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5,155 results for “resolution”
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>
A high-resolution GPM IMERG precipitation dataset for China
<p>The database of the paper: An attention mechanism based convolutional network for satellite precipitation downscaling over China</p>
Data: Guiding the design of SARS-CoV-2 genomic surveillance by estimating the resolution of outbreak detection
<p>Contains data necessary to reproduce the quantitative results related to a SARS-CoV-2 outbreak in NSW, Australia in the associated paper.</p> <p><strong>icpmr_delta_gisaid.csv</strong><br> Tabular data containing the GISAID accession numbers, dates of collection and submission for all sequences used in the NSW outbreak analysis. The epi set is available on GISAID as EPI_SET_220919ef. The <code>wgs_cluster</code> column contains identifiers of genomic clusters defined at ICPMR, NSW Health Pathology. A value of "Other" means that the sequence either did not belong to a cluster or was part of a cluster that contained fewer than 30 sequences in the study period, and sequences with this value should not be considered to form a single cluster.</p> <p><strong>icpmr_delta_gisaid.dists.tsv.gz</strong><br> Compressed pairwise SNP distance matrix in the format output by snp-dists. The script that creates this file from sequence data is available in the linked code archive.</p>
HiTIC-Monthly: A Monthly High Spatial Resolution (1 km) Human Thermal Index Collection over China during 2003–2020
<p>The monthly <strong>Hi</strong>gh spatial resolution human <strong>T</strong>hermal <strong>I</strong>ndex <strong>C</strong>ollection (<strong>HiTIC-Monthly</strong>) includes <strong><em>near-surface air temperature</em> </strong>(SAT) and 11 commonly used<strong> <em>human-perceived temperature</em> </strong>indices: indoor Apparent Temperature (AT<sub>in</sub>), outdoor shaded Apparent Temperature (AT<sub>out</sub>), Discomfort Index (DI), Effective Temperature (ET), Heat Index (HI), Humidex (HMI), Modified Discomfort Index (MDI), Net Effective Temperature (NET), simplified Wet Bulb Globe Temperature (sWBGT), Wet-Bulb Temperature (WBT), and Wind Chill Temperature (WCT). This dataset has a high spatial resolution of 1 km × 1 km and covers mainland China from January 2003 to December 2020. The overall R-square, root mean square error, and mean absolute error of the 12 thermal indices in the HiTIC dataset are 0.996, 0.693°C, and 0.512°C, respectively. It is stacked by year, and each stack is composed of 12 monthly images in the <strong>NetCDF format</strong>. The unit of the dataset is 0.01 degree Celsius (°C), and the values are stored in an integer type (Int16) for saving storage space, and need to be divided by 100 to get the values in degree Celcius when in use. The projection coordinate system of the dataset is Albers Equal Area Conic Projection. The naming rule and other detailed information can be found in “README.pdf”.</p> <p>If you have any questions when using the HiTIC-Monthly dataset, please feel free to contact Miss Hui Zhang via <a href="mailto:zhangh573@mail2.sysu.edu.cn">zhangh573@mail2.sysu.edu.cn</a>, Dr. Ming Luo via <a href="mailto:luom38@mail.sysu.edu.cn">luom38@mail.sysu.edu.cn</a>, or Dr. Yongquan Zhao via <a href="mailto:yqzhao@link.cuhk.edu.hk">zhaoyq66@mail.sysu.edu.cn</a>. More details on the procedure of producing the HiTIC-Monthly dataset and its accuracy assessment can be found in:</p> <p>Zhang, H., Luo, M., Zhao, Y., Lin, L., Ge, E., Yang, Y., Ning, G., Cong, J., Zeng, Z., Gui, K., Li, J., Chan, T. O., Li, X., Wu, S., Wang, P., and Wang, X.: HiTIC-Monthly: a monthly high spatial resolution (1 km) human thermal index collection over China during 2003–2020, Earth Syst. Sci. Data, 15, 359–381, https://doi.org/10.5194/essd-15-359-2023, 2023.</p>
Shoreline data at 30-m spatial resolution for 2001 coastal provinces or regions of the world, in geoJSON format.
<p>Region: 2001 coastal provinces or regions of the world</p> <p>Data fields:</p> <ol> <li>MEAN_SIG_WAVEHEIGHT</li> <li>TIDAL_RANGE</li> <li>CHLOROPHYLL</li> <li>TURBIDITY</li> <li>TEMP_MOISTURE</li> <li>EMU_PHYSICAL</li> <li>REGIONAL_SINUOSITY</li> <li>GHM</li> <li>MAX_SLOPE %</li> <li>OUTFLOW_DENSITY</li> <li>ERODIBILITY</li> <li>LENGTH_GEO</li> <li>ch_label</li> <li>river_label</li> <li>sinuosity_label</li> <li>slope_label</li> <li>tidal_label</li> <li>turbid_label</li> <li>wave_label</li> <li>CSU_Descriptor</li> <li>CSU_ID</li> </ol> <p>The data originally come from https://rmgsc.cr.usgs.gov/outgoing/ecosystems/Global/USGSEsriGlobalCoastalSegmentsv1.mpk</p> <p>The data are described in the following publication</p> <p>Roger Sayre, Suzanne Noble, Sharon Hamann, Rebecca Smith, Dawn Wright, Sean Breyer, Kevin Butler, Keith Van Graafeiland, Charlie Frye, Deniz Karagulle, Dabney Hopkins, Drew Stephens, Kevin Kelly, Zeenatul Basher, Devon Burton, Jill Cress, Karina Atkins, D. Paco Van Sistine, Beverly Friesen, Rebecca Allee, Tom Allen, Peter Aniello, Irawan Asaad, Mark John Costello, Kathy Goodin, Peter Harris, Maria Kavanaugh, Helen Lillis, Eleonora Manca, Frank Muller-Karger, Bjorn Nyberg, Rost Parsons, Justin Saarinen, Jac Steiner & Adam Reed (2019) A new 30 meter resolution global shoreline vector and associated global islands database for the development of standardized ecological coastal units, Journal of Operational Oceanography, 12:sup2, S47-S56, DOI: <a href="https://doi.org/10.1080/1755876X.2018.1529714">10.1080/1755876X.2018.1529714</a></p> <p>Metadata file (each file listed alongside the bounds in WGS84 latitude/longitude): province_files_bounds.json</p>
Open data for the article "Low-resistivity, high-resolution W-C electrical contacts fabricated by direct-write focused electron beam induced deposition"
<p>Open data for the article "Low-resistivity, high-resolution W-C electrical contacts fabricated by direct-write focused electron beam induced deposition", which will be published in Open Research Europe</p>
Shoreline data at 30-m spatial resolution for 298 coastal counties of the conterminous USA, in geoJSON format.
<p>Region: 298 coastal counties of the conterminous USA</p> <p>Data fields:</p> <ol> <li>MEAN_SIG_WAVEHEIGHT</li> <li>TIDAL_RANGE</li> <li>CHLOROPHYLL</li> <li>TURBIDITY</li> <li>TEMP_MOISTURE</li> <li>EMU_PHYSICAL</li> <li>REGIONAL_SINUOSITY</li> <li>GHM</li> <li>MAX_SLOPE %</li> <li>OUTFLOW_DENSITY</li> <li>ERODIBILITY</li> <li>LENGTH_GEO</li> <li>ch_label</li> <li>river_label</li> <li>sinuosity_label</li> <li>slope_label</li> <li>tidal_label</li> <li>turbid_label</li> <li>wave_label</li> <li>CSU_Descriptor</li> <li>CSU_ID</li> </ol> <p>The data originally come from https://rmgsc.cr.usgs.gov/outgoing/ecosystems/Global/USGSEsriGlobalCoastalSegmentsv1.mpk</p> <p>The data are described in the following publication</p> <p>Roger Sayre, Suzanne Noble, Sharon Hamann, Rebecca Smith, Dawn Wright, Sean Breyer, Kevin Butler, Keith Van Graafeiland, Charlie Frye, Deniz Karagulle, Dabney Hopkins, Drew Stephens, Kevin Kelly, Zeenatul Basher, Devon Burton, Jill Cress, Karina Atkins, D. Paco Van Sistine, Beverly Friesen, Rebecca Allee, Tom Allen, Peter Aniello, Irawan Asaad, Mark John Costello, Kathy Goodin, Peter Harris, Maria Kavanaugh, Helen Lillis, Eleonora Manca, Frank Muller-Karger, Bjorn Nyberg, Rost Parsons, Justin Saarinen, Jac Steiner & Adam Reed (2019) A new 30 meter resolution global shoreline vector and associated global islands database for the development of standardized ecological coastal units, Journal of Operational Oceanography, 12:sup2, S47-S56, DOI: <a href="https://doi.org/10.1080/1755876X.2018.1529714">10.1080/1755876X.2018.1529714</a></p> <p>Metadata file (each file listed alongside the bounds in WGS84 latitude/longitude): files_bounds.json</p> <p>Shoreline data files:</p> <p>us_county0_ref_shoreline.geojson<br> us_county1036_ref_shoreline.geojson<br> us_county111_ref_shoreline.geojson<br> us_county112_ref_shoreline.geojson<br> us_county113_ref_shoreline.geojson<br> us_county1148_ref_shoreline.geojson<br> us_county1149_ref_shoreline.geojson<br> us_county114_ref_shoreline.geojson<br> us_county115_ref_shoreline.geojson<br> us_county116_ref_shoreline.geojson<br> us_county117_ref_shoreline.geojson<br> us_county118_ref_shoreline.geojson<br> us_county1195_ref_shoreline.geojson<br> us_county119_ref_shoreline.geojson<br> us_county1200_ref_shoreline.geojson<br> us_county120_ref_shoreline.geojson<br> us_county121_ref_shoreline.geojson<br> us_county122_ref_shoreline.geojson<br> us_county123_ref_shoreline.geojson<br> us_county1246_ref_shoreline.geojson<br> us_county124_ref_shoreline.geojson<br> us_county125_ref_shoreline.geojson<br> us_county126_ref_shoreline.geojson<br> us_county1272_ref_shoreline.geojson<br> us_county1273_ref_shoreline.geojson<br> us_county1277_ref_shoreline.geojson<br> us_county127_ref_shoreline.geojson<br> us_county128_ref_shoreline.geojson<br> us_county129_ref_shoreline.geojson<br> us_county1302_ref_shoreline.geojson<br> us_county1304_ref_shoreline.geojson<br> us_county1305_ref_shoreline.geojson<br> us_county1306_ref_shoreline.geojson<br> us_county130_ref_shoreline.geojson<br> us_county1317_ref_shoreline.geojson<br> us_county131_ref_shoreline.geojson<br> us_county1324_ref_shoreline.geojson<br> us_county132_ref_shoreline.geojson<br> us_county133_ref_shoreline.geojson<br> us_county134_ref_shoreline.geojson<br> us_county135_ref_shoreline.geojson<br> us_county136_ref_shoreline.geojson<br> us_county137_ref_shoreline.geojson<br> us_county138_ref_shoreline.geojson<br> us_county139_ref_shoreline.geojson<br> us_county140_ref_shoreline.geojson<br> us_county141_ref_shoreline.geojson<br> us_county142_ref_shoreline.geojson<br> us_county143_ref_shoreline.geojson<br> us_county144_ref_shoreline.geojson<br> us_county145_ref_shoreline.geojson<br> us_county146_ref_shoreline.geojson<br> us_county147_ref_shoreline.geojson<br> us_county148_ref_shoreline.geojson<br> us_county149_ref_shoreline.geojson<br> us_county150_ref_shoreline.geojson<br> us_county151_ref_shoreline.geojson<br> us_county152_ref_shoreline.geojson<br> us_county153_ref_shoreline.geojson<br> us_county154_ref_shoreline.geojson<br> us_county155_ref_shoreline.geojson<br> us_county156_ref_shoreline.geojson<br> us_county157_ref_shoreline.geojson<br> us_county1585_ref_shoreline.geojson<br> us_county158_ref_shoreline.geojson<br> us_county1596_ref_shoreline.geojson<br> us_county159_ref_shoreline.geojson<br> us_county1605_ref_shoreline.geojson<br> us_county160_ref_shoreline.geojson<br> us_county161_ref_shoreline.geojson<br> us_county162_ref_shoreline.geojson<br> us_county163_ref_shoreline.geojson<br> us_county1649_ref_shoreline.geojson<br> us_county164_ref_shoreline.geojson<br> us_county1650_ref_shoreline.geojson<br> us_county1651_ref_shoreline.geojson<br> us_county1653_ref_shoreline.geojson<br> us_county1655_ref_shoreline.geojson<br> us_county1658_ref_shoreline.geojson<br> us_county165_ref_shoreline.geojson<br> us_county1667_ref_shoreline.geojson<br> us_county166_ref_shoreline.geojson<br> us_county167_ref_shoreline.geojson<br> us_county168_ref_shoreline.geojson<br> us_county169_ref_shoreline.geojson<br> us_county170_ref_shoreline.geojson<br> us_county171_ref_shoreline.geojson<br> us_county172_ref_shoreline.geojson<br> us_county173_ref_shoreline.geojson<br> us_county174_ref_shoreline.geojson<br> us_county175_ref_shoreline.geojson<br> us_county176_ref_shoreline.geojson<br> us_county177_ref_shoreline.geojson<br> us_county178_ref_shoreline.geojson<br> us_county179_ref_shoreline.geojson<br> us_county180_ref_shoreline.geojson<br> us_county181_ref_shoreline.geojson<br> us_county182_ref_shoreline.geojson<br> us_county183_ref_shoreline.geojson<br> us_county184_ref_shoreline.geojson<br> us_county185_ref_shoreline.geojson<br> us_county186_ref_shoreline.geojson<br> us_county187_ref_shoreline.geojson<br> us_county188_ref_shoreline.geojson<br> us_county189_ref_shoreline.geojson<br> us_county190_ref_shoreline.geojson<br> us_county191_ref_shoreline.geojson<br> us_county192_ref_shoreline.geojson<br> us_county193_ref_shoreline.geojson<br> us_county194_ref_shoreline.geojson<br> us_county195_ref_shoreline.geojson<br> us_county196_ref_shoreline.geojson<br> us_county197_ref_shoreline.geojson<br> us_county198_ref_shoreline.geojson<br> us_county199_ref_shoreline.geojson<br> us_county200_ref_shoreline.geojson<br> us_county201_ref_shoreline.geojson<br> us_county202_ref_shoreline.geojson<br> us_county203_ref_shoreline.geojson<br> us_county204_ref_shoreline.geojson<br> us_county205_ref_shoreline.geojson<br> us_county206_ref_shoreline.geojson<br> us_county207_ref_shoreline.geojson<br> us_county208_ref_shoreline.geojson<br> us_county209_ref_shoreline.geojson<br> us_county210_ref_shoreline.geojson<br> us_county2118_ref_shoreline.geojson<br> us_county211_ref_shoreline.geojson<br> us_county2125_ref_shoreline.geojson<br> us_county2127_ref_shoreline.geojson<br> us_county2128_ref_shoreline.geojson<br> us_county212_ref_shoreline.geojson<br> us_county2130_ref_shoreline.geojson<br> us_county2133_ref_shoreline.geojson<br> us_county213_ref_shoreline.geojson<br> us_county2148_ref_shoreline.geojson<br> us_county2149_ref_shoreline.geojson<br> us_county214_ref_shoreline.geojson<br> us_county215_ref_shoreline.geojson<br> us_county216_ref_shoreline.geojson<br> us_county217_ref_shoreline.geojson<br> us_county218_ref_shoreline.geojson<br> us_county219_ref_shoreline.geojson<br> us_county220_ref_shoreline.geojson<br> us_county221_ref_shoreline.geojson<br> us_county222_ref_shoreline.geojson<br> us_county223_ref_shoreline.geojson<br> us_county224_ref_shoreline.geojson<br> us_county225_ref_shoreline.geojson<br> us_county226_ref_shoreline.geojson<br> us_county227_ref_shoreline.geojson<br> us_county228_ref_shoreline.geojson<br> us_county229_ref_shoreline.geojson<br> us_county230_ref_shoreline.geojson<br> us_county231_ref_shoreline.geojson<br> us_county232_ref_shoreline.geojson<br> us_county233_ref_shoreline.geojson<br> us_county234_ref_shoreline.geojson<br> us_county235_ref_shoreline.geojson<br> us_county236_ref_shoreline.geojson<br> us_county237_ref_shoreline.geojson<br> us_county238_ref_shoreline.geojson<br> us_county239_ref_shoreline.geojson<br> us_county240_ref_shoreline.geojson<br> us_county241_ref_shoreline.geojson<br> us_county242_ref_shoreline.geojson<br> us_county243_ref_shoreline.geojson<br> us_county244_ref_shoreline.geojson<br> us_county245_ref_shoreline.geojson<br> us_county246_ref_shoreline.geojson<br> us_county2478_ref_shoreline.geojson<br> us_county247_ref_shoreline.geojson<br> us_county248_ref_shoreline.geojson<br> us_county2493_ref_shoreline.geojson<br> us_county249_ref_shoreline.geojson<br> us_county250_ref_shoreline.geojson<br> us_county2517_ref_shoreline.geojson<br> us_county2518_ref_shoreline.geojson<br> us_county251_ref_shoreline.geojson<br> us_county252_ref_shoreline.geojson<br> us_county2532_ref_shoreline.geojson<br> us_county253_ref_shoreline.geojson<br> us_county254_ref_shoreline.geojson<br> us_county255_ref_shoreline.geojson<br> us_county256_ref_shoreline.geojson<br> us_county257_ref_shoreline.geojson<br> us_county258_ref_shoreline.geojson<br> us_county259_ref_shoreline.geojson<br> us_county260_ref_shoreline.geojson<br> us_county261_ref_shoreline.geojson<br> us_county262_ref_shoreline.geojson<br> us_county263_ref_shoreline.geojson<br> us_county264_ref_shoreline.geojson<br> us_county265_ref_shoreline.geojson<br> us_county266_ref_shoreline.geojson<br> us_county267_ref_shoreline.geojson<br> us_county2688_ref_shoreline.geojson<br> us_county268_ref_shoreline.geojson<br> us_county269_ref_shoreline.geojson<br> us_county272_ref_shoreline.geojson<br> us_county2749_ref_shoreline.geojson<br> us_county275_ref_shoreline.geojson<br> us_county276_ref_shoreline.geojson<br> us_county277_ref_shoreline.geojson<br> us_county278_ref_shoreline.geojson<br> us_county279_ref_shoreline.geojson<br> us_county280_ref_shoreline.geojson<br> us_county281_ref_shoreline.geojson<br> us_county282_ref_shoreline.geojson<br> us_county283_ref_shoreline.geojson<br> us_county284_ref_shoreline.geojson<br> us_county285_ref_shoreline.geojson<br> us_county286_ref_shoreline.geojson<br> us_county287_ref_shoreline.geojson<br> us_county288_ref_shoreline.geojson<br> us_county289_ref_shoreline.geojson<br> us_county290_ref_shoreline.geojson<br> us_county291_ref_shoreline.geojson<br> us_county292_ref_shoreline.geojson<br> us_county293_ref_shoreline.geojson<br> us_county294_ref_shoreline.geojson<br> us_county295_ref_shoreline.geojson<br> us_county296_ref_shoreline.geojson<br> us_county297_ref_shoreline.geojson<br> us_county298_ref_shoreline.geojson<br> us_county299_ref_shoreline.geojson<br> us_county300_ref_shoreline.geojson<br> us_county301_ref_shoreline.geojson<br> us_county302_ref_shoreline.geojson<br> us_county303_ref_shoreline.geojson<br> us_county304_ref_shoreline.geojson<br> us_county305_ref_shoreline.geojson<br> us_county3069_ref_shoreline.geojson<br> us_county306_ref_shoreline.geojson<br> us_county307_ref_shoreline.geojson<br> us_county308_ref_shoreline.geojson<br> us_county309_ref_shoreline.geojson<br> us_county310_ref_shoreline.geojson<br> us_county311_ref_shoreline.geojson<br> us_county312_ref_shoreline.geojson<br> us_county313_ref_shoreline.geojson<br> us_county314_ref_shoreline.geojson<br> us_county315_ref_shoreline.geojson<br> us_county316_ref_shoreline.geojson<br> us_county317_ref_shoreline.geojson<br> us_county318_ref_shoreline.geojson<br> us_county319_ref_shoreline.geojson<br> us_county320_ref_shoreline.geojson<br> us_county321_ref_shoreline.geojson<br> us_county322_ref_shoreline.geojson<br> us_county323_ref_shoreline.geojson<br> us_county324_ref_shoreline.geojson<br> us_county325_ref_shoreline.geojson<br> us_county326_ref_shoreline.geojson<br> us_county327_ref_shoreline.geojson<br> us_county328_ref_shoreline.geojson<br> us_county329_ref_shoreline.geojson<br> us_county330_ref_shoreline.geojson<br> us_county331_ref_shoreline.geojson<br> us_county332_ref_shoreline.geojson<br> us_county333_ref_shoreline.geojson<br> us_county334_ref_shoreline.geojson<br> us_county335_ref_shoreline.geojson<br> us_county336_ref_shoreline.geojson<br> us_county337_ref_shoreline.geojson<br> us_county338_ref_shoreline.geojson<br> us_county339_ref_shoreline.geojson<br> us_county340_ref_shoreline.geojson<br> us_county341_ref_shoreline.geojson<br> us_county342_ref_shoreline.geojson<br> us_county343_ref_shoreline.geojson<br> us_county344_ref_shoreline.geojson<br> us_county345_ref_shoreline.geojson<br> us_county346_ref_shoreline.geojson<br> us_county347_ref_shoreline.geojson<br> us_county348_ref_shoreline.geojson<br> us_county349_ref_shoreline.geojson<br> us_county350_ref_shoreline.geojson<br> us_county351_ref_shoreline.geojson<br> us_county352_ref_shoreline.geojson<br> us_county353_ref_shoreline.geojson<br> us_county354_ref_shoreline.geojson<br> us_county416_ref_shoreline.geojson<br> us_county417_ref_shoreline.geojson<br> us_county418_ref_shoreline.geojson<br> us_county419_ref_shoreline.geojson<br> us_county420_ref_shoreline.geojson<br> us_county421_ref_shoreline.geojson<br> us_county422_ref_shoreline.geojson<br> us_county423_ref_shoreline.geojson<br> us_county685_ref_shoreline.geojson<br> us_county686_ref_shoreline.geojson<br> us_county700_ref_shoreline.geojson<br> us_county73_ref_shoreline.geojson<br> us_county74_ref_shoreline.geojson<br> us_county754_ref_shoreline.geojson<br> us_county79_ref_shoreline.geojson<br> us_county97_ref_shoreline.geojson</p> <p> </p> <p> </p>
Shoreline data at 30-m spatial resolution for 298 coastal counties of the conterminous USA, in geoJSON format.
<p>Region: 298 coastal counties of the conterminous USA</p> <p>Data fields:</p> <ol> <li>MEAN_SIG_WAVEHEIGHT</li> <li>TIDAL_RANGE</li> <li>CHLOROPHYLL</li> <li>TURBIDITY</li> <li>TEMP_MOISTURE</li> <li>EMU_PHYSICAL</li> <li>REGIONAL_SINUOSITY</li> <li>GHM</li> <li>MAX_SLOPE %</li> <li>OUTFLOW_DENSITY</li> <li>ERODIBILITY</li> <li>LENGTH_GEO</li> <li>ch_label</li> <li>river_label</li> <li>sinuosity_label</li> <li>slope_label</li> <li>tidal_label</li> <li>turbid_label</li> <li>wave_label</li> <li>CSU_Descriptor</li> <li>CSU_ID</li> </ol> <p>The data originally come from https://rmgsc.cr.usgs.gov/outgoing/ecosystems/Global/USGSEsriGlobalCoastalSegmentsv1.mpk</p> <p>The data are described in the following publication</p> <p>Roger Sayre, Suzanne Noble, Sharon Hamann, Rebecca Smith, Dawn Wright, Sean Breyer, Kevin Butler, Keith Van Graafeiland, Charlie Frye, Deniz Karagulle, Dabney Hopkins, Drew Stephens, Kevin Kelly, Zeenatul Basher, Devon Burton, Jill Cress, Karina Atkins, D. Paco Van Sistine, Beverly Friesen, Rebecca Allee, Tom Allen, Peter Aniello, Irawan Asaad, Mark John Costello, Kathy Goodin, Peter Harris, Maria Kavanaugh, Helen Lillis, Eleonora Manca, Frank Muller-Karger, Bjorn Nyberg, Rost Parsons, Justin Saarinen, Jac Steiner & Adam Reed (2019) A new 30 meter resolution global shoreline vector and associated global islands database for the development of standardized ecological coastal units, Journal of Operational Oceanography, 12:sup2, S47-S56, DOI: <a href="https://doi.org/10.1080/1755876X.2018.1529714">10.1080/1755876X.2018.1529714</a></p> <p>Metadata file (each file listed alongside the bounds in WGS84 latitude/longitude): files_bounds.json</p> <p>Shoreline data files:</p> <p>us_county0_ref_shoreline.geojson<br> us_county1036_ref_shoreline.geojson<br> us_county111_ref_shoreline.geojson<br> us_county112_ref_shoreline.geojson<br> us_county113_ref_shoreline.geojson<br> us_county1148_ref_shoreline.geojson<br> us_county1149_ref_shoreline.geojson<br> us_county114_ref_shoreline.geojson<br> us_county115_ref_shoreline.geojson<br> us_county116_ref_shoreline.geojson<br> us_county117_ref_shoreline.geojson<br> us_county118_ref_shoreline.geojson<br> us_county1195_ref_shoreline.geojson<br> us_county119_ref_shoreline.geojson<br> us_county1200_ref_shoreline.geojson<br> us_county120_ref_shoreline.geojson<br> us_county121_ref_shoreline.geojson<br> us_county122_ref_shoreline.geojson<br> us_county123_ref_shoreline.geojson<br> us_county1246_ref_shoreline.geojson<br> us_county124_ref_shoreline.geojson<br> us_county125_ref_shoreline.geojson<br> us_county126_ref_shoreline.geojson<br> us_county1272_ref_shoreline.geojson<br> us_county1273_ref_shoreline.geojson<br> us_county1277_ref_shoreline.geojson<br> us_county127_ref_shoreline.geojson<br> us_county128_ref_shoreline.geojson<br> us_county129_ref_shoreline.geojson<br> us_county1302_ref_shoreline.geojson<br> us_county1304_ref_shoreline.geojson<br> us_county1305_ref_shoreline.geojson<br> us_county1306_ref_shoreline.geojson<br> us_county130_ref_shoreline.geojson<br> us_county1317_ref_shoreline.geojson<br> us_county131_ref_shoreline.geojson<br> us_county1324_ref_shoreline.geojson<br> us_county132_ref_shoreline.geojson<br> us_county133_ref_shoreline.geojson<br> us_county134_ref_shoreline.geojson<br> us_county135_ref_shoreline.geojson<br> us_county136_ref_shoreline.geojson<br> us_county137_ref_shoreline.geojson<br> us_county138_ref_shoreline.geojson<br> us_county139_ref_shoreline.geojson<br> us_county140_ref_shoreline.geojson<br> us_county141_ref_shoreline.geojson<br> us_county142_ref_shoreline.geojson<br> us_county143_ref_shoreline.geojson<br> us_county144_ref_shoreline.geojson<br> us_county145_ref_shoreline.geojson<br> us_county146_ref_shoreline.geojson<br> us_county147_ref_shoreline.geojson<br> us_county148_ref_shoreline.geojson<br> us_county149_ref_shoreline.geojson<br> us_county150_ref_shoreline.geojson<br> us_county151_ref_shoreline.geojson<br> us_county152_ref_shoreline.geojson<br> us_county153_ref_shoreline.geojson<br> us_county154_ref_shoreline.geojson<br> us_county155_ref_shoreline.geojson<br> us_county156_ref_shoreline.geojson<br> us_county157_ref_shoreline.geojson<br> us_county1585_ref_shoreline.geojson<br> us_county158_ref_shoreline.geojson<br> us_county1596_ref_shoreline.geojson<br> us_county159_ref_shoreline.geojson<br> us_county1605_ref_shoreline.geojson<br> us_county160_ref_shoreline.geojson<br> us_county161_ref_shoreline.geojson<br> us_county162_ref_shoreline.geojson<br> us_county163_ref_shoreline.geojson<br> us_county1649_ref_shoreline.geojson<br> us_county164_ref_shoreline.geojson<br> us_county1650_ref_shoreline.geojson<br> us_county1651_ref_shoreline.geojson<br> us_county1653_ref_shoreline.geojson<br> us_county1655_ref_shoreline.geojson<br> us_county1658_ref_shoreline.geojson<br> us_county165_ref_shoreline.geojson<br> us_county1667_ref_shoreline.geojson<br> us_county166_ref_shoreline.geojson<br> us_county167_ref_shoreline.geojson<br> us_county168_ref_shoreline.geojson<br> us_county169_ref_shoreline.geojson<br> us_county170_ref_shoreline.geojson<br> us_county171_ref_shoreline.geojson<br> us_county172_ref_shoreline.geojson<br> us_county173_ref_shoreline.geojson<br> us_county174_ref_shoreline.geojson<br> us_county175_ref_shoreline.geojson<br> us_county176_ref_shoreline.geojson<br> us_county177_ref_shoreline.geojson<br> us_county178_ref_shoreline.geojson<br> us_county179_ref_shoreline.geojson<br> us_county180_ref_shoreline.geojson<br> us_county181_ref_shoreline.geojson<br> us_county182_ref_shoreline.geojson<br> us_county183_ref_shoreline.geojson<br> us_county184_ref_shoreline.geojson<br> us_county185_ref_shoreline.geojson<br> us_county186_ref_shoreline.geojson<br> us_county187_ref_shoreline.geojson<br> us_county188_ref_shoreline.geojson<br> us_county189_ref_shoreline.geojson<br> us_county190_ref_shoreline.geojson<br> us_county191_ref_shoreline.geojson<br> us_county192_ref_shoreline.geojson<br> us_county193_ref_shoreline.geojson<br> us_county194_ref_shoreline.geojson<br> us_county195_ref_shoreline.geojson<br> us_county196_ref_shoreline.geojson<br> us_county197_ref_shoreline.geojson<br> us_county198_ref_shoreline.geojson<br> us_county199_ref_shoreline.geojson<br> us_county200_ref_shoreline.geojson<br> us_county201_ref_shoreline.geojson<br> us_county202_ref_shoreline.geojson<br> us_county203_ref_shoreline.geojson<br> us_county204_ref_shoreline.geojson<br> us_county205_ref_shoreline.geojson<br> us_county206_ref_shoreline.geojson<br> us_county207_ref_shoreline.geojson<br> us_county208_ref_shoreline.geojson<br> us_county209_ref_shoreline.geojson<br> us_county210_ref_shoreline.geojson<br> us_county2118_ref_shoreline.geojson<br> us_county211_ref_shoreline.geojson<br> us_county2125_ref_shoreline.geojson<br> us_county2127_ref_shoreline.geojson<br> us_county2128_ref_shoreline.geojson<br> us_county212_ref_shoreline.geojson<br> us_county2130_ref_shoreline.geojson<br> us_county2133_ref_shoreline.geojson<br> us_county213_ref_shoreline.geojson<br> us_county2148_ref_shoreline.geojson<br> us_county2149_ref_shoreline.geojson<br> us_county214_ref_shoreline.geojson<br> us_county215_ref_shoreline.geojson<br> us_county216_ref_shoreline.geojson<br> us_county217_ref_shoreline.geojson<br> us_county218_ref_shoreline.geojson<br> us_county219_ref_shoreline.geojson<br> us_county220_ref_shoreline.geojson<br> us_county221_ref_shoreline.geojson<br> us_county222_ref_shoreline.geojson<br> us_county223_ref_shoreline.geojson<br> us_county224_ref_shoreline.geojson<br> us_county225_ref_shoreline.geojson<br> us_county226_ref_shoreline.geojson<br> us_county227_ref_shoreline.geojson<br> us_county228_ref_shoreline.geojson<br> us_county229_ref_shoreline.geojson<br> us_county230_ref_shoreline.geojson<br> us_county231_ref_shoreline.geojson<br> us_county232_ref_shoreline.geojson<br> us_county233_ref_shoreline.geojson<br> us_county234_ref_shoreline.geojson<br> us_county235_ref_shoreline.geojson<br> us_county236_ref_shoreline.geojson<br> us_county237_ref_shoreline.geojson<br> us_county238_ref_shoreline.geojson<br> us_county239_ref_shoreline.geojson<br> us_county240_ref_shoreline.geojson<br> us_county241_ref_shoreline.geojson<br> us_county242_ref_shoreline.geojson<br> us_county243_ref_shoreline.geojson<br> us_county244_ref_shoreline.geojson<br> us_county245_ref_shoreline.geojson<br> us_county246_ref_shoreline.geojson<br> us_county2478_ref_shoreline.geojson<br> us_county247_ref_shoreline.geojson<br> us_county248_ref_shoreline.geojson<br> us_county2493_ref_shoreline.geojson<br> us_county249_ref_shoreline.geojson<br> us_county250_ref_shoreline.geojson<br> us_county2517_ref_shoreline.geojson<br> us_county2518_ref_shoreline.geojson<br> us_county251_ref_shoreline.geojson<br> us_county252_ref_shoreline.geojson<br> us_county2532_ref_shoreline.geojson<br> us_county253_ref_shoreline.geojson<br> us_county254_ref_shoreline.geojson<br> us_county255_ref_shoreline.geojson<br> us_county256_ref_shoreline.geojson<br> us_county257_ref_shoreline.geojson<br> us_county258_ref_shoreline.geojson<br> us_county259_ref_shoreline.geojson<br> us_county260_ref_shoreline.geojson<br> us_county261_ref_shoreline.geojson<br> us_county262_ref_shoreline.geojson<br> us_county263_ref_shoreline.geojson<br> us_county264_ref_shoreline.geojson<br> us_county265_ref_shoreline.geojson<br> us_county266_ref_shoreline.geojson<br> us_county267_ref_shoreline.geojson<br> us_county2688_ref_shoreline.geojson<br> us_county268_ref_shoreline.geojson<br> us_county269_ref_shoreline.geojson<br> us_county272_ref_shoreline.geojson<br> us_county2749_ref_shoreline.geojson<br> us_county275_ref_shoreline.geojson<br> us_county276_ref_shoreline.geojson<br> us_county277_ref_shoreline.geojson<br> us_county278_ref_shoreline.geojson<br> us_county279_ref_shoreline.geojson<br> us_county280_ref_shoreline.geojson<br> us_county281_ref_shoreline.geojson<br> us_county282_ref_shoreline.geojson<br> us_county283_ref_shoreline.geojson<br> us_county284_ref_shoreline.geojson<br> us_county285_ref_shoreline.geojson<br> us_county286_ref_shoreline.geojson<br> us_county287_ref_shoreline.geojson<br> us_county288_ref_shoreline.geojson<br> us_county289_ref_shoreline.geojson<br> us_county290_ref_shoreline.geojson<br> us_county291_ref_shoreline.geojson<br> us_county292_ref_shoreline.geojson<br> us_county293_ref_shoreline.geojson<br> us_county294_ref_shoreline.geojson<br> us_county295_ref_shoreline.geojson<br> us_county296_ref_shoreline.geojson<br> us_county297_ref_shoreline.geojson<br> us_county298_ref_shoreline.geojson<br> us_county299_ref_shoreline.geojson<br> us_county300_ref_shoreline.geojson<br> us_county301_ref_shoreline.geojson<br> us_county302_ref_shoreline.geojson<br> us_county303_ref_shoreline.geojson<br> us_county304_ref_shoreline.geojson<br> us_county305_ref_shoreline.geojson<br> us_county3069_ref_shoreline.geojson<br> us_county306_ref_shoreline.geojson<br> us_county307_ref_shoreline.geojson<br> us_county308_ref_shoreline.geojson<br> us_county309_ref_shoreline.geojson<br> us_county310_ref_shoreline.geojson<br> us_county311_ref_shoreline.geojson<br> us_county312_ref_shoreline.geojson<br> us_county313_ref_shoreline.geojson<br> us_county314_ref_shoreline.geojson<br> us_county315_ref_shoreline.geojson<br> us_county316_ref_shoreline.geojson<br> us_county317_ref_shoreline.geojson<br> us_county318_ref_shoreline.geojson<br> us_county319_ref_shoreline.geojson<br> us_county320_ref_shoreline.geojson<br> us_county321_ref_shoreline.geojson<br> us_county322_ref_shoreline.geojson<br> us_county323_ref_shoreline.geojson<br> us_county324_ref_shoreline.geojson<br> us_county325_ref_shoreline.geojson<br> us_county326_ref_shoreline.geojson<br> us_county327_ref_shoreline.geojson<br> us_county328_ref_shoreline.geojson<br> us_county329_ref_shoreline.geojson<br> us_county330_ref_shoreline.geojson<br> us_county331_ref_shoreline.geojson<br> us_county332_ref_shoreline.geojson<br> us_county333_ref_shoreline.geojson<br> us_county334_ref_shoreline.geojson<br> us_county335_ref_shoreline.geojson<br> us_county336_ref_shoreline.geojson<br> us_county337_ref_shoreline.geojson<br> us_county338_ref_shoreline.geojson<br> us_county339_ref_shoreline.geojson<br> us_county340_ref_shoreline.geojson<br> us_county341_ref_shoreline.geojson<br> us_county342_ref_shoreline.geojson<br> us_county343_ref_shoreline.geojson<br> us_county344_ref_shoreline.geojson<br> us_county345_ref_shoreline.geojson<br> us_county346_ref_shoreline.geojson<br> us_county347_ref_shoreline.geojson<br> us_county348_ref_shoreline.geojson<br> us_county349_ref_shoreline.geojson<br> us_county350_ref_shoreline.geojson<br> us_county351_ref_shoreline.geojson<br> us_county352_ref_shoreline.geojson<br> us_county353_ref_shoreline.geojson<br> us_county354_ref_shoreline.geojson<br> us_county416_ref_shoreline.geojson<br> us_county417_ref_shoreline.geojson<br> us_county418_ref_shoreline.geojson<br> us_county419_ref_shoreline.geojson<br> us_county420_ref_shoreline.geojson<br> us_county421_ref_shoreline.geojson<br> us_county422_ref_shoreline.geojson<br> us_county423_ref_shoreline.geojson<br> us_county685_ref_shoreline.geojson<br> us_county686_ref_shoreline.geojson<br> us_county700_ref_shoreline.geojson<br> us_county73_ref_shoreline.geojson<br> us_county74_ref_shoreline.geojson<br> us_county754_ref_shoreline.geojson<br> us_county79_ref_shoreline.geojson<br> us_county97_ref_shoreline.geojson</p>
Annex 2: Supplementary Tables to 'Understanding Chinese hamster translation at sub-codon resolution'
<p>Annex 2 to PhD thesis titled 'Understanding Chinese hamster ovary cell translation at sub-codon resolution'. </p>
Model output data for Smith et al., "Effects of increasing the category resolution of the sea ice thickness distribution in a coupled climate model on Arctic and Antarctic sea ice"
<p>Model output data for Smith et al., "Effects of increasing the category resolution of the sea ice thickness distribution in a coupled climate model on Arctic and Antarctic sea ice", in review in Journal of Geophysical Research-Oceans, 2022. Details on CESM model settings and run setups can be found within the manuscript. </p>
Global daily high spatial resolution XCO2 product during 2019-2021
<p>Validation results are in preparation. The full dataset will be uploaded after the acceptance of our paper.</p>
Water isotopic composition (Oxygen-18 and Deuterium) from the EPICA Dome C ice core at 11 cm resolution
<p>This manuscript presents a compilation of high resolution (11 cm) water isotopic records including published and new measurements over the last 800 000 years on the EPICA Dome C ice core, Antarctica. Using this new water isotopes (δ18O and δD) combined dataset, we study the variability and possible influence of diffusion at multi-decadal to multi-centennial scale. We observe a stronger variability on the onset of the interglacial interval corresponding to a warm period.</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>
Datasets for Lorentz electron ptychography towards sub-nanometer resolution imaging of magnetic textures
<p>These data sets are the raw experimental data used in a Letter titled, Lorentz electron ptychography for imaging magnetic textures beyond the diffraction limit published on Nature Nanotechnology. The related paper should be cited whenever the datasets are used.</p> <p>Reference:</p> <p>Zhen Chen, Emrah Turgut, Yi Jiang, Kayla X. Nguyen, Matthew J. Stolt, Song Jin, Daniel C. Ralph, Gregory D. Fuchs, David A. Muller, Lorentz electron ptychography for imaging magnetic textures beyond the diffraction limit. Nature Nanotechnology, in press, https://doi.org/10.1038/s41565-022-01224-y (2022).</p> <p>The file format is Matlab's *.mat file with version 7.3.</p> <p>The diffraction patterns are stored as the variable 'cbed'.</p> <p>Experimental conditions can be found in data_info.txt and the related paper.</p> <p> </p>
NUUR-Texture500: A Diverse Dataset of High Resolution Homogeneous Textures
<p>"NUUR-Texture500" is a dataset of diverse texture images created to facilitate research on texture analysis and synthesis. The textures in this dataset are spatially homogeneous, ranging from regular to stochastic, typically containing repeated elements with random variations in position, shape, orientation and color. For a detailed description of the dataset construction and contents, readers should refer to the following paper:</p> <p>Jue Lin, Gaurav Sharma, Thrasyvoulos N. Pappas, "Towards Universal Texture Synthesis by Combining Texton Broadcasting with Noise Injection in StyleGAN-2", Journal of e-Prime - Advances in Electrical Engineering, Electronics and Energy (3) (2023), https://doi.org/10.1016/j.prime.2022.100092</p> <p>Permission to copy and use this dataset for noncommercial use is hereby granted provided this notice is retained in all copies and the dataset distribution and the paper mentioned below are clearly cited.</p> <p>Contacts:<br> Jue Lin: jue.lin@u.northwestern.edu<br> Gaurav Sharma: gaurav.sharma@rochester.edu<br> Thrasyvoulos N. Pappas: pappas@ece.northwestern.edu</p> <p>Disclaimer: </p> <p>The dataset is provided "as is" with ABSOLUTELY NO WARRANTY expressed or implied. Use at your own risk.</p> <p>Acknowledgment: </p> <p>The NUUR-Texture500 texture images are curated from a number of publicly accessible sources. We acknowledge and thank the original sites for their contributions:<br> Flickr www.flickr.com<br> NeedPix www.needpix.com<br> Pexels www.pexels.com<br> PickUpImage www.pickupimage.com<br> Pixabay www.pixabay.com<br> PublicDomainPictures www.publicdomainpictures.net<br> RawPixel www.rawpixel.com<br> Unsplash www.unsplash.com<br> WikimediaCommons commons.wikimedia.org</p> <p> </p>
High resolution shotgun metagenomics: the more data, the better?
<p>This data archive contains results generated using a high resolution shotgun metagenomics (HRSM) bioinformatic pipeline (ShotgunMG - https://jtremblay.github.io/shotgunmg.html) for the following projects:</p> <p>Human gut microbiome dataset: PRJNA588513</p> <p>Antarctic soil dataset: PRJNA513362</p> <p>Agricultural soil dataset: PRJNA513362</p> <p>Mock communities: PRJNA873699</p> <p>These analyses were performed in the context of evaluating if shallow shotgun metagenomic sequencing is an adequate approach to analyze SM sequencing data using a HRSM pipeline.</p> <p>Briefly, these PRJNA projects were analyzed using identical bioinformatic procedures, but using various initial raw sequencing data loads.</p> <p>Notable end results in each archive includes: de novo co-assembly (fasta files), contigs and genes abubance matrices. Beta-diversity (Bray-Curtis dissimilarity matrices), alpha diversity (richness, chao1, Simpson and Shannon indexes matrices), taxonomic summaries from the kingdom to up to the species level and Metagenome Assembled Genomes (MAGs).</p>
Text-fig. 4. Graphical visualization of Phytogeographic Reference Regions Assessment (PRRA) of nearest living relative genera of fossil-taxa from late Early Miocene Wiesa assemblage in eastern Germany. Analysis yields only NLRs which have modern distribution area (partly) in E and SE Asia. For relationships of fossil-taxa to nearest living relatives or ecological equivalents, see Tab. 6; taxa used for analysis marked with asterisks. Three geographic resolutions conducted: a – grid with 1.5° latitude/longitude resolution, b – grid with 2°, c – grid with 3°; similarity column indicates cooccurrences of genera of nearest living relatives in single grid box. Maximum value in our analysis: grid box marked with arrow in map a, located in western Yunnan Province, P. R. China and southern Kachin Province, NE Myanmar (east of Myitkyina city), area with 97.371 7–98.874 2° longitude and 24.586 7–25.837 5° latitude, yields 23 co-occurring species of 13 genera (Tab. 7). in Assessment Of Phytogeographic Reference Regions For Cenozoic Vegetation: A Case Study On The Miocene Flora Of Wiesa (Germany)
Text-fig. 4. Graphical visualization of Phytogeographic Reference Regions Assessment (PRRA) of nearest living relative genera of fossil-taxa from late Early Miocene Wiesa assemblage in eastern Germany. Analysis yields only NLRs which have modern distribution area (partly) in E and SE Asia. For relationships of fossil-taxa to nearest living relatives or ecological equivalents, see Tab. 6; taxa used for analysis marked with asterisks. Three geographic resolutions conducted: a – grid with 1.5° latitude/longitude resolution, b – grid with 2°, c – grid with 3°; similarity column indicates cooccurrences of genera of nearest living relatives in single grid box. Maximum value in our analysis: grid box marked with arrow in map a, located in western Yunnan Province, P. R. China and southern Kachin Province, NE Myanmar (east of Myitkyina city), area with 97.371 7–98.874 2° longitude and 24.586 7–25.837 5° latitude, yields 23 co-occurring species of 13 genera (Tab. 7).
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.