@article{keyhere,
  abstract = {Recent years have seen a dramatic increase in research and development of scientific workflow systems. These systems promise to make scientists more productive by automating data-driven and compute-intensive analyses. Despite many early achievements, the long-term success of scientific workflow technology critically depends on making these systems useable by "mere mortals", i.e.,�scientists who have a very good idea of the analysis methods they wish to assemble, but who are neither software developers nor scripting-language experts. With these users in mind, we identify a set of desiderata for scientific workflow systems crucial for enabling scientists to model and design the workflows they wish to automate themselves. As a first step towards meeting these requirements, we also show how the collection-oriented modeling and design (comad) approach for scientific workflows, implemented within the Kepler system, can help provide these critical, design-oriented capabilities to scientists.},
  added-at = {2008-09-21T20:31:12.000+0200},
  author = {McPhillips, Timothy and Bowers, Shawn and Zinn, Daniel and Ludaescher, Bertram},
  biburl = {https://www.bibsonomy.org/bibtex/205d2c76f045b03d89a20beada0b2305c/ludaesch},
  description = {ScienceDirect - Future Generation Computer Systems : Scientific workflow design for mere mortals},
  interhash = {5e5b98345cbb29d0a89ba5bf3aa1437c},
  intrahash = {05d2c76f045b03d89a20beada0b2305c},
  journal = {Future Generation Computer Systems},
  keywords = {provenance comad optimization workflow resilience},
  pages = {--},
  timestamp = {2008-09-21T20:31:12.000+0200},
  title = {Scientific workflow design for mere mortals},
  url = {http://www.sciencedirect.com/science/article/B6V06-4SYCPKX-1/2/7546e607112d0080d909a7c99aeb962d},
  volume = {In Press, Corrected Proof},
  year = 2008
}

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These systems promise to make scientists more productive by automating data-driven and compute-intensive analyses. Despite many early achievements, the long-term success of scientific workflow technology critically depends on making these systems useable by \"mere mortals\", i.e.,�scientists who have a very good idea of the analysis methods they wish to assemble, but who are neither software developers nor scripting-language experts. With these users in mind, we identify a set of desiderata for scientific workflow systems crucial for enabling scientists to model and design the workflows they wish to automate themselves. 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