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12,346 results for “derivatives”
CLDF dataset derived from Sawka et al.'s "Toka-Leya of Zambia" from 2019
<p>Cite the source of the dataset as:</p> <blockquote> <p>Sawka, Kenneth S. and Mbewe, Christopher and Josephat, Daka and Schwertfeger, Jacob (2019): Toka-Leya of Zambia: Southern Province: Livingstone, Kazungula, Zimba, and Kalomo District. SIL International.</p> </blockquote>
Figure 5.(a)Linear (y=0.45x + 57.74) dose-response relationship between plasma propranolol to % β- adrenergeric blockade derived from healthy study participants and translate into patients with angina pectoris. This image has been adapted from(Pine et al., 1975).-The Brain and Propranolol Pharmacokinetics in the Elderly
<p>Apharmacodynamic model,with parameters in the table below, may be used to visualize the<br> propranolol concentration-effect (β-blockade) relationship in patients suffering from angina pectoris.<br> These results have been adapted from the Pine et al article published in Circulation in 1975 which<br> identified a linear relationship plasma Propranolol (ng/mL) to an effect of % β-Adrenergic Blockade<br> in a single-oral dose of 40mg Propranolol in exercising individuals (Pine et al., 1975).</p>
Datasets for "Assessing satellite derived radiative forcing from snow impurities through inverse hydrologic modeling"
<p>This dataset contains observations and model output used in </p> <p>Matt, F. N., & Burkhart, J. F. (2018). Assessing satellite-derived radiative forcing from snow impurities through inverse hydrologic modeling. Geophysical Research Letters, 45. https://doi.org/10.1002/2018GL077133</p>
Space time cube for precipitation derived from 24h hours of metereological radar data
<p>The animation depicts a 24h space-time cube derived from radar-metereologic data recorded by the x-band radar station of the South African Weather Service for the Liebenbergvlei in the Freestate, South Africa on December 31 2001. The temporal resolution (z) is 5 Minutes, starting on 2001-12-31 00:00:00 hours, ending on 2001-12-31 23:55:00 hours. Spatial resulution please see. The spatial resolution (xy) is 1km, covering a radius of 200km from the radar station. Tempospatial zones of weak precispitation are colored in blue, zones of severe precipitation are colored in yellow.</p> <p>Data processing was done in GRASS v6.x, visualisation was done in Paraview.</p>
Molecular and functional variation in iPSC-derived sensory neurons
<p>These files contain summary statistics for gene expression QTLs and splicing QTLs from an RNA-sequencing study of iPSC-derived sensory neurons from 97 individuals.</p>
Annual minima and maxima of present day and future discharge, derived from PCR-GLOBWB
<p>Global dataset of annual minima and maxima for major river system in the world. Dataset provides the present day and 2C warming simulation derived from a combination of the Global Hydrological Model, PCR-GLOBWB (https://doi.org/10.5194/gmd-11-2429-2018) and the EC-EARTH Global Climate Model (https://doi.org/10.1007/s00382-011-1228-5). The large-ensemble dataset is part of the HiWAVES3 project (https://www.knmi.nl/research/weather-climate-models/projects/hiwaves3)</p>
A high-frequency and high-resolution image time series of the Gornergletscher - Swiss Alps - derived from repeated UAV surveys
<p>This dataset is based on aerial photographs of the Gornergletscher glacial system (Switzerland) collected during ten intensive UAV surveys carried out approximately every two weeks throughout the summer 2017.</p> <p>The final products consist in a series of 10 cm resolution ortho-images, Digital Elevation Models of the glacier surface, and Matching Maps that can be used to quantify ice surface displacements.</p>
Data for "On the impact of Citizen Science-derived data quality on deep learning based classification in marine images"
<p>This dataset contains all the annotations done by either citizen scientists or experts of the publication: "On the impact of Citizen Science-derived data quality on deep learning based classification in marine images"</p> <p><strong>CSP.csv</strong> -> CS annotations of the Citizen Science Primer-experiment</p> <p><strong>CSPExpert.csv</strong> -> Expert annotations of the Citizen Science Primer-experiment</p> <p><strong>CSS.csv</strong> -> CS annotations of the Citizen Science Study</p> <p><strong>CSSExpert.csv</strong> -> Expert annotations of the Citizen Science Study</p> <p>Visual exploration of the image data is possible in the BIIGLE 2.0 image annotation system at <a href="https://biigle.de/projects/139">https://biigle.de/projects/159</a> using the login <em><a href="mailto:cs@example.com">cs@example.com</a></em> and the password <em>plosonecs</em>.</p>
Orthophotos and DSMs derived from RPAS flights over the nature reserve Zwin in Flanders, Belgium
<p><strong>Study area</strong></p> <p>The Zwin is a nature reserve situated along the Belgian North Sea coast, northeast of Knokke, in the province of West-Flanders, Flanders, Belgium. The area is managed by the Flemish Agency for Nature and Forest and consists of a tidal marsh, coastal dunes with <em>Ammophila arenaria</em>, dune grasslands and/or shrub (<em>Hippophae rhamnoides</em>, <em>Salix repens</em>), and a transitional grassland zone that stretches from the inner edge of the coastal dunes into the polders.</p> <p><strong>Data collection</strong></p> <p>Data were collected by the <a href="http://www.inbo.be/en">Research Institute for Nature and Forest (INBO)</a> with a fixed wing drone Gatewing X100 in 2014 and 2015 (15 flights). RGB data were acquired using an off-the-shelf Ricoh GR Digital IV camera, with the following image bands: 1: red, 2: green, 3: blue, 4: alpha channel. CIR (color-infrared) data were acquired using a NIR-enabled Ricoh GR Digital IV camera, with the following info bands: 1: NIR, 2: red, 3: green, 4: alpha channel.</p> <p><strong>Data processing</strong></p> <p>The raw data were processed to Digital Surface Models and orthophotos by the <a href="https://vito.be/en">Flemish Institute for Technological Research (VITO)</a> in 2017. Images with coarse GPS coordinates were imported and processed in Agisoft PhotoScan Pro 1.4.x, a structure-from-motion (SfM) based photogrammetry software program. After extraction and matching of tie points, a bundle adjustment leads to a sparse point cloud and a refined set of camera position and orientation values. Ground control points (either artificially installed markers on the terrain, or other photo-identifiable points, measured on the ground with RTK GNSS) were used to further refine the camera calibration and obtain a pixel-level georeferencing accuracy. From there, a point cloud densification and classification into ground and non-ground points was performed, leading to a rasterized digital surface model (DSM) and digital terrain model (DTM). Finally, a true orthomosaic was projected onto the DTM.</p> <p><strong>Coordinate reference system</strong></p> <p>All geospatial data have the coordinate reference system <code>EPSG:31370 - Belgian Lambert 72</code>.</p> <p><strong>Files</strong></p> <ul> <li><strong>Raw flight data</strong>: images and logs collected by the drone during flight. These files are zipped per flight, with the date (<code>yyyymmdd</code>) and flight number (<code>x</code>) indicated in the file name (<code>flight_yyyymmdd_Zwin_x.zip</code>).</li> <li><strong>Processed data</strong>: Digital Surface Models (<code>filename_DSM.tif</code>) and orthophotos (<code>filename_Ortho.tif</code>) stitched together from the raw data. The included flights are indicated in the file name (e.g. 6 flights for <code>20150709_Zwin_1-3_20150710_Zwin_1-3_DSM.tif</code>).</li> <li><strong>Ground control points</strong>: fixed ground control points (GCP) were placed on 2014-04-07, coordinates of which are available in <code>GCP_20140407_Zwin_fixed.tsv</code>. These GCPs are visible (but fading over time) in all orthophotos except <code>20151012_Zwin_1-4_Ortho.tif</code> which covers a different area. Additional temporary GCPs were placed on 2014-04-07, 2014-04-10 and 2015-07-09 (visible in orthophotos of those dates), coordinates of which are available in the respective <code>GCP_yyyymmdd_Zwin.tsv</code> file.</li> </ul> <p><strong>Cloud Optimized GeoTIFF</strong></p> <p>The most efficient way to explore the processed data is by loading the <a href="https://www.cogeo.org/">Cloud Optimized GeoTIFFs</a> we created for each processed file. Copy one of the file URLs below and follow e.g. the <a href="https://www.cogeo.org/qgis-tutorial.html">QGIS tutorial</a> to load this type of file.</p> <ul> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20140407_Zwin_1-2_DSM.tif</code></li> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20140407_Zwin_1-2_Ortho.tif</code> CIR</li> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20140410_Zwin_1-3_DSM.tif</code></li> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20140410_Zwin_1-3_Ortho.tif</code> CIR</li> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20150709_Zwin_1-3_20150710_Zwin_1-3_DSM.tif</code></li> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20150709_Zwin_1-3_20150710_Zwin_1-3_Ortho.tif</code> RGB</li> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20151012_Zwin_1-4_DSM.tif</code></li> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20151012_Zwin_1-4_Ortho.tif</code> RGB</li> </ul> <p>See <a href="https://s3-eu-west-1.amazonaws.com/lw-remote-sensing/index.html">this page</a> for an overview of public INBO RPAS data.</p>
Orthophotos and DSMs/DTM derived from RPAS flights over the nature reserve Landschap De Liereman in Flanders, Belgium
<p><strong>Study area</strong></p> <p>Landschap De Liereman is a nature reserve situated in Oud-Turnhout, in the province of Antwerp, Flanders, Belgium. The area is managed by the nature conservation NGO Natuurpunt and consists of a diverse landscape of wet and dry heathlands, <em>Nardus</em> grasslands on siliceous soils, forests and transition mires, as well as some remaining arable fields and high-intensity agricultural grasslands.</p> <p><strong>Data collection</strong></p> <p>Data were collected by the <a href="http://www.inbo.be/en">Research Institute for Nature and Forest (INBO)</a> with a fixed wing drone Gatewing X100 in 2015 (7 flights). RGB data were acquired using an off-the-shelf Ricoh GR Digital IV camera, with the following image bands: 1: red, 2: green, 3: blue, 4: alpha channel.</p> <p>An additional flight campaign was commissioned by INBO and carried out by the <a href="https://vito.be/en">Flemish Institute for Technological Research (VITO)</a> in 2017 with a fixed wing drone SenseFly eBee. Multispectral data were acquired using a Parrot Sequoia camera, with the following image bands: 1: green, 2: red, 3: red edge, 4: NIR, 5: alpha channel. with a Parrot Sequoia camera. Raw data for this flight campaign are not available.</p> <p><strong>Data processing</strong></p> <p>The raw data were processed to Digital Surface Models and orthophotos by VITO in 2017. For the 2017 campaign by VITO, the outputs also include a Digital Terrain Model (DTM). Images with coarse GPS coordinates were imported and processed in Agisoft PhotoScan Pro 1.4.x, a structure-from-motion (SfM) based photogrammetry software program. After extraction and matching of tie points, a bundle adjustment leads to a sparse point cloud and a refined set of camera position and orientation values. From there, a point cloud densification and classification into ground and non-ground points was performed, leading to a rasterized digital surface model (DSM) and digital terrain model (DTM). Finally, a true orthomosaic was projected onto the DTM.</p> <p><strong>Coordinate reference system</strong></p> <p>All geospatial data have the coordinate reference system <code>EPSG:31370 - Belgian Lambert 72</code>.</p> <p><strong>Files</strong></p> <ul> <li><strong>Raw flight data</strong>: images and logs collected by the drone during flight. These files are zipped per flight, with the date (<code>yyyymmdd</code>) and flight number (<code>x</code>) indicated in the file name (<code>flight_yyyymmdd_Liereman_x.zip</code>). Raw data for the eBee flights are not available.</li> <li><strong>Processed data</strong>: Digital Surface Models (<code>filename_DSM.tif</code>) and orthophotos (<code>filename_Ortho.tif</code>) stitched together from the raw data. The included flights are indicated in the file name (7 flights for <code>20151008_Liereman_1-4_20151009_Liereman_1-3_DSM.tif)</code>.</li> <li><strong>Ground control points</strong>: not applicable for this dataset.</li> </ul> <p><strong>Cloud Optimized GeoTIFF</strong></p> <p>The most efficient way to explore the processed data is by loading the <a href="https://www.cogeo.org/">Cloud Optimized GeoTIFFs</a> we created for each processed file. Copy one of the file URLs below and follow e.g. the <a href="https://www.cogeo.org/qgis-tutorial.html">QGIS tutorial</a> to load this type of file.</p> <ul> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20151008_Liereman_1-4_20151009_Liereman_1-3_DSM.tif</code></li> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20151008_Liereman_1-4_20151009_Liereman_1-3_Ortho.tif</code> RGB</li> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20170718_Liereman_eBee_DSM.tif</code></li> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20170718_Liereman_eBee_DTM.tif</code></li> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20170718_Liereman_eBee_Ortho.tif</code> multispec</li> </ul> <p>See <a href="https://s3-eu-west-1.amazonaws.com/lw-remote-sensing/index.html">this page</a> for an overview of public INBO RPAS data.</p>
Orthophotos and DSMs derived from RPAS flights over wild boar damaged fields in Eigenbilzen in Flanders, Belgium
<p><strong>Study area</strong></p> <p>The study area in Eigenbilzen is situated in the agricultural zone east of the locality of Eigenbilzen, in the province of Limburg, Flanders, Belgium. The flights picture a wheat field where damage by wild boar is apparent.</p> <p><strong>Data collection</strong></p> <p>Data were collected by the <a href="https://www.inbo.be/en">Research Institute for Nature and Forest (INBO)</a> with a fixed wing drone Gatewing X100 in 2015 (2 flights). RGB data were acquired using an off-the-shelf Ricoh GR Digital IV camera, with the following image bands: 1: red, 2: green, 3: blue, 4: alpha channel. CIR (color-infrared) data were acquired using a NIR-enabled Ricoh GR Digital IV camera, with the following info bands: 1: NIR, 2: red, 3: green, 4: alpha channel.</p> <p><strong>Data processing</strong></p> <p>The raw data were processed to Digital Surface Models and orthophotos by INBO in 2015 using Agisoft PhotoScan Pro 1.0.4, a structure-from-motion (SfM) based photogrammetry software program.</p> <p><strong>Coordinate reference system</strong></p> <p>All geospatial data have the coordinate reference system <code>EPSG:31370 - Belgian Lambert 72</code>.</p> <p><strong>Files</strong></p> <ul> <li><strong>Raw flight data</strong>: images and logs collected by the drone during flight. These files are zipped per flight, with the date (<code>yyyymmdd</code>) and flight number (<code>x</code>) indicated in the file name (<code>flight_yyyymmdd_Bilzen_x.zip</code>).</li> <li><strong>Processed data</strong>: Digital Surface Models (<code>filename_DSM.tif</code>) and orthophotos (<code>filename_Ortho.tif</code>) stitched together from the raw data. The included flight is indicated in the file name (e.g. <code>20150728_Bilzen_1_DSM.tif</code>).</li> <li><strong>Ground control points</strong>: not applicable for this dataset.</li> </ul> <p><strong>Cloud Optimized GeoTIFF</strong></p> <p>The most efficient way to explore the processed data is by loading the <a href="https://www.cogeo.org/">Cloud Optimized GeoTIFFs</a> we created for each processed file. Copy one of the file URLs below and follow e.g. the <a href="https://www.cogeo.org/qgis-tutorial.html">QGIS tutorial</a> to load this type of file.</p> <ul> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20150728_Bilzen_1_Ortho.tif</code> RGB</li> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20150728_Bilzen_2_DSM.tif</code></li> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20150728_Bilzen_2_Ortho.tif</code> CIR</li> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20150728_Bilzen_1_DSM.tif</code></li> </ul> <p>See <a href="https://s3-eu-west-1.amazonaws.com/lw-remote-sensing/index.html">this page</a> for an overview of public INBO RPAS data.</p>
Orthophotos and DSMs derived from RPAS flights over the nature reserve Averbode Bos & Heide in Flanders, Belgium
<p><strong>Study area</strong></p> <p>Averbode Bos & Heide is a nature reserve situated near the locality of Averbode, in the province of Flemish-Brabant, Flanders, Belgium. The area is managed by the nature conservation NGO Natuurpunt and consists of wet and dry heathlands, inland dunes, forests and moorland pools. In the years prior to the drone flights, large stands of mostly coniferous trees were cut to enable ecological restoration of heathlands and moorland pools. The drone flight was triggered by a particular interest to monitor the effects of this restoration.</p> <p><strong>Data collection</strong></p> <p>Data were collected by the <a href="https://www.inbo.be/en">Research Institute for Nature and Forest (INBO)</a> with a fixed wing drone Gatewing X100 in 2015 and 2016 (6 flights). RGB data were acquired using an off-the-shelf Ricoh GR Digital IV camera, with the following image bands: 1: red, 2: green, 3: blue, 4: alpha channel. CIR (color-infrared) data were acquired using a NIR-enabled Ricoh GR Digital IV camera, with the following info bands: 1: NIR, 2: red, 3: green, 4: alpha channel.</p> <p><strong>Data processing</strong></p> <p>The raw data were processed to Digital Surface Models and orthophotos by the <a href="https://vito.be/en">Flemish Institute for Technological Research (VITO)</a> in 2017. Images with coarse GPS coordinates were imported and processed in Agisoft PhotoScan Pro 1.4.x, a structure-from-motion (SfM) based photogrammetry software program. After extraction and matching of tie points, a bundle adjustment leads to a sparse point cloud and a refined set of camera position and orientation values. From there, a point cloud densification and classification into ground and non-ground points was performed, leading to a rasterized digital surface model (DSM) and digital terrain model (DTM). Finally, a true orthomosaic was projected onto the DTM.</p> <p><strong>Coordinate reference system</strong></p> <p>All geospatial data have the coordinate reference system <code>EPSG:31370 - Belgian Lambert 72</code>.</p> <p><strong>Files</strong></p> <ul> <li><strong>Raw flight data</strong>: images and logs collected by the drone during flight. These files are zipped per flight, with the date (<code>yyyymmdd</code>) and flight number (<code>x</code>) indicated in the file name (<code>flight_yyyymmdd_ABH_x.zip</code>).</li> <li><strong>Processed data</strong>: Digital Surface Models (<code>filename_DSM.tif</code>) and orthophotos (<code>filename_Ortho.tif</code>) stitched together from the raw data. The included flights are indicated in the file name (e.g. 3 flights for <code>20150928_ABH_1-2_20151001_ABH_1_DSM.tif</code>).</li> <li><strong>Ground control points</strong>: not applicable for this dataset.</li> </ul> <p><strong>Cloud Optimized GeoTIFF</strong></p> <p>The most efficient way to explore the processed data is by loading the <a href="https://www.cogeo.org/">Cloud Optimized GeoTIFFs</a> we created for each processed file. Copy one of the file URLs below and follow e.g. the <a href="https://www.cogeo.org/qgis-tutorial.html">QGIS tutorial</a> to load this type of file.</p> <ul> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20150928_ABH_1-2_20151001_ABH_1_DSM.tif</code></li> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20150928_ABH_1-2_20151001_ABH_1_Ortho.tif</code> RGB</li> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20151001_ABH_2_DSM.tif</code></li> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20151001_ABH_2_Ortho.tif</code> RGB</li> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20160401_ABH_1_DSM.tif</code></li> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20160401_ABH_1_Ortho.tif</code> RGB</li> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20160401_ABH_2_DSM.tif</code></li> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20160401_ABH_2_Ortho.tif</code> CIR</li> </ul> <p>See <a href="https://s3-eu-west-1.amazonaws.com/lw-remote-sensing/index.html">this page</a> for an overview of public INBO RPAS data.</p>
Orthophotos and DSMs derived from RPAS flights over the nature reserve Kalmthoutse Heide in Flanders, Belgium
<p><strong>Study area</strong></p> <p>The Kalmthoutse Heide is a nature reserve situated north of Kalmthout, in the province of Antwerp, Flanders, Belgium. It is part of the Cross-Border Nature Park De Zoom - Kalmthoutse Heide in the Netherlands and Belgium. The Kalmthoutse Heide is managed by the Flemish Agency for Nature and Forest and consists of wet and dry heathlands, inland dunes, forests and moorland pools. In this area, there is particular interest in monitoring the encroachment of the heathlands by <em>Molinia caerulea</em> and <em>Campylopus introflexus</em>.</p> <p><strong>Data collection</strong></p> <p>Data were collected by the <a href="http://www.inbo.be/en">Research Institute for Nature and Forest (INBO)</a> with a fixed wing drone Gatewing X100 in 2015 and 2016 (8 flights). RGB data were acquired using an off-the-shelf Ricoh GR Digital IV camera, with the following image bands: 1: red, 2: green, 3: blue, 4: alpha channel.</p> <p><strong>Data processing</strong></p> <p>The raw data were processed to Digital Surface Models and orthophotos by the <a href="https://vito.be/en">Flemish Institute for Technological Research (VITO)</a> in 2017. Images with coarse GPS coordinates were imported and processed in Agisoft PhotoScan Pro 1.4.x, a structure-from-motion (SfM) based photogrammetry software program. After extraction and matching of tie points, a bundle adjustment leads to a sparse point cloud and a refined set of camera position and orientation values. Ground control points (either artificially installed markers on the terrain, or other photo-identifiable points, measured on the ground with RTK GNSS) were used to further refine the camera calibration and obtain a pixel-level georeferencing accuracy. From there, a point cloud densification and classification into ground and non-ground points was performed, leading to a rasterized digital surface model (DSM) and digital terrain model (DTM). Finally, a true orthomosaic was projected onto the DTM.</p> <p><strong>Coordinate reference system</strong></p> <p>All geospatial data have the coordinate reference system <code>EPSG:31370 - Belgian Lambert 72</code>.</p> <p><strong>Files</strong></p> <ul> <li><strong>Raw flight data</strong>: images and logs collected by the drone during flight. These files are zipped per flight, with the date (<code>yyyymmdd</code>) and flight number (<code>x</code>) indicated in the file name (<code>flight_yyyymmdd_KH_x.zip</code>).</li> <li><strong>Processed data</strong>: Digital Surface Models (<code>filename_DSM.tif</code>) and orthophotos (<code>filename_Ortho.tif</code>) stitched together from the raw data. The included flights are indicated in the file name (e.g. 3 flights for <code>20150717_KH_1-3_DSM.tif</code>).</li> <li><strong>Ground control points</strong>: temporary ground control points were placed for the first flights on 2015-07-17 (visible in <code>20150717_KH_1-3_Ortho.tif</code>). Coordinates for these are available in <code>GCP_20150717_KH.tsv</code>.</li> </ul> <p><strong>Cloud Optimized GeoTIFF</strong></p> <p>The most efficient way to explore the processed data is by loading the <a href="https://www.cogeo.org/">Cloud Optimized GeoTIFFs</a> we created for each processed file. Copy one of the file URLs below and follow e.g. the <a href="https://www.cogeo.org/qgis-tutorial.html">QGIS tutorial</a> to load this type of file.</p> <ul> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20150717_KH_1-3_DSM.tif</code></li> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20150717_KH_1-3_Ortho.tif</code> RGB</li> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20151020_KH_1-2_DSM.tif</code></li> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20151020_KH_1-2_Ortho.tif</code> RGB</li> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20151020_KH_3_DSM.tif</code></li> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20151020_KH_3_Ortho.tif</code> RGB</li> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20160205_KH_1-2_DSM.tif</code></li> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20160205_KH_1-2_Ortho.tif</code> RGB</li> </ul> <p>See <a href="https://s3-eu-west-1.amazonaws.com/lw-remote-sensing/index.html">this page</a> for an overview of public INBO RPAS data.</p>
SNP call data for: The current epidemic of the barley pathogen Ramularia collo-cygni derives from a recent population expansion and shows global admixture
<p>Ramularia Leaf Spot is becoming an ever increasing problem in main barley growing regions since the 1980s, causing up to 70% yield loss in extreme cases. Yet, the causal agent <em>Ramularia collo-cygni</em>, remains poorly studied. The diversity of the pathogen in the field thus far remains unknown. Furthermore, it is unknown to which extend the pathogen has a sexual reproductive cycle. To date, the teleomorph of <em>R. collo-cygni</em> has not been observed.</p> <p>To study the genetic diversity of <em>R. collo-cygni </em>and to get more insights into its biology, we sequenced the genomes of 19 <em>R. collo-cygn</em>i isolates from multiple geographic locations and diverse hosts. Here we share the SNP call data as well as the reference genome.</p> <p>The reference genome files and assembly can be found on ENI: GCA_900074925.1</p> <p>https://www.ebi.ac.uk/ena/data/view/GCA_900074925.1</p> <p>The raw sequence data is also available through ENI: ERX2296228</p> <p>https://www.ebi.ac.uk/ena/data/view/ERX2296228</p>
Summary Table of AHG Parameters, Hydraulics, Morphological and geophysical variables, and Suspending Sediment Concentration Derived at 1246 USGS River Monitoring Stations
<p>This table contains information about:</p> <p>1) At-A-Station Hydraulic Geometry Parameters,</p> <p>2) Hydraulics Variables,</p> <p>3) Morphological and Geophysical Variables, and</p> <p>4) Suspending Sediment Concentration and Fraction of Sand, Silt, and Clay,</p> <p>derived at 1246 USGS river monitoring stations across the conterminous United States. </p>
An MRI-Derived Neuroanatomical Atlas of the Fischer 344 Rat Brain for Automated Anatomical Segmentation
<p>This version of the dataset described in: <a href="https://www.biorxiv.org/content/10.1101/743583v2">https://www.biorxiv.org/content/10.1101/743583v2</a>, is outdated. Please refer to https://doi.org/10.5281/zenodo.3555556 for the current version and all future editions of the Fischer 344 neuroanatomical atlas.</p>
A global network of biomedical relationships derived from text
<p>This repository contains labeled, weighted networks of chemical-gene, gene-gene, gene-disease, and chemical-disease relationships based on single sentences in PubMed abstracts. All raw dependency paths are provided in addition to the labeled relationships.</p> <p>PART I: Connects dependency paths to labels, or "themes". Each record contains a dependency path followed by its score for each theme, and indicators of whether or not the path is part of the flagship path set for each theme (meaning that it was manually reviewed and determined to reflect that theme). The themes themselves are listed below and are in our paper (reference below).</p> <p>PART II: Connects sentences to dependency paths. It consists of sentences and associated metadata, entity pairs found in the sentences, and dependency paths connecting those entity pairs. Each record contains the following information:</p> <ul> <li>PubMed ID</li> <li>Sentence number (0 = title)</li> <li>First entity name, formatted</li> <li>First entity name, location (characters from start of abstract)</li> <li>Second entity name, formatted</li> <li>Second entity name, location</li> <li>First entity name, raw string</li> <li>Second entity name, raw string</li> <li>First entity name, database ID(s)</li> <li>Second entity name, database ID(s)</li> <li>First entity type (Chemical, Gene, Disease)</li> <li>Second entity type (Chemical, Gene, Disease)</li> <li>Dependency path</li> <li>Sentence, tokenized</li> </ul> <p>The "with-themes.txt" files only contain dependency paths with corresponding theme assignments from Part I. The plain ".txt" files contain all dependency paths.</p> <p>This release contains the annotated network for the <strong>September 15, 2019 version of PubTator</strong>. The version discussed in our paper, below, is an older one - from April 30, 2016. If you're interested in that network, it can be found in Version 1 of this repository. We will be releasing updated networks periodically, as the PubTator community continues to release new versions of named entity annotations for Medline each month or so.</p> <p>------------------------------------------------------------------------------------<br> REFERENCES</p> <p>Percha B, Altman RBA (2017) A global network of biomedical relationships derived from text. <em>Bioinformatics, </em>34(15): 2614-2624.<br> Percha B, Altman RBA (2015) Learning the structure of biomedical relationships from unstructured text. <em>PLoS Computational Biology,</em> 11(7): e1004216.</p> <p>This project depends on named entity annotations from the PubTator project:<br> https://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/PubTator/</p> <p>Reference:<br> Wei CH et. al., PubTator: a Web-based text mining tool for assisting Biocuration, Nucleic acids research, 2013, 41 (W1): W518-W522.</p> <p>Dependency parsing was provided by the Stanford CoreNLP toolkit (<strong>version 3.9.1</strong>):<br> https://stanfordnlp.github.io/CoreNLP/index.html</p> <p>Reference:<br> Manning, Christopher D., Mihai Surdeanu, John Bauer, Jenny Finkel, Steven J. Bethard, and David McClosky. 2014. The Stanford CoreNLP Natural Language Processing Toolkit In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 55-60.</p> <p>------------------------------------------------------------------------------------<br> THEMES</p> <p><strong>chemical-gene</strong><br> (A+) agonism, activation<br> (A-) antagonism, blocking<br> (B) binding, ligand (esp. receptors)<br> (E+) increases expression/production<br> (E-) decreases expression/production<br> (E) affects expression/production (neutral)<br> (N) inhibits</p> <p><strong>gene-chemical</strong><br> (O) transport, channels<br> (K) metabolism, pharmacokinetics<br> (Z) enzyme activity</p> <p><strong>chemical-disease</strong><br> (T) treatment/therapy (including investigatory)<br> (C) inhibits cell growth (esp. cancers)<br> (Sa) side effect/adverse event<br> (Pr) prevents, suppresses<br> (Pa) alleviates, reduces<br> (J) role in disease pathogenesis</p> <p><strong>disease-chemical</strong><br> (Mp) biomarkers (of disease progression)</p> <p><strong>gene-disease</strong><br> (U) causal mutations<br> (Ud) mutations affecting disease course<br> (D) drug targets<br> (J) role in pathogenesis<br> (Te) possible therapeutic effect<br> (Y) polymorphisms alter risk<br> (G) promotes progression</p> <p><strong>disease-gene</strong><br> (Md) biomarkers (diagnostic)<br> (X) overexpression in disease<br> (L) improper regulation linked to disease</p> <p><strong>gene-gene</strong><br> (B) binding, ligand (esp. receptors)<br> (W) enhances response<br> (V+) activates, stimulates<br> (E+) increases expression/production<br> (E) affects expression/production (neutral)<br> (I) signaling pathway<br> (H) same protein or complex<br> (Rg) regulation<br> (Q) production by cell population</p> <p>------------------------------------------------------------------------------------<br> FORMATTING NOTE</p> <p>A few users have mentioned that the dependency paths in the "part-i" files are all lowercase text, whereas those in the "part-ii" files maintain the case of the original sentence. This complicates mapping between the two sets of files.</p> <p>We kept the part-ii files in the same case as the original sentence to facilitate downstream debugging - it's easier to tell which words in a particular sentence are contributing to the dependency path if their original case is maintained. When working with the part-ii "with-themes" files, if you simply convert the dependency path to lowercase, it is guaranteed to match to one of the paths in the corresponding part-i file and you'll be able to get the theme scores.</p> <p>Apologies for the additional complexity, and please reach out to us if you have any questions (see correspondence information in the <em>Bioinformatics</em> manuscript, above).</p>
SRTM derived hydrological data for the SAFE Project
<b>Description: </b><p>These files contain flow direction and flow accumulation data for the area surrounding the SAFE Project. The data were derived using compiled Shuttle Radar Topograpy Mission (SRTM) 30m resolution elevation data for the region (<a href="https://zenodo.org/record/630004">https://zenodo.org/record/630004) using the GRASS GIS <code>r.watershed</code> tool.<br><br>Further details of the geoprocessing can be found here: </a><a href="https://www.safeproject.net/dokuwiki/safe_gis/hydrology">https://www.safeproject.net/dokuwiki/safe_gis/hydrology</a>.</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/1"><b>SAFE CORE DATA</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3490687">here</a></p><p><b>Files: </b>This dataset consists of 3 files: SAFE_SRTM_hydrology_metadata.xlsx, SRTM_Flow_Direction.tif, SRTM_Log_Flow_Accum.tif</p><p><b>SAFE_SRTM_hydrology_metadata.xlsx</b></p><p>This file only contains metadata for the files below</p><p><b>SRTM_Flow_Direction.tif</b></p><p>Description: Flow direction data in UTM 50N geotiff</p><p><b>SRTM_Log_Flow_Accum.tif</b></p><p>Description: Flow accumulation data in UTM50N geotiff</p><p><b>Date range: </b>2010-10-01 to 2019-10-01</p><p><b>Latitudinal extent: </b>4.0223 to 5.9761</p><p><b>Longitudinal extent: </b>116.0242 to 117.9758</p>
Text-fig. 2. Stratigraphic ranges of selected porambonitid taxa in West Gondwana (Iberica), Baltica, Perunica and Avalonia. Arrows indicate derivation of early Baltic porambonitids from Gondwanan Poramborthis (1), derivation of Porambonites in Perunica and Avalonia from Baltica (2), and expansion of Porambonites from Baltica and/or Avalonia to West Gondwana (3). in Porambonites Havliceki Sp. Nov., A New Brachiopod From The Šárka Formation (Darriwilian) From Bohemia And Its Contribution To Early History Of The Porambonitidae
Text-fig. 2. Stratigraphic ranges of selected porambonitid taxa in West Gondwana (Iberica), Baltica, Perunica and Avalonia. Arrows indicate derivation of early Baltic porambonitids from Gondwanan Poramborthis (1), derivation of Porambonites in Perunica and Avalonia from Baltica (2), and expansion of Porambonites from Baltica and/or Avalonia to West Gondwana (3).
Fig. 2 in Sarcocystis falcatula-like derived from opossum in Northeastern Brazil: In vitro propagation in avian cells, molecular characterization and bioassay in birds
Fig. 2. (A) A mature schyzont of Sarcocystis falcatula-like (Sarco-BA1 strain) in a permanent chicken cell line (UMNSAH/DF-1). May-Grüenwald-Giemsa stain. Bar = 20 μm. (B) Extracellular merozoites of Sarco-BA1 on a monolayer of UMNSAH/DF-1 cells. Bar = 10 μm.
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.