The dataset in the file "combined1.csv" included 383 patient's clinical data, including 7 variables: age, sex, location, aneurysm, nidus size, draining type, the number of draining veins, and hemorrhage. Female was defined as 0, male as 1. Superficial AVM was defined as 0, deep AVM as 1, infratentorial AVM as 2. Only superficial draining vein was defined as 0, mixed superficial and deep draining vein as 1. A single draining vein was defined as 1, multiple draining veins as 2. Ruptured AVM was defined as 1, unruptured AVM as 0.
The file "randomtestpredict.R" was the source file in RStudio, which was used to build and test prediction models based on the above data file.
Contributors:Veerle Van Oeckel, Maïté Verloigne, Benedicte Deforche, Nicola D. Ridgers, Elling Bere
Background: Sedentary behaviour guidelines recommend that individuals should regularly break up sitting time. Accurately monitoring such breaks is needed to inform guidelines concerning how regularly to break up sitting time and to evaluate intervention effects. We investigated the concurrent validity of questionnaire items assessing number of breaks in sitting time among children and adolescents.
Methods: Fifty-seven children and adolescents self-reported number of breaks from sitting taken at school, while watching TV and during other screen time activities. Participants also wore an activPAL monitor to objectively assess the number of sitting time breaks (frequency/hour). Concurrent validity was assessed using Spearman rank correlations.
Results: Self-reported number of breaks/hour at school showed good concurrent validity (ρ=0.676). Results were moderate to good for self-reported number of breaks/hour while watching TV (ρ range: 0.482 to 0.536) and moderate for self-reported number of breaks/hour in total screen time (ρ range: 0.377 to 0.468). Poor concurrent validity was found for self-reported number of breaks/hour during other screen time activities (ρ range: 0.157 to 0.274).
Conclusions: Only the questionnaire items about number of breaks at school and while watching TV appear to be appropriate for further use in research focussing on breaks in prolonged sitting among children and adolescents.
Contributors:Tünde Szabó, Márton Prorok, Bence Berkes
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.
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. Rmd): Datasets were generated with this R Notebook. It can be used to update datasets and customize the data generation process.
• 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.
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.
Contributors:Dominik Kaim, Jakub Taczanowski, Marcin Szwagrzyk, Krzysztof Ostafin
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.
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).
Contributors:H.M. Vu, Margaret Shanafield, Thong Nhat Tran, Daniel Partington, Okke Batelaan
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