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ZENODO
Dataset . 2022
License: CC BY NC SA
Data sources: Datacite
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ZENODO
Dataset . 2022
License: CC BY NC SA
Data sources: ZENODO
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ZENODO
Dataset . 2022
License: CC BY NC SA
Data sources: Datacite
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ZENODO
Dataset . 2022
License: CC BY NC SA
Data sources: Datacite
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ZENODO
Dataset . 2022
License: CC BY NC SA
Data sources: ZENODO
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https://doi.org/10.5281/zenodo...
Dataset . 2022
License: CC BY NC SA
Data sources: Sygma
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PROTEIN NAP Database

Authors: Stefanidis, Kiriakos; Tsatsou, Dorothea; Konstantinidis, Dimitrios; Gymnopoulos, Lazaros; Wilson-Barnes, Saskia; Hart, Kathryn; Cornelissen, Véronique; +8 Authors

PROTEIN NAP Database

Abstract

A dataset of expert-validated Nutrition and Physical Activity Plans (NAP), i.e., plans created and reviewed by experts in nutrition and physcial activity, as well as, medical experts. The dataset contains foods, meals, recipes, and physcial activities. The following databases have been used as input: USDA Food Composition Database, April 2018 (United States) Portuguese Food Composition Table (TCAP) Brazilian Food Composition Table (TACO) Peso, Uma Questão de Peso (Portugal)* Base de Datos del Centro de Enseñanza Superior de Nutrición y Dietética (CESNID Foundation, Spain)* Luján University Data Base (Argentina) Spanish Food Composition Database (BEDCA, UGR) French food composition (CIQUAL, ANSES) Composition tables of foods and Greek dishes (CTFG, HHF)* Food Composition Database for Epidemiological Studies in Italy (FCDBE, IEO)* Irish Food Composition Database (IFCD, UCC) Polish Foods Composition Database (PFCT, NFNI)* Tables of the Nutritional Composition of Food Consumed in Brazil of IBGE (TCNA, IBGE) McCance and Widdowson’s The Composition of Foods integrated dataset (Co FID) Mexican Food Equivalents System (Mexico)* * Items originating from these databases where not included in the offered file due to copyright issues; you may contact the authors directly for further inquiries regarding these items. Funding: The research leading to these results has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 817732 (PROTEIN: PeRsOnalized nutriTion for hEalthy living). © 2022 by the authors.

Keywords

personalized nutrition, meal plan recommendations, artificial intelligence

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
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