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FIG. 7 in Disentangling cryptic species in the Marasmius haematocephalus (Mont.) Fr. and M. siccus (Schwein.) Fr. species complexes (Agaricales, Basidiomycota)
FIG. 7. — Marasmius bambusinus (Fr.) Fr. (JO343): A, basidiomata; B, basidiospores; C, basidia; D, basidioles; E, pleurocystidia; F, cheilocystidia; G, Siccus-type broom cells of the pileipellis. Scale bar: A, 10.3 mm; B-G, 10 µm.
FIG. 19 in Disentangling cryptic species in the Marasmius haematocephalus (Mont.) Fr. and M. siccus (Schwein.) Fr. species complexes (Agaricales, Basidiomycota)
FIG. 19. — Marasmius cf. pallescens Murrill (JO426): A1, A2, basidiomata; B, basidiospores; C, basidioles; D, basidia; E, pleurocystidia; F, cheilocystidia; G, Siccus-type broom cells of the pileipellis. Scale bar: A1, 12.5 mm; A2, 10.5 mm; B-G, 10 µm.
FIG. 18 in Disentangling cryptic species in the Marasmius haematocephalus (Mont.) Fr. and M. siccus (Schwein.) Fr. species complexes (Agaricales, Basidiomycota)
FIG. 18. — Marasmius rubicundus (Singer) J.S. Oliveira, stat. nov. (JO464): A, basidiomata; B, basidiospores; C, basidia; D, basidioles; E, pleurocystidia; F, cheilocystidia; G, Siccus-type broom cells of the pileipellis. Scale bar: A, 19.2 mm; B-G, 10 µm.
FIG. 15 in Disentangling cryptic species in the Marasmius haematocephalus (Mont.) Fr. and M. siccus (Schwein.) Fr. species complexes (Agaricales, Basidiomycota)
FIG. 15. — Pictures of fresh basidiomata: A, Marasmius roseus J.S. Oliveira, sp. nov. (JO352); B, M. rubicundus (Singer) J.S. Oliveira, stat. nov. (JO464); C, M. cf. pallescens Murrill (JO426). Scale bars: 10 mm.
FIG. 14 in Disentangling cryptic species in the Marasmius haematocephalus (Mont.) Fr. and M. siccus (Schwein.) Fr. species complexes (Agaricales, Basidiomycota)
FIG. 14. — Marasmius haematocephalus (Mont.) Fr. (JO533): A1, A2, basidiomata; B, basidiospores; C, basidioles; D, basidia; E, pleurocystidia; F, cheilocystidia; G, Siccus-type broom cells of the pileipellis. Scale bar: A1, 8.9 mm; A2, 5.9 mm; B-G, 10 µm.
FIG. 11. — Marasmius castanocephalus J.S in Disentangling cryptic species in the Marasmius haematocephalus (Mont.) Fr. and M. siccus (Schwein.) Fr. species complexes (Agaricales, Basidiomycota)
FIG. 11. — Marasmius castanocephalus J.S. Oliveira, sp. nov. (JO523): A1, A2, basidiomata; B, basidiospores; C, basidioles; D, pleurocystidia; E, cheilocystidia; F, Siccus-type broom cells of the pileipellis. Scale bar: A1, 18 mm; A2, 10 mm; B-F, 10 µm.
FIG. 2 in Disentangling cryptic species in the Marasmius haematocephalus (Mont.) Fr. and M. siccus (Schwein.) Fr. species complexes (Agaricales, Basidiomycota)
FIG. 2. — Best-scored Maximum Likelihood Tree (TL = 0.164547) built from multilocus (nrITS and nrLSU) analysis based on siccus_cp1 in Fig. 1 (DATASET 2a). Support values are BS (70 to 100 %) and PP (0.95 to 1.0). Black, thick stems indicate high support and gray, thick stems indicate weak to moderate support. The gray shades highlight siccus_cp2a, siccus_cp2b, and M. siccus s. str. Blue arrows indicate the geographic origin of the strains. Global map is based on Olson & Dinerstein (2002) with forested areas depicted in their biogeographic realms.
FIG. 10. — Marasmius auranticapitatus J.S in Disentangling cryptic species in the Marasmius haematocephalus (Mont.) Fr. and M. siccus (Schwein.) Fr. species complexes (Agaricales, Basidiomycota)
FIG. 10. — Marasmius auranticapitatus J.S. Oliveira, sp. nov. (M. Capelari & L.A.S. Ramos 4554): A, basidiomata; B, basidiospores; C, basidioles; D, pleurocystidia; E, cheilocystidia; F, Siccus-type broom cells of the pileipellis. Scale bar: A, 10 mm; B-F, 10 µm.
FIG. 12. — Marasmius coasiaticus J.S in Disentangling cryptic species in the Marasmius haematocephalus (Mont.) Fr. and M. siccus (Schwein.) Fr. species complexes (Agaricales, Basidiomycota)
FIG. 12. — Marasmius coasiaticus J.S. Oliveira, sp. nov. (JO323): A, basidiomata; B, basidiospores; C, basidia; D, basidioles; E, pleurocystidia; F, cheilocystidia; G, Siccus-type broom cells of the pileipellis. Scale bar: A, 12.8 mm; B-G, 10 µm.
FIG. 5 in Disentangling cryptic species in the Marasmius haematocephalus (Mont.) Fr. and M. siccus (Schwein.) Fr. species complexes (Agaricales, Basidiomycota)
FIG. 5. — Best-scored Maximum Likelihood trees (left side, TL = 0.111755; 0.135069) and 50 % majority-rule consensus trees (right side, TL = 0.106492; 0.120539) of Bayesian analyses built from multilocus (nrITS + rpb2 + ef1-α) analyses (DATASET 4) of the haemat_cp2a (Fig. 3). Black, thick stems indicate high support and gray, thick stems indicate weak to moderate support. Orange shade indicates M. auranticapitatus J.S. Oliveira, sp. nov. and the pink shade indicates M. rubicundus (Singer) J.S. Oliveira, stat. nov. Trees on the top includes MC4554 (holotype of M. auranticapitatus J.S. Oliveira, sp. nov.).
FIG. 1. — 50 in Disentangling cryptic species in the Marasmius haematocephalus (Mont.) Fr. and M. siccus (Schwein.) Fr. species complexes (Agaricales, Basidiomycota)
FIG. 1. — 50 % majority-rule consensus tree (tree length – TL = 27.160273) of Bayesian analysis reconstructed from multilocus (nrITS and nrLSU) analysis of DATA- SET 1 (M. haematocephalus (Mont.) Fr., M. siccus (Schwein.) Fr. and close species). Support values are PP (0.95 to 1.0) and BS (70 to 100 %). Black, thick stems indicate high support and gray, thick stems indicate weak to moderate support. Global map is based on Olson & Dinerstein (2002) with depicted forested areas in their biogeographic realms. The red area on the map is the putative distribution coverage of haemat_cp1 (in gray shade) on continental and insular terrestrial areas; the blue area represents the putative distribution coverage of siccus_cp1 (in gray shade) based on Fig. S1 in the Appendix, with branches colored accordingly.
FIG. 9 in Disentangling cryptic species in the Marasmius haematocephalus (Mont.) Fr. and M. siccus (Schwein.) Fr. species complexes (Agaricales, Basidiomycota)
FIG. 9. — Pictures of fresh basidiomata: A, Marasmius auranticapitatus J.S. Oliveira, sp. nov. (M. Capelari & L.A.S. Ramos 4554); B, M. castanocephalus J.S. Oliveira, sp. nov. (JO523); C, M. coasiaticus J.S. Oliveira, sp. nov. (JO323). Scale bars: 10 mm.
FIG. 8 in Disentangling cryptic species in the Marasmius haematocephalus (Mont.) Fr. and M. siccus (Schwein.) Fr. species complexes (Agaricales, Basidiomycota)
FIG. 8. — Marasmius gardneri Singer (JO491): A1, A2, basidiomata; B, basidiospores; C, basidioles; D, pleurocystidia; E, cheilocystidia; F, Siccus-type broom cells of the pileipellis. Scale bar: A1, 10.8 mm; A2, 5.9 mm; B-F, 10 µm.
FIG. 3 in Disentangling cryptic species in the Marasmius haematocephalus (Mont.) Fr. and M. siccus (Schwein.) Fr. species complexes (Agaricales, Basidiomycota)
FIG. 3. — Best-scored Maximum Likelihood Tree (TL = 0.555713) built from multilocus (nrITS and nrLSU) analysis based on haemat_cp1 in Fig. 1 (DATASET 2b). Support values are BS (70 to 100 %) and PP (0.95 to 1.0). Black, thick stems indicate high support and gray, thick stems indicate weak to moderate support. The gray shades highlight the haemat_cp2a-e and the cryptic species in red rectangles if pileus color is a determinant constrain. Red arrows indicate the geographic origin of strains in the tree. Global map is based on Olson & Dinerstein (2002) with forested areas depicted in their biogeographic realms.
FIG. 4 in Disentangling cryptic species in the Marasmius haematocephalus (Mont.) Fr. and M. siccus (Schwein.) Fr. species complexes (Agaricales, Basidiomycota)
FIG. 4. — Best-scored Maximum Likelihood tree (left side, TL = 0.054576) and 50 % majority-rule consensus tree (right side, TL = 4.954404) of Bayesian analyses built from multilocus (nrLSU and nrITS) analyses (DATASET 3) of the haemat_cp2a (Fig. 3). Black, thick stems indicate high support and gray, thick stems indicate weak to moderate support. Dark red shade indicates M. haematocephalus (Mont.) Fr., the orange shade indicates M. auranticapitatus J.S. Oliveira, sp. nov. and the pink shade indicates M. rubicundus (Singer) J.S. Oliveira, stat. nov.
FIG. 1. — Ilytheomyces uncinatus W. Rossi & M in Fungal biodiversity profiles 111-120
FIG. 1. — Ilytheomyces uncinatus W. Rossi & M. Leonardi, sp. nov. from the type slide. Scale bar: 50 µm.
Physico-chemical properties of the top 120 m of two ice cores in Dronning Maud Land (East Antarctica)
<p>We provide in this dataset the complete records from the top 120 m of two ice cores drilled on two adjacent ice rises (Hammarryggen and Lokeryggen, ~100 km apart) located in coastal Dronning Maud Land, Antarctica. Density, water stable isotopes (δ<sup>18</sup>O, δD and d-excess), ions concentrations (Na<sup>+</sup>, K<sup>+</sup>, Mg<sup>+</sup>, Ca<sup>+</sup>, MSA, Cl<sup>-</sup>, SO<sub>4</sub><sup>2-</sup> and NO<sub>3</sub><sup>-</sup>), and continuous electrical conductivity measurement (ECM) records, as well as age models and resulting surface mass balance (SMB) are presented here. These ice cores are dated back to the end of the eighteenth century (CE 1793 ± 3 years for FK17and 1780 ± 5 years for TIR18) by manual and automatic multivariable layer counting and volcanic tie points validation. The SMB is calculated after correction for vertical strain rates.</p> <p>A detailed description of the measurement procedures, data treatments and uncertainties can be found in: Wauthy S., Tison, J.-L., Inoue, M., El Amri, S., Sun, S., Claeys, P., and Pattyn, F.: Physico-chemical properties of the top 120 m of two ice cores in Dronning Maud Land (East Antarctica): an open window on spatial and temporal regional variability of environmental proxies, submitted to Earth System Science Data (ESSD).</p> <p>These new records can be compared to those from other coastal ice cores in DML and to regional or global model results, and represent a great opportunity for further studies addressing the mechanisms behind SMB spatial and temporal variability in East Antarctica.</p> <p>The study was funded in the framework of the Mass2Ant project, contract n° BR/165/A2 by the Belgian Science Policy BELSPO.</p>
SPVPANELEX: Dataset containing aerial orthoimages (covering 257.93 km2 of the Spanish territory, with a spatial resolution of 0.5 m) labelled with photovoltaic panel information for binary recognition and semantic segmentation
<p>The data have been generated using scripts developed in Python with Open-Source libraries (GDAL/OGR and MapScript) to rasterize of vector cartography representing the photovoltaic (PV) panels instalations in urban, industrial, and rural areas. This PV panels cartography has been generated by manual digitalizing the PV panels found latest aerial orthofotographs available on June 1, 2021 from Plano Nacional de Ortofotografía Aérea (PNOA), produced by the National Geographic Institute of Spain, using the Web Map Service PNOA-MA.<br> <br> The dataset consists of 239,680 images of 256 × 256 pixels in size, in png format, labelled with Class_1: “Contains PV panel” and Class_2: “Does not contain PV panel”, that were pre-divided with a split criterion of 70:10:20%. in train, validation and test folders, respectively.<br> <br> The structure of the data is as follows:<br> 1-Panels-Ortho and 1-Panels-Mask contain the images featuring PV panels and their corresponding ground truth mask for training the semantic segmentation networks.<br> 1-Panels-Ortho and 2-NoPanels-Ortho contain images containing and not containing PV panels, for the training of binary recognition models of PV panels.<br> <br> Moreover, in each folder the structure is the same: train, test, validation containing 70%, 10% and 20% of the total images and masks of each type.<br> <br> 1-Panels-Ortho<br> |----Train<br> |----Test<br> -----Validation<br> <br> 1-Panels-Mask<br> |----Train<br> |----Test<br> -----Validation<br> <br> 2-NoPanels-Ortho<br> |----Train<br> |----Test<br> -----Validation</p>
CBRA: The first multi-annual (2016-2021) and high-resolution (2.5 m) building rooftop area dataset in China derived with Super-resolution Segmentation from Sentinel-2 imagery
<p>Large-scale and up-to-date maps of building rooftop area (BRA) are crucial for addressing policy decisions and sustainable development. In addition, as a fine-grained indicator of human activities, BRA could contribute to urban planning and energy modeling to provide benefits to human well-being. However, existing large-scale BRA datasets, such as those from Microsoft and Google, do not include China, hence there are no full-coverage maps of BRA in China. To this end, we produce the multi-annual China building rooftop area dataset (CBRA) with 2.5 m resolution from 2016-2021 Sentinel-2 images. The CBRA is the first full-coverage and multi-annual BRA data in China. The CBRA achieves good performance with the F1 score of 62.55% (+10.61% compared with the previous BRA data in China) based on 250,000 testing samples in urban areas, and the recall of 78.94% based on 30,000 testing samples in rural areas. </p> <p>The CBRA is organized as GeoTIFF (.tif) raster file format with a single band and GCS_WGS_1984 coordinate system. The pixel values are 0 and 255, with 0 representing the background and 255 representing the building rooftop area. Furthermore, to facilitate the use of the data, the CBRA is split into 215 tiles of spatial grid, named “CBRA_year_E/W**N/S**.tif”, where “year” is the sampling year, the “E/W**N/S**” is the latitude and longitude coordinates found in the upper left corner of the tile data.</p> <p> </p> <p>Version 2.0: In version 1.0, there were empty raster images (because they didn't contain buildings). In version 2.0, these raster images were removed.</p>
Supplementary material for: L. Coltofean-Arizancu, B. Gaydarska, S. Plutniak, L. Mary, M. Hlad, I. Algrain, B. Pasquini, S. Vandevelde, E. Stamataki, P. Janežič, B. Wouters, A. Sengeløv. "Harassment, Assault, Bullying and Intimidation (HABI) in Archaeology: Europe-wide Survey", DOI: 10.15184/aqy.2023.58
<p>Supplementary material for: L. Coltofean-Arizancu, B. Gaydarska, S. Plutniak, L. Mary, M. Hlad, I. Algrain, B. Pasquini, S. Vandevelde, E. Stamataki, P. Janežič, B. Wouter, A. Sengeløv. “Harassment, Assault, Bullying and Intimidation (HABI) in Archaeology: Europe-wide Survey”, <em>Antiquity</em>. DOI: <a href="https://doi.org/10.15184/aqy.2023.58">10.15184/aqy.2023.58</a>.</p>
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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.