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Long-term composited and land cover-adjusted Enhanced Normalized Difference Impervious Surface Index (ENDISI) for the greater Phoenix, Arizona, USA, metropolitan area and the surrounding Sonoran desert derived from annual and seasonal Landsat imagery, 1998 to 2020
This data package consists of multiple decades of Enhanced Normalized Difference Impervious Surface Index (ENDISI) raster data across the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) study area within metropolitan Phoenix, Arizona, USA, temporally aggregated by year and by four meteorological seasons (winter, spring, summer, fall). To serve as a proxy measurement of impervious surface and urbanization across years and seasons, we derived values of ENDISI – following the methods of Chen et al. 2019 from annual and seasonal composites of 30-m resolution Landsat 5-9 Level-2 Surface Reflectance imagery. Next, we corrected the underestimated ENDISI values of dark impervious surface cover and the overestimated ENDISI values of bright bare soils based on visible Landsat bands and 2020 land cover (Sabu et al. 2023). Finally, we exported images as individual GeoTIFF raster files, each with five bands corresponding values summarized annually (band 1) and seasonally (bands 2-5). All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season and the calculation of the spectral index, can be found in the data package metadata (see 'Methods and Protocols') and accompanying Javascript code. ### citations - Gorelick N, Hancher M, Dixon M, et al. (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18–27. https://doi.org/10.1016/j.rse.2017.06.031 - Sabu, S., Frazier, A., & Rashid, B. (2023). Land use and land cover (LULC) classification of the CAP LTER study area (central Arizona, USA) using Landsat imagery: 2015 and 2020 [Dataset]. Environmental Data Initiative. https://doi.org/10.6073/PASTA/BF18E5856215BD2D4DAB3B024BA87A7E
Long-term composited Enhanced Normalized Difference Impervious Surface Index (ENDISI) for the greater Phoenix, Arizona, USA, metropolitan area and the surrounding Sonoran desert derived from annual and seasonal Landsat imagery, 1998 to 2023
This data package consists of multiple decades of Enhanced Normalized Difference Impervious Surface Index (ENDISI) raster data across the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) study area within metropolitan Phoenix, Arizona, USA, temporally aggregated by year and by four meteorological seasons (winter, spring, summer, fall). To serve as a proxy measurement of impervious surface and urbanization across years and seasons, we derived values of ENDISI – following the methods of Chen et al. 2019 – from annual and seasonal composites of 30-m resolution Landsat 5-9 Level-2 Surface Reflectance imagery. Finally, we exported images as individual GeoTIFF raster files, each with five bands corresponding values summarized annually (band 1) and seasonally (bands 2-5). All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season and the calculation of the spectral index, can be found in the data package metadata (see 'Methods and Protocols') and accompanying Javascript code. ### citations - Gorelick N, Hancher M, Dixon M, et al. (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18–27. https://doi.org/10.1016/j.rse.2017.06.031
Dissolved Inorgainic Carbon concentration and Total Alkalinity from surface water samples collected in the GCE LTER domain near Sapelo Island, Georgia between May 2014 and December 2022.
Surface water samples were collected from GCE LTER sampling stations between May 2014 and December 2022. Monthly samples were collected from GCE 6 (high and low tide) and GCE 7 (high tide). Quarterly samples were collected from the remaining GCE sites, 4 sites along the Duplin River, and AL-02 ( the Altamaha River oceanic end-member station). These samples were analyzed for dissolved inorganic carbon (DIC) and total alkalinity (TA).
Composition and biodegradability of dissolved organic matter in supra-permafrost groundwater and surface waters near Simpson Lagoon, Alaska
Supra-permafrost groundwater (SPGW) is an important source of terrestrial dissolved organic matter (DOM) to the Arctic Ocean, yet few studies have investigated the quality or characteristics of this DOM. We sampled fresh SPGW, run-off, and rivers near Simpson Lagoon, Alaska during spring ice break-up (mid-June), summer open water (late July), and fall freeze-up (late September - early October). We measured dissolved organic carbon (DOC) concentrations in these samples and analyzed the composition of DOM using high-resolution mass spectrometry (Fourier transform ion cyclotron resonance mass spectrometry; FT-ICR MS). To measure biodegradable dissolved organic carbon (BDOC), we conducted an aerobic incubation experiment following the methods suggested by Vonk et al. (2015). Briefly, water samples were incubated at 20C for 28 days to measure DOC loss due to remineralization by in-situ microbial communities.
Total dissolved nitrogen (TDN), dissolved organic carbon (DOC), radiocarbon (14C-DOC), and stable carbon (13C-DOC) of surface waters from the Canning River watershed, 2019 and 2021
Sites along the Canning River mainstem and contributing streams near the Kavik River Camp, Alaska, were visited to track changes in stream and river total dissolved nitrogen (TDN) concentration, dissolved organic carbon (DOC) concentration, and the stable carbon (13C) and radiocarbon (14C) isotopic composition of DOC across transitions between the Brooks Range, Brooks foothills, and Arctic Coastal Plain. The dataset also includes water samples collected from lakes, springs, groundwater, and streams and rivers outside the Canning River watershed. Water samples were collected in late April and early August 2019 and in late July and early August 2021. Data include measurements of individual samples for TDN (milligrams nitrogen per liter), DOC (milligrams carbon per liter), carbon-13 of DOC (reported as delta-13C, per mil), carbon-14 of DOC (reported as fraction modern), and analytical error in the fraction modern values. Additional water chemistry data for these samples can be found in Koch et al. (2024). References: Koch, J. C., Connolly, C. T., Repasch, M., Best, H. R., Couvillion, C. S., Hunt, A. (2024). [Dataset] Hydrochemistry and age date tracers from springs, streams, and rivers in the Arctic National Wildlife Refuge, 2019-2022, U.S. Geological Survey data release, https://doi.org/10.5066/P95CXJIT.
Long-term composited land surface temperature for the greater Phoenix, Arizona, USA, metropolitan area and the surrounding Sonoran desert derived from annual and seasonal Landsat imagery, 1998 to 2023
This data package consists of multiple decades of land surface temperature (LST) raster data across the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) study area within metropolitan Phoenix, Arizona (USA), temporally aggregated by year and by four meteorological seasons (Winter, Spring, Summer, Fall). We derived LST values based on the thermal band from annual and seasonal composites of 30-m resolution Landsat 5-9 Level-2 Surface Reflectance imagery. All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season and the calculation of the spectral index, can be found in the data package metadata (see 'Methods and Protocols') and accompanying Javascript code. ### citations: - Gorelick N, Hancher M, Dixon M, et al. (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18–27. https://doi.org/10.1016/j.rse.2017.06.031
Concentrations and Surface Exchange of Air Pollutants at Harvard Forest EMS Tower since 1990
In North America, anthropogenic activities such as fossil fuel combustion and high-intensity agriculture have increased the inputs of nitrogen oxides in the atmosphere far above natural, biogenic inputs. The effect of this excess N depends on how it is distributed through the environment. If fixed N is deposited as nitrate in forests, it may act as a "fertilizer", stimulating growth and thus enhancing carbon sequestration. But when accumulated deposition exceeds the nutritional needs of the ecosystem, nitrogen saturation may result. Soil fertility declines due to leaching of cations and thus, carbon uptake diminishes. The balance between fertilization and saturation depends on the spatial and temporal extent of nitrogen deposition. Measurements of nitrogen oxide concentrations and fluxes made at Harvard Forest are intended to quantify the deposition of nitrogen oxides and to examine the rates for oxidation and deposition of reactive nitrogen that are critical in controlling how far the influence of nitrogen oxide emission sources extends. Measurements made to date indicate that dry deposition of NOy to the Harvard Forest canopy is controlled by advection from source regions, vertical mixing, and chemical reaction. The input is about equally divided between wet and dry deposition depending on the amount of precipitation. Southwesterly winds bring air from the major urban areas along the mid-Atlantic coast, whereas northwesterly wind bring air from less populated regions of northern New England and Canada. As a result, southwesterly winds transport higher concentrations and fluxes of NOx and NOy than northwesterly winds. In the summer, aerodynamically rough forests intercept NOx and emit reactive hydrocarbons that accelerate the oxidation of NOx to rapidly depositing species. As a result, much of the NOx emitted by North America is retained by the region in the summer. This deposition leads to a summertime decrease in reactive nitrogen concentrations and fluxes relati
WSC 2007 - 2012 Yahara Watershed surface water quality policies and practices created and implemented by public agencies
This dataset was created June 2012 - August 2013 to contribute to research under the Water Sustainability and Climate project. Interventions collected are those land-based policies and practices written and implemented by public agencies. Policies were implemented in Wisconsin's Yahara Watershed the period 2007-2012. They aim to improve surface water quality through nutrient (phosphorus and nitrogen) and sediment reduction. Interventions included in the mapping must have spatially-explicit, publicly available data through personal communication or website.
WSC - Water surface elevation (WSE) and water table depth (WTD) from 14 points at the Wibu field site, 2012-2013 growing seasons
Observation wells were installed for the purpose of continuously monitoring the water table level during the 2012 and 2013 growing seasons at the Wibu field site. These data were then used to study the yield response of corn to water table depth, soil texture, and growing season weather conditions (Zipper et al., in prep). The Wibu field site is a commercial agricultural field, which grew corn in the 2012, 2013, and 2014 growing seasons. See Zipper and Loheide (2014) Ag. For. Met. for more information about the field site. The 2012 growing season was characterized by severe drought, and the water table fell below the bottom of most wells in late June/early July.
PIE LTER surface elevation table (SET) pin height data from twelve marsh sites in northeast Massachusetts.
Surface elevation table (SET) measurements from 26 SETs at 9 marsh sites in the Plum Island Sound Long-Term Ecological Research Site in the Great Marsh, Massachusetts. SET measurements are useful for determining the relative elevation change of marsh sediments. Precise measurements of sediment elevation in marshes is useful for determining rates of elevation change in response to changes in sea level.
Sea-ice induced Southern Ocean subsurface warming and surface cooling in a warming climate: ROMS model data
<p><strong>Abstract:</strong></p> <p>This data set contains model output data from the regional ocean modelling system (ROMS) that was set up for the Southern Ocean (south of 24 °S; Haumann, 2016) to analyze the effects of changing surface freshwater and momentum fluxes on Southern Ocean water-mass changes over the period 1980 to 2011. All data is provided in NetCDF format. The data set contains 3 sets 40-year long model spin-up, control, and 3 perturbation simulations each. The sets differ by their model mean state to assess the effect of surface salinity biases on the results. One set of simulations are reference simulations where salinity is restored to the observed surface salinity during model spin-up, and the other two sets are simulations in which the restoring surface salinity has been altered by plus and minus 0.1 PSU, respectively. The control and perturbation simulations are 40-year extensions of the respective model spin-up simulations. In the perturbation simulations either the surface freshwater fluxes or momentum fluxes are instantaneously perturbed after the spin-up simulation to reflect the observation-derived changes in these surface fluxes and then held constant (at the perturbed level) for 40 years. They consist of sea-ice freshwater flux, glacial meltwater flux, and atmosphere-ocean momentum flux changes. Details on the model setup, forcing, and simulations can be obtained from the related research article by Haumann et al. (2020; https://doi.org/10.1029/2019AV000132).</p> <p><br> <strong>Contacts:</strong></p> <p>F. Alexander Haumann, ORCID: 0000-0002-8218-977X. Email: alexander.haumann@gmail.com</p> <p><br> <strong>License:</strong></p> <p>When using this data users must cite the original research article by Haumann et al. (2020) published in AGU Advances (https://doi.org/10.1029/2019AV000132). This model output data set is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p> <p><br> <strong>Citations:</strong></p> <p>Haumann, F. A., Gruber, N., Münnich, M. (2020): Sea-ice Induced Southern Ocean Subsurface Warming and Surface Cooling in a Warming Climate. AGU Advances, 1, e2019AV000132. https://doi.org/10.1029/2019AV000132</p> <p>Haumann, F. A., Gruber, N., Münnich, M. (2020): Sea-ice induced Southern Ocean subsurface warming and surface cooling in a warming climate: ROMS model data. (Version 1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3709154</p> <p><br> <strong>Data processing:</strong></p> <p>The model data provided here has been processed for analysis and, due to storage constraints, only variables and averages used in the related research article (Haumann et al., 2020) are published here. Years refer to model years since initiation, i.e. the start of the spin-up simulation, and reach a maximum of 80 at the end of the control and perturbation experiments. Averages are either annual means over an indicated period ('yearly'), overall means over an indicated period ('mean'), or monthly climatologies over an indicated period ('clim'). 'Yearly' and 'Clim' values are provided either for surface fields or vertically integrated fields ('surf'). 'Mean' values are provided on the native vertical model grid as 3D fields. 'Yearly' zonal mean ('zonalmean') values have been first interpolated to from the native vertical model grid to a regular vertical z-level grid and then zonally averaged. All data has been stored in NetCDF format and compressed using level 1 deflation (https://www.unidata.ucar.edu/blogs/developer/entry/netcdf_compression). Full monthly mean model output from these simulations and the forcing fields can be obtained from the corresponding author upon request (alexander.haumann@gmail.com).</p> <p><br> <strong>Standards:</strong></p> <p>Data files have been formatted as Network Common Data Form (NetCDF; https://www.unidata.ucar.edu/software/netcdf/) in Hierarchical Data Format, version 5 (https://portal.hdfgroup.org/display/knowledge/HDF5+Documentation)</p> <p><br> <strong>Contents:</strong></p> <p><em>Data</em><br> - ROMS_SO_d025_grd.nc, ROMS grid file (Note: one row of ghost points on either side of the grid), compressed netcdf-file<br> - ROMS_SO_d025_ref_spinup, ROMS output from reference spin-up simulation, 4 netcdf-files:<br> -- ROMS_SO_d025_ref_spinup_clim.31-40.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 31 to 40, netcdf-file<br> -- ROMS_SO_d025_ref_spinup_mean.31-40.nc, 3D averaged fields for years 31 to 40, netcdf-file<br> -- ROMS_SO_d025_ref_spinup_yearly.31-40.surf.nc, 2D surface or vertically integrated annual mean fields for years 31 to 40, netcdf-file<br> -- ROMS_SO_d025_ref_spinup_yearly.zlev.zonalmean.31-40.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 31 to 40, netcdf-file<br> - ROMS_SO_d025_minus01_spinup, ROMS output from minus 0.1 PSU sensitivity spin-up simulation, 4 netcdf-files:<br> -- ROMS_SO_d025_minus01_spinup_clim.31-40.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 31 to 40, netcdf-file<br> -- ROMS_SO_d025_minus01_spinup_mean.31-40.nc, 3D averaged fields for years 31 to 40, netcdf-file<br> -- ROMS_SO_d025_minus01_spinup_yearly.31-40.surf.nc, 2D surface or vertically integrated annual mean fields for years 31 to 40, netcdf-file<br> -- ROMS_SO_d025_minus01_spinup_yearly.zlev.zonalmean.31-40.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 31 to 40, netcdf-file<br> - ROMS_SO_d025_plus01_spinup, ROMS output from plus 0.1 PSU sensitivity spin-up simulation, 4 netcdf-files:<br> -- ROMS_SO_d025_plus01_spinup_clim.31-40.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 31 to 40, netcdf-file<br> -- ROMS_SO_d025_plus01_spinup_mean.31-40.nc, 3D averaged fields for years 31 to 40, netcdf-file<br> -- ROMS_SO_d025_plus01_spinup_yearly.31-40.surf.nc, 2D surface or vertically integrated annual mean fields for years 31 to 40, netcdf-file<br> -- ROMS_SO_d025_plus01_spinup_yearly.zlev.zonalmean.31-40.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 31 to 40, netcdf-file<br> - ROMS_SO_d025_ref_ctrl, ROMS output from reference control simulation, 4 netcdf-files:<br> -- ROMS_SO_d025_ref_ctrl_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_ref_ctrl_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_ref_ctrl_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_ref_ctrl_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_ref_ctrl_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file<br> -- ROMS_SO_d025_ref_ctrl_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file<br> - ROMS_SO_d025_minus01_ctrl, ROMS output from minus 0.1 PSU sensitivity control simulation, 4 netcdf-files:<br> -- ROMS_SO_d025_minus01_ctrl_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_minus01_ctrl_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_minus01_ctrl_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_minus01_ctrl_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_minus01_ctrl_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file<br> -- ROMS_SO_d025_minus01_ctrl_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file<br> - ROMS_SO_d025_plus01_ctrl, ROMS output from plus 0.1 PSU sensitivity control simulation, 4 netcdf-files:<br> -- ROMS_SO_d025_plus01_ctrl_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_plus01_ctrl_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_plus01_ctrl_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_plus01_ctrl_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_plus01_ctrl_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file<br> -- ROMS_SO_d025_plus01_ctrl_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file<br> - ROMS_SO_d025_ref_seaice, ROMS output from reference sea-ice freshwater flux perturbation simulation, 4 netcdf-files:<br> -- ROMS_SO_d025_ref_seaice_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_ref_seaice_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_ref_seaice_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_ref_seaice_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_ref_seaice_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file<br> -- ROMS_SO_d025_ref_seaice_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file<br> - ROMS_SO_d025_minus01_seaice, ROMS output from minus 0.1 PSU sensitivity sea-ice freshwater flux perturbation simulation, 4 netcdf-files:<br> -- ROMS_SO_d025_minus01_seaice_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_minus01_seaice_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_minus01_seaice_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_minus01_seaice_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_minus01_seaice_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file<br> -- ROMS_SO_d025_minus01_seaice_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file<br> - ROMS_SO_d025_plus01_seaice, ROMS output from plus 0.1 PSU sensitivity sea-ice freshwater flux perturbation simulation, 4 netcdf-files:<br> -- ROMS_SO_d025_plus01_seaice_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_plus01_seaice_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_plus01_seaice_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_plus01_seaice_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_plus01_seaice_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file<br> -- ROMS_SO_d025_plus01_seaice_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file<br> - ROMS_SO_d025_ref_glacial, ROMS output from reference glacial meltwater flux perturbation simulation, 4 netcdf-files:<br> -- ROMS_SO_d025_ref_glacial_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_ref_glacial_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_ref_glacial_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_ref_glacial_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_ref_glacial_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file<br> -- ROMS_SO_d025_ref_glacial_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file<br> - ROMS_SO_d025_minus01_glacial, ROMS output from minus 0.1 PSU sensitivity glacial meltwater flux perturbation simulation, 4 netcdf-files:<br> -- ROMS_SO_d025_minus01_glacial_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_minus01_glacial_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_minus01_glacial_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_minus01_glacial_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_minus01_glacial_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file<br> -- ROMS_SO_d025_minus01_glacial_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file<br> - ROMS_SO_d025_plus01_glacial, ROMS output from plus 0.1 PSU sensitivity glacial meltwater flux perturbation simulation, 4 netcdf-files:<br> -- ROMS_SO_d025_plus01_glacial_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_plus01_glacial_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_plus01_glacial_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_plus01_glacial_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_plus01_glacial_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file<br> -- ROMS_SO_d025_plus01_glacial_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file<br> - ROMS_SO_d025_ref_momentum, ROMS output from reference atmopshere-ocean momentum flux perturbation simulation, 4 netcdf-files:<br> -- ROMS_SO_d025_ref_momentum_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_ref_momentum_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_ref_momentum_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_ref_momentum_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_ref_momentum_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file<br> -- ROMS_SO_d025_ref_momentum_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file<br> - ROMS_SO_d025_minus01_momentum, ROMS output from minus 0.1 PSU sensitivity atmopshere-ocean momentum flux perturbation simulation, 4 netcdf-files:<br> -- ROMS_SO_d025_minus01_momentum_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_minus01_momentum_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_minus01_momentum_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_minus01_momentum_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_minus01_momentum_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file<br> -- ROMS_SO_d025_minus01_momentum_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file<br> - ROMS_SO_d025_plus01_momentum, ROMS output from plus 0.1 PSU sensitivity atmopshere-ocean momentum flux perturbation simulation, 4 netcdf-files:<br> -- ROMS_SO_d025_plus01_momentum_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_plus01_momentum_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_plus01_momentum_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file<br> -- ROMS_SO_d025_plus01_momentum_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file<br> -- ROMS_SO_d025_plus01_momentum_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file<br> -- ROMS_SO_d025_plus01_momentum_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file<br> <br> <em>File headers</em><br> - ROMS_SO_d025_grd:<br> angle [radians]: angle between xi axis and east<br> f [second-1]: Coriolis parameter at RHO-points<br> h [meter]: Final bathymetry at RHO-points<br> hraw [meter]: Working bathymetry at RHO-points<br> lat_rho [degree_north]: latitude of RHO-points<br> lat_psi [degree_north]: latitude of PSI-points<br> lat_u [degree_north]: latitude of U-points<br> lat_v [degree_north]: latitude of V-points<br> lon_rho [degree_east]: longitude of RHO-points<br> lon_psi [degree_east]: longitude of PSI-points<br> lon_u [degree_east]: longitude of U-points<br> lon_v [degree_east]: longitude of V-points<br> mask_rho [-]: mask on RHO-points<br> mask_u [-]: mask on U-points<br> mask_v [-]: mask on V-points<br> pm [meter-1]: curvilinear coordinate metric in XI<br> pn [meter-1]: curvilinear coordinate metric in ETA<br> spherical [char]: Grid type logical switch<br> - ROMS_SO_d025_*_clim.*.surf.nc, ROMS_SO_d025_*_yearly.*.surf.nc:<br> time [days since 0000-01-01 00:00:00]: time<br> lon [degree_east]: longitude<br> lat [degree_north]: latitude<br> zeta [meter]: averaged free-surface elevation<br> temp [degree_celsius]: averaged potential temperature<br> salt [psu]: averaged salinity<br> rho [kilogram meter-3]: averaged density anomaly from model reference density of 1027 kilogram meter-3<br> mld [meter]: averaged mixed layer depth<br> n2 [seconds-2]: averaged buoyancy frequency (upper 100 m)<br> n2temp [seconds-2]: averaged buoyancy frequency due to temperature (upper 100 m)<br> n2salt [seconds-2]: averaged buoyancy frequency due to salinity (upper 100 m)<br> heat_100 [10^18 J]: averaged ocean heat content (upper 100 m)<br> heat_100_2000 [10^18 J]: averaged ocean heat content (between 100 m and 2000 m)" ;<br> - ROMS_SO_d025_*_mean.*.nc:<br> time [days since 0000-01-01 00:00:00]: time<br> lon [degree_east]: longitude<br> lat [degree_north]: latitude<br> zeta [meter]: averaged free-surface elevation<br> temp [degree_celsius]: averaged potential temperature<br> salt [psu]: averaged salinity<br> rho [kilogram meter-3]: averaged density anomaly from model reference density of 1027 kilogram meter-3<br> - ROMS_SO_d025_*_yearly.zlev.zonalmean.*.nc:<br> time [days since 0000-01-01 00:00:00]: time<br> lat [degree_north]: latitude of RHO-points<br> lat_v [degree_north]: latitude of V-points<br> depth [meter]: depth of levels<br> temp [degree_celsius]: averaged potential temperature<br> salt [psu]: averaged salinity<br> rho [kilogram meter-3]: averaged density anomaly from model reference density of 1027 kilogram meter-3<br> moc [10^6 meter^3 second^-1]: averaged meridional overturning circulation on V-points<br> Missing values: missing values are filled as '-9.99e+20'.</p> <p><em>Metadata</em><br> - README.txt, metadata, text format</p> <p><br> <strong>References:</strong></p> <p>Haumann, F. A. (2016): Southern Ocean response to recent changes in surface freshwater fluxes. Doctoral Thesis. ETH Zurich. doi:10.3929/ethz-b-000166276.</p> <p><br> <strong>Acknowledgments:</strong></p> <p>This work was supported by ETH Research Grant CH2-01 11-1 and by the SNSF grant numbers P2EZP2_175162 and P400P2_186681, as well as NSF’s SOCCOM Project under NSF Award No. PLR-1425989.</p>
Short example of Biceps Brachii muscle surface HDEMG decomposition using the DEMUSE Tool
<p>This dataset contains 4 examples of synthetic high density surface EMG signals of the Biceps Brachii muscle and results of their decomposition into separate motor unit activity. It is intended as a demonstration of the DEMUSE Tool software for sEMG decomposition and as a basis for practical example of dataset preparation for the HybridNeuro project webinar on Data management and ethics (<a href="https://www.hybridneuro.feri.um.si/results.html#webinars">https://www.hybridneuro.feri.um.si/results.html#webinars</a>). Two sets of data are included: the raw simulated sEMG signals and the results of decomposition of those signals with the DEMUSE Tool.</p>
Multiscale Land Surface Parameters for Europe
<p><strong>General Description</strong></p> <p>The <em>Multiscale Land Surface Parameters for Europe</em> dataset is derived from <a href="../records/7676373">Global Ensemble DTM</a>. Data is computed using GRASS GIS and SAGA GIS. Original DTM data is in projection EPSG:4326, and reprojects to Equi7 (EPSG:27704), computes the parameters, and eventually reprojects to EPSG:3035. High resolution layers (120m downward in geo-hydrological parameters and 60m downward in others) are computed in tiles. In order to eliminate boundary effects and reprojection resampling, Regional land surface parameters have 3400 pixels overlap and local land surface 100 pixels overlap. Below is the list of land-surface parameters.</p> <ul> <li><strong>Local land-surface parameter</strong></li> </ul> <p><strong>slope in degree (slope): </strong>steepness at each cell</p> <p><strong>hillshade:</strong> visualizing of terrain determined by a light source and the slope and aspect of the elevation surface</p> <p><strong>easterness: </strong>cosine of aspect</p> <p><strong>northerness:</strong> sine of aspect</p> <p><strong>minimum curvature (minic): </strong>valleys in negative value and local convex landform in positive value</p> <p><strong>maximum curvature (maxic):</strong> ridges in positive values and local concave landform in negative value</p> <p><strong>positive openness (pos.openness): </strong>the "dominance" of an elevated location over its surroundings</p> <p><strong>negative openness (neg.openness):</strong> the "enclosure" of a lower location by elevated surroundings</p> <ul> <li><strong>Regional land-surface parameter</strong></li> </ul> <p><strong>sink removal DTM (nosink)</strong></p> <p><strong>flow accumulation (flow.accum): </strong>depiction of the flow convergence upslope pixels to downslope pixels</p> <p><strong>geomorphon classes (geomorphon):</strong> 9 terrain forms based on the line-of-sight neighbor pixels</p> <p><strong>specific catchment area (spec.catch.area.factor):</strong> the total catchment area divided by flow width</p> <p><strong>topographic wetness index (twi):</strong> a parameter describing the tendency of a cell to accumulate water</p> <p><strong>slope length and steepness factor (ls.factor):</strong> the S-factor measures the effect of slope steepness, and the L-factor defines the impact of slope length.</p> <p><strong>Data Details</strong></p> <ul> <li><strong>Time period:</strong> January 2000 – December 2022</li> <li><strong>Type of data:</strong> Land surface parameters of geomorphometry</li> <li><strong>How the data was collected or derived:</strong> Derived from <a href="../records/7676373">Global Ensemble DTM</a> in 30m using GRASS GIS and SAGA GIS running in a local HPC.</li> <li><strong>Coordinate reference system:</strong> EPSG:3035</li> <li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (900000 899000 7401000 5501000)</li> <li><strong>Spatial resolution:</strong> 60m, 120m, 240m, 480m, 960m</li> <li><strong>Image size: </strong>108,350 x 76,700; 54,175 x 38,350; 54,175 x 38,350; 13,544 x 9,588; 6,772 x 4,794<strong> </strong></li> <li><strong>File format:</strong> Cloud Optimized Geotiff (COG) format.</li> </ul> <p><strong>Support</strong></p> <p>If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">https://github.com/AI4SoilHealth/SoilHealthDataCube/issues</a></p> <p><strong>Name convention</strong></p> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:</p> <ol> <li><strong>generic variable name:</strong> slope = slope in degree</li> <li><strong>variable procedure combination:</strong> edtm = Ensemble digital terrain model</li> <li><strong>Position in the probability distribution / variable type:</strong> m = measurement</li> <li><strong>Spatial support:</strong> 60m, 120m, 240m, 480m, 960m</li> <li><strong>Depth reference:</strong> s = surface</li> <li><strong>Time reference begin time:</strong> 20000101 = 2000-01-01</li> <li><strong>Time reference end time:</strong> 20221231 = 2022-12-31</li> <li><strong>Bounding box:</strong> eu = Europe</li> <li><strong>EPSG code:</strong> epsg.3035 = EPSG:3035</li> <li><strong>Version code:</strong> v20240528 = 2024-05-28 (creation date)</li> </ol>
LAGOS-US LIMNO: Data module of surface water chemistry from 1975-2021 for lakes in the conterminous U.S.
The LAGOS-US LIMNO data package is one of the core data modules of LAGOS-US, an extensible research-ready platform designed to study the 479,950 lakes and reservoirs larger than or equal to 1 ha in the conterminous US (48 states plus the District of Columbia). The LIMNO module contains in situ observations of 47 parameters of lake physics, chemistry, and biology (hereafter referred to as chemistry) from lake surface samples (defined as observations taken from the epilimnion of a lake) obtained from the Water Quality Portal, the National Lakes Assessment (2007, 2012, 2017), and NEON programs. LIMNO provides 3,511,020 observations across all parameters collected between 1975 and 2021 from 20,329 lakes; the number of observations per lake ranged from 1 to 20,605 with a median of 32. The database design that supports the LAGOS-US research platform was created based on several important design features: lakes are the fundamental unit of consideration, all lakes in the spatial extent above the minimum size must be represented, and most information is connected to individual lakes. The design is modular, interoperable (the modules can be used with each other, as well as other comprehensive lake data products such as the USGS NHD), and extensible (future database modules can be developed and used in the LAGOS-US research platform by others). Users are encouraged to use the other two core data modules that are part of the LAGOS-US platform: LOCUS (location, identifiers, and physical characteristics of lakes and their watersheds) and GEO (characteristics defining geospatial and temporal ecological setting quantified at multiple spatial divisions) that are each found in their own data packages.
H2Ohio Wetland Monitoring Program Surface Water and Soil Nutrient Content from Wetlands across Ohio, USA (2021–2022).
This data package contains surface water and soil nutrient concentration datasets from wetland projects across Ohio, USA monitored by the H2Ohio Wetland Monitoring Program. Monitoring began in May 2021 and is ongoing. This data package will be updated yearly. In general, surface water samples are collected to measure concentrations of major nutrients, including inorganic nitrogen, ammonium-nitrogen, total nitrogen, dissolved reactive phosphorus, and total phosphorus. Sampling from major inflows and outflows is prioritized at flow-through wetland projects to support the calculation of nutrient filtration estimates using mass balance approaches. Surface water samples may also be collected from representative zones or hydrologic features with sufficient standing water (i.e., vernal pools, vegetated areas, interconnected smaller pond-like areas, etc.) to assess nutrient conditions and processes within the wetland system. The majority of surface water sampling (~monthly) occurs from March through December, with opportunistic sampling in January and February. Every effort is made to collect samples during hydrologic events (i.e., storms) as well as baseflow conditions. Concurrent with surface water sampling, hand-held multiparameter sensors are used to measure snapshots of physicochemical characteristics including dissolved oxygen, temperature, specific conductance, turbidity, and pH. Soil samples (0-5 cm) are collected in saturated and unsaturated zones at each identified soil "patch" determined from expert opinion, soil maps (Natural Resources Conservation Service), and/or hydrogeophysical assessment. Additionally, soil samples may be collected along major visible hydrologic or elevation gradients. Soil sampling occurs 1-3 times a year in select wetland projects.
Urban Heat and Desert Wildlife: Rodent Body Condition Across a Gradient of Surface Temperatures in the greater Phoenix, Arizona (USA) metropolitan area (2019-2020)
We live-trapped wild rodents from seven field sites spanning three strata of land-surface temperatures in the Phoenix, Arizona (USA) metropolitan area. We captured 116 adult pocket mice (Chaetodipus spp. and Perognathus spp.) and Merriam’s kangaroo rats (Dipodomys merriami) during 2019 and 2020 from mountainous urban parks and open spaces. Animal body condition was quantified as percent body fat (i.e., fat mass divided by body mass). We used a noninvasive quantitative magnetic resonance instrument to measure body condition.
Spatial surface water chemistry of Lake Mendota with FLAMe: 2014-2016
We mapped surface water chemistry in Lake Mendota 39 times between 2014 and 2016. We used a sensor-based and boat-mounted sensing platform to continuously measure underway water chemistry. Measurements were linked with global positioning systems (GPS) to create maps of surface water chemistry. Data have been provided in three formats (raw, hydraulic-corrected, and tau-corrected). Dataset is used for the publication, "Large spatial and temporal variability of carbon dioxide and methane in a eutrophic lake", https://doi.org/10.1029/2019JG005186
Urban Residential Surface and Subsurface Hydrology: Synergistic Effects of Low-Impact Features at the Parcel Scale
Accurately predicting the hydrologic effects of urbanization requires an understanding of how hydrologic processes are affected by low‐impact development practices. In this study, we explored how growing season surface runoff, deep drainage, and evapotranspiration on a residential parcel are affected by several low‐impact interventions, including three "impervious‐centric" interventions (disconnecting downspouts, disconnecting sidewalks, and adding a transverse slope to the driveway and front walk), two "pervious‐centric" interventions (decompacting soil and adding microtopography), and all possible "holistic" combinations. Results were compared to both a highly and moderately compacted baseline parcel under an average and a dry weather scenario for a temperate climate. We find that under reasonable assumptions for highly compacted soil, pervious areas are a major source of runoff and disconnecting impervious surfaces may be relatively less effective without improving soil conditions. Under both highly and moderately compacted soil conditions, combining efforts to decompact soil with impervious disconnection has a synergistic effect on reducing surface runoff and increasing deep drainage and evapotranspiration. All combinations of interventions enhance infiltration, but the partitioning of additional root zone water between deep drainage and evapotranspiration depends on the weather scenario. Importantly, when all low‐impact interventions are applied together, growing season deep drainage is higher than that from a vacant lot with no impervious surfaces. We infer that ecohydrologic interfaces between impervious and pervious areas are strong controls on urban hydrologic fluxes and that high‐resolution, process‐based models can be used to account for these interfaces and thereby improve predictions of the hydrologic effects of low‐impact interventions.
Euphausia superba length frequency from zooplankton collected with a 2-m, 700-um net towed from surface to 120 m, aboard Palmer LTER annual cruises off the coast of the Western Antarctic Peninsula, 1993 - 2024.
Euphausia superba standard lengths (SL) were measured at grid stations on the annual LTER cruises along the western Antarctic Peninsula (WAP). Annual cruises take place between late December to early February, except for the NBP21-13 cruise, which was November and December. Krill were collected with a 2x2 meter, 700um mesh net fitted with a flow meter and towed obliquely to 120m.
SBC LTER: Ocean: HFR-derived surface flow metrics, surface water retention times, and related factors in the Santa Barbara Channel (2012-2019)
This data package include three files: 1. daily maps of High-Frequency Radar (HFR) measured surface currents, indices of mesoscale eddy locations, and local retention times on a 2km grid; 2. monthly time series of wind stress, alongshore pressure gradient, surface current EOF principal components, vorticity, eddy area, eddy presence, and spatially averaged retention times from January 2012 to December 2019; 3. A MATLAB script for plotting the maps and timeseries. These data were processed in order to investigate the drivers of surface water retention in the Santa Barbara Channel, CA, details of which are available in the study: Brokaw, R.J., D.A. Siegel, and L. Washburn. Physical Drivers of Surface Water Retention in the Santa Barbara Channel. [In preparation for Journal of Geophysical Research: Oceans.]
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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.