The explosion of research activity as a result of the Covid-19 pandemic has accelerated the use of text-analytics to extract the insights and intelligence necessary for ST&I policy & management – e.g., competitive technical intelligence, R&D management, research evaluation, etc. VantagePoint software was recently used by B. P. Cabral, et.al. to identify top experts for a survey in "What are the most promising treatments and vaccine candidates for COVID-19? A global survey of experts involved in virus research"(full-text of paper available) and by C.V. Fry, et. al. to conduct a network analysis in "Consolidation in a crisis: Patterns of international collaboration in early COVID-19 research" (full-text of paper available).
VantagePoint text-mining software will be engaged in an NSF-funded RAPID project titled "Corona Virus -- Exploring Causes and Cures through Literature Based Discovery." Resolving the COVID-19 pandemic, and future crises, demands rapid and comprehensive access to pertinent research knowledge. This project promotes the progress of science to advance national health and welfare by enhancing capability to understand global research literature (i.e. tens of thousands of biomedical articles growing explosively week by week) and to hone in on findings that bear on combating the virus. The project will employ two distinct approaches to provide means to access vital findings, and tools to discover connections across disciplines and among causes, biomarkers, conditions, and treatments for COVID-19.
The first approach will use bibliometric and text analytic methods to help researchers elicit virus attributes, investigate contributing causes, and identify biomarkers. Bi-weekly summaries of key findings in the COVID-19 literature,arranged by topics, country, and organization will be posted at "http://www.techminingforglobalgood.org/open-covid-19-research-for-analysis/".
The second project apporoach will be to advance Literature Based Discovery methods to extract key components of a target domain and then explore other, distinct domains for potential causes, vital biomechanisms, and treatments that could be repurposed as novel COVID-19 treatment approaches. These methods can help relate seemingly distant research knowledge to help address future crises too.
We are excited to engage the opportunities a virtual conference platform offers for greater participation diversity and equity! With recordings of livestream video, pre-recorded video, Q&A and discussion boards, etc. accessible beyond the 3-day agenda, we envision an exceptionally thoughtful exchange of knowledge.
Submissions for Panels and Oral presentations are due by AUGUST 31, 2020, through the submission link at //www.gtmconference.org. The deadline for Power Talk abstract submissions is SEPTEMBER 15, 2020. Presenters must have access to the technology necessary to present virtually. A complete description of technology requirements can be found on the conference website.
VantagePoint led text-analysis in the 2019 Journal of Nanoparticle Research article "Updating a Search Strategy to Track Emerging Nanotechnologies" is cited in the recent National Academies of Sciences, Engineering, and Medicine report "A Quadrennial Review of the National Nanotechnology Initiative: Nanoscience, Applications, and Commercialization (2020), " showing that after U.S. dominance in the 2000's, EU-28 countries have consistently published more papers per year than the United States while China produces nearly double. After review of the data, the report recommends the United States overhaul its research priorities in nanotechnology and bolster funding to restore its leadership in the discipline.
Technology emergence has become a hot topic in R&D policy and management communities. Various methods of measuring technology emergence have been developed. However, there is little literature discussing how to evaluate the results identified by different methods. In "A 3-dimensional analysis for evaluating technology emergence indicators," Liu and Porter sharpen a promising Technology Emergence Indicator (TEI) set by assessing alternative formulations on three distinct datasets: Dye-Sensitized Solar Cells, Non-Linear Programming, and Nano-Enabled Drug Delivery. These TEIs are derived from a conceptual foundation including three attributes of emergence: persistence, community, and growth that are systematically addressed through a 3-dimensional evaluation framework. Comparing TEI behavior through sensitivity analyses shows good robustness for the measures. The TEI serve to distinguish emerging R&D topics in the field under study. They can further be used to identify highly active players publishing on those topics. Importantly, results show that identified emerging terms and topics persist to a strong degree; thus, they serve to predict highly active R&D foci within the technical domain under study.
Technological innovation is a dynamic process that spans the lifecycle of an idea, from scientific research to production. Within this process, there are few key innovations that significantly impact a technology's development, and the ability to identify and trace the development of these key innovations comes with a great payoff for researchers and technology managers. In "Exploring technology evolution pathways to Facilitate Technology Management: From a Technology Life Cycle Perspective", Huang et al, present a framework for identifying the technology's main evolutionary pathway of a technology. What is unique about this framework is that it introduces new indicators that reflect the connectivity and the modularity in the interior citation network to distinguish between the stages of a technology's development. The authors also show how information about a family of patents can be used to build a comprehensive patent citation network. Last, integrated approaches of main path analysis (MPA) -- namely global main path analysis and global key-route main analysis -- are applied for extracting technological trajectories at different technological stages. This approach is illustrated with Dye-Sensitized Solar Cells (DSSCs), a low-cost solar cell belonging to the group of thin film solar cells, contributing to the remarkable growth in the renewable energy industry. The results show how this approach can trace the main development trajectory of a research field and distinguish key technologies to help decision-makers manage the technological stages of their innovation processes more effectively.