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These workflows come from a paper by Bharathi, et al. [1]. There is another paper with more information about the workflows by Juve, et al. [2].
The code used to generate these workflows is available here. The code generator sometimes generates negative task runtimes, so watch out for that.
A large collection of DAXes similar to the ones listed above below is available here. Note that it is about 290MB.
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These workflows come from a report by Ramakrishnan and Gannon [23].
The Python code used to generate the DAX files below, as well as several others, can be downloaded here.
| Workflow Type | Figure in Report | Example | DAX |
|---|---|---|---|
| LEAD Mesoscale Meteorology | Figure 1 |  | leadmm.xml |
| LEAD ARPS Data Analysis System | Figure 2 |  | |
| LEAD Data Mining Workflow | Figure 3 |  | leaddm.xml |
| Storm Surge SCOOP Workflow | Figure 4 |  | |
| Floodplain Mapping | Figure 5 |  | floodplain.xml |
| Glimmer | Figure 6 |  | glimmer.xml |
| Gene2Life | Figure 7 |  | gene2life.xml |
| Motif Network | Figure 8 |  | |
| MEME-MAST | Figure 9 |  | mememast.xml |
| Molecular Sciences | Figure 10 |  | molsci.xml |
| Avian Flu | Figure 11 |  | |
| caDSR | Figure 12 |  | cadsr.xml |
| Pan-STARRS Load | Figure 13 |  | |
| Pan-STARRS Merge | Figure 14 |  | |
| McStas | Figure 15 |  | mcstas.xml |
The Python code used to generate the above DAX files, as well as several others, can be downloaded here.
[1] S. Bharathi, A. Chervenak, E. Deelman, G. Mehta, M.-H. Su, and K. Vahi, “Characterization of Scientific Workflows”, 3rd Workshop on Workflows in Support of Large Scale Science (WORKS 08), 2008.
[2] , "Characterizing and Profiling Scientific Workflows", Future Generation Computer Systems, 29:3, pp. 682–692, March 2013.
[3] L. Ramakrishnan and D. Gannon, "A Survey of Distributed Workflow Characteristics and Resource Requirements", Indiana University Technical Report TR671, 2008.