Project Outputs

The project's main output will be Kepler "actors", with documentation.
These will be hosted on Github, with dissemination: principally through peer-reviewed publications.
However our chief contribution will be promoting the conceptual approach: using a scientific workflow system of any sort.

Technologies and Features

Development
Kepler 2.3	Kepler
Stata 9 / 12	Stata 9 / 12
Rundo for Stata	Rundo for Stata
R studio	R studio
R	R
Microsoft visual C sharp express	Microsoft visual C sharp express
EMacs	EMacs
Java JDK (6u25)	Java JDK
Ant	Ant
Eclipse	Eclipse
Version control
Git hub	Git hub
TortoiseGit	TortoiseGit
Git	Git
Operating systems and tools
Putty	Putty
WinSCP	WinSCP
Windows	Windows
Linux Ubuntu	Linux Ubuntu
PGAdmin	PGAdmin
Admin
Dropbox	Dropbox
Skype	Skype
blogger.com	blogger.com

Target Customers

Our software tools will be used by epidemiologists, health researchers, statisticians, and other scientists whose work involves handling multiple datasets from different sources. The exemplar is research on the health impacts of extreme weather events, which requires merging meteorological, health and demographic data to permit statistical analysis of causal associations.

Project Description

The objective of this project is to develop a scientific workflow system to support data analysis for environmental epidemiology researchers, including workflow exemplars and training approaches.

We will develop software that will enable researchers to access, integrate and transform datasets from population, health and environmental domains, using current and future health impacts of Extreme Weather Events (EWE) as a case study.

A state-of-the-art scientific workflow system will be deployed at the ANU and developed to enable users to build and extend analytical tools. Tools will include methods to chain together tasks that perform operations in the domains of data acquisition, data transformation, mathematical operations, graphing, statistical analysis, and output. It will include both an operational web-based research platform as well as enhance traditional desktop client-side workflows, so that it boosts capacity without compromising expertise and trusted workflows.  The software ecosystem is summarised in the image below, and fully described at this page Click Here

The first demonstration of the system will be the creation of an online validated Extreme Weather Events (EWE) database from historical data that can be queried repeatedly, easily and effectively.
This will be merged with Health, Population and Climate Change scenario data to project future health impacts; and the impact assessment will be able to be easily updated with future additional health, population and weather data; or new Climate Change model versions.

The Team

Keith Dear: Project Manager I am a biostatistician and epidemiologist specialising in environmental health. My main area is the health impacts of climate change, especially the direct effects of heat and cold and how climate influences the range and transmissibility of vector-borne diseases such as Dengue.
Charmian Bennett: Epidemiologist I am an environmental epidemiologist with a background in environmental science and geography. My research focuses on the impacts of climate change on human health, especially the impacts of heat and other extreme weather events that will become more frequent and more severe as our climate changes.
Ivan Hanigan: Environmental scientist and database manager I am a multidisciplinary data manager and analyst. I have primarily worked in Environmental Epidemiology where I have honed my skills in manipulation of large databases and multivariate regression modelling.  I also have experience of scenario-based forecasting in climate change risk assessments.
Ian Szarka: Software engineer I am software developer at ANU with a having primarily worked on software for scientific applications. This has included environmental simulation models especially in hydrology, memory management of temporal data, decision support tools, forward propagation uncertainty analysis, multi-objective optimisation, and more recently processing of 3d geometries for rapid prototyping (‘3d printing’).