M. Matheny, S. Schlachter, L. Crouse, E. Kimmel, T. Estrada, M. Schumann, R. Armen, G. Zoppetti, and M. Taufer: ExSciTecH: Expanding Volunteer Computing to Explore Science, Technology, and Health. In Proceedings of the 2nd workshop on Analyzing and Improving Collaborative eScience with Social Networks (eSoN 12), October 2012, Chicago, Illinois, USA.

1. ExSciTecH: Expanding Volunteer Computing
to Explore Science, Technology, and Health
M.
Matheny1,
S.
Schlachter1,
L.M.
Crouse2,
E.T.
Kimmel2,
T.
Estrada1,
M.
Schumann3,
R.
Armen3,
G.
ZoppeB2,
and
M.
Taufer1
1University
of
Delaware
2University
of
Millersville
3Thomas
Jefferson
University

2. Volunteer Computing
• Volunteer Computing (VC) is a
form of distributed computing in
which volunteers donate
processing and storage resources
to computing projects.
• Every job represents a portion of a
larger problem whose
computation is divided into
smaller chunks and addressed in
parallel.
• Applications suited for VC include
searches in very large spaces,
parameter tuning, and data
analysis.
1

3. Volunteers
• Are not representative of the general population
• White males with a background in computers
• Paradigm causes them to be passive
• Lose interest in the project and uninstall the VC software within a few
months of participation
2

4. Main Contribution
• Problem: Volunteer Computing appeals to a very narrow demographic
– We want to utilize intuitive technologies and user interfaces to appeal
to historic minorities in Science, Technology, Engineering and
Mathematics (STEM)
• Problem: In VC participants are generally passive and not involved in the
research process
– We want to get volunteers to help solve important research problems
and make scientific discovery
3

5. Outline
• Background
• Motivation and Plan
• Implementation
• Game Testing
• Conclusion and Future Work
4

6. Outline
• Background
• Motivation and Plan
• Implementation
• Testing
• Conclusion and Future Work
5

7. Docking@Home
• Docking@Home is powered by Berkley Infrastructure for Network Computing
(BOINC)
6

8. D@H Scientific Goals
• Self-Docking
– Searching for protein inhibitors
– Targeted diseases
• HIV
• Breast cancer (trypsin)
• Cross-Docking
– Identifying drug side effects
– Look at proteins similar to the
disease protein
– Naïve approach: all to all
• All proteins
• All ligands
– This is where we want to utilize volunteers to reduce search space
7

9. Related Work
• Fold.it
– Developed by the same team as Rosetta@Home
– Aims to use volunteers to fold proteins through puzzles
– Complements VC system, does not integrate with VC system
• Bossa
– Developed by the BOINC team
– Volunteers use cognition, knowledge, and intelligence to solve
problems
• Luis von Ahn’s work
– Phetch – improving web accessibility
– Games with a purpose (GWAP)
– reCAPTCHA
8

10. Outline
• Background
• Motivation and Plan
• Implementation
• Testing
• Conclusion and Future Work
9

11. Docking@Home: As it stands
10

12. ExSciTecH: Learning Stage
11

13. ExSciTecH: Engaging Stage
12

14. ExSciTecH: Engaging Stage
13

15. ExSciTecH: the full circle
14

16. Outline
• Background
• Motivation and Plan
• Implementation
• Testing
• Conclusion and Future Work
15

17. BOINC Infrastructure
C L I E N T" S E R V E R"
Download"
Upload"
BOINC" CGI BOINC
Client" BOINC" Daemons"
DB"
Local File
System"
Back-end!
Client! Front-end!
BOINC"
Web
Browser"
BOINC! Web Interface!
16

18. BOINC + ExSciTecH
C L I E N T" S E R V E R"
Download"
Upload"
BOINC" CGI BOINC
Client" BOINC" Daemons"
DB"
Local File Learn" CGI
System" Game" Learn"
VMD" ExSciTecH"
Game DB" Daemons"
Engage CGI
Game" Engage"
Back-end!
Client! Front-end!
Player! Player" Teacher" BOINC"
Web
Teacher!
Browser"
BOINC! Web Interface!
17

19. ExSciTecH Client
• Modularly designed – each game is a separate program and the client
acts as a manager
18

20. Learning Games
• Teach volunteers about science without intimidating them
• Several levels available:
– High school student
– Under graduate student
– Graduate student
– Pharmaceutical student
– Professional chemist
• As players progress through the levels the game becomes more
challenging
• Familiarize volunteers with the science
• Give D@H more exposure
– Students in classrooms
19

21. Molecule Flashcards
• Players must identify or categorize a 3D molecule as it flies towards them
• If the player incorrectly identifies the molecule they’re given access to
additional information about it
20

22. Engaging Games
• Volunteers have been trained by the learning stage
• Building a job
– Short game
– Game objects correlate to job input parameters
• Protein (disease)
• Ligand (drug)
• Ligand Confirmation
• Ligand Rotation
• Submitting a job
– Game submits these parameters to the server
– Server builds a job based on these parameters
• Getting results
– Good results  More games!
– Volunteering CPU time  More games!
21

23. Drag’n’Dock
• Volunteers rotate a
protein and select a
ligand
• Then they fly a
spaceship over the
protein with the ligand
in tow
• They attempt to dock
the ligand in the protein
with the space ship
• The game submits data
to the server to build a
D@H work unit
22

24. Drag’n’Dock
• Volunteers rotate a
protein and select a
ligand
• Then they fly a
spaceship over the
protein with the ligand
in tow
• They attempt to dock
the ligand in the protein
with the space ship
• The game submits data
to the server to build a
D@H work unit
23

25. Drag’n’Dock
• Volunteers rotate a
protein and select a
ligand
• Then they fly a
spaceship over the
protein with the ligand
in tow
• They attempt to dock
the ligand in the protein
with the space ship
• The game submits data
to the server to build a
D@H work unit
24

26. Outline
• Background
• Motivation and Plan
• Implementation
• Testing
• Conclusion and Future Work
25

27. Testing the Molecule Flashcard Game
• What we want to learn:
– Do people learn more with the game than they do with a paper test?
– Do they enjoy our game more than a paper test?
– What improvements can we make to the game?
• How we tested:
– We took a group of students and had half of them attempt to identify
molecules with our flashcard game and half of them attempt to identify
molecules on a paper test
• What we measured:
– Time to complete the game/test
– Number of molecules correctly identified
– Survey with level of enjoyment and comments
26

28. Testing Setup
• 24 computer science students
• 10-minute introduction
– 14 undergraduate students
• Students split into two groups
– 10 graduate students
– Paper test
– Molecule flashcard game
Vs.
27

29. Results and Discussion
• Make more errors, but enjoy the game more
• Tend to be faster – less reflection
28

30. Results and Discussion
• Learning Curve
• Confusing 3D representations
29

31. Outline
• Background
• Motivation and Plan
• Implementation
• Testing
• Conclusion and Future Work
30

32. Conclusions and Future Work
• We can transform the way volunteers participate in VC projects
– More accessible
– More exciting
• We show students had a higher level of enthusiasm when using ExSciTecH
rather than traditional learning tools
• We identified improvements that could be made to the flashcard game:
– Variable speed
– Pause
– Skip and come back
• We are moving forward with the ExSciTecH development by continuing
development of engaging games
31

33. Acknowledgments
Thanks
to:
GCLab
group
M.
Matheny
(UD)
S.
Schlachter
(UD)
L.M.
Crouse
(U.
Millersville)
E.T.
Kimmel
(U.
Millersville)
T.
Estrada
(UD)
M.
Schumann
(TJU)
R.
Armen
(TJU)
G.
ZoppeB
(U.
Millersville)
Sponsors: Contact:
taufer@udel.edu
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