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 is available here. Note that it is about 290MB.
LIGO Inspiral Analysis
The code used to generate these workflows is available here.
Ramakrishnan and Gannon Workflows
|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|
|Motif Network||Figure 8|
|Molecular Sciences||Figure 10||molsci.xml|
|Avian Flu||Figure 11|
|Pan-STARRS Load||Figure 13|
|Pan-STARRS Merge||Figure 14|
The Python code used to generate the above DAX files was written in Python and , as well as several others, can be downloaded here.
 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.