This document discusses trends and issues in interdisciplinary research between ICT and social sciences. It touches on several topics including: - The emergence of data-driven science and use of digital tools for research - Debate around claims that large datasets can replace theories and models - Development of computational social science and e-science tools - New roles for data and need for contextualization of big data findings - Challenges of big data such as data gaps, biases, and ethical issues

1. ICT와 사회과학 지식간 학제간 연구동향 및 쟁점
- 이해를 넘어 활용으로
Virtual Knowledge Studio (VKS)
박한우 교수
영남대 언론정보학과
영남대사이버감성연구소장
아시아트리플헬릭스학회장
대구경북소셜미디어포럼고문
영국옥스퍼드인터넷연구소(전)
WCU웹보메트릭스사업단
네델란드왕립아카데미(전)
TEDxPalgong (전)
hanpark@ynu.ac.kr
www.hanpark.net

3. http://novaspivack.typepad.com/nova_spivacks_weblog/2007/02/steps_towards_a.html 에서 재인용

4. All models are wrong but some are useful
Emergence of data author on dataverse

5. Andersons claims
 Data is everything we need.
 We don't have to settle for models.
 Agnostic statistics.
 Out with every theory of human behavior.
 This approach to science — hypothesize,
model, test — is becoming obsolete.
 Petabytes allow us to say: "Correlation is
enough." We can stop looking for models.
 What can science learn from Google? E-
Science.

6. Computational (Social) Science
Park, H. W., & Leydesdorff, L. (2013 Work-In-Progress). Decomposing a Data-Driven Science Using a Scientometric Method.
 Focus on the methodological perspective based
on the use of new digital tools to manage the
data deluge.
 Development of e-science tools to automate
research process.
 Experimentation with new types of data
visualization.

7. http://www.nature.com/news/compu
-social-science-making-the-links-1.1

8. http://participatorysociety.org/wiki/index
.php?title=Online_Research

10. Why Data Science?
Savage and Burrows (2007, p.
886) lament, ―Fifty years ago,
academic social scientists might
be seen as occupying the apex
of the – generally limited – social
science research ‗apparatus‘.
Now they occupy an increasingly
marginal position in the huge
research infrastructure‖.
Bonacich, P. (2004).
The Invasion of the Physicists. Social Networks 26(3): 285-288

11. Global Communication 2team
(빅)데이터과학의도전
이론의 종말-증거기반 경영
Jeffrey Pfeffer, Robert I. Sutton (200
6)
How companies can bolster performance and
trump the competition through evidence-based
management, an approach to decision-making and
action that is driven by hard facts rather than half-
· 빅데이터의 등장으로 전통적인
과학 연구방법론 퇴색
· 인식의 한계치를 넘어선 데이터
(팩트가아닌패턴)

12. The Signal and the Noise:
Why Most Predictions Fail but Some Don't. Nate
Silver
I do not go as far as a Popper in asserting that such
theories are therefore unscientific or that they lack any
value. However, the fact that the few theories we can
test have produced quite poor results suggests that
many of the ideas we haven‘t tested are very wrong as
well. We are undoubtedly living with many delusions that
we do not even realize.
page 15

13. OECD (2012). OECD Technology Foresight
Forum 2012 - Harnessing data as a new source
of growth: Big data analytics and policies. OECD
Headquarters, Paris, France 22 October 2012

14. Big data and the end of theory?
 Does big data have the answers? Maybe some, but not all,
says - Mark Graham
 In 2008, Chris Anderson, then editor of Wired, wrote a
provocative piece titled The End of Theory. Anderson was
referring to the ways that computers, algorithms, and big data
can potentially generate more insightful, useful, accurate, or
true results than specialists or domain experts who
traditionally craft carefully targeted hypotheses and research
strategies.
 We may one day get to the point where sufficient quantities
of big data can be harvested to answer all of the social
questions that most concern us. I doubt it though. There will
always be digital divides; always be uneven data shadows;
and always be biases in how information and technology are
used and produced.
 And so we shouldn't forget the important role of specialists to
contextualize and offer insights into what our data do, and
maybe more importantly, don't tell us.http://www.guardian.co.uk/news/datablog/2012/mar/09/big-data-
theory

16. Yet, there still are serious problems to overcome. A
trenchant critique concerning the big data field as it is
nowadays came in the form of six statements intending
to temper unbridled enthusiasm. [42] These six
provocative statements are:
 Big data change the definition of knowledge;
 Claims to accuracy and objectivity are misleading;
 More data are not always better data;
 Taken out of context, big data loses its meaning;
 Just because it is accessible, it does not make it ethical;
and
 (Limited) access to big data creates a new digital divide.
Rousseau (2012)

17. Global Communication 2team
빅데이터에 대한 부정적인 시각 등장
-빅데이터의 가치
-저장, 분석 및 해석기술 한계 존재
-현재의 붐은 호들갑스러운 측면 존재
빅데이터 갭: Promise VS Capabilities
빅데이터의도전

18. http://www.forbes.com/sites/kashmirhill/2012/02/16/how-target-figured-out-a-teen-girl-was-pregnant-before-her-father-did/

19. 어떤 실험을 하는지 우리는 알고 있는가?
http://www.nature.com/news/facebook-experiment-boosts-us-voter-turnout-1.11401

20. 우리는 정확히 인지하지 못한 채 동의했다

22. This approach to science is attributed to the late Jim
Gray, one of the most influential computer scientists, at
Microsoft.

24. http://www.bbk.ac.uk/innovation/news-events/docs/s2/MEYER_new-triple-helix-environments.pdf

25. The Triple Helix 2 in Mode 2
• Internal transformation wit
hin each helix: e.g. an entr
epreneurial university
- University R&D plays a
role as an ‗entrepreneurial
mediator‘

26. Double, Triple, Quadruple Helix, …, an
d an N-tuple of Helices
http://www.leydesdorff.net/ntuple/index.htm

27. http://www.tandfonline.com/doi/pdf/10.1080/08109028.2011.641384

28. Applied and Basic research
Quest for
fundamental
understanding
?
Hig
h
Pure basic rese
arch (Bohr)
Use-inspired basic
research(Pasteur)
Lo
w
–
Pure applied resea
rch (Edison)
Low High
Considerations of use?
Pasteur's quadrant
Pasteur's quadrant is a label given to a class of scientific research methods that both seek
fundamental understanding of scientific problems, and, at the same time, seek to be
eventually beneficial to society. Louis Pasteur's research is thought to exemplify this type of
method, which bridges the gap between "basic" and "applied" research.[1] The term was
introduced by Donald Stokes in his book, Pasteur's Quadrant
http://en.wikipedia.org/wiki/Pasteur's_quadrant

32. http://www.euroreg-unicaconference.pl/unica_conference_presentation_09.pdf

33. http://www.euroreg-unicaconference.pl/unica_conference_presentation_09.pdf

36. http://pactlab-dev.spcomm.uiuc.edu/class/08SP/280/Diffusion-Certainty%20Lecture%20Notes.pdf

37. Re-setting science and innovation for the next 20
years: New Zealand, new futures,
new ways of science engaging with society? years
New Zealand, new futures,
new ways of science engaging with
society?
New Zealand, new futures,
new ways of science engaging with
society?
• New ways of doing science
The responsibilities of government, business and citizens may also move into
the realm of post-normal science in which people are credited with
multiple capacities and expertise that can support the co-production of
knowledge about sustainability alongside
e-Science: As society‘s ‗grand challenges‘ such as climate
change and food security demand more complex analysis of
ever-larger datasets, and global cooperation between scientists
and other stakeholders, many countries have begun to invest
in the infrastructure to support the sharing of knowledge (data,
models) and high-performance computing resources. T professional experts
http://scientists.org.nz/files/journal/2011-68/NZSR_68_1.pdf#page=26