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2,402 results for “East Asia”
Figs. 1-9 in Taxonomic Review of Zavreliella Kieffer from East Asia
Figs. 1-9. Zavreliella shidai sp. n., male. 1 wing 2 abdominal marking, dorsal view 3 legs, showing marking pattern 4 forefemur 5 foretibial apex 6 mid tibial apex 7 hind tibial apex 8 hypopygium 9 details of superior volsella.
Figs. 10-16 in Taxonomic Review of Zavreliella Kieffer from East Asia
Figs. 10-16. Zavreliella shidai sp. n., pupa. 10 frontal apotome, with details of cephalic tubercles in upper corners 11 thoracic horn 12 thorax 13 details of basal ring 14 tergites 15 anal comb of VIII 16 anal lobe, showing male sac.
A downscaled 0.05-Degree Monthly Solar-Induced Chlorophyll Fluorescence Product derived using AVHRR Data in East Asia (1995-2003)
<p>Downscaling techniques offer the opportunity to utilize coarse-spatial-resolution SIF products for investigating carbon cycles and ecological processes at finer resolutions. Here, we generated a new monthly SIF product, DSIF_EA0.05, at a resolution of 0.05° in East Asia from July 1995 to June 2003. The random forest kriging (RFK) approach was employed, incorporating GOME SIF, AVHRR data, ERA5 climate data, and using the optimal explanatory variables. The unit of SIF is mW m<sup>-2</sup> nm<sup>-1</sup> sr<sup>-1</sup>. The selected variables were daily maximum values of air temperature (T<sub>air</sub>), skin temperature (T<sub>skin</sub>), fraction of absorbed photosynthetically active radiation (fPAR), near-infrared reflectance of vegetation (NIRv), downward shortwave radiation (SR<sub>down</sub>), and precipitation. To verify the reliability of DSIF_EA0.05 and the advantages over original GOME SIF, this dataset has been validated with the original GOME SIF, ground gross primary productivity (GPP) data from eight flux sites, and two other SIF products at 1-degree and 0.05-degree resolutions from SCIAMACHY SIF and downscaled SCIAMACHY SIF datasets.</p>
ELITE land surface temperature: hourly seamless 0.02° LST over East Asia (2021)
<p>The <strong>E</strong>ssential therma<strong>L</strong> <strong>I</strong>nfrared remo<strong>T</strong>e s<strong>E</strong>nsing (<strong>ELITE</strong>) product suite currently has four types of products, including land surface temperature (LST: clear-sky and all-sky), emissivity (NBE: narrowband emissivity; BBE: broadband emissivity; and spectral emissivity), the component of surface radiation and energy budget (SLUR: surface longwave upwelling radiation; SLDR: surface longwave downward radiation SLDR; SLNR: surface longwave net radiation), and the component of Earth’s radiation budget (OLR; outgoing longwave radiation; RSR: reflected solar radiation). The spatial-temporal resolutions of the ELITE products are mainly determined by the employed satellite data sources. For more information about ELITE products, please refer to the website (<a href="https://elite.bnu.edu.cn">https://elite.bnu.edu.cn</a>).</p> <p>This dataset is the ELITE hourly seamless 0.02 ° LST dataset over East Asia (2016-2021). Firstly, the iTES algorithm is employed to retrieve the Himawari-8/AHI LST. Secondly, the CLDAS LST is corrected to eliminate its system deviation. Finally, the multi-scale Kalman filter is employed to fuse Himawari-8/AHI LST and the bias-corrected CLDAS LST to generate 0.02 ° hourly seamless LST. The in situ validation results show that the root mean square error (RMSE) of the seamless LST is about 3k. The temporal resolution and spatial resolution of this dataset are 1 hour and 0.02°, respectively.</p> <p>This is the seamless LST dataset in 2021. Please<a href="../records/10668883" target="_blank" rel="noopener"> <strong><em>click here</em></strong></a> to download the ELITE LST product in Januray-June, 2022.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: East Asia (0-60°N, 80°E-140°E)</li> <li>Temporal Coverage: 2021</li> <li>Spatial Resolution: 0.02 °</li> <li>Temporal Resolution: one hour</li> <li>Data Format: Geotiff</li> <li>Scale: 0.01</li> </ul> <p><strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong></p> <ol> <li>Dong, S., Cheng, J., Shi, J., Shi, C., Sun, S., & Liu, W. (2022). A Data Fusion Method for Generating Hourly Seamless Land Surface Temperature from Himawari-8 AHI Data. Remote Sensing, 14, 5170</li> <li>Zhou, S., & Cheng, J. (2020). An Improved Temperature and Emissivity Separation Algorithm for the Advanced Himawari Imager. IEEE Transactions on Geoscience and Remote Sensing, 58(10), 7105-7124.</li> </ol> <p>If you have any questions, please contact Prof. Jie Cheng (Jie_Cheng@bnu.edu.cn).</p>
ELITE land surface temperature: hourly seamless 0.02° LST over East Asia (2020)
<p>The <strong>E</strong>ssential therma<strong>L</strong> <strong>I</strong>nfrared remo<strong>T</strong>e s<strong>E</strong>nsing (<strong>ELITE</strong>) product suite currently has four types of products, including land surface temperature (LST: clear-sky and all-sky), emissivity (NBE: narrowband emissivity; BBE: broadband emissivity; and spectral emissivity), the component of surface radiation and energy budget (SLUR: surface longwave upwelling radiation; SLDR: surface longwave downward radiation SLDR; SLNR: surface longwave net radiation), and the component of Earth’s radiation budget (OLR; outgoing longwave radiation; RSR: reflected solar radiation). The spatial-temporal resolutions of the ELITE products are mainly determined by the employed satellite data sources. For more information about ELITE products, please refer to the website (<a href="https://elite.bnu.edu.cn">https://elite.bnu.edu.cn</a>).</p> <p>This dataset is the 0.02 ° hourly seamless LST dataset over East Asia (2016-2021). Firstly, the iTES algorithm is employed to retrieve the Himawari-8/AHI LST. Secondly, the CLDAS LST is corrected to eliminate its system deviation. Finally, the multi-scale Kalman filter is employed to fuse Himawari-8/AHI LST and the bias-corrected CLDAS LST to generate 0.02 ° hourly seamless LST. The in situ validation results show that the root mean square error (RMSE) of the seamless LST is about 3k. The temporal resolution and spatial resolution of this dataset are 1 hour and 0.02°, respectively.</p> <p>This is the ELITE seamless LST product in 2020. Please <a href="https://zenodo.org/record/8260245"><strong><em>click here</em></strong></a> to download the ELITE LST product in 2019 and <a href="https://zenodo.org/record/8260240"><strong><em>click here</em></strong></a> to download the ELITE LST product in 2021.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: East Asia (0-60°N, 80°E-140°E)</li> <li>Temporal Coverage: 2020</li> <li>Spatial Resolution: 0.02 °</li> <li>Temporal Resolution: one hour</li> <li>Data Format: Geotiff</li> <li>Scale: 0.01</li> </ul> <p><strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong></p> <ol> <li>Dong, S., Cheng, J., Shi, J., Shi, C., Sun, S., & Liu, W. (2022). A Data Fusion Method for Generating Hourly Seamless Land Surface Temperature from Himawari-8 AHI Data. Remote Sensing, 14, 5170</li> <li>Zhou, S., & Cheng, J. (2020). An Improved Temperature and Emissivity Separation Algorithm for the Advanced Himawari Imager. IEEE Transactions on Geoscience and Remote Sensing, 58(10), 7105-7124.</li> </ol> <p>If you have any questions, please contact Prof. Jie Cheng (Jie_Cheng@bnu.edu.cn).</p> <p> </p>
ELITE land surface temperature: hourly seamless 0.02° LST over East Asia (2018)
<p>The <strong>E</strong>ssential therma<strong>L</strong> <strong>I</strong>nfrared remo<strong>T</strong>e s<strong>E</strong>nsing (<strong>ELITE</strong>) product suite currently has four types of products, including land surface temperature (LST: clear-sky and all-sky), emissivity (NBE: narrowband emissivity; BBE: broadband emissivity; and spectral emissivity), the component of surface radiation and energy budget (SLUR: surface longwave upwelling radiation; SLDR: surface longwave downward radiation SLDR; SLNR: surface longwave net radiation), and the component of Earth’s radiation budget (OLR; outgoing longwave radiation; RSR: reflected solar radiation). The spatial-temporal resolutions of the ELITE products are mainly determined by the employed satellite data sources. For more information about ELITE products, please refer to the website (<a href="https://elite.bnu.edu.cn">https://elite.bnu.edu.cn</a>).</p> <p>This dataset is the 0.02 ° hourly seamless LST dataset over East Asia (2016-2021). Firstly, the iTES algorithm is employed to retrieve the Himawari-8/AHI LST. Secondly, the CLDAS LST is corrected to eliminate its system deviation. Finally, the multi-scale Kalman filter is employed to fuse Himawari-8/AHI LST and the bias-corrected CLDAS LST to generate 0.02 ° hourly seamless LST. The in situ validation results show that the root mean square error (RMSE) of the seamless LST is about 3k. The temporal resolution and spatial resolution of this dataset are 1 hour and 0.02°, respectively.</p> <p>This is the ELITE seamless LST product in 2018. Please <a href="https://zenodo.org/record/8266456"><strong><em>click here</em></strong></a> to download the ELITE LST product in 2017 and <a href="https://zenodo.org/record/8260245"><strong><em>click here</em></strong></a> to download the ELITE LST product in 2019.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: East Asia (0-60°N, 80°E-140°E)</li> <li>Temporal Coverage: 2018</li> <li>Spatial Resolution: 0.02 °</li> <li>Temporal Resolution: one hour</li> <li>Data Format: Geotiff</li> <li>Scale: 0.01</li> </ul> <p><strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong></p> <ol> <li>Dong, S., Cheng, J., Shi, J., Shi, C., Sun, S., & Liu, W. (2022). A Data Fusion Method for Generating Hourly Seamless Land Surface Temperature from Himawari-8 AHI Data. Remote Sensing, 14, 5170</li> <li>Zhou, S., & Cheng, J. (2020). An Improved Temperature and Emissivity Separation Algorithm for the Advanced Himawari Imager. IEEE Transactions on Geoscience and Remote Sensing, 58(10), 7105-7124.</li> </ol> <p>If you have any questions, please contact Prof. Jie Cheng (Jie_Cheng@bnu.edu.cn).</p> <p> </p>
ELITE land surface temperature: hourly seamless 0.02° LST over East Asia (2019)
<p>The <strong>E</strong>ssential therma<strong>L</strong> <strong>I</strong>nfrared remo<strong>T</strong>e s<strong>E</strong>nsing (<strong>ELITE</strong>) product suite currently has four types of products, including land surface temperature (LST: clear-sky and all-sky), emissivity (NBE: narrowband emissivity; BBE: broadband emissivity; and spectral emissivity), the component of surface radiation and energy budget (SLUR: surface longwave upwelling radiation; SLDR: surface longwave downward radiation SLDR; SLNR: surface longwave net radiation), and the component of Earth’s radiation budget (OLR; outgoing longwave radiation; RSR: reflected solar radiation). The spatial-temporal resolutions of the ELITE products are mainly determined by the employed satellite data sources. For more information about ELITE products, please refer to the website (<a href="https://elite.bnu.edu.cn">https://elite.bnu.edu.cn</a>).</p> <p>This dataset is the 0.02 ° hourly seamless LST dataset over East Asia (2016-2021). Firstly, the iTES algorithm is employed to retrieve the Himawari-8/AHI LST. Secondly, the CLDAS LST is corrected to eliminate its system deviation. Finally, the multi-scale Kalman filter is employed to fuse Himawari-8/AHI LST and the bias-corrected CLDAS LST to generate 0.02 ° hourly seamless LST. The in situ validation results show that the root mean square error (RMSE) of the seamless LST is about 3k. The temporal resolution and spatial resolution of this dataset are 1 hour and 0.02°, respectively.</p> <p>This is the ELITE seamless LST product in 2019. Please <a href="https://zenodo.org/record/8256087"><strong><em>click here</em></strong></a> to download the ELITE LST product in 2018 and <a href="https://zenodo.org/record/8264798"><strong><em>click here</em></strong></a> to download the ELITE LST product in 2020.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: East Asia (0-60°N, 80°E-140°E)</li> <li>Temporal Coverage: 2019</li> <li>Spatial Resolution: 0.02 °</li> <li>Temporal Resolution: one hour</li> <li>Data Format: Geotiff</li> <li>Scale: 0.01</li> </ul> <p><strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong></p> <ol> <li>Dong, S., Cheng, J., Shi, J., Shi, C., Sun, S., & Liu, W. (2022). A Data Fusion Method for Generating Hourly Seamless Land Surface Temperature from Himawari-8 AHI Data. Remote Sensing, 14, 5170</li> <li>Zhou, S., & Cheng, J. (2020). An Improved Temperature and Emissivity Separation Algorithm for the Advanced Himawari Imager. IEEE Transactions on Geoscience and Remote Sensing, 58(10), 7105-7124.</li> </ol> <p>If you have any questions, please contact Prof. Jie Cheng (Jie_Cheng@bnu.edu.cn).</p> <p> </p>
Impact of Convection-permitting and Model Resolution on the Simulation of Mesoscale Convective System Properties over East Asia: companion dataset
<p>This folder includes the intermediate data for the following manuscript:</p><p>Ding et al., Impact of Convection-permitting and Model Resolution on the Simulation of Mesoscale Convective System Properties over East Asia</p><p>The simulations were done using ICON-NWP (ICON Numerical Weather Prediction) model, version 2.6.1, over Asian monsoon region (62E–150E, 5.5N–54.5N) for 2020 summer. At the moment, we upload the intermediate data for MCS tracking. For more data, please contact the authors.</p>
Revisiting of Carex sect. Confertiflorae s.l. (Cyperaceae): new data from molecular and morphological evidence and first insights on Carex biogeography in East Asia
Open the record for dataset details and reuse information.
Data from: Multiple refugia from penultimate glaciations in East Asia demonstrated by phylogeography and ecological modelling of an insect pest
Open the record for dataset details and reuse information.
Barn Swallows (Hirundo rustica) in East Asia show strong tolerance against nest mites
Open the record for dataset details and reuse information.
Cretaceous coastal mountain building and potential impacts on climate change in East Asia
Open the record for dataset details and reuse information.
Figs 139–152 in Taxonomy of the katydids (Orthoptera: Tettigoniidae) from East Asia and adjacent islands. Communication 16
Figs 139–152. Elbenia: 139 – E. (Elbenia) eudigitata sp. n.; 140–149 – E. (Aequapteron)
Figs 63–79 in Taxonomy of the katydids (Orthoptera: Tettigoniidae) from East Asia and adjacent islands. Communication 16
Figs 63–79. Elbenia (Elbenia), male: 63–68 – E. (E.) carinata sp. n.; 69 – E. (E.)
Figs 1–12 in Taxonomy of the katydids (Orthoptera: Tettigoniidae) from East Asia and adjacent islands. Communication 16
Figs 1–12. Elbenia, male: 1–3 – E. (Tamdaopteron) ryabovi sp. n.; 4–6 – E. (T.) daedala
Genetic diversity, genetic differentiation and demographic history of Cryptomeria (Cupressaceae), a Tertiary relict plant in East Asia based on RAD sequencing
<p>Genetic structure and distribution patterns of modern floras are strongly affected by climatic change and geographical isolation. In the present study, we applied restriction-site-associated DNA sequencing (RAD-seq) to analyze the genetic structure and to simulate the demographic history of two extant <em>Cryptomeria</em> species in Japan (<em>C. japonica</em>) and Southeastern China (<em>C</em>. <em>japonica</em> var. <em>sinensis</em>). Thirteen natural populations representing the entire species distributed in East Asia were collected from Japan and China. At the species level, the genetic diversity of <em>Cryptomeria</em> was moderate (<em>H<sub>o</sub></em> = 0.217, <em>H<sub>e</sub></em> = 0.203) with a significant genetic differentiation among populations (85.30%, P < 0.001), especially between Japan and China lineages (<em>F</em><sub>ST</sub> = 0.147). Except for the Lushan (LS) population in China, all populations were clustered into two lineages (Japanese and Chinese), which was consistent with their geographical distribution. Approximate Bayesian computations (ABC) model indicated that the current two geographical lineages diverged from a common ancestral lineage and that their divergence time was about 0.417 ~ 0.139 million years ago (Mya). Geographical isolation, climate change in the Quaternary, and human disturbance played important roles in genetic variation and distribution patterns of <em>Cryptomeria</em> in East Asia. Our results shed light on the speciation processes of <em>Cryptomeria</em> and provide a reference for the conservation of this species.</p>
Anthropogenic disturbance driving population decline of a dominant tree in East Asia evergreen broadleaved forests over the last 11,000 years
<p><span>Current biodiversity loss was generally considered to be caused by </span><span>anthropogenic disturbance</span><span>, but when anthropogenic activities began to impact biodiversity loss is still </span><span>controversial</span><span>. One hypothesis suggested it was from the industrial era, while others proposed that the anthropogenic disturbance had already resulted in biodiversity decline since the Early Holocene. To test these hypotheses, we focus on subtropical East Asia, which has witnessed a land use and anthropogenic history since the Early Holocene. We selected the unique vegetation of evergreen broadleaved forests (EBLFs), using a genomic approach to infer the demographic history of a dominant plant (<em>Litsea</em> <em>elongata</em>) of EBLFs, and to further detangle the impact of climate change and/or anthropogenic disturbance on effective population size fluctuation. Nine well-defined geographical clades were identified within extant populations of <em>L</em>. <em>elongata</em>. The estimated historical population sizes of these clades all contracted, indicating persistent population decline over the last 11,000 years. Significant correlation was detected between demographic history and three anthropogenic disturbance factors, rather than the climate change in the Holocene. Therefore, we provided the <span>dataset used for<span> phylo</span></span>g<span>enomic analysis</span>,<span> </span><span> demographic history inference</span><span>, and correlation analysis in this study.</span></span></p>
Data from: Inference of selective force on house mice genomes during secondary contact in East Asia
<p>The house mouse (<em>Mus musculus</em>), commensal to humans, has spread globally via human activities, leading to secondary contact between genetically divergent subspecies. This pattern of genetic admixture can provide insights into the selective forces at play in this well-studied model organism. Our analysis of 163 house mouse genomes, mainly from East Asia, revealed substantial admixture between the subspecies<em> castaneus</em> and <em>musculus</em>, particularly in Japan and southern China. We revealed, despite the admixture, that all Y chromosomes in the East Asian samples belonged to the <em>musculus</em>-type haplogroup, potentially explained by genomic conflict under sex ratio distortion due to varying copy numbers of ampliconic genes on sex chromosomes. We also investigated the influence of natural selection on the post-hybridization of the subspecies <em>castaneus</em> and <em>musculus</em> in Japan. Even though the genetic background of most Japanese samples closely resembles the subspecies<em> musculus</em>, certain genomic regions overrepresented the <em>castaneus</em>-like genetic components, particularly in immune-related genes. Furthermore, a large genomic block containing a vomeronasal/olfactory receptor gene cluster predominantly harbored <em>castaneus</em>-type haplotypes in the Japanese samples, highlighting the possible role of olfaction-based recognition in shaping hybrid genomes.</p>
Figure 26 in Molecular and morphological confirmation of Isometrus maculatus (DeGeer 1778) (Scorpiones Buthidae) from Northeast India and East Asia
Figure 26. Distribution of Isometrus specimens analysed here (see Figs 24–25).
Figs 8–12 in REVIEW OF LEAF BEETLES OF THE SUBGENUS HYPERICIA BEDEL, 1899 (COLEOPTERA: CHRYSOMELIDAE: CHRYSOLINA) FROM EAST ASIA
Figs 8–12. Aedeagus in dorsal and lateral view. 8 – Chrysolina difficilis Motschulsky
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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
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.