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Institut Pasteur & INRIA Futurs Participatory programming and the Scope of Mutual Responsibility: Balancing scientific, design and software commitment.

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Presentation on theme: "Institut Pasteur & INRIA Futurs Participatory programming and the Scope of Mutual Responsibility: Balancing scientific, design and software commitment."— Presentation transcript:

1 Institut Pasteur & INRIA Futurs Participatory programming and the Scope of Mutual Responsibility: Balancing scientific, design and software commitment Catherine Letondal Wendy E. Mackay

2 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 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 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 5 Users as participants Education: Biology: Computer Science: Goals: Biology research: Tools for biologists: CS research: Results: Local software: Distributed software: Biology articles: CS articles: Computer scientists Bioinfor- maticians Biologists

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

7 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 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 9 Participatory design activities Timeline of participatory design activities (1996 - 2004)

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

11 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 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 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 14 ‘ Reflection: 3 poles Computational medium Participatory design Scientific hypotheses Local developments Active theories Problem solving Scientific scenarios Informal scientific discussions Software design

15 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 16 Participatory Participatory Design Participatory Science Participatory Problem Solving Participatory Teaching Participatory Programming *

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

18 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 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 20 Course projects Put in images Participatory Teaching

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

22 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 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 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 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 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 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 28 Human-Computer Interaction - 3 themes New design methods: participatory design co-adaptive systems New interaction techniques: paradigms & interaction models empirical studies New software tools: engineering interactive systems toolbox

29 29 Utilisateur Situated Interaction Computers Users Environment Artefacts

30 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 31 Multi-disciplinary approach design psychology sociology anthropology industrial design typography graphic design social sciences Interactive systems engineering architecture computer science electronics mechanical engineering optics physiology

32 Novel design methods

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

34 [interaction paradigms and models]

35 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 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 37 A-book

38 [novel software tools]

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

40 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 41 Conclusion Next generation of interactive environments new design methods new interaction techniques new software tools


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