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711 results for “Tibetan”
3-D velocity field of the Tibetan Plateau due to land water loading
<h3>Basic information:</h3> <p>This dataset includes a series of 3-D loading deformation velocity fields, which are supplements to the GRL paper entitled "Present-Day Three-Dimensional Crustal Deformation Velocity of the Tibetan Plateau Due to Multi-Component Land Water Loading" [<a href="https://doi.org/10.1029/2024GL108684">https://doi.org/10.1029/2024GL108684</a>]. The deformation velocities are fitted using long time span data during 2000-2020, and the detailed description of the data processing and calculation methods can be found through the GRL paper. There are results of three different grid resolutions (0.5x0.5, 0.25x0.25, 0.1x0.1), and for distinction, different file naming suffixes are used. For example, '0point5grids' indicates the results are in 0.5-degree grid resolutions (0.5x0.5), and so forth.</p> <h3>Application scenario:</h3> <p>The velocity fields here can be directly used for the analysis of crustal deformation or used for the separation of land water-induced loading deformation within geodetic deformation velocity fields over Tibetan Plateau. There are results of all the six main land water components, including soil moisture (SM) [Table S1], snow water equivalent (SWE) [Table S2], glacier [Table S3], lake [Table S4], permafrost (PM) [Table S5] and groundwater storage (GWS) [Table S6], thus users can choose one or some they focus on, or directly choose the sum of all the six main components (i.e., GRACE-inferred total terrestrial water storage [Table S7]).</p> <h3>Citation: </h3> <p>When using this dataset, please cite the GRL paper: Jiao, J., Pan, Y., Ren, D., & Zhang, X. (2024). Present-day three-dimensional crustal deformation velocity of the Tibetan Plateau due to multi-component land water loading. <em>Geophysical Research Letters</em>, 51, e2024GL108684. <a href="https://doi.org/10.1029/2024GL108684">https://doi.org/10.1029/2024GL108684</a></p> <h3>Contents:</h3> <p>Table S1. 3-D velocity field of the Tibetan Plateau due to the loading of soil moisture (SM).</p> <p>Table S2. 3-D velocity field of the Tibetan Plateau due to the loading of snow water equivalent (SWE).</p> <p>Table S3. 3-D velocity field of the Tibetan Plateau due to the loading of glacier.</p> <p>Table S4. 3-D velocity field of the Tibetan Plateau due to the loading of lake.</p> <p>Table S5. 3-D velocity field of the Tibetan Plateau due to the loading of permafrost (PM).</p> <p>Table S6. 3-D velocity field of the Tibetan Plateau due to the loading of groundwater storage (GWS).<br>Table S7. 3-D velocity field of the Tibetan Plateau due to the loading of GRACE-inferred total terrestrial water storage (TWS).</p>
A long term hourly eddy covariance dataset of consistently processed CO2 and H2O Fluxes from the Tibetan Alpine Steppe at Nam Co (2005 - 2019)
<p>The data set contains nearly 15 years of eddy covariance data from an alpine steppe ecosystem on the central Tibetan Plateau. The data was processed following standardized quality control methods to allow for comparability between the different years of our record and with other data sets. To ensure meaningful estimates of ecosystem atmosphere exchange, careful application of the following correction procedures and analyses was necessary: (1) Due to the remote location, continuous maintenance of the eddy covariance (EC) system was not always possible, so that cleaning and calibration of the sensors was performed irregularly. Furthermore, the high proportion of bare soil and high wind speeds led to accumulation of dirt in the measurement path of the infrared gas analyzer (IRGA). The installation of the sensor in such a challenging environment resulted in a considerable drift in CO2 and H2O gas density measurements. If not accounted for, this concentration bias may distort the estimation of the carbon uptake. We applied a modified drift correction procedure following Fratini et al. (2014) which, instead of a linear interpolation between calibration dates, uses the CO2 concentration measurements from the Mt. Waliguan atmospheric observatory as reference time series. (2) We applied rigorous quality filtering of the calculated fluxes to retain only fluxes which represent actual physical processes. (3) During the long measurement period, there were several buildings constructed in the near vicinity of the EC system. We investigated the influence of these obstacles on the turbulent flow regime to identify fluxes with uncertain land cover contribution and exclude them from subsequent computations. (4) We calculated the de-facto standard correction for instrument surface heating during cold conditions (hereafter called sensor self heating correction) following Burba et al. (2008) and a revision of the original method following Frank and Massman (2020). (5) Subsequently, we applied the traditional and widely used gap filling procedure following Reichstein et al. (2005) to provide a more complete overview of the annual net ecosystem CO2 exchange. (6) We estimated the flux uncertainty by calculating the random flux error (RE) following Finkelstein and Sims (2001) and by using the standard deviation of the fluxes used for gap filling (NEE_fsd) as a measure for spatial and temporal variation.</p> <p>References:</p> <ol> <li>Burba, G. G., McDermitt, D. K., Grelle, A., Anderson, D., and XU, L. (2008). Addressing the influence of instrument surface heat exchange on the measurements of CO2 flux from open-path gas analyzers, Global Change Biology, 14, 1854-1876, <a href="https://doi.org/10.1111/j.1365-2486.2008.01606.x">https://doi.org/10.1111/j.1365-2486.2008.01606.x</a>.</li> <li>Finkelstein, P. L. and Sims, P. F. (2001). Sampling error in eddy correlation flux measurements, J. Geophys. Res. Atmos., 106, 3503–3509, doi:10.1029/2000JD900731.</li> <li>Frank, J. M. and Massman, W. J.: A new perspective on the open-path infrared gas analyzer self-heating correction, Agricultural and Forest Meteorology, 290, 107986, doi:10.1016/j.agrformet.2020.107986, 2020.</li> <li>Fratini, G., McDermitt, D. K., and Papale, D. (2004). Eddy-covariance flux errors due to biases in gas concentration measurements: origins, quantification and correction, Biogeosciences, 11, 1037-1051, <a href="https://doi.org/10.5194/bg-11-1037-2014">https://doi.org/10.5194/bg-11-1037-2014</a>.</li> <li>Reichstein, M., Falge, E., Baldocchi, D., Papale, D., Aubinet, M., Berbigier, P., Bernhofer, C., Buchmann, N., Gilmanov, T., Granier, A., Grunwald, T., Havrankova, K., Ilvesniemi, H., Janous, D., Knohl, A., Laurila, T., Lohila, A., Loustau, D., Matteucci, G., Meyers, T., Miglietta, F., Ourcival, J.-m., Pumpanen, J., Rambal, S., Rotenberg, E., Sanz, M., Tenhunen, J., Seufert, G., Vaccari, F., Vesala, T., Yakir, D., and valentini, R. (20050. On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm, Global Change Biology, 11, 1424-1439, <a href="https://doi.org/10.1111/j.1365-2486.2005.001002.x">https://doi.org/10.1111/j.1365-2486.2005.001002.x</a>.</li> </ol>
Tibetan for Spacy 1.1
<p><strong>Tibetan for SpaCy </strong>is a language model for Tibetan designed for use in the SpaCy environment. The model was trained using SpaCy. It uses an external tokenizer, <a href="https://github.com/OpenPecha/Botok">Botok</a>, to segment the Tibetan and replaces the Tibetan syllable-separator (<em>tseg</em>) with white spaces where it occurs as a word separator. SpaCy was then told to interpret the input as English. This produces good results with standard vocabulary, but fails with unrecognised words. The project is currently working on a more sophisticated version of Tibetan for SpaCy. The package includes a list of stop words.</p> <p><strong>Tibetan for SpaCy</strong> was developed by James Engels as part of <a href="(https:/research.uni-leipzig.de/diverge/">Divergent Discourses</a>, a joint project between SOAS University of London and Leipzig University, funded by the AHRC in the UK and the DFG in Germany.</p> <p>The project developed Tibetan for SpaCy particularly for users who want to use Tibetan texts within the <strong>Leipzig Corpus Miner </strong>(<a href="https://ilcm.informatik.uni-leipzig.de/">iLCM</a>), an advanced text-mining interface designed for social scientists. The instructions for using Tibetan for SpaCy within the iLCM are in the readme file below. These instructions assume the user has already downloaded and installed the iLCM, which can be found <a href="https://ilcm.informatik.uni-leipzig.de/">here</a>. </p> <p>Please acknowledge the Divergent Discourses project if using this material (note that ultimately copyright belongs to the two participating universities). Contact: rb75@soas.ac.uk </p>
Visual Dictionary of Tibetan Verb Valency: Data
<p>This repository contains a JSON version of the data powering the <a href="https://bit.ly/VisualDictionary-TibetanValency">Visual Dictionary of Tibetan Verb Valency</a> together with its documentation. The structure of the data is explained in the documentation section on 'Dictionary data'.</p> <p>The Visual Dictionary of Tibetan Verb Valency was produced as part of the UKRI-funded project <a href="https://gtr.ukri.org/projects?ref=AH%2FP004644%2F1">Lexicography in Motion a History of the Tibetan Verb</a> (LIM) at SOAS.</p>
Mechanism for the Uplift of Gongga Shan in the Southeastern Tibetan Plateau Constrained by 3D Magnetotelluric Data
<p>The *.data, *.rho, and *.zip files are associated with a paper titled 'Mechanism for the Uplift of Gongga Shan in the Southeastern Tibetan Plateau Constrained by 3D Magnetotelluric Data' in Geophysical Research Letters published in 2022. On the basis of this data and inversion model, we addressed that the rapid uplift of the Gongga Shan massif likely occurred by the underthrusting of the Yangtze Craton. More details about the electrical resistivity model and its interpretations can be found in our journal paper. </p> <p>All the resulting files from ModEM are included in the 'ModEM_Inversion_Results.zip'. All the figures in the paper and supplementary are included in the 'GRL_All_Figures.zip' and 'Figure_S5_All_Responses.zip'.</p> <p>The resulting model and data output in ModEM format can be found in .rho and .data files. The ModEM is an open-source code package for MT 3D inversion, which is provided by Gary Egbert, Anna Kelbert, and Naser Meqbel and can be found on this website: <a href="https://sites.google.com/site/modularem/download">https://sites.google.com/site/modularem/download</a>. </p> <p>Please note that the 3D resistivity model files in general format includes four columns -- longitude, latitude, depth, and resistivity, the one who wants to plot the model via GMT, MATLAB, Surface, etc., can find these files in 'Gongga_3D_Resistivity_Model_Files.zip'. In this zip, you will find the resistivity model of each horizontal slice of different depths and a file including all the slices. A MATLAB script called 'see_slice.m' is included in the folder which can help to quickly view these resistivity slices.</p>
Long-Term Net Primary Productivity Dataset of the Tibetan Plateau from 1982 to 2013
<ul><li>This dataset is generated by the advanced CASA model, encompassing vegetation net primary productivity (NPP) raster data for the Tibetan Plateau from 1982 to 2013. The model's input parameters comprise NDVI time series, monthly average temperature, monthly total precipitation, monthly total solar radiation, and vegetation type. The data is provided at an 8 km × 8 km spatial resolution and is formatted in ENVI format (.dat).</li><li>Remarkably, during cross-validation with the MODIS 500m resolution product (MOD17A3HGF.061), it exhibited significantly strong correlations, with the correlation coefficients (R) ranging from 0.74 to 0.82.</li><li>Please cite this dataset as<br>Tan, Q., Sun, G., & Pang, Y. (2023). Long-Term Net Primary Productivity Dataset of the Tibetan Plateau from 1982 to 2013 (V1.0) [Data set]. Zenodo. https://doi.org/<a href="https://doi.org/10.5281/zenodo.10040818">10.5281/zenodo.10040818</a></li></ul>
Phlorest phylogeny derived from Sagart et al. 2019 'Dated language phylogenies shed light on the ancestry of Sino-Tibetan'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Sagart L, Jacques G, Lai Y, Ryder RJ, Thouzeau V, Greenhill SJ, List J- M. 2019 Dated language phylogenies shed light on the ancestry of Sino-Tibetan. Proceedings of the National Academy of Sciences, 201817972.</p> </blockquote>
Phlorest phylogeny derived from Zhang et al 2019 'Phylogenetic evidence for Sino-Tibetan origin in northern China in the Late Neolithic'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Zhang M, Yan S, Pan W, & Jin L. 2019. Phylogenetic evidence for Sino-Tibetan origin in northern China in the Late Neolithic. Nature, 569, 112–115.</p> </blockquote>
Integrated disturbance mapping over the Tibetan Plateau based on multiple detection algorithms
<p>This dataset contains the mapped representation of vegetation disturbance across the Tibetan Plateau, rendered at a 30-meter spatial resolution. The dataset comprehensively illustrates the extent of vegetation disturbance on the Tibetan Plateau during the period spanning 1986 to 2020. Notably, the map employs a categorization system, with values assigned to distinct classes: 0 denotes areas of disturbed vegetation, 1 denotes regions characterized by undisturbed vegetation, and 2 denotes zones devoid of vegetation.</p>
Seasonal Terrestrial Water Load Modulation of Seismicity at the Southeastern Margin of the Tibetan Plateau Constrained by GNSS and GRACE Data
<p>Data Set S1. The earthquake catalog is used to decluster aftershocks and background events, and the time range is from July 2004 to July 2021. This data set includes 672585 events in the study area.</p> <p>Data Set S2. Focal mechanism solutions of M ≥ 4 earthquakes at the southeastern margin of the Tibetan Plateau. The data set includes 634 solutions of earthquakes M ≥ 4, and the time range is from 2009 to 2017.</p>
Geographical and geological GIS boundaries of the Tibetan Plateau and adjacent mountain regions
<p><strong>Introduction</strong></p> <p>Geographical scale, in terms of spatial extent, provide a basis for other branches of science. This dataset contains newly proposed geographical and geological GIS boundaries for the <strong>Pan-Tibetan Highlands </strong>(new proposed name for the High Mountain Asia), based on geological and geomorphological features. This region comprises the <strong>Tibetan Plateau</strong> and three adjacent mountain regions: the <strong>Himalaya</strong>, <strong>Hengduan Mountains</strong> and <strong>Mountains of Central Asia</strong>, and boundaries are also given for each subregion individually. The dataset will benefit quantitative spatial analysis by providing a well-defined geographical scale for other branches of research, aiding cross-disciplinary comparisons and synthesis, as well as reproducibility of research results.</p> <p>The dataset comprises three subsets, and we provide three data formats (.shp, .geojson and .kmz) for each of them. Shapefile format (.shp) was generated in ArcGIS Pro, and the other two were converted from shapefile, the conversion steps refer to 'Data processing' section below. The following is a description of the three subsets:</p> <p>(1) The GIS boundaries we newly defined of the Pan-Tibetan Highlands and its four constituent sub-regions, i.e. the Tibetan Plateau, Himalaya, Hengduan Mountains and the Mountains of Central Asia. All files are placed in the "Pan-Tibetan Highlands (Liu et al._2022)" folder.</p> <p>(2) We also provide GIS boundaries that were applied by other studies (cited in Fig. 3 of our work) in the folder "Tibetan Plateau and adjacent mountains (Others’ definitions)". If these data is used, please cite the relevent paper accrodingly. In addition, it is worthy to note that the GIS boundaries of Hengduan Mountains (Li et al. 1987a) and Mountains of Central Asia (Foggin et al. 2021) were newly generated in our study using Georeferencing toolbox in ArcGIS Pro.</p> <p>(3) Geological assemblages and characters of the Pan-Tibetan Highlands, including Cratons and micro-continental blocks (Fig. S1), plus sutures, faults and thrusts (Fig. 4), are placed in the "Pan-Tibetan Highlands (geological files)" folder.</p> <p>Note: <strong>High Mountain Asia</strong>: The name ‘High Mountain Asia’ is the only direct synonym of Pan-Tibetan Highlands, but this term is both grammatically awkward and somewhat misleading, and hence the term ‘Pan-Tibetan Highlands’ is here proposed to replace it. <strong>Third Pole</strong>: The first use of the term ‘Third Pole’ was in reference to the Himalaya by Kurz & Montandon (1933), but the usage was subsequently broadened to the Tibetan Plateau or the whole of the Pan-Tibetan Highlands. The mainstream scientific literature refer the ‘Third Pole’ to the region encompassing the Tibetan Plateau, Himalaya, Hengduan Mountains, Karakoram, Hindu Kush and Pamir. This definition was surpported by geological strcture (Main Pamir Thrust) in the western part, and generally overlaps with the ‘Tibetan Plateau’ <em>sensu lato</em> defined by some previous studies, but is more specific.</p> <p>More discussion and reference about names please refer to the paper. The figures (Figs. 3, 4, S1) mentioned above were attached in the end of this document.</p> <p> </p> <p><strong>Data processing</strong></p> <p>We provide three data formats. Conversion of shapefile data to kmz format was done in ArcGIS Pro. We used the <em>Layer to KML</em> tool in Conversion Toolbox to convert the shapefile to kmz format. Conversion of shapefile data to geojson format was done in R. We read the data using the <em>shapefile</em> function of the raster package, and wrote it as a geojson file using the <em>geojson_write</em> function in the geojsonio package.</p> <p> </p> <p><strong>Version</strong></p> <p>Version 2022.1.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>This study was supported by the Strategic Priority Research Program of Chinese Academy of Sciences (XDB31010000), the National Natural Science Foundation of China (41971071), the Key Research Program of Frontier Sciences, CAS (ZDBS-LY-7001). We are grateful to our coauthors insightful discussion and comments. We also want to thank professors Jed Kaplan, Yin An, Dai Erfu, Zhang Guoqing, Peter Cawood, Tobias Bolch and Marc Foggin for suggestions and providing GIS files.</p> <p> </p> <p><strong>Citation</strong></p> <p>Liu, J., Milne, R. I., Zhu, G. F., Spicer, R. A., Wambulwa, M. C., Wu, Z. Y., Li, D. Z. (2022). Name and scale matters: Clarifying the geography of Tibetan Plateau and adjacent mountain regions. Global and Planetary Change, In revision</p> <p> </p> <p>Jie Liu & Guangfu Zhu. (2022). Geographical and geological GIS boundaries of the Tibetan Plateau and adjacent mountain regions (Version 2022.1). https://doi.org/10.5281/zenodo.6432940</p> <p> </p> <p><strong>Contacts</strong></p> <p>Dr. Jie LIU: E-mail: <a>liujie@mail.kib.ac.cn</a>;</p> <p>Mr. Guangfu ZHU: <a>zhuguangfu@mail.kib.ac.cn</a></p> <p>Institution: Kunming Institute of Botany, Chinese Academy of Sciences</p> <p>Address: 132# Lanhei Road, Heilongtan, Kunming 650201, Yunnan, China</p> <p> </p> <p><strong>Copyright</strong></p> <p>This dataset is available under the Attribution-ShareAlike 4.0 International (<a href="https://creativecommons.org/licenses/by-sa/4.0/">CC BY-SA 4.0</a>).</p>
Data file for: Three-Dimensional Electrical Imaging Across the Cona Woka Rift and Yalaxiangbo Dome in Southern Tibetan Plateau
<p>The magnetotellurics data were used to study the lithospheric electrical structure across the Cona Woka rift and Yalaxiangbo dome in the southern Tibetan plateau, conducted by Institute of Geophysical and Geochemical Exploration, Chinese Academy of Geological Sciences. The data file of CN6.dat was generated by the Matlab code EM3DVP.</p> <p>You are recommended to refer to the Kelbert et al., 2014 paper: https://doi.org/10.1016/j.cageo.2014.01.010 for a brief understanding of the data file formats.</p> <p> </p>
Classical Tibetan Word Embeddings
<p>Classical Tibetan word embeddings trained with FastText based on the 2018 version of the BDRC corpus, a segmented version of which is available on Zenodo:</p> <p>Meelen, Marieke, & Roux, Élie. (2020). The Annotated Corpus of Classical Tibetan (ACTib) - Version 2.0 (Segmented & POS-tagged) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3951503</p> <p>This is the first version trained with default FastText settings (100D) for a pilot study on Chinese-Tibetan crosslinguistic Semantic Textual Similarity:</p> <p>Felbur, Rafal, Marieke Meelen & Paul Vierthaler (2022), 'Crosslinguistic Semantic Textual Similarity of Buddhist Chinese and Classical Tibetan' in <em>Journal of Open Humanities Data</em>.</p> <p>This research was done with generous funding from the Open Philology project. This project (running 2018–2022) is funded by the European Research Council (ERC) under the Horizon 2020 program (Advanced Grant agreement No 741884). It is based at the Leiden University Institute for Area Studies.</p>
Intensified atmospheric branch of the hydrological cycle over the Tibetan Plateau during the Last Interglacial from a dynamical downscaling perspective
<p>We provide the datasets run for investigating the atmospheric branch of the hydrological cycle over the Tibetan Plateau during the Last Interglacial (127 ka), based on the mesoscale Weather Research and Forecasting (WRF) model driven by the Community Earth System Model (CESM). We upload summer mean of the model outputs from the WRF over the Tibetan Plateau used in estimating the atmospheric branch of the hydrological cycle.</p>
The 0.1° stem area index dataset over Tibetan Plateau from 1981 to 2018
<p><strong>Description:</strong></p> <p>Based on the method of Zeng et al. (2002), we built the monthly stem area index (Ls) data product over the Tibetan Plateau (TP) at 0.1°×0.1° spatial resolution from 1981-09 to 2018-12 by using leaf area index data (LAI) from GLASS (Liang et al., 2021) and grass fractional cover data from Lawrence and Chase (2007). Here, We revised the method, considering that grass is completely green (no WGS) from May to August, the Ls,min is set to 0; in September when the grass begins to wither (Xiao et al., 2023), its Ls value is obtained by subtracting the Lgv in September from that in August; from October (when grass turns completely withered) to the April of next year, the Ls is calculated without adding of withered leaves, considering the small magnitude of LAI (<0.2) in non-growing season; the monthly remaining rate of withered leaves and stems (α) is obtained from the observed total area of leaf and stem data of Xiao et al. (2023), which actually represents the neutralization of monthly removal of dead leaves and the withering part, especially in October. The calculation of Ls starts from September, 1981. Based on the above, the Ls is calculated on the sub-grid scale, then it multiplies by the fractional vegetation cover of grass to obtain the stem area index on the grid scale.</p> <p><strong>Dataset Characteristics:</strong></p> <p>Spatial Coverage: 75º~105ºE, 25º~40ºN;</p> <p> Temporal Coverage: Sep. 1981-Dec. 2018;</p> <p> Spatial Resolution: 0.1º;</p> <p> Temporal Resolution: Monthly;</p> <p> Data Format: NetCDF.</p> <p><strong>Citation</strong><strong>:</strong></p> <p>Qi, Q., Yang, K., Li, H., Ai, L., Wang, C., Wu, T. (2024). Negative impacts of the withered grass stems on winter snow cover over the Tibetan Plateau. Agric. For. Meteorol., 352, 110053. https://doi.org/10.1016/j.agrformet.2024.110053</p> <p>If you have any questions, please contact <strong>Dr. Kai Yang (yangkai@lzu.edu.cn)</strong></p>
CLDF dataset derived from Sagart et al.'s "Sino-Tibetan Database of Lexical Cognates" from 2019
<p>Cite the source of the dataset as:</p> <blockquote> <p>Laurent Sagart, Jacques, Guillaume, Yunfan Lai, and Johann-Mattis List (2019): Sino-Tibetan Database of Lexical Cognates. Jena: Max Planck Institute for the Science of Human History.</p> </blockquote>
Typical Village UAV Aerial Photography Dataset in northeastern Tibetan Plateau (2022)
<p>This dataset was collected during a field survey in the Hehuang Valley, located in the northeastern Tibetan Plateau, in July and August 2022. Using a DJI Mavic 2 Pro equipped with a Hasselblad L1D-20c camera, over 4,600 aerial photographs were captured from 55 typical villages across the region. These images were processed into high-resolution orthophotos using Agisoft PhotoScan 1.25 software, resulting in ultra-high-precision orthophoto data for the 55 villages. The "Village Information" section provides detailed information on each village, including its full name, abbreviation, latitude and longitude coordinates, and elevation. This dataset accurately reflects the overall situation, spatial patterns, and surrounding environment of the typical villages, offering a high-resolution data source for spatial structure analysis, land use mapping, and correction tasks.</p>
Ultra-high Resolution Land Use Data Set of Typical Villages in Northeastern Tibetan Plateau
<p>This dataset was collected by a research team during a field investigation in the Hehuang Valley of Qinghai Province from July to August 2022. Using the DJI Mavic2pro equipped with a Hasselblad L1D-20c camera, 55 typical villages were selected in the Hehuang Valley and over 4600 aerial photographs were obtained using drone photogrammetry technology as raw data. Using AgisfphotoScan 1.25 software to synthesize orthophoto images with a spatial resolution of 0.05m. The vector data of human settlement boundaries in villages was extracted through visual interpretation. Based on the object-oriented human-machine interaction interpretation method, 55 typical village land use datasets in 2022 were obtained (including forests, grasslands, forest land, cultivated land, water bodies, roads, unused land, and building land, totaling 8 categories). By establishing 1050 sample points and using confusion matrix analysis, it was found that the overall accuracy of the dataset was 96.86%, with a Kappa coefficient of 0.95. It can accurately reflect the spatial form, land use composition, and surrounding environment of typical villages. Aerial photographs all have longitude, latitude, and altitude information, providing ultra-high resolution data sources for village spatial structure analysis, land use mapping, and analysis work, effectively assisting in the improvement of human housing and rural revitalization strategies.</p>
Named-Entity Recognition for Modern Tibetan Newspapers: Tagset, Guidelines and Training Data
<p>This dataset, tagset and guidelines were the output of a six-month incubator project on the feasibility of developing Named-Entity Recognition (NER) for modern Tibetan, primarily for use with contemporary Tibetan-language newspapers and media published inside the PRC. The project was carried out by the Mongolian and Inner Asian Studies Unit at Cambridge University’s Department of Social Anthropology. It was funded by an incubator grant from Cambridge Language Sciences. The project title was “Named-Entity Recognition in Tibetan and Mongolian Newspapers.” The Project PI was Dr Hildegard Diemberger (Cambridge), the Coordinator and Lead Author was Dr Robert Barnett (SOAS), and Senior Advisers were Dr Nathan Hill (SOAS), Dr Marieke Meelen (Cambridge), and Dr Thomas White (Cambridge). <br> <br> Although some forms of NER and other NLP procedures have been developed within China for modern Tibetan (see Liu, Nuo <em>et al</em>, 2011), the data underlying those initiatives have not been made publicly available and their findings cannot be tested or reproduced. Significant work on developing NLP for Tibetan has been carried out outside China, but has focused largely on classical Tibetan and religious texts (see Hill & Garrett, Edward, 2017). </p> <p>The Cambridge incubator project therefore produced a tagset, guidelines and training data for developing NER for modern Tibetan, with a focus on historical and political analysis of contemporary newspapers, media and other public documents in Tibetan. We compiled 3.11m syllables of data in Tibetan extracted from articles downloaded from Chinese-language news aggregator sites within China, primarily tibet.cpc.people.com.cn and tibet.people.com.cn. From this data, we selected texts containing 280,000 syllables in Tibetan, grouped in 26,000 utterances/sentences (available on request). Using Lighttag, an online annotation site, we developed a tagset for NER consisting of 17 tags (and one for wrong segmentation if using segmented data). We annotated approximately 186,000 syllables, leading to 9,884 annotations. Of these, after discounting flawed data, we produced training data containing c.6,700 annotations. We carried out the secondary, manual review offline (for our method of converting Lighttag data for offline review, see the attached report “Using Spreadsheets to Review Annotations Offline.pdf”), and found an error rate of 3.6%. The final total of reviewed annotations was 6,624. </p> <p>The dataset, tagset, guidelines and reports were developed and documented by Robert Barnett, with assistance from Tsering Samdrup, Dr Hill and Dr Meelen. Primary annotation was by Tsering Samdrup, assisted by Dr Barnett.<br> <br> The datasets published here include: </p> <ol> <li>The <strong>tagseet guidelines and annotation manual</strong>, including the 17-tag tagset, guidelines, and recommendations ("NER for Modern Tibetan-tagset and guidelines.pdf").</li> <li>The <strong>tagged training data </strong>in .csv format ("Tibetan NER Training Data-tagged, reviewed wth context-v10-UTF-8.csv") and .xls format ("Tibetan NER Training Data-tagged with context-v10-UTF-8.xlsx"). This includes 6,624 reveiwed annotations, arranged according to the Tibetan alphabet together with the tags and context (utterance) for each annotation.</li> <li>The <strong>raw annotation results </strong>downloaded from Lighttag as .json files ("Raw Training Data for NER in Modern Tibetan -Jobs2-11-JSON.zip") and as .xls files ("Training Data for NER in Modern Tibetan -Jobs2-11-XLS.zip"). These include 10 "tasks" or datasets of articles scraped from Tibetan-language websites within Tibet. </li> <li>A <strong>guide to preparing Lighttag annotation results for manual review offline </strong>(“Using Spreadsheets to Review Annotations Offline.pdf”).</li> </ol> <p>The project's findings regarding the status of NER and NLP for vertical Mongolian are available at DOI: 10.5281/zenodo.5103499.</p>
Validation of MODIS11A2 LST and glacier surface heatwave during 2001-2020 over Tibetan Plateau
<p>1,Validation of MODIS11A2 LST in 2019 using AWS temperature on the glacier</p> <p>2,Validation of MODIS11A2 LST during 2001-2020 using CMA station temperature over the Tibetan Plateau</p> <p>3,Glacier surface heatwave during 2001-2020 over the Tibetan Plateau glacier </p>
ScienceDex guides
Understand access before you commit
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.