The Coronavirus Disease 2019 (COVID-19) pandemic presents a great public health challenge around the world, especially given the urgency to identify effective drugs and develop a vaccine in a short period of time. Globally, there are several drug and vaccine candidates currently in clinical trials, yet it is not yet clear which will prove successful. This study addresses this gap by mapping the treatments and vaccine candidates currently in clinical trials and assessing the opinions on these candidates of virus-related researchers from all over the world. Clinical trial data were obtained from ClinicalTrials.gov and the survey’s respondents were authors of recent scientific publications related to viruses, SARS virus, coronavirus, and COVID-19 indexed in the Web of Science Core Collection. The results show that remdesivir, immunoglobulin from cured patients and plasma are considered the most promising treatments, and ChAdOx1 and mRNA-1273 the most promising vaccine candidates. They also indicate that a vaccine could be available within eighteen months.
For a copy of a pre-print go to https://doi.org/10.2196/preprints.22483
Author(s): Bernardo Pereira Cabral, Luiza Braga, Fabio Batista Mota
Organization(s): Oswaldo Cruz Foundation, Federal University of Bahia
Source: pre-print Journal of Medical Internet Research
This paper seeks to understand whether a catastrophic and urgent event, such as the first months of the COVID-19 pandemic, accelerates or reverses trends in international collaboration, especially in and between China and the United States. A review of research articles produced in the first months of the COVID-19 pandemic shows that COVID-19 research had smaller teams and involved fewer nations than pre-COVID-19 coronavirus research. The United States and China were, and continue to be in the pandemic era, at the center of the global network in coronavirus related research, while developing countries are relatively absent from early research activities in the COVID-19 period. Not only are China and the United States at the center of the global network of coronavirus research, but they strengthen their bilateral research relationship during COVID-19, producing more than 4.9% of all global articles together, in contrast to 3.6% before the pandemic. In addition, in the COVID-19 period, joined by the United Kingdom, China and the United States continued their roles as the largest contributors to, and home to the main funders of, coronavirus related research. These findings suggest that the global COVID-19 pandemic shifted the geographic loci of coronavirus research, as well as the structure of scientific teams, narrowing team membership and favoring elite structures. These findings raise further questions over the decisions that scientists face in the formation of teams to maximize a speed, skill trade-off. Policy implications are discussed.
For FULL-TEXT go to https://doi.org/10.1371/journal.pone.0236307
Author(s): Caroline V. Fry, Xiaojing Cai, Yi Zhang, Caroline S. Wagner
Organization(s): University of Hawai’i at Manoa, The Ohio State University, University of Technology Sydney
Source: PloS One
- A bibliometric analysis on agents for sludge dewatering was conducted.
- Highly cited papers support applied research.
- Japan is application-oriented and USA performed well in both fundamental research and applied research.
- Developing countries still need to optimize their pattern or promote technology innovation.
- Interdisciplinary collaboration covering the lifecycle of industrial chain is needed.
In recent years, agents for sludge dewatering have received extensive attention both from fundamental and applied research. Papers in the field of sludge dewatering agents are mainly concerned with the synthesis and characterization of agents as well as their interaction mechanisms with sludge. On the other hand, patents, which are characteristically different than papers, involve the invention and application of sludge dewatering agents. However, these researches rarely use bibliometric technology and social network analysis to provide a better understanding of the fundamental and applied researches in this specific field. This paper presents a quantitative analysis based on a total of 338 (out of 1126) core papers from Web of Science Expanded and 961 (out of 4678) core patents from Derwent Innovation to provide valuable suggestions that may promote further development in the field of sludge dewatering agents. The core point of this paper is to explore the relationship between fundamental research and applied research in the field of sludge dewatering agents through citation, characteristics of different countries/regions, collaborations of research area and organizations. Our results underline that highly cited papers support applied research by citating information among them. With respect to countries, Japan paid more attention to applied research, whereas the USA performed well both in fundamental research and applied research. China has high quantity in both papers and patents in agents for sludge dewatering. However, the papers per capita of population of China is low and the pattern still needs to optimized to improve the quality in this field. The unique development path revealed that developing countries still have a long way to go in terms of technology innovation. In addition, collaboration network of research areas has shown strong cooperation among applied researches, however they lack connection with fundamental sciences, such as physics or chemistry. Furthermore, collaboration network of organizations disclosed the necessity for upstream enterprises (agent producers) to work closely with downstream enterprises (agent users) in the sludge dewatering.
Author(s): Gengping Zhang, Qi Shi, Qiannan Li, Hongtao Wang, Heyang Yuan, Wenjing Guo, Yufei Lu
Organization(s): Tongji University
Source: Journal of Cleaner Production
Access to full-text of this paper is available through August 20, 2020 at https://authors.elsevier.com/a/1bKYd98SGmQ4B
- Thirteen teams strive to distinguish emerging research topics in synthetic biology.
- Analyses of ten years of article abstracts predict topics in the next two years.
- Augmenting, consolidating, embedding, and clustering text help detect emergence.
- Analyses of citation patterns and research networking also help discern emergence.
We conducted a contest to predict highly active research topics. Participants analyzed ten years of Web of Science abstract records in a target technological domain (synthetic biology) so as to indicate cutting edge sub-topics likely to be actively pursued in the following two years. We describe contest procedures and results provided by thirteen participating teams.
Contestants used various topical and other fields in the abstract records; some augmented with external data. They applied at least 19 diverse methods in deriving emerging topics predicted to be actively researched in the coming two years. Besides topical text analyses, contestants variously brought to bear both backward and forward citation analyses, and network analyses, to help identify topics apt to be highly researched in the near future. This communal exercise on forecasting near-future research activity using a wide array of text analytic and other bibliometric tools provides a stimulating resource.
Author(s): Alan L. Porter, Denise Chiavetta, Nils C. Newman
Organization(s): Search Technology, Inc.
Source: Technological Forecasting and Social Change
Efforts to involve data science in policy analysis can be traced back decades but transforming analytic findings into decisions is still far from straightforward task. Data-driven decision-making requires understanding approaches, practices, and research results from many disciplines, which makes it interesting to investigate whether data science and policy analysis are moving in parallel or whether their pathways have intersected. Our investigation, from a bibliometric perspective, is driven by a comprehensive set of research questions, and we have designed an intelligent bibliometric framework that includes a series of traditional bibliometric approaches and a novel method of charting the evolutionary pathways of scientific innovation, which is used to identify predecessor–descendant relationships in technological topics. Our investigation reveals that data science and policy analysis have intersecting lines, and it can foresee that a cross-disciplinary direction in which policy analysis interacting with data science has become an emergent area in both communities. However, equipped with advanced data analytic techniques, data scientists are moving faster and further than policy analysts. The empirical insights derived from our research should be beneficial to academic researchers and journal editors in related research communities, as well as policy-makers in research institutions and funding agencies.
Author(s): Yi Zhang, Alan L. Porter, Scott Cunningham, Denise Chiavetta, Nils Newman
Organization(s): University of Technology Sydney, Search Technology Inc., University of Strathclyde
Source: IEEE Transactions on Engineering Management
In an era of exponential technological growth, business intelligence professionals are more in need than ever of an organized patent landscape in which to conduct technology forecasting and industry positioning. However, the construction of such a system requires time and trained experts, both of which are expensive investments for such a small part of any actual analysis. A natural solution is to employ machine learning (ML), a branch of artificial intelligence that uses statistical information to find patterns and make inferences. The primary benefit of using ML is that these algorithms do not require explicit instruction. In this paper, I present an analysis of feature selection for automatic patent categorization. For a corpus of 7,309 patent applications from the World Patent Information (WPI) Test Collection (Lupu, 2019), I assign International Patent Classification (IPC) section codes using a modified Naïve Bayes classifier. I compare precision, recall, and f-measure for a variety of meta-parameter settings including data smoothing and acceptance threshold. Finally, I apply the optimized model to IPC class and group codes and compare the results of patent categorization to academic literature.
Author(s): Caitlin Cassidy
Organization(s): Search Technology
Source: World Patent Information
Recent years have witnessed an incipient shift in science policy from a focus mainly on academic excellence to a focus that also takes into account “societal impact”. This shift raises the question as to whether medical research has given proper attention to the diseases imposing the greatest burden on society. Therefore, with the aim of identifying correlations between research funding priorities and public demand in health, we examine grants issued by the major medical research funding bodies of China and the UK during the decade 2006-2017 and compare the focus of their funded projects with the diseases that carry the highest burden of death, risk, or loss of health. The results indicate that the funding decisions of both nations do correspond to the illnesses with the highest health impact on their citizens. For both regions, the greatest health concerns surround non-communicable diseases, and neoplasms and cardiovascular disease in particular. In China, national health priorities have remained focused on these illnesses for the benefit of its own population, whereas the UK has funded a wider variety of research, extending to projects with impacts outside its borders to some developing countries. Additionally, despite an increased incidence of mental illness and HIV/AIDs in China, there is evidence that less priority has been given to these conditions. Both of these health areas seem to require more attention from China’s national funding agencies and the society in general. Methodologically, this study can serve as an example of how to conduct analyses related to public health issues by combining informetric methods and data with data and tools from other fields, thereby inspiring other scientometrics studies.
For FULL-TEXT download at DOI: 10.31219/osf.io/ckpf8
Author(s): Lin Zhang, Wenjing ZHAO, Jianhua Liu, Gunnar Sivertsen, Ying HUANG
Organization(s): Wuhan University, KU Leuven, Beijing Wanfang Data Ltd., Nordic Institute for Studies in Innovation Research and Education (NIFU)
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. This research sharpens 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. Our TEIs derive from a conceptual foundation including three attributes of emergence: persistence, community, and growth that we systematically address 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.
DOI: https://doi.org/10.1007/s11192-020-03432-6 432-6
Author(s): Xiaoyu Liu, Alan L. Porter
Organization(s): Beijing Institute of Technology, Search Technology
Identifying key research themes is an effective way to chart knowledge structures in a field of research and, in turn, stimulate new ideas and innovation. Most thematic analyses of a research field are based on some form of network analysis, e.g., citations and cowords, and most of these networks are made up of cohesive, highly overlapping groups of nodes. Based on the suggestion that the “universal features” of networks are to be found in these overlapping communities, we argue that these same communities in a keyword network should reveal the key research themes in a field of study. With no traditional method with which to test our theory, we combined a cluster percolation algorithm with a Word2Vec model, and in a case study on information science, we were not only able to detect the overlapping communities in a keyword similarity network, but we also found a new perspective on the importance of overlapping communities as a way to identify a field’s key research themes.
Author(s): Lu Huang, Fangyan Liu, Yi Zhang
Organization(s): Beijing Institute of Technology, University of Technology Sydney
Source: IEEE Transactions on Engineering Management
The ability to identify the scholarship of individual authors is essential for performance evaluation. A number of factors hinder this endeavor. Common and similarly spelled surnames make it difficult to isolate the scholarship of individual authors indexed on large databases. Variations in name spelling of individual scholars further complicates matters. Common family names in scientific powerhouses like China make it problematic to distinguish between authors possessing ubiquitous and/or anglicized surnames (as well as the same or similar first names). The assignment of unique author identifiers provides a major step toward resolving these difficulties. We maintain, however, that in and of themselves, author identifiers are not sufficient to fully address the author uncertainty problem. In this study we build on the author identifier approach by considering commonalities in fielded data between authors containing the same surname and first initial of their first name. We illustrate our approach using three case studies.
For FULL-TEXT see https://doi.org/10.2478/jdis-2019-0006
Author(s): Stephen F. Carley, Alan L. Porter, Jan L. Youtie
Organization(s): Georgia Institute of Technology
Source: Journal of Data and Information Science
Year: 2019 (online. 2017 print)