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404 results for “Beijing”
Refined Land cover for Beijing, Shanghai, Ningbo in China and Paris Region, Velika Gorica, Aarhus in Europe under different scenarios in 2030
<p>Europe and China Refined Land Cover 2030 (ECRLC2030) was derived from historical landcover observations, natural geographical, location, and socio-economic factors and the Conversion of Land Use and its Effects at Small Regional Extent model (CLUE-S). With a spatial resolution of 60m, landcover under three different scenarios were simulated: the business-as-usual scenario (BAU), the market-liberal scenario (MLS), and the ecological protection scenario (EPS).</p>
A Construction Waste Landfill Dataset of Two Districts in Beijing, China from High Resolution Satellite Images
<p>CWLD_model project shows scripts and instructions on how to use this dataset to train a segmentation model. requirements.txt files provide the libraries you need to run your project. The README.md document details the deployment process and features of each module.</p> <p>You can also visit the GitHub page for scripts and instructions on how to use this dataset for visualizing and plotting basic statistics. The models and the code to execute them are released on <a href="https://github.com/huangleinxidimejd/CWLD_Model">https://github.com/huangleinxidimejd/CWLD_Model</a>.</p> <h2>Training details</h2> <p>The model was trained with two GPUs, an Nvidia GeForce RTX 2080Ti, and the following parameters:</p> <ul> <li>'train_batch_size': 4,</li> <li>'val_batch_size': 4,</li> <li>'train_crop_size': 512,</li> <li>'val_crop_size': 512,</li> <li>'lr': 0.001, # the learning rate used during training. It determines how quickly the model learns from the data</li> <li>'Epoch Times': 200,</li> <li>'gpu': correct,</li> <li>'weight_decay': 5E-4,</li> <li>'Momentum': 0.9,</li> <li>'print_freq': 100,</li> <li>'predict_step': 5,</li> </ul> <h2>usage</h2> <ul> <li>After downloading the dataset from Zenodo, place the train and val files from the Deep Learning Datasets file into the data folder of the CWLD semantic segmentation model.</li> <li>Open: CWLD_ Open the root directory in CWLD_model/dataset/ and start training with the WasteSeg_Train.py file. The modelss module provides five convolutional networks, Improved_DeeplabV3_plus, PSPNet, ResNet, SegNet, and UNet, which can be selected and modified accordingly.</li> <li>The utils package provides a large number of data processing tools to use.</li> <li>The trained model can be predicted from a EvalSeg.py file.</li> </ul>
CLDF dataset derived from Beijing Daxue's "Chinese Character Pronunciations" from 1962
<p>Cite the source of the dataset as:</p> <blockquote> <p>Běijīng Dàxué 北京大学 (1962): Hànyǔ fāngyán zìhuì 漢語方音字彙 [Chinese dialect character pronunciation list]. Beijing: Wenzi Gaige.</p> </blockquote>
CLDF dataset derived from Beijing University's "Chinese Dialect Vocabularies" from 1964
<p>Cite the source of the dataset as:</p> <blockquote> <p>Běijīng Dàxué 北京大学 (1964): Hànyǔ fāngyán cíhuì 汉语方言词汇 [Chinese dialect vocabularies]. Beijing: Wenzi Gaige.</p> </blockquote>
Measurements of Ice Nucleating Particles in Beijing, China - Data and processing code
<p>Dataset needed to replicate findings published in the journal article "Measurements of Ice Nucleating Particles in Beijing, China, published in the Journal of Geophysical Research. The dataset contains the following:</p> <p>1. Data files containing raw data from a Continuous Flow Diffusion Chamber - Ice Activation Spectrometer (CFDC-IAS), in comma-delimited format).</p> <p>2. Data and processing files for analysis of backward air trajectories as an Igor Pro 8 packed experiment package file. Igor Pro is available from www.wavemetrics.com and a free 30-day trial version can be used to export data to other formats.</p> <p>3. Data and processing files for analysis of CFDC, APS and meteorological data, including data in the form of waves, as part of an Igor Pro 8 packed experiment package.</p>
Morphology and biomass allometry data of vines in Beijing
<p>Lacking mechanical support tissue, vines may have distinctive allometric laws, which may differ from the predictions of theoretical models for self-supporting plants such as metabolic scaling theory (MST). Here, we collected 260 branches of 12 garden vine species to analyze the allometric relationships between diameter, length, leaf, and stem biomass and the effects of biotic and abiotic factors on the allometric relationships of vine branches using mixed-effect model ANOVA. Our results showed that the scaling exponents of diameter-length of vine branch had significant inter-species differences and were higher than the predictions of MST. Besides, the exponents of herbaceous vines were higher than woody vines. However, leaf-stem biomass of vine branches showed isometric relationships, which supported the predictions of MST and there was no difference among species and life forms. Both biotic and abiotic factors had weak effects on exponents of allometric relationships, while significant effects on constants. These results confirmed that vines invest more in elongation growth compared to self-supporting plants, but the partitioning of biomass follows a strict biophysical constraint. Our findings emphasize the significance of mechanical support tissue proportion in plant stems, which is a critical determinant in allometric scaling relationships.</p>
Figures 1–5 in A survey of the tribe Mesosini Mulsant (Coleoptera: Cerambycidae) from Beijing, China
Figures 1–5. Agelasta (Mesolophus) marmorata (Pic, 1927) and Mesosa (Perimesosa) atrostigma Gressitt, 1942. 1–2. A. marmorata (1. Female, from Beijing, Huairou; 2. Male, from Yunnan). 3–5. M. atrostigma (3. Female, from Beijing, Miyun, photographed by Shengping Yu; 4. Male, from Beijing, Huairou, photographed by Guoyue Yu; 5. Male, from Beijing, Fangshan, photographed by Guoyue Yu). Scale bars: 1–2 = 5.0 mm.
Figures 10–15 in A survey of the tribe Mesosini Mulsant (Coleoptera: Cerambycidae) from Beijing, China
Figures 10–15. Mesosa (Mesosa) myops (Dalman, 1817). 10. Male, from Beijing, Yanqing. 11. Female, from Beijing, Miyun. 12. Female, from Beijing, Pinggu. 13. Male, from Beijing, Pinggu, showing the absence of one black spot on pronotum (Fig. 13a). 14. Female, from Beijing, Haidian, misidentified as M. stictica (Fig. 14a). 15. Male, from Beijing, Mentougou, showing the most round spots on pronotum. Scale bars = 5.0 mm (except 13a and 14a).
Figures 6–9 in A survey of the tribe Mesosini Mulsant (Coleoptera: Cerambycidae) from Beijing, China
Figures 6–9. Mesosa (Perimesosa) hyunchaei Yamasako & Hasegawa, 2009. 6–7. Male, from Beijing, Huairou, dorsal and lateral view. 8. Male, from Jiangsu, Jurong, Baohuashan. 9. Female, from Beijing, Mentougou. Scale bars = 5.0 mm.
Dataset for Influence of Aerosol Chemical Composition on Condensation Sink Efficiency and New Particle Formation in Beijing
<p>This dataset includes one year long measurements of particle number size distributions, chemical composition of PM2.5, gaseous precursors, and meteorological parameters in urban Beijing, China, from March 1, 2018, to March 1, 2019. It is the supplementary data for "Influence of Aerosol Chemical Composition on Condensation Sink Efficiency and New Particle Formation in Beijing", which is published by Environmental Science & Technology Letter. Please cite: Wei Du, Jing Cai, Feixue Zheng, Chao Yan, Ying Zhou, Yishuo Guo, Biwu Chu, Lei Yao, Liine M. Heikkinen, Xiaolong Fan, Yonghong Wang, Runlong Cai, Simo Hakala, Tommy Chan, Jenni Kontkanen, Santeri Tuovinen, Tuukka Petäjä, Juha Kangasluoma, Federico Bianchi, Pauli Paasonen, Yele Sun, Veli-Matti Kerminen, Yongchun Liu, Kaspar R. Daellenbach, Lubna Dada, and Markku Kulmala Environmental Science & Technology Letters Article ASAP DOI: 10.1021/acs.estlett.2c00159</p>
Data for "Measurement Report: A Multi-Year Study on the Impacts of Chinese New Year Celebrations on Air 1 Quality in Beijing, China."
<p>These are the datasets that have been used for the article "Measurement Report: A Multi-Year Study on the Impacts of Chinese New Year Celebrations on Air 1 Quality in Beijing, China," which is published in the journal <em>Atmospheric Chemistry and Physic</em><em>s</em>, by Foreback et al. (2022).</p>
Figure 12. Cryptodrassus beijing Lin & Li in Taxonomy notes on twenty-eight spider species (Arachnida: Araneae) from Asia
Figure 12. Cryptodrassus beijing Lin & Li, sp. nov., holotype male, left palp. A. Prolateral view; B. Ventral view; C. Retrolateral view. Arrows show apophyses. Abbreviations: C—conductor; E—embolus; SD—sperm duct; ST—subtegulum. Scale bars = 0.1 mm.
Figure 14. Cryptodrassus beijing Lin & Li in Taxonomy notes on twenty-eight spider species (Arachnida: Araneae) from Asia
Figure 14. Cryptodrassus beijing Lin & Li, sp. nov., paratypes male (A–B) and female (C–D). A, C. Habitus, dorsal view; B, D. Same, lateral view. Scale bars = 0.5 mm.
Figure 13. Cryptodrassus beijing Lin & Li in Taxonomy notes on twenty-eight spider species (Arachnida: Araneae) from Asia
Figure 13. Cryptodrassus beijing Lin & Li, sp. nov., paratype female. A. Epigyne, ventral view; B. Vulva, dorsal view. Abbreviations: CD—copulatory duct; CO—copulatory opening; FD—fertilization duct; G—spermathecal gland; S—spermatheca. Scale bars = 0.1 mm.
Data for Survival probabilities of atmospheric particles: comparison based on theory, cluster population simulations, and observations in Beijing
<p>Data for<em> Survival probabilities of atmospheric particles: comparison based on theory, cluster population simulations, and observations in Beijing </em>(https://doi.org/10.5194/acp-2022-484)</p> <p>Contact Santeri Tuovinen (santeri.tuovinen@helsinki.fi) for more details.</p>
Figure 1 in Description of Laimaphelenchus sinensis n. sp. (Nematoda: Aphelenchoididae) from declining Chinese pine, Pinus tabuliformis in Beijing, China
Figure 1: Line drawings of LaimaphelenChus sinensis n. sp. A: Entire female; B: Entire male; C: Anterior region; D: Female posterior region showing vulva and post-uterine sac; E: Lateral lines F, G: Female tail terminus; H: Male tail; I: Spicule. (Scale bars = A, B = 20µ m; C-I = 10µ m).
Figure 3 in Description of Laimaphelenchus sinensis n. sp. (Nematoda: Aphelenchoididae) from declining Chinese pine, Pinus tabuliformis in Beijing, China
Figure 3: Phylogenetic relationships of LaimaphelenChus sinensis n. sp. and aphelenchid nematodes based on full length of 18 S rDNA. The 100001st Bayesian tree inferred from 18 S rDNA under TVM + I + G model. AphelenChus avenae (JQ348399) served as the outgroup species. Posterior probability values exceeding 50% are given on appropriate clades.
Figure 2 in Description of Laimaphelenchus sinensis n. sp. (Nematoda: Aphelenchoididae) from declining Chinese pine, Pinus tabuliformis in Beijing, China
Figure 2: Light photomicrographs of LaimaphelenChus sinensis n. sp. A: Entire female; B: Entire male; C: Lateral lines; D: Anterior region; E: Female posterior region showing vulva and postuterine sac; F, G: Vulval regions; H: Female tail; I-K: Female tail terminus; L-N: Male tails arrows showing position of caudal papillae (Scale bars = A, B = 20 µm; C-N = 10µ m; Abbreviations: ex, excretory pore).
Figure 4 in Description of Laimaphelenchus sinensis n. sp. (Nematoda: Aphelenchoididae) from declining Chinese pine, Pinus tabuliformis in Beijing, China
Figure 4: Phylogenetic relationships of LaimaphelenChus sinensis n. sp. and aphelenchid nematodes based on D2-D3 expansion segments of 28 S rDNA. The 100001st Bayesian tree inferred from 28 S rDNA under TIM2 + I + G model. AphelenChus avenae (JQ348400) served as the outgroup species. Posterior probability values exceeding 50% are given on appropriate clades.
Xitaizi Experimental Watershed dataset, Beijing, China
<p><span>Bimodal runoff behavior, characterized by two distinct peaks in flow response, often leads to significant stormflow and associated flooding. Understanding and characterizing this phenomenon is crucial for effective flood forecasting. However, this runoff behavior has been understudied and poorly understood in semi-humid regions. In this study, we investigated the response characteristics and occurrence conditions of bimodal hydrograph based on the hydrometric and isotope data spanning 10 years in a semi-humid forested watershed in North China.</span></p> <p><span>In this dataset, we provide essential observational data from the Xitaizi Experimental Watershed in North China, including information on rainfall, groundwater levels, soil water content, streamflow, and isotopic measurements </span><span>(δ</span><sup><span>18</span></sup><span>O).</span></p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.