(Big) Data for Research for "Science, Technology and Entrepreneurship Class: 14" 2017/1/11 16:40-19:00

1. Big Data for Research
for“Science,Technology andEntrepreneurship:Class14”
2017/1/11 16:40-18:20 19:00
GRIPS SciREX Center/Specialist
NISTEP/Visiting Researcher
Hitotsubashi University MIC/Affiliate Researcher
Toyo University/Lecturer
Yasushi HARA
ya-hara@grips.ac.jp
twitter: harayasushi
facebook: yasushi.hara

2. Contents
• Introduction
• Database List
• J-global/KAKEN database
• RPA System
• Good Design Prize Database
• Scopus
• Web of Science
• PATSTAT
• Showcases
• Researcher network of Dr. Ryoji Noyori
• Identifying Core Paper for Dr. Satoshi Omura
• Knowledge flow analysis for Blue LED.
• Conclusion

3. Introduction:
Data(base) for Research.
• Research Question List
• Who’s the Star Scientist? ex. (Zucker and Darby 1997)
• Impact of Open Journal movement?
• The Paper written by PhD holder has more citation than the paper by non-
PhD holder? (Shimizu and Hara 2011)
• The high number of backward citation induces the high number of forward
citation?
• What is the Role of Self-citation?
• Technological Trajectory could be traced via data?
• Ranking of authors/organization in typical scientific field.
• Etc….
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4. Framework of Innovation Indicators
(Pakes and Griliches 1984)
Other
Economic
Factors
Non-Knowledge Factors
of Production Output:
Productivity
Firm’s Value
Patent
Patenting
Propensity
Inputs to Innovation
R&D, designing,
marketing research etc…
Knowhow and
First Mover Advantage
Paper
In-tangible
knowledge
1/11/2017 4

5. Data; from micro, meso to macro
Macro
National/Global level
Meso
Industry/firm level
(University/Company)
Micro
Individual Level
(Scientist/Inventor)
PATENT
- Inventor
- Assignee
- Patent
Number
- IPC
- Patent Family
- Non Patent
Literature
PAPER
- Author
- Organization
- Category
- Acknowledgement
DESIGN
- No.
- Designer Name
FUND
- No.
- Tied Patent/Paper N.
Science
Linkage
Economic Census
Innovation
Survey(NISTEP)
INPUT-OUTPUT
TABLE (I/O)
Macro
Economic
Model
Funding Database
Press Release
Survey of Research
and Development
(Statistics JAPAN)
SNA (System of National
Accounts; GDP)

6. Data Source Type Duration Source Availability Accessibility
Scopus (Web Interface) Publication 1970-
2017
Elsevier Researcher/Student/Staff in GRIPS Web from GRIPS network
Scopus (XML data; 1.4TB) Publication 1998-
2015
Elsevier Researcher/Student/Staff in SciREX
Center/GIST
Workstation (accessible via
grips_faculty)
Thomson Innovation PATENT 1850-
2016
Thomson
Reuters
1 user account license Web from GRIPS network
PATSTAT PATENT 1960-
2016
EPO Researcher/Staff in SciREX Center Workstation (accessible via
grips_faculty)
Web of Science
(Web Interface)
Publication 1993-
2016
Thomson
Reuters
Researcher/Student/Staff in GRIPS Web from GRIPS network
Web of Science (XML data) Publication 1980-2016 Thomson
Reuters
Researcher/Student/Staff in GRIPS
(max. 5 users)
Workstation (accessible via
grips_faculty)
J-global Publication/PATENT 1964-
2016
JST Researcher/Staff in SciREX Center Workstation (internal)
FMDB database FUNDING 1980-
2016
JST Researcher/Staff in SciREX Center [only
for Arimoto/Kuroda/Ikeuchi/Michel and
HARA]
Workstation (internal)
NIKKEI Press Release Database Press Release 2003-
2014
NIKKEI Researcher/Staff in SciREX Center/GIST Workstation (internal)
NIKKEI Article (Japanese) Database Newspaper article 1995-
2015
NIKKEI Researcher/Staff in SciREX Center/GIST Workstation (internal)
Input-Output Table I/O table 1995-2011 Applied Researcher/Staff in SciREX Center Workstation (internal)
Good Design Award Database Product Database 1959-2016 JDP Creative Commons Workstation / Web
2017/1/11 6
THEDATASETINGIST/SciREXCentre

7. Data; from micro to macro
Macro
National/Global level
Meso
Industry/firm level
(University/Company)
Micro
Individual Level
(Scientist/Inventor)
PATENT
- Inventor
- Assignee
- Patent
Number
- IPC
- Patent Family
- Non Patent
Literature
PAPER
- Author
- Organization
- Category
- Acknowledgement
DESIGN
- No.
- Designer Name
FUND
- No.
- Tied Patent/Paper
Science
Linkage
Economic Census
Innovation
Survey(NISTEP)
INPUT-OUTPUT
TABLE
Macro
Economic
Model
Funding Database
IIP Patent
[JPO]
PATSTAT
[EPO]
PatentsView
[USPTO]
Web of
Science
J-global
DWPI
Scopus
NISTEP
Design
Patent DB
KAKEN DB
Press Release
Survey of Research
and Development
(Statistics JAPAN)
SNA (System of National
Accounts; GDP)
Good Design
DB

8. But,
Very Unfortunately….
• There is no “exhaustively complete”
database
• Disambiguation is always needed.
• Fluctuation in organization
• Fluctuation in author name
• Fluctuation in country
• Fluctuation in paper category
• When using scientific paper database for
your own research, you will need to (1)
choose appropriate database for research
question, (2) and clean up the database to
get rid of the fluctuation in every single
entries.
2017/1/11 8

9. 1. J-global/KAKEN Database
2017/1/11 9

10. Dataset & Software
• Dataset: J-global and KAKEN database
• Managed by JST Information Planning Department
• J-global
• PATENT/Scientific Paper Database
• Covering Scientific Paper written in Japanese
• KAKEN-DB
• Database of KAKEN Fund
2017/1/11 10

11. Data Structure of J-global
・Researcher Information
・Bibliographic Information
・Patent Information
Forward Citation
Backward Citation
・Institutional Information
2017/1/11 11

12. Data Structure of KAKEN DB
・Researcher Information
・Institution Information
・Scientific Paper tied with KAKEN
・PATENT tied with KAKEN
・Funding Information (KAKEN)
2017/1/11 12

13. 0
200000
400000
600000
800000
1000000
1200000
1400000
1600000
1800000
2017/1/11 13

14. Article, 28672296
Proceedings,
7968231
Reports,
134279
Year Book,
48840
, 10308
Review, 8244
Newsletter, 4457
Patents, 1641
Statistics, 1084
Abstract,Index,
417
Dissertation,
38
Bibliography,
4
JPN, 1
Other, 1544
Article Proceedings Reports Year Book
Review Newsletter Patents Statistics Abstract,Index
Dissertation Bibliography JPN
2017/1/11 14

15. 2017/1/11 15

16. Value of the database?
• Feasible for Science, Technology and Innovation Activity in JAPAN
• Better to use Scopus and/or Thomson Innovation, PATSTAT if aim to
seek global comparison.
• Use with KAKEN-DB
• Treated Group vs. Control Group analysis
• Connect with Web of Science and Scopus based on ID
2017/1/11 16

17. 2. Creating Your Own Database
based on RPA (Robotics Process
Automation) system
2017/1/11 17

18. Create Database: BizRobo
• Automatic Data Cloaning
System from Web/PDF
• Making Web Scraping through
GUI
• Web Scraping : data scraping
used for extracting data from
websites.
• Fetching the data from Web
and/or PDF document, making
database sheet in
CSV/Excel/SQL format.
• Based on Kapow Katalyst.
• https://www.youtube.com/wa
tch?v=EHsOI51dPo0
Source: https://bizrobo.com/case/20161019_564.html2017/1/11 18

19. Screen Shot

20. 3. Good Design Prize Database

21. Data Description
• Principal Implementing Business
• Category
• Company
• Outline
• Producer
• Director
• Designer
• Based on Creative Commons

22. Value of the database?
• Connecting the dots
• Role of the design itself and the designer in R&D process.
• In-house R&D or Collaborative Research in Design?
• Harmonization between design and technology
• Binding the data between Patent, Design Patent.
• Role of Designer in R&D process.
• Availability
• Creative Commons License
• Freely used by SQL schema (coming soon)

23. 4. Web of Science

24. Web of Science
• Supplied by Clarivate Analytics, Inc.
• Variable Set available in Scopus
Database
• Author’s Name
• Title
• Journal Title
• Backward Citation
• Forward Citation
• Affiliation and its address
• Data Coverage
• 1900-2016 (in Hitotsubashi)
• 1993-2016 (in GRIPS)

25. Information embed in Web of Science
Abstract
Title
Author Name
Journal Name
and Pages
Publish Year
Keyword
Organization Name
and Address
Publisher
Classification
Type of Article and
Language
Backward and
Forward Citation
2017/1/11 25

26. Backward Citation and Forward Citation
time : t
"An Approach to the Study of
Entrepreneurship," THE TASKS OF
ECONOMIC HISTORY (Supplemental
Issue of THE JOURNAL OF
ECONoMIc HISTORY), VI (1946), 1-15
Oscar Lange, "A Note on
Innovations," Review of Economic
Statistics, XXV (1943), 19-25
F. W. Taussig, Inventors and
Money-Makers (New York: The
Macmillan Company, 1915).
Fritz Redlich, The Molding of American
Banking—Men and Ideas (New York: Hafner
Publishing Company, 1947).
Robert A. Gordon, Business
Leadership in the Large Corporation
(Washington, D.C.: The Brookings
Institution, 1945).
F. J. Marquis and S. J. Chapman on the
managerial stratum ,of the Lancashire cotton
industry in the Journal of the Royal Statistical
Society, LXXV, Pt. III (1912). 293-306.
Forward CitationBackward Citation
・Finding from Backward citation
-- How the author uses prior knowledge?
-- When there prior knowledge has been
emerged?
-- What’s the core knowledge for scientific
discovery?
・Data Could not retrieve from Backward Citation
-- Critical Scientific Discoveries which did not cite
-- Cited Knowledge but did not cite in References
page
・Finding from Forward Citation
-- The Importance of Paper itself
-- “Standing on the shoulders of the giants”
-- Knowledge Diffusion
-- Is the paper still anew?
・Data Could not retrieve from Forward citation
-- Number is Number. Do the accumulative
number of Forward citation reflect the
importance of paper (kinda Research Question)
-- 1-tier citation did not cover the core prior
knowledge.
2017/1/11 26

27. Research Example: Dr. Ryoji Noyori’s citation network
Ryoji Noyori
• Emeritus professor of Nagoya University
• Nobel Prize in Chemistry (2001)
• Born: 3 Sep. 1938, Japan
• Affiliation at the time of the award: Nagoya
University, Japan
• Prize motivation: “for their work on chirally
catalysed hydrogenation reactions”
• Field: Industrial chemistry, organic chemistry
By using co-author-network analysis, we try
to ensure
(1) trajectory of his research,
(2) relationship with co-recipient Laureates;
Dr. William S. Knowles and Dr. K. Barry
Sharpless http://www.nobelprize.org/nobel_prizes/chemistry/laureates/2001/

28. Case 1: Dr. Ryoji Noyori
• 1-tier co-authorship
• Dr. Knowleds and Dr. Noyori
• No direct co-authorship
• Dr. Sharpless and Dr. Noyori
• It would be highly related to
laboratory system of
scientific community
especially in chemistry and
captured out the spill over
process between scientists.
Co-authorship between Dr. Noyori and Dr. Sharpless
Data: Web of Knowledge / Visualization: VantagePoint

29. Case 1: Dr. Ryoji Noyori
Co-authorship of Dr. Noyori (1976-1980 ; in organization)
Co-authorship of Dr. Noyori (1981-1990 ; in organization)
• Continuous Collaboration with
pharmaceutical company, and in other
sector (ex. Nippon steel Chem.)

30. 5. SCOPUS

31. Scopus
• Supplied by Elsevier, Inc.
• Variable Set available in Scopus Database
• Author’s Name
• Title
• Journal Title
• Backward Citation
• Forward Citation
• Affiliation and its address
• Funding
• Impact Factor
+
・Corresponding Author
・Scientific Ordering (sequence number of authorship).
・EID (Scopus Paper ID)
・Author ID
・Affiliation ID
• Data Set: 1998-2015

32. Scopus XML Data.

33. Value of the database?
• Very Comprehensive dataset for
scientific paper
• Disambiguation for author name
and organization name.
• Availability
• Web : Accessible via GRIPS Network
• XML : Through Workstation
(ID/password required)
• SQL : Coming Soon! (Based on
MySQL)
• Comparison between Scopus and
Web of science
• Accumulated Journal list is differ
See (Del Bo. 2016)

34. Suppl.
Data Coverage Comparison between J-global and Scopus
Source: http://researchmap.jp/outline/sympo2015/rmapsympo2015_lecture04.pdf
Foreign Journal (海外誌)
Japanese Journal (国内誌)
Scopus: 20,246 Journals.
J-global: 14,525 Journals.
J-global [Japanese Journal]:
9,514 Journals.
J-global [Foreign Journal]:
5,011 Journals.
Scopus [Foreign Journal]:
19,829 Journals.
Scopus [Japanese Journal]:
417 Journals.
9,162
1,168 3,843
352 65
15,986
Analyzed on December 2014 by JST.

35. Research Example(2) :
Core papers analysis of Dr. Satoshi Omura
(Nobel Prize in Physiology or Medicine)
• Identifying Two Core Papers from scientific contribution in Nobel Prize Website
• http://www.nobelprize.org/nobel_prizes/medicine/laureates/2015/advanced-medicineprize2015.pdf :
References
• “One of these new Streptomyces strains, found in soil nearby a golf course in Ito, Japan, would turn out to be
the strain (Streptomyces avermitilis) producing Avermectin, (Burg et al., 1979). This strain, was also later
called Streptomyces avermectinius.”
• Ōmura subsequently applied novel techniques in molecular biology, chemistry, and microbiology to determine
the taxonomic status of the organism and later proposed the name Streptomyces avermectinius, based on
genetic studies in which the sequence of the genome was established (Ikeda et al., 1987, 1999, 2001 and
2003).
Two Core Papers
• （１）Burg, R.W., Miller, B.M., Baker, E.E., Birnbaum, J., Currie, S.A., Hartman, R., Kong, Y.,
Monaghan, R.L., Olson, G., Putter, I., Tu- nac, J.B., Hallick, H., Stapley, E.O., Ruiko O., Omura S.
Avermectins, new family of potent anthelmintic agents: producing organism and fermentation.
Antimicrob. Agents Chemotherap. 15(3):361-367, 1979.
• （２）Ikeda, H., Kotaki, H., Omura, S. Genetic studies of avermectin biosynthesis in Streptomyces
avermitilis. J Bacteriol. 169(12):5615-5621, 1987.
351/11/2017 SciREX Center © YASUSHI HARA 2016

36. Core Paper of Dr. Omura (1)
• Accumulative Number
of Forward Citation
• Web of science
• 527(until 2015)
• Scopus
• 378(until 2015)
• ※. Scopus does not
cover the whole data
before 1996
• # of Citation has been
increased in these five
years. 0
5
10
15
20
25
30
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
361/11/2017 SciREX Center © YASUSHI HARA 2016

37. Core Paper of Dr. Omura (1)
• Organizations that cite Dr. Omura’s paper
Organization # of citation
MERCK SHARP DOHME RES LABS 118
KITASATO UNIV 32
KITASATO INST 27
CHINA AGR UNIV 23
CHINESE ACAD SCI 17
USDA ARS 11
UNIV GLASGOW 8
CORNELL UNIV 7
PFIZER INC 7
AUBURN UNIV 6
COMSATS INST INFORMAT TECHNOL 6
UNIV LONDON IMPERIAL COLL SCI TECHNOL 6
USDA 6
GOVT COLL UNIV 4
SHANGHAI JIAO TONG UNIV 4
UNIV CALIF RIVERSIDE 4
UNIV MONTREAL 4
UNIV WISCONSIN 4
37
・Pharmaceutical Companies
- Merck 118
- Pfizer 7
1/11/2017 SciREX Center © YASUSHI HARA 2016

38. 6. PATSTAT

39. PATSTAT
• Patent Database by EPO
• Data Coverage
• Application
• Inventors
• Priorities
• Patents
• IPC Classes
• Citations
• Non Patent Literature
2017/1/11 39

40. Data Structure of PATSTAT
http://documents.epo.org/projects/babylon/eponet.nsf/0/95da6bccf12e54a1c1257aa1002e2d
1d/$FILE/patstat_data%20elements_v1.1.pdf
3/8/2015 40

41. Data Structure of PATSTAT (cont.)
3/8/2015 41

42. PATSTAT ER Graph
3/8/2015 42

43. Pros and Cons of the database
• Pros
• Covering EPO/USPTO/JPO patent database.
• Well Structured Database
• You’ll need the knowledge of SQL query language.
• Cons
• Need supplemental database for JPO patent.
• IIP patentdatabase (free) or patR.
• PATENT Family Control
• Use Thomson Innovation;
• Accessibility
• Thorough SQL server.

44. Research Example3; Shuji Nakamura
• Created Efficient Blue LEDs
• Lighting plays a major role in our quality
of life. The development of light-
emitting diodes (LEDs) has made more
efficient light sources possible. Creating
white light that can be used for lighting
requires a combination of red, green,
and blue light.
• Blue LEDs proved to be much more
difficult to create than red and green
diodes. During the 1980s and 1990s
Isamu Akasaki, Hiroshi Amano, and Shuji
Nakamura successfully used the
difficult-to-handle semiconductor
gallium nitride to create efficient blue
LEDs.
http://www.nobelprize.org/nobel_prizes/physics/laureates/2014/nak
amura-facts.html

45. Micro-level analysis
Aim
Try to capture the knowledge flow from basic science to disruptive
innovation
Methods
Citation Analysis
Based on the BLUE LED patent issued by Shuji Nakamura in 2007, trace the
data of backward citation to realize the knowledge flow among
inventor/researcher and organizations.
This study based on JST/RISTEX funding program fusibility study by Dr. Hiroshi
Shimizu, associate professor in Institute of Innovation Research, Hitotsubashi
University
45

46. Numbers of Papers and Patents
46

47. Method and Approach
• Method and Approach
• To capture up the trajectory of technological development numerically, build
up “citation tree” for
• 1.) Ensuring the role of organization.
• 2.) Identifying the “main path (= most influenced patent and/or paper in
each decade)” of trajectory.
• 3.) Under 1.) and 2.), determining whether the existence of “cru node “and
its scientist.
• Data
• Patent [USPTO, JP Patent Library] / Paper [ISI Web of
Knowledge/Science]
471/11/2017 SciREX Center © YASUSHI HARA 2016 47

48. 1: Defining the starting point:
Shuji Nakamura’s most-cited patent [US7205220]
for blue LED.
2: Referring [inventor-cited] whole forward
citation data of starting point
3: Under 2., referring forward citation data
of 3-tier paper and/or patents.
4: Repeating these procedure for 5 times.
Sum: Fetches about 1,000 paper/patents
and its forward citation data. [from 1903 to
2007]
48
“Citation Tree” build algorism
patent
patent
paper
paper
paper
paper
paperpaper
patent
patent
paper
paper
paper
patentpatent
paper
patent
patent
patent
paper
paper
paper
paper
paperpatent
patent
paper
patent
paper
Starting point
1/11/2017 SciREX Center © YASUSHI HARA 2016 48

49. 1930s 1960s 1970s 1990s 2000s1980s
1/11/2017 SciREX Center © YASUSHI HARA 2016 49
 Big Firm in United States; such as IBM, Bell
Telephone Laboratories, RCA Corporation, and
Texas Instruments, invested and yielded
enormous research outputs in 1970s.
 In late 1970s, the knowledge accumulated in
US were used by firm and university in Japan
which could be traced via citation path.
Knowledge from 1910 to 2007
(in organizations)

50. So, how to use these databases?

51. How to Use the Database?
• Use Freely via Web interface
• Web of Science(Web), Scopus (Web)
• Accessible via GRIPS Network
• Input-Output Table
• Call: ya-hara@grips.ac.jp
• Accessible via Workstation Server
• Web of Science(XML), Scopus(XML -> SQL), PATSTAT(SQL)
• Assign IDs and Password
• Accessible via grips_faculty network
• Call : ya-hara@grips.ac.jp
2017/1/11 51

52. How to Use the Database? (2)
• Managed via ID
• Thomson Innovation
• Ask ya-hara@grips.ac.jp
• Database with User restriction
• NIKKEI Press Release (CSV), KAKEN DB (SQL), J-global Database (SQL)
• Based on Collaborative Agreement with JST and NIKKEI
• Current Member: Prof. Arimoto, Prof. Kuroda, Ikeuchi, HARA, and Post. Doc Micheal
• Based on Creative Commons
• Good Design Award Database
• Accessible via SQL database.
2017/1/11 52

53. Conclusion
• Conclusion
• Data analysis alone is IN VAIN. Research question First.
• Then you should find appropriate database, and fetching the data to make
quantitative analysis.
• Data Cleaning and Disambiguation is mandatory.
• Advanced
• Better to learn SQL/NoSQL schema.

54. THANKS
ya-hara@grips.ac.jp
twitter: harayasushi / facebook: yasushi.hara