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the QoS-WSC test data-set is introduced and provided for the researchers, to be used for QoS-aware web-services discovery and composition. Service specification files (WSDL files) of this data-set have been generated by injecting QoS index vectors into service specification files of the WSC05 data-set. The injected QoS index vectors have been randomly picked from the QWS ver2.0 data-set. For generating service discovery and composition requests, the QoS importance coefficients vector has also been added to requests available in the WSC05 data-set. The optimal answer is also calculated and recorded for each request.
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During the 7~9th century, the Tibetan Empire constituted a superpower between Tang Empire and the Abbasid Caliphate: one that played a significant role in geopolitics in Asia during the Early Medieval Period. It is unclear what factors led to the rise and rapid decline of this powerful empire: the only united regime in the Tibetan history. We present sub-annual scale precipitation and decadal-scale temperature records in the central Tibetan Plateau, indicating that the height of this empire coincided with a two centuries long interval of uncharacteristically warm and humid climate. The ameliorated climate enabled the expansion of arable land and increased agricultural production. This has implications for agricultural production in alpine regions including Tibetan Plateau in context of current global warming. The elevation data of the Tibetan Plateau (26°00’-39°47’N, 73°19’-104°47’E) was downloaded for the GDEMDEM 30M digital elevation data from the Geospatial Data Cloud (http://www.gscloud.cn/sources/list_dataset/421?cdataid=302&pdataid=10&datatype=gdem_utm2#dlv=Wzg4LFswLDEwLDEsMF0sW1siZGF0YWlkIiwxXV0sW10sOTld). Because the dataset is 3.48 GB in zipped format (14.98GB in raster format), which can be downloaded from the original website. If there are any problems, please contact us directly.
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This zip file contains data and codes of manuscript "Maximum Frequency Deviation Assessment with Semi-Supervised Clustering based on Metric Learning". It consists of 4 folders: (1) example_system folder: IEEE 39-bus model and simplified provincial power system of China in PSS/E format. (2) codes_to_generate_samples folder: Python codes to generate samples of the two systems in the example_system folder. (3) samples folder: samples generated with codes in the codes_to_generate_samples folder. In the manuscript, 10,000 samples were generated. However, only 1,000 samples are uploaded here to save space. (4) codes_for_machine_learning: codes for implementing the model proposed in this manualscript.
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Data used to generate figures in the manuscript, excluding RNA-seq and ChIP-seq data, which can instead be downloaded from Gene Expression Omnibus: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE122456.
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The research data includes: the original spectrogram and visualized feature image; the denoised spectrogram; the cropped spectrogram;; results of deep-learning models; the result of this article(Result of experiment); core code; and the core code.
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MATLAB simulation code and scripts reproducing all figures in Tikhonov, Kachru, Fisher (2020), "A model for the interplay between tradeoff plasticity and evolution in changing environment". Optional pre-computed simulation data included to speed-up figure plotting; remove or rename any data file to rerun the relevant simulations from scratch.
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This dataset contains 26 sub-datasets of common distribution, including Beta, Exponential, Log-Normal, Normal, Triangular, Uniform, bi-modal. The size of datasets varying from 1000 points to 1 million points. The distributions are generated with various parameter settings.
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PDF in each folder shows un-cropped Western blots that were used to generate main and supplementary Figures. Raw microscopic images were also included in the data set.
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Propionic acidemia (PA) is an autosomal recessive metabolic disorder caused by the deficiency of the mitochondrial protein propionyl-CoA carboxylase (PCC) and associated with pathogenic variants in either of the two genes, PCCA or PCCB. In the present study, three PA patients were diagnosed by using gas chromatography-mass spectrometry(GC-MS), tandem mass spectrometry (MS/MS) and Molecular diagnostic methods. All patients had onset in neonatal period. One patient died of infection and metabolic decompensation, and the other two had mild to moderate developmental delay/mental retardation. Mutation analysis of the PCCA gene identified compound heterozygous c.1288C>T(p.R430X) and c.2002G>A(p.G668R) in patient 1, homozygous c.1426C>T(p.R476X) in patient 2; mutation analysis of the PCCB gene identified compound heterozygousc.359_360del AT(p.Y120Cfs*40) and c.1398+1G>A in patient 3. Three novel mutations (c.1288C>T, c.359_360del AT and c.1398+1G>A were identified in PCCA and PCCB genes. Among them, in the PCCA gene, c.1288C>T(p.R430X) was a nonsense mutation, resulting in a truncated protein. c.359_360del AT was a frameshift mutation, leading to p.Y120Cfs*40 mutation of amino acid sequence in PCCB. c.1398+1G>A was a splicing mutation, causing skipping of the exons 13-14. In conclusion, the novel mutations of this study will expands the mutation spectrum of PA.
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This dataset contains sensor traces (multivariate time series) of six data acquisition campaigns performed by autonomous aquatic drones involved in water monitoring. A total of 5.6 hours of navigation are available, with data coming from both lakes and rivers, and from different locations in Italy and Spain. The monitored variables concern both the internal state of the drone (e.g., battery voltage, GPS position and signals to propellers) and the state of the water (e.g., temperature, dissolved oxygen and electrical conductivity). Data were collected in the context of the EU-funded Horizon 2020 project INTCATCH (http://www.intcatch.eu) which aims to develop a new paradigm in the monitoring of river and lake water quality. Both autonomous and manual drive is used in different parts of the navigation.
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