“Tech Mining” R&D Literature – for Research Assessment & Forecasting Innovation Pathways Alan...

“Tech Mining” R&D Literature –for Research Assessment &

Forecasting Innovation PathwaysAlan Porter

Search Technology, Inc.&

Georgia Tech

[email protected]

Agenda: Mining R&D Literature

1. The Data2. Tech Mining3. Research Assessment

◦ Measures◦ Maps

4. Forecasting Innovation Pathways

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

Alan Porter

#4: Papers cited by #3

Tracking multi-generational research knowledge transferwith• Interdisciplinarity metrics• Science overlay mapping•“Specialization” scores (Diversity of areas of publication)•Science overlay maps (Location of publications among ISI Subject Categories)

•Coherence measures (do #3 papers draw upon distinct topics?)•[ “Bibliographic Coupling” measures available – e.g., % shared references]

#3: Papers cited by #2

•Integration scores (Average diversity of areas of citation)•Science citation maps•Bibliographic coupling

#2: Main Level (e.g., research outputs of a target program) – publication overlay maps

#1: Papers Citing Level #2 Papers – Citing Paper Overlay Maps [Knowledge Diffusion]

•Diffusion scores•Science Citing Overlay Maps•Relative engagement by ISI Subject Categories

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

Web of Science (“WOS”)

Case ExamplesGetting to

the data- usually via internet

Case Examples

Getting the data

- search

- within databases

- retrieve abstract records electronically

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: * 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]

AU Oliver-Hoyo, M Gerber, RWTI From the research bench to the teaching laboratory: Gold nanoparticle layeringSO JOURNAL OF CHEMICAL EDUCATIONDT ArticleC1 N Carolina State Univ, Dept Chem, Raleigh, NC 27695 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 16TC 1PY 2007VL 84IS 7BP 1174EP 1176SC Chemistry, Multidisciplinary; Education, Scientific Disciplines

Getting “SCs” = easy; Getting “Cited SCs” is more challenging

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

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.?

R&D Abstract Record Data Mining

Tech MiningAlan L. Porter and Scott W. CunninghamJohn Wiley & Sons Inc., 2005


Tools and Techniques: Tech Mining

The Tech Mining Process


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.

TechMining: MOT Issues, Questions, and Indicators

~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

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?


A.Fit growth models to trend data to gauge technology maturation.

B.Understand R&D processes within an organization – key players, relationships &

C.Gauge commercialization timetable: Pie Chart - % of R&D publications by industry vs. academic vs. government.

D.Competitive/collaborative analysis -- compare IPCs between companies (unique/common).


Some of the 200+ MOT Indicators

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


DILEMMA“Does this technology offer strong commercialization prospects?”

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: Identify high% of publications by industry compared to government and academics


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


Innovation Indicators

Who? Where?

What? When?

How? & Why? – Need human analyst to interpret the data

Tech Mining Questions to Answer from field-structured data


1. Spell out the Intelligence questions and how to answer them

2. Get suitable data3. Search (iterate) & retrieve ~abstract records4. Import into text mining software (VantagePoint)5. Clean the data6. Analyze 7. Visualize (Map)8. Integrate with Internet analyses & expert

opinion9. Summarize; Interpret; Communicate (multi-

dimensionally)!10. Standardize and semi-automate where possible


Tech Mining“How to”

How does this fit with NRCC-KM efforts?

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


Data for Tech Mining: Six types

On-line Data Sources Custom DataCambridge Scientific Abstracts Factiva Patbase Comma/tab delimited tablesDelphion ISI Web Of Knowledge Questel-Orbit Microsoft Excel and AccessDialog Lexis Nexis SilverPlatter SmartChartsEBSCOHost Micropatent STN XMLEi Engineering Village Ovid Thomson Innovation

Databases Record/Field ToolsAerospace Focust Pascal Combine duplicate recordsArt Abstracts Food Sci & Tech Patent Citation Index Remove duplicate recordsBiobase Foodline Market PCT Create “frankenrecords”Biological Abstracts Foodline Science PCTPAT (merge records fromBiological Sciences Forege Phin dissimilar sources)Biosis Frosti Pira Classify recordsBiotechno FSTA Pluspat Merge fieldsBusiness & Industry Gale PROMT PROMT Clean up fieldsCAPlus (AnaVist export) GeoRef PsycINFO Apply thesauriCassis Global Reporter PubMedCBNB IFIPAT Rapra Claims IFIUDB Recent RefsComputer & Info Systems INPADOC Reference ManagerCorrosion INSPEC Science Citation IndexCurrent Contents IPA SciSearchDerwent Biotech Abstracts ISD ScopusDerwent Innovations Index ITRD Tech ResearchDerwent World Patent Index JAPIO ToxFile Ei Compendex JICST TransportEMBase Kosmet USAppsEnCompass Literature LGST USPat EnCompass Patents MATBUS WaternetEnergy Medline WaterResAbsEnergySciTech METADEX Web of ScienceEngineering Materials Abstr Mgmt and Org Studies WeldaSearch Envr Sci & Pollution Mgmt Micropatent Materials Wisdomain ERIC MobilityEuroPat NSF AwardsFamPat NTIS

VantagePoint Import Filters and Tools

A wealth of diverse

information sources for innovation

management

1. A Newly Emerging Science & Technology (NEST)

2. Combining technical intelligence from multiple database analyses – to answer:

a. What? / When?b. Who? / What?

3. Seeking to Forecast Innovation Pathwaysa. Illustrating lots of Tech Mining toolsb. To be used selectively – focusing on

the target questions!


Forecasting Innovation Pathways (FIP) Case Example: Dye-Sensitized Solar Cells (“DSSCs”)

Georgia Tech group has compiled nanotechnology R&D records from several databases

◦Modular, Boolean search (2006; update 2012)One area of “nano” focus – solar cellsHere, 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]


DSSCs: Background

Tech Mining the Data

1. Search on your topic in a target database2. Download to your computer3. Use text mining software to help clean &

analyze4. 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]

5. Look to do:◦ Check fields◦ Cleaning the data◦ Basic analyses (lists of the content of a field;

matrices made of 2 lists)◦ Maps

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, 601-621.

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

2005 2006 2007 2008 20090

50

100

150

200

250

300

350

400

450

Case ExamplesWho? Top among 993 InstitutionsAuthor Affiliations #Baku State Univ 291Natl Acad Sci Azerbaijan 254Azerbaijan Acad Sci 234Azerbaijan Natl Acad Sci 155Natl Acad Sci 106Russian Acad Sci 66Azerbaijan Tech Univ 61Azerbaijan State Oil Acad 49Gazi Univ 42Gebze Inst Technol 35Yildiz Tech Univ 34Azerbaijan Med Univ 31Ankara Univ 27Univ Rostock 22Middle E Tech Univ 20Tabriz Univ Med Sci 20

Issues to consider:A. Data cleaning

[combining name variations]

B. How to handle out-of-country institutions?

Case ExamplesWith whom? Top Collaborating Countries

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

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

Case Examples What Research Areas?

Macro-disciplines #Chemistry 475Materials Sci 382Engineering 333Physics 231Biomed Sci 105Geosciences 101Clinical Med 68Computer Sci 51Infectious Diseases 42Agri Sci 38Ecol Sci 33Env Sci & Tech 17Cognitive Sci 15Health Issues 7Policy Sciences 7Psychology 4Business & Mgt 3Folklore 3Language & Linguistics 1Literature, British Isles 1Social Studies 1

Macro-disciplines are basedon factor analysis of a year’sworth of Web of Science (2007)cross-journal citations[thanks to Leydesdorff and Rafols]

Case ExamplesResearch Profile: Azerbaijan 2005-09 by Disciplines (top 5)Macro-Discipline Author Affiliations Key Terms Authors Year

Top 3 Top 5 Top 3 2008-09Chemistry[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

Cognitive Sci

Agri Sci

Biomed Sci

Chemistry

Physics

Engineering

Env Sci & Tech

Mtls Sci

Infectious Diseases

Psychology

Social Studies

Clinical Med

Computer SciBusiness & MGT

Geosciences

Ecol Sci

Economics Politics & Geography

Health & Social Issues

Cognitive Sci.

Agri Sci

Biomed Sci

Chemistry

Physics

Engineering

Env Sci & Tech

Mtls Sci

Infectious Diseases

Psychology

Social Studies

Clinical Med

Computer Sci.Business & MGT

GeosciencesEcol Sci

Econ. Polit. & Geography

Health & Social Issues

Azerbaijan Research, 2005-09 on Global Map of Science, SCI-SSCI 2007

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

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.900.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

HSD vs Control

HSDControl Groups

Integration by Project

Sp

ecia

liza

tio

n b

y P

roje

ct

MoreInterdisciplinary

MoreDisciplinary

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

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

Thomson Data Analyzer Map Principles

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]

Study research networks

Science Mapping

Governance

Visions

Co-citation Mapof the most citedauthors bythe 307nano social sciencepapers[Use Auto-corr onhi cited Authors]

Evolutionary Economics

Perception

Ethics

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?

NSF Research Assessments

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

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

Co-authoring among CAS institutes on nano[partial network map]

CAS Grad School shows hi centrality

1. Identify and Map the participating research domains, over time

2. Elucidate the intellectual & social research networks involved

3. Gauge how interdisciplinary the projects are4. Look for impacts of the research support on

researchers’ emphases, productivity, and teaming

ROLE/REESE Research Evaluation Targets

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

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 Research Activities

Project BB

Project A

Project K

Project Y

Project F

Project C

Project E

Project CC

Project W

Project B

Project T

Project U

Project D

Project DD

Project N

Project G

Project I

Project EE

Project Z

Project V

Project R

Project J

Project X

Project AA

Project M Projec

t P

Project Q

Project OProjec

t L

Project S

Project H

HSD Co-authoring

Project BB

Project A

Project K

Project Y

Project F

Project C

Project E

Project CC

Project W

Project B

Project T

Project U

Project D

Project DD

Project N

Project G

Project I

Project EE

Project Z

Project V

Project R

Project J

Project X

Project AA

Project M Projec

t P

Project Q

Project OProjec

t L

Project S

Project H

HSD Co-authoring+ citing

Measures & maps How much output? Extent and nature of collaboration?

Research Assessment

“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


STAGE ONEUnderstand the NEST and its TDS (Technology Delivery System)

Step A: Characterize the technology’s nature

Step B: Model the TDS

STAGE TWOTech Mine

Step C: Profile R&D

Step D: Profile innovation actors & activities

Step E: Determine potential applications

Step J: Engage experts

STAGE THREEForecast likely innovation paths

Step F: Lay out alternative innovation pathways

Step G: Explore innovation components

Step H: Perform Technology Assessment

Step J: Engage experts

STAGE FOURSynthesize & report Step I: Synthesize and Report

10 Steps (non-linear!) to Forecast Innovation Pathways (FIP)


Methods & Data Sources vis-à-vis Analytical Steps

Analytical StepsStep J. Expert checking

Bibliometric analysesSCI & Compendex research publications

Derwent patents

Factiva business & context data

A: Understand the NEST & specify the driving questions X

B: Model the TDS XC: Profile R&D X X XD: Identify key Actors X X X XE: Identify Applications X X X XF: Lay out alternative innovation pathways X

G: Explore innovation elements required X X X X

H: Perform Technology Assessments X X X

I: Synthesize & report XJ: Expert Checking ~

STAGE ONEUnderstand the technology and its Technology Delivery System (TDS)



STAGE TWOTech Mine

Step C: Profile R&DStep D: Profile innovation actors & activitiesStep E: Determine potential applications

Step J: Engage expertsSTAGE THREEForecast likely innovation paths

Step F: Lay out alternative innovation pathwaysStep G: Explore innovation components


Step J: Engage expertsSTAGE FOURSynthesize & report Step I: Synthesize and Report



Step A: Trends in Solar Cell Sub-technologies


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


Step B: Model the Technology Delivery System (TDS)

Step B: Basic DSSC Technology Delivery System

Who? [Enterprise(s) to innovate?]

What? [Potent Environmental Influences on innovation prospects?]

STAGE ONEUnderstand the technology and its TDS (Technology Delivery System)



STAGE TWOTech Mine

Step C: Profile R&D










Step C: When? Projecting Nano-enhanced Solar Cell Research Activity


Actual data Projected data

Step C: Where? Geo-map: Nano-enhanced Solar Cells – European Institutions >=10 papers


SCI EI DWPI Factiva

Samsung SDI Co LTD 52* 38 65* 4Sharp Co Ltd 27* 24 17* 4

Nippon Oil Corp 15* 35 27* 10*Hayashibara Biochem Labs Inc 14* 9 0 0

Fujikura Ltd 12* 8 17* 9*Chemicrea Co Ltd 10* 8 0 0

Sumitomo Osaka Cement Co Ltd 10* 3 3 2Toshiba Co Ltd 9* 7 2 1

Konarka Technologies Inc 7* 11 11* 9*DONG JIN SEMICHEM CO LTD 0 1 16* 8*

SONY CORP 10 10 17* 17*Evonik Degussa GmbH 0 0 0 15*STMicroelectronics NV 0 0 0 12*

Data Systems & Software Inc 0 0 0 8*Dongjin Semichem Co Ltd 0 1 0 8*

Dyesol Ltd 3 3 2 8*

Step D: Who? Leading DSSC Companies across Databases


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.


Step D: DSSCs “Glass Houses”

2 distinct inventor teams --

The upper team has the 6 “glass

wall” related patents


Step D: SamsungPatent Analyses:

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.




STAGE TWOTech Mine

Step C: Profile R&DStep D: Profile innovation actors & activitiesStep E: Determine potential applications


Step F: Lay out alternative innovation pathwaysStep G: Explore innovation components





Hunt for local experts willing to engage Key – faculty, but especially technical PhD

students Workshops


Step J: Engage Experts

time

Envisioned Application Areas

Goa

ls

present Short/Medium Term

Anticipated potential Product Platforms

Functionalities Expected to made

available

Nanostructures that are expected to be

applied to solar cells

Advances in Material R&D

Long Term

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

Nanoparticle

Quantum Dot

Nanowires

Carbon nanotubes

Single-crystalline silicon

Multi-crystalline silicon

Cadmium sulfide (CdS)

Cadmium telluride (CdTe)

TiO2, ZnO

Organic Materials

Amorphous silicon

Copper indium diselenide (CIS)

Niche

Conventional Solar Cells

Si - FilmSolar Cells

Compound Semiconductor Film Solar Cells

OrganicSolar Cells

Dye sensitizedSolar Cells

3DSolar Cells

Quantum dotSolar Cells

GRID CONNECTED OFF GRIDPERSONAL PRODUCTS

time

Envisioned Application Areas

Goa

ls

present Short/Medium Term

Silic

on

-base

d t

hin

film

sola

r ce

lls

Conventional Solar Cells

Anticipated potential Product Platforms

Functionalities Expected to made

available

Nanostructures that are expected to be

applied to solar cells

Advances in Material R&D

Si - FilmSolar Cells

Compound Semiconductor Film Solar Cells

Large surface Area to increase light absorption

New film deposition tech

reduces cost

Long Term

Large surface area could help

charge separation

Multiple exciton generation (MEG)

Tailor optical properties through

its size

Nanoparticle

Quantum Dot

Nanowires

Carbon nanotubes

Single-crystalline silicon

Multi-crystalline silicon

Cadmium sulfide (CdS)

Cadmium telluride (CdTe)

TiO2, ZnO

Organic Materials

Amorphous silicon

Copper indium diselenide (CIS)

OrganicSolar Cells

Well embedded

Niche

Niche markets

Dye sensitizedSolar Cells

3DSolar Cells

Quantum dotSolar Cells

Nan

opar

ticl

e-bas

ed s

olar

cel

ls

Qua

ntum

Dot

-bas

ed s

olar

cel

ls

GRID CONNECTED OFF GRIDPERSONAL PRODUCTS

Scalability?

Issu

e:Li

fe C

ycle

C

ost

Du

rati

on

of

Go

v.

Ince

nti

ves?

Alignment with market

needs?

NanomaterialRegulation?





STAGE TWOTech Mine

Step C: Profile R&D










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, 171-182, 2003.

Porter, A.L., Schoeneck, D.J., Roessner, D., and Garner, J. (2010). Practical research proposal and publication profiling, Research Evaluation, 19(1), 29-44.

Carley, S., and Porter, A.L., A forward diversity index, Scientometrics, to appear -- DOI: 10.1007/s11192-011-0528-1.

Research Assessment References

Science Mapping ReferencesScience 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), 351-374.• 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), 348-362.

• Boyack, K. W., Börner, K. & Klavans, R. (2009). Mapping the structure and evolution of chemistry research. Scientometrics, 79(1), 45-60.

• Klavans, R. & Boyack, K. W. (2009). Toward a Consensus Map of Science. Journal of the American Society for Information Science and Technology, 60(3), 455-476.

• Places & Spaces: http://www.scimaps.org/ 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), 1823-1835.

• 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), 1871-1887, 2010.

• Rafols, I. and Meyer, M. (2009) Diversity and Network Coherence as indicators of interdisciplinarity: case studies in bionanoscience. Scientometrics, 82(2), 263-287.DOI 10.1007/s11192-009-0041-y.

• Porter, A.L., and Youtie, J., Where Does Nanotechnology Belong in the Map of Science?, Nature-Nanotechnology, Vol. 4, 534-536, 2009.

Interdisciplinarity References• National Academies Keck Futures Initiative: //www.keckfutures.org• 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. 117-147.

• Porter, A.L., Roessner, J.D., and Heberger, A.E., How Interdisciplinary is a Given Body of Research?, Research Evaluation, Vol. 17, No. 4, 273-282, 2008.

• Porter, A.L., and Rafols, I. (2009), Is Science Becoming more Interdisciplinary? Measuring and Mapping Six Research Fields over Time, Scientometrics, 81(3), 719-745.

• Rafols, I., and Meyer, M., Diversity and network coherence as indicators of interdisciplinarity: case studies in bionanoscience, Scientometrics 82, 263-287, 2010.

• Stirling, A. (2007). A general framework for analysing diversity in science, technology and society. Journal of The Royal Society Interface, 4(15), 707-719.

• 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), 14-26.

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), 267-285.

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.


FIP References

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), 30-36.

Cunningham, S.W., Porter, A.L., and Newman, N.C. (2006), Tech Mining, special issue of Technological Forecasting & Social Change, 73 (8), 915-1060.

Porter, A.L. (2007), How ‘Tech Mining’ Can Enhance R&D Management, Research Technology Management, 50 (2), 15-20.

Porter, A.L. (2009), Technology Monitoring – Tech Mining, in Ashton, W.B. and Hohhof, B. (Eds.), Competitive Technical Intelligence, Competitive Intelligence Foundation, Alexandria, VA., 125-129.

Porter, A.L., and Newman, N.C. (2011), Tech Mining Success Stories, Technology Management Report, Center for Innovation Management Studies (CIMS), Spring, 17-19.

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.


TechMining References

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 www.leydesdorff.net/overlaytoolkit

Global Tech Mining Conference, in conjunction with the Atlanta Conference on Science & Innovation Policy, 25-28 Sep., 2013, Atlanta www.atlantaconference.org/

Global Tech Mining – forthcoming special issues of Technological Forecasting & Social Change, and of Technology Analysis & Strategic Management

Resources

http://ip.thomsonreuters.com/training/thomson-data-analyzer

http://ip.thomsonreuters.com/training/thomson-data-analyzer

Outtakes

Bases

1. Using multiple data resources for research assessment

◦ Publications – mainly via Web of Science◦ Citations – via Web of Science◦ Patents (not today)

2. Data cleaning and analyses◦ Using Thomson Data Analyzer (TDA) or

VantagePoint software3. Visualization

◦ Using VantagePoint together with Aduna, Pajek, Excel, Gephi, etc.

Heuristics of diversity

(Stirling, 1998; 2007)(Rafols and Meyer,

2009)

Diversity:‘attribute of a system whose elements may be apportioned into categories’

Characteristics: Variety: Number of distinctive categoriesBalance: Evenness of the distribution Disparity: Degree to which the categories

are different.

Variety

Balance Disparity

Herfindahl (concentration): i pi2

[** Shannon & Herfindahldo not includeDisparity]

Shannon (Entropy): i pi ln pi

Dissimilarity: i di

Generalised Diversity (Stirling) ij(ij) (pipj)a (dij)

b

Bibliographic Coupling

Meta Overlay, HSD Citing

Bio & Medical Sciences

Env, Ag & Geo Sciences

Physical Sciences & Engr

Social & Behavioral Sciences

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