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BackgroundIn these trying times, we developed an R package about bibliographic references on coronaviruses. Working with reproducible research principles based on open science, disseminating scientific information, providing easy access to scientific production on this particular issue, and offering a rapid integration in researchers’ workflows may help save time in this race against the virus, notably in terms of public health. ObjectiveThe goal is to simplify the workflow of interested researchers, with multidisciplinary research in mind. With more than 60,500 medical bibliographic references at the time of publication, this package is among the largest about coronaviruses. MethodsThis package could be of interest to epidemiologists, researchers in scientometrics, biostatisticians, as well as data scientists broadly defined. This package collects references from PubMed and organizes the data in a data frame. We then built functions to sort through this collection of references. Researchers can also integrate the data into their pipeline and implement them in R within their code libraries. ResultsWe provide a short use case in this paper based on a bibliometric analysis of the references made available by this package. Classification techniques can also be used to go through the large volume of references and allow researchers to save time on this part of their research. Network analysis can be used to filter the data set. Text mining techniques can also help researchers calculate similarity indices and help them focus on the parts of the literature that are relevant for their research. ConclusionsThis package aims at accelerating research on coronaviruses. Epidemiologists can integrate this package into their workflow. It is also possible to add a machine learning layer on top of this package to model the latest advances in research about coronaviruses, as we update this package daily. It is also the only one of this size, to the best of our knowledge, to be built in the R language.

This article is about the two conflicting goals when teaching statistics or data science courses based on real-world data in a business school environment. We propose to look at structured socio-economic data about the Canadian economy. Canada was ranked 8th in 2017 by Open Data Watch (Government of Canada) for its data accessibility policy. Statistics Canada offers several ways to access data across its over 11,000 data tables. We built an R package to ease access to Statistics Canada's open economic data. With this package, we offer students another option to collect data about the Canadian economy.

The purpose of this study is to contribute to an investigation of the role of European integration as a potential growth driver or convergence engine for emerging Europe. We classify emerging Europe into two groups of economies: (1) 11 new EU member states from Central and Eastern Europe; and (2) six EU candidate countries from the Western Balkans. Our results are also highly relevant for the so-called 'association trio' within the EU's framework of Eastern partnership. The more specific research objectives are to explore (1) what the main driving forces behind growth dynamics in post-communist economies in Europe are; and (2) whether the experience of fast-reforming new EU member states can be replicated or adapted to the EU candidate countries. We provide evidence that the process of European integration holds strong transformative power to accelerate institutional progress and support economic growth. [ABSTRACT FROM AUTHOR]

This research analyzes the speeches and interviews of high-level European Central Bank decision makers from 1997 to 2021 to identify clusters of prominent topics. Transparency is a crucial aspect of modern monetary policy, and public speeches and interviews by central bankers play a vital role in achieving it. Our study employs structural topic modeling to compare the prevalence of topics during the Global Financial Crisis, pandemic crisis, and periods of heightened inflation. Additionally, we explore the impact of central bank independence on official rhetoric. [ABSTRACT FROM AUTHOR]

We propose a simple theoretical model emphasizing the importance of a multi-sided platform (MSP) in fostering innovation. This model aims to assess the effects of structuring industries around an MSP on innovation dynamics and emphasizes the impact of cross-platform competition. It teaches us how to encourage cross-platform competition in terms of competition policy. The outcome is threefold. To begin, the presence of an MSP is critical for market innovation. Second, our findings indicate that skewed market power in favor of the MSP may stifle innovation in this industry, even if the negative impact on the industry's rate of innovation is not immediately apparent. Finally, we demonstrate that industries with multiple MSPs have a higher rate of innovation. The model's conclusions emphasize the critical importance of preserving the contestability of digital markets through competition rules enforcement. Even if the inherent technical characteristics of this industry result in a situation of dominance, competition rules should aim to preserve the possibility of market competition through, among other things, interoperability requirements, data portability requirements, and control of exclusivity clauses. [ABSTRACT FROM AUTHOR]