Search Technology Inc.

Atlanta, GA, United States

Search Technology Inc.

Atlanta, GA, United States
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Shapira P.,University of Manchester | Shapira P.,Georgia Institute of Technology | Youtie J.,Georgia Institute of Technology | Porter A.L.,Georgia Institute of Technology | Porter A.L.,Search Technology Inc.
Scientometrics | Year: 2010

This article examines the development of social science literature focused on the emerging area of nanotechnology. It is guided by the exploratory proposition that early social science work on emerging technologies will draw on science and engineering literature on the technology in question to frame its investigative activities, but as the technologies and societal investments in them progress, social scientists will increasingly develop and draw on their own body of literature. To address this proposition the authors create a database of nanotechnology-social science literature by merging articles from the Web of Science's Social Science Citation Index and Arts and Humanities Citation Index with articles from Scopus. The resulting database comprises 308 records. The findings suggest that there are multiple dimensions of cited literature and that social science citations of other social scientists' works have increased since 2005. © 2010 Akadémiai Kiadó, Budapest, Hungary.

Ma J.,Beijing Institute of Technology | Porter A.L.,Georgia Institute of Technology | Porter A.L.,Search Technology Inc
Scientometrics | Year: 2015

As a basic knowledge resource, patents play an important role in identifying technology development trends and opportunities, especially for emerging technologies. However patent mining is restricted and even incomplete, because of the obscure descriptions provided in patent text. In this paper, we conduct an empirical study to try out alternative methods with Derwent Innovation Index data. Our case study focuses on nanoenabled drug delivery (NEDD) which is a very active emerging biomedical technology, encompassing several distinct technology spaces. We explore different ways to enhance topical intelligence from patent compilations. We further analyze extracted topical terms to identify potential innovation pathways and technology opportunities in NEDD. © Akadémiai Kiadó, Budapest, Hungary 2014

Zhang Y.,Beijing Institute of Technology | Zhou X.,Beijing Institute of Technology | Porter A.L.,Georgia Institute of Technology | Porter A.L.,Search Technology Inc. | Vicente Gomila J.M.,Polytechnic University of Valencia
Scientometrics | Year: 2014

Competitive technical intelligence addresses the landscape of both opportunities and competition for emerging technologies, as the boom of newly emerging science & technology (NEST)-characterized by a challenging combination of great uncertainty and great potential-has become a significant feature of the globalized world. We have been focusing on the construction of a "NEST Competitive Intelligence" methodology that blends bibliometric and text mining methods to explore key technological system components, current R&D emphases, and key players for a particular NEST. This paper emphasizes the semantic TRIZ approach as a useful tool to process "Term Clumping" results to retrieve "problem & solution (P&S)" patterns, and apply them to technology roadmapping. We attempt to extend our approach into NEST Competitive Intelligence studies by using both inductive and purposive bibliometric approaches. Finally, an empirical study for dye-sensitized solar cells is used to demonstrate these analyses. © 2014 Akadémiai Kiadó, Budapest, Hungary.

Carley S.,Georgia Institute of Technology | Porter A.L.,Georgia Institute of Technology | Porter A.L.,Search Technology Inc.
Scientometrics | Year: 2012

We introduce an indicator to measure the diffusion of scientific research. Consistent with Stirling's 3-factor diversity model, the diffusion score captures not only variety and balance, but also disparity among citing article cohorts. We apply it to benchmark article samples from six 1995 Web of Science subject categories (SCs) to trace trends in knowledge diffusion over time since publication. Findings indicate that, for most SCs, diffusion scores steadily increase with time. Mathematics is an outlier. We employ a typology of citation trends among benchmark SCs and correlate this with diffusion scores. We also find that self-cites do not, in most cases, significantly influence diffusion scores. © 2011 Akadémiai Kiadó, Budapest, Hungary.

Zhang Y.,Beijing Institute of Technology | Zhou X.,Beijing Institute of Technology | Porter A.L.,Georgia Institute of Technology | Porter A.L.,Search Technology Inc. | And 2 more authors.
Scientometrics | Year: 2014

In recent years, the Triple Helix model has identified feasible approaches to measuring relations among universities, industries, and governments. Results have been extended to different databases, regions, and perspectives. This paper explores how bibliometrics and text mining can inform Triple Helix analyses. It engages Competitive Technical Intelligence concepts and methods for studies of Newly Emerging Science & Technology (NEST) in support of technology management and policy. A semantic TRIZ approach is used to assess NEST innovation patterns by associating topics (using noun phrases to address subjects and objects) and actions (via verbs). We then classify these innovation patterns by the dominant categories of origination: Academy, Industry, or Government. We then use TRIZ tags and benchmarks to locate NEST progress using Technology Roadmapping. Triple Helix inferences can then be related to the visualized patterns. We demonstrate these analyses via a case study for dye-sensitized solar cells. © 2013 Akadémiai Kiadó, Budapest, Hungary.

Garner J.,Search Technology Inc | Porter A.L.,Search Technology Inc | Porter A.L.,Georgia Institute of Technology | Newman N.C.,IISC
Scientometrics | Year: 2014

Research that integrates the social and natural sciences is vital to address many societal challenges, yet is difficult to arrange, conduct, and disseminate. This paper compares diffusion of the research supported by a unique U.S. National Science Foundation program on Human and Social Dynamics ("HSD") with a matched group of heavily cited papers. We offer a measure of the distance of cites between the Web of Science Category ("WoSC") in which a publication appears and the WoSC of the journal citing it, and find that HSD publications are cited more distantly than are comparison publications. We provide another measure-citation velocity-finding that HSD publications are cited with similar lag times as are the comparison papers. These basic citation distance and velocity measures enrich analyses of research knowledge diffusion patterns. © 2014 Akadémiai Kiadó, Budapest, Hungary.

Porter A.L.,Search Technology Inc. | Newman N.C.,Search Technology Inc.
Technovation | Year: 2011

Open Innovation presses the case for timely and thorough intelligence concerning research and development activities conducted outside one's organization. To take advantage of this wealth of R&D, one needs to establish a systematic "tech mining" process. We propose a 5-stage framework that extends literature review into research profiling and pattern recognition to answer posed technology management questions. Ultimately one can even discover new knowledge by screening research databases. Once one determines the value in mining external R&D, tough issues remain to be overcome. Technology management has developed a culture that relies more on intuition than on evidence. Changing that culture and implementing effective technical intelligence capabilities is worth the effort. P&G's reported gains in innovation call attention to the huge payoff potential. © 2011 Elsevier Ltd.

Agency: NSF | Branch: Standard Grant | Program: | Phase: SCIENCE OF SCIENCE POLICY | Award Amount: 149.92K | Year: 2016

This project examines the usefulness of the Open Researcher & Contributor ID (ORCID) as a method for identifying journal article authors, and as a basis for detecting emerging scientific topics studied by these authors. Authors of scientific works may use non-standard ways of reporting their names and affiliations in journal articles and other publications, change affiliations over their careers, or have names that are similar to those of other authors. Such variations can make it difficult to correctly match authors with their publications and institutional affiliations. Against an assortment of government or publisher registration methods and machine learning approaches to deal with these problems, ORCID stands out for its potential to extend across methods and nations with its open source approach. Improved capabilities for identifying authors are vital in studying scientific mobility, networks, the contribution of authors to the emergence of new scientific topics, and other subjects in the Science of Science and Innovation Policy domain. More needs to be understood about the strengths and weaknesses in ORCID coverage by country, field, and other areas for it to be useful in Science of Science and Innovation Policy studies.

The project uses two bibliometric methods to examine ORCID usage over time and by author characteristics. First, the project uses comparative sampling by disciplinary area, and considers ORCID coverage within country, organization, and citation distribution categories in these disciplinary areas. Second, the project investigates the potential of ORCID in identifying emerging science-driven technologies by employing it in the development of an emergence indicator based on a semi-automated numerical scoring system. The emergence indicator is designed to take topical terms with recent, sharp increases in publication and/or patent activity and examine them in the context of identified scholars to potentially highlight concentrations of research and development activity on emergent fields of science and technology. The resulting intelligence is intended to be useful in informing government, commercial, and academic analyses of leading-edge players and other contributors to rising science and technology domains.

Agency: NSF | Branch: Continuing grant | Program: | Phase: REAL | Award Amount: 564.92K | Year: 2014

This project, conducted by Search Technology, Inc., and Georgia Institute of Technology, will seek to better understand how research ideas are communicated in science, technology, engineering and mathematics (STEM) education. The project will use bibliometric analysis to study patterns in how research publications cite each other, mapping the flow of ideas within STEM education research as well as cognitive science research. This project will help show what are the key influences on STEM education research, and what makes STEM education research influential. This project will aim to help the STEM education research community develop and make better use of its research base, and work effectively with researchers from other fields. The Division of Research on Learning in the Education and Human Resources directorate supports this work as part of its mission to advance STEM education research, including research on STEM learning.

The first stage of the project will involve analyzing data bases covering a broad range of published research, examining patterns of citation. The second stage will focus on the influential report, How People Learn, published by the National Research Council, and include textual analysis of content. The third stage will involve outreach to the research community.

Agency: NSF | Branch: Standard Grant | Program: | Phase: | Award Amount: 111.77K | Year: 2010

The goal of the project is to understand the multidisciplinary and interdisciplinary (MIDR) nature of education and learning research and how it has developed over time, with a focus on cognitive science, an area of particular importance to education research. Investigators from Search Technology Inc will study proposals funded by two NSF education research programs (ROLE and REESE). Using bibliometric methods they devised, they will examine the references cited in the proposals to characterize the MIDR nature of the projects. They will also analyze the publications produced by the projects and track the dissemination of that research knowledge via the papers that cite those publications. The investigators will focus additionally on the work of key cognitive scientists and highly-cited ROLE/REESE-derived publications. Using social network analysis and science mapping, the investigators will track STEM community engagement with this educational research.

The project seeks to advance measurement, analysis, and visualization tools to help quantify science and science policy. In particular, these offer empirical evidence on much-touted, but little measured, MIDR. The results should have important implications for STEM education researchers who wish to understand trends in their fields, for cognitive science and education research communities that have traditionally not engaged one anothers literatures, and for science policy analysts and program developers.

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