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1,977 results for “2007”
Figures 1–2 in A REDESCRIPTION OF ZAVRELIA BRAGREMIA GUO & WANG, 2007 (DIPTERA: CHIRONOMIDAE) Abstract
Figures 1–2. Zavrelia bragremia Guo & Wang, 2007, male. 1, head and antenna, scale = 100 μm; 2, eye, scale = 50 μm.
Figures 7–10 in A REDESCRIPTION OF ZAVRELIA BRAGREMIA GUO & WANG, 2007 (DIPTERA: CHIRONOMIDAE) Abstract
Figures 7–10. Zavrelia bragremia Guo & Wang, 2007, male. 7, holotype hypopygium, dorsal view; 8, hypopygium (BDN: G5A69), dorsal view; 9, hypopygium (BDN: G5A53), dorsal view; 10, median volsellae. Scales = 50 μm.
SuperDARN Grid data in netCDF format (2007-Jan)
<p>2007-Jan SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, 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>
Fig. 62. Colletes zygophyllum Kuhlmann, 2007 in Taxonomic revision of the southern African Colletes fasciatus species group (Hymenoptera: Colletidae)
Fig. 62. Colletes zygophyllum Kuhlmann, 2007, ♂. A. Habitus, lateral view. B. Head. C. Scutum, dorsal view. D. Metasomal terga 1 and 2, dorsal view. E. Metasoma, dorsal view. F. Hind basitarsus, lateral view. G. Metasomal sternum 7, dorsal view. H. Gonostylus, lateral view.
Fig. 61. Colletes zygophyllum Kuhlmann, 2007 in Taxonomic revision of the southern African Colletes fasciatus species group (Hymenoptera: Colletidae)
Fig. 61. Colletes zygophyllum Kuhlmann, 2007, ♀. A. Habitus, lateral view. B. Head. C. Scutum, dorsal view. D. Metasomal terga 1 and 2, dorsal view. E. Metasoma, dorsal view. F. Hind tibia, lateral view.
Fig. 47. Colletes nieuwoudtvillei Kuhlmann, 2007 in Taxonomic revision of the southern African Colletes fasciatus species group (Hymenoptera: Colletidae)
Fig. 47. Colletes nieuwoudtvillei Kuhlmann, 2007, ♂. A. Habitus, lateral view. B. Head. C. Scutum, dorsal view. D. Metasomal terga 1 and 2, dorsal view. E. Metasoma, dorsal view. F. Hind basitarsus, lateral view. G. Metasomal sternum 7, dorsal view. H. Gonostylus, lateral view.
Fig. 60. Colletes watmoughi Kuhlmann, 2007 in Taxonomic revision of the southern African Colletes fasciatus species group (Hymenoptera: Colletidae)
Fig. 60. Colletes watmoughi Kuhlmann, 2007, ♀. A. Habitus, lateral view. B. Head. C. Scutum, dorsal view. D. Metasomal terga 1 and 2, dorsal view. E. Metasoma, dorsal view. F. Hind tibia, lateral view.
Fig. 38. Colletes knersvlaktei Kuhlmann, 2007 in Taxonomic revision of the southern African Colletes fasciatus species group (Hymenoptera: Colletidae)
Fig. 38. Colletes knersvlaktei Kuhlmann, 2007, ♂. A. Habitus, lateral view. B. Head. C. Scutum, dorsal view. D. Metasomal terga 1 and 2, dorsal view. E. Metasoma, dorsal view. F. Hind basitarsus, lateral view. G. Metasomal sternum 7, dorsal view. H. Gonostylus, lateral view.
Fig. 34. Colletes karooensis Kuhlmann, 2007 in Taxonomic revision of the southern African Colletes fasciatus species group (Hymenoptera: Colletidae)
Fig. 34. Colletes karooensis Kuhlmann, 2007, ♀. A. Habitus, lateral view. B. Head. C. Scutum, dorsal view. D. Metasomal terga 1 and 2, dorsal view. E. Metasoma, dorsal view. F. Hind tibia, lateral view.
Fig. 35. Colletes karooensis Kuhlmann, 2007 in Taxonomic revision of the southern African Colletes fasciatus species group (Hymenoptera: Colletidae)
Fig. 35. Colletes karooensis Kuhlmann, 2007, ♂. A. Habitus, lateral view. B. Head. C. Scutum, dorsal view. D. Metasomal terga 1 and 2, dorsal view. E. Metasoma, dorsal view. F. Hind basitarsus, lateral view. G. Metasomal sternum 7, dorsal view. H. Gonostylus, lateral view.
Fig. 29. Colletes gessi Kuhlmann, 2007 in Taxonomic revision of the southern African Colletes fasciatus species group (Hymenoptera: Colletidae)
Fig. 29. Colletes gessi Kuhlmann, 2007, ♂. A. Habitus, lateral view. B. Head. C. Scutum, dorsal view. D. Metasomal terga 1 and 2, dorsal view. E. Metasoma, dorsal view. F. Hind basitarsus, lateral view. G. Metasomal sternum 7, dorsal view. H. Gonostylus, lateral view.
Fig. 28. Colletes gessi Kuhlmann, 2007 in Taxonomic revision of the southern African Colletes fasciatus species group (Hymenoptera: Colletidae)
Fig. 28. Colletes gessi Kuhlmann, 2007, ♀. A. Habitus, lateral view. B. Head. C. Scutum, dorsal view. D. Metasomal terga 1 and 2, dorsal view. E. Metasoma, dorsal view. F. Hind tibia, lateral view.
Fig. 19. Colletes eardleyi Kuhlmann, 2007 in Taxonomic revision of the southern African Colletes fasciatus species group (Hymenoptera: Colletidae)
Fig. 19. Colletes eardleyi Kuhlmann, 2007, ♀. A. Habitus, lateral view. B. Head. C. Scutum, dorsal view. D. Metasomal terga 1 and 2, dorsal view. E. Metasoma, dorsal view. F. Hind tibia, lateral view.
Fig. 4. Colletes abnormis Kuhlmann, 2007 in Taxonomic revision of the southern African Colletes fasciatus species group (Hymenoptera: Colletidae)
Fig. 4. Colletes abnormis Kuhlmann, 2007, ♀. A. Habitus, lateral view. B. Head. C. Scutum, dorsal view. D. Metasomal terga 1 and 2, dorsal view. E. Metasoma, dorsal view. F. Hind tibia, lateral view.
Fig. 5. Colletes abnormis Kuhlmann, 2007 in Taxonomic revision of the southern African Colletes fasciatus species group (Hymenoptera: Colletidae)
Fig. 5. Colletes abnormis Kuhlmann, 2007, ♂. A. Habitus, lateral view. B. Head. C. Scutum, dorsal view. D. Metasomal terga 1 and 2, dorsal view. E. Metasoma, dorsal view. F. Hind basitarsus, lateral view. G. Metasomal sternum 7, dorsal view. H. Gonostylus, lateral view.
Fig. 16. Colletes cyanonitidus Kuhlmann, 2007 in Taxonomic revision of the southern African Colletes fasciatus species group (Hymenoptera: Colletidae)
Fig. 16. Colletes cyanonitidus Kuhlmann, 2007, ♀. A. Habitus, lateral view. B. Head. C. Scutum, dorsal view. D. Metasomal terga 1 and 2, dorsal view. E. Metasoma, dorsal view. F. Hind tibia, lateral view.
Fig. 17. Colletes cyanonitidus Kuhlmann, 2007 in Taxonomic revision of the southern African Colletes fasciatus species group (Hymenoptera: Colletidae)
Fig. 17. Colletes cyanonitidus Kuhlmann, 2007, ♂. A. Habitus, lateral view. B. Head. C. Scutum, dorsal view. D. Metasomal terga 1 and 2, dorsal view. E. Metasoma, dorsal view. F. Hind basitarsus, lateral view. G. Metasomal sternum 7, dorsal view. H. Gonostylus, lateral view.
Monthly aggregated GLASS FAPAR V6 (250 m): 95th percentile monthly time-series (2007)
<p><strong>List of Subdatasets:</strong></p> <ul> <li>Long-term data: <a href="https://doi.org/10.5281/zenodo.8381409">2000-2021</a></li> <li>5th percentile (p05) monthly time-series: <a href="https://doi.org/10.5281/zenodo.8408654">2000</a>, <a href="https://doi.org/10.5281/zenodo.8411611">2001</a>, <a href="https://doi.org/10.5281/zenodo.8412712">2002</a>, <a href="https://doi.org/10.5281/zenodo.8413021">2003</a>, <a href="https://doi.org/10.5281/zenodo.8413689">2004</a>, <a href="https://doi.org/10.5281/zenodo.8414639">2005</a>, <a href="https://doi.org/10.5281/zenodo.8411609">2006</a>, <a href="https://doi.org/10.5281/zenodo.8414085">2007</a>, <a href="https://doi.org/10.5281/zenodo.8414960">2008</a>, <a href="https://doi.org/10.5281/zenodo.8415476">2009</a>, <a href="https://doi.org/10.5281/zenodo.8415686">2010</a>, <a href="https://doi.org/10.5281/zenodo.8412154">2011</a>, <a href="https://doi.org/10.5281/zenodo.8414082">2012</a>, <a href="https://doi.org/10.5281/zenodo.8411364">2013</a>, <a href="https://doi.org/10.5281/zenodo.8414933">2014</a>, <a href="https://doi.org/10.5281/zenodo.8415414">2015</a>, <a href="https://doi.org/10.5281/zenodo.8412246">2016</a>, <a href="https://doi.org/10.5281/zenodo.8414083">2017</a>, <a href="https://doi.org/10.5281/zenodo.8411366">2018</a>, <a href="https://doi.org/10.5281/zenodo.8415203">2019</a>, <a href="https://doi.org/10.5281/zenodo.8415549">2020</a>, <a href="https://doi.org/10.5281/zenodo.8387608">2021</a></li> <li>50th percentile (p50) monthly time-series: <a href="https://doi.org/10.5281/zenodo.8408710">2000</a>, <a href="https://doi.org/10.5281/zenodo.8408798">2001</a>, <a href="https://doi.org/10.5281/zenodo.8408866">2002</a>, <a href="https://doi.org/10.5281/zenodo.8415319">2003</a>, <a href="https://doi.org/10.5281/zenodo.8415619">2004</a>, <a href="https://doi.org/10.5281/zenodo.8415878">2005</a>, <a href="https://doi.org/10.5281/zenodo.8416080">2006</a>, <a href="https://doi.org/10.5281/zenodo.8416619">2007</a>, <a href="https://doi.org/10.5281/zenodo.8417164">2008</a>, <a href="https://doi.org/10.5281/zenodo.8417513">2009</a>, <a href="https://doi.org/10.5281/zenodo.8417708">2010</a>, <a href="https://doi.org/10.5281/zenodo.8415669">2011</a>, <a href="https://doi.org/10.5281/zenodo.8416000">2012</a>, <a href="https://doi.org/10.5281/zenodo.8416542">2013</a>, <a href="https://doi.org/10.5281/zenodo.8417055">2014</a>, <a href="https://doi.org/10.5281/zenodo.8417467">2015</a>, <a href="https://doi.org/10.5281/zenodo.8415747">2016</a>, <a href="https://doi.org/10.5281/zenodo.8416333">2017</a>, <a href="https://doi.org/10.5281/zenodo.8416835">2018</a>, <a href="https://doi.org/10.5281/zenodo.8417326">2019</a>, <a href="https://doi.org/10.5281/zenodo.8417589">2020</a>, <a href="https://doi.org/10.5281/zenodo.8388078">2021</a></li> <li>95th percentile (p95) monthly time-series: <a href="https://doi.org/10.5281/zenodo.8408949">2000</a>, <a href="https://doi.org/10.5281/zenodo.8409059">2001</a>, <a href="https://doi.org/10.5281/zenodo.8409154">2002</a>, <a href="https://doi.org/10.5281/zenodo.8409362">2003</a>, <a href="https://doi.org/10.5281/zenodo.8416487">2004</a>, <a href="https://doi.org/10.5281/zenodo.8417029">2005</a>, <a href="https://doi.org/10.5281/zenodo.8417833">2006</a>, <a href="https://doi.org/10.5281/zenodo.8417996">2007</a>, <a href="https://doi.org/10.5281/zenodo.8418308">2008</a>, <a href="https://doi.org/10.5281/zenodo.8418669">2009</a>, <a href="https://doi.org/10.5281/zenodo.8418986">2010</a>, <a href="https://doi.org/10.5281/zenodo.8417649">2011</a>, <a href="https://doi.org/10.5281/zenodo.8417816">2012</a>, <a href="https://doi.org/10.5281/zenodo.8417959">2013</a>, <a href="https://doi.org/10.5281/zenodo.8418253">2014</a>, <a href="https://doi.org/10.5281/zenodo.8418625">2015</a>, <a href="https://doi.org/10.5281/zenodo.8417759">2016</a>, <a href="https://doi.org/10.5281/zenodo.8417898">2017</a>, <a href="https://doi.org/10.5281/zenodo.8418076">2018</a>, <a href="https://doi.org/10.5281/zenodo.8418442">2019</a>, <a href="https://doi.org/10.5281/zenodo.8418751">2020</a>, <a href="https://doi.org/10.5281/zenodo.8392976">2021</a></li> </ul> <p><strong>General Description</strong></p> <p>The <i>monthly aggregated Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)</i> dataset is derived from <abbr title="glass.umd.edu/FAPAR/MODIS/250m/">250m 8d GLASS V6 FAPAR</abbr>. The data set is derived from Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance and LAI data using several other FAPAR products (MODIS Collection 6, GLASS FAPAR V5, and PROBA-V1 FAPAR) to generate a bidirectional long-short-term memory (Bi-LSTM) model to estimate FAPAR. The dataset time spans from March 2000 to December 2021 and provides data that covers the entire globe. The dataset can be used in many applications like land degradation modeling, land productivity mapping, and land potential mapping. The dataset includes:</p> <ul> <li><strong>Long-term:</strong></li> </ul> <p>Derived from monthly time-series. This dataset provides linear trend model for the p95 variable: (1) slope beta mean (p95.beta_m), p-value for beta (p95.beta_pv), intercept alpha mean (p95.alpha_m), p-value for alpha (p95.alpha_pv), and coefficient of determination R<sup>2</sup> (p95.r2_m).</p> <ul> <li><strong>Monthly time-series:</strong></li> </ul> <p>Monthly aggregation with three standard statistics: (1) 5th percentile (p05), median (p50), and 95th percentile (p95). For each month, we aggregate all composites within that month plus one composite each before and after, ending up with 5 to 6 composites for a single month depending on the number of images within that month.</p> <p><strong>Data Details</strong></p> <ul> <li><strong>Time period:</strong> March 2000 – December 2021</li> <li><strong>Type of data:</strong> Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)</li> <li><strong>How the data was collected or derived:</strong> Derived from 250m 8 d GLASS V6 FAPAR using Python running in a local HPC. The time-series analysis were computed using the <a href="https://github.com/scikit-map/scikit-map">Scikit-map</a> Python package.</li> <li><strong>Statistical methods used:</strong> for the long-term, Ordinary Least Square (OLS) of p95 monthly variable; for the monthly time-series, percentiles 05, 50, and 95.</li> <li><strong>Limitations or exclusions in the data:</strong> The dataset does not include data for Antarctica.</li> <li><strong>Coordinate reference system:</strong> EPSG:4326</li> <li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (-180.00000, -62.0008094, 179.9999424, 87.37000)</li> <li><strong>Spatial resolution:</strong> 1/480 d.d. = 0.00208333 (250m)</li> <li><strong>Image size:</strong> 172,800 x 71,698</li> <li><strong>File format:</strong> Cloud Optimized Geotiff (COG) format.</li> </ul> <p><strong>Support</strong></p> <p>If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: <a href="https://github.com/Open-Earth-Monitor/Global_FAPAR_250m/issues">https://github.com/Open-Earth-Monitor/Global_FAPAR_250m/issues</a></p> <p><strong>Reference</strong></p> <p>Hackländer, J., Parente, L., Ho, Y.-F., Hengl, T., Simoes, R., Consoli, D., Şahin, M., Tian, X., Herold, M., Jung, M., Duveiller, G., Weynants, M., Wheeler, I., (2023?) "Land potential assessment and trend-analysis using 2000–2021 FAPAR monthly time-series at 250 m spatial resolution", submitted to PeerJ, preprint available at: <a href="https://doi.org/10.21203/rs.3.rs-3415685/v1">https://doi.org/10.21203/rs.3.rs-3415685/v1</a></p> <p><strong>Name convention</strong></p> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:</p> <ol> <li><strong>generic variable name:</strong> fapar = Fraction of Absorbed Photosynthetically Active Radiation</li> <li><strong>variable procedure combination:</strong> essd.lstm = Earth System Science Data with bidirectional long short-term memory (Bi–LSTM)</li> <li><strong>Position in the probability distribution / variable type:</strong> p05/p50/p95 = 5th/50th/95th percentile</li> <li><strong>Spatial support:</strong> 250m</li> <li><strong>Depth reference:</strong> s = surface</li> <li><strong>Time reference begin time:</strong> 20000301 = 2000-03-01</li> <li><strong>Time reference end time:</strong> 20211231 = 2022-12-31</li> <li><strong>Bounding box:</strong> go = global (without Antarctica)</li> <li><strong>EPSG code:</strong> epsg.4326 = EPSG:4326</li> <li><strong>Version code:</strong> v20230628 = 2023-06-28 (creation date)</li> </ol>
Liana species of the Barro Colorado Island 50-ha plot 2007 and 2017 censuses
Open the record for dataset details and reuse information.
Data package for NutNet project: Compositional variation in grassland plant communities (60 sites, 2007-2020)
Data associated with a manuscript examining compositional variation in grassland plant communities around the globe. We used a globally distributed experiment to examine variation in species composition within 60 grasslands on 6 continents. Each site had an identical experimental and sampling design: 24 plots x 4 years. We expressed compositional variation within each site—not across sites—using abundance- and incidence-based metrics of the magnitude of dissimilarity (Bray-Curtis and Sorensen, respectively), abundance- and incidence-based measures of the relative importance of replacement (balanced variation and species turnover, respectively), and species richness at two scales (per plot-year (alpha) and per site (gamma)). We assessed species composition separately for each site and then compared patterns among sites, asking: (1) How does small-scale compositional variation differ among grasslands?; (2) Can compositional variation within a site be predicted by its biotic and abiotic context?; and (3) Does a combination of metrics enhance our understanding of the ecological processes at individual sites? This data package includes the site-specific values of each metric and the explanatory variables used to predict differences in compositional variation among sites. Scripts to conduct analyses and create figures and tables are also provided.
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