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Community Data Evaluation using a Semantically Enhanced Modelling Process. , Mohammed Haji 1 , Peter Dew 1 , Chris Martin 1,2 1 School of Computing, University of Leeds 2 School of Chemistry, University of Leeds. e-mail: mhh@comp.leeds.ac.uk. Content.

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community data evaluation using a semantically enhanced modelling process

Community Data Evaluation using a Semantically Enhanced Modelling Process

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

e-mail: mhh@comp.leeds.ac.uk

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

2

community data evaluation
Community Data Evaluation
  • 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
  • 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 .

3

capturing provenance data
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.

4

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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

9

evaluation
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 don’t 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 won’t [record the provenance].”

10

conclusion
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

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