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275 results for “Spatial resolution”
Sensitivity of a Coarse-Resolution Global Ocean Model to a Spatially Variable Neutral Diffusivity - ACCESS-OM2 data and plotting routines
<p>This repository contains the processed data and plotting routines associated with the article</p> <p>Holmes, Groeskamp, Stewart and McDougall (2022), Sensitivity of a Coarse-Resolution Global Ocean Model to a Spatially Variable Neutral Diffusivity, Journal of Advances in Modeling Earth Systems (JAMES), doi: 10.1029/2021MS002914, http://dx.doi.org/10.1029/2021MS002914</p> <p>The contents includes post-processed data output from the 1-degree ACCESS-OM2 ocean-sea-ice model simulations and the python/jupyter plotting routines required to make the plots.</p> <p>The processing script is Holmes2022JAMES_Neutral_Diffusion_ACCESS-OM2_Plotting_Script.ipynb. The data files consist of time-averages or time series of certain metrics processed using NCO tools from the raw ACCESS-OM2 simulation output.</p>
Dataset of the determination of the topographic spatial resolution of a confocal point sensor with a type ASG material measure
<p>These original measurement data relate to the publication: J. Schaude, A. C. Gröschl, T. Hausotte: Effect of a Misidentified Centre of a Type ASG Material Measure on the Determined Topographic Spatial Resolution of an Optical Point Sensor, Metrology 2(1), p. 19-32, 2022, <a href="https://doi.org/10.3390/metrology2010002">https://doi.org/10.3390/metrology2010002</a>. Please refer to this open access publication for a detailed description of the measurement setup and procedure.</p> <p>All data are in ASCII-format. Each file contains four columns, where column one to three are the <em>x</em>, <em>y</em>, and <em>z</em>-coordinates of the positioning system and column four is the signal of the photodetector.</p> <p><strong>Content of the folders</strong></p> <p>10_Plane: Axial probings on 18 points just outside the grooves.</p> <p>20_Edges: Lateral probings from each point just outside the grooves in the direction of the roughly determined centre of the material measure.</p> <p>30_PlaneArea: Repeated axial probing on a plane area near the material measure.</p> <p>40_RadialMeasurement: Radial measurement of the material measure by lateral (radial) probings conducted on different heights (referring to the distance to the plane fitted to the measuring points of 10_Plane), and radii (referring to the centre of the circle fitted to the edges determined in 20_Edges). Each radial probing has its own data file, with the name of the data file being “radial_probing {radius in m} {height in m} {data and time of probing}.txt”.</p> <p>50_LineMeasurement: Lateral probings conducted on different heights (referring to the distance to the plane fitted to the measuring points of 10_Plane), different lateral offsets (referring to the centre of the circle fitted to the edges determined in 20_Edges) and on two angles (on the groove (0°) and the adjacent top level (10°)). Please refer to sec. 5.2 of the aforementioned publication for a detailed description. Each lateral probing has its own data file, with the name of the file being “lateral probing {angle in °} {offset in m} {height in m} {data and time of probing}.txt”.</p> <p><strong>Acknowledgement</strong></p> <p>This project 20IND07 TracOptic has received funding from the EMPIR programme co-financed by the Participating States and from the European Union’s Horizon 2020 research and innovation programme. Funder name: European Metrology Programme for Innovation and Research (EMPIR); Funder ID: 10.13039/10001413</p>
Gastruloids as in vitro models of embryonic blood development with spatial and temporal resolution
<p>Rawdata (images and flow cytometry) for the manuscript "Gastruloids as in vitro models of embryonic blood development with spatial and temporal resolution". <br> </p>
Shoreline data at 30-m spatial resolution for regions of the USA, in geoJSON format. Region 3: Lousiana/Florida border to Georgia/South Carolina border
<p>Data file: SE_USA_Louisiana_Georgia_ref_shoreline.geojson</p> <p>Region: Lousiana/Florida border to Georgia/South Carolina border</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> <p>ABSTRACT</p> <p>A new 30-m spatial resolution global shoreline vector (GSV) was developed from annual composites of 2014 Landsat satellite imagery. The semi-automated classification of the imagery was accomplished by manual selection of training points representing water and non-water classes along the entire global coastline. Polygon topology was applied to the GSV, resulting in a new characterisation of the number and size of global islands. Three size classes of islands were mapped: continental mainlands (5), islands greater than 1 km<sup>2</sup> (21,818), and islands smaller than 1 km<sup>2</sup> (318,868). The GSV represents the shore zone land and water interface boundary, and is a spatially explicit ecological domain separator between terrestrial and marine environments. The development and characteristics of the GSV are presented herein. An approach is also proposed for delineating standardised, high spatial resolution global ecological coastal units (ECUs). For this coastal ecosystem mapping effort, the GSV will be used to separate the nearshore coastal waters from the onshore coastal lands. The work to produce the GSV and the ECUs is commissioned by the Group on Earth Observations (GEO), and is associated with several GEO initiatives including GEO Ecosystems, GEO Marine Biodiversity Observation Network (MBON) and GEO Blue Planet.</p> <p>https://www.tandfonline.com/doi/full/10.1080/1755876X.2018.1529714</p>
Shoreline data at 30-m spatial resolution for regions of the USA, in geoJSON format. Region 4: Mexico/Texas border to Lousiana/Georgia border
<p>Data file: S_USA_Texas_Louisiana_ref_shoreline.geojson</p> <p>Region: Mexico/Texas border to Lousiana/Georgia border</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> <p>ABSTRACT</p> <p>A new 30-m spatial resolution global shoreline vector (GSV) was developed from annual composites of 2014 Landsat satellite imagery. The semi-automated classification of the imagery was accomplished by manual selection of training points representing water and non-water classes along the entire global coastline. Polygon topology was applied to the GSV, resulting in a new characterisation of the number and size of global islands. Three size classes of islands were mapped: continental mainlands (5), islands greater than 1 km<sup>2</sup> (21,818), and islands smaller than 1 km<sup>2</sup> (318,868). The GSV represents the shore zone land and water interface boundary, and is a spatially explicit ecological domain separator between terrestrial and marine environments. The development and characteristics of the GSV are presented herein. An approach is also proposed for delineating standardised, high spatial resolution global ecological coastal units (ECUs). For this coastal ecosystem mapping effort, the GSV will be used to separate the nearshore coastal waters from the onshore coastal lands. The work to produce the GSV and the ECUs is commissioned by the Group on Earth Observations (GEO), and is associated with several GEO initiatives including GEO Ecosystems, GEO Marine Biodiversity Observation Network (MBON) and GEO Blue Planet.</p> <p>https://www.tandfonline.com/doi/full/10.1080/1755876X.2018.1529714</p>
Shoreline data at 30-m spatial resolution for regions of the USA, in geoJSON format. Region 5: California
<p>Data file: W_USA_California_ref_shoreline.geojson</p> <p>Region: Mexico/California border to California/Oregon border</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> <p>ABSTRACT</p> <p>A new 30-m spatial resolution global shoreline vector (GSV) was developed from annual composites of 2014 Landsat satellite imagery. The semi-automated classification of the imagery was accomplished by manual selection of training points representing water and non-water classes along the entire global coastline. Polygon topology was applied to the GSV, resulting in a new characterisation of the number and size of global islands. Three size classes of islands were mapped: continental mainlands (5), islands greater than 1 km<sup>2</sup> (21,818), and islands smaller than 1 km<sup>2</sup> (318,868). The GSV represents the shore zone land and water interface boundary, and is a spatially explicit ecological domain separator between terrestrial and marine environments. The development and characteristics of the GSV are presented herein. An approach is also proposed for delineating standardised, high spatial resolution global ecological coastal units (ECUs). For this coastal ecosystem mapping effort, the GSV will be used to separate the nearshore coastal waters from the onshore coastal lands. The work to produce the GSV and the ECUs is commissioned by the Group on Earth Observations (GEO), and is associated with several GEO initiatives including GEO Ecosystems, GEO Marine Biodiversity Observation Network (MBON) and GEO Blue Planet.</p> <p>https://www.tandfonline.com/doi/full/10.1080/1755876X.2018.1529714</p>
Shoreline data at 30-m spatial resolution for regions of the USA, in geoJSON format. Region 2: Georgia/South Carolina border to North Carolina/Delaware border
<p>Data file: E_USA_SouthCarolina_NorthCarolina_ref_shoreline.geojson</p> <p>Region: Georgia/South Carolina border to North Carolina/Delaware border</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> <p>ABSTRACT</p> <p>A new 30-m spatial resolution global shoreline vector (GSV) was developed from annual composites of 2014 Landsat satellite imagery. The semi-automated classification of the imagery was accomplished by manual selection of training points representing water and non-water classes along the entire global coastline. Polygon topology was applied to the GSV, resulting in a new characterisation of the number and size of global islands. Three size classes of islands were mapped: continental mainlands (5), islands greater than 1 km<sup>2</sup> (21,818), and islands smaller than 1 km<sup>2</sup> (318,868). The GSV represents the shore zone land and water interface boundary, and is a spatially explicit ecological domain separator between terrestrial and marine environments. The development and characteristics of the GSV are presented herein. An approach is also proposed for delineating standardised, high spatial resolution global ecological coastal units (ECUs). For this coastal ecosystem mapping effort, the GSV will be used to separate the nearshore coastal waters from the onshore coastal lands. The work to produce the GSV and the ECUs is commissioned by the Group on Earth Observations (GEO), and is associated with several GEO initiatives including GEO Ecosystems, GEO Marine Biodiversity Observation Network (MBON) and GEO Blue Planet.</p> <p>https://www.tandfonline.com/doi/full/10.1080/1755876X.2018.1529714</p>
Shoreline data at 30-m spatial resolution for regions of the USA, in geoJSON format. Region 7: Alaska
<p>Data file: USA_Alaska_ref_shoreline.geojson</p> <p>Region: Alaska</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>
HiTIC-Monthly: A Monthly High Spatial Resolution (1 km) Human Thermal Index Collection over China during 2003–2020
<p>The monthly <strong>Hi</strong>gh spatial resolution human <strong>T</strong>hermal <strong>I</strong>ndex <strong>C</strong>ollection (<strong>HiTIC-Monthly</strong>) includes <strong><em>near-surface air temperature</em> </strong>(SAT) and 11 commonly used<strong> <em>human-perceived temperature</em> </strong>indices: indoor Apparent Temperature (AT<sub>in</sub>), outdoor shaded Apparent Temperature (AT<sub>out</sub>), Discomfort Index (DI), Effective Temperature (ET), Heat Index (HI), Humidex (HMI), Modified Discomfort Index (MDI), Net Effective Temperature (NET), simplified Wet Bulb Globe Temperature (sWBGT), Wet-Bulb Temperature (WBT), and Wind Chill Temperature (WCT). This dataset has a high spatial resolution of 1 km × 1 km and covers mainland China from January 2003 to December 2020. The overall R-square, root mean square error, and mean absolute error of the 12 thermal indices in the HiTIC dataset are 0.996, 0.693°C, and 0.512°C, respectively. It is stacked by year, and each stack is composed of 12 monthly images in the <strong>NetCDF format</strong>. The unit of the dataset is 0.01 degree Celsius (°C), and the values are stored in an integer type (Int16) for saving storage space, and need to be divided by 100 to get the values in degree Celcius when in use. The projection coordinate system of the dataset is Albers Equal Area Conic Projection. The naming rule and other detailed information can be found in “README.pdf”.</p> <p>If you have any questions when using the HiTIC-Monthly dataset, please feel free to contact Miss Hui Zhang via <a href="mailto:zhangh573@mail2.sysu.edu.cn">zhangh573@mail2.sysu.edu.cn</a>, Dr. Ming Luo via <a href="mailto:luom38@mail.sysu.edu.cn">luom38@mail.sysu.edu.cn</a>, or Dr. Yongquan Zhao via <a href="mailto:yqzhao@link.cuhk.edu.hk">zhaoyq66@mail.sysu.edu.cn</a>. More details on the procedure of producing the HiTIC-Monthly dataset and its accuracy assessment can be found in:</p> <p>Zhang, H., Luo, M., Zhao, Y., Lin, L., Ge, E., Yang, Y., Ning, G., Cong, J., Zeng, Z., Gui, K., Li, J., Chan, T. O., Li, X., Wu, S., Wang, P., and Wang, X.: HiTIC-Monthly: a monthly high spatial resolution (1 km) human thermal index collection over China during 2003–2020, Earth Syst. Sci. Data, 15, 359–381, https://doi.org/10.5194/essd-15-359-2023, 2023.</p>
Shoreline data at 30-m spatial resolution for 2001 coastal provinces or regions of the world, in geoJSON format.
<p>Region: 2001 coastal provinces or regions of the world</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> <p>Metadata file (each file listed alongside the bounds in WGS84 latitude/longitude): province_files_bounds.json</p>
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> <p>Metadata file (each file listed alongside the bounds in WGS84 latitude/longitude): files_bounds.json</p> <p>Shoreline data files:</p> <p>us_county0_ref_shoreline.geojson<br> us_county1036_ref_shoreline.geojson<br> us_county111_ref_shoreline.geojson<br> us_county112_ref_shoreline.geojson<br> us_county113_ref_shoreline.geojson<br> us_county1148_ref_shoreline.geojson<br> us_county1149_ref_shoreline.geojson<br> us_county114_ref_shoreline.geojson<br> us_county115_ref_shoreline.geojson<br> us_county116_ref_shoreline.geojson<br> us_county117_ref_shoreline.geojson<br> us_county118_ref_shoreline.geojson<br> us_county1195_ref_shoreline.geojson<br> us_county119_ref_shoreline.geojson<br> us_county1200_ref_shoreline.geojson<br> us_county120_ref_shoreline.geojson<br> us_county121_ref_shoreline.geojson<br> us_county122_ref_shoreline.geojson<br> us_county123_ref_shoreline.geojson<br> us_county1246_ref_shoreline.geojson<br> us_county124_ref_shoreline.geojson<br> us_county125_ref_shoreline.geojson<br> us_county126_ref_shoreline.geojson<br> us_county1272_ref_shoreline.geojson<br> us_county1273_ref_shoreline.geojson<br> us_county1277_ref_shoreline.geojson<br> us_county127_ref_shoreline.geojson<br> us_county128_ref_shoreline.geojson<br> us_county129_ref_shoreline.geojson<br> us_county1302_ref_shoreline.geojson<br> us_county1304_ref_shoreline.geojson<br> us_county1305_ref_shoreline.geojson<br> us_county1306_ref_shoreline.geojson<br> us_county130_ref_shoreline.geojson<br> us_county1317_ref_shoreline.geojson<br> us_county131_ref_shoreline.geojson<br> us_county1324_ref_shoreline.geojson<br> us_county132_ref_shoreline.geojson<br> us_county133_ref_shoreline.geojson<br> us_county134_ref_shoreline.geojson<br> us_county135_ref_shoreline.geojson<br> us_county136_ref_shoreline.geojson<br> us_county137_ref_shoreline.geojson<br> us_county138_ref_shoreline.geojson<br> us_county139_ref_shoreline.geojson<br> us_county140_ref_shoreline.geojson<br> us_county141_ref_shoreline.geojson<br> us_county142_ref_shoreline.geojson<br> us_county143_ref_shoreline.geojson<br> us_county144_ref_shoreline.geojson<br> us_county145_ref_shoreline.geojson<br> us_county146_ref_shoreline.geojson<br> us_county147_ref_shoreline.geojson<br> us_county148_ref_shoreline.geojson<br> us_county149_ref_shoreline.geojson<br> us_county150_ref_shoreline.geojson<br> us_county151_ref_shoreline.geojson<br> us_county152_ref_shoreline.geojson<br> us_county153_ref_shoreline.geojson<br> us_county154_ref_shoreline.geojson<br> us_county155_ref_shoreline.geojson<br> us_county156_ref_shoreline.geojson<br> us_county157_ref_shoreline.geojson<br> us_county1585_ref_shoreline.geojson<br> us_county158_ref_shoreline.geojson<br> us_county1596_ref_shoreline.geojson<br> us_county159_ref_shoreline.geojson<br> us_county1605_ref_shoreline.geojson<br> us_county160_ref_shoreline.geojson<br> us_county161_ref_shoreline.geojson<br> us_county162_ref_shoreline.geojson<br> us_county163_ref_shoreline.geojson<br> us_county1649_ref_shoreline.geojson<br> us_county164_ref_shoreline.geojson<br> us_county1650_ref_shoreline.geojson<br> us_county1651_ref_shoreline.geojson<br> us_county1653_ref_shoreline.geojson<br> us_county1655_ref_shoreline.geojson<br> us_county1658_ref_shoreline.geojson<br> us_county165_ref_shoreline.geojson<br> us_county1667_ref_shoreline.geojson<br> us_county166_ref_shoreline.geojson<br> us_county167_ref_shoreline.geojson<br> us_county168_ref_shoreline.geojson<br> us_county169_ref_shoreline.geojson<br> us_county170_ref_shoreline.geojson<br> us_county171_ref_shoreline.geojson<br> us_county172_ref_shoreline.geojson<br> us_county173_ref_shoreline.geojson<br> us_county174_ref_shoreline.geojson<br> us_county175_ref_shoreline.geojson<br> us_county176_ref_shoreline.geojson<br> us_county177_ref_shoreline.geojson<br> us_county178_ref_shoreline.geojson<br> us_county179_ref_shoreline.geojson<br> us_county180_ref_shoreline.geojson<br> us_county181_ref_shoreline.geojson<br> us_county182_ref_shoreline.geojson<br> us_county183_ref_shoreline.geojson<br> us_county184_ref_shoreline.geojson<br> us_county185_ref_shoreline.geojson<br> us_county186_ref_shoreline.geojson<br> us_county187_ref_shoreline.geojson<br> us_county188_ref_shoreline.geojson<br> us_county189_ref_shoreline.geojson<br> us_county190_ref_shoreline.geojson<br> us_county191_ref_shoreline.geojson<br> us_county192_ref_shoreline.geojson<br> us_county193_ref_shoreline.geojson<br> us_county194_ref_shoreline.geojson<br> us_county195_ref_shoreline.geojson<br> us_county196_ref_shoreline.geojson<br> us_county197_ref_shoreline.geojson<br> us_county198_ref_shoreline.geojson<br> us_county199_ref_shoreline.geojson<br> us_county200_ref_shoreline.geojson<br> us_county201_ref_shoreline.geojson<br> us_county202_ref_shoreline.geojson<br> us_county203_ref_shoreline.geojson<br> us_county204_ref_shoreline.geojson<br> us_county205_ref_shoreline.geojson<br> us_county206_ref_shoreline.geojson<br> us_county207_ref_shoreline.geojson<br> us_county208_ref_shoreline.geojson<br> us_county209_ref_shoreline.geojson<br> us_county210_ref_shoreline.geojson<br> us_county2118_ref_shoreline.geojson<br> us_county211_ref_shoreline.geojson<br> us_county2125_ref_shoreline.geojson<br> us_county2127_ref_shoreline.geojson<br> us_county2128_ref_shoreline.geojson<br> us_county212_ref_shoreline.geojson<br> us_county2130_ref_shoreline.geojson<br> us_county2133_ref_shoreline.geojson<br> us_county213_ref_shoreline.geojson<br> us_county2148_ref_shoreline.geojson<br> us_county2149_ref_shoreline.geojson<br> us_county214_ref_shoreline.geojson<br> us_county215_ref_shoreline.geojson<br> us_county216_ref_shoreline.geojson<br> us_county217_ref_shoreline.geojson<br> us_county218_ref_shoreline.geojson<br> us_county219_ref_shoreline.geojson<br> us_county220_ref_shoreline.geojson<br> us_county221_ref_shoreline.geojson<br> us_county222_ref_shoreline.geojson<br> us_county223_ref_shoreline.geojson<br> us_county224_ref_shoreline.geojson<br> us_county225_ref_shoreline.geojson<br> us_county226_ref_shoreline.geojson<br> us_county227_ref_shoreline.geojson<br> us_county228_ref_shoreline.geojson<br> us_county229_ref_shoreline.geojson<br> us_county230_ref_shoreline.geojson<br> us_county231_ref_shoreline.geojson<br> us_county232_ref_shoreline.geojson<br> us_county233_ref_shoreline.geojson<br> us_county234_ref_shoreline.geojson<br> us_county235_ref_shoreline.geojson<br> us_county236_ref_shoreline.geojson<br> us_county237_ref_shoreline.geojson<br> us_county238_ref_shoreline.geojson<br> us_county239_ref_shoreline.geojson<br> us_county240_ref_shoreline.geojson<br> us_county241_ref_shoreline.geojson<br> us_county242_ref_shoreline.geojson<br> us_county243_ref_shoreline.geojson<br> us_county244_ref_shoreline.geojson<br> us_county245_ref_shoreline.geojson<br> us_county246_ref_shoreline.geojson<br> us_county2478_ref_shoreline.geojson<br> us_county247_ref_shoreline.geojson<br> us_county248_ref_shoreline.geojson<br> us_county2493_ref_shoreline.geojson<br> us_county249_ref_shoreline.geojson<br> us_county250_ref_shoreline.geojson<br> us_county2517_ref_shoreline.geojson<br> us_county2518_ref_shoreline.geojson<br> us_county251_ref_shoreline.geojson<br> us_county252_ref_shoreline.geojson<br> us_county2532_ref_shoreline.geojson<br> us_county253_ref_shoreline.geojson<br> us_county254_ref_shoreline.geojson<br> us_county255_ref_shoreline.geojson<br> us_county256_ref_shoreline.geojson<br> us_county257_ref_shoreline.geojson<br> us_county258_ref_shoreline.geojson<br> us_county259_ref_shoreline.geojson<br> us_county260_ref_shoreline.geojson<br> us_county261_ref_shoreline.geojson<br> us_county262_ref_shoreline.geojson<br> us_county263_ref_shoreline.geojson<br> us_county264_ref_shoreline.geojson<br> us_county265_ref_shoreline.geojson<br> us_county266_ref_shoreline.geojson<br> us_county267_ref_shoreline.geojson<br> us_county2688_ref_shoreline.geojson<br> us_county268_ref_shoreline.geojson<br> us_county269_ref_shoreline.geojson<br> us_county272_ref_shoreline.geojson<br> us_county2749_ref_shoreline.geojson<br> us_county275_ref_shoreline.geojson<br> us_county276_ref_shoreline.geojson<br> us_county277_ref_shoreline.geojson<br> us_county278_ref_shoreline.geojson<br> us_county279_ref_shoreline.geojson<br> us_county280_ref_shoreline.geojson<br> us_county281_ref_shoreline.geojson<br> us_county282_ref_shoreline.geojson<br> us_county283_ref_shoreline.geojson<br> us_county284_ref_shoreline.geojson<br> us_county285_ref_shoreline.geojson<br> us_county286_ref_shoreline.geojson<br> us_county287_ref_shoreline.geojson<br> us_county288_ref_shoreline.geojson<br> us_county289_ref_shoreline.geojson<br> us_county290_ref_shoreline.geojson<br> us_county291_ref_shoreline.geojson<br> us_county292_ref_shoreline.geojson<br> us_county293_ref_shoreline.geojson<br> us_county294_ref_shoreline.geojson<br> us_county295_ref_shoreline.geojson<br> us_county296_ref_shoreline.geojson<br> us_county297_ref_shoreline.geojson<br> us_county298_ref_shoreline.geojson<br> us_county299_ref_shoreline.geojson<br> us_county300_ref_shoreline.geojson<br> us_county301_ref_shoreline.geojson<br> us_county302_ref_shoreline.geojson<br> us_county303_ref_shoreline.geojson<br> us_county304_ref_shoreline.geojson<br> us_county305_ref_shoreline.geojson<br> us_county3069_ref_shoreline.geojson<br> us_county306_ref_shoreline.geojson<br> us_county307_ref_shoreline.geojson<br> us_county308_ref_shoreline.geojson<br> us_county309_ref_shoreline.geojson<br> us_county310_ref_shoreline.geojson<br> us_county311_ref_shoreline.geojson<br> us_county312_ref_shoreline.geojson<br> us_county313_ref_shoreline.geojson<br> us_county314_ref_shoreline.geojson<br> us_county315_ref_shoreline.geojson<br> us_county316_ref_shoreline.geojson<br> us_county317_ref_shoreline.geojson<br> us_county318_ref_shoreline.geojson<br> us_county319_ref_shoreline.geojson<br> us_county320_ref_shoreline.geojson<br> us_county321_ref_shoreline.geojson<br> us_county322_ref_shoreline.geojson<br> us_county323_ref_shoreline.geojson<br> us_county324_ref_shoreline.geojson<br> us_county325_ref_shoreline.geojson<br> us_county326_ref_shoreline.geojson<br> us_county327_ref_shoreline.geojson<br> us_county328_ref_shoreline.geojson<br> us_county329_ref_shoreline.geojson<br> us_county330_ref_shoreline.geojson<br> us_county331_ref_shoreline.geojson<br> us_county332_ref_shoreline.geojson<br> us_county333_ref_shoreline.geojson<br> us_county334_ref_shoreline.geojson<br> us_county335_ref_shoreline.geojson<br> us_county336_ref_shoreline.geojson<br> us_county337_ref_shoreline.geojson<br> us_county338_ref_shoreline.geojson<br> us_county339_ref_shoreline.geojson<br> us_county340_ref_shoreline.geojson<br> us_county341_ref_shoreline.geojson<br> us_county342_ref_shoreline.geojson<br> us_county343_ref_shoreline.geojson<br> us_county344_ref_shoreline.geojson<br> us_county345_ref_shoreline.geojson<br> us_county346_ref_shoreline.geojson<br> us_county347_ref_shoreline.geojson<br> us_county348_ref_shoreline.geojson<br> us_county349_ref_shoreline.geojson<br> us_county350_ref_shoreline.geojson<br> us_county351_ref_shoreline.geojson<br> us_county352_ref_shoreline.geojson<br> us_county353_ref_shoreline.geojson<br> us_county354_ref_shoreline.geojson<br> us_county416_ref_shoreline.geojson<br> us_county417_ref_shoreline.geojson<br> us_county418_ref_shoreline.geojson<br> us_county419_ref_shoreline.geojson<br> us_county420_ref_shoreline.geojson<br> us_county421_ref_shoreline.geojson<br> us_county422_ref_shoreline.geojson<br> us_county423_ref_shoreline.geojson<br> us_county685_ref_shoreline.geojson<br> us_county686_ref_shoreline.geojson<br> us_county700_ref_shoreline.geojson<br> us_county73_ref_shoreline.geojson<br> us_county74_ref_shoreline.geojson<br> us_county754_ref_shoreline.geojson<br> us_county79_ref_shoreline.geojson<br> us_county97_ref_shoreline.geojson</p> <p> </p> <p> </p>
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> <p>Metadata file (each file listed alongside the bounds in WGS84 latitude/longitude): files_bounds.json</p> <p>Shoreline data files:</p> <p>us_county0_ref_shoreline.geojson<br> us_county1036_ref_shoreline.geojson<br> us_county111_ref_shoreline.geojson<br> us_county112_ref_shoreline.geojson<br> us_county113_ref_shoreline.geojson<br> us_county1148_ref_shoreline.geojson<br> us_county1149_ref_shoreline.geojson<br> us_county114_ref_shoreline.geojson<br> us_county115_ref_shoreline.geojson<br> us_county116_ref_shoreline.geojson<br> us_county117_ref_shoreline.geojson<br> us_county118_ref_shoreline.geojson<br> us_county1195_ref_shoreline.geojson<br> us_county119_ref_shoreline.geojson<br> us_county1200_ref_shoreline.geojson<br> us_county120_ref_shoreline.geojson<br> us_county121_ref_shoreline.geojson<br> us_county122_ref_shoreline.geojson<br> us_county123_ref_shoreline.geojson<br> us_county1246_ref_shoreline.geojson<br> us_county124_ref_shoreline.geojson<br> us_county125_ref_shoreline.geojson<br> us_county126_ref_shoreline.geojson<br> us_county1272_ref_shoreline.geojson<br> us_county1273_ref_shoreline.geojson<br> us_county1277_ref_shoreline.geojson<br> us_county127_ref_shoreline.geojson<br> us_county128_ref_shoreline.geojson<br> us_county129_ref_shoreline.geojson<br> us_county1302_ref_shoreline.geojson<br> us_county1304_ref_shoreline.geojson<br> us_county1305_ref_shoreline.geojson<br> us_county1306_ref_shoreline.geojson<br> us_county130_ref_shoreline.geojson<br> us_county1317_ref_shoreline.geojson<br> us_county131_ref_shoreline.geojson<br> us_county1324_ref_shoreline.geojson<br> us_county132_ref_shoreline.geojson<br> us_county133_ref_shoreline.geojson<br> us_county134_ref_shoreline.geojson<br> us_county135_ref_shoreline.geojson<br> us_county136_ref_shoreline.geojson<br> us_county137_ref_shoreline.geojson<br> us_county138_ref_shoreline.geojson<br> us_county139_ref_shoreline.geojson<br> us_county140_ref_shoreline.geojson<br> us_county141_ref_shoreline.geojson<br> us_county142_ref_shoreline.geojson<br> us_county143_ref_shoreline.geojson<br> us_county144_ref_shoreline.geojson<br> us_county145_ref_shoreline.geojson<br> us_county146_ref_shoreline.geojson<br> us_county147_ref_shoreline.geojson<br> us_county148_ref_shoreline.geojson<br> us_county149_ref_shoreline.geojson<br> us_county150_ref_shoreline.geojson<br> us_county151_ref_shoreline.geojson<br> us_county152_ref_shoreline.geojson<br> us_county153_ref_shoreline.geojson<br> us_county154_ref_shoreline.geojson<br> us_county155_ref_shoreline.geojson<br> us_county156_ref_shoreline.geojson<br> us_county157_ref_shoreline.geojson<br> us_county1585_ref_shoreline.geojson<br> us_county158_ref_shoreline.geojson<br> us_county1596_ref_shoreline.geojson<br> us_county159_ref_shoreline.geojson<br> us_county1605_ref_shoreline.geojson<br> us_county160_ref_shoreline.geojson<br> us_county161_ref_shoreline.geojson<br> us_county162_ref_shoreline.geojson<br> us_county163_ref_shoreline.geojson<br> us_county1649_ref_shoreline.geojson<br> us_county164_ref_shoreline.geojson<br> us_county1650_ref_shoreline.geojson<br> us_county1651_ref_shoreline.geojson<br> us_county1653_ref_shoreline.geojson<br> us_county1655_ref_shoreline.geojson<br> us_county1658_ref_shoreline.geojson<br> us_county165_ref_shoreline.geojson<br> us_county1667_ref_shoreline.geojson<br> us_county166_ref_shoreline.geojson<br> us_county167_ref_shoreline.geojson<br> us_county168_ref_shoreline.geojson<br> us_county169_ref_shoreline.geojson<br> us_county170_ref_shoreline.geojson<br> us_county171_ref_shoreline.geojson<br> us_county172_ref_shoreline.geojson<br> us_county173_ref_shoreline.geojson<br> us_county174_ref_shoreline.geojson<br> us_county175_ref_shoreline.geojson<br> us_county176_ref_shoreline.geojson<br> us_county177_ref_shoreline.geojson<br> us_county178_ref_shoreline.geojson<br> us_county179_ref_shoreline.geojson<br> us_county180_ref_shoreline.geojson<br> us_county181_ref_shoreline.geojson<br> us_county182_ref_shoreline.geojson<br> us_county183_ref_shoreline.geojson<br> us_county184_ref_shoreline.geojson<br> us_county185_ref_shoreline.geojson<br> us_county186_ref_shoreline.geojson<br> us_county187_ref_shoreline.geojson<br> us_county188_ref_shoreline.geojson<br> us_county189_ref_shoreline.geojson<br> us_county190_ref_shoreline.geojson<br> us_county191_ref_shoreline.geojson<br> us_county192_ref_shoreline.geojson<br> us_county193_ref_shoreline.geojson<br> us_county194_ref_shoreline.geojson<br> us_county195_ref_shoreline.geojson<br> us_county196_ref_shoreline.geojson<br> us_county197_ref_shoreline.geojson<br> us_county198_ref_shoreline.geojson<br> us_county199_ref_shoreline.geojson<br> us_county200_ref_shoreline.geojson<br> us_county201_ref_shoreline.geojson<br> us_county202_ref_shoreline.geojson<br> us_county203_ref_shoreline.geojson<br> us_county204_ref_shoreline.geojson<br> us_county205_ref_shoreline.geojson<br> us_county206_ref_shoreline.geojson<br> us_county207_ref_shoreline.geojson<br> us_county208_ref_shoreline.geojson<br> us_county209_ref_shoreline.geojson<br> us_county210_ref_shoreline.geojson<br> us_county2118_ref_shoreline.geojson<br> us_county211_ref_shoreline.geojson<br> us_county2125_ref_shoreline.geojson<br> us_county2127_ref_shoreline.geojson<br> us_county2128_ref_shoreline.geojson<br> us_county212_ref_shoreline.geojson<br> 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us_county254_ref_shoreline.geojson<br> us_county255_ref_shoreline.geojson<br> us_county256_ref_shoreline.geojson<br> us_county257_ref_shoreline.geojson<br> us_county258_ref_shoreline.geojson<br> us_county259_ref_shoreline.geojson<br> us_county260_ref_shoreline.geojson<br> us_county261_ref_shoreline.geojson<br> us_county262_ref_shoreline.geojson<br> us_county263_ref_shoreline.geojson<br> us_county264_ref_shoreline.geojson<br> us_county265_ref_shoreline.geojson<br> us_county266_ref_shoreline.geojson<br> us_county267_ref_shoreline.geojson<br> us_county2688_ref_shoreline.geojson<br> us_county268_ref_shoreline.geojson<br> us_county269_ref_shoreline.geojson<br> us_county272_ref_shoreline.geojson<br> us_county2749_ref_shoreline.geojson<br> us_county275_ref_shoreline.geojson<br> us_county276_ref_shoreline.geojson<br> us_county277_ref_shoreline.geojson<br> us_county278_ref_shoreline.geojson<br> us_county279_ref_shoreline.geojson<br> us_county280_ref_shoreline.geojson<br> us_county281_ref_shoreline.geojson<br> us_county282_ref_shoreline.geojson<br> us_county283_ref_shoreline.geojson<br> us_county284_ref_shoreline.geojson<br> us_county285_ref_shoreline.geojson<br> us_county286_ref_shoreline.geojson<br> us_county287_ref_shoreline.geojson<br> us_county288_ref_shoreline.geojson<br> us_county289_ref_shoreline.geojson<br> us_county290_ref_shoreline.geojson<br> us_county291_ref_shoreline.geojson<br> us_county292_ref_shoreline.geojson<br> us_county293_ref_shoreline.geojson<br> us_county294_ref_shoreline.geojson<br> us_county295_ref_shoreline.geojson<br> us_county296_ref_shoreline.geojson<br> us_county297_ref_shoreline.geojson<br> us_county298_ref_shoreline.geojson<br> us_county299_ref_shoreline.geojson<br> us_county300_ref_shoreline.geojson<br> us_county301_ref_shoreline.geojson<br> us_county302_ref_shoreline.geojson<br> us_county303_ref_shoreline.geojson<br> us_county304_ref_shoreline.geojson<br> us_county305_ref_shoreline.geojson<br> us_county3069_ref_shoreline.geojson<br> us_county306_ref_shoreline.geojson<br> us_county307_ref_shoreline.geojson<br> us_county308_ref_shoreline.geojson<br> us_county309_ref_shoreline.geojson<br> us_county310_ref_shoreline.geojson<br> us_county311_ref_shoreline.geojson<br> us_county312_ref_shoreline.geojson<br> us_county313_ref_shoreline.geojson<br> us_county314_ref_shoreline.geojson<br> us_county315_ref_shoreline.geojson<br> us_county316_ref_shoreline.geojson<br> us_county317_ref_shoreline.geojson<br> us_county318_ref_shoreline.geojson<br> us_county319_ref_shoreline.geojson<br> us_county320_ref_shoreline.geojson<br> us_county321_ref_shoreline.geojson<br> us_county322_ref_shoreline.geojson<br> us_county323_ref_shoreline.geojson<br> us_county324_ref_shoreline.geojson<br> us_county325_ref_shoreline.geojson<br> us_county326_ref_shoreline.geojson<br> us_county327_ref_shoreline.geojson<br> us_county328_ref_shoreline.geojson<br> us_county329_ref_shoreline.geojson<br> us_county330_ref_shoreline.geojson<br> us_county331_ref_shoreline.geojson<br> us_county332_ref_shoreline.geojson<br> us_county333_ref_shoreline.geojson<br> us_county334_ref_shoreline.geojson<br> us_county335_ref_shoreline.geojson<br> us_county336_ref_shoreline.geojson<br> us_county337_ref_shoreline.geojson<br> us_county338_ref_shoreline.geojson<br> us_county339_ref_shoreline.geojson<br> us_county340_ref_shoreline.geojson<br> us_county341_ref_shoreline.geojson<br> us_county342_ref_shoreline.geojson<br> us_county343_ref_shoreline.geojson<br> us_county344_ref_shoreline.geojson<br> us_county345_ref_shoreline.geojson<br> us_county346_ref_shoreline.geojson<br> us_county347_ref_shoreline.geojson<br> us_county348_ref_shoreline.geojson<br> us_county349_ref_shoreline.geojson<br> us_county350_ref_shoreline.geojson<br> us_county351_ref_shoreline.geojson<br> us_county352_ref_shoreline.geojson<br> us_county353_ref_shoreline.geojson<br> us_county354_ref_shoreline.geojson<br> us_county416_ref_shoreline.geojson<br> us_county417_ref_shoreline.geojson<br> us_county418_ref_shoreline.geojson<br> us_county419_ref_shoreline.geojson<br> us_county420_ref_shoreline.geojson<br> us_county421_ref_shoreline.geojson<br> us_county422_ref_shoreline.geojson<br> us_county423_ref_shoreline.geojson<br> us_county685_ref_shoreline.geojson<br> us_county686_ref_shoreline.geojson<br> us_county700_ref_shoreline.geojson<br> us_county73_ref_shoreline.geojson<br> us_county74_ref_shoreline.geojson<br> us_county754_ref_shoreline.geojson<br> us_county79_ref_shoreline.geojson<br> us_county97_ref_shoreline.geojson</p>
Global daily high spatial resolution XCO2 product during 2019-2021
<p>Validation results are in preparation. The full dataset will be uploaded after the acceptance of our paper.</p>
HiP-RI: High-resolution spatial assessment of precipitation using in-situ and remote sensing data in the Cordillera Blanca, Peru
<p>The HiP-RI product was obtained from CHIRP, PERSIANN and GPM datasets, also vegetation products (NDVI-BOKU), topography (DEM SRTM) and data from 38 meteorological stations (2012-2020) were used to estimate precipitation in the Cordillera Blanca, northern sector of the Peruvian Andes. The observed data underwent quality control. A Gaussian filter, resampling and temporal homogenization at monthly scale were applied to the raster data. Subsequently, a linear regression model was built with the different datasets that served as predictors for precipitation spatialization. This allowed obtaining the best R2 values between the in situ data and those estimated with the model (HiP-RI). The results obtained were satisfactory with R2 values higher than 0.60 and an RMSE = 54%.</p>
Code and data for: Decoupling channel count from field-of-view and spatial resolution in single-sensor imaging systems for fluorescence image-guided surgery
<p><em>Significance</em></p> <p>Near-infrared fluorescence image-guided surgery is often thought of as a spectral imaging problem where the channel count is the critical parameter, but it should also be thought of as a multiscale imaging problem where the field-of-view and spatial resolution are similarly important.</p> <p><em>Aim</em></p> <p>Conventional imaging systems based on division-of-focal-plane architectures suffer from a strict relationship between the channel count on one hand and the field-of-view and spatial resolution on the other, but bioinspired imaging systems that combine stacked photodiode image sensors and long-pass/short-pass filter arrays offer a weaker tradeoff.</p> <p><em>Approach</em></p> <p>In this paper, we explore how the relevant changes to the image sensor and associated image processing routines affect image fidelity during image-guided surgeries for tumor removal in an animal model of breast cancer and nodal mapping in women with breast cancer.</p> <p><em>Results</em></p> <p>We demonstrate that a transition from a conventional imaging system to a bioinspired one, along with optimization of the image processing routines, yields improvements in multiple measures of spectral and textural rendition relevant to surgical decision-making.</p> <p><em>Conclusions</em></p> <p>These results call for a critical examination of the devices and algorithms that underpin image-guided surgery to ensure that surgeons receive high-quality guidance and patients receive high-quality outcomes as these technologies enter clinical practice.</p>
Spatial patterns of uncertainty in climate exposure metrics for North America at 1km resolution
<p>The data provided below represents the degree of uncertainty or variation between 8 individual general circulation models (GCM) for three metrics commonly used to assess the intensity of exposure to climate change. The three exposure metrics (forward and backward <a href="https://adaptwest.databasin.org/pages/adaptwest-velocitywna">climatic velocity</a> and <a href="https://adaptwest.databasin.org/pages/climatic-dissimilarity">local climatic dissimilarity</a>) were calculated based on the first two principal components (PC) scores derived from <a href="https://adaptwest.databasin.org/pages/climatic-dissimilarity">11 different climate variables</a>. Frameworks and heuristics supporting climate adaptation for conservation often rely on projections of climate change or climate exposure. However, projections of climate change vary among alternative GCM outputs, different emissions scenarios, and different future time periods. The potential for these model predictions to vary geographically presents a source of uncertainty in assigning climate-informed conservation strategies to landscapes. Regions with high agreement among predictions could be more confidently assigned a climate-informed strategy, whereas regions with less agreement among predictions may require a more cautious approach. More information on the data can be found at https://adaptwest.databasin.org/pages/uncertainty-climate-metrics.</p>
A High-Resolution Spatial Room Impulse Response Database
<p><strong>A High-Resolution Spatial Room Impulse Response Database:</strong></p> <p>A Database of various SRIRs measured in three rooms, for two receiver and 3 source positions each. The positions are shown in the floor plans. </p> <p><strong>Naming Convention:</strong></p> <p><strong>Receivers</strong>: SMA (DRIRs): spherical microphone array impulse responses on a 2702 sampling point Lebedev grid.</p> <p> KU100 (BRIRs): Neumann KU100 dummy head impulse responses on a 360 sampling point horizontal grid</p> <p> Omni_ir: Omnidirectional impulse responses measured with an Earthworks M30 microphone </p> <p><strong>Receiver positions:</strong> P1, P2 as indicated in the floor plans</p> <p><strong>Source positions:</strong> LSL: left speaker, LSR: right speaker, LSC: center speaker as indicated in the floor plans </p> <p><strong>Rooms: </strong>Audiolab, Classroom, Audimax</p> <p>_______________________________________________________________________________________</p> <p><strong>Contact:</strong><br> Tim Lübeck, Johannes M. Arend, and Christoph Pörschmann<br> TH Köln - University of Applied Sciences<br> Institute of Communications Engineering<br> Department of Acoustics and Audio Signal Processing<br> Betzdorfer Str. 2, D-50679 Cologne, Germany</p> <p><a href="https://www.th-koeln.de/personen/tim.luebeck/">https://www.th-koeln.de/personen/tim.luebeck/</a></p> <p><a href="https://www.th-koeln.de/personen/johannes.arend/">https://www.th-koeln.de/personen/johannes.arend/</a></p> <p><a href="http://www.th-koeln.de/personen/christoph.poerschmann/">https://www.th-koeln.de/personen/christoph.poerschmann/</a><br> <br> </p>
Imaging Mass Cytometry Images (APP1) from: A SIMPLI (Single-cell Identification from MultiPLexed Images) approach for spatially resolved tissue phenotyping at single-cell resolution.
<p>Four µm-thick sections were cut from the APP1 FFPE block with a microtome and used for staining with a panel of 26 antibodies targeting the main immune, stromal and epithelial cell populations of the gastrointestinal tract (Supplementary Table 2). The optimal dilution of each antibody in the panel was identified by staining and ablating FFPE appendix sections. The resulting images were reviewed by a mucosal immunologist (J.S.) and the dilution giving the best signal to background ratio was selected for each antibody (Supplementary Table 2). To perform the staining for IMC, slides were dewaxed after a one-hour incubation at 60°C, rehydrated and heat-induced antigen retrieval was performed with a pressure cooker in Antigen Retrieval Reagent-Basic (R&D Systems). Slides were incubated in a 10% BSA (Sigma), 0.1% Tween (Sigma), and 2% Kiovig (Shire Pharmaceuticals) Superblock Blocking Buffer (Thermo Fisher) blocking solution at room temperature for two hours. Each antibody was added to a primary antibody mix at the selected concentration in blocking solution and incubated overnight at 4°C. After two washes in PBS and PBS-0.1% Tween, the slides were treated with the DNA intercalator Cell-ID™ Intercalator-Ir (Fluidigm) (containing the two iridium isotopes 191Ir and 193Ir) 1.25 mM in a PBS solution. After a 30-minute incubation, the slides were washed once in PBS and once in MilliQ water and air-dried. The stained slides were then loaded in the Hyperion Imaging System (Fluidigm) imaging module to obtain light-contrast high resolution images of approximately four mm<sup>2</sup>. These images were used to select the ROI in each slide. For APP1, a one mm<sup>2</sup> ROI containing a lymphoid follicle in its whole depth alongside a portion of lamina propria and of epithelium was selected. ROIs were ablated at a o µm/pixel resolution and 200 Hz frequency.</p>
SPVPANELEX: Dataset containing aerial orthoimages (covering 257.93 km2 of the Spanish territory, with a spatial resolution of 0.5 m) labelled with photovoltaic panel information for binary recognition and semantic segmentation
<p>The data have been generated using scripts developed in Python with Open-Source libraries (GDAL/OGR and MapScript) to rasterize of vector cartography representing the photovoltaic (PV) panels instalations in urban, industrial, and rural areas. This PV panels cartography has been generated by manual digitalizing the PV panels found latest aerial orthofotographs available on June 1, 2021 from Plano Nacional de Ortofotografía Aérea (PNOA), produced by the National Geographic Institute of Spain, using the Web Map Service PNOA-MA.<br> <br> The dataset consists of 239,680 images of 256 × 256 pixels in size, in png format, labelled with Class_1: “Contains PV panel” and Class_2: “Does not contain PV panel”, that were pre-divided with a split criterion of 70:10:20%. in train, validation and test folders, respectively.<br> <br> The structure of the data is as follows:<br> 1-Panels-Ortho and 1-Panels-Mask contain the images featuring PV panels and their corresponding ground truth mask for training the semantic segmentation networks.<br> 1-Panels-Ortho and 2-NoPanels-Ortho contain images containing and not containing PV panels, for the training of binary recognition models of PV panels.<br> <br> Moreover, in each folder the structure is the same: train, test, validation containing 70%, 10% and 20% of the total images and masks of each type.<br> <br> 1-Panels-Ortho<br> |----Train<br> |----Test<br> -----Validation<br> <br> 1-Panels-Mask<br> |----Train<br> |----Test<br> -----Validation<br> <br> 2-NoPanels-Ortho<br> |----Train<br> |----Test<br> -----Validation</p>
Gene expression dataset of the Spatially Resolved Single-cell Translatomics at Molecular Resolution
<p>Here are the gene expression datasets of RIBOmap included in "<strong>Spatially Resolved Single-cell Translatomics at Molecular Resolution</strong>" from Zeng et al. Please refer to the README file for more detailed information. </p> <p> </p> <p><strong>Abstract</strong></p> <p>The precise control of mRNA translation is a crucial step in post-transcriptional gene regulation of cellular physiology. However, it remains a major challenge to systematically study mRNA translation at the transcriptomic scale with spatial and single-cell resolution. Here, we report the development of RIBOmap, a three-dimensional (3D) in situ profiling method to detect mRNA translation of thousands of genes simultaneously in intact cells and tissues. By applying RIBOmap to 981 genes in HeLa cells, we revealed a remarkable dependency of translation on cell-cycle stages and subcellular localization. Furthermore, we profiled single-cell translatomes of 5,413 genes in adult mouse brain tissues yielding a spatial cell atlas of 119,173 cells. The pairwise spatial mapping of single-cell translatome and transcriptome in two adjacent mouse brain slices revealed cell-type and brain-region-dependent translational regulation and suggested a translation remodeling during oligodendrocyte lineage maturation. The spatial translatome profiling detected widespread patterns of localized translation in neuronal and glial cells in intact brain tissue networks. Together, RIBOmap presents the first spatially resolved single-cell translatomics technology, accelerating our understanding of protein synthesis in the context of subcellular architecture, cell types, and tissue anatomy.</p>
ScienceDex guides
Understand access before you commit
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.