Structural analysis and evolutionary exploration based on the research topic network of a field: a case in high-frequency trading

This study aims to systematically analyze the distribution dynamics of research topics and uncover the development state of the research in the specific field, which will provide a practical reference for developing professional subject knowledge services in the era of big data. The research topic network is constructed and analyzed using methods and tools of scientometrics. Basic statistics on network characteristics are performed to reveal the research status. Community detection, node ordering, and other steps are conducted to generate the evolutionary alluvial diagram. Then, relevant results are analyzed to explore the knowledge structure of the specific field and evolutionary context of research topics. Visualization analysis on the network structure of the latest period is executed to distinguish related concepts and predict the research trends. Taking high-frequency trading (HFT) as a case, this study achieves diversified scientometrics analysis of the research topic network and multi-dimensional evolution exploration of the relevant research topics in the specific field, which obtaining some knowledge insights. (1) Six major topics in HFT: liquidity & market microstructure, market efficiency, financial market, incomplete market, cointegration & price discovery, and event study. (2) The research focus about markets gradually transferred from international to emerging, meanwhile continuous attention to volatility/risk related issues. (3) The emphasis will change from theory to practice, technologies (big data, etc.) and theories (behavioral finance, etc.) will have more interaction with HFT. An effective research idea is proposed to reveal the knowledge structure of field and analyze the evolutionary context of research topics, which demonstrating the knowledge insights

Research on the Evolution of Journal Topic Mining Based on the BERT-LDA Model

Scientific papers are an important form for researchers to summarize and display their research results. Information mining and analysis of scientific papers can help to form a comprehensive understanding of the subject. Aiming at the ignorance of contextual semantic information in current topic mining and the uncertainty of screening rules in association evolution research, this paper proposes a topic mining evolution model based on the BERT-LDA model. First, the model combines the contextual semantic information learned by the BERT model with the word vectors of the LDA model to mine deep semantic topics. Then construct topic filtering rules to eliminate invalid associations between topics. Finally, the relationship between themes is analyzed through the theme evolution, and the complex relationship between the themes such as fusion, diffusion, emergence, and disappearance is displayed. The experimental results show that, compared with the traditional LDA model, the topic mining evolution model based on BERTLDA can accurately mine topics with deep semantics and effectively analyze the development trend of scientific and technological paper topics

Study on open science: The general state of the play in Open Science principles and practices at European life sciences institutes

Nowadays, open science is a hot topic on all levels and also is one of the priorities of the European Research Area. Components that are commonly associated with open science are open access, open data, open methodology, open source, open peer review, open science policies and citizen science. Open science may a great potential to connect and influence the practices of researchers, funding institutions and the public. In this paper, we evaluate the level of openness based on public surveys at four European life sciences institute