Alan Porter Search Technology, Inc. & Georgia Tech

“Tech Mining” R&D Literature – for Research Assessment & Forecasting Innovation Pathways
Alan Porter Search Technology, Inc. & Georgia Tech Pick this or the previous cover slide

Agenda: Mining R&D Literature
4/16/2017 Agenda: Mining R&D Literature The Data Tech Mining Research Assessment Measures Maps Forecasting Innovation Pathways 2

Alan Porter Mixed background B.S. in Chemical Engineering (Caltech)
4/16/2017 Alan Porter Mixed background B.S. in Chemical Engineering (Caltech) PhD in Engineering/Psychology (UCLA) Research focus Technology intelligence, forecasting & assessment Faculty – Georgia Tech (Prof Emeritus) Industrial & Systems Engineering Public Policy, and taught 10 years as well in Management (Management of Technology – “MOT”) Small Business – Search Technology Decision aiding in complex environments since 1980 Since 1994, develop & apply text mining software focusing on Science, Technology & Innovation (ST&I) Search Technology, 2012 3

Tracking multi-generational research knowledge transfer with
#1: Papers Citing Level #2 Papers – Citing Paper Overlay Maps [Knowledge Diffusion] Tracking multi-generational research knowledge transfer with Interdisciplinarity metrics Science overlay mapping Diffusion scores Science Citing Overlay Maps Relative engagement by ISI Subject Categories #2: Main Level (e.g., research outputs of a target program) – publication overlay maps “Specialization” scores (Diversity of areas of publication) Science overlay maps (Location of publications among ISI Subject Categories) Integration scores (Average diversity of areas of citation) Science citation maps Bibliographic coupling #3: Papers cited by #2 Coherence measures (do #3 papers draw upon distinct topics?) [ “Bibliographic Coupling” measures available – e.g., % shared references] #4: Papers cited by #3

4/16/2017 Web of Science (“WOS”) Indexes publications from ~12,000 leading journals Recently >1.5 million papers per year Includes several databases Science Citation Index Expanded (SCI) Social Sciences Citation Index (SSCI) Arts & Humanities Citation Index (A&HCI) Conference Proceedings Provides field-structured abstract records Classify journals into Subject Categories (“SCs”) – presently, 224 for SCI + SSCI Provide Cited References for each paper – we apply thesauri to associate to Cited SCs Separately search for Citing records for each paper to discern Citing SCs

Getting to the data - usually via internet
Case Examples

Case Examples Getting the data - search - within databases - retrieve abstract records electronically

Search (Publications) Results
Search (Publications) Results * Nominal search on “Alivisatos, A P” (one of the PIs) * Not all are articles * Co-author, year, institution information available to help filter * Note Subject Areas = “SCs”

Cited Reference Search Results:
Cited Reference Search Results: * Hypothetical search on “Kuhn, D” (not one of our PIs) * Not just Kuhn, the education researcher * Multiple citing articles (to be downloaded) * Includes cites to non-WOS-indexed items (“Carn S Cogn”) * Includes cites to co-authored items (…Kuhn)

Sample WOS Abstract Record (excerpted) [Retrieved Publications and/or Citing Articles]
4/16/2017 AU Oliver-Hoyo, M Gerber, RW TI From the research bench to the teaching laboratory: Gold nanoparticle layering SO JOURNAL OF CHEMICAL EDUCATION DT Article C1 N Carolina State Univ, Dept Chem, Raleigh, NC USA. AB … CR BENTLEY AK, 2005, J CHEM EDUC, V82, P765 BOLSTAD DB, 2002, J CHEM EDUC, V79, P1101 HALE PS, 2005, J CHEM EDUC, V82, P775, … NR 16 TC 1 PY 2007 VL 84 IS 7 BP 1174 EP 1176 SC Chemistry, Multidisciplinary; Education, Scientific Disciplines Note that this could be based on various similarity measures: Cited SCs Cited Authors - content analyses Getting “SCs” = easy; Getting “Cited SCs” is more challenging

Case Examples Import into text mining software for cleaning & analyses [Thomson Data Analyzer (TDA), ~like VantagePoint]

R&D Abstract Record Data Mining
4/16/2017 Extract available field information (authors, affiliations, etc.) “Text mine” to derive new field information: “cited author,” “cited Subject Category,” etc. Clean – i.e., Disambiguate -- authors, affiliations List Cleanup (fuzzy matching – e.g., almost the same) Apply thesaurus (e.g., to combine variations) Let’s take a look at the software: Thomson Data Analyzer (VantagePoint) But first, we introduce Tech Mining QUESTIONS about R&D abstract records, etc.? Use Biosketeches to help associate Bookkeeping and tagging challenge on citation searches as the referenced publications lack co-author names

Tools and Techniques: Tech Mining
Alan L. Porter and Scott W. Cunningham John Wiley & Sons Inc., 2005 Search Technology, 2012

The Tech Mining Process
Tech Mining’s key message is to START WITH THE ISSUES TO BE ADDRESSED, not with data mining. Worth noting the multiple professional types potentially engaged Search Technology, 2012

TechMining: MOT Issues, Questions, and Indicators
39 MOT Questions What? What’s hot? Fit into tech landscape? New frontiers at fringe? Drivers? Competing technologies? Likely development paths? Who? Who are available experts? Which universities or labs lead? ~200 Innovation Indicators Mapping of topic clusters within the technology 3-D trend charts for topic clusters Ratio of conference to journal papers (benchmarked) Scorecard rate-of-change metrics for topic clusters Time slices to show evolution of topical emphases Topic growth modeling (S-curve) fit & extrapolation Profile table of main players Pie chart: Company vs. Academic vs. Government publishing Spreading (or constricting) # of players by topic 13 MOT Issues R&D Portfolio Mgt R&D Project Initiation Engr Project Initiation New Product Development Strategic Planning Track/forecast emerging or breakthrough technologies etc. To illustrate, picked 1 issue particularly pertinent to use of patent info at a creative stage ~13 questions pertain to this Issue Many indicators relate to one of these - 2 types: What and Who - some indicators are straightforward, others are conceptually richer innovation indicators – use Co vs. Acad vs. Gov example NOTE: multiplicative: 13 Q’s X 9 indicators ~ >100 indicators to address 1 issue * This slide can shortcut use of the long list slides; if so used, those can be appended for reference Search Technology, 2012

Some of the 200+ MOT Indicators
4/16/2017 Some of the 200+ MOT Indicators Fit growth models to trend data to gauge technology maturation. Understand R&D processes within an organization – key players, relationships & Gauge commercialization timetable: Pie Chart - % of R&D publications by industry vs. academic vs. government. Competitive/collaborative analysis -- compare IPCs between companies (unique/common). Search Technology, 2012

DILEMMA “What are the global opportunities?”
4/16/2017 DILEMMA “What are the global opportunities?” MANAGEMENT ACTIVITY R&D portfolio selection R&D project initiation Engineering project initiation New product development New market development Merger Acquisition of intellectual property (IP) Intellectual asset management Open innovation Competitive intelligence Future technology opportunity analysis Strategic technology planning Technology roadmapping RELEVANT INDICATOR EXAMPLE: Geo-plot patent assignee concentration Search Technology, 2012

4/16/2017 MANAGEMENT ACTIVITY R&D portfolio selection R&D project initiation Engineering project initiation New product development New market development Merger Acquisition of intellectual property (IP) Intellectual asset management Open innovation Competitive intelligence Future technology opportunity analysis Strategic technology planning Technology roadmapping DILEMMA “Does this technology offer strong commercialization prospects?” RELEVANT INDICATOR EXAMPLE: Identify high% of publications by industry compared to government and academics Search Technology, 2012

Innovation Indicators
Technology Life Cycle Indicators - e,g, growth curve location & projection Innovation Context Indicators - e.g., presence or absence of success factors (funding, standards, infrastructure, etc.) Product Value Chain and Market Prospects Indicators - e.g., applications, sectors engaged Search Technology, 2012

Tech Mining Questions to Answer from field-structured data
4/16/2017 Where? Who? What? When? How? & Why? – Need human analyst to interpret the data Search Technology, 2012 20

4/16/2017 Tech Mining“How to” Spell out the Intelligence questions and how to answer them Get suitable data Search (iterate) & retrieve ~abstract records Import into text mining software (VantagePoint) Clean the data Analyze Visualize (Map) Integrate with Internet analyses & expert opinion Summarize; Interpret; Communicate (multi- dimensionally)! Standardize and semi-automate where possible Works nicely to initiate a live VP demo How does this fit with NRCC-KM efforts? Search Technology, 2012 21

Data for Tech Mining: Six types
Technical Information ST&I databases (e.g., Web of Science; Derwent World Patent Index) [field-structured data] Internet Sources (e.g., Googling) Technical Expertise Contextual Information Business, competition, customer, financial, or policy content databases (e.g., Thomson One; Factiva) Internet Sources (e.g., blogs, website profiling) Business Expertise Search Technology, 2012

A wealth of diverse information sources for innovation management
On-line Data Sources Custom Data Cambridge Scientific Abstracts Factiva Patbase Comma/tab delimited tables Delphion ISI Web Of Knowledge Questel-Orbit Microsoft Excel and Access Dialog Lexis Nexis SilverPlatter SmartCharts EBSCOHost Micropatent STN XML Ei Engineering Village Ovid Thomson Innovation Databases Record/Field Tools Aerospace Focust Pascal Combine duplicate records Art Abstracts Food Sci & Tech Patent Citation Index Remove duplicate records Biobase Foodline Market PCT Create “frankenrecords” Biological Abstracts Foodline Science PCTPAT (merge records from Biological Sciences Forege Phin dissimilar sources) Biosis Frosti Pira Classify records Biotechno FSTA Pluspat Merge fields Business & Industry Gale PROMT PROMT Clean up fields CAPlus (AnaVist export) GeoRef PsycINFO Apply thesauri Cassis Global Reporter PubMed CBNB IFIPAT Rapra Claims IFIUDB Recent Refs Computer & Info Systems INPADOC Reference Manager Corrosion INSPEC Science Citation Index Current Contents IPA SciSearch Derwent Biotech Abstracts ISD Scopus Derwent Innovations Index ITRD Tech Research Derwent World Patent Index JAPIO ToxFile Ei Compendex JICST Transport EMBase Kosmet USApps EnCompass Literature LGST USPat EnCompass Patents MATBUS Waternet Energy Medline WaterResAbs EnergySciTech METADEX Web of Science Engineering Materials Abstr Mgmt and Org Studies WeldaSearch Envr Sci & Pollution Mgmt Micropatent Materials Wisdomain ERIC Mobility EuroPat NSF Awards FamPat NTIS A wealth of diverse information sources for innovation management VantagePoint Import Filters and Tools

4/16/2017 Forecasting Innovation Pathways (FIP) Case Example: Dye-Sensitized Solar Cells (“DSSCs”) A Newly Emerging Science & Technology (NEST) Combining technical intelligence from multiple database analyses – to answer: What? / When? Who? / What? Seeking to Forecast Innovation Pathways Illustrating lots of Tech Mining tools To be used selectively – focusing on the target questions! Search Technology, 2012 24

4/16/2017 DSSCs: Background Georgia Tech group has compiled nanotechnology R&D records from several databases Modular, Boolean search (2006; update 2012) One area of “nano” focus – solar cells Here, we spotlight Dye-Sensitized Solar Cells (DSSCs) – work by Guo Ying & Ma Tingting with Huang Lu, Doug Robinson, & others Invented by O’Regan and Grätzel (1991) Promising “3d Generation” solar cells Commercialization still in its infancy Striving to track from research to innovation [Forecasting Innovation Pathways] Search Technology, 2012 25

Search on your topic in a target database Download to your computer
4/16/2017 Tech Mining the Data Search on your topic in a target database Download to your computer Use text mining software to help clean & analyze Let’s take a look at the DSSC data in TDA software Combination of search results from 2 databases [Web of Science + EI Compendex] 6056 abstract records [We’ll be showing “Research Assessment” results from other data; then return to DSSCs to Forecast Innovation Pathways] Look to do: Check fields Cleaning the data Basic analyses (lists of the content of a field; matrices made of 2 lists) Maps 26

Azerbaijan’s Research Profile
Very basic research questions to demonstrate country-level profiling [see reference below for an in-depth country profile] Who, what, where, when? How active is Azerbaijan? Changes recently? In what research areas? Leading research institutions? Schoeneck, D.J., Porter,A.L., Kostoff, R.N., and Berger, E.M., Assessment of Brazil’s research literature, Technology Analysis and Strategic Management, 23 (6) 2011, Step 2 – Convince Mgt to re-enter domain. Second aha – He is plugged into Ceramics ME community; solutions not there. Then did broad analysis of ceramic research (not limited to engines). Found preponderence ~95% of ceramic pubs electrically, not mechanically, oriented. They may have solutions to problems we have! Surfacing pistons as a goal – coating problem. Ceramic solution (but not from mechanical domain). Bridge terminology – e.g., vapor deposition (vs. coating). “Top 10” list identified Sandia; they pointed to the company. Can you coat piston back to specs? They could, without additional machining. Abrams tanks coming back from the gulf – diseal turbine; sand wears out blades. So coat pistons and turbin blades. Could get even better fit – coat a abit over-spec; ceramic wear off in barrel to outdo original mfg. This is operational. Did truck engines too. SUCCESS!

Case Examples When? Trend in Azerbaijan Publication in Journals indexed by Web of Science

Who? Top among 993 Institutions
Case Examples Author Affiliations # Baku State Univ 291 Natl Acad Sci Azerbaijan 254 Azerbaijan Acad Sci 234 Azerbaijan Natl Acad Sci 155 Natl Acad Sci 106 Russian Acad Sci 66 Azerbaijan Tech Univ 61 Azerbaijan State Oil Acad 49 Gazi Univ 42 Gebze Inst Technol 35 Yildiz Tech Univ 34 Azerbaijan Med Univ 31 Ankara Univ 27 Univ Rostock 22 Middle E Tech Univ 20 Tabriz Univ Med Sci Issues to consider: Data cleaning [combining name variations] How to handle out-of-country institutions?

With whom? Top Collaborating Countries
Case Examples Countries # Azerbaijan 1439 Turkey 286 Russia 112 Iran 109 Germany 67 USA 59 England 31 Italy 30 Japan 24 Ukraine 20 Wales Switzerland 17 France 16 Canada 14 Uzbekistan 11

Who: Funded the research?
Case Examples Funding Organization # INTAS 17 Russian Foundation for Basic Research 8 TUBITAK 7 Turkish State Planning Committee 5 Gazi University BAP 3 NATO

What Research Areas? Macro-disciplines # Chemistry 475 Materials Sci
Case Examples Macro-disciplines # Chemistry 475 Materials Sci 382 Engineering 333 Physics 231 Biomed Sci 105 Geosciences 101 Clinical Med 68 Computer Sci 51 Infectious Diseases 42 Agri Sci 38 Ecol Sci 33 Env Sci & Tech 17 Cognitive Sci 15 Health Issues 7 Policy Sciences Psychology 4 Business & Mgt 3 Folklore Language & Linguistics 1 Literature, British Isles Social Studies Macro-disciplines are based on factor analysis of a year’s worth of Web of Science (2007) cross-journal citations [thanks to Leydesdorff and Rafols]

Research Profile: Azerbaijan 2005-09 by Disciplines (top 5)
Case Examples Macro-Discipline Author Affiliations Key Terms Authors Year Top 3 Top 5 Chemistry[475] Natl Acad Sci Azerbaijan [119] Baku State Univ [95] Azerbaijan Acad Sci [48] synthesis [72] thermodynamic properties [27] Density [24] Water [23] methanol [21] Abdulagatov, I M [25] Magerramov, A M [19] Chyragov, F M [18] 48% of 475 Materials Sci[382] Azerbaijan Acad Sci [95] Baku State Univ [66] Azerbaijan Natl Acad Sci [64] effect [29] TlInS2 [19] Incommensurate phase [17] CRYSTALS [17] SINGLE-CRYSTALS [14] Suleymanov, R A [16] Altindal, S [14] Tagiev, O B [13] Mammadov, T S [13] 51% of 382 Engineering[333] Natl Acad Sci Azerbaijan [83] Baku State Univ [74] Azerbaijan Acad Sci [38] methanol [14] Initial stresses [11] sufficient conditions [10] thermodynamic properties [10] approximation [10] boundedness [10] Akbarov, S D [22] Guliyev, V S [16] Khanmamedov, A K [9] Abdulagatov, I M [9] Nasibov, S M [9] 50% of 333 Physics[231] Azerbaijan Acad Sci [58] Baku State Univ [47] Azerbaijan Natl Acad Sci [35] MODEL [22] PHYSICS [12] SCATTERING [10] VARIABILITY [10] SYSTEMS [9] Shahverdiev, E M [13] Shore, K A [13] Aliev, T M [12] Sultansoy, S [12] 51% of 231 Biomed Sci[105] Baku State Univ [27] Azerbaijan Med Univ [9] Azerbaijan Acad Sci [7] EFFICIENCY [10] sturgeons [8] diencephalon [7] CYTOARCHITECTONIC ANALYSIS [7] Azerbaijan [7] EXPRESSION [7] organization [7] Zeynalov, R [9] Musayev, I [9] Rustamov, E K [8] Dadasheva, N [8] 39% of 105

221 SC Base Map – Sciences + Social Sciences
Agri Sci Ecol Sci Infectious Diseases Env Sci & Tech Clinical Med Geosciences Biomed Sci Chemistry Cognitive Sci Mtls Sci Health & Social Issues Engineering Psychology Physics Computer Sci Business & MGT Social Studies Economics Politics & Geography

Azerbaijan Research, 2005-09 on Global Map of Science, SCI-SSCI 2007
Env Sci & Tech Agri Sci Infectious Diseases Ecol Sci Geosciences Clinical Med Chemistry Mtls Sci Engineering Biomed Sci Cognitive Sci. Health & Social Issues Psychology Physics Computer Sci. Business & MGT Social Studies Econ. Polit. & Geography

Interdisciplinary Research Metrics
National Academies Keck Futures Initiative (15-year program) to boost interdisciplinary research in the US Measure interdisciplinarity for program evaluation For a body of research Extract papers’ cited references Associate cited journals to Web of Science (WOS) Subject Categories (SCs) Matrix of SC by SC interrelationships For given paper set, calculate “Integration” – breadth of SCs drawn upon “Specialization” – concentration of publication activity “Diffusion” – diversity of SCs citing the research

More Interdisciplinary
More Disciplinary Bubble size representing Funding amount More Interdisciplinary

Multiple Mapping Approaches
Science mapping Research Network Mapping [Social Network Analyses] Co-authoring; co-citation; co-term; etc. Bibliographic coupling Geo-mapping For regional & cluster analyses

Thomson Data Analyzer Map Principles
4/16/2017 Nodes = entities mapped; larger implies more activity (but relative to full data set, so differences among a relatively homogeneous mapped set may not show up) Multi-Dimensional Scaling (“MDS”) representations Closer proximity suggests stronger relationship (association) Accuracy is not guaranteed because of the dimensional reduction from N-D to 2-D Position on X & Y axes has no inherent meaning Path-erasing Algorithm added to indicate relationship Heavier links (lines) indicate stronger relationship Absence of a link only means that relationship is less than the arbitrary threshold selected In preparing maps, we vary threshold to show relationships most effectively

Study research networks
4/16/2017 Study research networks From publications Mainly compare: Before vs. After Secondarily, examine those deriving from NSF support From citations By researcher publications, or proposals To researcher publications For Target & Comparison Group researchers Networks based on Social links [e.g., co-authoring] Intellectual links [e.g., cross-citing or bibliographic coupling on SCs, topics, or whatever]

Ethics Co-citation Map of the most cited authors by the 307 nano social science papers [Use Auto-corr on hi cited Authors] Perception Governance Visions Evolutionary Economics Science Mapping

4/16/2017 NSF Research Assessments RCN (Research Coordination Networks) Program Can we see researcher network enrichment, Before to After? HSD (Human & Social Dynamics) and CMG (Collaborations in Math & Geosciences) Programs How interdisciplinary (compared to ~similar projects)? REESE (Research & Evaluation on Education in Science & Engineering) Program How is Cognitive Science engaging with STEM education, over time?

Topical Themes of Proposal Reference Title Phrases
4/16/2017 Topical Themes of Proposal Reference Title Phrases Extract noun phrases using Natural Language Processing (NLP) in VantagePoint Consolidate term variations using “fuzzy matching” Group like terms and build a thesaurus for the area Could use to group proposals Can analyze emerging research themes Can probe further to identify who is active on what topics [a factor map]

Nanotechnology – MISO/CAS Analyses
4/16/2017 Nanotechnology – MISO/CAS Analyses by Ruimin Pei, CAS Using Georgia Tech Web of Science (SCI) nano dataset Compare Multi-Institute Scientific Organizations (“MISOs”): CAS (China) RAS (Russian Academy of Sciences) CNRS (France) CNR (Italy) CSIC (Spain Done with Michael Kayat and colleagues at UTEK in 2007 for an ICIC presentation

Co-authoring among CAS institutes on nano
[partial network map] CAS Grad School shows hi centrality

ROLE/REESE Research Evaluation Targets
4/16/2017 ROLE/REESE Research Evaluation Targets Identify and Map the participating research domains, over time Elucidate the intellectual & social research networks involved Gauge how interdisciplinary the projects are Look for impacts of the research support on researchers’ emphases, productivity, and teaming

Fig. 7. RCN Project -- Researcher Collaboration: Before vs
Fig. 7. RCN Project -- Researcher Collaboration: Before vs. After NSF program funding Blackwell Co-Authoring

HSD Research Activities
4/16/2017 HSD Research Activities Key on the Year 2004 HSD awards (33 Projects; 28 with papers in WOS or Scopus) Publications deriving from the awards One interest: how much collaboration Within projects? Across projects?

HSD Co-authoring HSD Derived Co-Authoring Project C Project D
Project A Project B Project H Project J Project F Project E Project G Project I Project N Project O Project M Project L Project P Project K Project T Project Q Project R Project S Project V Project U HSD Derived Co-Authoring Project Y Project X Project Z Project W HSD Co-authoring Project AA Project CC Project BB Project DD Project EE

HSD Co-authoring + citing
Project C Project D Project A Project B Project H Project J Project F Project E Project G Project I Project N Project O Project M Project L Project P Project K Project T Project Q Project R Project S Project V Project U HSD Derived Co-Authoring + Cited Author Project Y Project X Project Z Project W HSD Co-authoring + citing Project AA Project CC Project BB Project DD Project EE

Extent and nature of collaboration?
4/16/2017 Research Assessment Measures & maps How much output? Extent and nature of collaboration?

Using multiple information resources in combination
“FIP” Using multiple information resources in combination to Forecast Innovation Pathways (“FIP”) for New & Emerging Science & Technology to inform Technology Management Illustrating via Nano-Dye Sensitized Solar Cells - “DSSCs” Thanks to Guo Ying, Ma Tingting, and Huang Lu, Beijing Institute of Technology, and Doug Robinson, Nano-UK & University of Twente Search Technology, 2010

10 Steps (non-linear!) to Forecast Innovation Pathways (FIP)
STAGE ONE Understand the NEST and its TDS (Technology Delivery System) Step A: Characterize the technology’s nature Step B: Model the TDS STAGE TWO Tech Mine Step C: Profile R&D Step D: Profile innovation actors & activities Step E: Determine potential applications Step J: Engage experts STAGE THREE Forecast likely innovation paths Step F: Lay out alternative innovation pathways Step G: Explore innovation components Step H: Perform Technology Assessment STAGE FOUR Synthesize & report Step I: Synthesize and Report Search Technology, 2012

Methods & Data Sources vis-à-vis Analytical Steps
Step J. Expert checking Bibliometric analyses SCI & Compendex research publications Derwent patents Factiva business & context data A: Understand the NEST & specify the driving questions X B: Model the TDS C: Profile R&D D: Identify key Actors E: Identify Applications F: Lay out alternative innovation pathways G: Explore innovation elements required H: Perform Technology Assessments I: Synthesize & report J: Expert Checking ~

STAGE ONE Understand the technology and its Technology Delivery System (TDS) Step A: Characterize the technology’s nature Step B: Model the TDS STAGE TWO Tech Mine Step C: Profile R&D Step D: Profile innovation actors & activities Step E: Determine potential applications Step J: Engage experts STAGE THREE Forecast likely innovation paths Step F: Lay out alternative innovation pathways Step G: Explore innovation components Step H: Perform Technology Assessment STAGE FOUR Synthesize & report Step I: Synthesize and Report Search Technology, 2012

Step A: Trends in Solar Cell Sub-technologies
How important of these two? do they need this to forecast the like pathway? Search Technology, 2012

Step B: Model the Technology Delivery System (TDS)
4/16/2017 Step B: Model the Technology Delivery System (TDS) Simple, but effective “boxes and arrows” modeling Focus on: What is needed to deliver a technology-enhanced product (an innovation) to market? [Technology Enterprise – depict along X axis] What external forces & influences need be recognized and addressed? [Contextual factors – depict off the X axis] Identify key players and leverage points Obtain reviews from multiple perspectives Search Technology, 2012 59

Step B: Basic DSSC Technology Delivery System
What? [Potent Environmental Influences on innovation prospects?] Who? [Enterprise(s) to innovate?]

STAGE ONE Understand the technology and its TDS (Technology Delivery System) Step A: Characterize the technology’s nature Step B: Model the TDS STAGE TWO Tech Mine Step C: Profile R&D Step D: Profile innovation actors & activities Step E: Determine potential applications Step J: Engage experts STAGE THREE Forecast likely innovation paths Step F: Lay out alternative innovation pathways Step G: Explore innovation components Step H: Perform Technology Assessment STAGE FOUR Synthesize & report Step I: Synthesize and Report Search Technology, 2012

Step C: When? Projecting Nano-enhanced Solar Cell Research Activity
4/16/2017 Step C: When? Projecting Nano-enhanced Solar Cell Research Activity Actual data Projected data Search Technology, 2012 62

4/16/2017 Step C: Where? Geo-map: Nano-enhanced Solar Cells – European Institutions >=10 papers Search Technology, 2012 63

Step D: Who? Leading DSSC Companies across Databases
4/16/2017 Step D: Who? Leading DSSC Companies across Databases SCI EI DWPI Factiva Samsung SDI Co LTD 52* 38 65* 4 Sharp Co Ltd 27* 24 17* Nippon Oil Corp 15* 35 10* Hayashibara Biochem Labs Inc 14* 9 Fujikura Ltd 12* 8 9* Chemicrea Co Ltd Sumitomo Osaka Cement Co Ltd 3 2 Toshiba Co Ltd 7 1 Konarka Technologies Inc 7* 11 11* DONG JIN SEMICHEM CO LTD 16* 8* SONY CORP 10 Evonik Degussa GmbH STMicroelectronics NV Data Systems & Software Inc Dongjin Semichem Co Ltd Dyesol Ltd Search Technology, 2012 64

Step D: DSSCs  “Glass Houses”
Who? ~19 or so patent families Samsung prominent (6) Find out more – Profile Samsung 54 patent families ~2 inventor teams 1 team with 28 patents has all 6 of these [network map next] We could analyze their emphases – e.g., Manual Code concentrations Discrete devices Electro-(in)organics Polymer applications, etc. Search Technology, 2012

Step D: Samsung Patent Analyses:
2 distinct inventor teams -- The upper team has the 6 “glass wall” related patents Search Technology, 2012

Step E: Focused DSSC Cross-Charting: Tracking Materials to Technology to Functions to Applications
Next steps: Consider ways to enhance key attributes; Consider “TDS” aspects; Determine “Who” is active on particular elements.

Step J: Engage Experts Hunt for local experts willing to engage
Key – faculty, but especially technical PhD students Workshops Search Technology, 2012

Goals Niche time Envisioned Application Areas
GRID CONNECTED OFF GRID PERSONAL PRODUCTS Niche Conventional Solar Cells Si - Film Compound Semiconductor Film Solar Cells Organic Dye sensitized 3D Quantum dot Anticipated potential Product Platforms Large surface Area to increase light absorption New film deposition tech reduces cost Large surface area could help charge separation Multiple exciton generation (MEG) Tailor optical properties through its size Functionalities Expected to made available Goals Nanoparticle Quantum Dot Nanowires Carbon nanotubes Nanostructures that are expected to be applied to solar cells Single-crystalline silicon Multi-crystalline silicon Cadmium sulfide (CdS) Cadmium telluride (CdTe) TiO2, ZnO Organic Materials Amorphous silicon Copper indium diselenide (CIS) Advances in Material R&D time present Short/Medium Term Long Term

Alignment with market needs? Duration of Gov. Incentives?
Well embedded Niche markets GRID CONNECTED OFF GRID PERSONAL PRODUCTS Envisioned Application Areas Alignment with market needs? Niche Quantum dot Solar Cells Conventional Solar Cells Dye sensitized Solar Cells Compound Semiconductor Film Solar Cells Anticipated potential Product Platforms 3D Solar Cells Si - Film Solar Cells Organic Solar Cells Issue: Life Cycle Cost Large surface Area to increase light absorption Large surface area could help charge separation Silicon-based thin film solar cells Tailor optical properties through its size Functionalities Expected to made available Goals Nanoparticle-based solar cells New film deposition tech reduces cost Quantum Dot-based solar cells Multiple exciton generation (MEG) Duration of Gov. Incentives? Nanoparticle Nanostructures that are expected to be applied to solar cells Nanowires Nanomaterial Regulation? Scalability? Quantum Dot Carbon nanotubes Single-crystalline silicon Cadmium sulfide (CdS) TiO2, ZnO Advances in Material R&D Multi-crystalline silicon Copper indium diselenide (CIS) Organic Materials Amorphous silicon Cadmium telluride (CdTe) Search Technology, 2012 time present Short/Medium Term Long Term

Research Assessment References
4/16/2017 Porter, A.L., Newman, N.C., Myers, W., and Schoeneck, D., Projects and Publications: Interesting Patterns in U.S. Environmental Protection Agency Research, Research Evaluation, Vol. 12, No. 3, , 2003. Porter, A.L., Schoeneck, D.J., Roessner, D., and Garner, J. (2010). Practical research proposal and publication profiling, Research Evaluation, 19(1), Carley, S., and Porter, A.L., A forward diversity index, Scientometrics, to appear -- DOI: /s Report has Fig co-authoring among particular project researchers Beofre/After Fig various highly cited AU maps (diff bases) Citing AU’s - reduce our 60,000 citing articles to from 2001 on & eliminating 2 special ROLE projects (Mtls Res Ctr; Marine Biol proj). 168 w >= 6 articles citing >= 6 ROLE/REESE researchers eliminate if citing pubs linked to <= 2 awards - find 81 w >= 10 citing articles linked to 3-5 awards - union – 249 hi citing authors 68 really highly citing >= 10 awards’ researchers cited or >=10 pubs citing work related to >=6 awards Then 2 ways: - based on similarity among cited researchers first - based on similarity among the MDs represented second

Science Mapping References
Science Maps Chen, C. (2003) Mapping Scientific Frontiers: The Quest for Knowledge Visualization, Springer, London Boyack, K. W., Klavans, R. & Börner, K. (2005). Mapping the backbone of science. Scientometrics, 64(3), Leydesdorff, L. and Rafols, I. (2009) A Global Map of Science Based on the ISI Subject Categories. Journal of the American Society for Information Science and Technology, 60(2), Boyack, K. W., Börner, K. & Klavans, R. (2009). Mapping the structure and evolution of chemistry research. Scientometrics, 79(1), Klavans, R. & Boyack, K. W. (2009). Toward a Consensus Map of Science. Journal of the American Society for Information Science and Technology, 60(3), Places & Spaces: Science Overlay Maps Rafols, I. & Leydesdorff, L. (2009). Content-based and Algorithmic Classifications of Journals: Perspectives on the Dynamics of Scientific Communication and Indexer Effects. Journal of the American Society for Information Science and Technology, 60(9), Rafols, I., Porter, A.L., and Leydesdorff, L., (2010) Science overlay maps: A new tool for research policy and library management, Journal of the American Society for Information Science & Technology, 61 (9), , 2010. Rafols, I. and Meyer, M. (2009) Diversity and Network Coherence as indicators of interdisciplinarity: case studies in bionanoscience. Scientometrics, 82(2), DOI /s y. Porter, A.L., and Youtie, J., Where Does Nanotechnology Belong in the Map of Science?, Nature-Nanotechnology, Vol. 4, , 2009.

Interdisciplinarity References
National Academies Keck Futures Initiative: // National Academies Committee on Facilitating Interdisciplinary Research, Committee on Science, Engineering and Public Policy (COSEPUP) (2005). Facilitating interdisciplinary research. (National Academies Press, Washington, DC). Klein, J. T. (1996), Crossing boundaries: Knowledge, disciplinarities, and interdisciplinarities. (University Press of Virginia, Charlottesville, VA.). Porter, A.L., Cohen, A.S., Roessner, J.D., and Perreault, M. Measuring Researcher Interdisciplinarity, Scientometrics, Vol. 72, No. 1, 2007, p Porter, A.L., Roessner, J.D., and Heberger, A.E., How Interdisciplinary is a Given Body of Research?, Research Evaluation, Vol. 17, No. 4, , 2008. Porter, A.L., and Rafols, I. (2009), Is Science Becoming more Interdisciplinary? Measuring and Mapping Six Research Fields over Time, Scientometrics, 81(3), Rafols, I., and Meyer, M., Diversity and network coherence as indicators of interdisciplinarity: case studies in bionanoscience, Scientometrics 82, , 2010. Stirling, A. (2007). A general framework for analysing diversity in science, technology and society. Journal of The Royal Society Interface, 4(15), Wagner, C.S., Roessner, J.D., Bobb, K., Klein, J.T., Boyack, K.W., Keyton, J., Rafols, I., and Borner, K. (2011), Approaches to understanding and measuring interdisciplinary scientific research (IDR): A review of the literature, Journal of Informetrics, 5(1),

FIP References 4/16/2017 Porter, A.L., Guo, Y., Huang, L., and Robinson, D.K.R., Forecasting Innovation Pathways: The Case of Nano- enhanced Solar Cells, ITICTI - International Conference on Technological Innovation and Competitive Technical Intelligence, Beijing, December, 2010. Robinson, D.K.R., Huang, L., Guo, Y., and Porter, A.L. (2013), Forecasting Innovation Pathways for New and Emerging Science & Technologies, Technological Forecasting & Social Change, 80 (2), Huang, L., Guo, Y., Zhu, D., Porter, A.L., Youtie, J., and Robinson, D.K.R., Organizing a Multidisciplinary Workshop for Forecasting Innovation Pathways: The Case of Nano-Enabled Biosensors, Atlanta Conference on Science and Innovation Policy, 2011. Report has Fig co-authoring among particular project researchers Beofre/After Fig various highly cited AU maps (diff bases) Citing AU’s - reduce our 60,000 citing articles to from 2001 on & eliminating 2 special ROLE projects (Mtls Res Ctr; Marine Biol proj). 168 w >= 6 articles citing >= 6 ROLE/REESE researchers eliminate if citing pubs linked to <= 2 awards - find 81 w >= 10 citing articles linked to 3-5 awards - union – 249 hi citing authors 68 really highly citing >= 10 awards’ researchers cited or >=10 pubs citing work related to >=6 awards Then 2 ways: - based on similarity among cited researchers first - based on similarity among the MDs represented second Search Technology, 2012

TechMining References
4/16/2017 Porter, A.L., and Cunningham, S.W. (2005), Tech Mining: Exploiting New Technologies for Competitive Advantage, Wiley, New York. Porter, A.L. (2005), Tech Mining, Competitive Intelligence Magazine, 8 (1), Cunningham, S.W., Porter, A.L., and Newman, N.C. (2006), Tech Mining, special issue of Technological Forecasting & Social Change, 73 (8), Porter, A.L. (2007), How ‘Tech Mining’ Can Enhance R&D Management, Research Technology Management, 50 (2), Porter, A.L. (2009), Technology Monitoring – Tech Mining, in Ashton, W.B. and Hohhof, B. (Eds.), Competitive Technical Intelligence, Competitive Intelligence Foundation, Alexandria, VA., Porter, A.L., and Newman, N.C. (2011), Tech Mining Success Stories, Technology Management Report, Center for Innovation Management Studies (CIMS), Spring, Porter, A.L., Guo, Y., and Chiavetta, D. (to appear), Tech Mining: Text mining and visualization tools, as applied to nano-enhanced solar cells, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. Report has Fig co-authoring among particular project researchers Beofre/After Fig various highly cited AU maps (diff bases) Citing AU’s - reduce our 60,000 citing articles to from 2001 on & eliminating 2 special ROLE projects (Mtls Res Ctr; Marine Biol proj). 168 w >= 6 articles citing >= 6 ROLE/REESE researchers eliminate if citing pubs linked to <= 2 awards - find 81 w >= 10 citing articles linked to 3-5 awards - union – 249 hi citing authors 68 really highly citing >= 10 awards’ researchers cited or >=10 pubs citing work related to >=6 awards Then 2 ways: - based on similarity among cited researchers first - based on similarity among the MDs represented second Search Technology, 2012

4/16/2017 Resources Text mining software like that used: //ip.thomsonreuters.com/training/thomson-data-analyzer Ongoing Research on Interdisciplinarity & to make your own science overlay maps: //idr.gatech.edu/ or Global Tech Mining Conference, in conjunction with the Atlanta Conference on Science & Innovation Policy, Sep., 2013, Atlanta Global Tech Mining – forthcoming special issues of Technological Forecasting & Social Change, and of Technology Analysis & Strategic Management Report has Fig co-authoring among particular project researchers Beofre/After Fig various highly cited AU maps (diff bases) Citing AU’s - reduce our 60,000 citing articles to from 2001 on & eliminating 2 special ROLE projects (Mtls Res Ctr; Marine Biol proj). 168 w >= 6 articles citing >= 6 ROLE/REESE researchers eliminate if citing pubs linked to <= 2 awards - find 81 w >= 10 citing articles linked to 3-5 awards - union – 249 hi citing authors 68 really highly citing >= 10 awards’ researchers cited or >=10 pubs citing work related to >=6 awards Then 2 ways: - based on similarity among cited researchers first - based on similarity among the MDs represented second

Outtakes 4/16/2017

Bases Using multiple data resources for research assessment
4/16/2017 Bases Using multiple data resources for research assessment Publications – mainly via Web of Science Citations – via Web of Science Patents (not today) Data cleaning and analyses Using Thomson Data Analyzer (TDA) or VantagePoint software Visualization Using VantagePoint together with Aduna, Pajek, Excel, Gephi, etc. 80

Diversity: ‘attribute of a system whose elements may be apportioned into categories’ Characteristics: Variety: Number of distinctive categories Balance: Evenness of the distribution Disparity: Degree to which the categories are different. Heuristics of diversity (Stirling, 1998; 2007) (Rafols and Meyer, 2009) [** Shannon & Herfindahl do not include Disparity] Variety Shannon (Entropy): i pi ln pi Herfindahl (concentration):  i pi2 Dissimilarity:  i di Balance Disparity Generalised Diversity (Stirling) ij(ij) (pipj)a (dij)b

Bibliographic Coupling

Meta Overlay, HSD Citing
Env, Ag & Geo Sciences Bio & Medical Sciences Physical Sciences & Engr Social & Behavioral Sciences

Alan Porter Search Technology, Inc. & Georgia Tech

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