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8,553 results for “west”
Global Airborne Observatory: Submarine Groundwater Discharge on West Hawaii Island
<p>Mapped submarine groundwater discharge (SGD) for the west coast of Hawaii Island. A full description of the data source and methodology is available at:</p> <p>Asner, G.P., N.R. Vaughn, and J. Heckler. 2024. Operational mapping of submarine groundwater discharge into coral reefs: Application to West Hawaii Island. Oceans 5, 547-559. https://doi.org/10.3390/oceans5030031</p> <p>There are two data layers:</p> <ol> <li>Estimated point sources of SGD</li> <li>Estimated dischage areas of SGD </li> </ol> <p>SGD discharge point sources as a point layer and SGD discharge areas are provided as polygon layers, both in GeoJSON format. The point source layer includes a Field LocCertainty, describing the certainty in percent that the discharge location is correctly identified. The discharge area layer includes fields InsideC and OutsideC, which average thermal sensor temperature in Celsius inside and outside, respectively, as well as fields for the temperature difference (dTemp) and the size of the dicharge area in hectares (ha). Both layers use the WGS84 coordinate system with spatial coordinates giving positions as degrees longitiude and latitude.</p>
IN02003 Changu Narayana Pillar West Shaft Inscription. Sanskrit XML file, draft epidoc edition
<p>IN02003 Changu Narayana Pillar West Shaft Inscription. Sanskrit XML file (without metadata). Draft epidoc edition to be incorporated into 'Siddham' archive</p>
Regional climate simulations of surface precipitation and temperature for West Africa using COSMO-CLM based on MPI-LR (ECHAM6) and RCP4.5
<p>Regional climate model COSMO-CLM (CCLM) simulations with a horizontal resolution of 0.11° (approx. 12 km) for sub-Saharan West Africa under current and future climate conditions. The CCLM is driven by initial and lateral boundary conditions from the MPI-LR (ECHAM6), based on the emission scenario RCP4.5. The downscaled MPI-LR (ECHAM6) data for surface precipitation (P) and surface temperature (Tmin, Tmax) are provided for the baseline period (1981-2010) and two future time slices, i.e. the 2021–2050 and the 2071–2100 period. </p> <p> </p>
Magnetic Field Measurements above a Phonolite Diatreme near Rockeskyll, West Eifel, Germany
Open the record for dataset details and reuse information.
Water Body Checklists 2019: Inner Seas off the West Coast of Scotland Species List
Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.<p></p>List of species collected from the Inner Seas off the west coast of Scotland using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.
Water Body Checklists: Inner Seas off the West Coast of Scotland Species List
Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.<p></p>List of species collected from the Inner Seas off the west coast of Scotland using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.
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>
Resource use strategies, resistance and tolerance to aerial biomass removal in Argentina mid-west native plants
<p>Dataset of the PhD Thesis from Lucas D. Gorné:<br> - Gorné LD. 2018. Estrategias de uso de recursos, resistencia y tolerancia a la remoción de biomasa aérea en plantas nativas del centro-oeste de Argentina. Tesis del Doctorado en Ciencias Biológicas. Facultad de Ciencias Exactas, Físicas y Naturales. Universidad Nacional de Córdoba. Córdoba, Argentina. https://ri.conicet.gov.ar/handle/11336/87925.</p>
West Midlands Combined Area MATSim model
<p>An agent-based model of West Midlands Combined Area for 2022 composed of:</p> <ol> <li>a synthetic population of individuals and households with socio-demographic attributes and residential locations,</li> <li>a set of individuals' weekly schedules of activities,</li> <li>a WMCA environment model: road and public transport networks, public transport services, buildings and facilities.</li> </ol> <p>The model can be used as input for MATSim simulations.</p>
Graph 6: Stagings of Heiner Müller's Plays in Germany (East and West).
<p>Graph 6 shows the number of stagings of Heiner Müller’s plays in East and West Germany from 1957-1990.</p>
A lack of population structure characterizes the invasive Lonicera japonica in West Virginia and across eastern North America
<p>Figure S1. Mig-seq primers used in the current study.</p> <p>Dataset S1. SNP data in .vcf format for Lonicera japonica.</p> <p>Figure S2: STRUCTURE analyses. Left: Delta K plot showing the optional number of ancestral population clusters (based on Evanno et al. 2015 method). Right: Ancestry plots from analysis with ParallelStructure for k = 3 (above) and k = 5 (below). Colors correspond to each ancestral cluster.</p>
CLDF dataset derived from 'West Old Turkic' by András Róna-Tas and Árpád Berta from 2011
<p>Cite the source of the dataset as:</p> <blockquote> <p>Róna-Tas, András, & Berta, Árpád. (2011) West Old Turkic. Harrassowitz Verlag, Wiesbaden</p> </blockquote>
Material stock map of CONUS - Mid West
<p>Humanity’s role in changing the face of the earth is a long-standing concern, as is the human domination of ecosystems. Geologists are debating the introduction of a new geological epoch, the ‘anthropocene’, as humans are ‘overwhelming the great forces of nature’. In this context, the accumulation of artefacts, i.e., human-made physical objects, is a pervasive phenomenon. Variously dubbed ‘manufactured capital’, ‘technomass’, ‘human-made mass’, ‘in-use stocks’ or ‘socioeconomic material stocks’, they have become a major focus of sustainability sciences in the last decade. Globally, the mass of socioeconomic material stocks now exceeds 10e14 kg, which is roughly equal to the dry-matter equivalent of all biomass on earth. It is doubling roughly every 20 years, almost perfectly in line with ‘real’ (i.e. inflation-adjusted) GDP. In terms of mass, buildings and infrastructures (here collectively called ‘built structures’) represent the overwhelming majority of all socioeconomic material stocks.</p> <p>This dataset features a detailed map of material stocks in the CONUS on a 10m grid based on high resolution Earth Observation data (Sentinel-1 + Sentinel-2), crowd-sourced geodata (OSM) and material intensity factors.</p> <p><strong>Spatial extent</strong><br> This subdataset covers the <strong>Mid West CONUS</strong>, i.e.</p> <ul> <li>IA</li> <li>IL</li> <li>IN</li> <li>MI</li> <li>MN</li> <li>MO</li> <li>OH</li> <li>WI</li> </ul> <p>For the remaining CONUS, see the <em>related identifiers</em>.</p> <p><strong>Temporal extent</strong><br> The map is representative for ca. 2018.</p> <p><strong>Data format</strong><br> The data are organized by states. Within each state, data are split into 100km x 100km tiles (EQUI7 grid), and mosaics are provided.</p> <p>Within each tile, images for area, volume, and mass at 10m spatial resolution are provided. Units are m², m³, and t, respectively. Each metric is split into buildings, other, rail and street (note: In the paper, other, rail, and street stocks are subsumed to mobility infrastructure). Each category is further split into subcategories (e.g. building types).</p> <p>Additionally, a grand total of all stocks is provided at multiple spatial resolutions and units, i.e.</p> <ul> <li>t at 10m x 10m</li> <li>kt at 100m x 100m</li> <li>Mt at 1km x 1km</li> <li>Gt at 10km x 10km</li> </ul> <p>For each state, mosaics of all above-described data are provided in GDAL VRT format, which can readily be opened in most Geographic Information Systems. File paths are relative, i.e. DO NOT change the file structure or file naming. </p> <p>Additionally, the grand total mass per state is tabulated for each county in <em>mass_grand_total_t_10m2.tif.csv</em>. County FIPS code and the ID in this table can be related via <em>FIPS-dictionary_ENLOCALE.csv</em>.</p> <p><strong>Material layers</strong><br> Note that material-specific layers are not included in this repository because of upload limits. Only the totals are provided (i.e. the sum over all materials). However, these can easily be derived by re-applying the material intensity factors from (see <em>related identifiers</em>):</p> <p>A. Baumgart, D. Virág, D. Frantz, F. Schug, D. Wiedenhofer, Material intensity factors for buildings, roads and rail-based infrastructure in the United States. Zenodo (2022), <a href="https://doi.org/10.5281/zenodo.5045337.">doi:10.5281/zenodo.5045337.</a></p> <p><strong>Further information</strong><br> For further information, please see the publication.<br> A web-visualization of this dataset is available here.<br> Visit our <a href="https://boku.ac.at/understanding-the-role-of-material-stock-patterns-for-the-transformation-to-a-sustainable-society-mat-stocks">website</a> to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.</p> <p><strong>Publication</strong><br> D. Frantz, F. Schug, D. Wiedenhofer, A. Baumgart, D. Virág, S. Cooper, C. Gomez-Medina, F. Lehmann, T. Udelhoven, S. van der Linden, P. Hostert, H. Haberl. Weighing the US Economy: Map of Built Structures Unveils Patterns in Human-Dominated Landscapes. <em>In prep</em></p> <p><strong>Funding</strong><br> This research was primarly funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950). Workflow development was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—Project-ID 414984028-SFB 1404.</p> <p><strong>Acknowledgments</strong><br> We thank the European Space Agency and the European Commission for freely and openly sharing Sentinel imagery; USGS for the National Land Cover Database; Microsoft for Building Footprints; Geofabrik and all contributors for OpenStreetMap.This dataset was partly produced on <a href="https://eodc.eu/">EODC</a> - we thank Clement Atzberger for supporting the generation of this dataset by sharing disc space on EODC.</p>
Material stock map of CONUS - West Coast
<p>Humanity’s role in changing the face of the earth is a long-standing concern, as is the human domination of ecosystems. Geologists are debating the introduction of a new geological epoch, the ‘anthropocene’, as humans are ‘overwhelming the great forces of nature’. In this context, the accumulation of artefacts, i.e., human-made physical objects, is a pervasive phenomenon. Variously dubbed ‘manufactured capital’, ‘technomass’, ‘human-made mass’, ‘in-use stocks’ or ‘socioeconomic material stocks’, they have become a major focus of sustainability sciences in the last decade. Globally, the mass of socioeconomic material stocks now exceeds 10e14 kg, which is roughly equal to the dry-matter equivalent of all biomass on earth. It is doubling roughly every 20 years, almost perfectly in line with ‘real’ (i.e. inflation-adjusted) GDP. In terms of mass, buildings and infrastructures (here collectively called ‘built structures’) represent the overwhelming majority of all socioeconomic material stocks.</p> <p>This dataset features a detailed map of material stocks in the CONUS on a 10m grid based on high resolution Earth Observation data (Sentinel-1 + Sentinel-2), crowd-sourced geodata (OSM) and material intensity factors.</p> <p><strong>Spatial extent</strong><br> This subdataset covers the <strong>West Coast CONUS</strong>, i.e.</p> <ul> <li>CA</li> <li>OR</li> <li>WA</li> </ul> <p>For the remaining CONUS, see the <em>related identifiers</em>.</p> <p><strong>Temporal extent</strong><br> The map is representative for ca. 2018.</p> <p><strong>Data format</strong><br> The data are organized by states. Within each state, data are split into 100km x 100km tiles (EQUI7 grid), and mosaics are provided.</p> <p>Within each tile, images for area, volume, and mass at 10m spatial resolution are provided. Units are m², m³, and t, respectively. Each metric is split into buildings, other, rail and street (note: In the paper, other, rail, and street stocks are subsumed to mobility infrastructure). Each category is further split into subcategories (e.g. building types).</p> <p>Additionally, a grand total of all stocks is provided at multiple spatial resolutions and units, i.e.</p> <ul> <li>t at 10m x 10m</li> <li>kt at 100m x 100m</li> <li>Mt at 1km x 1km</li> <li>Gt at 10km x 10km</li> </ul> <p>For each state, mosaics of all above-described data are provided in GDAL VRT format, which can readily be opened in most Geographic Information Systems. File paths are relative, i.e. DO NOT change the file structure or file naming. </p> <p>Additionally, the grand total mass per state is tabulated for each county in <em>mass_grand_total_t_10m2.tif.csv</em>. County FIPS code and the ID in this table can be related via <em>FIPS-dictionary_ENLOCALE.csv</em>.</p> <p><strong>Material layers</strong><br> Note that material-specific layers are not included in this repository because of upload limits. Only the totals are provided (i.e. the sum over all materials). However, these can easily be derived by re-applying the material intensity factors from (see <em>related identifiers</em>):</p> <p>A. Baumgart, D. Virág, D. Frantz, F. Schug, D. Wiedenhofer, Material intensity factors for buildings, roads and rail-based infrastructure in the United States. Zenodo (2022), <a href="https://doi.org/10.5281/zenodo.5045337.">doi:10.5281/zenodo.5045337.</a></p> <p><strong>Further information</strong><br> For further information, please see the publication.<br> A web-visualization of this dataset is available here.<br> Visit our <a href="https://boku.ac.at/understanding-the-role-of-material-stock-patterns-for-the-transformation-to-a-sustainable-society-mat-stocks">website</a> to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.</p> <p><strong>Publication</strong><br> D. Frantz, F. Schug, D. Wiedenhofer, A. Baumgart, D. Virág, S. Cooper, C. Gomez-Medina, F. Lehmann, T. Udelhoven, S. van der Linden, P. Hostert, H. Haberl. Weighing the US Economy: Map of Built Structures Unveils Patterns in Human-Dominated Landscapes. <em>In prep</em></p> <p><strong>Funding</strong><br> This research was primarly funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950). Workflow development was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—Project-ID 414984028-SFB 1404.</p> <p><strong>Acknowledgments</strong><br> We thank the European Space Agency and the European Commission for freely and openly sharing Sentinel imagery; USGS for the National Land Cover Database; Microsoft for Building Footprints; Geofabrik and all contributors for OpenStreetMap.This dataset was partly produced on <a href="https://eodc.eu/">EODC</a> - we thank Clement Atzberger for supporting the generation of this dataset by sharing disc space on EODC.</p>
Material stock map of CONUS - South West
<p>Humanity’s role in changing the face of the earth is a long-standing concern, as is the human domination of ecosystems. Geologists are debating the introduction of a new geological epoch, the ‘anthropocene’, as humans are ‘overwhelming the great forces of nature’. In this context, the accumulation of artefacts, i.e., human-made physical objects, is a pervasive phenomenon. Variously dubbed ‘manufactured capital’, ‘technomass’, ‘human-made mass’, ‘in-use stocks’ or ‘socioeconomic material stocks’, they have become a major focus of sustainability sciences in the last decade. Globally, the mass of socioeconomic material stocks now exceeds 10e14 kg, which is roughly equal to the dry-matter equivalent of all biomass on earth. It is doubling roughly every 20 years, almost perfectly in line with ‘real’ (i.e. inflation-adjusted) GDP. In terms of mass, buildings and infrastructures (here collectively called ‘built structures’) represent the overwhelming majority of all socioeconomic material stocks.</p> <p>This dataset features a detailed map of material stocks in the CONUS on a 10m grid based on high resolution Earth Observation data (Sentinel-1 + Sentinel-2), crowd-sourced geodata (OSM) and material intensity factors.</p> <p><strong>Spatial extent</strong><br> This subdataset covers the <strong>South West CONUS</strong>, i.e.</p> <ul> <li>AZ</li> <li>NM</li> <li>NV</li> <li>TX</li> </ul> <p>For the remaining CONUS, see the <em>related identifiers</em>.</p> <p><strong>Temporal extent</strong><br> The map is representative for ca. 2018.</p> <p><strong>Data format</strong><br> The data are organized by states. Within each state, data are split into 100km x 100km tiles (EQUI7 grid), and mosaics are provided.</p> <p>Within each tile, images for area, volume, and mass at 10m spatial resolution are provided. Units are m², m³, and t, respectively. Each metric is split into buildings, other, rail and street (note: In the paper, other, rail, and street stocks are subsumed to mobility infrastructure). Each category is further split into subcategories (e.g. building types).</p> <p>Additionally, a grand total of all stocks is provided at multiple spatial resolutions and units, i.e.</p> <ul> <li>t at 10m x 10m</li> <li>kt at 100m x 100m</li> <li>Mt at 1km x 1km</li> <li>Gt at 10km x 10km</li> </ul> <p>For each state, mosaics of all above-described data are provided in GDAL VRT format, which can readily be opened in most Geographic Information Systems. File paths are relative, i.e. DO NOT change the file structure or file naming. </p> <p>Additionally, the grand total mass per state is tabulated for each county in <em>mass_grand_total_t_10m2.tif.csv</em>. County FIPS code and the ID in this table can be related via <em>FIPS-dictionary_ENLOCALE.csv</em>.</p> <p><strong>Material layers</strong><br> Note that material-specific layers are not included in this repository because of upload limits. Only the totals are provided (i.e. the sum over all materials). However, these can easily be derived by re-applying the material intensity factors from (see <em>related identifiers</em>):</p> <p>A. Baumgart, D. Virág, D. Frantz, F. Schug, D. Wiedenhofer, Material intensity factors for buildings, roads and rail-based infrastructure in the United States. Zenodo (2022), <a href="https://doi.org/10.5281/zenodo.5045337.">doi:10.5281/zenodo.5045337.</a></p> <p><strong>Further information</strong><br> For further information, please see the publication.<br> A web-visualization of this dataset is available here.<br> Visit our <a href="https://boku.ac.at/understanding-the-role-of-material-stock-patterns-for-the-transformation-to-a-sustainable-society-mat-stocks">website</a> to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.</p> <p><strong>Publication</strong><br> D. Frantz, F. Schug, D. Wiedenhofer, A. Baumgart, D. Virág, S. Cooper, C. Gomez-Medina, F. Lehmann, T. Udelhoven, S. van der Linden, P. Hostert, H. Haberl. Weighing the US Economy: Map of Built Structures Unveils Patterns in Human-Dominated Landscapes. <em>In prep</em></p> <p><strong>Funding</strong><br> This research was primarly funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950). Workflow development was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—Project-ID 414984028-SFB 1404.</p> <p><strong>Acknowledgments</strong><br> We thank the European Space Agency and the European Commission for freely and openly sharing Sentinel imagery; USGS for the National Land Cover Database; Microsoft for Building Footprints; Geofabrik and all contributors for OpenStreetMap.This dataset was partly produced on <a href="https://eodc.eu/">EODC</a> - we thank Clement Atzberger for supporting the generation of this dataset by sharing disc space on EODC.</p>
AgriCarbon-EO Winter wheat Net Ecosystem Exchange and Biomass over South-west France at 10 m resolution
<p>Dataset contains the outputs of the AgriCarbon-EO</p> <p>An agronomical modeling tool for the carbon and water flux estimates by Bayesian assimilation of S2 and LandSat8 remote sensing data into the Prosail radiative transfer model and the SAFYE-CO2 crop model.<br> -----------------------<br> -for TILE : T31TCJ <br> -for year: 2017<br> -for Winter wheat crops<br> - at 10 m resolution</p> <p> </p> <p>Maps:<br> -file: "GLA_statmap.tif"<br> Description: A raster with 4 bands containing respectively:<br> *The R2 of the GLAI observed by satellite and simulations from 2016/11/01 until 2017/08/01<br> *The RMSE of the GLAI observed by satellite and simulations from 2016/11/01 until 2017/08/01<br> *The Bias of the GLAI observed by satellite and simulations from 2016/11/01 until 2017/08/01<br> *The number of images that are assimilated into SAFYE-CO2 from 2016/11/01 until 2017/08/01</p> <p>-file: "emerg_wheat_2017.tif"<br> Description: A raster with 2 bands containing respectively:<br> *The mean value of emerg retrieved by the SAFYE-CO2 inversion in days of simulation (the simulation begins the 01/01/2016).<br> *The standard deviation of emerg retrieved by the SAFYE-CO2 inversion.<br> <br> -file: "LUEa_wheat_2017.tif"<br> Description: A raster with 2 bands containing respectively:<br> *The mean value of LUEa retrieved by the SAFYE-CO2 inversion in g/MJ.<br> *The standard deviation of LUEa retrieved by the SAFYE-CO2 inversion in g/MJ.</p> <p>-file: "SENa_wheat_2017.tif"<br> Description: A raster with 2 bands containing respectively:<br> *The mean value of Sena retrieved by the SAFYE-CO2 inversion in °C.<br> *The standard deviation of Sena retrieved by the SAFYE-CO2 inversion in °C.</p> <p>-file: "SENb_wheat_2017.tif"<br> Description: A raster with 2 bands containing respectively:<br> *The mean value of SENb retrieved by the SAFYE-CO2 inversion.<br> *The standard deviation of SENb retrieved by the SAFYE-CO2 inversion.</p> <p>-file: "PRT_La_wheat_2017.tif"<br> Description: A raster with 2 bands containing respectively:<br> *The mean value of DAM retrieved by the SAFYE-CO2 inversion.<br> *The standard deviation of DAM retrieved by the SAFYE-CO2 inversion.</p> <p>-file: "DAM_wheat_2017.tif"<br> Description: A raster with 2 bands containing respectively:<br> *The mean value of DAM retrieved by the SAFYE-CO2 inversion in g/m2.<br> *The standard deviation of DAM retrieved by the SAFYE-CO2 inversion in g/m2.</p> <p>-file: "NEP_wheat_2017.tif"<br> Description: A raster with 2 bands containing respectively:<br> *The mean value of NEP retrieved by the SAFYE-CO2 inversion in g/m2.<br> *The standard deviation of NEP retrieved by the SAFYE-CO2 inversion in g/m2.</p> <p>-file: "NECB_exportG_wheat_2017.tif"<br> Description: A raster with 2 bands containing respectively:<br> *The mean value of NECB retrieved by the SAFYE-CO2 inversion in g/m2 , considering an export scénario with grains export only.<br> *The standard deviation of NECB_exportG retrieved by the SAFYE-CO2 inversion in g/m2.</p> <p>-file: "NECB_exportGLS_wheat_2017.tif"<br> Description: A raster with 2 bands containing respectively:<br> *The mean value of NECB retrieved by the SAFYE-CO2 inversion in g/m2, considering an export scénario with grains, stems and leaves.<br> *The standard deviation of NECB_exportGLS retrieved by the SAFYE-CO2 inversion in g/m2.</p> <p> </p> <p>Shapefiles a GIS: <br> -file: "S2_TILE_T31TCJ.shp"<br> Description: shape file of the contour of the T231 TCJ sentinel2 tile <br> -file: "FR_AUR.shp"<br> Description: shape file of the contour of AURADE experimental field <br> -file: "FR_AUR_TOWER.shp"<br> Description: shape file of the location of the AURADE eddy covariance flux tower<br> -file: "POI_2017.shp"<br> Description: shape file of the location of points of interest that illustrate the ... paper<br> -file: "ESU_DAM.shp"<br> Description: shape file of the contour of the plots where dry biomass samples were taken.<br> -file: "ESU_DAM_points.shp"<br> Description: shape file of the location of the points where dry biomass samples were taken.<br> -file: "mapT31TCJ_spamaps.qgz"<br> QGIS project file for the visualisation of the NEP maps.<br> </p>
Measurements of ethylene production (using the acetylene reduction assay) as a proxy for nitrogen fixation of epiphytes on seagrass in West Falmouth Harbor during July from 2005 through 2019.
West Falmouth Harbor (West Falmouth, MA, USA) has been experiencing a dramatic increase in nitrogen loading from an upgradient municipal wastewater treatment facility since the early 2000’s. As part of a long-term study into the effects of this nitrogen enrichment, we have measured nitrogen fixation rates of seagrass-associated epiphytes using the acetylene reduction technique. Samples were taken annually in July at two sites, one in the well-flushed outer basin (OH) and one in the inner basin closer to the dominant groundwater N source (Snug Harbor, SH). Additional data are presented in 2019 at 18 sites spatially distributed through the seagrass bed to assess spatial heterogeneity. Individual replicate data are presented. These data are in support of a manuscript submitted to the journal Biogeochemistry by Marino et al, submitted for publication (12/2022).
Measurements of water column chemistry taken hourly over 24-hour periods at three sites in West Falmouth Harbor from 2006 to 2019
West Falmouth Harbor (West Falmouth, MA, USA) has been experiencing a dramatic increase in nitrogen loading from an upgradient municipal wastewater treatment facility since the early 2000’s. As part of a long-term study into the effects of this nitrogen enrichment, we have been measuring water chemistry at stations throughout the harbor to examine nutrient concentrations along the gradient from the highest loaded areas of the site to the most well-flushed. Water samples were taken hourly over 24-hour periods at 3 stations between 2006 and 2019. Due to covid restrictions on field and laboratory work, samples were not collected in 2020-2021; sample collection resumed in 2022 and data will be added after analysis. One station is in the well-flushed outer basin (OH) and one in the inner basin closer to the dominant groundwater N source (Snug Harbor, SH). These two were sampled at least once per year between 2006 and 2019. In two years, samples in the OH were taken at a location approximately 140m from the long-term site from this dataset. Those data can be accessed at doi:10.6073/pasta/73408abf801827966041c219f4222c1f. A third station is in the middle between the two, and was sampled in 2016 and 2018. Samples were processed for ammonium, phosphate, nitrate + nitrite, total nitrogen, and total phosphorus. On some dates, additional samples were run for silicate and chlorophyll. Salinity is reported for all samples. Samples were collected with an ISCO autosampler and stored on ice until analysis. Full analysis details and quality control methods are available in Hayn et al. 2014 (doi: 10.1007/s12237-013-9699-8).
Porewater sulfide, sediment sulfur, and organic matter data in West Falmouth Harbor, 2017-2019
West Falmouth Harbor (West Falmouth, MA, USA) has been experiencing a dramatic increase in nitrogen loading from an upgradient municipal wastewater treatment facility since the early 2000’s. As part of a long-term study into the effects of this nitrogen enrichment, each summer in 2017-2019 we sampled sediment and porewater to assess the relationship between belowground sulfur and carbon pools and seagrass health. This dataset contains information on porewater sulfide, porewater sulfate to chloride molar ratios, sediment organic matter, and sediment total sulfur from sites across three basins of West Falmouth Harbor that receive varying inputs of nitrogen. The data supports findings reported in Haviland et al., 2022 (https://doi.org/10.1002/lno.12025).
Isotopes and content of carbon, nitrogen, and sulfur of eelgrass (Zostera marina) from West Falmouth Harbor from 2005 through 2019
West Falmouth Harbor (West Falmouth, MA, USA) has been experiencing a dramatic increase in nitrogen loading from an upgradient municipal wastewater treatment facility since the early 2000’s. As part of a long-term study into the effects of this nitrogen enrichment, we have been assessing changes in the extent and health of the eelgrass (Zostera marina) community within the harbor. Eelgrass shoots were collected during the summer and run for carbon (C), nitrogen (N), and sulfur (S) content and isotopic composition. Samples were analyzed from 7 stations in the more well-flushed outer harbor (OH), 4 stations in the middle of the harbor (MH), and 6 stations in the inner portion of the harbor in close proximity to a high groundwater nitrate source (Snug Harbor, SH). Carbon data is available from 2011 through 2018, nitrogen data from 2006 through 2018, and sulfur data from 2005 through 2019 with a few samples from 2021. Sulfur results have been published in Haviland et al. 2022 (doi: 10.1002/lno.12025).
ScienceDex guides
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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.