Nicholas Anderson

Data intensive biomedical research is increasingly integrative; knowledge gained from a spectrum of disciplines and tools is generated, collected and applied to aid in the analysis and description of biological phenomena. Though there are many evolving approaches and much effort to bring a synthesis of disciplines to biological research, we have little understanding of how researchers are coping with the longitudinal experimental data management challenges involved with day-to-day experimental work.
This research uses multiple theoretical frameworks and data collection methods to identify issues involved in the use of information-rich research tools and techniques by academic laboratory research groups. Experience from a broad evaluation of common information management issues affecting the local biomedical research community was used to inform a case study protocol for the study of information technology in laboratory settings. This protocol was then used to design a focused case study of microarray gene expression analysis (MGEA) information use and workflow in academic laboratories. MGEA is a methodology that due to its large generated raw data sets, expensive measurement equipment and complex analysis procedures requires collaboration with specialists in biostatistics and bioinformatics to aid researchers in effective inquiry. As such, the academic use of MGEA methodologies is a representative example of information management challenges that are necessary to provide integrative biomedical research support. This case study approach was then used to evaluate the utility and transferability of the protocol to other laboratory information management issues.
This work seeks to explore two fundamental issues: The first is the development of methods to capture the full complexity and cost of planning, collaboration and analysis needed to complete data-rich academic biomedical experiments. The second is to use these methods to explore the use of a representative technology, and to assess the degree to which an exploratory case-study approach can serve to inform bioinformatics design, implementation and support.