Category Archives: Research Type

Early social science research about Big Data

Research Examples, ST&I policy

Recent emerging technology policies seek to diminish negative impacts while equitably and responsibly accruing and distributing benefits. Social scientists play a role in these policies, but relatively little quantitative research has been undertaken to study how social scientists inform the assessment of emerging technologies. This paper addresses this gap by examining social science research on ‘Big Data’, an emerging technology of wide interest. This paper analyzes a dataset of fields extracted from 488 social science and humanities papers written about Big Data. Our focus is on understanding the multi-dimensional nature of societal assessment by examining the references upon which these papers draw. We find that eight sub-literatures are important in framing social science research about Big Data. These results indicate that the field is evolving from general sociological considerations toward applications issues and privacy concerns. Implications for science policy and technology assessment of societal implications are discussed.

http://spp.oxfordjournals.org/content/early/2016/06/23/scipol.scw021.abstract

Author(s): Jan Youtie, Alan L. Porter and Ying Huang
Organization(s): Georgia Institute of Technology, Beijing Institute of Technology
Source: Science and Public Policy
Year: 2016

Leveraging patent landscape analysis and IP competitive intelligence for competitive advantage

CTI, Patents, Research Examples, Research Type

Patent landscape and the accompanying IP competitive intelligence involves understanding and anticipating the competitive environment within which a company operates. More specifically, IP competitive intelligence highlights emerging IP risks, provides patent portfolio benchmarking, monitors competitor technology development efforts, and predicts commercialization of technology.

This paper provides a framework for patent landscape and IP competitive intelligence as driven by strategic intent. This paper advocates the benefits of both “quantitative” statistical analysis and “qualitative” human intelligence for IP competitive intelligence. Moreover, this paper defines four Levels of IP analysis with pruned examples for effective competitive intelligence.

http://www.sciencedirect.com/science/article/pii/S0172219016000193

Author: Yateen R. Pargaonkar
Organization: Chevron Energy Technology Company
Source: World Patent Information
Year: 2016

How Does National Scientific Funding Support Emerging Interdisciplinary Research: A Comparison Study of Big Data Research in the US and China

Data Type, Funding, Interdisciplinarity, Research Examples, Research Type

How do funding agencies ramp-up their capabilities to support research in a rapidly emerging area? This paper addresses this question through a comparison of research proposals awarded by the US National Science Foundation (NSF) and the National Natural Science Foundation of China (NSFC) in the field of Big Data. Big data is characterized by its size and difficulties in capturing, curating, managing and processing it in reasonable periods of time. Although Big Data has its legacy in longstanding information technology research, the field grew very rapidly over a short period. We find that the extent of interdisciplinarity is a key aspect in how these funding agencies address the rise of Big Data. Our results show that both agencies have been able to marshal funding to support Big Data research in multiple areas, but the NSF relies to a greater extent on multi-program funding from different fields. We discuss how these interdisciplinary approaches reflect the research hot-spots and innovation pathways in these two countries.

FULL-TEXT at http://dx.doi.org/10.1371/journal.pone.0154509

Author(s): Ying Huang, Yi Zhang, Jan Youtie, Alan L. Porter, Xuefeng Wang
Organization(s): Beijing Institute of Technology; Georgia Institute of Technology
Source: PLoS ONE
Year: 2016

A scientometric comparative study of single-walled and multi-walled carbon nanotubes research

Research Examples, Research profiling, Research Type

In the present study, we aim to quantitatively investigate and compare the intellectual landscapes of single-walled carbon nanotubes (SWCNTs) and multi-walled carbon nanotubes (MWCNTs) research between 2000 and 2014. The overall intellectual structure of these fields is illustrated by emerging trends of bursting keywords and thematic concentrations of co-cited references. This study is based on two sets of bibliographic records retrieved from the Web of Science database. The SWCNTs dataset contains 18,700 original research and review articles. The MWCNTs dataset, consisting of 23,584 records, is also collected from the database. We find that both domains have scrutinized chemical concepts which underlie the properties of the materials. Recent thematic trends show that MWCNTs research focuses on the improvement of the material while SWCNTs research lays more emphasis on their applications. In conclusion, it is argued that SWCNTs and MWCNTs have co-evolved. At the same time, both fields are distinctively diverging with their own scientific concerns.

http://dl.acm.org/citation.cfm?id=2857163

Author(s): Geet Lahoti, Meen Chul Kim, Jan Youtie, Alan L Porter, Chuck Zhang, Ben Wang, and Diana Hicks
Organization(s): Georgia Institute of Technology
Source: Proceedings of the 78th ASIS&T Annual Meeting: Information Science with Impact: Research in and for the Community
Year: 2015

Identifying target for technology mergers and acquisitions using patent information and semantic analysis

Data Type, Patents, Research Examples, Strategic planning

Technology plays an increasingly important role in today’s enterprise competition. Technology mergers and acquisitions (Tech M&A), as an effective way to acquire external technology resources rapidly, have attracted attention from researchers because of their potential realization of value through synergy. A big challenge that faces corporate managers and government policy makers is how to identify the appropriate target to support effective technology integration. In this study, we develop a model of target selection of Tech M&A from the perspective of technology relatedness and R&D capability. We present the results relating to M&A in the field of cloud computing in China.

http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7273128

Author(s): Lu Huang, Lining Shang, Kangrui Wang, Alan L Porter, and Yi Zhang
Organization(s): Beijing Institute of Technology
Source: 2015 Portland International Conference on Management of Engineering and Technology
Year: 2015

Analyzing collaboration networks and developmental patterns of nano-enabled drug delivery (NEDD) for brain cancer

Network analysis, Research Examples, Research Type

The rapid development of new and emerging science & technologies (NESTs) brings unprecedented challenges, but also opportunities. In this paper, we use bibliometric and social network analyses, at country, institution, and individual levels, to explore the patterns of scientific networking for a key nano area – nano-enabled drug delivery (NEDD). NEDD has successfully been used clinically to modulate drug release and to target particular diseased tissues. The data for this research come from a global compilation of research publication information on NEDD directed at brain cancer. We derive a family of indicators that address multiple facets of research collaboration and knowledge transfer patterns. Results show that: (1) international cooperation is increasing, but networking characteristics change over time; (2) highly productive institutions also lead in influence, as measured by citation to their work, with American institutes leading; (3) research collaboration is dominated by local relationships, with interesting information available from authorship patterns that go well beyond journal impact factors. Results offer useful technical intelligence to help researchers identify potential collaborators and to help inform R&D management and science & innovation policy for such nanotechnologies.

Full-text article at http://www.beilstein-journals.org/bjnano/single/articleFullText.htm?publicId=2190-4286-6-169

author(s): Ying Huang, Jing Ma, Alan L Porter, Seokbeom Kwon, and Donghua Zhu
Organization(s): Beijing Institute of Technology
Source: Beilstein Journal of Nanotechnology
Year: 2015

Technology roadmapping for competitive technical intelligence

CTI, Research Examples

Understanding the evolution and emergence of technology domains remains a challenge, particularly so for potentially breakthrough technologies. Though it is well recognized that emergence of new fields is complex and uncertain, to make decisions amidst such uncertainty, one needs to mobilize various sources of intelligence to identify known–knowns and known–unknowns to be able to choose appropriate strategies and policies. This competitive technical intelligence cannot rely on simple trend analyses because breakthrough technologies have little past to inform such trends, and positing the directions of evolution is challenging. Neither do qualitative tools, embracing the complexities, provide all the solutions, since transparent and repeatable techniques need to be employed to create best practices and evaluate the intelligence that comes from such exercises. In this paper, we present a hybrid roadmapping technique that draws on a number of approaches and integrates them into a multi-level approach (individual activities, industry evolutions and broader global changes) that can be applied to breakthrough technologies. We describe this approach in deeper detail through a case study on dye-sensitized solar cells. Our contribution to this special issue is to showcase the technique as part of a family of approaches that are emerging around the world to inform strategy and policy.d to inform strategy and policy.

Author(s): Yi Zhang, Douglas KR Robinson, Alan L Porter, Donghua Zhu, Guangquan Zhang, Jie Lu
Organization(s): Beijing Institute of Technology,  Université de Paris-Est
Source: Technological Forecasting and Social Change
Year: 2015

Human Optimization Research: International Activity (Full-Text)

Research Examples, Research profiling

The present scientometric study was commissioned by the Chief Scientist Network of Defence Research and Development Canada (DRDC). It provides an overview of international research activity and collaboration networks in the field of human optimization. This is the second study in a series on human optimization, where the first was focused on the Canadian landscape. To identify major players, their collaboration networks and key research topics in the international landscape, 7,656 references, dated 2005-2015, to relevant unclassified publications were retrieved and analyzed using text mining software and a variety of visualization tools. 114 research topics were categorized into five (non-mutually exclusive) metagroups including Ethics, Physiological issues,
Computational/Cognitive issues, Automation/Robotics and Means of Enhancement. Internationally, research is most focused on Computational/Cognitive issues.
Visualizations of the 114 research topics showed great interconnection between them, displaying three main clusters; which speaks to the fact that research in this domain is quite interdisciplinary. Examining the research momentum of the topics reveals that 33 of the topics can be considered to be emerging (i.e. growing at a notable rate despite a relatively low publication count). While these emerging topics (e.g. transcranial stimulation or neurophysiology), in and of themselves, are not necessarily emerging topics in the broader picture of scientific research, it may be that within the field of human optimization, these topics represent an emerging angle of research. An analysis of the geographic distribution of the publications revealed that the US dominates the field in terms of total number of publications. However, Switzerland has both the greatest rate of collaboration (82%) as well as the highest average annual growth rate for 2012-2015 (70%). Most of the top countries are collaborating with each other. International collaboration networks are rather sparse amongst the top collaborating countries in that the top affiliations may have many different international colleagues but with very few repeated co-publications. Notable exceptions are described in the report. Recommendations for further study include, among others, a formal comparison with the Canadian landscape, additional analysis of the Means of enhancement metagroup, and a deeper exploration of the top countries’ collaboration networks.

FULL-TEXT at NCR paper

Author: Erica Wiseman
Organization: National Research Council of Canada
Source: NRC-CNRC Knowledge Management
Year: 2016

How Multidisciplinary are the Multidisciplinary Journals Science and Nature?

Interdisciplinarity, Research Examples

Interest in cross-disciplinary research knowledge interchange runs high. Review processes at funding agencies, such as the U.S. National Science Foundation, consider plans to disseminate research across disciplinary bounds. Publication in the leading multidisciplinary journals, Nature and Science, may signify the epitome of successful interdisciplinary integration of research knowledge and cross-disciplinary dissemination of findings. But how interdisciplinary are they? The journals are multidisciplinary, but do the individual articles themselves draw upon multiple fields of knowledge and does their influence span disciplines? This research compares articles in three fields (Cell Biology, Physical Chemistry, and Cognitive Science) published in a leading disciplinary journal in each field to those published in Nature and Science. We find comparable degrees of interdisciplinary integration and only modest differences in cross-disciplinary diffusion. That said, though the rate of out-of-field diffusion might be comparable, the sheer reach of Nature and Science, indicated by their potent Journal Impact Factors, means that the diffusion of knowledge therein can far exceed that of leading disciplinary journals in some fields (such as Physical Chemistry and Cognitive Science in our samples).

FULL-TEXT at http://dx.doi.org/10.1371/journal.pone.0152637

Author(s): Gregg E. A. Solomon , Stephen Carley, and Alan L. Porter
Organization(s): Harvard University, Georgia Institute of Technology
Source: PlosONE
Year: 2016

Collaboration and change in the research networks of five Energy Frontier Research Centers

Research evaluation, Research Examples

Emphasizing the university research center model, from 2009 to 2014 the US Department of Energy (DOE) funded a first round of over 40 Energy Frontier Research Centers (EFRCs) spread out among 100 institutions. Early in its implementation, however, the EFRC model received criticism from scholars warning that the arrangements of the EFRCs did not provide adequate governance structures for coordinating research efforts. In this article, we seek to begin answering a call for ‘systematic and rigorous study of the implementation of EFRCs’ by studying a sample of five EFRCs and their individual members. We find that despite lacking formal mechanisms for coordinating research, EFRCs increase coauthorships among EFRC members, especially new coauthorships. Moreover, EFRC members’ research quality increases after each EFRC is formed. Through negative-binomial regression analysis on individual researcher outcomes, we find that stronger preexisting networks increase coauthorship among EFRC members. This finding supports the idea that preexisting research collaboration networks are indicative of research coordination mechanisms that researchers have discovered or established for themselves prior to becoming members of a research center. We posit that new research centers may leverage research coordination mechanisms embedded in preexisting coauthorship relations, rather than imposing new research coordination mechanisms.

http://rev.oxfordjournals.org/content/early/2016/03/22/reseval.rvw006.abstract#aff-2

Author(s): Alexander M. Smith, Samson Yuxiu Lai, Jonah Bea-Taylor, Rebecca B. M. Hill and Nabil Kleinhenz
Organization: Georgia Institute of Technology
Source: Research Evaluation
Year: 2016