, CO, USA
<p>This dataset contains meteorology and snow observation data collected at sites in the southwestern Colorado Rocky Mountains during water years 2019-2021. Data collection had an emphasis on paired open-forest sites and included three forested elevations. In total, we present 270 snow pit observations, 4,019 snow depth measurements, and three years of meteorological forcing from two weather stations (one in a meadow, the other in an adjacent forest). The dataset is described in a forthcoming publication of the same name: <em>A meteorology and snow dataset from adjacent forested and meadow sites at Crested Butte, CO, USA</em> (Bonner et al., 2022).</p> <p>All snow observation and meteorological forcing data are available as both .nc and .mat files.<br> Additionally, original digitized copies of snow pit observations are provided as .gsheet/.xlxs files.</p> <p>This dataset will continue to be updated, via this repository, as additional years of data are collected.</p>
Satellite-derived chlorophyll-a concentrations for Lake Harsha (USA) using Mixture Density Networks and Sentinel-2 and Landsat 8 imagery
<p>This dataset contains satellite-derived chlorophyll-a data of Lake Harsha (USA) for the period 21 Mar. 2013 - 01 Feb. 2021. Chlorophyll-a concentrations have been calculated using Mixture Density Networks and Sentinel-2 and Landsat 8 imagery.</p> <p>Mixture Density Networks are a class of neural networks that tackle the inverse problem by modelling the multimodal distribution of target variables using a mixture of Gaussians. For more information, please refer to the following:</p> <ul> <li>Pahlevan, N., Smith, B., Alikas, K., Anstee, J., et al. (2022). Simultaneous retrieval of selected optical water quality indicators from Landsat-8, Sentinel-2, and Sentinel-3. <em>Remote Sensing of Environment, 270</em>, 112860</li> <li>Smith, B., Pahlevan, N., Schalles, J., et al. (2021). A Chlorophyll-a Algorithm for Landsat-8 Based on Mixture Density Networks. <em>Frontiers in Remote Sensing, 1</em></li> <li>Pahlevan, N., Smith, B., Schalles, J., et al. (2020). Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in inland and coastal waters: A machine-learning approach. <em>Remote Sensing of Environment, 240</em>, 111604</li> </ul>
Cuzzone2024: Ice sheet model simulations reveal polythermal ice conditions existed across the NE USA during the Last Glacial Maximum
<p>Here you will find model output associated with Cuzzone et al. (2024) for simulations conducted to reconstruct the Last Glacial Maximum conditions across the Northeast United States. Model output is available as: 1) Simulated Ensemble Mean LGM Ice Thickness, 2.) Simulated Ensemble Mean LGM Velocity 3.) Simulated Ensemble Mean LGM Velocity in X and Y direction, and 4.) The simulated LGM thermal state, shown as the Model ensemble agreement for warm and cold-based ice.</p> <p>These outputs are given for 3 model domains: 1) The Northeast USA (NE Domain), 2) The Adirondack Mountains (ADK), 3) The White Mountains (White), and 4) Mount Katahdin (Kat).</p> <p>Model output is given in .tif format, and the Map Projection is ESPG: 4326 , WGS 84</p> <p>Units for model output is:</p> <p>1) Ice Thickness: meters</p> <p>2) Velocity (vel, vx, vy): meters/yr</p> <p>3) Model Thermal Agreement: -5 to 5</p> <p>-5: All ensemble members agree cold-based ice</p> <p>-4: 4/5 ensemble members agree cold-based ice</p> <p>-3: 3/5 ensemble members agree cold-based ice</p> <p>-2: 2/5 ensemble members agree cold-based ice</p> <p>-1: 1/5 ensemble members agree cold-based ice</p> <p>0: 50% ensemble members either cold or warm-based</p> <p>1: 1/5 ensemble members agree cold-based ice</p> <p>2: 2/5 ensemble members agree cold-based ice</p> <p>3: 3/5 ensemble members agree cold-based ice</p> <p>4: 4/5 ensemble members agree cold-based ice</p> <p>5: 5/5 ensemble members agree cold-based ice</p>
Core log descriptions and sediment grain size data for Hurricane Ian sediment cores collected in Lee County, Florida, USA
<p>These data represent qualitative and quantitative measurements of sediment cores collected from various environments following the landfall of Hurricane Ian. These sediment cores were collected using pound coring techniques up to 2m into the subsurface to characterize the sedimentological signature of storm deposits resulting from Hurricane Ian. More details regarding these measurements and interpretations of storm deposits can be found in the folllowing manuscript:</p> <p>McCormick, W.M., Briggs, T.R., Hauptman, L.H., Wang, P., Morphologic and sedimentological signatures resulting from Hurricane Ian, southwest Florida, USA: Insight into intra-storm bidirectional sediment transport processes (In Review). </p>
Data, Analytical Code, and Model Outputs From: "Green is the New Black: Outcomes of Post-Fire Tree Planting Across the Interior West, USA"
<p>This archive includes data (locations of tree plantings, one-year survival records, remotely sensed canopy cover change), statistical code, and model outputs from Rodman et al. (2024). For more information on specific information, processing methods, and data formats, see "README.md" or "README.html" files associated with this archive</p>
Carbon Storage in Beaver Meadows of the Sierra Nevada, USA
<p><span>The purpose of this study was to examine the effects of active beaver dams on sequestered carbon in subalpine valleys of the Sierra Nevada across a temporal scale. Specifically, carbon stored in floodplain sediments and above and below ground vegetation was measured in 8 beaver meadows to quantify carbon storage.</span></p>
AI-related patents (WIPO, category G06N) and market capitalisation by companies registering at least 2 new ones in 2019, sorted into four global regions (China, USA, EEA, rest of the world)
<p>NOTE: for some reason the pptx and previews keep getting munged on this supposedly permanent arxiv, but the data is still there, unchanged, and you can see how the pptx should look in either the jpg, or the the article.</p> <p>Datasets and presentations concerning the strength of the EU and "the rest of the world" relative to China and the USA, for the purpose of illustrating and counteracting / better informing narratives concerning a "new AI cold war". The materials authored by us may be freely used under the terms of the MIT License, which appears in its entirety in both the dataset and the presentation. The other materials are only curated by us, taken from Twitter as examples of misinformation pertaining to this concern.</p> <p>As of 28 June, this work now also appears in a formal publication: Joanna J. Bryson, Helena Malikova; Is There an AI Cold War?. <em><em>Global Perspectives</em></em> 2021; 2 (1): 24803. doi: <a href="https://doi.org/10.1525/gp.2021.24803">https://doi.org/10.1525/gp.2021.24803</a></p> <p>Authors: The original analysis was conducted primarily by Malikova in collaboration with Bryson. An associated publication is anticipated where Bryson is the lead author.</p> <p>Contributors: independently followed Malikova's procedures to check her work. Inconsistencies were triple checked and resolved.</p>
Orogens of Big Sky Country: Reconstructing the Deep-Time Tectonothermal History of the Beartooth Mountains, Montana and Wyoming, USA (Supporting Information)
<p>Supporting datasets for Ronemus et al., "Orogens of Big Sky Country: Reconstructing the Deep-Time Tectonothermal History of the Beartooth Mountains, Montana and Wyoming, USA" in review at <em>Tectonics </em>as of August 16, 2022.</p> <p>Dataset S1. Detailed analytical settings and data for zircon U-Pb geochronology</p> <p>Dataset S2. Biotite <sup>40</sup>Ar/<sup>39</sup>Ar analytic results</p> <p>Dataset S3. Zircon (U-Th)/He analytic results</p> <p>Dataset S4. Apatite and zircon grain photomicrographs with measurements</p> <p>Dataset S5. Apatite (U-Th-Sm)/He analytic results</p> <p>Dataset S6. QTQt input and results files</p> <p> </p> <p>Datasets S1, S2, S3, and S5 are also available in the Tectonics submission supporting information.</p>
Detecting coarse beach sediment using remotely sensed imagery at the FRF, Duck, NC, USA: Labeled images, deep learning model, testing data, and predictions.
<p>This data record contains 5 zip files all used to build and use a semantic segmentation model to operate on beach imagery taken at the Field Research Facility (FRF) in Duck, North Carolina, USA. All data is from 2015-2021</p> <p>The `training_data.zip` contains all data used to train the ML model. All images come from the north facing (c1) camera. This zip file includes: a list of classes used to label the imagery, and folders of 107 images, 107 sparse annotations (doodles), 107 labels, and 107 overlays. All labeling was done with the open-source labeling tool ‘Doodler (Buscombe et al., 2021).</p> <p>The `model.zip` file contains the ML model, and associated metadata. This includes: a JSON model configuration file, a figure showing model training statistics, an `.npz` file of model training output, a list of training and validation files, the model as an h5 file and in the Tensorflow ‘saved model’ format. All modeling was done with Segmentation Gym (Buscombe & Goldstein 2022).</p> <p>The `test_data_c6.zip` file contains all data from the south facing (c6) camera to test the ML model. This includes: a list of classes used to label the imagery, and folders of 10 images, 10 sparse annotations (doodles), 10 labels, and 10 overlays. All labeling was done with the open-source labeling tool ‘Doodler (Buscombe et al., 2021). Testing the model with this data was done with codes in: https://github.com/ebgoldstein/FRF_GrainSize</p> <p>The `test_data_c1.zip` file contains all data from the north facing (c1) camera to test the ML model. This includes: a list of classes used to label the imagery, and folders of 10 images, 10 sparse annotations (doodles), 10 labels, and 10 overlays. All labeling was done with an open-source labeling tool ‘Doodler (Buscombe et al., 2021). Testing the model with this data was done with codes in: https://github.com/ebgoldstein/FRF_GrainSize</p> <p>The `predictions.zip` file contains 4418 images from the north facing (c1) camera that were run through the trained segmentation model as well as the resulting output (presented as side-by-side image and overlays). These images were created using codes in Segmentation Gym (Buscombe & Goldstein 2022).</p>
CoastSeg: Shoreline data at 30-m spatial resolution for 298 coastal counties of the conterminous USA, in geoJSON format.
<p>Region: 298 coastal counties of the conterminous USA</p> <p>Data fields:</p> <ol> <li>MEAN_SIG_WAVEHEIGHT</li> <li>TIDAL_RANGE</li> <li>CHLOROPHYLL</li> <li>TURBIDITY</li> <li>TEMP_MOISTURE</li> <li>EMU_PHYSICAL</li> <li>REGIONAL_SINUOSITY</li> <li>GHM</li> <li>MAX_SLOPE %</li> <li>OUTFLOW_DENSITY</li> <li>ERODIBILITY</li> <li>LENGTH_GEO</li> <li>ch_label</li> <li>river_label</li> <li>sinuosity_label</li> <li>slope_label</li> <li>tidal_label</li> <li>turbid_label</li> <li>wave_label</li> <li>CSU_Descriptor</li> <li>CSU_ID</li> </ol> <p>The data originally come from https://rmgsc.cr.usgs.gov/outgoing/ecosystems/Global/USGSEsriGlobalCoastalSegmentsv1.mpk</p> <p>The data are described in the following publication</p> <p>Roger Sayre, Suzanne Noble, Sharon Hamann, Rebecca Smith, Dawn Wright, Sean Breyer, Kevin Butler, Keith Van Graafeiland, Charlie Frye, Deniz Karagulle, Dabney Hopkins, Drew Stephens, Kevin Kelly, Zeenatul Basher, Devon Burton, Jill Cress, Karina Atkins, D. Paco Van Sistine, Beverly Friesen, Rebecca Allee, Tom Allen, Peter Aniello, Irawan Asaad, Mark John Costello, Kathy Goodin, Peter Harris, Maria Kavanaugh, Helen Lillis, Eleonora Manca, Frank Muller-Karger, Bjorn Nyberg, Rost Parsons, Justin Saarinen, Jac Steiner & Adam Reed (2019) A new 30 meter resolution global shoreline vector and associated global islands database for the development of standardized ecological coastal units, Journal of Operational Oceanography, 12:sup2, S47-S56, DOI: <a href="https://doi.org/10.1080/1755876X.2018.1529714">10.1080/1755876X.2018.1529714</a></p>
Metal and nutrient content from submerged aquatic macrophytes collected from Southern Coeur d'Alene Lake, Idaho (USA) in August 2018.
<p>This repository contains data and R scripts used to produce the analyses reported in the manuscript listed below. See the Readme.txt and metadata.csv files for more explanation. The .R file can be used to unbundle the .tar.gz file via the packrat library. The data and script files are contained in the .tar.gz file. </p> <p>Scofield, B.D., Fields, S.F. & Chess, D.W. Aquatic macrophytes show distinct spatial trends in contaminant metal and nutrient concentrations in Coeur d’Alene Lake, USA. <em>Environ Sci Pollut Res</em> (2023). <a href="https://doi.org/10.1007/s11356-023-27211-x">https://doi.org/10.1007/s11356-023-27211-x</a></p>
Mean Annual Herbaceous Cover for the Sagebrush Biome, USA (2020 - 2022)
<p><strong>Abstract: </strong>Cheatgrass (Bromus tectorum) and other invasive annual grasses are the single largest threat to sagebrush rangeland health and resilience (Doherty et al. 2022). To address this challenge, NRCS’ Working Lands for Wildlife, the Western Governors Association (WGA), and diverse partners are helping implement a new proactive spatial plan to tackle invasive annuals known as “Defend the Core” (Maestas et al. 2022). Foundational to implementing this new approach is the creation of a common spatial map of invasion severity to guide strategic actions. In 2020, a WGA-led cheatgrass working group an annual herbaceous cover map that summarized the extent of annuals using three remotely-sensed data products for the years 2016 - 2018 (Maestas et al. 2020). This updated product reports annual herbaceous cover for the years 2020 - 2022 using only cover data from the Rangeland Analysis Platform. Data coverage includes all rangelands within the U.S. sagebrush biome. </p> <p><strong>Purpose: </strong>The goal of the annual herbaceous cover map is to support a common spatial strategy for tackling invasive annual grasses across the western U.S. As with all remote sensing-based products, the map presented here is best used alongside local knowledge and data. The map is intended to facilitate cross-boundary regional planning, and it is anticipated that state and local partners will further refine priority areas for management using additional information.</p> <p><strong>Methodology: </strong>This product used the Rangeland Analysis Platform V3 cover product from years 2020, 2021, and 2022. A mean composite was generated from the yearly raster data using the ‘annual herbaceous functional type’ (AFG) layer, representing percent cover of annual forb and grasses. The methodology for producing the cover product is described in Allred et al. 2021. Cover error for AFG in RAP Cover V3 was 7.0% (MAE) and 11.0% (RMSE). More information can be found at <a href="https://rangelands.app/products/">https://rangelands.app/products/</a>. The data is clipped to the extent of the sagebrush biome using the data from Jeffries and Finn (2019). </p> <p>Some important considerations must be kept in mind when using this product. First, the data layer depicts cover for all annual herbaceous species, not just invasive annual grasses. However, annual herbaceous cover is a useful surrogate for invasive annuals on arid rangelands in the sagebrush biome where native annuals typically represent a small proportion of vegetation cover most years. Second, the product reflects modeled predictions, so error must also be considered. This data product is best suited to highlight patterns of invasive annuals where they are known to be widely distributed and cannot be used in isolation to confirm the absence of invasive species. </p> <p><strong>Time Period of Data:</strong></p> <ul> <li>Start Date: 2020-01-01</li> <li>End Date: 2022-12-31</li> </ul> <p><strong>Coordinate Reference System</strong>: Data are in WGS84 Geographic Coordinate System (EPSG:4326); spatial resolution is approximately 30m.</p> <p><strong>Data format: </strong>Cloud Optimized GeoTiff</p> <p><strong>Data Value: </strong>Percent (%) annual herbaceous cover</p> <p><strong>Data type: </strong>Byte </p> <p><strong>Nodata value: </strong>255</p> <p><strong>Bounding Coordinates:</strong></p> <ul> <li>West: -122.116081408</li> <li>East: -102.260259357</li> <li>North: 49.0016614443</li> <li>South: 34.2918384983</li> </ul> <p><strong>Keywords:</strong></p> <ul> <li>Terrestrial ecosystems</li> <li>Vegetation</li> <li>Invasive species</li> <li>Grassland ecosystems</li> <li>Remote sensing</li> <li>Grasslands</li> <li>Cheatgrass</li> <li>Great Basin</li> <li>Biota</li> <li>Geoscientific information</li> </ul> <p><strong>Access Constraints: </strong>This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit <a href="http://creativecommons.org/licenses/by/4.0/">http://creativecommons.org/licenses/by/4.0/</a> Data are provided "as is" without warranty of any kind, express or implied.</p> <p><strong>Use Constraints:</strong> None. </p> <p><strong>Previous Version(s): </strong>Maestas et al. 2020</p> <p><strong>Data Credit: </strong>University of Montana, USDA-NRCS</p> <p><strong>Data Attribution: </strong>Allred et al. 2021</p>
Bastrop (TX, USA) forest fire cross-sensor change detection images
<p><strong>Abstract. </strong></p> <p>This dataset is composed of a set of four images acquired by different sensors over the Bastrop County, Texas (USA). On September 4, 2011, the region has been struck by ‘‘the most destructive wildland-urban interface wildfire in Texas history’’ which caused 2 casualties, more than 1300 destroyed buildings and almost burned entirely the Bastrop county state park.</p> <p>This dataset is composed of a pair of pre- and post-event images from the same sensor, the Landsat 5 TM (L5T1 and L5T2), which are completed by a post-event of another sensor, the Advanced Land Imager (ALI) from the Earth Observing (EO-1) mission, acquired very shortly after the L5T2 (denoted ALIT2). These three scenes are very similar to each other and no apparent changes between L5T2 and ALIT2 are visible, since images were acquired within 1 day. Differences between these pre- and post-event pairs are only due to burned forest since they were acquired at a 16 days interval during summer. We also dispose of a fourth image of the same area acquired one year and 9 months after the forest fire by the Landsat 8 Operational Land Imager (OLI, L8T2 hereafter). The differences between L5T1 and L8T2 are significant, due both to sun/sensor angles and the long temporal interval between acquisitions. A whole new series of building has been constructed in the burn scar and many cultivated crops are at a different stage of growth.</p> <p>Table : Dataset description</p> <pre><code class="language-markdown">| | Pre-event | Post-event 1 | Post-event 2 | Post-event 3 | |--------- |--------------|--------------|--------------|---------------| | Filename | t1_L5 | t2_L5 | t2_ALI | t2_L8 | | Sensor | Landast 5 TM | Landsat 5TM | EO-1 ALI | Landsat 8 OLI | | Channels | 7 | 7 | 9a | 7b | | GSD | 30, 120 | 30, 120 | 30 | 30 | | Sp. Range | [0.45–2.35, | [0.45–2.35, | [0.4–2.4] | [0.43–2.25] | | [\mu m] | 10.40–12.50] | 10.40–12.50] | | |</code></pre> <p>We prepared the ground truth for pairs of change detection problems by photo-interpretation and relying on the maps provided on the emergency response website [1]. In the ground truth involving the L5T1-L8T2 problem we also included changes related to vegetation density and vegetation/bare soil transitions, since these changes are of the same spectral class as those related to the burned scar.</p> <p>This data has been collected from the NASA LP DAAC Program [2], we are free to redistribute the data. For this reason we provide the Bastrop data and the ground truth we defined and used in these experiments (subset of original crops and ground truth).</p> <p><strong>Data Files</strong></p> <p>The archive contains the following files:</p> <pre><code>data ├── t1_L5.tif ├── t2_L5.tif ├── t2_ALI.tif ├── t2_L8.tif ├── ROI_1.tif ├── ROI_2.tif ├── Cross-sensor-Bastrop-data.mat </code></pre> <p>Where:</p> <ul> <li>`t1_L5.tif` is the pre-event image acquired by Landsat 5 TM</li> <li>`t2_L5.tif` is the post-event image acquired by Landsat 5 TM - `t2_ALI.tif` is the post-event image acquired by EO-1 ALI</li> <li>`t2_L8.tif` is the post-event image acquired by Landsat 8 OLI</li> <li>`ROI_1.tif` is the ground truth for the change detection problem between `t1_L5.tif` and `t2_L5.tif`</li> <li>`ROI_2.tif` is the ground truth for the change detection problem between `t1_L5.tif` and `t2_L8.tif`</li> <li>`Cross-sensor-Bastrop-data.mat` is a Matlab file containing the data in a more convenient format for Matlab users</li> </ul> <p><strong>Citation</strong></p> <p>If you are using this dataset, please cite the following paper:</p> <pre><code>@article{volpi2015jisprs, author = {Michele Volpi and Gustau Camps-Valls and Devis Tuia}, title = {Spectral alignment of multi-temporal cross-sensor images with automated kernel canonical correlation analysis}, journal = {ISPRS Journal of Photogrammetry and Remote Sensing}, volume = {107}, pages = {50-63}, year = {2015}, doi = {https://doi.org/10.1016/j.isprsjprs.2015.02.005}, url = {https://www.sciencedirect.com/science/article/pii/S0924271615000404}, issn = {0924-2716}, }</code></pre> <p>Volpi, M., Camps-Valls, G., Tuia, D. (2015). Spectral alignment of multi-temporal cross-sensor images with automated kernel canonical correlation analysis, ISPRS Journal of Photogrammetry and Remote Sensing, Volume 107, 2015, Pages 50-63. https://doi.org/10.1016/j.isprsjprs.2015.02.005</p>
Experiment replication - Plattes 1639 (Johns Hopkins University, Baltimore, USA, 02/28/2023 - 03/01/2023)
<p>Replication in laboratory of an experiment reported by Gabriel Plattes, <em>A Discovery of Subterraneall Treasure</em> (1639):</p> <p>“Now for a plaine demonstration [of how mineral ores are generated], let this experiment following be tryed, and I make no question, but that it will satisfie every one that hath an inquisitive disposition. Let there bee had a great retort of glasse, and let the same be halfe filled with brimstone, sea-coale, and as many bituminous and sulphurious subterraneall substances as can bee gotten: then fill the necke thereof halfe full with the most free earth from stones that can be found, but thrust it not in too hard, then let it bee luted, and set in an open furnace to distill with a temperate fire, which may onely kindle the said substances, and if you worke exquisitely, you shall finde the said earth petrified, and turned into a stone: you shall also finde cracks and chinkes in it, filled with the most tenacious, clammy, and viscous parts of the said vapours, which ascended from the subterraneall combustible substances. Wereby it appeareth that the same thing is done by Nature, and that the rocks and craggy mountaines are caused by the vapours of bituminous and sulphurious substances kindled in the bowells of the earth, of which there bee divers so well knowne, that they neede not bee heere mentioned [...]” (pp. 5-7). This experiment is also mentioned in Emerton 1984, p. 249. See also Debus 1961.</p> <p>“Take a peece of the blacke fat earth, which is usually digged up in the west countrey, where there are such a multitude of firre trees covered therewith, and which the people use to cut in the forme of bricks, and to drye them, & so to burne them in stead of coales; use this substance as you did the other earth in the beginning of the booke, to find out the natural cause of rocks, stones, and mettalls, and let it receive the vapours of the cumbustible substances, and you shall find this fat earth hardned into a plaine coale; even as you found the other leane earth hardned into a stone” (p. 50).</p>
Experiment replication - Plattes 1639 (Johns Hopkins University, Baltimore, USA, 6/15/2022)
<p>Replication in laboratory of an experiment reported by Gabriel Plattes, <em>A Discovery of Subterraneall Treasure</em> (1639):</p> <p>“Now for a plaine demonstration [of how mineral ores are generated], let this experiment following be tryed, and I make no question, but that it will satisfie every one that hath an inquisitive disposition. Let there bee had a great retort of glasse, and let the same be halfe filled with brimstone, sea-coale, and as many bituminous and sulphurious subterraneall substances as can bee gotten: then fill the necke thereof halfe full with the most free earth from stones that can be found, but thrust it not in too hard, then let it bee luted, and set in an open furnace to distill with a temperate fire, which may onely kindle the said substances, and if you worke exquisitely, you shall finde the said earth petrified, and turned into a stone: you shall also finde cracks and chinkes in it, filled with the most tenacious, clammy, and viscous parts of the said vapours, which ascended from the subterraneall combustible substances. Wereby it appeareth that the same thing is done by Nature, and that the rocks and craggy mountaines are caused by the vapours of bituminous and sulphurious substances kindled in the bowells of the earth, of which there bee divers so well knowne, that they neede not bee heere mentioned [...]” (pp. 5-7). This experiment is also mentioned in Emerton 1984, p. 249. See also Debus 1961.</p> <p>“Take a peece of the blacke fat earth, which is usually digged up in the west countrey, where there are such a multitude of firre trees covered therewith, and which the people use to cut in the forme of bricks, and to drye them, & so to burne them in stead of coales; use this substance as you did the other earth in the beginning of the booke, to find out the natural cause of rocks, stones, and mettalls, and let it receive the vapours of the cumbustible substances, and you shall find this fat earth hardned into a plaine coale; even as you found the other leane earth hardned into a stone” (p. 50).</p>
Under-ice lake physicochemical data, Missisquoi Bay and Shelburne Pond, Vermont, USA, 2014-2015
This dataset contains under-ice physicochemical data from Missisquoi Bay and Shelburne Pond in Vermont, USA collected during the 2013-2014 and 2014-2015 winters and spring thaw periods. The main dataset contains data for ice depths, water column sampling depths, nutrient chemistry, chlorophyll a, and relevant physicochemical variables. Ancillary data includes nearby air temperature data for the study periods as well as river discharge data for contributing or nearby tributaries. Data collection is complete.
A hurricane alters the relationship between mangrove cover and marine subsidies in Texas, USA: 2014-2019
We experimentally manipulated black mangrove (Avicennia germinans) cover in ten large plots and over five years (2014-2019) quantified the effects of mangrove cover on subsidies of floating organic material (wrack) into coastal wetlands. We hypothesized that the change from salt marsh to mangrove vegetation would alter the permeability of the intertidal habitat, and thus alter the nature of subsidies from marine to intertidal habitats. Data from field surveys of wrack distribution showed that as mangrove cover increased from zero to 100%, wrack cover and thickness decreased by ~60%, the distance that wrack penetrated into the plots decreased by ~70%, and the percentage of the wrack trapped in the first six m of the plot tripled. Data from wrack samples indicated that wrack samples collected from the fringe were ~3 times heavier than those from the interior of plots. Animals were ~40% more abundant in samples from the interior than from the fringe of plots, but this trend was not statistically significant due to low replication of interior samples. Data from a wrack experiment revealed that animal abundance and species composition varied between the fringe and interior of the plots, and between microhabitats dominated by salt marsh versus mangrove vegetation. Increasing mangrove cover decreased the relative importance of marine subsidies into the intertidal at the plot level, but concentrated subsidies at the front edge of the mangrove stand. Storms, however, may temporarily override mangrove attenuation of wrack inputs.
Spatial and Social Behavior of Acanthurus triostegus on Moorea (French Polynesia) and Palmyra Atoll (USA), 2017-2018
These data describe the schooling behavior of coral reef fish on Moorea (French Polynesia) and Palmyra Atoll (USA) in 2017 and 2018. Data are grouped into two sets of observations: 1) surveys measuring the abundance of reef fish and the proportion of those fish occurring in schools, and 2) behavioral observations including time spent grazing and GPS tracks of schooling and solitary Acanthurus triostegus.
Lake ecosystem metabolism estimates from 3 locations in Lake Sunapee, NH, USA during the summer stratified period from June to September 2018
Surface water lake ecosystem metabolism daily estimates during the 2018 summer stratified period (04 June - 22 Sept) at three locations within Lake Sunapee (NH, USA). Estimates at each site used previously published data from high-frequency temperature and dissolved oxygen sensors deployed in the lake: the Deep Site (LSPA et al., 2021a: full citation in Methods) and the Herrick Cove and Georges Mills sites (Ward et al., 2021: full citation in methods). The Deep Site was located near Loon Island in the main basin of the lake with 12 m total depth and the dissolved oxygen sensor was deployed 1 m below surface. The Herrick Cove site was in the north east cove of the lake with 6.5 m total depth at site and the dissolved oxygen sensor was deployed 1.75 m below surface. The Georges Mills site was in the northwest cove of the lake with 7 m total depth at site and the dissolved oxygen sensor deployed 1.75 m below surface. We used an inverse modeling approach, where the lake ecosystem model predicted diel changes in dissolved oxygen to estimate daily volumetric rates of gross primary production (GPP), respiration (R), and net ecosystem metabolism (NEM) using the in-lake buoy measurements at each site and wind and surface PAR from the meteorological station at the Deep Site buoy (LSPA et al., 2021b). Raw metabolism estimates were QA/QC'd to generate this final metabolism estimate dataset following protocols described in the Methods section of this dataset.
Meteorological data for the Manitou Experimental Forest, Colorado, USA, 1936-1997
The Manitou Experimental Forest is an outdoor research laboratory in Colorado, USA, that has been run by the USDA Forest Service’s Rocky Mountain Research Station since 1936. This data publication contains meteorological data collected at the Manitou Experimental Forest from 1936-11-11 to 1997-06-24. Precipitation amount, current temperature, maximum temperature, and minimum temperature were collected at daily to weekly intervals over most of this period. Precipitation type, aboveground wind speed, ground wind speed, and wind direction were collected at daily to weekly intervals over a portion of the period.
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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.