Community Data Evaluation using a Semantically Enhanced Modelling Process

1. 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

2. Content • Community Data Evaluation using a Semantically Enhanced Modelling Process • Capturing Provenance and Data • Current practices and the Electronic Lab Notebook • Evaluation • Conclusion 2

3. 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

4. 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

5. Current Evaluation Processes for the MCM 5

6. Envisioned Evaluation Processes 6

7. ELN Process 7

8. Prompts displayed when changing the chemical mechanism; Editing a reaction Adding a new reaction ELN Screenshots 8

9. 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

10. 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

11. 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 11