Institut Pasteur & INRIA Futurs

1. Participatory programming and the Scope of Mutual Responsibility:Balancing scientific, design and software commitmentCatherine LetondalWendy E. Mackay Institut Pasteur & INRIA Futurs

2. Research setting • Institut Pasteur, France • World leader in biological research • Created by Louis Pasteur in Paris in 1850’s • Diverse research environment • Diverse computer environment • Over seven years: • multiple long and short-term participatory design projects

3. Computing in biology • Biologists rely on a variety of software tools: • Algorithms, databases, editors, websites with on-line tools • Two main types: • • large scale projects (genomic centers, .gov or .europ, etc.) • • local developments (often not distributed)

4. Computing in Biology • Needs: • • simple data manipulation and editing • • database search, retrieval and browsing • • tools for running standard data analyses • • tools for constructing scientific hypotheses • • information management tools • Evolution • • dynamic software activity • • developed by domain experts for specific purposes • • small, single-purpose tools • • recycling program chunks vs. reuse of designed modules • • designed modules evolve quickly

5. Users as participants Biologists Bioinfor-maticians Computer scientists Education: Biology: Computer Science: Goals: Biology research: Tools for biologists: CS research: Results: Local software: Distributed software: Biology articles: CS articles:

6. Pasteur user survey Discomfort, help needed • Five user categories: • lo-hi use level • lo-hi comfort level Learners (15%) Occasional Users (36%) Extensive computing Little computing Young Scientists (15%) Non-UNIX (15%) Gurus (6%) Comfort, autonomy

7. Informatics in Biology Course • Teach practicing and junior biologists basic programming skill • • Programming autonomy • • Improved communication with computer scientists • Course: 11 sessions, 16 per year, 130 total students • 20 Professors: 40% biologists, 50% computer scientists, 10% other • 60% Pasteur, 40% exterior • Subjects: Programming, databases, web programming, • Participatory design • Activities: Lectures, workshops, seminars & projects (+50%)

8. Software R&D projects • biok Data analysis & visualisation environment for biologists. • 1. Analyze biological data, e.g. DNA, sequences 2. Support tailorability and end-user programming • A-book Augmented laboratory notebook, • integrates on-line data with paper lab notebook. • Mobyle Environment for searching, discovering, running & • combining bioinformatics analysis tools. • i3DMo Tk widget for 3D structure visualization

9. Participatory design activities • Timeline of participatory design activities (1996 - 2004)

10. Reflecting on Participatory Programming • Activities: • Interviews • Group meetings • Questionnaire • Feedback: • Workshops • Course projects

11. Boundary objects • Concrete: • Software tools • Abstract: • Program concepts • -------- • Installed software • Small local software artifacts, spreadsheets and scripts • (one purpose only) • Projects (from the course) - exercises • Biology problems: • Biologist: the problem • CS: instance of a problem

12. Reflection: 3 poles • Most research labs move between 2 poles: • Computational medium and Scientific Hypotheses • Responsibilities: • Software act as Boundary objects • But we do not see boundary roles • Skills: often overlap • Biologists who program, CS who know about biology • Responsibilities: rarely overlap • « Me, I’m not a biologist » or « Me, I’m not a computer scientist » • Image of 2 poles:

13. Reflection: 3 poles • If we add a 3rd pole, participatory design, • we open opportunities for better communication and better tools • Participatory design as a ‘boundary activity’ • Weak coupling • Image with third pole

14. Reflection: 3 poles • ‘ Computationalmedium Problemsolving Softwaredesign Activetheories Localdevelopments Informalscientific discussions Participatorydesign Scientifichypotheses Scientificscenarios

15. Reflection: 3 poles • Most research labs move between: • Computational medium • Scientific Hypotheses • If we add • Participatory Design • We open opportunities for better communication and better tools • (weak coupling)

16. * Participatory • Participatory Design • Participatory Science • Participatory Problem Solving • Participatory Teaching • Participatory Programming

17. Participatory Design Mackay et al. (2000) Observation Evaluation Brainstorming Prototyping

18. Participatory Problem-Solving • Designing scientific algorithms: • Who has responsibility for the quality of a scientific hypothesis? • Problem: major misunderstanding • Biologists assume software will help identify scientific questions • Computer scientists assume biologists know the questions • Biologists work with a ‘problem’ • Computer scientists consider this to be an ‘instance’ of a problem • Example: biologist is looking yyy gene in a xxx • CS wants to write an algorithm to find this gene in any animal

19. Participatory Problem-Solving • Scientific modeling workshops allow participatory problem defining • Participatory prototyping of scientific algorithms • to express scientific problems and sketch potential solutions • Illusion of software ‘magic’ - somehow the software will find the right research question. (Not poor biologists - it’s the tool that creates the illusion) • Example 1: 2 workshops to help define the scientific question: • Data: Families of genes vs. Interactions : • What can we do with this data? • Workshop 1: Brainstorming: • Help identify that the biologists didn’t already have a clear idea of the question - Posed all kinds of different questions (create ‘question’ space) • Workshop 2: Prototyping: • Highly-prepared in advance based on questions that emerged from brainstorming, with lots of data examples • Data examples projected on whiteboard, + paper proto stuf • Refined and validated the question. • What happened - continued to define the question - but this time, seriously evaluating and developing ideas. • Result: • Moving beyond interactive software design to algorithm design

20. Participatory Teaching • Course projects • Put in images

21. End-user Programming today • Spreadsheets • Scripts • Small databases • Specialised software • Web applications • Distributed software • Algorithms spreadsheets

22. Programming by the user • Biologists program but do not want to be programmers • Need flexibile environment to allow non-anticipated usage • Tool : Biok • MAP = Meta-Application Protocol • Manipulation at multiple levels: • Data (DNA sequences, etc.) • Meta-data (alignment, gap…)

23. Participatory Programming • Participatory design for end-user programming • vs. • End-user programming to help the design • Goal : either: end-user programmable software • Or using end-user programming to help in the design

24. Participatory Science • Workshops • Serve as a point of discussion for scientific ideas • 1 example of PD to explore and specify a scientific design • Why teach participatory design to biologists? • Knowing that it exists, they can demand more and create a better mode of communication with programmers

25. Teaching Participatory Design to Biologists • Wendy’s course … • Approach: • A bit strange, but we actively TEACH biologists to do pd. • (they have both roles of being biologists primarily • but also learning to program - and learning PD as • a part of programming, either to communicate with • other programmers or to help them do their own designs…) • Examples from project • Really linked to the issue that who does what..

26. Conclusion • - summary view of the 3 poles (as in the paper): • Diagram of three poles • PD -> CM <- science : collaboratively building a CM • CM -> PD <- science : Providing input to the participatory • activities • CM -> science <- PD : Mediating scientific hypotheses

27. Boundary objects • Boundary objects are scientific objects which both inhabit several intersecting social worlds and satisfy the informational requirements of each of them. Boundary objects are objects that are both plastic enough to adapt to local needs and the constraints of the several parties employing them, yet robust enough to maintain a common identity across sites. They are weakly structured in common use and become strongly structured in individual site use. These objects may be abstract or concrete. … The creation and management of boundary objects is a key process in developing and maintaining coherence across intersecting social worlds. • (Star & Griesemer, 1989)

28. Human-Computer Interaction - 3 themes • New design methods: • participatory designco-adaptive systems • New interaction techniques: • paradigms & interaction models • empirical studies • New software tools: • engineering interactive systems • toolbox

29. Environment Artefacts Users Situated Interaction Computers Utilisateur

30. Research Strategy • The concept of ‘scientific’ has evolved Chaotic phenomena: small changes lead to large efffects Co-evolution : human-system interaction Triangulation : necessary for ‘real’ problems • Inspiration from other disciplines Interactive systems are not ‘natural phenomena’ Requires a mix of scientific,engineering and ‘design’ strategies together • Example : architecture combines science, engineering et design

31. Multi-disciplinary approach social sciences anthropology physiology sociology industrial design psychology Interactive systems graphic design design typography computer science architecture electronics mechanicalengineering optics engineering

32. Novel design methods

33. Strategies for understanding users Scientific perspective Design perspective Collect data Analyze data Inform designers Get design inspiration Reflect on daily activities Redefine the design problem Engineering perspective technical trade-offs ensure that it works “in situ”

34. [interaction paradigms and models]

35. Mixed reality and tangible interfaces • How do we manage physical and on-line documents? • Why is it so hard to eliminate paper? • Mixed reality Links between physical artifacts and on-line information • Tangible interfaces Physical objects represent data and software tools

36. Mixed reality : A-book • Physical object augmented with software: • Paper notebook • ‘Magic lens’ • Capture hand-written data • Links and search for data • on-line Patent: INRIA

37. A-book

38. [novel software tools]

39. How can we move the curve? Compromise between power and simplicity Power of expression simplicity

40. For more information • Website http://insitu.lri.fr/ • Mediated ommunication http://www.lri.fr/~roussel/projects/ • Participatory design and mixed reality http://www.lri.fr/~mackay/research.html • Information visualisation http://www.lri.fr/~fekete/InfovisToolkit http://www.cs.umd.edu/hcil/millionvis/ • Instrumental interaction and the CPN2000 project http://www.lri.fr/~mbl/INSTR/ http://www.daimi.au.dk/CPnets/CPN2000/

41. Conclusion • Next generation of interactive environments • new design methods • new interaction techniques • new software tools