"Big data" driven tech mining and ST&I management: an introduction.

Huang, Y; Wang, X; Zhang, Y; Chiavetta, D; Porter, AL

"Big data" driven tech mining and ST&I management: an introduction.

Huang, Y Wang, X Zhang, Y

Chiavetta, D Porter, AL

Permalink

Publisher:: SPRINGER
Publication Type:: Journal Article
Citation:: Scientometrics, 2022, 127, (9), pp. 5227-5231
Issue Date:: 2022

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

The embargo period expires on 24 Aug 2023

Download Accepted versionAdobe PDF (555.23 kB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Huang, Y
dc.contributor.author	Wang, X
dc.contributor.author	Zhang, Y https://orcid.org/0000-0002-7731-0301
dc.contributor.author	Chiavetta, D
dc.contributor.author	Porter, AL
dc.date.accessioned	2023-01-19T00:11:18Z
dc.date.available	2022-08-15
dc.date.available	2023-01-19T00:11:18Z
dc.date.issued	2022
dc.identifier.citation	Scientometrics, 2022, 127, (9), pp. 5227-5231
dc.identifier.issn	0138-9130
dc.identifier.issn	1588-2861
dc.identifier.uri	http://hdl.handle.net/10453/165161
dc.description.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.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	SPRINGER
dc.relation.ispartof	Scientometrics
dc.relation.isbasedon	10.1007/s11192-022-04507-2
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.rights	This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s11192-022-04507-2
dc.subject	0807 Library and Information Studies, 1605 Policy and Administration
dc.subject.classification	Science Studies
dc.title	"Big data" driven tech mining and ST&I management: an introduction.
dc.type	Journal Article
utslib.citation.volume	127
utslib.location.activity	Switzerland
utslib.for	0807 Library and Information Studies
utslib.for	1605 Policy and Administration
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2023-08-24T00:00:00+1000Z
dc.date.updated	2023-01-19T00:11:16Z
pubs.issue	9
pubs.publication-status	Published
pubs.volume	127
utslib.citation.issue	9

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.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/165161