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This repository includes:

  1. one ontology for nutritional epidemiologic research. It was developed according to three well-developed standards agreed by the nutrition science community.
  2. Python code to manage a graph database of nutritional epidemiologic papers.
  3. Python code to manage a graph database of FAO food-based dietary guidelines.

Ontology for Nutritional Epidemiology (ONE)

Homepage & BioPortal

Nutritional epidemiology is a specific research area. The generic ontologies for food science, nutrition science or medical science failed to cover the specific characteristics of nutritional epidemiologic studies. As a result, we developed the ontology for nutritional epidemiology (ONE) in order to describe nutritional epidemiologic studies accurately.

A Python module for food-based dietary guidelines annotation

It has been tested in Python v3.8.5 and Neo4j v3.5.6.

  1. FAO guidelines extract and store metadata of food-based dietary guidelines through the FAO website;
  2. FAO guidelines-get urls and extract the list of urls and country/area names of the FAO guidelines;
  3. FAO guidelines annotation (an example).py: an example to run the code.

Figure 1. The FAO homepage of food-based dietary guidelines

Figure 2. Extract the list of countries/areas from the FAO website

Figure 3. Extract the urls of food-based dietary guidelines from the FAO website

Figure 4. The urls of food-based dietary guidelines from the FAO website

Figure 5. Run the web crawler to process all the urls

Figure 6. Identify the key metadata of the Portugal guidelines (an example)

Figure 7. Query the developed neo4j database: area

Figure 8. Query the developed neo4j database: food item

Figure 9. Identify corresponding ontology terms (Food Ontology)

Figure 10. Identifier/ontology term

Figure 11. A drafted dashboard

A Python module for nutrition article annotation

It has been tested in Python v3.7.4 and Neo4j v3.5.6.

A Python module was developed to process content and STROBE-nut annotations of nutritional epidemiologic papers in XML format:

  1. extract and store metadata of papers through the “Springer Nature API Portal” or in local XML files respectively;
  2. annotate the reporting completeness of papers according to the STROBE-nut reporting guidelines;
  3. visualize the statistics of reporting completeness of papers; visualize the reporting frequency of the STROBE-nut items.


Please download the code under the folder "imports".

Publications (ontology and graph database)

Yang, C.; Hawwash, D.; De Baets, B.; Bouwman, J.; Lachat, C. (2020) Perspective: Towards automated tracking of content and evidence appraisal of nutrition research. Advances in Nutrition.

Yang, C., De Baets, B., & Lachat, C. (2019). From DIKW pyramid to graph database: a tool for machine processing of nutritional epidemiologic research data. In 2019 IEEE International Conference on Big Data (Big Data) (pp. 5202–5205). Los Angeles, CA, USA: IEEE.

Yang, C.; Ambayo, H.; De Baets, B.; Kolsteren, P.; Thanintorn, N.; Hawwash, D.; Bouwman, J.; Bronselaer, A.; Pattyn, F.; Lachat, C. (2019) An Ontology to Standardize Research Output of Nutritional Epidemiology: From Paper-Based Standards to Linked Content. Nutrients, 11, 1300.

Publications (data standards)

Pinart, M., Nimptsch, K., Bouwman, J., Dragsted, L. O., Yang, C., De Cock, N., Lachat, C., et al. (2018). Joint data analysis in nutritional epidemiology : identification of observational studies and minimal requirements. JOURNAL OF NUTRITION, 148(2), 285–297.

Yang, C.; Pinart, M.; Kolsteren, P.; Van Camp, J.; De Cock, N.; Nimptsch, K.; et al.(2017) Perspective: Essential study quality descriptors for data from nutritional epidemiologic research. Advances in Nutrition, 8(5):639–51.

Lachat, C., Hawwash, D., Ocké, M. C., Berg, C., Forsum, E., Hörnel, A., Larsson, C., et al. (2016). Strengthening the Reporting of Observational Studies in Epidemiology-Nutritional Epidemiology (STROBE-nut): an extension of the STROBE Statement. PLOS MEDICINE, 13(6).


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