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  • Data set for publication in Cell Host Microbe, Bruggisser et al: Cell-specific targeting by Clostridium perfringens β-toxin unraveled: the role of CD31 as the toxin receptor.
    Data Types:
    • Image
    • Tabular Data
    • Dataset
    • Document
    • Text
  • The dataset is constructed for a project that investigates the coverage and the role of Semantic Scholar (S2) search engine in condunting secondary studies in software engineering.
    Data Types:
    • Software/Code
    • Tabular Data
    • Dataset
    • Text
  • Swaim, et al., Cell Reports, 2020.
    Data Types:
    • Dataset
    • File Set
  • Real-time tracking of the spatial diffusion of airborne diseases, and especially COVID-19 is in the focal point of both recent academic studies and policymaking. Airborne pathogens are handed over by interpersonal encounters. Therefore, agent-based modelling provides a useful approach to grasp the complex and interrelated nature of spatiotemporal movement and the geographical spread of infectious diseases. Although technology development rendered it to be feasible to track the spatial spread of infected individuals, the spatial scale of data retrieval can cause challenging bottlenecks for academic analysis. Samples on community-scale, for instance, by crowdsourced data as well as the global level of international aircraft movements are addressed. However, regional-scale spread of airborne diseases conveyed by human mobility rarely comes into focus. By directing our efforts to the level of countrywide diffusion, we aim to disclose the spatial component of airborne pathogens’ infection carried over by interpersonal encounters. The mobile cell dataset we applied here is especially suitable to estimate the number of interpersonal encounters, that is enabled by co-locating the same space with an infected person within a definite timeframe. Consequently, we considered mobile phone data driven co-location as ‘locational chance’ of airborne pathogen spreading. The volume of spread, as we argue, is dependent on the interpersonal connections. According to the current results, the geographical spread of COVID-19 is dominantly carried over by latently infected individuals, who transmit the disease without showing any symptoms. We modelled the interpersonal encounters of a set of randomly chosen latent infected as an indicator of the further geographical spread of the disease. We applied two various sets of models running: one, that is based on real archive data, and the other, that simulates current mobility patterns ordered by relocation restrictions.
    Data Types:
    • Video
    • Tabular Data
    • Dataset
    • File Set
  • The following files are associated with the manuscript "Flavones’ and Flavonols’ Antiradical Structure–Activity Relationship—A Quantum Chemical Study", MDPI Antioxidants, 2020 (https://doi.org/10.3390/antiox9060461). They include geometries of molecules used therein and allows to reproduce the obtained results.
    Data Types:
    • Dataset
    • Text
  • The presented cross-sectional dataset can be employed to analyze the governmental, trade, and competitiveness relationships of official COVID-19 reports. It contains 18 COVID-19 variables generated based on the official reports of 138 countries, as well as an additional 2203 governance, trade, and competitiveness indicators from the World Bank Group GovData360 and TCdata360 platforms in a preprocessed form. The current version was compiled on May 25, 2020. Please cite as: • (Data in Brief article) Data generation: • Data generation (data_generation. Rmd): Datasets were generated with this R Notebook. It can be used to update datasets and customize the data generation process. Datasets: • Country data (country_data.txt): country data. • Metadata (metadata.txt): the metadata of selected GovData360 and TCdata360 indicators. • Joint dataset (joint_dataset.txt): the joint dataset of COVID-19 variables and preprocessed GovData360 and TCdata360 indicators. • Correlation matrix (correlation_matrix.txt): the Kendall rank correlation matrix of the joint dataset.
    Data Types:
    • Software/Code
    • Dataset
    • Text
  • The COVID-19 pandemic is a worldwide public health crisis. A vaccine with efficacy against SARS-CoV-2, the pathogen that causes COVID-19, is needed. While most vaccines under investigation are optimized to generate an antibody response, we hypothesize that peptide vaccines containing optimized epitope regions with concurrent B cell, CD4+ T cell, and CD8+ T cell stimulation would drive both humoral and cellular immunity with high specificity, potentially avoiding undesired effects such as antibody-dependent enhancement (ADE), all the while providing a platform with fast manufacturing potential and with high shelf-life stability. Here we combine computational prediction of T cell epitopes with recently published B cell epitope mapping studies to propose optimized peptide vaccines for SARS-CoV-2. We begin with an exploration of the predicted T cell epitope space in SARS-CoV-2, with interrogation of HLA-I and -II epitope overlap, protein source, concurrent human/murine coverage, and allelic space. The T cell vaccine candidates were selected by further considering their predicted affinities for MHC-I and MHC-II alleles across the human population (as well as H2-b/H2-d murine coverage to support preclinical studies), predicted immunogenicity, viral protein abundance, sequence conservation, and co-localization of MHC-I and -II epitopes. The predicted B cell epitope regions were selected by starting from responses identified in linear epitope mapping studies of patient serum and filtering to select those with high molecular dynamics-derived surface accessibility, high sequence conservation, spatial localization within functional domains of the spike glycoprotein (RBD, FP, and HR regions), and avoidance of glycosylation sites. From 58 initial candidates, three B cell epitope regions were identified using these criteria. By combining these B cell and T cell analyses, we propose a set of human and murine-compatible SARS-CoV-2 vaccine peptide candidates.
    Data Types:
    • Other
    • Sequencing Data
    • Tabular Data
    • Dataset
    • Text
  • The zip file contains two directories, which include waveform data for two reference earthquakes in Sheng et al. (2020), recorded by a dense seismic array in Weiyuan, China. Instrument responses have been removed.
    Data Types:
    • Dataset
    • File Set
  • The dataset presents the historical railway network of Galicia and Austrian Silesia – two regions of the Habsburg Empire, covering more than 80 000 km2, currently divided among Czechia, Poland and Ukraine. The network covers the times of railway appearance and the most dynamic development of the 19th and beginning of the 20th century, up to 1914 – the outbreak of the First World War. The data can be characterized by unprecedented positional accuracy, as they were reconstructed based on the current railway network, which resulted in almost no shifts in space. Most of the lines were reconstructed based on OpenStreetMap data, and the lines, which were closed-down between 1914 and 2019, and are no longer available in spatial datasets, were reconstructed based on high-resolution satellite imageries and historical maps. Altogether, the network covers nearly 5000 km on 127 lines. The data are accompanied by a set of attributes, i.e. year of construction, length, starting and final point, type (normal, narrow-gauge, etc.). It can be used in many different applications including historical accessibility mapping, migrations, economic development, the impact of past human activities on current environmental and socio-economic processes, like land use change drivers, landscape fragmentation, invasion of new species and many more. Data are available for download in the shp format. Acknowledgments This research was funded by the Ministry of Science and Higher Education, Republic of Poland under the frame of “National Programme for the Development of Humanities” 2015–2020, as a part of the GASID project (Galicia and Austrian Silesia Interactive Database 1857–1910, 1aH 15 0324 83).
    Data Types:
    • Dataset
    • File Set
  • Data used in paper: H. M. Vu, M. Shanafield, T. T. Nhat, D. Partington, and O. Batelaan, 2020, Mapping catchment-scale unmonitored groundwater abstractions: Approaches based on soft data. Journal of Hydrology: Regional Studies
    Data Types:
    • Dataset
    • Document
    • File Set