Modelling the cost differential between healthy and current diets: the New Zealand case study

Background: Evidence on whether healthy diets are more expensive than current diets is mixed due to lack of robust methodology. The aim of this study was to develop a novel methodology to model the cost differential between healthy and current diets and apply it in New Zealand. Methods: Prices of common foods were collected from 15 supermarkets, 15 fruit/vegetable stores and from the Food Price Index. The distribution of the cost of two-weekly healthy and current household diets was modelled using a list of commonly consumed foods, a set of min and max quantity/serves constraints for each, and food group and nutrient intakes based on dietary guidelines (healthy diets) or nutrition survey data (current diets). The cost differential between healthy and current diets was modelled for several diet, prices and policy scenarios. Acceptability of resulting meal plans was validated. Results: The average cost of healthy household diets was 40 and $60 cheaper than current diets due to large energy intakes. Discretionary foods and takeaway meals contributed 30-40% to the average cost of current diets. This cost differential could be reduced if fruits and vegetables became exempt from Goods and Services Tax. Healthy diets were cheaper with an allowance for discretionary foods and more expensive when including takeaway meals. Conclusion: Healthy New Zealand diets were on average more expensive than current diets, but one-quarter of healthy diets were cheaper than the average cost of current diets. The impact of diet composition, types of prices and policies on the cost differential was substantial. The methodology can be used in other countries to monitor the cost differential between healthy and current household diets

How do different science disciplines represent and compute over ‘space’?

Peer Reviewe

From Reproducible to Explainable GIScience (Short Paper)

Communicating deep understanding between humans is key to the effective application and sharing of science, and this is critical in GIScience because much of what we do has practical implications in the modelling and governance of societal and environmental systems. Reproducible and explainable science is needed for public trust, for informed governance, for productivity and for global sustainability [Vicente-Saez et al., 2021]. This article summarises some of the more recent research on reproducibility from outside of GIScience, gives practical guidance to current best practice from a GIScience perspective, provides a clearer road-map towards reproducibility and adds in the additional step of explainable GIScience as our final goal

Ontology use for semantic e-science

Abstract. Ontologies are being widely used in online science activities, or e-Science, mostly in roles related to managing and integrating data resources and workflows. We suggest this use has focused on enabling e-science infrastructures to operate more efficiently, but has had less emphasis on scientific knowledge innovation. A greater focus on online innovation can be achieved through more explicit representation of scientific artifacts such as theories and models, and more online tools to enable scientists to directly generate and test such representations. This should lead to regular use of ontologies by scientists as part of their routine online activity

Research Data–Preserve, Share, Reuse, Publish, or Perish

<div>Slides for a talk given on October the 5th, 2015, as part of the COMPASS Spring Seminar Series 2015 by Prof Mark Gahegan from the Centre for eResearch</div><div><br></div>Abstract: Researchers need a variety of data services to support their work, from archives, to backups, through to data sharing and eventually data publishing.  And it is very clear that our funding agencies will soon follow suit with those in the USA, EU, Australia and elsewhere and require researchers to make publicly funded research data available to others in most cases, though of course ensuring confidentiality of individuals where necessary.  The talk will begin by describing some of these services, what we understand that researchers and funders need and how we go about ensuring such services are provided by our institutions. But this is just the beginning.  In the burgeoning era of open (and sometimes data-led) research, new possibilities and challenges for how we describe, find, share and reuse data are waiting around every corner.  Some of these may radically change how we conduct research, some could dramatically improve the effectiveness of the research sector at large.  What we think of as data, and even as research, will change as a result

Is Your Dataset a Mule?

It's a bunch of slides, that I cobbled together in a hurry