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  • The database contains cross-border exposures (as a % of total exposures) to individual countries for the 61 largest European banks over the years 2010-2015. Getting a complete overview of the cross-border positions of European banks is challenging, as there are no regular reporting standards for banks’ foreign exposures split by country. One option is to focus on banks’ foreign subsidiaries. This may however lead to a significant underestimation of a bank’s foreign activities, especially since European banks conduct around half of their foreign activities vis branches. With this dataset we aim to provide a more complete picture of banks' cross-border exposures and collected the data ourselves from public sources. We collected the data for the 61 largest European banks. These banks together represent around two-third of total European banking assets. The 61 banks together invest in 138 different countries (the countries are presented by their ISO2 codes). Data on cross-border exposures are primarily obtained from annual reports, and, when needed, supplemented with data stemming from the public EBA stress tests conducted in 2011 and 2013, and CRD IV country-by-country reporting.Due to the absence of a standard reporting format some assumptions and simplifications had to be made. First of all, the majority of banks report their foreign exposures in loans or assets, but some banks use the net income as the reporting unit. As we are especially interested in banks’ credit exposures to other countries, we had an order of preference for exposures reported in i) loans; ii) assets; and iii) net income. Second, we aimed for cross-border exposures at the country level as for our analysis we link home and host country characteristics. However, sometimes only information on banks’ exposures to a group of countries (e.g. Western Europe) or continents (e.g. Asia) was available. In those cases, we simply reported the exposures to groups of countries or continents (see columns EN-EX in the database). Third, the data collection resulted in an almost complete overview of the foreign exposures of the 61 European banks. For only a small portion of foreign exposures (3.6% of the total foreign exposures or 1.1% of the total assets) we do not know to which region or country these belong . This is the case when banks report their remaining foreign exposures as “other” without mentioning the countries belonging to this group (here we used "in-sample" estimation). Note that it also happens that a bank reports about its cross-border exposures quite granular for one year (i.e. exposures to multipe individual countries), and the subsequent year only reports the total exposures to "rest of europe" and "america". In those cases information from the years with granular information is used to make assumptions about the other years' cross-border exposures.
    Data Types:
    • Tabular Data
    • Dataset
  • UKIP Local Elections
    Data Types:
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    • File Set
  • WT is a modified version of Tephra2 to reconstruct tephra fall from bending plume, which often occurs in windy condition.
    Data Types:
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    • File Set
  • An isothermal implementation of Smoothed Dissipative Particle Dynamics (SDPD) for LAMMPS is presented. SDPD is useful for hydrodynamics simulations at mesoscale where the effect of thermal fluctuations are important, but a molecular dynamics simulation is prohibitively expensive. We have used this package to simulate diffusion of spherical colloids. The results (particularly the long-time behaviour of velocity autocorrelation function) are in agreement with theoretical models that take hydrodynamic interactions into account.
    Data Types:
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    • File Set
  • Appendix 1: Mineral chemistry Data Appendix 2: Precision and acuraccy Appendix 3: Compilated data for isotopic geochemistry
    Data Types:
    • Tabular Data
    • Dataset
  • Raw pollen counts and full pollen diagram from the RZ section, Nangqian Basin, Tibet, China
    Data Types:
    • Tabular Data
    • Dataset
  • We present a parallel implementation of a direct solver for Poisson’s equation on extreme-scale supercomputers with accelerators. We introduce a chunked-pencil decomposition as the domain-decomposition strategy to distribute work among processing elements to achieve improved scalability at high counts of accelerators. Chunked-pencil decomposition enables overlapping MPI communication and data transfer between the central processing units (CPUs) and the graphics processing units (GPUs). It enables contiguous message transfer among the nodes and improves data locality by keeping neighboring elements in adjacent memory locations while permitting the use of shared memory for certain segments of the algorithm when possible. We study two different communication patterns within the chunked-pencil decomposition. The first pattern fully overlaps the communication with data transfer and aims to speedup the overall turnaround time. The second pattern concentrates on low memory usage and is more network friendly than the first pattern for computations at extreme scale. In our parallel implementation, we interleave OpenACC with MPI to support computations on the GPU or the CPU. The numerical solution and its formal second order of accuracy is verified using the method of manufactured solutions for various combinations of boundary conditions. Additionally, we used PittPack within an incompressible flow solver to further validate its accuracy and as well as demonstrate its versatility as a software package. We performed weak scaling analysis with up to 1.1 trillion Cartesian mesh points distributed over 16384 GPUs on a petascale leadership class supercomputer.
    Data Types:
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  • MS profile
    Data Types:
    • Dataset
    • File Set
  • A Mobile Monitoring System (MMS) has been designed having into account the use of technology with high sensor accuracy and with the capacity to be installed easily and quickly in different cardinal locations, distribution spaces, volumes and at different heights of a tertiary in-use building located in Leioa (Bilbao).Two types of MMS have been designed with the aim of doing two types of analysis, one intended to do the indoor air temperature uncertainty analysis and another focused on doing the outdoor air temperature uncertainty analysis. Eight tripods compose the indoor MMS with twenty sensors at different heights, which have been installed in different building’s offices in order to collect indoor temperature measurements at different heights and locations. As well, eight sensors compose the outdoor MMS to collect data of outdoor temperature measurements. Both MMS have been integrated to the existing Building Automation System (BAS) of the tertiary building; some data collected by the BAS have also been taken into account for the indoor and outdoor temperature uncertainty analysis. Seven tests have been carried out, five tests for the indoor temperature analysis and two tests for outdoor analysis. In the case of indoor MMS, one test has collected temperature data from all sensors together in the same place and at the same height, and four tests were carried out in four different offices with different typologies; all tests have been performed using the MMS at different periods using the same eight tripods. With the outdoor MMS, again, one test has collected temperature data of all sensors together located at the building’s roof and the final test has been carried out installing the sensors around the envelope of the building at different heights and cardinal locations. The potential of the datasets from the indoor and outdoor MMSs is based on the rigorous data collection process, which allows making an analysis of: temperature uncertainties, temperature stratification, temperature spatial behaviour and temperature behaviour analysis due to impact of solar radiation, heating system and electricity consumption.
    Data Types:
    • Tabular Data
    • Dataset
  • This is a dataset consisting of the SBAS LOS land deformation and the differences between TanDEM-X and SRTM DEMs over a mining area located in Zonguldak district, Turkey. The land deformation was calculated based on the Copernicus Sentinel 1A/B SAR data collected between Jan 2018 and Nov 2019. The dataset allows for the study of the relationship between the deformations and differences between DEMs captured some 11 years apart. The data are in ESRI's shop format. The raster is in tiff format.
    Data Types:
    • Dataset
    • File Set
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