Chris J. Martin

Semantically-Enhanced Model-Experiment-Evaluation Processes (SeMEEPs) within the Atmospheric Chemistry Community

The scientific model development process is often documented in an ad-hoc unstructured manner leading to difficulty in attributing provenance to data products. This can cause issues when the data owner or other interested stakeholder seeks to interpret the data at a later date. In this paper we discuss the design, development and evaluation of a Semantically-enhanced Electronic Lab-Notebook to facilitate the capture of provenance for the model development process, within the atmospheric chemistry community. We then proceed to consider the value of semantically enhanced provenance within the wider community processes, Semantically-enhanced Model-Experiment Evaluation Processes (SeMEEPs), that leverage data generated by experiments and computational models to conduct evaluations.

A user-orientated approach to provenance capture and representation for in silico experiments, explored within the atmospheric chemistry community

We present a novel user-orientated approach to provenance capture and representation for in silico experiments, contrasted against the more systems-orientated approaches that have been typical within the e-Science domain. In our approach, we seek to capture the scientists reasoning in the form of annotations as an experiment evolves, while using the scientists terminology in the representation of process provenance. Our user-orientated approach is applied in a case study within the atmospheric chemistry domain: we consider the design, development and evaluation of an electronic laboratory notebook, a provenance capture and storage tool, for iterative model development.

Data Governance in the Sustainable Smart City

The wisdom of ‘smart’ development increasingly shapes urban sustainability in Europe and beyond. Yet, the ‘smart city’ paradigm has been critiqued for favouring technological solutions and business interests over social inclusion and urban innovation. Despite the rhetoric of ‘citizen-centred approaches’ and ‘user-generated data’, the level of stakeholder engagement and public empowerment is still in question. It is unclear how smart city initiatives are developing common visions according to the principles of sustainable urban development. This paper examines how data governance in particular is framed in the new smart city agenda that is focused on sustainability. The challenges and opportunities of data governance in sustainability-driven smart city initiatives are articulated within a conceptual Framework on Sustainable Smart City Data Governance. Drawing on three cases from European countries and a stakeholder survey, the paper shows how governance of data can underpin urban smart and sustainable development solutions. The paper presents insights and lessons from this multi-case study, and discusses risks, challenges, and future research.