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1,868 results for “Spatial Data”
Scale dependent spatial structuring of mountain river large bed elements maximizes flow resistance - Data Revision
<p>Datasets and R code related to manuscript entitled, "Scale dependent spatial structuring of mountain river large bed elements maximizes flow resistance". See '0_READ_ME.rtf' file for additional description of available files.</p>
Spatial Scaling Challenge - Additional Data. COST Action CA17134 SENSECO. Working Group 1
<p>This dataset contains the data additional data provided for the <strong>SPATIAL SCALING CHALLENGE</strong> organized in the framework of the <strong>SENSECO COST Action CA17143</strong> “Optical synergies for spatiotemporal SENsing of Scalable ECOphysiological traits” (<a href="https://www.senseco.eu/">https://www.senseco.eu/</a>), by the <strong>Working Group 1</strong><strong>.</strong> “Closing the scaling gap: from leaf measurements to satellite images” (<a href="https://www.senseco.eu/working-groups/wg1-scaling-gap/">https://www.senseco.eu/working-groups/wg1-scaling-gap/</a>).</p> <p> </p> <p>The main dataset, documentation and scripts of the <strong>SPATIAL SCALING CHALLENGE</strong> must be downloaded from<strong>: <a href="https://doi.org/10.5281/zenodo.6451335">https://doi.org/10.5281/zenodo.6451335</a></strong></p> <p>This additional dataset includes half-hourly time series of down-welling incoming spectral irradiance (W m<sup>-2</sup> µm<sup>-1</sup>) in the visible and near-infrared domains as measured by a field spectroradiometer operating in a nearby ecosystem station. The inclusion of these data in the Spatial Scaling Challenge is at the discretion of the participants. </p> <p> </p> <p>The <strong>SPATIAL SCALING CHALLENGE</strong> aims at gathering the community’s expertise and knowledge to tackle the scaling problems posed by variables of different nature. These experiences will be summarized in a journal article where all the participants are invited to contribute. The exercise is internationally open. Ph.D. students, early career and senior researchers, spin-offs, and companies working in the field of remote sensing of vegetation ecophysiology are welcome to participate.</p> <p> </p> <p><strong>STILL OPEN FOR PARTICIPATION! New deadline 31<sup>st</sup> of October 2022.</strong></p> <p> </p> <p>Follow all the <strong>communications and updates</strong> of the <strong>SPATIAL SCALING CHALLENGE</strong> in the RG site: <strong><a href="https://www.researchgate.net/project/Spatial-Scaling-Challenge-COST-Action-CA17134-SENSECO-Working-Group-1">https://www.researchgate.net/project/Spatial-Scaling-Challenge-COST-Action-CA17134-SENSECO-Working-Group-1</a></strong>.</p> <p> </p> <p> </p>
Location-based augmented reality (LBAR) spatial data test
<p>This repository gathers video data (screen capture) collected on a field test conducted on the 11th of May 2022, at the HEIG-VD in Yverdon-les-Bains, Switzerland.<br> <br> The goal of the test was to submit LBAR interfaces to different sources of spatial data. The 5 conditions compared were:<br> <br> 1) ARCore interface (visual odometry) fed with position and orientation data provided by the mobile device’s embedded Inertial Measurment Unit (IMU) and GNSS measurment unit.<br> 2) ARCore interface (visual odometry) fed with orientation data provided by the mobile device’s embedded Inertial Measurment Unit (IMU), and with position data provided by an external REDcatch GNSS/RTK measurment unit.<br> 3) A-Frame + LBAR.js interface fed with position and orientation data provided by the mobile device’s embedded Inertial Measurment Unit (IMU) and GNSS measurment unit.<br> 4) A-Frame + LBAR.js interface fed with orientation data provided by the mobile device’s embedded Inertial Measurment Unit (IMU), and with position data provided by an external REDcatch GNSS/RTK measurment unit.<br> 5) A-Frame + LBAR.js interface fed with position and orientation data provided by an external Inertial Navigation Station Xsens MTi-680g (IMU + GNSS/RTK).</p>
Data for the manuscript: Demographic basis of spatially structured fluctuations in a threespine stickleback metapopulation
<p>Uncovering the demographic basis of population fluctuations is a central goal of population biology. This is particularly challenging for spatially structured populations, which require disentangling synchrony in demographic rates from coupling via immigration. In this study, we fit a stage-structured metapopulation model to a 29-year times series of threespine stickleback abundance in the heterogeneous and productive Lake Myvatn, Iceland. The lake comprises two basins (North and South) connected by a channel through which the stickleback disperse. The model includes time-varying demographic rates, allowing us to assess the potential contributions of recruitment and survival, spatial coupling via immigration, and demographic transience to the population's large fluctuations in abundance. Our analyses indicate that recruitment was only modestly synchronized between the two basins, whereas survival probabilities of adults were more strongly synchronized, contributing to cyclic fluctuations in the lake-wide population size with a period of approximately six years. The analyses further show that the two basins are coupled through immigration, with the North Basin subsidizing the South Basin and playing a dominant role in driving the lake-wide dynamics. Our results show that cyclic fluctuations of a metapopulation can be explained in terms of the combined effects of synchronized demographic rates and spatial coupling.</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>
Supporting Data: Do spatial climate messages increase pro-environmental engagement? Evidence from a survey experiment on public transport
<p>Additional material for the paper:</p> <p>Victor von Loessl, Eva Weingärtner & Sonja Zitzelsberger (2022) Do spatial climate messages increase pro-environmental engagement? Evidence from a survey experiment on public transport, Journal of Environmental Economics and Policy, DOI: 10.1080/21606544.2022.2097960</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> 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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> 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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>
Data used for developing a parameterization for spatial distribution of solar irradiance over rugged terrain
<p>This dataset includes data and results produced while developing a parameterization for the spatial distribution of solar radiation over mountainous terrain. The method is designed for applications in Earth System Models.</p> <p>The four items included in this dataset are the following:</p> <p><strong>GFDL_preproc_dems</strong> includes digital elevations maps derived from the Shuttle Radar Topography Mission (SRTM) for three sample domains.</p> <p><strong>single-time-step </strong>Includes atmospheric optical properties used as input for Monte Carlo simulations.</p> <p><strong>gmd_2021_grids_light_*</strong> Include maps of terrain parameters derived from SRTM elevation data, and the partition of the domains in homogeneous tiles (sub-grid units) for different number and types of of land units.</p> <p><strong>rmc_simul_res</strong> results of the Monte Carlo simulations, consisting of maps of simulated solar radiation for different domains and solar angles.</p>
Data for: Co-evolution of dormancy and dispersal in spatially autocorrelated landscapes
<p>The evolution of dispersal can be driven by spatial processes, such as landscape structure, and temporal processes, such as disturbance. Dormancy, or dispersal in time, is generally thought to evolve in response to temporal processes. In spite of broad empirical and theoretical evidence of trade-offs between dispersal and dormancy, we lack evidence that spatial structure can drive the evolution of dormancy. Here, we develop a simulation-based model of the joint evolution of dispersal and dormancy in spatially heterogeneous landscapes. We show that dormancy and dispersal are each favored under different landscape conditions, but not simultaneously under any of the conditions we tested. We further show that, when dispersal distances are short, dormancy can evolve directly in response to landscape structure. In this case, selection is primarily driven by benefits associated with avoiding kin competition. Our results are similar in both highly simplified and realistically complex landscapes.</p>
Data for: A spatial framework for prioritizing biochar application to arable land: a case study for Sweden
<p>The uploaded data is related to the publication: <em>A spatial framework for prioritizing biochar application to arable land: a case study for Sweden</em>, and contains the following:</p> <p>(I) Raster files for three different biochar prioritization narratives.</p> <p>(II) High-resolution biochar use indication maps (in JPEG) for different prioritization narratives. </p>
Hierarchical trait filtering at different spatial scales determines beetle assemblages in deadwood: Additional data
<p>Contains data used in the following publication:</p> <p>Felix Neff, Jonas Hagge, Rafael Achury, Didem Ambarlı, Christian Ammer, Peter Schall, Sebastian Seibold, Michael Staab, Wolfgang W. Weisser, Martin M. Gossner (2022). <em>Hierarchical trait filtering at different spatial scales determines beetle assemblages in deadwood. </em>Functional Ecology. <a href="https://doi.org/10.1111/1365-2435.14186">https://doi.org/10.1111/1365-2435.14186</a></p> <p>These are complementary data, which are needed to reproduce the analyses. Most data are archived in the Biodiversity Exploratories Information System (<a href="https://doi.org/10.17616/R32P9Q">https://doi.org/10.17616/R32P9Q</a>).</p> <p>The following data are included:</p> <ul> <li><strong>BELongDead_Subplots_Normal.csv</strong>: List of <em>normal</em> subplots within the BELongDead projects (only these were included in the analyses)</li> <li><strong>Body_length.csv</strong>: Body length data for study species assembled from Freude et al. (1965-1998)</li> <li><strong>Lightness_completion.csv</strong>: Colour lightness recorded from measured individuals and photos from coleonet.de (Lompe, 2002)</li> <li><strong>Name_standardisation.csv</strong>: Dataset used to standardise taxonomic names from different sources</li> <li><strong>Saproxylic_species_sub.csv</strong>: List of saproxylic species (according to Schmidl & Bussler (2004)), which were recorded in the project</li> <li><strong>Similar_Species.csv</strong>: List of similar species for species with missing traits. Based on these, missing traits were estimated</li> </ul> <p><strong>References</strong></p> <p>Freude, H., Harde, K. W., & Lohse, G. A. (1965–1998). <em>Die Käfer Mitteleuropas Band 1-15</em>. Goecke und Evers.</p> <p>Lompe, A. (2002). <em>Käfer Europas</em>. <a href="http://coleonet.de/">http://coleonet.de/</a></p> <p>Schmidl, J., & Bussler, H. (2004). Ökologische Gilden xylobionter Käfer Deutschlands. <em>Naturschutz und Landschaftsplanung</em>, <em>36</em>(7), 202–218.</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>
Count data, spatial data, environmental data - Dee Estuary Waders 1970-2020
<p>Combined raw ecological field data, WeBS data, and environmental data used to investigate the spatio-temporal drivers of wader community on the Dee estuary (UK).</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.