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"Big data" driven tech mining and ST&I management: an introduction.
Huang, Ying; Wang, Xuefeng; Zhang, Yi; Chiavetta, Denise; Porter, Alan L.
  • Huang Y; Center for Studies of Information Resources, School of Information Management, Wuhan University, Wuhan, China.
  • Wang X; Center for Science, Technology and Education Assessment (CSTEA), Wuhan University, Wuhan, China.
  • Zhang Y; Centre for R&D Monitoring (ECOOM) and Department of MSI, KU Leuven, Leuven, Belgium.
  • Chiavetta D; School of Management and Economics, Beijing Institute of Technology, Beijing, China.
  • Porter AL; Faculty of Engineering and Information Technology, Centre for Artificial Intelligence, University of Technology Sydney, Sydney, Australia.
Scientometrics ; 127(9): 5227-5231, 2022.
Article in English | MEDLINE | ID: covidwho-2014327
ABSTRACT
Since the first Global Tech Mining (GTM) conference was held in Atlanta in 2011, the GTM conference has created a platform to connect tech mining researchers, exchange ideas and research progress, and promote collaborations. When it came to its 10th anniversary in 2020, COVID-19 forced the GTM conference into an online format. In tumultuous times for ST&I research activity, the GTM conference sought to focus on several issues How to better collect and combine multiple "large data" sources? How to analyze these data effectively? And how to utilize these results more powerfully in ST&I management? In this collection, 15 papers are selected after evaluating by the science advisory committee, the guest editor team, and our peer review experts to address the following aspects regarding "tech mining" (1) DATA Maximizing the potential of traditional and novel data; (2)

METHODS:

Advancing and integrating methods; (3) APPLICATIONS Innovative analyses translating to usefulintelligence.
Keywords

Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies Language: English Journal: Scientometrics Year: 2022 Document Type: Article Affiliation country: S11192-022-04507-2

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies Language: English Journal: Scientometrics Year: 2022 Document Type: Article Affiliation country: S11192-022-04507-2