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483 results for “Qinghai”
Fig. 4 in Phylogeography of (Nemacheilidae): Responded to the Mid-Pleistocene Climate Transition in the Qinghai-Tibetan Plateau.
Fig. 4. TCS network generated by Popart based on cytochrome b haplotypes for the T. stenura in QTP. Numbers in the networks represent haplotype designations and the areas of the circles are proportional to haplotype frequency; Black dots represent missing intermediate haplotypes.
Fig. 1 in Phylogeography of (Nemacheilidae): Responded to the Mid-Pleistocene Climate Transition in the Qinghai-Tibetan Plateau.
Fig. 1. Map of the study area and geographical distribution of sampling sites in this study. The sites are numbered according to table 1.
Fig. 1 in First report on the molecular detection of Enterocytozoon bieneusi in livestock and wildlife around Qinghai Lake in the Qinghai-Tibetan Plateau area, China
Fig. 1. Phylogenetic tree of E. bieneusi isolates and reference based on ITS region sequence of small-subunit ribosomal RNA (SSU rRNA) genes.
Fig. 1-2 in Two new Cryptophagidae (Coleoptera) from Qinghai (China)
Fig. 1-2: (1) Cryptophagus hongguanus nov.sp., paratype, female; (2) C. hongguanus nov.sp., holotype, parameres.
Two-step fusion method for generating 1 km seamless multi-layer soil moisture with high accuracy in the Qinghai-Tibet plateau
<p>Current remote sensing techniques fail to observe and generate large scale multi-layer soil moisture (SM) due to the inherent features of the satellite sensors. The lack of comprehensive understanding of multi-layer SM hinders the sustainable development of agriculture, hydrology, and food security. In order to overcome the depth barrier of traditional SM assimilation and downscaling methods, we developed a Two-step Multi-layer SM Downscaling (TMSMD) framework by fusing multi-source remotely sensed, reanalysis, and in-situ data through both machine learning and state-of-the-art deep learning models to generate multi-layer SM. The produced multi-layer SM was characterized by high resolution (1 km), high spatio-temporal continuity (cloud-free and daily), and high accuracy (i.e., 3H data). Firstly, the coarse resolution SMAP SM was downscaled to 1 km spatial resolution using LightGBM to weaken the effects of scale mismatch issue and provide high-resolution input for the subsequent calibration. Results indicated that the downscaled SMAP SM remained high consistency with the original SMAP SM product. With the high-resolution inputs, we calibrated the downscaled SMAP SM using multi-layer in-situ SM through state-of-the-art attention-based LSTM. Results demonstrated that the average PCC, RMSE, ubRMSE, and MAE were improved by 22.3%, 50.7%, 26.2%, and 56.7% compared to SMAP L4 SM while 38.5%, 52.1%, 29.5%, and 58.7% compared to downscaled SMAP SM. Further spatio-temporal and comparative analysis confirmed that the multi-layer SM produced by the TMSMD framework had excellent performance in capturing the spatial and temporal dynamics. In conclude, the proposed TMSMD framework successfully generated 3H multi-layer SM data and is promising for accurate assessment and monitoring in agriculture, water resources, and environmental domains.</p> <p> </p> <p>The remaining data will be uploaded soon.</p>
A 1 km daily soil moisture dataset over the Qinghai-Tibet Plateau (2001-2010)
<p>Soil moisture is the key variable of water and energy cycle, but the long-term, high-resolution soil moisture data with high accuracy is still relatively lacking on the Qinghai-Tibet Plateau. Therefore, we provide the 1 km seamless daily soil moisture data over the Qinghai-Tibet Plateau during 2001-2010 (named as BTCH). Firstly, several predictors including the vegetation index (NDVI and EVI), land surface temperature (LST), evapotranspiration, precipitation, topography (DEM, aspect, slope, TWI), soil properties and three soil moisture related indices (SWCI SIWSI VSDI) were utilized. Five machine/deep learning methods including the artificial neural network (ANN), convolutional neural network (CNN), residual neural networks (ResNet), the long short-term memory network (LSTM) and XGBoost were trained for each year taking the ESA CCI soil moisture data as target. Then the Bayesian three-cornered hat method was adopted for model integration and the final dataset was generated. Evaluaion against four in-situ measurement networks shows that the BTCH dataset has relativey high accuracy both as station and network scales with mean unbiased RMSE values of 0.048 m3/m3 and 0.034 m3/m3. The dataset can be used for various regional hydrological, meteorological, ecological analysis and modeling.</p>
The Long-term, High-accuracy and Seamless Soil Moisture (LHS-SM) dataset over the Qinghai-Tibet Plateau: part 1 (2001-2010)
<p>Soil moisture (SM) is a vital variable in the water-energy cycle and characterizing its spatiotemporal dynamics is crucial for understanding the impacts of climate change. Although substantial efforts have been devoted to derive SM data at fine scale, there is still a research gap in obtaining the long-term, high-accuracy and high-resolution SM data over the Qinghai-Tibet Plateau (QTP) due to its complex topography. Therefore, this study generated the long-term, high-accuracy and seamless soil moisture (LHS-SM) dataset over the QTP during 2001-2020 using a two-step downscaling method. First the daily SM data from the Climate Change Initiative program of the European Space Agency (ESA CCI) was downscaled to 1km utilizing five machine learning approaches. Then a dynamic data merging method that considers the spatiotemporal nonstationary error was applied to derive the final LHS-SM data. Results indicated that LHS-SM data exhibited satisfying accuracy (mean R = 0.55, ubRMSE = 0.049 m³/m³) and certain improvement to the ESA CCI SM data both at station and network scales. The dataset can be used for various regional hydrology, meteorology, ecological analysis and modeling.</p>
A dataset for methane concentrations and fluxes for alpine permafrost streams and rivers on the East Qinghai-Tibet Plateau
This dataset is a collation of 3-year direct measurement values of CH4 concentrations and fluxes for EQTP streams and rivers, along with information on location, hydrological, physical, and chemical conditions of the study sites. Given the rarity and high value of this EQTP data set, it will be very valuable for the next update to global C flux estimates.
Dataset of trend-preserving bias-corrected daily temperature, precipitation and wind from NEX-GDDP and CMIP5 in the Qinghai-Tibet Plateau——Part Ⅱ
<p>A bias-corrected dataset containing daily meteorological data of the Qinghai-Tibet Plateau has been generated, by using a trend-preserving bias-correction, the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) approach together with a high-quality gridded meteorological dataset based on ground observation (CN05.1). The data set contains daily bias-corrected values of maximum/minimum near-surface air temperature, precipitation and mean near-surface wind speed from 15 models from the Fifth Phase of the Coupled Model Intercomparison Project (CMIP5) and their downscaled high-resolution dataset (NEX-GDDP) in the Qinghai-Tibet Plateau (QTP) during 1986-2095. This dataset can provide important reference for the study on future climate change and its impacts in the Qinghai-Tibet Plateau region.</p> <p><strong>Note: For Tmin in historical periods, the values larger than 2606 refer to no data. Set them to NaN before using, for example (Matlab): Tmin(Tmin>2606)=nan;</strong></p> <p>More details about this dataset can be found in the article: S. Chen, T. Ye, W. Liu, A. Wang and P. Shi. Evaluation and bias correction of the historical and future near-surface climate forcing in NEX-GDDP and CMIP5 over the Qinghai-Tibet plateau[J], Plateau Meteorology (in Chinese), 2020, DOI: 10.7522/j.issn.1000-0534. 2020. 00019.</p>
Dataset of trend-preserving bias-corrected daily temperature, precipitation and wind from NEX-GDDP and CMIP5 in the Qinghai-Tibet Plateau——Part Ⅰ
<p>A bias-corrected dataset containing daily meteorological data of the Qinghai-Tibet Plateau has been generated, by using a trend-preserving bias-correction, the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) approach together with a high-quality gridded meteorological dataset based on ground observation (CN05.1). The data set contains daily bias-corrected values of maximum/minimum near-surface air temperature, precipitation and mean near-surface wind speed from 15 models from the Fifth Phase of the Coupled Model Intercomparison Project (CMIP5) and their downscaled high-resolution dataset (NEX-GDDP) in the Qinghai-Tibet Plateau (QTP) during 1986-2095. This dataset can provide important reference for the study on future climate change and its impacts in the Qinghai-Tibet Plateau region.</p> <p><strong>Note: For Tmax in historical periods, the values larger than 2606 refer to no data. Set them to NaN before using, for example (Matlab): Tmax(Tmax>2606)=nan;</strong></p> <p>More details about this dataset can be found in the article: S. Chen, T. Ye, W. Liu, A. Wang and P. Shi. Evaluation and bias correction of the historical and future near-surface climate forcing in NEX-GDDP and CMIP5 over the Qinghai-Tibet plateau[J], Plateau Meteorology (in Chinese), 2020, DOI: 10.7522/j.issn.1000-0534. 2020. 00019.</p>
Data from: 'In and out of' the Qinghai-Tibet Plateau and the Himalayas: centers of origin and diversification compared across five clades of Eurasian montane and alpine passerine birds
<p>Encompassing some of the major hotspots of biodiversity on Earth, large mountain systems have long held the attention of evolutionary biologists. The region of the Qinghai-Tibet Plateau (QTP) is considered a biogeographic source for multiple colonization events into adjacent areas including the northern Palearctic. The faunal exchange between the QTP and adjacent regions could thus represent a one-way street ('out of' the QTP). However, immigration into the QTP region has so far received only little attention, despite its potential to shape faunal and floral communities of the QTP. In this study, we investigated centers of origin and dispersal routes between the QTP, its forested margins and adjacent regions for five clades of alpine and montane birds of the passerine superfamily Passeroidea (Johansson et al., 2008; Selvatti et al., 2015). We performed an ancestral area reconstruction using BioGeoBEARS and inferred a time-calibrated backbone phylogeny for 279 taxa of Passeroidea. The oldest endemic species of the QTP was dated to the early Miocene (ca. 18 Ma). Several additional QTP endemics evolved in the mid to late Miocene (12–7 Ma). The inferred centers of origin and diversification for some of our target clades matched the 'out of Tibet hypothesis' or the 'out of Himalayas hypothesis' for others they matched the 'into Tibet hypothesis'. Three radiations included multiple independent Pleistocene colonization events to regions as distant as the Western Palearctic and the Nearctic. We conclude that faunal exchange between the QTP and adjacent regions was bidirectional through time, and the QTP region has thus harbored both centers of diversification and centers of immigration.</p>
Figure 8 in A new species of Gammarus Fabricius, 1775 (Crustacea: Amphipoda: Gammaridae) from extreme high-altitude Lake Tong Tso, Qinghai-Tibetan Plateau
Figure 8. Gammarus pontual sp. nov., male paratype (MZUSP 40977): (A-C) Pleopods 1-3, dorsal view, respectively. Scale bars: 1.0 mm.
Figure 7 in A new species of Gammarus Fabricius, 1775 (Crustacea: Amphipoda: Gammaridae) from extreme high-altitude Lake Tong Tso, Qinghai-Tibetan Plateau
Figure 7. Gammarus pontual sp. nov., male holotype (MZUSP 40976): (A-C) Epimeral plates 1-3, lateral view, respectively. Male paratype (MZUSP 40977): (D-F) Uropods 1-3, lateral view, respectively; (G) Telson, dorsal view. Scale bars: 0.5 mm for G; 2.0 mm for A-C; 1.0 mm for the remaining.
Figure 4 in A new species of Gammarus Fabricius, 1775 (Crustacea: Amphipoda: Gammaridae) from extreme high-altitude Lake Tong Tso, Qinghai-Tibetan Plateau
Figure 4. Gammarus pontual sp. nov., male paratype (MZUSP 40977): (A) Right gnathopod 1, lateral view; (B) Left gnathopod 1, mesial view. Scale bars: 1.0 mm.
Figure 1 in A new species of Gammarus Fabricius, 1775 (Crustacea: Amphipoda: Gammaridae) from extreme high-altitude Lake Tong Tso, Qinghai-Tibetan Plateau
Figure 1. Gammarus pontual sp. nov., male holotype (MZUSP 40976): (A) Habitus, lateral view; (B-D) Urosomites 1-3, dorsal view, respectively. Scale bars: 10.0 mm for A; 2.0 mm for B-D.
Figure 3 in A new species of Gammarus Fabricius, 1775 (Crustacea: Amphipoda: Gammaridae) from extreme high-altitude Lake Tong Tso, Qinghai-Tibetan Plateau
Figure 3. Gammarus pontual sp. nov., male paratype (MZUSP 40977): (A) Left maxilla 1, dorsal view; (B) Detail of the right palp of maxilla 1, dorsal view; (C) Left maxilla 2, dorsal view; (D) Left maxilliped, dorsal view. Scale bars: 0.1 mm for B; 1.0 mm for D; 0.5 mm for the remaining.
Figure 2 in A new species of Gammarus Fabricius, 1775 (Crustacea: Amphipoda: Gammaridae) from extreme high-altitude Lake Tong Tso, Qinghai-Tibetan Plateau
Figure 2. Gammarus pontual sp. nov., male paratype (MZUSP 40977): (A) Head, lateral view; (B) Right antenna 1, lateral view; (C) Right antenna 2, lateral view; (D) Upper lip, dorsal view; (E) Lower lip, dorsal view; (F) Left mandible, mesial view; (G) Right mandible, mesial view. Scale bars: 1.0 mm.
Figure 6 in A new species of Gammarus Fabricius, 1775 (Crustacea: Amphipoda: Gammaridae) from extreme high-altitude Lake Tong Tso, Qinghai-Tibetan Plateau
Figure 6. Gammarus pontual sp. nov., male paratype (MZUSP 40977): (A) Right pereopod 3, lateral view; (B) Right pereopod 4, lateral view; (C) Right pereopod 5, lateral view; (D) Right pereopod 6, lateral view; (E) Right pereopod 7, lateral view. Scale bars: 1.0 mm.
Figure 5 in A new species of Gammarus Fabricius, 1775 (Crustacea: Amphipoda: Gammaridae) from extreme high-altitude Lake Tong Tso, Qinghai-Tibetan Plateau
Figure 5. Gammarus pontual sp. nov., male paratype (MZUSP 40977): (A) Right gnathopod 2, lateral view; (B) Left gnathopod 2, mesial view. Scale bars: 1.0 mm.
Data from: Plant biodiversity responds more strongly to climate warming and anthropogenic activities than microbial biodiversity in the Qinghai-Tibetan alpine grasslands
<p>Biodiversity serves as the fundamental underpinning for ecosystem functions and services. As a result of human-induced global change, there is a growing awareness of the substantial alterations in terrestrial aboveground biodiversity, particularly within alpine regions. However, it remains uncertain whether belowground biodiversity will exhibit similar responses, both in terms of magnitude and manner, to anthropogenic global changes as aboveground biodiversity.</p> <p>Here, we conducted a meta-analysis to assess the impacts of warming, nutrient addition, and grazing on plant and soil microbial biodiversity in alpine grasslands on the Qinghai-Tibetan Plateau, which are known to be climate-sensitive and vulnerable. The analysis included 819 experimental observations from 152 studies, focusing on species richness, Shannon diversity, and Pielou's evenness.</p> <p>We found that plant biodiversity exhibited greater sensitivity to climate warming and anthropogenic activities compared to soil microbial biodiversity. Specifically, plant richness and Shannon diversity were reduced by warming and nutrient addition, while plant evenness was increased by grazing. However, only microbial richness was increased by grazing and microbial evenness was increased by warming slightly.</p> <p>The responses of biodiversity to climate warming and anthropogenic activities were modulated by multiple factors. Specifically, the negative effects of warming on plant biodiversity were more pronounced in long-term experiments under warmer or drier environmental conditions. The negative effects of nitrogen addition on biodiversity were enhanced by the intensity and duration of nitrogen treatment. Appropriate intensity and frequency of grazing were beneficial to sustaining plant biodiversity. Soil microbial biodiversity was weakly regulated, where bacterial Shannon diversity was more sensitive to nutrient addition, while fungal species richness was sensitive to grazing.</p> <p><strong>Synthesis: </strong>Our findings reveal a mismatch between aboveground plant and belowground microbial biodiversity in response to climate warming and anthropogenic activities in alpine grasslands, with plant biodiversity being more sensitive. In the context of future global change, plant biodiversity may be at greater risk than soil microbial biodiversity. In addition, biodiversity responses of different experimental and environmental conditions should be distinguished, and more attention is needed on biodiversity conservation in alpine steppe, or areas with warmer and drier environmental conditions, high-intensity fertilization or heavy grazing. </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)
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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.