Our hope is that Boa for COVID-19 can help with solving the COVID-19 problem by analyzing text for COVID-19 papers. Some examples of such questions include but are not limited to:
The BoaC project is led by Hridesh Rajan at Iowa State University and Robert Dyer at University of Nebraska - Lincoln. The project also involves PhD students Yijia Huang and Rangeet Pan. We have also significantly benefited from our collaborators Simon Geletta, a professor of public health, Jianqiang Zhang, and Tomislav Jelesijević both faculty in veterinary medicine and experts in virology and coronaviruses.
The registration is to prevent abuse of our limited computational resources by web robots and to limit certain attacks on our cyberinfrastructure. We have experienced bots automatically crafting and submitting queries to launch denial of service attacks against our cyberinfrastructure.
The registration is very quick and one of us is usually able to activate your account at a short notice.
The BoaC cyberinfrastructure already provides the following capabilities and we are adding more capabilities as we speak.
Basic search capabilities. BoaC's search capabilities include:
Advanced text analysis. The BoaC infrastructure also provides the capability to search through every part of the paper including the body of the paper and references. This data is stored in a structured form and your analytics can browse over that structure. This includes the ability to:
Copyright © 2012–2015, Iowa State University of Science and Technology. All rights reserved.
This material is based upon work supported by the US National Science Foundation (NSF) under grants CCF-19-34884, CNS-15-13263, CNS-15-12947, CCF-14-23370, CCF-13-49153, CCF-11-17937, CCF-10-17334, and CCF-10-18600. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.