LCI data and data quality

Life cycle inventory (LCI) is becoming an established environmental management tool that quantifies all resource usage and waste generation associated with providing specific goods or services to society. LCIs are increasingly used by industry as well as policy makers to provide a holistic ‘macro’ overview of the environmental profile of a good or service. This information, effectively combined with relevant information obtained from other environmental management tools, is very useful in guiding strategic environmental decision making. LCIs are very data intensive. There is a risk that they imply a level of accuracy that does not exist. This is especially true today, because the availability of accurate LCI data is limited. Also, it is not easy for LCI users, decision-makers and other interested parties to differentiate between ‘good quality’ and ‘poor quality’ LCI data. Several data quality requirements for ‘good’ LCI data can be defined only in relation to the specific study in which they are used. In this paper we show how and why the use of a common LCI database for some of the more commonly used LCI data, together with increased documentation and harmonisation of the data quality features of all LCI data, is key to the further development of LCI as a useful and pragmatic environmental management tool. Initiatives already underway to make this happen are also described.     

Linking data to models: data regression

Mathematical models are an essential tool in systems biology, linking the behaviour of a system to the interactions between its components. Parameters in empirical mathematical models must be determined using experimental data, a process called regression. Because experimental data are noisy and incomplete, diagnostics that test the structural identifiability and validity of models and the significance and determinability of their parameters are needed to ensure that the proposed models are supported by the available data.     

Maintaining data integrity in microarray data management

Gene expression microarrays are a relatively new technology, dating back just a few years, yet they have already become a very widely used tool in biology, and have evolved to a wide range of applications well beyond their original design intent. However, while the use of microarrays has expanded, and the issues of performance optimization have been intensively studied, the fundamental issue of data integrity management has largely been ignored. Now that performance has improved so greatly, the shortcomings of data integrity control methods constitute a greater percent of the stumbling blocks for investigators. Microarray data are cumbersome, and the rule up to this point has mostly been one of hands-on transformations, leading to human errors which often have dramatic consequences. We show in this review that the time lost on such mistakes is enormous and dramatically affects results; therefore, mistakes should be mitigated in any way possible. We outline the scope of the data integrity issue, to survey some of the most common and dangerous data transformations, and their shortcomings. To illustrate, we review some case studies. We then look at the work done by the research community on this issue (which admittedly is meager up to this point). Some data integrity issues are always going to be difficult, while others will become easier-one of our goals is to expedite the use of integrity control methods. Finally, we present some preliminary guidelines and some specific approaches that we believe should be the focus of future research.     

Taming data

A challenge in systems-level investigations of the immune response is the principled integration of disparate data sets for constructing predictive models. InnateDB (Lynn et al., 2008; http://www.innatedb.ca), a publicly available, manually curated database of experimentally verified molecular interactions and pathways involved in innate immunity, is a powerful new resource that facilitates such integrative systems-level analyses.