Thesis subject

Data sharing in food systems: a landscape analysis (BSc/MSc)

Are you an ambitious student looking for a thesis in the food domain related to data? Embark on a journey with FoodDataQuest, a project funded by the European Union. Join a dynamic international consortium of 15 institutions from eight European countries that harness data-driven innovations to promote sustainable food systems. This initiative offers an opportunity for thesis research in areas leveraging artificial intelligence and machine learning to tackle environmental and social challenges in the agri-food industry.

Short description:

The agri-food industry faces numerous challenges dealing with societal, public health, individual nutrition, environmental, food waste and overall sustainability. There is an urgent need for innovative solutions to address these challenges. The EU FoodDataQuest project recognizes these challenges and wants to tackle them with advanced data driven strategies. By collecting more data from new sources and unconventional players the project is going to tackle social and ecological challenges by advanced data driven solution, driven by Artificial Intelligence (AI) and Machine learning algorithms.

During this thesis you will work in an interdisciplinary, international team to map the current landscape of private data sharing in food systems from farm to fork. This thesis is a collaboration between Wageningen University and Wageningen Economic Research (WEcR). You will identify, analyze, and map current practices and systems of private data sharing in the food system. This work will be done both by desk research into multiple data repositories (for example in 4TU.ResearchData, DANS-EASY, Zenodo), and interviews with stakeholders in the food systems domain.

Objectives:

  1. Map the current landscape of private data sharing in food systems by defining the main actors, their roles/functions, business processes, data flows, and possible influencing drivers and enablers.
  2. Identify bottlenecks, barriers, and challenges for data sharing in food systems
  3. Develop recommendations to improve data sharing in food systems.

Tasks:

  • (Semi) structured interviews with stakeholders in the consortium and the food industry
  • Systematic search in data repositories
  • Co-authoring of a scientific publication

Literature:

  • Deroo, E., & Maes, E. (2023). Building a European framework for the secure and trusted data space for agriculture: Up-to-date online inventory (D1.1). Flanders Research Institute for Agriculture, Fisheries and Food (EV ILVO). https://agridataspace-csa.eu/
  • Kazantsev, N., Islam, N., Zwiegelaar, J., Brown, A., & Maull, R. (2023). Data sharing for business model innovation in platform ecosystems: From private data to public good.Technological Forecasting and Social Change,192, 122515.

Requirements:

  • Courses: Business information Analytics (INF-37306) OR Data Science Concepts (INF-34306) OR Artificial Intelligence for Food and Health (INF-36803) OR Data Science Applications for Food and Consumer Science (YSS-35803) OR Management and Engineering of Information Systems (INF-31306)
  • Required skills/knowledge: Food and health, Interest in working in an interdisciplinary, international team.

Key words: Data Science, Information Systems, Information technology, Management Information Systems, Quantitative Social Science

Contact person(s):

Cor Verdouw (cor.verdouw@wur.nl)

Joep Tummers (WEcR) (joep.tummers@wur.nl)