Data for: Surely you don't eat parsnip skins? Categorising the edibility of food waste

Published: 15 May 2019 | Version 1 | DOI: 10.17632/rynhc7p97y.1

Description of this data

The dataset is connect to a study that presents a novel methodology to categorise food waste into food (edible parts) and its associated inedible parts, accounting for cultural differences. The methodology section describes how a survey was used to obtain information on whether people eat certain parts of food and, irrespective of if they eat those parts, whether they consider them edible. The method allows existing food-waste definitions that require a split between edible parts (wasted food) and associated inedible parts to be put into practice in a transparent and reproducible way.

The questionnaire was distributed by an on-line polling company (Populus) using an on-line poll between 20th and 22nd September 2017. The sample of 1,092 adults was a nationally representative sample of adults in the UK. Quotas on age, gender and region were set. Targets for quotas and weights were taken from the 2012 National Readership Survey, a random probability face-to-face survey conducted annually with 34,000 adults. The data were weighted by Populus to the known profile of the UK using age, gender, and government office region, social grade, taken a foreign holiday in the last 3 years, tenure, number of cars in the household and working status.

Experiment data files

This data is associated with the following publication:

Surely you don’t eat parsnip skins? Categorising the edibility of food waste

Published in: Resources, Conservation & Recycling

Latest version

  • Version 1


    Published: 2019-05-15

    DOI: 10.17632/rynhc7p97y.1

    Cite this dataset

    Quested, Thomas (2019), “Data for: Surely you don't eat parsnip skins? Categorising the edibility of food waste”, Mendeley Data, v1


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Surveys, Food Waste


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The files associated with this dataset are licensed under a Attribution-NonCommercial 3.0 Unported licence.

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You are free to adapt, copy or redistribute the material, providing you attribute appropriately and do not use the material for commercial purposes.