Value modelling for a research data set pipeline

Dr Chris Jackett1, Ms Pamela Brodie1, Dr Simon Pigot1

1CSIRO, Castray Esplanade, Battery Point, Australia

Abstract:

The acquisition and management of research data is fundamental to the scientific process. However, the underlying value of data sets are rarely understood or analysed. There is a growing view in the research data management community that data sets should be considered more like a core asset, rather than a means-to-an-end in the production of scientific publications. The inherent value of a research data set comprises many factors including the operational costs of data acquisition, survey design and sampling methods, publication output and impact factor, as well as the quality of data management practices used to achieve Findable, Accessible, Interoperable and Reusable (FAIR) data.

 

This work proposes a mixed data set valuation methodology designed to produce an estimate of the value of research data sets. The initial valuation model is broken down into three components: fiscal, academic and data. A custom algorithm is proposed to combine the various valuation metrics into a single estimate of the value range of a data set. This model could also be used to make future projections, providing an indication of how the value of a data set could be influenced by different data collection and management scenarios.

 

The valuations and projections produced by this model would give a clear understanding about the value of research data sets. This approach would provide a quantitative framework that could be used to inform scientific decision-making processes.


Biography:

Chris Jackett is a software engineer in the CSIRO Information and Data Centre who specialises in designing and developing software systems for research data management. Chris has previous experience developing remote sensing data acquisition systems, data storage solutions for drone-based multispectral imagery, computational systems for the optimisation of aquacultural planning, and web application development using modern frameworks, tools and techniques. His PhD research investigated a range of mathematical, statistical and computational mechanisms for improving the quality of recorded satellite data, including deconvolution and spatial resolution enhancement. Chris completed a Graduate Diploma in marine science through the joint CSIRO-UTAS Quantitative Marine Science (QMS) program which focused on a range of quantitative approaches to a wide variety of marine applications.

 

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