This Article 
 Bibliographic References 
 Add to: 
Collaborative e-Science Experiments and Scientific Workflows
July/August 2011 (vol. 15 no. 4)
pp. 39-47
Adam Belloum, University of Amsterdam
Marcia A. Inda, University of Amsterdam
Dmitry Vasunin, University of Amsterdam
Vladimir Korkhov, University of Amsterdam
Zhiming Zhao, University of Amsterdam
Han Rauwerda, University of Amsterdam
Timo M. Breit, University of Amsterdam
Marian Bubak, AGH University of Science and Technology, Poland
Luis O. Hertzberger, University of Amsterdam

Recent advances in Internet and grid technologies have greatly enhanced scientific experiments' life cycle. In addition to compute- and data-intensive tasks, large-scale collaborations involving geographically distributed scientists and e-infrastructure are now possible. Scientific workflows, which encode the logic of experiments, are becoming valuable resources. Sharing these resources and letting scientists worldwide work together on one experiment is essential for promoting knowledge transfer and speeding up the development of scientific experiments. Here, the authors discuss the challenges involved in supporting collaborative e-Science experiments and propose support for different phases of the scientific experimentation life cycle.

1. Z. Zhao, A. Belloum, and M. Bubak, "Special Section on Workflow Systems and Applications in e-Science," Future Generation Computer Systems, vol. 25, no. 5, 2009, pp. 525–527.
2. B. Ludscher et al., "Scientific Workflow Management and the Kepler System," Concurrency and Computation: Practice & Experience, vol. 18, no. 10, 2006, pp. 1039–1065.
3. T. Oinn et al., "Taverna: A Tool for the Composition and Enactment of Bioinformatics Workflows," Bioinformatics, vol. 20, no. 17, 2004, pp. 3045–3054.
4. K. Lee et al., "Adaptive Workflow Processing and Execution in Pegasus," Proc. 3rd Int'l Conf. Grid and Pervasive Computing Symposia/Workshops, IEEE CS Press, 2008, pp. 99–106.
5. V. Korkhov et al., "VLAM-G: Interactive Dataflow Driven Engine for Grid-Enabled Resources," Scientific Programming, vol. 15, no. 3, 2007, pp. 173–188.
6. D. De Roure et al., "Towards Open Science: The myExperiment Approach," Special Issue, Concurrency and Computation: Practice and Experience from the Microsoft eScience Workshop, vol. 22, no. 17, 2010, pp. 2335–2353.
7. B. Shneiderman, "Science 2.0," Science,7 Mar. 2008.
8. C. Wroe et al., "Recycling Workflows and Services through Discovery and Reuse," Concurrency and Computation: Practice and Experience, vol. 19, no. 2, 2007, pp. 181–194.
9. Y. Gil et al., "Examining the Challenges of Scientific Workflows," Computer, vol. 40, no. 12, 2007, pp. 24–32.
10. C. Wald, "Scientists Embrace Openness," Science,9 Apr. 2010; previous_issues/articles/2010_04_09 caredit.a1000036.
11. H. Afsarmanesh et al., "VLAM-G: A Grid-Based Virtual Laboratory," Scientific Programming, vol. 10, no. 2, 2002, pp. 173–181.
12. V. Guevara-Mass et al., "Semantic Workflow Discovery in VL-e," Proc. Knowledge Grid Workshop Information Society (IST) 2006 — Strategies for Leadership, Elsevier, 2006; .
13. T. Glatard et al., "fMRI Analysis on EGEE with the VBrowser and MOTEUR," Proc. 3rd Enabling Grids for e-Science (EGEE) User Forum, 2008; .
14. A. Wibisono et al., "WS-VLAM: Towards a Scalable Workflow System on the Grid," Proc. 16th IEEE Int'l Symp. High Performance Distributed Computing, IEEE CS Press, 2007.
15. E. Hubbell, W.M. Liu, and R. Mei, "Robust Estimators for Expression Analysis," Bioinformatics, vol. 18, no. 12, 2002, pp. 1585–1592.
1. C.A.D. Leguy et al., "Estimation of Distributed Arterial Mechanical Properties Using a Wave Propagation Model in a Reverse Way," Medical Engineering Physics, vol. 32, no. 9, 2010, pp. 957–967.
2. E. Elts et al., "Grid Workflows for Molecular Simulations in Chemical Industry," High Performance Computing in Science and Eng., part 7, SpringerLink, 2010, pp. 651–662.
3. S. Olabarriaga et al., "A Virtual Laboratory for Medical Image Analysis," IEEE Trans. Information Technology in Biomedicine, vol. 14, no. 4, 2010, pp. 979–985.
1. P. Li et al., "Systematic Integration of Experimental Data and Models in Systems Biology," BMC Bioinformatics, vol. 11, article no. 582, 2010; doi:10.1186/1471-2105-11-582.
2. M. Bubak et al., "Virtual Laboratory for Collaborative Applications," Handbook of Research on Computational Grid Technologies for Life Sciences, Biomedicine, and Healthcare, IGI Global, 2009, pp. 531–551.
3. R. Strijkers et al., "Network Resource Control for Grid Workflow Management Systems," Proc. 6th World Congress on Services, IEEE CS Press, 2010, pp. 318–325.
4. R. Strijkers et al., "AMOS: Using the Cloud for On-Demand Execution of e-Science Applications," IEEE e-Science Conf., IEEE CS Press, 2010, pp. 331–338.

Index Terms:
WSRF, experiment life cycle, workflow management systems, e-Science
Adam Belloum, Marcia A. Inda, Dmitry Vasunin, Vladimir Korkhov, Zhiming Zhao, Han Rauwerda, Timo M. Breit, Marian Bubak, Luis O. Hertzberger, "Collaborative e-Science Experiments and Scientific Workflows," IEEE Internet Computing, vol. 15, no. 4, pp. 39-47, July-Aug. 2011, doi:10.1109/MIC.2011.87
Usage of this product signifies your acceptance of the Terms of Use.