Data Centric Art, Science, and Humanities
- 1. Data Centric Art, Science, and Humanities 김홍기 서울대학교 의생명지식공학연구실 Biomedical Knowledge Engineering Laboratory
- 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
- 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. Key Challenges of Data Centric Science Source: Pacific Northwest
- 6. Big Data (Large Volumes)? Fast Data Processing Big Analytics Deep insight
- 7. Open Data Space in Biology
- 9. Data Silos
- 10. Source: BioPax Relating and Linking
- 11. Linked Open Data
- 12. Layers of Biological Research (Vertical Liking) System Science Interrelationships, Dynamics Reductionism Time Space Context Components System Biology Structural Biology
- 13. Complexity Analysis (Network Biology) Source: Barabasi(Nature Reviews, 2004)
- 14. Assortative vs. Disassortative Networks Social Network Biological or Technological Network
- 15. Governmental Open Data in Healthcare
- 16. Collaborative and Multi-disciplinary Research neuroscientists physicians statisticians computer scientists
- 17. Scientific Investigation with Transdisciplinarity Disciplinay Xxx xxx Adapted from: www.hent.org/transdisciplinary.htm Interdisciplinary Transdisciplinary Multidisciplinary
- 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. 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. 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. 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. Semantic Data for Historical Informatics 독일의 변천과정 Source: Bykau et.al. (J Data Semantics, 2012)
- 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
- 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. 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. Example: Glowing landscape shows river history (Daniel E. Coe)
- 29. Example: The family tree for All in the family (James Grady)
- 30. Bach Cello Suites visualized
- 31. 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
- 32. Collective Creativity 32 / 10 ∎ No more Einstein or too many Einsteins