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  • Icequakes, micro-seismicity caused by glacier deformation and motion, provide important information to study glacier dynamics and its responses to environmental changes at various temporal and spatial scales. In this study, we apply a multi-dimensional autoregressive maximum-likelihood algorithm to obtain 12 icequake templates on the Urumqi Glacier No. 1, China, and detect 65,363 icequakes through template matching. Centroid location of the 12 templates indicate that most icequakes are caused by surface crevasses inside the glacier, which are characterized by dominant surface waves. The icequakes show seasonal variation with more events in summer because of faster ice flow due to high temperature and precipitation. In winter, however, the icequakes, on par with those in summer, suggest considerable glacier growth in cold weather. Because of higher ice flow velocity due to low tide, the number of icequakes has two daily peaks which seems to negatively correlate with semi-diurnal solid tide.
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  • This video dataset comprises (44) recordings of the human gait with (396) edited videos, there is also available a dataset of geometric features with (1431) files acquired from the video dataset that can be used for identifying people by human gait or for analyzing gait on various purposes. Each directory has the following structure: One directory with original videos of 44 people walking. Some parameters are, Video format AVI, stable background, resolution of 1280x700 p. Each video recording registers a person walking from left to right and back three times. One directory (Track A) with 44 edited videos with the first walking pass from left to right and back. A subdirectory (Sx_Track 1_Right) with 44 edited videos with the first walking pass from left to right. A subdirectory (Sx_Track 1_Left) with 44 edited videos with the first walking pass from right to left. This directory comprises 132 videos in total. One directory (Track B) with 44 edited videos with the second walking pass from left to right and back. A subdirectory (Sx_Track 1_Right) with 44 edited videos with the second walking pass from left to right. A subdirectory (Sx_Track 1_Left) with 44 edited videos with the second walking pass from right to left. This directory comprises 132 videos in total. One directory (Track C) with 44 edited videos with the third walking pass from left to right and back. A subdirectory (Sx_Track 1_Right) with 44 edited videos with the third walking pass from left to right. A subdirectory (Sx_Track 1_Left) with 44 edited videos with the third walking pass from right to left. This directory comprises 132 videos in total. One directory V-Geometric features with .dat files acquired from the video dataset with geometric features from the rectangle drawn over the silhouettes during a time. The geometric features were acquired with image processing techniques and are comprised of width, height, and area over time [1,2] nnW subdirectory: 477 .dat files with registered information about the behavior of the rectangle width during the gait record. nnH subdirectory: 477 .dat files with registered information about the behavior of the rectangle height during the gait record. nnA subdirectory: 477 .dat files with registered information about the behavior of the rectangle area during the gait record. References: [1] Senigagliesi, L.; Ciattaglia, G.; De Santis, A.; Gambi, E. People Walking Classification Using Automotive Radar. Electronics 2020, doi:10.3390/electronics9040588 [2] Kececi, Aybuke, Armağan Yildirak, Kaan Ozyazici, Gulsen Ayluctarhan, Onur Agbulut, and Ibrahim Zincir. 2020. Implementation of Machine Learning Algorithms for Gait Recognition. doi:10.1016/j.jestch.2020.01.005 [3] Figueiredo, Joana, Cristina P. Santos, and Juan C. Moreno. 2018. Automatic Recognition of Gait Patterns in Human Motor Disorders using Machine Learning: A Review. Vol. 53. doi: 10.1016/j.medengphy.2017.12.006
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  • This dataset is a geomorphic classification of the shelf features surrounding Lord Howe Island and Balls Pyramid. This dataset provides information on the size, extent and type of features which occur around the shelves, which can be used for a broad range of marine planning and research purposes. Shelves were classified into shelf region (inner, mid, outer) and geomorphic features. Features include an extensive submerged fossil reefs, ridges and patch reefs, sandy basins, paleochannels, modern fringing reef, shallow lagoon, shelf edge terraces and shelf break. Broad seafloor features were visually interpreted through digitisation in ArcGIS v10.1 using terminology consistent with international nomenclature and national standards. The classification of geomorphic features extends upon the interpretation of Balls Pyramid shelf undertaken by Linklater et al. (2015). Full description of methods is outlined in the following open-access publication, accessible by the following link: http://www.mdpi.com/2076-3263/8/1/11/htm
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  • This research aims to provide an understanding of how absorptive capacity occurs in startups, which maintains interorganizational relationships with large companies. The research used a qualitative multiple case study design and the investigation of seven startups constituted the study corpus. The data were analyzed using statistical analysis, content analysis, and business process analysis. In the study, the software Iramuteq (Interface of R pour les Analyzes Multidimensionnelles de Textes et de Questionnaires) was used as a tool to aid the qualitative analysis of textual data. In a complementary way, the SPSS Statistics 17 software was used to treat the survey data.
    Data Types:
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  • This data set contains the source files of the SeGa4Biz framework including metamodels, model instances, and transformation rules.
    Data Types:
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  • The outbreak of coronavirus and the infectious disease it causes (COVID-19) have taken different paths around the world, with countries experiencing different rates of infection, case prevalence and mortality. This simultaneous yet heterogenous process presents a natural experiment for understanding some of the reasons for such different experiences of the same shock. This paper looks at the privatization of healthcare as one key determinant of this pattern. We use a cross-section dataset covering 147countries with the latest available data. Controlling for per capita income, health inequality and several other control variables, we find that a 10% increase in private health expenditure relates to a 4.3% increase in COVID-19 cases and a 4.9% increase in COVID-19 related mortality. Globalization also has a small positive effect on COVID-19 prevalence, while higher hospital capacity (in beds per 1,000 people) is significant in lowering COVID-19 mortality. The findings suggest caution regarding policies which privatize healthcare systems in order to boost efficiency or growth in the short-run, as these reduce countries' long-term preparedness for dealing with pandemics.
    Data Types:
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  • It involves raw data and preprocessed files used for statistical analysis and the training of computational models. Please see the readme.txt files under each folder to get further information about the files inside that folder.
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  • This is a small Word2Vec Model n-gram (n= 1-3) built from Twitter chatter about COVID-19. The objective of this model is to capture semantic representations of COVID-19-related concepts. We will be updating this dataset over time. Please see the following script if you are having difficulty loading the model: https://bitbucket.org/asarker/lexexp
    Data Types:
    • Software/Code
    • Dataset
  • The dataset was constructed to examine Vietnamese student’s learning habit during the school suspension time due to the novel coronavirus - SARS-CoV-2 (Covid-19), responds to the call of open research to prevent potential effects of coronavirus (Elsevier, 2020). The questionnaires were spread over a network of educational communities on Facebook from 7th to 28th of February 2020. Using the snowball sampling method, researchers delivered the survey to teachers and parents to confirm the consent form before they forward it to their students and children. In order to measure the influence of student’s socioeconomic status and occupational aspirations over their learning habit during school closure, the survey includes three major groups of questions: (1) Individual demographic, includes family socioeconomic status, school types, and occupational aspirations; (2) Student’s learning habits, include hours of learning before and during school suspension time, with and without other people’s support; and (3) Student’s perception on self-learning during the disease. There was a total of 920 clicks on the survey link, but only 460 responses with consent form were received. The incurable answers (e.g., year of birth is before 2009, more than 20 hours of learning per day, etc.) have been eliminated. Finally, the dataset includes 420 valid observations.
    Data Types:
    • Tabular Data
    • Dataset
    • Document
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