Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
21
datasets available to search
ShareScore release 0.9.0
Dataset results
21 results for “GeoJSON”
Europa Chaos Block Shapefiles and Geojson Files in Lower RegMap Images
<p>The included dataset includes the raw polygon shapefiles of the outlines for chaos blocks on Europa in the lower half of the RegMap images. The outlines were generated using the standard definitions of blocks and further subdivision in morphology of plates and knobs based on those previously published by Leonard et al. (2022) in which the author of this dataset is the same that created the majority of the Leonard et al. (2022) dataset. Images used to generate the outlines were the RegMap images within the Photogrammetrically Controlled Galileo Image Mosaics of Europa, produced by the USGS Astrogeology (Bland et al., 2021). The shapefiles do not include the entire metadata and will be uploaded at a later date, but information about chaos block morphology, area in sq km, lon/lat location of center, and chaos terrain location are included. Also included within this dataset are the Geojson files (produced by Marina Dunn) that complement the ArcMap shapefiles, so they could be implemented into other programs more easily. Both datasets have yet to be peer reviewed. </p><p> </p><p> </p><p><strong>References</strong></p><p>Leonard, E.J., Howel, S.M., Mills, A., Senske, D.A., Patthoff, D.A., Hay, H.C.F.C., and Pappalardo, R.T. (2022). Finding Order in Chaos: Quantitive Predictors of Chaos Terrain Morphology on Europa, Volume 49, Issue 8, doi: <a href="https://doi.org/10.1029/2021GL097309">10.1029/2021GL097309.</a></p><p>Bland, Michael T., Weller, Lynn A., Archinal, Brent A., Smith, Ethan, Wheeler, Benjamin H. (2021). Improving the Usability of Galileo and Voyager Images of Jupiter's Moon Europa, Earth and Space Science, Volume 8, Issue 12, doi: <a href="https://doi.org/10.1029/2021EA001935">10.1029/2021EA001935</a>.</p>
CoastSeg: Shoreline data at 30-m spatial resolution for 298 coastal counties of the conterminous USA, in geoJSON format.
<p>Region: 298 coastal counties of the conterminous USA</p> <p>Data fields:</p> <ol> <li>MEAN_SIG_WAVEHEIGHT</li> <li>TIDAL_RANGE</li> <li>CHLOROPHYLL</li> <li>TURBIDITY</li> <li>TEMP_MOISTURE</li> <li>EMU_PHYSICAL</li> <li>REGIONAL_SINUOSITY</li> <li>GHM</li> <li>MAX_SLOPE %</li> <li>OUTFLOW_DENSITY</li> <li>ERODIBILITY</li> <li>LENGTH_GEO</li> <li>ch_label</li> <li>river_label</li> <li>sinuosity_label</li> <li>slope_label</li> <li>tidal_label</li> <li>turbid_label</li> <li>wave_label</li> <li>CSU_Descriptor</li> <li>CSU_ID</li> </ol> <p>The data originally come from https://rmgsc.cr.usgs.gov/outgoing/ecosystems/Global/USGSEsriGlobalCoastalSegmentsv1.mpk</p> <p>The data are described in the following publication</p> <p>Roger Sayre, Suzanne Noble, Sharon Hamann, Rebecca Smith, Dawn Wright, Sean Breyer, Kevin Butler, Keith Van Graafeiland, Charlie Frye, Deniz Karagulle, Dabney Hopkins, Drew Stephens, Kevin Kelly, Zeenatul Basher, Devon Burton, Jill Cress, Karina Atkins, D. Paco Van Sistine, Beverly Friesen, Rebecca Allee, Tom Allen, Peter Aniello, Irawan Asaad, Mark John Costello, Kathy Goodin, Peter Harris, Maria Kavanaugh, Helen Lillis, Eleonora Manca, Frank Muller-Karger, Bjorn Nyberg, Rost Parsons, Justin Saarinen, Jac Steiner & Adam Reed (2019) A new 30 meter resolution global shoreline vector and associated global islands database for the development of standardized ecological coastal units, Journal of Operational Oceanography, 12:sup2, S47-S56, DOI: <a href="https://doi.org/10.1080/1755876X.2018.1529714">10.1080/1755876X.2018.1529714</a></p>
CoastSeg: 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>
CoastSeg: Shoreline data at 30-m spatial resolution for 5x5 degree regions of the world, in geoJSON format.
<p><em><strong>CoastSeg: global 30-m shoreline in 5x5 degree chunks</strong></em></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): global_5x5grid.geojson</p>
CoastSeg: Shoreline data at 30-m spatial resolution for 5x5 degree regions of the world, in geoJSON format. Version 2.
<p><em><strong>CoastSeg: global 30-m shoreline in 5x5 degree chunks</strong></em></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): global_5x5grid.geojson</p>
CoastSeg: 30-m atlas of the coastal shoreline attributes of California, in geoJSON format.
<p><strong>CoastSeg: 30-m atlas of the coastal shoreline attributes of California, in geoJSON format.</strong></p> <p>This is a shoreline atlas of California at 30m resolution, to support analysis of CoastSat/CoastSeg-derived shoreline time-series and other shoreline data, and miscellaneous analyses of coastal shoreline data. The dataset consists of a GeoJSON files containing a 30-m shoreline estimate for California, based on an analysis of 2014 Landsat imagery (Sayre et al., 2019). This shoreline vector has been attributed with the following fields that may be useful in analyses of shoreline patterns and regional variability:</p> <ol> <li>MEAN_SIG_WAVEHEIGHT (m)</li> <li>TIDAL_RANGE (m)</li> <li>CHLOROPHYLL (mg/L)</li> <li>TURBIDITY</li> <li>TEMP_MOISTURE (descriptive)</li> <li>EMU_PHYSICAL (descriptive)</li> <li>REGIONAL_SINUOSITY</li> <li>GHM</li> <li>MAX_SLOPE (%)</li> <li>OUTFLOW_DENSITY</li> <li>ERODIBILITY (descriptive)</li> <li>LENGTH_GEO</li> <li>ch_label (descriptive)</li> <li>river_label (descriptive)</li> <li>sinuosity_label (descriptive)</li> <li>slope_label (descriptive)</li> <li>tidal_label (descriptive)</li> <li>turbid_label (descriptive)</li> <li>wave_label (descriptive)</li> <li>CSU_Descriptor (descriptive)</li> <li>CSU_ID</li> <li>elevation (m)</li> <li>aspect (degrees N)</li> <li>slope (degrees)</li> </ol> <p>Fields 1 to 21 inclusive originally come from raw data https://rmgsc.cr.usgs.gov/outgoing/ecosystems/Global/USGSEsriGlobalCoastalSegmentsv1.mpk, which is described in Sayre et al (2019)</p> <p>Fields 22 and 24 come from raw data originally in the U.S. Geological Survey Elevation Derivatives for National Applications (EDNA) database (https://www.usgs.gov/centers/eros/science/usgs-eros-archive-digital-elevation-elevation-derivatives-national), accessed through Earth Explorer and processed in QGIS.</p> <p>The figure shows distributions of selected quantities. A python script to reproduce this plot is provided</p> <p>A subset of numeric-only variables and descriptive-only variables has also been prepared and made available. A CSV version of the full dataset is also provided</p> <p> </p> <p><strong>References</strong></p> <ol> <li>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></li> <li><a href="https://doi.org/10.5066/F7TD9VTQ">Elevation Derivatives for National Applications (EDNA) Seamless Three-Dimensional Hydrologic Database Digital Object Identifier (DOI) number: /10.5066/F7TD9VTQ</a></li> </ol> <p> </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>
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> 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>
Great Britain coastline boundary (modified from 2011 Census boundary data) [GeoJSON]
<p><strong>Original purpose</strong></p> <p>This coastline boundary dataset was originally derived for research on population proximity to the UK coast. It required adaptation of boundary files in order to prevent areas close to major rivers from being counted as ‘coastal’. With no single definition of what ‘coastal’ is, we took a decision to cut off the coastline where major estuaries/rivers narrowed to approximately 1km. The original publication that used this approach and informed the development of the dataset is cited below (Wheeler et al, 2012).</p> <p>Please note therefore that this is a somewhat arbitrary definition of what is coastal, and you will need to make sure this definition is appropriate for your application for this to be useful.</p> <p><strong>Method & Data Format</strong></p> <ul> <li>Original source data: UK Census 2011 Lower-layer Super Output Areas / Data Zones – full resolution / Mean High Water version.</li> <li>LSOA/DZ boundaries were dissolved to create outline boundary at Mean High Water.</li> <li>Major estuaries/rivers were manually truncated where they narrowed to approximately 1km width.</li> <li>Data are provided as a GeoJSON file</li> <li>Co-ordinate system is British National Grid (EPSG 27700)</li> </ul> <p><strong>Original data source & copyright</strong></p> <p>This boundary dataset was derived from Ordnance Survey/Office for National Statistics/Scottish Government data, under Open Government Licence. Its use/re-use is dependent on appropriate citation and acknowledgement of the original source data.</p> <p>Licence: Adapted and redistributed under Open Government Licence: <a href="https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/">https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/</a></p> <p>Copyright statements to appear on any maps/publications containing these data:</p> <p><strong>Contains National Statistics data © Crown copyright and database right 2012</strong></p> <p><strong>Contains Ordnance Survey data © Crown copyright and database right 2012</strong></p> <p><strong>Copyright Scottish Government, contains Ordnance Survey data © Crown copyright and database right (2012).</strong></p> <p> </p> <p><strong>Citation and Attribution</strong></p> <p>The original source of the approach and methodology for this coastal definition should be cited as:</p> <p>Wheeler, B.W., White, M., Stahl-Timmins, W., Depledge, M.H., 2012. Does living by the coast improve health and wellbeing? Health and Place 18: 5, 1198-1201. doi: 10.1016/j.healthplace.2012.06.015</p> <p>The boundary dataset requires the copyright statements as above to be stated on any publication/redistribution.</p> <p>The adapted data are redistributed here under CC-BY Licence - <a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a></p>
GeoJSON files for the MCSC's Trucking Industry Decarbonization Explorer (Geo-TIDE)
<h1>Summary</h1> <p>Geojson files used to visualize geospatial layers relevant to identifying and assessing trucking fleet decarbonization opportunities with the MIT Climate & Sustainability Consortium's Geospatial Trucking Industry Decarbonization Explorer (Geo-TIDE) tool.</p> <h1>Relevant Links</h1> <p>Link to the <a href="https://climatedata.mit.edu/faf5/transportation/">online version of the tool</a> (requires creation of a free user account).</p> <p><a href="https://github.com/mcsc-impact-climate/FAF5-Analysis">Link to GitHub repo</a> with source code to produce this dataset and deploy the Geo-TIDE tool locally.</p> <h1>Funding </h1> <p>This dataset was produced with support from the MIT Climate & Sustainability Consortium.</p> <h1>Original Data Sources</h1> <p>These geojson files draw from and synthesize a number of different datasets and tools. The original data sources and tools are described below:</p> <table> <tbody> <tr> <td><strong>Filename(s)</strong></td> <td><strong>Description of Original Data Source(s)</strong></td> <td><strong>Link(s) to Download Original Data<br></strong></td> <td><strong>License and Attribution for Original Data Source(s)</strong></td> </tr> <tr> <td> <p>faf5_freight_flows/*.geojson</p> <p>trucking_energy_demand.geojson</p> <p>highway_assignment_links_*.geojson</p> <p>infrastructure_pooling_thought_experiment/*.geojson</p> </td> <td> <p>Regional and highway-level freight flow data obtained from the <a href="https://faf.ornl.gov/faf5/">Freight Analysis Framework Version 5</a>. Shapefiles for FAF5 region boundaries and highway links are obtained from the <a href="https://geodata.bts.gov/search?collection=Dataset">National Transportation Atlas Database</a>. Emissions attributes are evaluated by incorporating data from the <a href="https://rosap.ntl.bts.gov/view/dot/42632/dot_42632_DS2.zip">2002 Vehicle Inventory and Use Survey</a> and the <a href="https://greet.anl.gov/">GREET lifecycle emissions tool</a> maintained by Argonne National Lab.</p> </td> <td> <p><a href="https://geodata.bts.gov/datasets/usdot::freight-analysis-framework-faf5-regions">Shapefile for FAF5 Regions</a></p> <p><a href="https://geodata.bts.gov/datasets/usdot::freight-analysis-framework-faf5-network-links">Shapefile for FAF5 Highway Network Links</a></p> <p><a href="https://faf.ornl.gov/faf5/data/download_files/FAF5.5.1_2018-2022.zip">FAF5 2022 Origin-Destination Freight Flow database</a></p> <p><a href="https://ops.fhwa.dot.gov/freight/freight_analysis/faf/faf_highway_assignment_results/FAF5_2022_HighwayAssignmentResults_04_07_2022.zip">FAF5 2022 Highway Assignment Results</a></p> <p> </p> </td> <td> <p><strong>Attribution for Shapefiles:</strong> United States Department of Transportation Bureau of Transportation Statistics National Transportation Atlas Database (NTAD). Available at: https://geodata.bts.gov/search?collection=Dataset. </p> <p><strong>License for Shapefiles:</strong> This NTAD dataset is a work of the United States government as defined in 17 U.S.C. § 101 and as such are not protected by any U.S. copyrights. This work is available for unrestricted public use.</p> <p><strong>Attribution for Origin-Destination Freight Flow database:</strong> <a href="https://www.ornl.gov/ntrc/" target="_blank" rel="noopener">National Transportation Research Center</a> in the <a href="https://www.ornl.gov/" target="_blank" rel="noopener">Oak Ridge National Laboratory</a> with funding from the <a href="https://www.bts.gov/" target="_blank" rel="noopener">Bureau of Transportation Statistics</a> and the <a href="https://www.fhwa.dot.gov/" target="_blank" rel="noopener">Federal Highway Administration</a>. Freight Analysis Framework Version 5: Origin-Destination Data. Available from: https://faf.ornl.gov/faf5/Default.aspx. Obtained on Aug 5, 2024. In the public domain. </p> <p><strong>Attribution for the 2022 Vehicle Inventory and Use Survey Data:</strong> United States Department of Transportation Bureau of Transportation Statistics. Vehicle Inventory and Use Survey (VIUS) 2002 [supporting datasets]. 2024. https://doi.org/10.21949/1506070 </p> <p><strong>Attribution for the GREET tool (original publication):</strong> Argonne National Laboratory Energy Systems Division Center for Transportation Research. GREET Life-cycle Model. 2014. Available from <a href="https://greet.anl.gov/files/greet-model&ved=2ahUKEwiAuryGsd6HAxVMFlkFHaafHNUQFnoECBUQAQ&usg=AOvVaw29kokx-ZurrfBFsjji9UM2">this link</a>.</p> <p><strong>Attribution for the GREET tool (2022 updates):</strong> Wang, Michael, et al. Summary of Expansions and Updates in GREET® 2022. United States. https://doi.org/10.2172/1891644</p> </td> </tr> <tr> <td>grid_emission_intensity/*.geojson</td> <td> <p>Emission intensity data is obtained from the <a href="https://www.epa.gov/egrid/download-data">eGRID database</a> maintained by the United States Environmental Protection Agency.</p> <p>eGRID subregion boundaries are obtained as a shapefile from the <a href="https://www.epa.gov/egrid/egrid-mapping-files">eGRID Mapping Files</a> database.</p> </td> <td> <p><a href="https://www.epa.gov/system/files/documents/2024-01/egrid2022_data.xlsx">eGRID database</a></p> <p><a href="https://www.epa.gov/system/files/other-files/2024-05/egrid2022_subregions_shapefile.zip">Shapefile with eGRID subregion boundaries</a></p> </td> <td> <p><strong>Attribution for eGRID data: </strong>United States Environmental Protection Agency: eGRID with 2022 data. Available from https://www.epa.gov/egrid/download-data. In the public domain.</p> <p><strong>Attribution for shapefile:</strong> United States Environmental Protection Agency: eGRID Mapping Files. Available from https://www.epa.gov/egrid/egrid-mapping-files. In the public domain.</p> </td> </tr> <tr> <td> <p>US_elec.geojson</p> <p>US_hy.geojson</p> <p>US_lng.geojson</p> <p>US_cng.geojson</p> <p>US_lpg.geojson</p> </td> <td>Locations of direct current fast chargers and refueling stations for alternative fuels along U.S. highways. Obtained directly from the <a href="https://afdc.energy.gov/corridors">Station Data for Alternative Fuel Corridors</a> in the Alternative Fuels Data Center maintained by the United States Department of Energy Office of Energy Efficiency and Renewable Energy. </td> <td> <p><a href="https://developer.nrel.gov/api/alt-fuel-stations/v1.geojson?access=public&status=E&country=US&download=true&utf8_bom=true&api_key=srJv3MBMvbZaDHrSssCEnhPK4IijuOLTsvs6l35L&fuel_type=ELEC&ev_charging_level=dc_fast&ev_connector_type=J1772COMBO&beta_min_j1772combo_150plus_port_count=4&response_format=beta_dot_corridors">US_elec.geojson</a></p> <p><a href="https://developer.nrel.gov/api/alt-fuel-stations/v1.geojson?access=public&status=E&country=US&download=true&utf8_bom=true&api_key=srJv3MBMvbZaDHrSssCEnhPK4IijuOLTsvs6l35L&fuel_type=HY&hy_is_retail=true">US_hy.geojson</a></p> <p><a href="https://developer.nrel.gov/api/alt-fuel-stations/v1.geojson?access=public&status=E&country=US&download=true&utf8_bom=true&api_key=srJv3MBMvbZaDHrSssCEnhPK4IijuOLTsvs6l35L&fuel_type=LNG">US_lng.geojson</a></p> <p><a href="https://developer.nrel.gov/api/alt-fuel-stations/v1.geojson?access=public&status=E&country=US&download=true&utf8_bom=true&api_key=srJv3MBMvbZaDHrSssCEnhPK4IijuOLTsvs6l35L&fuel_type=CNG&cng_fill_type=Q&cng_psi=3600">US_cng.geojson</a></p> <p><a href="https://developer.nrel.gov/api/alt-fuel-stations/v1.geojson?access=public&status=E&country=US&download=true&utf8_bom=true&api_key=srJv3MBMvbZaDHrSssCEnhPK4IijuOLTsvs6l35L&fuel_type=LPG&lpg_include_secondary=false">US_lpg.geojson</a></p> </td> <td> <p><strong>Attribution:</strong> U.S. Department of Energy, Energy Efficiency and Renewable Energy. Alternative Fueling Station Corridors. 2024. Available from: <a href="https://afdc.energy.gov/corridors" target="_new" rel="noreferrer">https://afdc.energy.gov/corridors</a>. In the public domain. </p> <p> </p> <p>These data and software code ("Data") are provided by the National Renewable Energy Laboratory ("NREL"), which is operated by the Alliance for Sustainable Energy, LLC ("Alliance"), for the U.S. Department of Energy ("DOE"), and may be used for any purpose whatsoever.</p> </td> </tr> <tr> <td>daily_grid_emission_profiles/*.geojson</td> <td> <p>Hourly emission intensity data obtained from <a href="https://www.electricitymaps.com/data-portal/united-states-of-america">ElectricityMaps</a>.</p> </td> <td> <p>Original data can be downloaded as csv files from the <a href="https://www.electricitymaps.com/data-portal/united-states-of-america">ElectricityMaps United States of America database</a></p> <p><a href="https://raw.githubusercontent.com/electricitymaps/electricitymaps-contrib/master/web/geo/world.geojson">Shapefile with region boundaries used by ElectricityMaps</a></p> </td> <td> <p><strong>License:</strong> <a href="https://opendatacommons.org/licenses/odbl/" target="_blank" rel="noopener">Open Database License (ODbL)</a>. Details here: https://www.electricitymaps.com/data-portal</p> <p><strong>Attribution for csv files:</strong> Electricity Maps (2024). United States of America 2022-23 Hourly Carbon Intensity Data (Version January 17, 2024). Electricity Maps Data Portal. https://www.electricitymaps.com/data-portal.</p> <p><strong>Attribution for shapefile with region boundaries:</strong> ElectricityMaps contributors (2024). electricitymaps-contrib (Version v1.155.0) [Computer software]. https://github.com/electricitymaps/electricitymaps-contrib.</p> </td> </tr> <tr> <td> <p>gen_cap_2022_state_merged.geojson </p> <p>trucking_energy_demand.geojson</p> </td> <td> <p>Grid electricity generation and net summer power capacity data is obtained from the <a href="https://www.eia.gov/electricity/data/state/">state-level electricity database</a> maintained by the United States Energy Information Administration. </p> <p> </p> <p>U.S. state boundaries obtained from <a href="https://www.sciencebase.gov/catalog/item/52c78623e4b060b9ebca5be5">this United States Department of the Interior U.S. Geological Survey ScienceBase-Catalog</a>.</p> </td> <td> <p><a href="https://www.eia.gov/electricity/data/state/annual_generation_state.xls">Annual electricity generation by state</a></p> <p><a href="https://www.eia.gov/electricity/data/state/existcapacity_annual.xlsx">Net summer capacity by state</a></p> <p><a href="https://www.sciencebase.gov/catalog/file/get/52c78623e4b060b9ebca5be5?facet=tl_2012_us_state">Shapefile with U.S. state boundaries</a></p> </td> <td> <p><strong>Attribution for electricity generation and capacity data: </strong>U.S. Energy Information Administration (Aug 2024). Available from: https://www.eia.gov/electricity/data/state/. In the public domain. </p> </td> </tr> <tr> <td>electricity_rates_by_state_merged.geojson</td> <td> <p>Commercial electricity prices are obtained from the <a href="https://www.eia.gov/electricity/data.php">Electricity database</a> maintained by the United States Energy Information Administration.</p> </td> <td> <p><a href="https://www.eia.gov/electricity/data/state/sales_annual_a.xlsx">Electricity rate by state</a></p> <p> </p> </td> <td><strong>Attribution:</strong> U.S. Energy Information Administration (Aug 2024). Available from: https://www.eia.gov/electricity/data.php. In the public domain. </td> </tr> <tr> <td> <p>demand_charges_merged.geojson</p> <p>demand_charges_by_state.geojson</p> </td> <td> <p>Maximum historical demand charges for each state and zip code are derived from a dataset compiled by the National Renewable Energy Laboratory in this <a href="https://data.nrel.gov/submissions/74">this Data Catalog.</a></p> </td> <td><a href="https://data.nrel.gov/system/files/74/Demand%20charge%20rate%20data.xlsm">Historical demand charge dataset</a></td> <td> <p>The original dataset is compiled by the National Renewable Energy Laboratory (NREL), the U.S. Department of Energy (DOE), and the Alliance for Sustainable Energy, LLC ('Alliance').</p> <p><strong>Attribution:</strong> McLaren, Joyce, Pieter Gagnon, Daniel Zimny-Schmitt, Michael DeMinco, and Eric Wilson. 2017. 'Maximum demand charge rates for commercial and industrial electricity tariffs in the United States.' NREL Data Catalog. Golden, CO: National Renewable Energy Laboratory. Last updated: July 24, 2024. DOI: 10.7799/1392982.</p> </td> </tr> <tr> <td> <p>eastcoast.geojson</p> <p>midwest.geojson</p> <p>la_i710.geojson</p> <p>h2la.geojson</p> <p>bayarea.geojson</p> <p>saltlake.geojson</p> <p>northeast.geojson</p> </td> <td> <p>Highway corridors and regions targeted for heavy duty vehicle infrastructure projects are derived from a <a href="https://www.energy.gov/articles/biden-harris-administration-announces-funding-zero-emission-medium-and-heavy-duty-vehicle">public announcement</a> on February 15, 2023 by the United States Department of Energy.</p> <p>The shapefile with Bay area boundaries is obtained from <a href="https://geodata.lib.berkeley.edu/catalog/ark28722-s7hs4j">this Berkeley Library dataset</a>.</p> <p>The shapefile with Utah county boundaries is obtained from <a href="https://gis.utah.gov/products/sgid/boundaries/county/">this dataset</a> from the Utah Geospatial Resource Center. </p> </td> <td> <p><a href="https://spatial.lib.berkeley.edu/public/ark28722-s7hs4j/data.zip">Shapefile for Bay Area country boundaries</a></p> <p><a href="https://opendata.arcgis.com/datasets/90431cac2f9f49f4bcf1505419583753_0.zip">Shapefile for counties in Utah</a></p> <p> </p> </td> <td> <p><strong>Attribution for public announcement:</strong> United States Department of Energy. Biden-Harris Administration Announces Funding for Zero-Emission Medium- and Heavy-Duty Vehicle Corridors, Expansion of EV Charging in Underserved Communities (2023). Available from https://www.energy.gov/articles/biden-harris-administration-announces-funding-zero-emission-medium-and-heavy-duty-vehicle.</p> <p><strong>Attribution for Bay area boundaries:</strong> San Francisco (Calif.). Department Of Telecommunications and Information Services. Bay Area Counties. 2006. In the public domain. </p> <p><strong>Attribution for Utah boundaries:</strong> Utah Geospatial Resource Center & Lieutenant Governor's Office. Utah County Boundaries (2023). Available from https://gis.utah.gov/products/sgid/boundaries/county/. </p> <p><strong>License for Utah boundaries:</strong> <a href="https://gis.utah.gov/documentation/policy/license/#license">Creative Commons 4.0 International License</a>. </p> </td> </tr> <tr> <td>incentives_and_regulations/*.geojson</td> <td> <p>State-level incentives and regulations targeting heavy duty vehicles are collected from the <a href="https://afdc.energy.gov/laws/state">State Laws and Incentives database</a> maintained by the United States Department of Energy's Alternative Fuels Data Center. </p> </td> <td>Data was collected manually from the <a href="https://afdc.energy.gov/laws/state">State Laws and Incentives database</a>.</td> <td> <p><strong>Attribution:</strong> U.S. Department of Energy, Energy Efficiency and Renewable Energy, Alternative Fuels Data Center. State Laws and Incentives. Accessed on Aug 5, 2024 from: https://afdc.energy.gov/laws/state. In the public domain. </p> <p> </p> <p>These data and software code ("Data") are provided by the National Renewable Energy Laboratory ("NREL"), which is operated by the Alliance for Sustainable Energy, LLC ("Alliance"), for the U.S. Department of Energy ("DOE"), and may be used for any purpose whatsoever.</p> </td> </tr> <tr> <td> <p>costs_and_emissions/*.geojson</p> <p>diesel_price_by_state.geojson</p> <p>trucking_energy_demand.geojson</p> </td> <td> <p>Lifecycle costs and emissions of electric and diesel trucking are evaluated by adapting the model developed by <a href="https://chemrxiv.org/engage/chemrxiv/article-details/656e4691cf8b3c3cd7c96810">Moreno Sader et al.</a>, and calibrated to the <a href="https://runonless.com/run-on-less-electric-depot-reports/">Run on Less dataset</a> for the Tesla Semi collected from the 2023 PepsiCo Semi pilot by the North American Council for Freight Efficiency.</p> <p>In addition to the data sources outlined in <a href="https://chemrxiv.org/engage/chemrxiv/article-details/656e4691cf8b3c3cd7c96810">Moreno Sader et al.</a> et al. and the <a href="https://runonless.com/run-on-less-electric-depot-reports/">Run on Less dataset</a>, this dataset incorporates:</p> <ul> <li>Emission intensity data from the <a href="https://www.epa.gov/egrid/download-data">eGRID database</a>, described elsewhere in this metadata. </li> <li>Commercial electricity price data from the US EIA <a href="https://www.eia.gov/electricity/data.php">Electricity database</a>, described elsewhere in this metadata. </li> <li><a href="https://data.nrel.gov/submissions/74">Maximum historical demand charges</a> from the National Renewable Energy Laboratory, described elsewhere in this metadata. </li> <li>Max motor power estimate of 942,900W and frontal area of 10.7 m^s for the Tesla Semi from <a href="https://www.motormatchup.com/catalog/Tesla/Semi-Truck/2022/Empty">motormatchup.com.</a></li> <li>Drag coefficient estimate of 0.36 for the Tesla Semi from <a href="https://www.notateslaapp.com/tesla-reference/963/everything-we-know-about-the-tesla-semi">notateslaapp.com.</a></li> <li>Estimates best-in-class truck rolling resistance of 0.0044 from a <a href="https://www.lrrb.org/pdf/201539.pdf">Rolling Resistance Validation report</a> prepared by the Minnesota Department of Transportation Office of Transportation System Management.</li> <li><a href="https://www.eia.gov/petroleum/gasdiesel/">Historical diesel prices</a> by state from the United States Energy Information Administration.</li> <li>Estimate of best in class diesel powertrain engine efficiency of 44% from a <a href="https://theicct.org/sites/default/files/publications/EU-HDV-Tech-Potential_ICCT-white-paper_14072017_vF.pdf">Fuel Efficiency Technology report</a> by the International Council on Clean Transportation.</li> </ul> </td> <td> <p> </p> <p><a href="https://runonless.com/wp-content/uploads/ROL23-Web-data.zip">NACFE Run on Less dataset</a></p> <p><a href="https://www.eia.gov/petroleum/gasdiesel/xls/psw18vwall.xls">Historical diesel prices</a></p> <p> </p> </td> <td> <p><strong>Attribution for original truck model:</strong> Moreno Sader K, Biswas S, Jones R, Mennig M, Rezaei R, Green WH. Battery Electric Long-Haul Trucking in the United States: A Comprehensive Costing and Emissions Analysis. ChemRxiv. 2023; doi:10.26434/chemrxiv-2023-48zsc (link to <a href="https://colab.research.google.com/drive/124rFu_4vHx4cP6SODtdzCxnUmLY50wbW?usp=sharing">colab notebook</a> included as supplementary material).</p> <p><strong>Attribution for GitHub repository with adapted code for the truck model:</strong> Eamer, D., Moreno-Sader, K., & Biswas, S. (2024). Green_Trucking_Analysis (Version 0.1.0) [Computer software]. https://doi.org/10.5281/zenodo.13205854</p> <p><strong>Attribution for GitHub repository with analysis of the NACFE Run on Less dataset (provides inputs to Eamer, D., Moreno-Sader, K., & Biswas, S. (2024) cited above):</strong> Eamer, D. (2024). PepsiCo_NACFE_Analysis (Version 0.1.0) [Computer software]. https://doi.org/10.5281/zenodo.13173390</p> <p><strong>Attribution for <a href="https://runonless.com/run-on-less-electric-depot-reports/">Run on Less dataset</a>: </strong>North American Countil for Freight Efficiency (2023). Run on Less – Electric DEPOT data. Available from: https://runonless.com/run-on-less-electric-depot-reports/ </p> <p><strong>Attribution for data from MotorMatchup:</strong> 2022 Tesla Semi Truck Empty Specs. Available from: https://www.motormatchup.com/catalog/Tesla/Semi-Truck/2022/Empty. Copyright 2024 by MotorMatchup</p> <p><strong>Attribution for data from Not a Tesla App:</strong> Not a Tesla App. Everything We Know About the Tesla Semi. 2024. Available from: <a href="https://www.notateslaapp.com/tesla-reference/963/everything-we-know-about-the-tesla-semi" target="_new" rel="noreferrer">https://www.notateslaapp.com/tesla-reference/963/everything-we-know-about-the-tesla-semi</a></p> <p><strong>Attribution for historical diesel prices:</strong> U.S. Energy Information Administration (Aug 2024). Available from: https://www.eia.gov/petroleum/gasdiesel/. In the public domain.</p> <p><strong>Attribution for best in class diesel powertrain efficiency:</strong> Delgado O, Rodríguez F, Muncrief R. Fuel Efficiency Technology in European Heavy-Duty Vehicles: Baseline and Potential for the 2020–2030 Time Frame. 2017. Available from: <a href="https://theicct.org/sites/default/files/publications/EU-HDV-Tech-Potential_ICCT-white-paper_14072017_vF.pdf" target="_new" rel="noreferrer">https://theicct.org/sites/default/files/publications/EU-HDV-Tech-Potential_ICCT-white-paper_14072017_vF.pdf</a>.</p> </td> </tr> <tr> <td> <p>electrolyzer_operational.geojson</p> <p>electrolyzer_installed.geojson</p> <p>electrolyzer_planned_under_construction.geojson</p> <p> </p> </td> <td> <p>Data on locations and capacities of planned, under-construction, installed, operational electrolyzers was obtained from <a href="https://www.hydrogen.energy.gov/docs/hydrogenprogramlibraries/pdfs/23003-electrolyzer-installations-united-states.pdf?Status=Master">this DOE Hydrogen Program Record</a>.</p> </td> <td>Data was extracted manually from <a href="https://www.hydrogen.energy.gov/docs/hydrogenprogramlibraries/pdfs/23003-electrolyzer-installations-united-states.pdf?Status=Master">this DOE Hydrogen Program Record</a>.</td> <td><strong>Attribution:</strong> Arjona, Vanessa. DOE Hydrogen Program Record: Electrolyzer Installations in the United States. 2023. Available from https://www.hydrogen.energy.gov/docs/hydrogenprogramlibraries/pdfs/23003-electrolyzer-installations-united-states.pdf?Status=Master. </td> </tr> <tr> <td> <p>grid_emission_intensity/*.geojson</p> <p>gen_cap_2022_state_merged.geojson </p> <p>trucking_energy_demand.geojson</p> <p>electricity_rates_by_state_merged.geojson</p> <p>demand_charges_merged.geojson</p> <p>demand_charges_by_state.geojson</p> <p>trucking_energy_demand.geojson</p> <p>costs_and_emissions/*.geojson</p> <p>diesel_price_by_state.geojson</p> <p>trucking_energy_demand.geojson</p> </td> <td> <p>U.S. state boundaries obtained from <a href="https://www.sciencebase.gov/catalog/item/52c78623e4b060b9ebca5be5">this United States Department of the Interior U.S. Geological Survey ScienceBase-Catalog</a>.</p> </td> <td> </td> <td><strong>Attribution: </strong>U.S. Department of Commerce, U.S. Census Bureau, Geography Division. State boundaries (generalized for mapping). 2011. In the public domain.</td> </tr> <tr> <td> <p>refinery.geojson</p> </td> <td> <p>Locations and production rates of hydrogen from refineries are obtained from the following two complementary datasets on the <a href="https://h2tools.org">Hydrogen Tools Portal</a>:</p> <p><br>1) <a href="https://h2tools.org/hyarc/hydrogen-data/captive-purpose-refinery-hydrogen-production-capacities-individual-us">Captive, On-Purpose, Refinery Hydrogen Production Capacities at Individual U.S. Refineries</a>, and </p> <p><br>2) <a href="https://h2tools.org/hyarc/hydrogen-data/merchant-hydrogen-plant-capacities-north-america">Merchant Hydrogen Plant Capacities in North America</a></p> </td> <td> <p><a href="https://h2tools.org/file/9338/download?token=0IWTving">Dataset for Captive, On-Purpose, Refinery Hydrogen Production Capacities at Individual U.S. Refineries</a></p> <p><a href="https://h2tools.org/file/2050/download?token=Wp-XDY-h">Dataset for Merchant Hydrogen Plant Capacities in North America</a></p> </td> <td> <p><strong>Attribution: </strong>Copyright © 2024 by H2Tools; H2 Tools is intended for public use. It was built, and is maintained, by the Pacific Northwest National Laboratory with funding from the DOE Office of Energy Efficiency and Renewable Energy's Hydrogen and Fuel Cell Technologies Office. All Rights Reserved. </p> </td> </tr> <tr> <td> <p>Truck_Stop_Parking.geojson</p> <p>infrastructure_pooling_thought_experiment/*.geojson</p> </td> <td> <p>Obtained from the DOT Bureau of Transportation Statistics's <a href="https://geodata.bts.gov/datasets/usdot::truck-stop-parking">Truck Stop Parking database</a></p> </td> <td> <p>Original dataset can be downloaded using the Shapefile download link at https://geodata.bts.gov/datasets/usdot::truck-stop-parking (link for hosted download changes regularly). </p> </td> <td> <p><strong>Attribution: </strong>United States Department of Transportation Bureau of Transportation Statistics National Transportation Atlas Database (NTAD). Truck Stop Parking. Available at https://geodata.bts.gov/datasets/usdot::truck-stop-parking. </p> <p><strong>License:</strong> This NTAD dataset is a work of the United States government as defined in 17 U.S.C. § 101 and as such are not protected by any U.S. copyrights. This work is available for unrestricted public use.</p> </td> </tr> <tr> <td> <p>Principal_Port.geojson</p> </td> <td> <p>Obtained from the DOT Bureau of Transportation Statistics's <a href="https://geodata.bts.gov/datasets/usdot::principal-ports-1/about">Principal Ports database</a></p> </td> <td> <p>Original dataset can be downloaded using the Shapefile download link at https://geodata.bts.gov/datasets/usdot::principal-ports-1 (link for hosted download changes regularly). </p> </td> <td> <p><strong>Attribution: </strong>United States Department of Transportation Bureau of Transportation Statistics National Transportation Atlas Database (NTAD). Truck Stop Parking. Available at https://geodata.bts.gov/datasets/usdot::principal-ports-1. </p> <p><strong>License:</strong> This NTAD dataset is a work of the United States government as defined in 17 U.S.C. § 101 and as such are not protected by any U.S. copyrights. This work is available for unrestricted public use.</p> <p><strong> </strong></p> </td> </tr> <tr> <td> <p>ZEF_Corridor_Strategy/*.geojson</p> </td> <td> <p>Visualizes the corridors, facilities, and hubs targeted by the National Zero-Emission Freight Corridor Strategy, a framework developed by the U.S. Joint Office of Energy and Transportation to support the coordinated deployment of medium- and heavy-duty zero-emission vehicle (ZEV) infrastructure along critical freight corridors. The strategy, outlined in the publication <a href="https://driveelectric.gov/files/zef-corridor-strategy.pdf">National Zero-Emission Freight Corridor Strategy</a>, identifies priority corridors and infrastructure investment needs to accelerate the transition to zero-emission medium- and heavy-duty vehicles.</p> </td> <td> <p>Original dataset can be downloaded from https://driveelectric.gov/files/zef-gis-files.zip</p> </td> <td> <p><strong>Attribution:</strong> Chu, K.-C. (J.), Miller, K. G., Schroeder, A., Gilde, A., & Laughlin, M. (2024, September). <em>National Zero-Emission Freight Corridor Strategy: Prioritizing investments, planning, and deployment for medium- and heavy-duty vehicle fueling infrastructure to advance zero-emission freight along our nation’s corridors</em>. Joint Office of Energy and Transportation; U.S. Department of Energy. Accessed from: https://driveelectric.gov/files/zef-corridor-strategy.pdf.</p> </td> </tr> </tbody> </table> <p> </p>
Test for Spondylus of the Americas Database- GeoJSON
<p>The is a test of the storage of data collected using KoboToolbox form - https://ee.kobotoolbox.org/x/Mig5zlqC</p><p>This is the GeoJSON format file. </p>
10x Xenium GeoJSON
Open the record for dataset details and reuse information.
CoastSeg: estimate of zone of potential shoreline change, California and southeast USA Atlantic (FL, GA, SC, NC), in geoJSON format.
<p><em><strong>CoastSeg: estimate of zone of potential shoreline change, California and southeast USA Atlantic (FL, GA, SC, NC), in geoJSON format.</strong></em></p> <ul> <li>Data have been made by Daniel Buscombe, Marda Science.</li> <li>Data cover the shorelines of five states</li> <li>A single geoJSON file per region. Data outline the extent of potential shoreline change.</li> <li>A 30-m vector defining the average shoreline, and a 30-m vector defining the limit of erodible material, were constructed and merged, then buffered, and manually edited.</li> <li>It is designed to be used in conjunction with the program CoastSeg https://github.com/Doodleverse/CoastSeg , for masking shoreline estimates outside of reasonable spatial bounds.</li> </ul> <p> </p>
Shoreline data at 30-m spatial resolution for regions of the USA, in geoJSON format. Region 1: Delaware/Virginia border to Maine/Canada border
<p>Data file: NE_USA_Delaware_Maine_ref_shoreline.geojson</p> <p>Region: Delaware/Virginia border to Maine/Canada 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> <p> </p> <p> </p> <p> </p> <p> </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.