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Fig. 1 in PAST DISTRIBUTION OF TILIA-FEEDING PHYLLONORYCTER MICROMOTH (LEPIDOPTERA: GRACILLARIIDAE) IN THE RUSSIAN FAR EAST BASED ON SURVEY OF HISTORICAL HERBARIUM
Fig. 1. Accumulation of Tilia herbarium during one century period in the Russian Far East (A) and number of herbarized Tilia specimens with Phyllonorycter mines (B). VLA –
Fig. 3 in PAST DISTRIBUTION OF TILIA-FEEDING PHYLLONORYCTER MICROMOTH (LEPIDOPTERA: GRACILLARIIDAE) IN THE RUSSIAN FAR EAST BASED ON SURVEY OF HISTORICAL HERBARIUM
Fig. 3. Historical records of Tilia-feeding Phyllonorycter occurrence in the Russian Far East retrieved from on one century-old herbarium. For locations 1–61 see Table 1. In the left
Data used in the paper: Historical and current environmental selection on functional traits of trees in the Atlantic Forest biodiversity hotspot
<p>This repository contains phylogenetic and functional trait data, raster files, and tables with sampling information and references used in the article "Historical and current environmental selection on functional traits of trees in the Atlantic Forest biodiversity hotspot" by Silva, J.L.A., Souza, A., and Vitória, A.P. Journal of Vegetation Science, <a href="https://doi.org/10.1111/jvs.13049">https://doi.org/10.1111/jvs.13049</a> .</p> <p>Description of files:</p> <p>(1) "Species-level_Trait_Data_Silva_et_al._2021.csv": This file contains species-specific mean trait values and the plant growth form of the 2,122 studied species, whenever available. Trait values were compiled from public sources such as original papers, master and doctoral dissertations, and global trait databases.</p> <p>(2) "Phylogenetic_Tree_Silva_et_al._2021.txt": This file contains the phylogenetic tree of the 2,122 studied species.</p> <p>(3) "CWM_Trait_Maps.zip": This file contains seven rasters of spatially contiguous surfaces produced by Ordinary Kriging Interpolation using Community-Weighted Means (CWM) of each functional trait.</p> <p>(4) "References-abundance-data.csv": This file contains sampling details and the references used to compile species abundance data for each studied site.</p> <p>(5) "References-trait-data.csv": This file contains sampling details and the references used to compile functional trait data.</p> <p> </p>
Input data for: Combining global tree cover loss data with historical national forest-cover maps to look at six decades of deforestation and forest fragmentation in Madagascar.
<p>This repository includes input data used in the following article:</p> <p><strong>Vieilledent G., C. Grinand, F. A. Rakotomalala, R. Ranaivosoa, J.-R. Rakotoarijaona, T. F. Allnutt, and F. Achard.</strong> Combining global tree cover loss data with historical national forest-cover maps to look at six decades of deforestation and forest fragmentation in Madagascar.</p> <p>For this article, data have been processed with a R/GRASS script. The development version of this script is available on GitHub at https://github.com/ghislainv/deforestation-maps-Mada. The last release of this script is archived on Zenodo: [DOI: 10.5281/zenodo.1118484].</p>
Output data from: Combining global tree cover loss data with historical national forest-cover maps to look at six decades of deforestation and forest fragmentation in Madagascar.
<p>This repository includes output data from the following article:</p> <p><strong>Vieilledent G., C. Grinand, F. A. Rakotomalala, R. Ranaivosoa, J.-R. Rakotoarijaona, T. F. Allnutt, and F. Achard</strong>. Combining global tree cover loss data with historical national forest-cover maps to look at six decades of deforestation and forest fragmentation in Madagascar.</p> <p>This repository includes Madagascar forest cover (forXXXX.tif), forest density (fordensXXXX.tif), distance to forest edge (dist_edge_XXXX.tif) and forest fragmentation index (fragXXXX.tif) for the years 1953, 1973, 1990, 2000, 2005, 2010 and 2014. Data are available as GeoTIFF raster files at 30m resolution in the UTM 38S projection (EPSG:32738).</p>
Historical time-series reconstruction benchmark dataset of Landsat bi-monthly aggregates from GLAD ARD-2 at 30-m resolution with stratified sampling based on ESA CCI
<h2>Description</h2> <p>Historical time-series reconstruction benchmark dataset presented here is designed for evaluating and comparing the performance of time series reconstruction methods in the context of land cover change detection. The dataset is based on the European Space Agency Climate Change Initiative (ESA CCI) land cover dataset, which has been aggregated into 18 classes to facilitate analysis. The dataset includes information on land cover dynamics from 2000 to 2020, focusing on identifying and characterizing changes in land cover over time.</p> <h3><strong>Data Collection and Processing:</strong></h3> <p>The dataset is derived from the ESA CCI land cover dataset, which provides information on land cover classes at a global scale. The original dataset, containing 37 land cover classes, was aggregated into 18 classes based on similarity. Pixels with stable land cover over the study period and pixels with one or multiple land cover changes were identified and grouped into strata for sampling purposes.</p> <p>Sampling points were selected using a stratified sampling design, ensuring representation across different land cover classes and change scenarios. Approximately 2600 points were selected from each stratum, resulting in a total of 51,978 sampling points. The selected points were uniformly distributed along the strata, with spatial variations accounted for.</p> <p>Bimonthly time series data were extracted for each sampling point from 1997 to 2022, capturing temporal dynamics in land cover. Artificial gaps were introduced into the time series data to simulate real-world data loss, allowing for the evaluation of time series reconstruction methods under varying gap densities.</p> <p>The time series values were extracted from Landsat GLAD imagery using the specified spectral bands, including blue, green, red, NIR, SWIR1, SWIR2, and thermal bands. Additionally, a clear quality band was also extracted.</p> <h3>Data Details</h3> <ul> <li><strong>Time Period:</strong> 1997-01-01 to 2022-12-31</li> <li><strong>Type of Data: </strong>R data frame / Geopackage points.</li> <li><strong>Collection/Derivation:</strong> Derived from Landsat ARD v2, processed with Scikit-map.</li> <li><strong>Coordinate Reference System:</strong> EPSG:4326</li> <li><strong>Bounding Box:</strong> All the globe</li> <li><strong>File Format:</strong> RDS</li> </ul> <p> </p> <h3><strong>Reclassified Classes of ESA CCI Land Cover Dataset</strong></h3> <table> <tbody> <tr> <td> <div> <div> <p><strong>Aggregated Class Code</strong></p> </div> </div> </td> <td> <div> <div> <p><strong>Aggregated Class Label</strong></p> </div> </div> </td> <td> <div> <div> <p><strong>Original ESA CCI Classes</strong></p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>10</p> </div> </div> </td> <td> <div> <div> <p>Cropland rainfed</p> </div> </div> </td> <td> <div> <div> <p>10, 11, 12</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>30</p> </div> </div> </td> <td> <div> <div> <p>Mosaic cropland | natural vegetation</p> </div> </div> </td> <td> <div> <div> <p>30, 40</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>50</p> </div> </div> </td> <td> <div> <div> <p>Tree cover broadleaved evergreen</p> </div> </div> </td> <td> <div> <div> <p>50</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>60</p> </div> </div> </td> <td> <div> <div> <p>Tree cover broadleaved deciduous</p> </div> </div> </td> <td> <div> <div> <p>60, 61, 62</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>70</p> </div> </div> </td> <td> <div> <div> <p>Tree cover needleleaved evergreen</p> </div> </div> </td> <td> <div> <div> <p>70, 71, 72</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>80</p> </div> </div> </td> <td> <div> <div> <p>Tree cover needleleaved deciduous</p> </div> </div> </td> <td> <div> <div> <p>80, 81, 82</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>90</p> </div> </div> </td> <td> <div> <div> <p>Tree cover mixed leaf type</p> </div> </div> </td> <td> <div> <div> <p>90</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>100</p> </div> </div> </td> <td> <div> <div> <p>Mosaic tree and shrub | herbaceous cover</p> </div> </div> </td> <td> <div> <div> <p>100, 110</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>120</p> </div> </div> </td> <td> <div> <div> <p>Shrubland</p> </div> </div> </td> <td> <div> <div> <p>120, 121, 122</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>150</p> </div> </div> </td> <td> <div> <div> <p>Sparse vegetation</p> </div> </div> </td> <td> <div> <div> <p>150, 151, 152, 153</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>160</p> </div> </div> </td> <td> <div> <div> <p>Tree cover flooded</p> </div> </div> </td> <td> <div> <div> <p>160, 170</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>180</p> </div> </div> </td> <td> <div> <div> <p>Shrub or herbaceous cover flooded</p> </div> </div> </td> <td> <div> <div> <p>180</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>200</p> </div> </div> </td> <td> <div> <div> <p>Bare areas</p> </div> </div> </td> <td> <div> <div> <p>200, 201, 202</p> </div> </div> </td> </tr> </tbody> </table> <p>In the table, each row represents a reclassified land cover class, identified by a unique code. The 'Original ESA CCI Classes' column lists the specific land cover classes from the European Space Agency Climate Change Initiative dataset that are grouped together to form each broader category. Note that land cover classes not listed in this table were retained in their original value and were not reclassified.</p> <h3><strong>File Format</strong></h3> <p>The dataset comprises observations spanning from January 1997 to November 2022, capturing data for 51,978 samples.</p> <ul> <li>blue.rds: Time series data for the blue spectral band.</li> <li>green.rds: Time series data for the green spectral band.</li> <li>red.rds: Time series data for the red spectral band.</li> <li>nir.rds: Time series data for the near-infrared (NIR) spectral band.</li> <li>swir1.rds: Time series data for the shortwave infrared 1 (SWIR1) spectral band.</li> <li>swir2.rds: Time series data for the shortwave infrared 2 (SWIR2) spectral band.</li> <li>thermal.rds: Time series data for the thermal infrared band.</li> <li>clear.rds: Time series data for the clear quality band, used for masking out cloudy observations.</li> </ul> <p>How open the files in R:</p> <p><code>blue <- readRDS("blue.rds")</code></p> <p>To open the files in Python, you need to the <code>pyreadr</code> library:</p> <p><code>import pyreadr</code><br><code>blue = pyreadr.read_r('blue.rds')</code></p> <p> </p>
Figure 5 in A new species of Andean lizard Proctoporus (Squamata: Gymnophthalmidae) from montane forest of the Historic Sanctuary of Machu Picchu, Peru
Figure 5. Type locality of Proctoporus machupicchu: (A, C) Montane forest, (B) Urubamba River, (D) Habitat of Proctoporus machupicchu. Photo: (6 A–C) Luis Mamani; 6 D (Javier Farfan).
Figure 3 in A new species of Andean lizard Proctoporus (Squamata: Gymnophthalmidae) from montane forest of the Historic Sanctuary of Machu Picchu, Peru
Figure 3. Dorsal and ventral views of living specimens of Proctoporus machupicchu. A–B adult female (MHNC 13362); C–D adult female (MHNC 13513); and E–F inmature male (MHNC 13373); and G not collected of immature male showing femoral pores.
Figure 4 in A new species of Andean lizard Proctoporus (Squamata: Gymnophthalmidae) from montane forest of the Historic Sanctuary of Machu Picchu, Peru
Figure 4. Map showing the distribution of Proctoporus species known from southeast of Peru, based on species listed in Appendix I and in Uzzell (1970), Doan and Castoe (2003), Doan et al. (2005), Chavez et al. (2011), and Goicoechea et al. (2013). Green circle, Proctoporus machupicchu sp. nov.; blue triangle, P. bolivianus; blue square, P. carabaya; red square, P. chasqui; blue circle, P. iridescens; blue pentagon, P. kiziriani; red circle, P. lacertus; red triangle, P. unsaacae; and red pentagon P. sucullucu.
Fig. 2 in A new species of Andean lizard Proctoporus (Squamata: Gymnophthalmidae) from montane forest of the Historic Sanctuary of Machu Picchu, Peru
Fig. 2. (A, B, C) Head of the holotype of Proctoporus machupicchu (MHNC 13362), lateral, dorsal, and ventral view; and (D, E, F) Paratype (MHNC 13373) lateral, dorsal. and ventral view of the head. Scale bar 5 mm.
Corpus of Historical German, Bochum
<p>This publication contains the datasets and annotation scheme from the paper: Stefanie Dipper, Cora Haiber, Anna Maria Schröter, Alexandra Wiemann, Maike Brinkschulte (2024) Universal Dependencies: Extensions for Modern and Historical German. In: Proceedings of LREC-COLING 2024.</p>
Investigating historical drivers of latitudinal gradients in polyploid plant biogeography: A multi-clade perspective
<p><em>Premise of the Study</em></p> <p>The proportion of polyploid plants in a community increases with latitude, and different hypotheses have been proposed about which factors drive this pattern. Here, we aim to understand the historical causes of the latitudinal polyploidy gradient using a combination of ancestral state reconstruction methods. Specifically, we assess whether (1) polyploidization enables movement to higher latitudes (i.e., polyploidization precedes occurrences in higher latitudes) or (2) higher latitudes facilitate polyploidization (i.e., occurrence in higher latitudes precedes polyploidization).</p> <p><em>Methods</em></p> <p>We reconstruct the ploidy states and ancestral niches of 1,032 angiosperm species at four paleoclimatic time slices ranging from 3.3 million years ago to the present, comprising taxa from four well-represented clades: Onagraceae, Primulaceae, <em>Solanum</em> (Solanaceae), and Pooideae (Poaceae). We use ancestral niche reconstruction models alongside a customized discrete character evolution model to allow reconstruction of states at specific time slices. Patterns of latitudinal movement are reconstructed and compared in relation to inferred ploidy shifts.</p> <p><em>Key Results</em></p> <p>We find that no single hypothesis applies equally well across all analyzed clades. While significant differences in median latitudinal occurrence were detected in the largest clade, Poaceae, no significant differences were detected in latitudinal movement in any clade.</p> <p><em>Conclusions</em></p> <p>Our preliminary study is the first to attempt to connect ploidy changes to continuous latitudinal movement, but we cannot favor one hypothesis over another. Given that patterns seem to be clade-specific, a larger number of clades must be analyzed in future studies for generalities to be drawn.</p>
Figure 7 in Historical and morphological review of the subgenus Indonthophagus Kabakov, 2006 of Onthophagus Latreille, 1802 (Coleoptera: Scarabaeidae: Scarabaeinae: Onthophagini)
Figure 7. Onthophagus (Indonthophagus) mopsus (Fabricius, 1792), lectotype labels. Photo by Katerine Child (OUMNH).
Figure 1 in Historical and morphological review of the subgenus Indonthophagus Kabakov, 2006 of Onthophagus Latreille, 1802 (Coleoptera: Scarabaeidae: Scarabaeinae: Onthophagini)
Figure 1. Onthophagus (Indonthophagus) mopsus (Fabricius, Figure 2. Onthophagus (Indonthophagus) turbatus Walker, 1792) [Pakistan, Islamabad Capital Territory]. 1a) ♀ head, 1858 [Sri Lanka, Uva prov.]. Detail of pronotal posterolateral from above. 1b) Epipharynx. Photos by A. Degiovanni, ed- area. Photos by A. Degiovanni, edited by G. Fiumi.
Figure 9 in Historical and morphological review of the subgenus Indonthophagus Kabakov, 2006 of Onthophagus Latreille, 1802 (Coleoptera: Scarabaeidae: Scarabaeinae: Onthophagini)
Figure 9. Onthophagus (Indonthophagus) turbatus Walker, 1858 [syntype, medium ♂: Sri Lanka]. 9a) Male dorsal habitus [photo and editing by G. Fiumi]. 9b) Parameres, lateral view. 9c) Parameres, dorsal view. 9d) Endophallus. AcS: accessory endophallites. LC: lamella copulatrix. Rs: raspulae. Photos by A. Degiovanni, edited by G. Fiumi.
Figure 6 in Historical and morphological review of the subgenus Indonthophagus Kabakov, 2006 of Onthophagus Latreille, 1802 (Coleoptera: Scarabaeidae: Scarabaeinae: Onthophagini)
Figure 6. Onthophagus (Indonthophagus) mopsus (Fabricius, 1792) [major ♂: Pakistan, Islamabad Capital Territory]. 6a) Dorsal habitus; 6b) Parameres, lateral view. 6c) Parameres, dorsal view. 6d) Endophallus. AcS: accessory endophallites. LC: lamella copulatrix. Rs: raspulae. Photos by A. Degiovanni, edited by G. Fiumi.
Figure 4 in Historical and morphological review of the subgenus Indonthophagus Kabakov, 2006 of Onthophagus Latreille, 1802 (Coleoptera: Scarabaeidae: Scarabaeinae: Onthophagini)
Figure 4. Onthophagus (Indonthophagus) ensifer Boucomont, 1914 [major ♂: India, Tamil Nadu st.]. 4a) Dorsal habitus. 4b) Parameres, lateral view. 4c) Parameres, dorsal view. 4d) Endophallus. AcS: accessory endophallites. LC: lamella copulatrix. Rs: raspula. Photos by A. Degiovanni, edited by G. Fiumi.
Figure 11 in Historical and morphological review of the subgenus Indonthophagus Kabakov, 2006 of Onthophagus Latreille, 1802 (Coleoptera: Scarabaeidae: Scarabaeinae: Onthophagini)
Figure 11. Onthophagus (incertae sedis) spinifex (Fabricius, 1781) [major ♂: Pakistan, Capital Territory]. 11a) Dorsal habitus. 11b) Parameres, lateral view. 11c) Parameres, dorsal view. 11d) Endophallus. AcS: accessory endophallites. LC: lamella copulatrix. Photos by A. Degiovanni, edited by G. Fiumi.
Figure 3 in Historical and morphological review of the subgenus Indonthophagus Kabakov, 2006 of Onthophagus Latreille, 1802 (Coleoptera: Scarabaeidae: Scarabaeinae: Onthophagini)
Figure 3. Onthophagus (Indonthophagus) aeneopiceus d'Orbigny, 1902 [major ♂: Yemen, Ibb gov.]. 3a) Dorsal habitus. 3b) Parameres, lateral view. 3c) Parameres, dorsal view. 3d) Endophallus. AcS: accessory endophallites. LC: lamella copulatrix. Rs: raspulae. Photos by A. Degiovanni, edited by G. Fiumi [from Ziani and Keith (2023)].
Figure 10 in Historical and morphological review of the subgenus Indonthophagus Kabakov, 2006 of Onthophagus Latreille, 1802 (Coleoptera: Scarabaeidae: Scarabaeinae: Onthophagini)
Figure 10. Onthophagus (Indonthophagus) turbatus Walker, 1858 [major ♂: Pakistan, Mansehra prov.]. Dorsal habitus. Photos by A. Degiovanni, edited by G. Fiumi.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.