Richard's Tasks for this Week:

• Finish the write up, or at least make it much stronger than it currently is. 
  • Email the write up to David de Roure after going over the database with Karthik.
• Digest what each paper says, sort them into subfolders and tags
  • Position ourselves in relation to other current works. 
  • Write up notes for each relevant paper. 
  • Differentiate methods papers from workflow papers.
  • Look at  how Kepler is being used outside of myExperiment. 
• Do tier 2/3 workflows manually. Should take 5-10 minutes each. 
  • Do 300+ (as much as possible) Taverna ones. Time yourself. 
• Research the differences between T1  and T2.
  • Implications for complexity?
• Make slides for Berlin; email them out by Thursday.
  
  

Questions:

• Everyone: 
  • Get subfolders on the new version of Mendeley
  • Diachronic study on new  workflows - is this possible?
• Karthik:
  • Whatever happened to  that conference in Tennessee? Eden? Nimbus?
  • How is the Methods in Ecology and Evolution paper  is going.
  • What can we wring out of Sparql? When can we meet?
• Bill, what happened to SciencePipes?
• Bertram: 
  • What about Galaxy?  Did they ever get back to us?
  • How much time do you have, what do you want to read? Shall I presort the papers for you/us?
    
    

Write up sections to be done:
http://dl.dropbox.com/u/4245277/Understanding%20Workflows.docx

- methods, teir 1/2/3, selection  processes...
- Organise in terms of 2,3 tiers of  information 
      1: high level - author,  when,  how many, nodes, workflow links, goal
        2: of  nodes, \% of data  aquisition, \% of data sources, type of nodes,  models?
           3: metadata sufficiency

•     How are we going to visualise this? 
  
  

Notes, Hypotheses and Questions: 

What gap are we filling? Some open questions about this: 

• Scaling > scaling ones are asumed to be more popular (Pegasus, cagrid)
• automation > automating ones are more downloaded
• provenance > More downloaded, more cited.
• Visualisations > easier to follow (How does one test this?)
• reproducible > Are myExperiment workflows cited more? Is myExperiment?
• (Null hypotheis, tiers)
• What users use these? Are they mostly project devs? What works?
• Are we dependant only on downloads?
• What about closed systems? Science Pipes? How are these different
• Applications > Is this important? What field it is in?
• Template > Or do they constantly reinvent the wheel? What about one offs?
• Are they stand alone? Or are they only plumbing, not the whole system?
• Less inpout over time > doesmore complexity lend itself ot more complexity in the input?
• What are the weak points > reusint? Training? 
• Example ones - are these copied? Are templates carried thorugh culturally?
• Sharing issues: what about privacy concerns? IRBs?
• What's out there? is myExperiment a good enough show of the state of the art? Or are people using shell scripts and the r scripts? What does these suggest?
• What about non-published work?
• What's with subsetting, translation, transformation?
• Does the information flowing through the workflow change anything? The input versus the output? Are any of these able to be linked easily? 
• Live constant visualisation should be more common as time goes on.
• Human interactive components should be becoming more common. At the same time, workflows should be more able to have detached execution. 
• Embedded workflows should be more common. 
• Different workflow systems should be converging over time, both in possible stages, in application, and in natural language descriptions. 
• More applications should be considered over time. cf. http://pegasus.isi.edu/applications.php
• There should be more results over time - not just in data, but in purposes. 
• Linear workflows should become less linear over time. 
• Information retrieval should become less common. 
• Workflows should in general be used by bioinformatics. 
• Downloads will be dependant on the amount of natural language descriptions. 
• There will be a proliferation of shims.
• Most workflows will use the same repositories. 
• Most workflows integrate similar QA/QC steps.
• Workflows will have a disparity in their use on public repositories and their function in the literature. 
• Workflows will decay with time. 
• Knowledge discovery worfklows will become more common with proliferation of databases. 
• Composite actors (which collapse details into abstract components) should proliferate.
• More workflows will have smart reruns. 
• More workflows will have smart semantic links. 
• The content should, over time, more predictably alter the nature and course, shape, and tempo of the work flow. 
• Workflows should become reduced in their components - hwat used to be one workflow should become one segment. 
• Workflows will be more cited in publications.
• Workflow visualisations will become more (less?) popular in the literature. 
• Parallel execution should become more common. 
• Non annotation-based approaches should become more common. 
• The type of workflow and application will influence the data provenance. 
  • "Workflow provenance refers to the record of the history  of the derivation of some dataset in a scientific workflow. The amount  of information recorded for workflow provenance varies, depending on  application needs. For example, workflow provenance of a scientific  result may include details about the type and model of external devices  used, as well as the versions of softwares used for deriving the result."