Profile of Honorable Mentions:
In response to this year’s call for the Eugene Garfield Award for Innovation in Citation Analysis, we received submissions from 18 different countries. The award targets early-career researchers, and we were pleased with the response rate and overall quality of applications.
The submissions contained many different lines of investigation using Web of Science citation data, several of which were unexpected and creative. The applications can be grouped under the following themes:
- Patterns of novelty, long-term citation impact and so-called ‘sleeping beauties’
- Enhancements to citation-based indicators addressing data quality or indicator limitations, such as author credit challenges
- Selection of experts using different ranking algorithms
- Understanding how research teams incorporate expertise and knowledge differently
- Understanding the evolution of ideas over time
- Improved information retrieval using citation information
- The selection of papers for systematic reviews
- Understanding university-industry collaborations or the structure of the scientific system
- Selecting journals for library collection management or assessing journal citation trends
- The rhetorical context of citations.
The applications were of a high standard, proposing sound approaches to important problems, with a half dozen reflecting the creativity that Dr. Garfield exhibited in his long career. These standouts were provided to an external panel of judges in order to assess novelty and innovation.
When we established the award earlier this year, we intended to select one recipient, but based on the feedback of our panel of external judges, and the high standard exhibited, it was decided that two additional early-career researchers be recognized with Honorable Mention awards. These investigators will each receive a monetary prize of $2,500 and access to Web of Science data. This will help them to continue their research as well as participate in important international conferences, undertake additional training, cover author-processing fees or offset data or software license fees.
The following are brief profiles of the two Honorable Mention winners.
Russell Funk is Assistant Professor of Strategic Management and Entrepreneurship within the Carlson School of Management at the University of Minnesota. He earned a PhD in 2014 from the University of Michigan with a concentration in Economic Sociology and Organizations.
His independent research program is focused on two main areas: understanding collaboration dynamics in knowledge-intensive teams and developing novel measures of innovation using large scale-databases. His submission to the Garfield award focused on this second area, namely the application of citation data to novel measures of innovation.
Funk aims to overcome a gap with existing quantitative measures of innovation by developing indicators that distinguish innovations that consolidate the status quo in a given technology area from those that destabilize the knowledge base. In a paper published in Management Science in 2016, he and his collaborator Jason Owen-Smith of the University of Michigan report on the development of the CD indicator using patent citation networks to determine if a discovery consolidates (C) or destabilizes (D) existing knowledge. Distinguishing between these different types of discovery allows companies, universities and governments to better understand and facilitate innovations across a wide range of industries.
In his submission for the Garfield Award, Funk proposes using high-quality citation data within the Web of Science to apply the CD indicator to discoveries reported in scientific literature. We look forward to supporting his efforts to apply the CD indicator to scholarly and academic research.
Saeed Ul Hassan is Assistant Professor at Information Technology University (ITU), Punjab, Pakistan. He was awarded a PhD from the Asian Institute of Technology, Thailand in 2012.
His research applies advanced computer-science techniques to scholarly communication data such as Web of Science citation indexes and social media data sets. In important work studying the semantic context of scholarly knowledge flows to be presented later this year at ISSI 2017 in China, he and his collaborators apply machine-learning techniques to classify citation context of a full-text corpus, an important and active area for scientometricians.
Hassan applied his scientometric expertise to contribute to national-level analyses of research excellence and international collaboration, including in the Middle East, an emerging scientific region. Additionally, he is working to bolster bibliometric and scientometric researchers in Pakistan, and we hope that recognition by Clarivate Analytics as an Honorable Mention will support those efforts.
Congratulations to them both. Watch this space later this week for the announcement of our inaugural recipient of the award.
References:
Russell J. Funk, Jason Owen-Smith (2016) A Dynamic Network Measure of Technological Change. Management Science (Published online in Articles in Advance 22 Mar 2016. http://dx.doi.org/10.1287/mnsc.2015.2366
Saeed-Ul Hassan, Anam, A., Asghar, A., Naif, A. (2017) Measuring Scientific Knowledge Flows by Deploying Citation Context Analysis using Machine Learning Approach on PLoS ONE Full Text
Proceedings of the 16th International Conference On Scientometrics & Informetrics
(in Press)
