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91 results for “type series”
Analysis of Wild Type and Siah2 KO time series liver transcriptome from male and female mice
GEO Series GSE182836. Mus musculus. 44 samples. Type: Expression profiling by high throughput sequencing.
Analysis of Wild Type and Siah2 KO time series liver transcriptome from male and female mice [ZT0-ZT21]
GEO Series GSE182834. Mus musculus. 32 samples. Type: Expression profiling by high throughput sequencing.
Gene expression profiles of wild type and Actinin 4 point mutant knock-in adult podocyte cells at 2, 5 and 44 weeks following TRAP purification. (GUDMAP Series ID: 49)
GEO Series GSE53156. Mus musculus. 25 samples. Type: Expression profiling by array.
Time series XBP1s ChIP-Seq in wild-type mice and XBP1 liver-specific knockout mice
GEO Series GSE130760. Mus musculus. 13 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Time series transcritpome in wild-type mice and XBP1 liver-specific knockout mice
GEO Series GSE130890. Mus musculus. 96 samples. Type: Expression profiling by high throughput sequencing.
Appendix III. Morphological variation within the type series of Acanthodactylus montanus sp. nov. HT: holotype, PT: paratype. Ad.: adult, Subad.: subadult, Juv.: juvenile. F: female, M: male. Cf. Material and methods for the other abbreviations used. in Morphology and multilocus phylogeny of the Spiny-footed Lizard (Acanthodactylus erythrurus) complex reveal two new mountain species from the Moroccan Atlas
Appendix III. Morphological variation within the type series of Acanthodactylus montanus sp. nov. HT: holotype, PT: paratype. Ad.: adult, Subad.: subadult, Juv.: juvenile. F: female, M: male. Cf. Material and methods for the other abbreviations used.
FIGURE 1 in Deciphering a hundred-year-old puzzle: the type series of Bryconamericus exodon (Characiformes: Characidae)
FIGURE 1. Card with curatorial notes on the type series of Bryconamericus exodon after being moved from the Indiana University to the California Academy of Sciences.
The Use of Topical Nasal Steroids for Skin Reactions to Continuous Glucose Monitoring System, Among Children and Youth With Type 1 Diabetes Mellitus: Case Series
ClinicalTrials.gov study NCT03594565. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
Gene expression profiles of the P1 bladder isolated from SMAA or SMAGA (Acta2 or Actg2) transgenic and wild type mice. (GUDMAP Series ID: 2)
GEO Series GSE4816. Mus musculus. 12 samples. Type: Expression profiling by array.
National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data (2020)
<p>This data set contains information on the agricultural land use in Germany for the year 2020.<br> The map was derived from dense time series of Sentinel-2 and Landsat 8 data, Sentinel-1 monthly composites and addtional environmental data. It is based on the methods described in <a href="https://www.sciencedirect.com/science/article/pii/S0034425721005514">Blickensdörfer et al. 2022</a> and can be seen as a continuation of the dataset provided under: <a href="http://zenodo.org/record/5153047#.YWFyXn1CREZ">https://zenodo.org/record/5153047#.YWFyXn1CREZ</a>.<br> The maps can be explored online in a <a href="https://ows.geo.hu-berlin.de/webviewer/landwirtschaft/">webviewer</a>.</p> <p>Due to specific user needs the class catalogue was slightly modified but a translation key (Table 1) and a translated map version (*_V1.tif) is provided. However, it has to be noted that some rather small classes in the previous maps were not differentiated anymore (e.g., onions, carrots, asparagus).Thus, the classes 34, 43, 92, 130, 140, 181 and 182 were excluded from the raster and legend files.</p> <p> </p> <p>Table 1: Updated class catalogue and translation key to the class catalogue used in Blickensdörfer et al. 2022.</p> <table> <tbody> <tr> <td> <p><strong>New class code (V2) </strong></p> </td> <td> <p><strong>Class name (V2)</strong></p> </td> <td> <p><strong>Class code (V1)</strong></p> </td> <td> <p><strong>Class name (V1)</strong></p> </td> </tr> <tr> <td> <p>1101</p> </td> <td> <p>Winter wheat</p> </td> <td> <p>31</p> </td> <td> <p>Winter wheat</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p>34</p> </td> <td> <p>Other winter cereals</p> </td> </tr> <tr> <td> <p>1102</p> </td> <td> <p>Winter barley</p> </td> <td> <p>33</p> </td> <td> <p>Winter barley</p> </td> </tr> <tr> <td> <p>1103</p> </td> <td> <p>Winter rye</p> </td> <td> <p>32</p> </td> <td> <p>Winter rye</p> </td> </tr> <tr> <td> <p>1201</p> </td> <td> <p>Spring barley</p> </td> <td> <p>41</p> </td> <td> <p>Spring barley</p> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>43</p> </td> <td> <p>Other spring cereals</p> </td> </tr> <tr> <td> <p>1202</p> </td> <td> <p>Oat</p> </td> <td> <p>42</p> </td> <td> <p>Spring oat</p> </td> </tr> <tr> <td> <p>1300</p> </td> <td> <p>Maize</p> </td> <td> <p>91</p> </td> <td> <p>Maize (silage)</p> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>92</p> </td> <td> <p>Maize (grain)</p> </td> </tr> <tr> <td> <p>1401</p> </td> <td> <p>Potatoe</p> </td> <td> <p>100</p> </td> <td> <p>Potatoe</p> </td> </tr> <tr> <td> <p>1402</p> </td> <td> <p>Sugar beet</p> </td> <td> <p>80</p> </td> <td> <p>Sugar beet</p> </td> </tr> <tr> <td> <p>1501</p> </td> <td> <p>Rapeseed</p> </td> <td> <p>50</p> </td> <td> <p>Winter rapeseed</p> </td> </tr> <tr> <td> <p>1502</p> </td> <td> <p>Sunflower</p> </td> <td> <p>70</p> </td> <td> <p>Sunflower</p> </td> </tr> <tr> <td> <p>1611</p> </td> <td> <p>Peas</p> </td> <td> <p>60</p> </td> <td> <p>Legume</p> </td> </tr> <tr> <td> <p>1612</p> </td> <td> <p>Broad beans</p> </td> <td> </td> <td> </td> </tr> <tr> <td> <p>1613</p> </td> <td> <p>Lupine</p> </td> <td> </td> <td> </td> </tr> <tr> <td> <p>1614</p> </td> <td> <p>Soy</p> </td> <td> </td> <td> </td> </tr> <tr> <td> <p>1603</p> </td> <td> <p>Vegetables</p> </td> <td> <p>120</p> </td> <td> <p>Strawberry</p> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>130</p> </td> <td> <p>Asparagus</p> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>140</p> </td> <td> <p>Onion</p> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>181</p> </td> <td> <p>Carrot</p> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>182</p> </td> <td> <p>Other leafy vegetables</p> </td> </tr> <tr> <td> <p>1602</p> </td> <td> <p>Cultivated grassland</p> </td> <td> <p>10</p> </td> <td> <p>Grassland</p> </td> </tr> <tr> <td> <p>200</p> </td> <td> <p>Permanent grassland</p> </td> <td> </td> <td> </td> </tr> <tr> <td> <p>3003</p> </td> <td> <p>Fallow land</p> </td> <td> </td> <td> </td> </tr> <tr> <td> <p>3001</p> </td> <td> <p>Small woody features</p> </td> <td> <p>555</p> </td> <td> <p>Small woody features</p> </td> </tr> <tr> <td> <p>3002</p> </td> <td> <p>Other areas</p> </td> <td> <p>999</p> </td> <td> <p>Other agricultural areas</p> </td> </tr> <tr> <td> <p>4001</p> </td> <td> <p>Grapevine</p> </td> <td> <p>110</p> </td> <td> <p>Grapevine</p> </td> </tr> <tr> <td> <p>4002</p> </td> <td> <p>Hops</p> </td> <td> <p>150</p> </td> <td> <p>Hops</p> </td> </tr> <tr> <td> <p>4003</p> </td> <td> <p>Orchard</p> </td> <td> <p>160</p> </td> <td> <p>Orchards</p> </td> </tr> </tbody> </table> <p> </p> <p>All optical satellite data were downloaded, pre-processed and structured in an analysis-ready data (ARD) cube using the open-source software FORCE - Framework for Operational Radiometric Correction for Environmental monitoring (Frantz, D., 2019; <a href="https://force-eo.readthedocs.io/en/latest/">https://force-eo.readthedocs.io/en/latest/</a> last accessed: 12. April 2022), before environmental and SAR data were included in the ARD cube. </p> <p>The models were trained in FORCE and applied to all areas in Germany that were defined as agricultural land, small woody features, heathland or peatland in ATKIS DLM 2020 (Geobasisdaten: © GeoBasis-DE / BKG (2020)). Post-processing of the final maps included applying a sieve filter, the exclusion of classes other than grasslands and small woody features above 900 m (based on the Digital Elevation Model for Germany BKG (2015)) and the exclusion of grapevine and hops areas that were not labelled as the respective permanent crop in ATKIS DLM (BKG (2020); labelled as other agricultural areas in the final map). <br> </p> <p>The maps are provided as GeoTiff files together with QGIS legend files for visualization. </p> <p> </p> <p>References:</p> <p>Blickensdörfer, L., Schwieder, M., Pflugmacher, D., Nendel, C., Erasmi, S., & Hostert, P. (2022). Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany. Remote Sensing of Environment, 269, 112831</p> <p>BKG, Bundesamt für Kartographie und Geodäsie (2015). Digitales Geländemodell Gitterweite 10 m. DGM10. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/dgm10.pdf (last accessed: 28. April 2022). </p> <p>BKG, Bundesamt für Kartographie und Geodäsie (2018). Digitales Basis-Landschaftsmodell. <br> https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/basis-dlm.pdf (last accessed: 28. April 2022).</p> <p>Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, 1124.</p> <p> </p> <p><a href="https://zenodo.org/record/5153047#.YhYwgpYxmUn">National-scale crop type maps for Germany </a>© 2022 by Schwieder, Marcel; Erasmi, Stefan; Nendel, Claas; Hostert, Patrick is licensed under <a href="http://creativecommons.org/licenses/by/4.0/?ref=chooser-v1">CC BY 4.0. </a></p>
National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data (2017, 2018 and 2019)
<p>Detailed maps of agricultural landscapes are a valuable data source for manifold applications, such as environmental modelling, biodiversity monitoring or the support of agricultural statistics. Satellites from the European Copernicus program, especially, Sentinel-1 and Sentinel-2, as well as the Landsat missions operated by NASA/USGS, acquire data with a spatial resolution (10 m to 30 m) that is sufficient to identify field structures in complex agricultural landscapes. Time series of combined Sentinel-2 and Landsat data facilitate to differentiate crop types with a high thematic detail based on differences in land surface phenology. However, large data gaps due to frequent cloud cover may hamper such classification approaches. </p> <p>We thus combined dense interpolated times series of Sentinel-2A/B and Landsat data with monthly composites of Sentinel-1 backscatter data to overcome periods with high cloud contamination. To further account for regional variations along the agroecological gradient within Germany, we additionally included a broad set of spatially explicit environmental data in a random forest classification model. </p> <p>All optical satellite data were downloaded, pre-processed and structured in an analysis-ready data (ARD) cube using the open-source software FORCE - Framework for Operational Radiometric Correction for Environmental monitoring (Frantz, D., 2019; <a href="https://force-eo.readthedocs.io/en/latest/">https://force-eo.readthedocs.io/en/latest/</a> last accessed: 19. August 2021), before environmental and SAR data were included in the ARD cube. </p> <p>For each year (2017, 2018 and 2019) we trained an individual random forest model with 24 agricultural classes. Each model was independently validated with area adjusted overall accuracies of 80% (2017), 79% (2018), and 78% (2019). Further details regarding the data and methods used as well as class wise accuracies can be found in Blickensdörfer et al. (2022). </p> <p>The final models were applied to areas in Germany that were defined as agricultural land in ATKIS DLM 2018 (Geobasisdaten: © GeoBasis-DE / BKG (2018)). Post-processing of the final maps included applying a sieve filter, the exclusion of classes other than grasslands and small woody features above 900 m (based on the Digital Elevation Model for Germany BKG (2015)) and the exclusion of grapevine/hops areas that were not labelled as the respective permanent crop in ATKIS DLM (labelled as other agricultural areas in the final map). </p> <p>The maps are provided as GeoTiff files together with a QGIS legend file for visualization. </p> <p>Class catalogue:</p> <p>10 Grassland<br> 31 Winter wheat<br> 32 Winter rye<br> 33 Winter barley<br> 34 Other winter cereal<br> 41 Spring barley<br> 42 Spring oat<br> 43 Other spring cereal<br> 50 Winter rapeseed<br> 60 Legume<br> 70 Sunflower<br> 80 Sugar beet<br> 91 Maize<br> 92 Maize (grain)<br> 100 Potato<br> 110 Grapevine<br> 120 Strawberry<br> 130 Asparagus<br> 140 Onion<br> 150 Hops<br> 160 Orchard<br> 181 Carrot<br> 182 Other vegetables<br> 555 Small woody features<br> 999 Other agricultural areas</p> <p> </p> <p>Blickensdörfer, L., Schwieder, M., Pflugmacher, D., Nendel, C., Erasmi, S., & Hostert, P. (2022). Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany. Remote Sensing of Environment, 269, 112831</p> <p>BKG, Bundesamt für Kartographie und Geodäsie (2015). Digitales Geländemodell Gitterweite 10 m. DGM10. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/dgm10.pdf (last accessed: 19. August 2021). </p> <p>BKG, Bundesamt für Kartographie und Geodäsie (2018). Digitales Basis-Landschaftsmodell. <br> https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/basis-dlm.pdf (last accessed: 19. August 2021).</p> <p>Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, 1124.</p> <p> </p> <p><a href="https://zenodo.org/record/5153047#.YhYwgpYxmUn">National-scale crop type maps for Germany </a>© 2021 by Blickensdörfer, Lukas; Schwieder, Marcel; Pflugmacher, Dirk; Nendel, Claas; Erasmi, Stefan; Hostert, Patrick is licensed under <a href="http://creativecommons.org/licenses/by/4.0/?ref=chooser-v1">CC BY 4.0. </a></p>
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