Data Centric Art, Science, and Humanities

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2013 한국데이터사이언스 창립기념 심포지움 발표 - 서울대 김홍기 교수

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  • The FRIENDLY COMPETITION BETWEEN ART AND SCIENCE will develop a transdisciplinary approach in THINKING DESIGN research and education to solve complex problems of the global society . The 2.0 MODEL of Transdisciplinary Design (TransDesign) is focused for improving creativity to a new generation of thinking designers who want to address pressing social issues using new ideas, tools, and methods. Students work in trans-disciplinary teams, consider issues from multiple perspectives in art and science to develop a emerging and future projects of design as a process for transforming the way we live in the contemporary knowledge society. The key-themes of the project are : -->Reflective collaboration – working flexibly trans-disciplinary teams to solve highly complex problems of social innovation --> Complexity modeling – visually modeling complex systems and social structures to yield new creative insights --> Critical reframing – examining problems and turning them into design opportunities of innovation --> Design-led research – articulating a research problem and exploring it through a thinking design process --> Fitness prototyping – discovering an appropriate resolution of a problem that belongs to multiple designing area. While traditional industrial design may work in conventional business consultancies for industry , the thinking designer will also be qualified to apply their skills in areas outside of traditional design realms that are the focus on social innovation. Modern THINKING DESIGN may work in developing new careers that involve for instance: structuring health care policy; food and nutritional innovation , perspectives in rebuilding social infrastructure; rethinking public/private education, develop micro-businesses management , and organize nongovernmental organizations, etc.. etc.. . Paolo Manzelli
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Data Centric Art, Science, and Humanities

  1. 1. Data Centric Art, Science, and Humanities 김홍기 서울대학교 의생명지식공학연구실 Biomedical Knowledge Engineering Laboratory
  2. 2. Why Data Centric? ∎ Big Data(?) ∎ 개방, 공유, 융합, 협력은 시대정신 ∎ 창조경제(?), 집단지성, 집단창의성 ∎ 데이터는 쉽게, 그리고 순식간에 이동 ∎ Pervasive, real-time data everywhere ∎ 데이터는 손쉽게 가공처리 가능 ∎ 데이터의 부가가치는 매우 높을 수 있음 ∎ So many computational tools and methodologies  Analytics & Visualization
  3. 3. Source: Pacific Northwest
  4. 4. How Science Works ∎ “Philosophies of Funding”, Cell, 2009. ∎ 가설중심의 과학적 연 구 데이터 중심의 대 규모 융합연구 ∎ Fragmentation  Integration ∎ 빅데이터 기반의 새로 운 가설의 발견과 집단 지성 기반의 데이터 분 석과 피드백의 중요성 강조됨 ∎ 현실적 문제의 정의와 연구결과물의 공유에 있어 사회구성원의 참 여가 강조됨
  5. 5. Key Challenges of Data Centric Science Source: Pacific Northwest
  6. 6. Big Data (Large Volumes)?  Fast Data Processing  Big Analytics  Deep insight
  7. 7. Open Data Space in Biology
  8. 8. 데이터의 다양성(Heterogeneity, Diversity)
  9. 9. Data Silos
  10. 10. Source: BioPax Relating and Linking
  11. 11. Linked Open Data
  12. 12. Layers of Biological Research (Vertical Liking) System Science Interrelationships, Dynamics Reductionism Time Space Context Components System Biology Structural Biology
  13. 13. Complexity Analysis (Network Biology) Source: Barabasi(Nature Reviews, 2004)
  14. 14. Assortative vs. Disassortative Networks Social Network Biological or Technological Network
  15. 15. Governmental Open Data in Healthcare
  16. 16. Collaborative and Multi-disciplinary Research neuroscientists physicians statisticians computer scientists
  17. 17. Scientific Investigation with Transdisciplinarity Disciplinay Xxx xxx Adapted from: www.hent.org/transdisciplinary.htm Interdisciplinary Transdisciplinary Multidisciplinary
  18. 18. Association vs. Bisociation ∎ Association is most commonly used in ICT technologies to discover new information relevant to the evidence already known to the user. ∎ BISOCIATION occurs when two seemingly unrelated things are shown to have unanticipated connections. ∎ Context-crossing “associations” that are often needed in innovative domains ∎ The history of engineering and science is full of serendipitous discoveries, which are based on bisociative processes.
  19. 19. Bisociation의 예: Swanson Linik A CB Articles about an AB relationship. Articles about a BC relationship. AB BC AB and BC are complementary but disjoint : They can reveal an implicit relationship between A and C in the absence of any explicit relation. suggest a novel hypothesis that connects A with C, an implicit but not explicit connection. To call attention to possible implicit links between the various text passages that are selected. Source: Swanson. 2003. A literature based Approach to Scientific Discovery. http://hdl.handle.net/10027/41
  20. 20. Magnesium-deficient rat as a model of epilepsy. Lab Animal Sci 28:680-5, 1978 The relation of migraine and epilepsy. Brain 92: 285-300, 1969 A magnesium 8011 C migraine 2756 An unintended link Venn diagram: sets of Medline records; A,C are disjoint. 22 45 B epilepsy An example based on title words in Medline
  21. 21. 인문학의 분야 » Korean Studies » English Literature » European Studies » Cultural Studies » Linguistics » Other Languages and Literatures » Philosophy » History and Philosophy of Science » History of Ideas » History » Environmental Studies » Multicultural Studies » Classics and Ancient History » Archeology » History of Art, Architecture, Design » Law » Theology and Religious Studies » Communication and Media Studies » Music and History of Music » Film Studies » Drama and Theatre Studies » Studies of other Performing Arts » Medical Humanities » Women’s Studies
  22. 22. Semantic Data for Historical Informatics 독일의 변천과정 Source: Bykau et.al. (J Data Semantics, 2012)
  23. 23. Data Journalism ∎ Data-driven journalism as process ∎ Raw data needs to be (1) available, (2) filtered for patterns, (3) visualized to help people understand the meaning and (4) the data needs to be turned into stories ∎ Mostly use open data with open source tools ∎ Can help a journalist tell a complex story through engaging infographics Source: Wikipedia
  24. 24. Example (Data Journalism)
  25. 25. Musicology as a ‘data-rich’ discipline ∎ A computer program can take as input a representation of a score and produces as output an analysis of that music.  ‘what is the cause of emotion in music?’ ∎ Music Information Retrieval ∎ Music Recognition ∎ Data driven research on music history ∎ Multi-modal research (Music + Image)
  26. 26. Data Art ∎ Data artists paint a picture with data to construct imaginative representations of the world in their own way ∎ Creative visualizations can translate terabytes of data into meaningful business information ∎ Touch will be the next generation user interface for data, spanning to every screen and every surface around you ∎ Everybody will be able to create his or her own data art with data painting tools 26 / 10
  27. 27. Example: Glowing landscape shows river history (Daniel E. Coe)
  28. 28. Example: The family tree for All in the family (James Grady)
  29. 29. Bach Cello Suites visualized
  30. 30. Art & Science ∎ 미래의 산업과 과학기술에서의 예술가의 역할은 더욱 중 요해질 것 같다. 예술에 대한 내 나름의 정의는 "chaos와 order" 사이에 긴장감(tension)을 창조해 내는 것이다. 지나친 복잡함과 혼돈의 상태는 정보의 엔트로피 (Claude Shannon의 개념)가 높고, 불확실성이 높으며, 인지적 과부하로 인해 이해를 힘들게 된다. 지나친 질서 와 당연하게 받아들여진 규칙성(regularity)은 지루함을 느끼게 만든다. 과학의 발견은 자연 혹은 사회 현상으로 부터 규칙성을 찾아내는 과정이다. 예술가의 역할은 chaos에서 motif(일종의 미적 패턴)를 창조하고, 일반인 들에게 익숙한 현상에서 질서를 깨는 혼돈을 창조하는 것이 아닐까? 이런 점에서 예술가의 직관은 현상을 바라 보는 초월적(meta 수준의) 관점을 제공해 줌으로써 과학 에 창조적 긴장감을 줄 수 있지 않을까? - 김홍기
  31. 31. Collective Creativity 32 / 10 ∎ No more Einstein or too many Einsteins
  32. 32. Collective Creativity

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