Research Profiling – “Tech Mining” WebResearch Profiling Tech Mining Web of Science topical search results

Cells• Nano-enhanced Thin-film Solar Cells

Alan PorterDirector of R&D, Search Technology, Inc. Nano enhanced Thin film Solar Cells, gy,

[& Georgia Tech]aporter@searchtech.comp @

ForINGENIO, Valencia, 2010, ,

Topics1. Introduction to “Tech Mining”g2. Research and Development (R&D) Profiling --

Illustrated for Nano-enhanced thin-film SolarIllustrated for Nano-enhanced, thin-film Solar Cells

3 Discussion3. Discussion

Stages in Mining R&D Information Resources1. Literature review (within research community)2 T h Mi i2. Tech Mining

• Multiple Science, Technology and Innovation (“ST&I”)data to mine

• To generate effective intelligenceg g3. Research Profiling: Characterizing a body of

research publication activityresearch publication activity• Focus on select research activities• Characterize the “research landscape”

4. Structured Knowledge Discovery -- Literature-g yBased Discovery (“LBD”)

How do you extract effectiveHow do you extract effective intelligence from electronic

ST&I information resources?

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

Search Technology, 2010

Start with the questions!Start with the questions!

13 MOT Issues ~200 Innovation Indicators39 MOT QuestionsTech Mining Framework

• R&D Portfolio MgtR&D P j t

• Mapping of topic clusters within the technology3 D d h f i

What?• What’s hot?

• R&D Project Initiation

• Engr Project Initiation

• 3-D trend charts for topic clusters

• Ratio of conference to journal papers (benchmarked)

• Fit into tech landscape?• New frontiers at fringe?• Drivers?

Initiation• New Product

Development• Strategic

papers (benchmarked)• Scorecard rate-of-change

metrics for topic clusters• Time slices to show evolution

• Competing technologies?• Likely development paths?Who?Strategic

Planning• Track/forecast

emerging or

Time slices to show evolution of topical emphases

• Topic growth modeling (S-curve) fit & extrapolation

• Who are available experts?• Which universities or labs

lead?breakthrough technologies

• etc.

p

• Profile table of main players• Pie chart: Company vs.

MOT Issues, Questions, and Indicators

Academic vs. Government publishing

• Spreading (or constricting) # of players by topicand Indicators players by topic

“Answers”: Innovation IndicatorsAnswers : Innovation Indicators• Technology Life Cycle IndicatorsTechnology Life Cycle Indicators

- e,g, growth curve location & projection

• Innovation Context Indicators- e g presence or absence of success factors- e.g., presence or absence of success factors (funding, standards, infrastructure, etc.)

• Product Value Chain and MarketProduct Value Chain and Market Prospects Indicators- e.g., applications, sectors engagedg , pp , g g

How to do Tech Mining (for Research Profiling): 8 steps

1 Spell out the questions and how to answer1. Spell out the questions and how to answer them

2 Get suitable data2. Get suitable data3. Search (iterate)4 I t i t t t i i ft (4. Import into text mining software (e.g.,

VantagePoint)5 Cl th d t5. Clean the data6. Analyze & interpret 7. Represent the information well – communicate!8. Standardize and semi-automate where possible

Six information types

Technical Information Contextual Information

Six information types

• Science, Technology & Innovation (“ST&I”)

• Business, competition, customer policy& Innovation ( ST&I )

Databases (e.g., Web of Science; CSCD

customer, policy, popular content Databases (e gof Science; CSCD,

Thomson Innovation)• Internet Sources

Databases (e.g., Thomson One)

• Internet Sources (e g• Internet Sources(e.g., Googling)

• Internet Sources (e.g., blogs, website profiling)

• Technical Expertiseprofiling)

• Business Expertise

On-line Data Sources Custom DataCambridge Scientific Abstracts Factiva Patbase Comma/tab delimited tablesDelphion ISI Web Of Knowledge Questel‐Orbit Microsoft Excel and AccessDi l L i N i Sil Pl tt S tCh tDialog 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 SciSearch

A wealth of diverse

Derwent Biotech Abstracts ISD ScopusDerwent Innovations Index ITRD Tech ResearchDerwent World Patent Index JAPIO ToxFile Ei Compendex JICST TransportEMBase Kosmet USApps

information sources for

EnCompass Literature LGST USPat EnCompass Patents MATBUS WaternetEnergy Medline WaterResAbsEnergySciTech METADEX Web of ScienceEngineering Materials Abstr  Mgmt and Org Studies WeldaSearch 

innovation managementg g g g

Envr Sci & Pollution Mgmt  Micropatent Materials Wisdomain ERIC MobilityEuroPat  NSF AwardsFamPat NTIS

VantagePoint Import Filters and Tools

g

Case Examples

Getting to the datathe data- usually via internet

Case Examples

Getting the datathe data

- search within databases

- retrieve abstract records electronically

Case ExamplesImport into text mining software for cleaning & analyses

Nano-enhanced Thin-film Solar CellsNano-enhanced Thin-film Solar Cells

Analysis of Global Research Activities ywith Future Prospects

Ying Guo

Ph.D. Candidate, Beijing Institute of TechnologyVisiting Student, Georgia Institute of Technology

Alan L. PorterLu HuangLu Huang

International Association for Management of Technology, 2009

P ibl ST&I P li k Q ti Wh t ti l• Possible ST&I Policy-maker Question: What are national R&D strengths and weaknesses?

• Possible Technology Manager Questions: How to gauge relative opportunities for collaborative development, as well as monitor emerging competitors? g g p

Who Global

What

When Research Activities with Our Papers

Tech Mining

Where

Why

Future Prospects

g

Why

HowNeed more expert inputs (we’re working on this)

We look at such aspects as:We look at such aspects as:

1. What research fields are involved?– science overlay maps

2 Quantity publication numbers and trends2. Quantity -- publication numbers and trends

3. Diversity -- national contrasts

4 Q lit it ti4. Quality -- citations

5. Patterns of research networking---using VantagePoint

6. “Hot” topics

Data:

a global dataset of defined “thin film acquired the dataset containing 1659nano publications

downloaded from the SCI

and (solar or photovoltaic)” as our search expression

containing 1659 records for time period from 2001 to mid-2008mid 2008

Basic Dataset Search Expression Resulting Dataset

The Three Core VantagePoint gAnalytical Tools•• LISTSLISTS• CO-OCCURRENCE• CO-OCCURRENCE

MATRICES• MAPS• MAPS

KEY: Co-occurrence statistics to find relationshipsp• Count the relative degree to which “terms” (e.g.,

keywords, author names, organizations, years) appear together in particular documents in the set

• The higher the co-occurrence, the stronger the t ti l l ti hipotential relationship

“Top N’s” Available1 Document types (e g articles)1. Document types (e.g., articles)2. Publication Years (essential for trend analyses)3 Ti Cit d3. Times Cited4. Countries5. Affiliations6 Funding agencies6. Funding agencies7. Authors8 J l ( S )8. Journals (or Sources)9. Key terms10. Subject Categories11. Macro-Disciplines11. Macro Disciplines12. Organization Types

Nano-enhanced Solar Cell Web of Science Subject Category Concentrations of the Leading CountriesCategory Concentrations of the Leading Countries

USA India Germany Japan ChinaUSA India Germany Japan China

Materials Science 126 132 83 68 63Materials Science, Multidisciplinary

126 132 83 68 63

Physics Applied 112 56 92 68 53Physics, Applied 112 56 92 68 53Physics, Condensed Matter 59 72 80 47 46Ch i t Ph i l 8282 2626 28 34 32Chemistry, Physical 8282 2626 28 34 32Energy & Fuels 2626 4949 16 9 10M t i l S i C ti 24 21 26 17 21Materials Science, Coatings & Films

24 21 26 17 21

Academic-Corporate-Government Publishing by Countryp g y y

Cross-national Collaboration

% International

USA India Germany Japan China France UK SouthKorea

Mexico Spain

Cooperation (among top 10)

USA 20 1% 288 5 16 5 6 5 3 9 8 1USA 20.1% 288 5 16 5 6 5 3 9 8 1India 26.4% 5 239 4 15 4 5 20 10Germany 27.1% 16 4 195 10 2 8 8 1 4

Japan 24.2% 5 15 10 182 4 2 5 2 1China 10.4% 6 2 4 182 2 2 1 2France 24.8% 5 4 8 2 2 113 4 3UK 34.5% 3 5 8 5 2 4 84 1 1South 52.2% 9 20 1 2 1 1 69 2Korea

Mexico 38.5% 8 10 1 2 2 65 2Spain 17 5% 1 4 3 1 2 63p 17.5% 1 4 3 1 2 63

Dye Sensitized Solar Cell („DSSC“) research by organization type over Time (from SCI)organization type, over Time (from SCI)

# of author affiliations/paper for DSSC publications (SCI)

Nano-Structured ZnO Thin-film Solar Cells Publication by Countries and Years

14

12 China

India

8

10 Japan

USA

6Mexico

Germany

ChinaJapan

2

4 SouthKoreaSpain

2001

Mexico

South Korea0

2

France

2001 2002 2003 2004 2005 2006 2007

France

IAMOT 2009

90%

100%

France

%

70%

80% France

Spain

SouthK

40%

50%

60% KoreaGermany

Mexico

20%

30%

40% USA

Japan

India

0%

10%

20%

China

0%2001 2002 2003 2004 2005 2006 2007

Nano-Structured ZnO Thin-film Solar Cells Publication: Top 10 countries by Years – note the Bumpiness for Spain

DSSC Publications (SCI) with % 2006 or later

Projecting Nano-enhanced Solar Cell Research Activity

Actual data Projected data

Research publication activity and citation (impact) characteristics

USA

2000ns

1000

1500

f ci

tatio

n

IndiaGermanyUK

Japan500

1000

ality

-# o

f

IndiaChinaFrance

South KoreaMexico

Spain0

0 50 100 150 200 250 300 350

qua

0 50 100 150 200 250 300 350

act iv ity -# of records

• Nodes above the diagonal suggest relatively higher quality (US and UK). Below the diagonal, the closer to the diagonal, the higher the quality of thatBelow the diagonal, the closer to the diagonal, the higher the quality of that country’s research.

ZnO attracts increasing attention in recent years and is on trend to catch up with TiO2

“Hot topic” shown by relative trends

ZnO attracts increasing attention in recent years and is on trend to catch up with TiO2

“ Hot Topics“ –ratio-recent # Records Top 20 Key Terms

1.14 47 conjugated polymer pas Ratio of Occurrences

0.85 74 fabrication0.85 61 TiO20.74 66 chemical vapor deposition

2007-08 to those in 2001-06

0.65 28 amorphous silicon0.53 72 morphology0.52 94 semiconductor0 50 48 f ll0.50 48 fullerene0.48 49 zinc oxide0.46 51 microstructure0 41 65 l i0.41 65 spray pyrolysis0.36 49 heterojunction0.32 37 CdTe0 29 102 l t d iti0.29 102 electrodeposition0.28 92 CuInSe20.24 21 anatase0 22 39 h i l b th d iti0.22 39 chemical bath deposition0.17 21 Cu(In0.00 37 sol-gel0 00 22 h t d ti it0.00 22 photoconductivity

0.44 Top 20 Key Terms combined

Recent Entrants

• We need not restrict the temporal comparison to key terms or topicskey terms or topics

• Same modus operandi can be applied to identify new or recent entrants to the research (e g firstnew or recent entrants to the research (e.g., first papers on the topic from a given organization)Another variant is the inverse to look for• Another variant is the inverse – to look for which participants seem to have abandoned the topic (no publications since Year X)topic (no publications since Year X)

Visualization (Maps)1. VantagePoint Mapsg p Auto-correlation maps Cross-correlation maps Cross-correlation maps Factor maps

2 S i l N t k A l i (SNA)2. Social Network Analysis (SNA)3. Science Overlay Maps4. Geo-mapping

Factor Map (Principal Components Analysis) groups terms based on their tendency to co-occur across records

Social Network Analysis (SNA)• VantagePoint offers several application opportunities Create a sub-dataset for a given country or organizationCreate a sub dataset for a given country or organization Within that target group, for the given research topic, explore

research network connectionsresearch network connections• Examples CollaborationsCollaborations Shared interests Discrepancies between interests & collaborationDiscrepancies between interests & collaboration

• Rich options Highly co-cited authors (e g nano in social sciences) Highly co-cited authors (e.g., nano in social sciences) Highly co-citing authors Bibliographic coupling (shared referencing) Bibliographic coupling (shared referencing)

Auto-Correlation MapsNETFSC Research networking comparison

USS (dispersed) vs Germany (1 central organization)Auto-Correlation Map

Affiliation (Name Only) (>6)

All li k h

Emor y Uni vEmor y Uni v Auto-Correlation Map

Affiliation (Name Only) (>5)USA Germany

USS (dispersed) vs Germany (1 central organization)

All links shown> 0.75 0 (0)0.50 - 0.75 0 (0)0.25 - 0.50 0 (0)< 0.25 3 (0)

Nor t hwest er n Uni vNor t hwest er n Uni v

All links shown> 0.75 0 (0)0.50 - 0.75 0 (0)0.25 - 0.50 2 (0)< 0.25 11 (0)

Uni v Lei pzi gUni v Lei pzi g

Hahn Mei t ner I nst Ber l i n GmbHHahn Mei t ner I nst Ber l i n GmbH

Fr ee Uni v Ber l i nFr ee Uni v Ber l i n

MI TMI THahn Mei t ner I nst Ber l i n GmbHHahn Mei t ner I nst Ber l i n GmbH

Uni v Wur zbur gUni v Wur zbur g

Bul gar i an Acad SciBul gar i an Acad Sci

Uni v Massachuset t sUni v Massachuset t sUni v Del awar eUni v Del awar e

Uni v Cal i f Los Angel esUni v Cal i f Los Angel es Johns Hopki ns Uni vJohns Hopki ns Uni v

Uni v Massachuset t sUni v Massachuset t s

Uni v Fl or i daUni v Fl or i daTech Uni v Dar mst adtTech Uni v Dar mst adt

Uni v Cal i f Sant a Bar bar aUni v Cal i f Sant a Bar bar a

Penn St at e Uni vPenn St at e Uni v Uni v St ut t gar tUni v St ut t gar t

Uni v Gi essenUni v Gi essen

Uni v Washi ngt onUni v Washi ngt on

Penn St at e Uni vPenn St at e Uni v

Nat l Renewabl e Ener gy LabNat l Renewabl e Ener gy Lab

Uni v Er l angen Nur nber gUni v Er l angen Nur nber g

Tech Uni v Muni chTech Uni v Muni ch

Max Pl anck I nst Pol ymer ResMax Pl anck I nst Pol ymer Res

Gi f u Uni vGi f u Uni v

Science Overlay Mapping1 Start with Web of Science file in VantagePoint1. Start with Web of Science file in VantagePoint

• List the Subject Categories or• Cited Subject Categories (somewhat complicated process)• Cited Subject Categories (somewhat complicated process)• Output a vector file of SCs or Cited SCs

2 In Pajek2. In Pajek• Select the SCI (175 SC) or SCI+SSCI (221 SC) base map

[thanks to Ismael Rafols & Loet Leydesdorff]y• Edit your map

3. In MS Powerpoint• Overlay on the appropriate base map

4. Or, go to www.idr.gatech.edu/ -- select “Upload Map”

Science Overlay Map [see: www.idr.gatech.edu – includes “how to make your own map” and full citations]

GeosciencesAgri Sci

Ecol SciInfec tious Diseases

EnvSci & Tech

Biomed Sci.

Chemistr yClinical Med

Chemi st r y Physi cal

Ener gy & Fuel s

Cognitive Sci

E S i

Health Sci

Mat er i al s Sci ence, Mul t i di sci pl i nar y

Physi cs Appl i ed

Chemi st r y, Physi cal

Mat er i al s Sci ence, Coat i ngs & Fi l ms

Engr SciMtls Sci

Physi cs, Appl i ed

Physi cs, Condensed Mat t er

Computer Sci

PhysicsPhysics

Nano-Thin-Film Publications 2001-08 DistributionOv erlay ov er base 175 Subject Category Science Map

Ley desdorff &Raf ols (Forthcoming) –

Geo-map: Nano-enhanced Solar Cells – European Institutions >=10 papers

Resources• www.theVantagePoint.com – offers multiple papers and

some case analyses• Tech Mining by Alan Porter and Scott Cunningham, Wiley,

2005.Porter A L Kongthon A Lu J C Research Profiling:• Porter, A.L., Kongthon, A., Lu, J-C., Research Profiling: Improving the Literature Review, Scientometrics, Vol. 53, p. 351-370, 2002.351 370, 2002.

• Interdisciplinarity and Science Overlay Mapping:www.idr.gatech.edu/

• Additional analyses (papers) using these tools:• www.tpac.gatech.edu/• www nanopolicy gatech edu/• www.nanopolicy.gatech.edu/

• alan.porter@isye.gatech.edu

Opportunitiespp• Future-oriented Technology Analyses Conference

Sevilla, May 12-13, 2011 – see jrc-ipts-foresight@ec.europa.eu

C f S• Tech Mining Workshop + Atlanta Conference on Science, Technology & Innovation Policy, Atlanta, Sep 13-17, 2011

Discussion1 R h fili th WIDENED lit i1. Research profiling – the WIDENED lit review To inform ST&I policy-making To assist R&D management To assist R&D management For researchers to situate their work

2 Empirical ST&I analyses of use to you?2. Empirical ST&I analyses of use to you? Web of Science (or other research databases) Other R&D information (e g patents)Other R&D information (e.g., patents) Downstream data (e.g., business, public interest)

3. Further analyses of interest to you:3. Further analyses of interest to you: Environmental (competitive; natural; organizational) Economic development (clusters; geo-maps)p ( ; g p ) ??