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FIG. 3 in A cool-water bryozoan association from the La Pola Formation (Sandbian, Ordovician) of Argentine Precordillera
FIG. 3. — Microfacies of the main bryozoan-bearing rocks (lenticular quartz-bioclastic-rich sandstones) containing fragmented bryozoans (PS, Pseudostictoporella simplex n. sp.; AB, Albardonia bifoliata n. gen., n. sp.; LV, Lunaferamita virginiensis Utgaard, 1981; HE, Heterotrypa enodis n. sp.), brachiopod shells, crinoids, calcareous algae (CA), as well as lithoclasts in the siliciclastic matrix; A, CEGH-UNC 27520 b; B, CEGH-UNC 27530 e. Scale bars: 5 mm.
FIG. 2. — A in A cool-water bryozoan association from the La Pola Formation (Sandbian, Ordovician) of Argentine Precordillera
FIG. 2. — A, stratigraphic chart showing main Ordovician units (including La Pola Formation) of the Argentinean Precordillera disposed according north-south distribution; B, lithological column of the La Pola section in the Villicum range.
FIG. 10 in High diversity of pimpline parasitoid wasps (Hymenoptera, Ichneumonidae, Pimplinae) from the lowermost Eocene Fur Formation (Denmark)
FIG. 10. — Holotype of Xanthopimpla ciboisae n. sp. (FUR-10046), photograph and interpretative drawing, where dotted lines represent uncertain and/or interpolated interpretations. Scale bar: 2 mm.
FIG. 7 in High diversity of pimpline parasitoid wasps (Hymenoptera, Ichneumonidae, Pimplinae) from the lowermost Eocene Fur Formation (Denmark)
FIG. 7. — Specimens of Epitheronia stigmatica (Henriksen, 1922), n. comb.: A, C, holotype, deposited at the Natural History Museum in Copenhagen; B, D, specimen MOL-MM-3141. Photographs (A, B), detail (E) and interpretative drawings (B, D), where dotted lines represent uncertain and/or interpolated interpretations. Scale bars: 2 mm.
FIG. 4 in High diversity of pimpline parasitoid wasps (Hymenoptera, Ichneumonidae, Pimplinae) from the lowermost Eocene Fur Formation (Denmark)
FIG. 4. — Holotype of Crusopimpla minuta n. sp. (FUR-13076), photograph (A), detail (B) and interpretative drawing (C), where dotted lines represent uncertain and/or interpolated interpretations. Scale bars: A, C, 1 mm; B, 0.5 mm.
FIG. 3 in High diversity of pimpline parasitoid wasps (Hymenoptera, Ichneumonidae, Pimplinae) from the lowermost Eocene Fur Formation (Denmark)
FIG. 3. — Holotype of Crusopimpla elongata n. sp. (FUR-11220), photograph (A) and interpretative drawing (B), where dotted lines represent uncertain and/or interpolated interpretations. Scale bar: 1 mm.
FIG. 9 in High diversity of pimpline parasitoid wasps (Hymenoptera, Ichneumonidae, Pimplinae) from the lowermost Eocene Fur Formation (Denmark)
FIG. 9. — Holotype of Theronia? nigriscutum n. sp. (MOL-MHM-5412), photograph (A) and interpretative drawing (B), where dotted lines represent uncertain and/ or interpolated interpretations. Scale bar: 2 mm.
FIG. 2 in High diversity of pimpline parasitoid wasps (Hymenoptera, Ichneumonidae, Pimplinae) from the lowermost Eocene Fur Formation (Denmark)
FIG. 2. — Holotype of Crusopimpla collina n. sp. (FUR-14680), photograph (A), detail (B) and interpretative drawing (C), where dotted lines represent uncertain and/or interpolated interpretations; D, wing vein names on photographs of the extant Darwin wasp Rhimphoctona obscuripes (Holmgren, 1860). Scale bars: A, C, 2 mm; B, 200 µm.
FIG. 8 in High diversity of pimpline parasitoid wasps (Hymenoptera, Ichneumonidae, Pimplinae) from the lowermost Eocene Fur Formation (Denmark)
FIG. 8. — Holotype of Theronia? furensis n. sp. (FUR-11207), photograph (A) and interpretative drawing (B), where dotted lines represent uncertain and/or interpolated interpretations. Blue lines indicate a fossil Diptera which is partly overlaying the Darwin wasp fossil. One fore and the two hind wings have been moved up and down, respectively, for clarity. Scale bar: 1 mm.
FIG. 11 in High diversity of pimpline parasitoid wasps (Hymenoptera, Ichneumonidae, Pimplinae) from the lowermost Eocene Fur Formation (Denmark)
FIG. 11. — Holotype of Xanthopimpla crescendae n. sp. (FUR-11214), photograph (A) with details and interpretative drawing (B), where dotted lines represent uncertain and/or interpolated interpretations. Scale bar: 1 mm.
FIG. 1 in High diversity of pimpline parasitoid wasps (Hymenoptera, Ichneumonidae, Pimplinae) from the lowermost Eocene Fur Formation (Denmark)
FIG. 1. — Examples of colour preservation in pimpline Darwin wasps from the Fur Formation: A, paratype FUR-13817 of Crusopimpla violina n. sp.; B, paratype FUR-11216 of C. violina n. sp.; C, paratype FUR- 11219 of C. rettigi n. sp.; D, paratype FUR-13065 of Xanthopimpla crescendae n. sp.; E, paratype FUR-10918 of X. crescendae n. sp.; F, specimen FUR-12707 of Epitheronia stigmatica (Henriksen, 1922), n. comb. Scale bars: 1 mm.
FIG. 6 in High diversity of pimpline parasitoid wasps (Hymenoptera, Ichneumonidae, Pimplinae) from the lowermost Eocene Fur Formation (Denmark)
FIG. 6. — Holotype of Crusopimpla violina n. sp. (FUR-13061), photograph (A) and interpretative drawing (B), where dotted lines represent uncertain and/or interpolated interpretations. Scale bar: 2 mm.
FIG. 5 in High diversity of pimpline parasitoid wasps (Hymenoptera, Ichneumonidae, Pimplinae) from the lowermost Eocene Fur Formation (Denmark)
FIG. 5. — Holotype of Crusopimpla rettigi n. sp. (FUR-11209), photograph (A) and interpretative drawing (B), where dotted lines represent uncertain and/or interpolated interpretations. Scale bar: 2 mm.
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>
Dataset to "Hydride Formation Diminishes CO2 Reduction Rate on Palladium"
<p>Dataset to "Hydride Formation Diminishes CO2 Reduction Rate on Palladium" as published in ChemPhysChem, 20 (2019), 1398-1403</p>
SuperDARN data in netCDF format (1994-Apr)
<p>1994-Apr SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.</p><p>The RST is available here: https://github.com/SuperDARN/rst</p><p>The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.</p><p>Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (<a href="http://vt.superdarn.org/tiki-index.php?page=Radar+Overview">SuperDARN Radar Overview</a>)</p><p><strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong></p><p>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'</p>
SuperDARN data in netCDF format (1993-Nov)
<p>1993-Nov SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.</p><p>The RST is available here: https://github.com/SuperDARN/rst</p><p>The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.</p><p>Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (<a href="http://vt.superdarn.org/tiki-index.php?page=Radar+Overview">SuperDARN Radar Overview</a>)</p><p><strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong></p><p>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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