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26,784 results for “M.”
ROV cm-scale seafloor surveys at 1840 m depth in Monterey Canyon
<p>This dataset contains 5-cm lateral resolution multibeam sonar bathymetry and 1-cm lateral resolution lidar bathymetry from ~1840 m water depth in Monterey Canyon, offshore Moss Landing, California. The data were acquired by a remotely operated vehicle (ROV) aboard the R/V Western Flyer operated by the Monterey Bay Aquarium Research Institute (MBARI). The multibeam sonar and lidar data were collected using a Reson SeaBat 7125 400-kHz multibeam sonar and a 3D at Depth SL1 subsea lidar, respectively.</p> <p>These surveys were all conducted between November 2015 and March 2017 as one component of the Coordinated Canyon Experiment (CCE) led by MBARI geologist Charlie Paull, in collaboration with researchers from the United States Geological Survey, the Ocean University of China, the National Oceanography Centre in Southampton, England, and the University of Hull, England.</p> <p>For a detailed description of the methodology used to collect, process, and grid these data, please refer to the associated journal article: <em><strong><This info will be updated once the paper is published></strong></em></p> <p>The primary results from the CCE were described in:</p> <p>Paull, C.K., Talling, P.J., Maier, K.L., Parsons, D., Xu, J., Caress, D.W., Gwiazda, R., Lundsten, E.M., Anderson, K., Barry, J.P., Chaffey, M., O'Reilly, T., Rosenberger, K.J., Gales, J.A., Kieft, B., McGann, M., Simmons, S.M., McCann, M., Sumner, E.J., Clare, M.A., Cartigny, J., (2018). Powerful turbidity currents driven by dense basal layers. Nature Communications, 9: 1-9. <a href="https://doi.org/10.1038/s41467-018-06254-6">https://doi.org/10.1038/s41467-018-06254-6</a></p> <p>A general description of the CCE can be found in an article from the 2017 MBARI annual report: <a href="https://annualreport.mbari.org/2017/story/coordinated-canyon-experiment">https://annualreport.mbari.org/2017/story/coordinated-canyon-experiment</a></p> <p><br> This dataset contains the following gridded bathymetry data in both GeoTIFF and ESRI ASCII grid format (.asc) in WG84 Geographic (EPSG: 4326):<br> <br> 5-cm lateral resolution multibeam sonar bathymetry grids:</p> <ol> <li>CCE_LASS_5cm_multibeam_MontereyCanyon_November2015 <ul> <li>Multibeam bathymetry data collected between November 18 - 20, 2015</li> </ul> </li> <li>CCE_LASS_5cm_multibeam_MontereyCanyon_May2016 <ul> <li>Multibeam bathymetry data collected between May 10 - 12, 2016</li> </ul> </li> <li>CCE_LASS_5cm_multibeam_MontereyCanyon_October2016 <ul> <li>Multibeam bathymetry data collected between October 26 - 28, 2016</li> </ul> </li> <li>CCE_LASS_5cm_multibeam_MontereyCanyon_March2017 <ul> <li>Multibeam bathymetry data collected between March 7 - 12th, 2016</li> </ul> </li> </ol> <p>1-cm lateral resolution lidar bathymetry grids:</p> <ol> <li>CCE_LASS_1cm_lidar_MontereyCanyon_May2016 <ul> <li>a portion of the survey area mapped by lidar between May 10 - 12, 2016</li> </ul> </li> </ol>
Archive of the analysis results of the microtremor data obtained from a seismic array with a radius of 0.58 m distributed to the participants of the blind prediction experiments for the ESG6 symposium
<p>This is a supplemental material of the paper "Array-size dependency of the upper limit wavelength normalized by array radius for the standard spatial autocorrelation method" by Ikuo Cho, published in Earth, Planets and Space. It consists of the analysis results of the microtremor data observed using a seismic array with a radius of 0.58 m, which were distributed to the participants of the blind prediction experiments in ESG6. It involves all analysis results and script files to draw Figure 1 of the paper. See the "Availability of data and materials" section of the paper to download the original observed data and analysis code. See the main text of the paper for the details of the analysis.</p>
Data collection for Section M in the northeastern Bay of Bengal
<p>Observational Data from the China-Myanmar joint cruise, in relation to the publication</p> <p>An intrathermocline eddy observed in the northeastern Bay of Bengal</p> <p>Xiwu Zhou, Yun Qiu, Xinyu Lin, Hui Teng, Cherry Aung</p>
NPM Packages (~1.9M packages)
<p>- input.json.gz contains the raw NPM data dump from the replicate.npmjs.org database (downloaded on June 4th, 2022) in the following format:</p> <pre><code class="language-json">{ "total_rows": 1997865, "offset": 0, "rows": [ { "id": "-", "key": "-", "value": { "rev": "5-92e28c1ea7f24d165faa202aabd92669" }, "doc": { "_id": "-", "_rev": "5-92e28c1ea7f24d165faa202aabd92669", "name": "-", "dist-tags": { "latest": "0.0.1" }, "versions": { "0.0.1": { "name": "-", "version": "0.0.1", "license": "UNLICENSED", "keywords": [], "main": "dist/index.js", "types": "dist/index.d.ts", "scripts": { "test": "echo \"Error: no test specified\" && exit 1", "start": "node --require ts-node/register src", "build": "tsc" }, "devDependencies": { "@types/node": "13.9.0", "ts-node": "8.6.2", "typescript": "3.8.3" }, "description": "...", "_id": "-@0.0.1", "_nodeVersion": "13.3.0", "_npmVersion": "6.14.4", "dist": {...}, "maintainers": [ { "name": "parzhitsky", "email": "parzhitsky@gmail.com" } ], "_npmUser": { "name": "parzhitsky", "email": "parzhitsky@gmail.com" }, "directories": {}, "_npmOperationalInternal": { "host": "s3://npm-registry-packages", "tmp": "tmp/-_0.0.1_1585828132159_0.6185838126718755" }, "_hasShrinkwrap": false } }, "time": { "created": "2020-04-02T11:48:52.159Z", "0.0.1": "2020-04-02T11:48:52.339Z", "modified": "2022-04-04T10:06:01.120Z" }, "maintainers": [ { "email": "support@npmjs.com", "name": "npm-support" } ], "description": "...", "keywords": [], "license": "UNLICENSED", "readme": "...", "readmeFilename": "README.md" } }, ... ] }</code></pre> <p>- streamedout.json.gz contains a processed file that has the following format</p> <pre><code class="language-json">{"pkgs": [ { "name": "react", "versions": { "1.00": { "timestamp": "06-05-2022T10:00:01", "dependencies": { "name": "^1.0.2" } } } }, ... ]}</code></pre> <p> </p>
Fig. 2 in Myriophyllum rubricaule sp. nov., a M. aquaticum look-alike only known in cultivation
Fig. 2. Maximum likelihood tree based on ITS (partial 18S, ITS1, 5.8S, ITS2, partial 28S) sequences. The Tamura 3 parameter model was used (Tamura 1992). The tree with the highest log likelihood (-1398.71) is shown. Bootstrap values after 1000 replicates are expressed as percentages. A discrete Gamma distribution was used to model evolutionary rate differences among sites (5 categories (+G, parameter = 0.4031)). The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. Laurembergia tetrandra AP 33 was used as an outgroup.
Fig. 1 in Myriophyllum rubricaule sp. nov., a M. aquaticum look-alike only known in cultivation
Fig. 1. Plants, prior to collection, of M. rubricaule Valk. & Duist. sp. nov. (Valkenburg 3495; red stem) growing amidst M. aquaticum (Valkenburg 3494; green stem) in an artificial pond at a golf course, illustrating the more modest dimensions and color differences (13 July 2009, Grashoek, the Netherlands).
Fig. 3 in Myriophyllum rubricaule sp. nov., a M. aquaticum look-alike only known in cultivation
Fig. 3. Maximum likelihood tree based on concatenated trnK3-matK sequences. This tree was inferred by using the Maximum Likelihood method and Tamura 3 parameter model (Tamura 1992). The tree with the highest log likelihood (-2853.86) is shown. Bootstrap values after 1000 replicates are expressed as percentages. The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. Laurembergia tetrandra AP 33 was used as an outgroup.
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>
Generating Annual 4-m Land Cover Products over 28 Metropolises in China during 2000-2020
<p>A 4-m fine-grained land cover products over the 28 provincial capital cities in China (mainly in temperate monsoon climate and subtropical monsoon climate zone, and categorized into 7 classes, i.e., cropland, forest, grass, shrub, water, impervious surface, and bare land) is generated through the proposed model; A 4-m annual land cover products during 2000-2020 in Wuhan, China is generated through the proposed model.</p> <p>To validate the effectiveness of the derived products, three verification approaches are employed. 1) evaluation by the consistency with the contemporary products including 10-m Esri Land Cover, 10-m FROMGLC10, and 30-m GlobeLand30 ; 2) evaluation by the publicly certified 4-m annotation dataset GID; 3) evaluation by the third-party-created annotation samples. The validation results can be viewed in the manuscript, and the reproduction executation details can be viewed at Readme.txt.</p> <p>The products is in .tif format, and validaiton region is in ENVI/hdr format. Users need to install ENVI software or Arcgis to read the product.</p> <p>"open_source_DETNet_code_and_simple_demo.rar" is open source code and the corresponding intruction.</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>
Mapping 10-m global impervious surface area (GISA-10m) using multi-source geospatial data
<p>Artificial impervious surface area (ISA) documents human footprints. Accurate, timely, and detailed ISA datasets are therefore essential for global climate change and urban planning. However, due to the lack of sufficient training samples and operational mapping methods, global ISA mapping at 10-m resolution is still lacking. To this end, we proposed a global ISA mapping method leveraging multi-source geospatial data. Based on the existing satellite-derived ISA maps and the crowdsourcing OpenStreetMap (OSM), 58 million training samples were extracted via a series of temporal, spatial, spectral, and geometric rules. Combined with over 2.7 million Sentinel optical and radar images on the Google Earth Engine, we produced the 10 m global ISA dataset (GISA-10m). Based on the test samples that are independent to the training set, GISA-10m embraced an overall accuracy greater than 86%. In addition, the GISA-10m was comprehensively compared with the existing global ISA datasets, and the superiority of GISA-10m was demonstrated. </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> 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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> 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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> 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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>
FIG. 4. — Erythroxylum labatii E.Bidault & M in Novelties in Erythroxylum P.Browne (Erythroxylaceae) from the Comoros Archipelago: two new, range-restricted and threatened species, and notes on the Mount Choungi biogeographical singularity
FIG. 4. — Erythroxylum labatii E.Bidault & M.Pignal, sp. nov.: A, stem and immature fruits; B, inflorescence with flower buds and stem portion with cataphylls; C, long-styled flower; D, mature fruit; E, short-styled flower; A, D, photos by J.-N. Labat, B, E, photos by E. Bidault, C, photo by G. Viscardi.
FIG. 3. — Erythroxylum labatii E.Bidault & M in Novelties in Erythroxylum P.Browne (Erythroxylaceae) from the Comoros Archipelago: two new, range-restricted and threatened species, and notes on the Mount Choungi biogeographical singularity
FIG. 3. — Erythroxylum labatii E.Bidault & M.Pignal, sp. nov.: A, detail of stem; B, long-styled flower; C, short-styled flower with corolla fallen; D, short-styled flower; E, immature fruit; F, mature fruit; G, habit, dry; H, detail of inflorescence with flower buds; I, detail of cataphyll, abaxial side; A, C, G-I, Labat et al. 3212 (P); B, D, Bidault et al. 63 (P); E, F, Labat et al. 3787 (P). Drawings by Anne-Hélène Paradis. Scale bars: A, B, 2 mm; C, 1 mm; D, 2 mm; E, F, 3 mm; G, 2 cm; H, 2 mm; I, 1 mm.
Text-fig. 3. Fossil fruits of Fraxinus: photos and drawings. a–e: F. zlatkoi MENG-XIOA WU et J.HUANG sp. nov., a–c – the holotype image and its line drawing (XTBG-PC-LH3-0341), d, e – paratype image and its line drawing (XTBGPC-LH3-1138); f–k: Fraxinus cf. honshuensis TANAI et ONOE, f–h – XTBG-PC-LH2-0111, i–k – XTBG-PC-LH1-0578; l, m: enlargement of (b) and its line drawing; n, o: enlargement of (g) and its line drawing. Arrows in (i) and (n) refer to the calyxes. Scale bars = 5 mm for a–k, scale bars = 1 mm for i–o. in Fraxinus L. (Oleaceae) Fruits From The Early Oligocene Of Southwest China And Their Biogeographic Implications
Text-fig. 3. Fossil fruits of Fraxinus: photos and drawings. a–e: F. zlatkoi MENG-XIOA WU et J.HUANG sp. nov., a–c – the holotype image and its line drawing (XTBG-PC-LH3-0341), d, e – paratype image and its line drawing (XTBGPC-LH3-1138); f–k: Fraxinus cf. honshuensis TANAI et ONOE, f–h – XTBG-PC-LH2-0111, i–k – XTBG-PC-LH1-0578; l, m: enlargement of (b) and its line drawing; n, o: enlargement of (g) and its line drawing. Arrows in (i) and (n) refer to the calyxes. Scale bars = 5 mm for a–k, scale bars = 1 mm for i–o.
ScienceDex guides
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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.