Community Data Evaluation using a Semantically Enhanced Modelling Process

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Community Data Evaluation using a Semantically
Enhanced Modelling Process

• e-mail mhh_at_comp.leeds.ac.uk

• , Mohammed Haji 1, Peter Dew 1, Chris Martin 1,2
• 1 School of Computing, University of Leeds
• 2 School of Chemistry, University of Leeds

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Content

• Community Data Evaluation using a Semantically
  Enhanced Modelling Process
• Capturing Provenance and Data
• Current practices and the Electronic Lab Notebook
• Evaluation
• Conclusion

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Community Data Evaluation

• Progress in many scientific communities depends
  on complementary
• experimental and theoretical development.
• These communities require high quality data to
  evaluate findings.
• - Our primary community is the Atmospheric
  Community .

• The Motivation
• Study how to transition from today's ad-hoc
  process practises
• Sustainable process of
• Gathering, community evaluation and sharing data
  models between scientists
• Minimising changes to proven working practises
  of the scientist
• Operate within world-wide co-laboratories

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Capturing Provenance Data

• Provenance is captured in three forms namely
  Inline (during the experiment execution), pre-hoc
  and post-hoc, before and after the experiment.
• Broadly speaking there are two categories for
  capturing provenance data in e-Science projects
• System oriented There are usually tightly
  coupled with the workflow paradigm and seek to
  automatically capture provenance.
• User oriented Adopting key practises from the
  scientific approach and use domain specific
  scientific terminologies.
• In this research we seek to develop a user
  oriented approach and reconcile with the system
  orientation to automate process provenance
  capture. Specifically capturing inline
  annotation.

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Current Evaluation Processes for the MCM
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Envisioned Evaluation Processes
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ELN Process
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ELN Screenshots

• Prompts displayed when changing the chemical
  mechanism
• Editing a reaction
• Adding a new reaction

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Evaluation Methodology

• In-depth interviews with members of the
  atmospheric chemistry model group at Leeds,
  covering
• Demonstration of the prototype
• User testing of the prototype
• Discussion of scenarios involving the use of the
  prototype.
• Analysis
• Interviews recorded and transcribed
• Analysed using techniques from grounded theory

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Evaluation

• Barriers to adoption
• Effort required at modelling time for provenance
  capture
• in your lab book you can write down what ever
  you want but with an ELN it is going to take
  time to go through the different protocol steps.
  
• When asked if they would use an ELN requiring a
  similar amount of user input to the prototype the
  response was positive
• Yeah, I think it would be a good thing. I dont
  think it is too much extra work.
• Rather than viewing the prompts for user
  annotation as interruption to their normal work
  the user recognised the value of being prompted
• is a good way to do it because otherwise you
  wont record the provenance.

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Conclusion

• Outlined the Community Data Evaluation using a
  Semantically Enhanced Modelling Process and the
  ELN.
• The work is focused on a user-oriented approach
  using domain specific scientific terminologies.
• Showed the community evaluation vision.
• Discussed the ELN evaluation method.
• Future work
• Carry out further investigation into the
  atmospheric chemistry community.
• Look into other community that would benefit from
  this work such as Geomagnetism.
• Acknowledgement
• Peter Jimack, David Allen and Mike Pilling