Chemistry

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Chemistry
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Early Artificial Intelligence

• MECHEM
• Valdés-Pérez, R.E. Some Recent Human-Computer
  Discoveries in Science and What Accounts or Them.
  AI Magazine, 16 (3). 37-44, 1995.
• FAHRENHEIT
• Zytkow, J.M., Combining many searches in the
  FAHRENHEIT discovery system. in Proceedings of
  the 4th International workshop on machine
  learning, (San Mateo, 1987),281-7.
• Zytkow, J.M. Integration of knowledge and method
  in real-world discovery. ACM SIGART Bulletin, 2
  (4). 179-184, 1991.

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Chemistry Informatics

• Information Retrieval
• http//www.scientificpsychic.com/az.html
• Project Halo
• http//www.projecthalo.com/
• E-notebooks
• http//wiki.myexperiment.org/index.php/Main_Page

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Scientific Discovery A View from the Trenches

• Catherine Blake
• Meredith Rendall
• University of North Carolina at Chapel Hill

Discovery Science 2006
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Motivation

• Discovery systems which solve tasks
  cooperatively with a domain expert are likely to
  have an important role, because in any nontrivial
  domain, it will be virtually impossible to
  provide the system with a complete theory which
  is anyway constantly evolving Simon,
  Valdés-Pérez, Sleeman, 1997
• as developers realize the need to provide
  explicit support for human intervention, we will
  see even more productive systems and even more
  impressive discoveries Langley, 1998
• Discovery is an inherently complex task Kuhn
  ,1996

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Goal

• Model the day-to-day processes of scientists
• Day-to-day activities reflect
• the human cognitive processing surrounding
  discovery
• the complex socio-technical environments in which
  successful discovery tools will eventually be
  embedded
• Key questions
• What is their definition of Discovery ?
• How do they arrive at their research question ?
• How do they transition from an initial idea to
  publication ?

Hypothesis Generation
Hypothesis Verification
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Study Design

• Recruitment
• Experienced scientists (7-45 yrs)
• Chemists and Chemical Engineers who are part of
  the Center for the Environmentally Responsible
  Solvents and Processes (CERSP)
• Response rate 84 (21/25)
• Semi-structured interviews
• Critical incident technique
• consider a recent research paper that you have
  published
• consider seminal papers in your field

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Participants
Average Experience 24 yrs
ID Interview Title Area of Research Experience (yrs)
A 67 Director Biochemical Engineering 32
B 55 Assistant Professor Colloid Science and Engineering 10
C 51 Associate Professor Polymer Design and Synthesis 12
D 51 Professor Semiconductor Surface Chemistry 34
E 43 Professor Polymer Chemistry 16
F 60 Professor Nano-electronics and Photonics 10
G 50 Professor Electronic Materials Synthesis 26
H 59 Director Polymer Design and Synthesis 35
I 39 Assistant Professor Colloidal Macromolecular Physics 14
J 61 Professor Nano-electronics and Photonics 36
K 58 Associate Professor Bioorganic Chemistry 7
L 44 Professor Rheology 13
M 41 Professor Organometallic Chemistry 31
N 54 Professor Polymer Theory 23
O 41 Professor Electrochemistry 46
P 56 Professor Synthetic Organometallic Chemistry 37
Q 5 Associate Professor Surface and Interface Polymers 10
R Associate Professor Polymer Thin Films 20
S 53 Director Polymer Synthesis 16
X 56 Professor Neutron Scattering 35
Y 33 Professor Chemical Reaction Engineering 40
Chemists Chemical Engineers
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Data Collection

• Interviews
• interview time fixed to 1 hour
• conducted in interviewees office
• Work flow process design
• Outline the process you used to from your initial
  idea to the first published paper
• Activity cards
• Reading - Thinking - Online searching
• Books - Journals - Experimenting
• Analyzing - Writing - Discussion
• Organizing
• Extra cards
• Repeat cards - Wild card

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Interview Questions

• Discovery Questions
• What is your definition of discovery ?
• What evidence convinced you that the paper
  addressed the initial research questions ?
• What factors limited the adoption and deployment
  of the discovery ?
• How did you arrive at the research question ?
• What if any existing evidence prompted the
  study/experiment ?
• Were there any alternative explanations ?
• Information Usage questions
• Other than the scientific literature, what
  information resources do you draw from to aid in
  your research processes ?
• How many articles did you read last month that
  related to each of those projects ?
• Is that typical of how many articles you read in
  a month for research projects ?
• Do you read articles for another purpose ? If so
  what?
• How many hours do you spend reading journal
  articles for research projects?
• Which journals do you typically read and draw
  from ?
• How would you characterize the journals that you
  read- are they only within your domain, or do you
  read journals that would be considered
  non-traditional in your research ?
• If you only have a few minutes to read an
  article, what parts would you read?
• What do you do with the article once you have
  read it ?

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Work Flow Example
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Data Analysis

• 25/27 recorded interviews transcribed verbatim
• 2/27 interview notes transcript
• Interviews - qualitative analysis
• All transcripts coded using NVivo 7
• Bottom-up theme identification
• Reveals similarities and differences
• Work flow process qualitative and quantitative
• Transcribed descriptions
• Calculated frequency of activities
• Interactions and transitions identified

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(No Transcript)
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(1) Novelty
Discovery Definition

• new insight (G and M)
• obviously novel and new and doesnt exist in the
  literature (L)
• finding something new and unexpected (P)
• learning something that hasnt really been well
  understood before (G)
• not previously seen (M)
• it discovery opens the door to exploration
  (O).

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(2) Build on existing ideas
Discovery Definition

• from my standards, one has only less than ten
  completely new ideas in their lifetime, and so,
  most of the time you are sort of doing some
  modifications on a new idea or something (N)
• Even supposedly the most creative peopleI dont
  think things are cut from a whole cloth anymore.
  I think there arent any more cloths without big
  holes anymore (I)
• Everything has precedent, in my opinion (M)

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(3) Have a practical application
Discovery Definition

• there are lots of firsts that are wearing the
  shirt of a different color or making a new isorun
  that nobody cares about. I dont put those in a
  discovery category. On the other hand, NSF likes
  to try to support research that is going to lead
  to discoveries that will be transformational and
  start new areas of research. Those are hard
  things to do and dont happen very often. (M)

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(4) Balance experimentation and theory
Discovery Definition

• . as an experimentalist, I treat analyzing
  data as let me try to decide whether or not what
  I have measured is real before I get too excited
  about it (I)
• Theres this added level of, almost, engineering
  that we build a system to see if we can mimic
  what nature does. If we can mimic what nature
  does then that tells us that we do understand it
  as well as we think that we do (K)

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(5) Simplicity
Discovery Definition

• A discovery doesnt have to be something that is
  very hard. It could be something very simple that
  you can get to work. It doesnt have to be
  tedious work or years spent. Some people to see
  something simple might say it is way too simple,
  but as long as it is an elegant thing and hasnt
  been thought out already I think thats fine.
  Its a discovery (L)

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(1) Discussion
Arriving at a Research Question

• Talking to people outside my field is something
  I would always do. I would never consider it a
  waste of my time to talk with people (Q)
• hearing about other stuff (K)

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(2) Previous projects
Arriving at a Research Question

• Ive been doing that since 1972 (D)
• mistakes, with scientists challenging Is it
  really wrong? No. It might be interesting (E)
• There are times when an investigation finds
  itself off course and heading in an unanticipated
  direction, which may be for a variety of reasons
  including the original idea looking less and less
  promising to the unexpected outcome is very
  exciting and potentially a new area of science
  (P).

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(3) Combining expertise
Arriving at a Research Question

• different things came together in this project
  and when I was visiting a lab in France, I was
  talking to experimentalist and theorist there.
  They had some very strange results they couldnt
  explain, and on the other side we were working on
  this different part of the same problem. So the
  two things clicked. Thats how, from a discussion
  in France and our research at EKC, this idea came
  that this must be new. (N)

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(4) Reading literature
Arriving at a Research Question

• finding an article chemically offensive (E)
• Literature inspires questions such as Is there
  a better way to separate X, Is there a cheaper
  way to do Y, or Can this approach be applied to
  Z? (A)

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Overview
Work Flow Process

• 26 process diagrams
• literature-related cards
• comprised more than 30 of the activities in all
• chemists read (Tenopir, et al., 2003) and
  download more articles (Davis Solla, 2003) than
  other sciences
• Repeat cards
• indeterminate number of repeats
• added one extra transition from last to first step

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Transition Model (gt7)
Work Flow Process
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Co-occurrence model
Work Flow Process
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Results

• Definition of discovery
• Novelty is not everything
• Simplicity and linking to practical applications
• System design implications
• Link to previous literatures
• Describe and justify new discoveries
• Use open box algorithms
• Explanation based systems

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Results

• Arriving at a research question
• a collaborative process
• build on previous projects
• System design implications
• Integrate email or IM
• Enable group annotations and discussions
• Enable easy access of previous papers analyses

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Results

• Transforming a research idea to publication
• The work flow process is
• Tightly coupled
• Highly iterative
• System design implications
• Integrate data within the workflow
• Allow iteration between activities

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Conclusions

• Discovery definition
• Novelty - Balance theory and experimentation
• Build on existing ideas - Practical application
• Simplicity
• Hypothesis generation
• Discussion - Previous experiments
• Combine expertise - Read literature
• Hypothesis validation
• Iterative - Tightly coupled
• This is just the first step
• Next Step Operationalize these design
  recommendations