Chemical models for, and the role of data and provenance in, an atmospheric chemistry community

This thesis presents research at the interface of the e-Science and atmospheric chemistry disciplines. Two inter-related research topics are addressed: first, the development of computational models of the troposphere (i.e. in silico experiments); and secondly, provenance capture and representation for data produced by these computational models. The research was conducted using an ethnographic approach, seeking to develop in-depth understanding of current working practices, which then informed the research itself. The research focused on the working practices of a defined research community; the users and developers of the MCM (Master Chemical Mechanism). The MCM is a key data and information repository used by researchers, with an interest in atmospheric chemistry, across the world.

A computational modelling system, the OSBM (Open Source Box Model) was successfully developed to encourage researchers to make use of the MCM, within their in silico experiments. Taking advantage of functionality provided by the OSBM, the use of in situ experimental data to constrain zero dimensional box models was explored. Limitations of current methodologies for constraining zero dimensional box models were identified, particularly associated with the use of piecewise constant interpolation and the averaging of constraint data. Improved methodologies for constraining zero dimensional box models were proposed, tested and demonstrated to offer gains in the accuracy of the model results and the efficiency of the model itself.

Current data generation and provenance related working practices, within the MCM community, were mapped. An opportunity was identified to apply Semantic Web technologies to improve working practices associated with gathering and evaluating feedback from in silico experiments, to inform the ongoing development of the MCM. These envisioned working practices rely on researchers, performing in silico experiments, that make use of the MCM, capturing data and provenance using an ELN (Electronic Laboratory Notebook). A prototype ELN, employing a user-orientation approach to provenance capture and representation, was then successfully designed, implemented and evaluated. The evaluation of this prototype ELN highlighted the importance of adopting a holistic approach to the development of provenance capture tools and the difficulties of balancing researchers’ requirements for flexibility and structure their scientific processes.