Our fellow team will present their network-driven study summarizing the fields related to the recent COVID-19 works.
When: September 15 at 3 p.m. ET
Where: Register for this webinar here. After registering, you will receive a confirmation email containing information about joining the meeting.
Abstract: Studies on COVID-19 have been flooding preprint repositories over the past few months, with many researchers changing their focus study the pandemic. This is happening so fast that it has become hard for researchers to catch up with the literature. At the same time, it is impossible to address the relevance of these studies based on citations or other metrics that take time to build up. Automated methods to summarize or provide an overview of these studies could be very useful for researchers to keep track of their fields of study and focus on what may be relevant for them.
In this project, we propose a network-driven study on summarizing the fields related to the recent COVID-19 works. We plan to start by building citation networks among the most recent literature and then apply community detection and keyword extraction to better understand how it is organized. Next, we are interested in exploring some questions about the works they cite, for instance, and answer some questions such as: are they dormant studies that only got cited in recent times? what are the main disciplines they are based on? what is the amount of applied and theoretical papers on the field? This can be accomplished by building citation networks now based on the referenced literature. In this framework, authors of recent studies could also be tracked over time in the data (according to their previous publications) to understand the team formation and expertise behind the emergence of these new studies.
The proposed study could lead to the development of interactive visualizations and other tools that can be used by researchers to assess the relevance of the new COVID-19 works according to their main interests or fields of study. Also, this is an opportunity to promote the importance of funding science (pure and applied) and show that during an unexpected crisis, society may need knowledge and resources of research not seem like a priority in the past.
Given the fast pacing aspect of COVID-19 related research, it is desirable to have means to accelerate tasks such as data acquisition and preprocessing, aspects that could be accomplished with help from CADRE, since it already provides a unified environment to access scholarly datasets. Thus, we are confident that its infrastructure would be beneficial to the proposed study.
Filipi Nascimento Silva joined the IU Network Science Institute as an Assistant Research Scientist after working for a year as a visiting scholar at Indiana University's Luddy School of Informatics, Computing & Engineering. He received his Ph. D. in Physics at the São Carlos Institute of Physics (University of São Paulo). His primary research interests include studying and implementing new techniques for analyzing, modeling, and understanding real-world systems through a joint combination of complex networks, machine learning, and data visualization. These approaches have been applied to a wide range of scientific areas, including bioinformatics, text analysis, science of science, information science, and urban networks. Dr. Nascimento Silva's current research focus is on developing a new set of interactive network visualization tools for researchers and on studying the structure and dynamics of scientific fields over time.