WDCC News Q3: Investment program (WU + WR) Data Science/AI

We would like to draw attention to courses that address the topics of semantic interoperability and FAIR data sharing at a research level.

How do you make your research data FAIR? At a new postgraduate school course “Towards FAIR Data Management” will work with tools and best practices for FAIR data. The aim is to work hands-on with the use cases present at WUR, and coming closer to the vision of improved data sharing. In-person parts of the course are taking place on October 26, November 3 and 10, 2022. More details and registration information can be found here: FAIR data management Oct/Nov 2022 | PE&RC (pe-rc.nl)

Further, at the Master and PhD student level, the topics of semantic annotations and interoperability will be covered in “Linked Data" course, that will be given in period 2 (details and registration can be found in OSIRIS). Also, opportunities to bring in and co-develop own projects are provided.

WUR Data Science and Artificial Intelligence Fellowships

Last year, 6 innovative Data Science and Artificial Intelligence projects were competitively selected out of the open call open for younger WUR researchers. Six projects were selected, and are running now, positioned at WU, WR and different chair groups.

We have recorded videos about the Fellowship program and the individual projects, and have published them on the WUR YouTube channel, so that the ongoing research developments can be broadly disseminated.

See the videos here.

You are welcome to get in contact and collaborate with the talented researchers!

The final, in-person event, where the projects’ outcomes will be presented is being planned for mid-November 2022.

Next Level Data Science and Artificial Intelligence Fellowships

We are organizing a number of hybrid positions (PhD or postdoc) with other chair groups, combining domain research with data science and artificial intelligence. We have received 29 Expressions of Interest and the assessment process is currently taking place. The intention of this process is to find and advance domain challenges with innovations in data science and AI.

In parallel, we are investigating possible applications with other chair groups to an externally funded (NWO) call for newly appointed researchers. This call originates from the Netherlands AI Coalition as part of the Talent Program of AINed. Its aim is to attract and retain AI talents at Dutch universities. The first round has opened, we expect a second round to open at the end of next year. Furter ideas are very welcome.

EU Grants

Two new EU funded grants started this September both related to Data Science/Artificial Intelligence and the European Green Deal! Another two were approved, and are expected to start in January 2022!

• will exploit artificial intelligence and data science for impactful spraying commands in agricultural robots. The project aims to reduce the field application of chemicals (fertilizers and pesticides), with a consortium of 9 partners, 3 million Euro budget (520K for WUR) and two trials (wheat and apples). The WUR team will further develop the concepts of learning Digital Twins, developed in the Digital Farm of the Future flagship. Keep track of the project website, here: https://smartdroplets.eu/
• The project aims to establish the European Green Deal Data Space, its community of practice, and develop a roadmap for its implementation for 2024-2027! The project addresses three Green Deal actions: 2030 Biodiversity strategy, Zero-pollution Action Plan & Climate Change Adaptation Strategy. With 2 million Euro funding (70K for WUR), GREAT builds on the strengths of a consortium of 11 partners & 3 affiliated entities with direct links to +1000 stakeholders & +100 initiatives operating at national & international level. The network of stakeholders directly connected with the consortium spans across multiple sectors (e.g., Land, Ocean & Maritime, Atmosphere & Climate, Security & Safety, Built Environments). WDCC together with GRS contributes to this work.
• (led by INRA, WU/WR participation, ESG/PSG and NPEC) brings together the European Research Infrastructures (RI) on plant phenotyping (EMPHASIS), ecosystems experimentation (AnaEE), long-term observation (eLTER) and data management and bioinformatics (ELIXIR). They join their forces to co-develop new tools and methods for the identification of future-proofed combinations of species, genotypes and management practices in front of the most likely climatic scenarios across Europe. Ambitioning to go beyond current highly instrumented but often spatially and temporally limited installations, PHENET derived services will allow wide access to enlarged sources of in-situ phenotypic and environmental data thanks to (i) new AI-based multi (agroecology-related) traits multi-sensors devices (ii) to unleashed access to high resolution Earth Observation data connected to ground based data, (iii) FAIR data support for connection with (iv) new generation of predictive modeling solutions encompassing AI and digital twins. With a consortium of 30 partners and a total budget of 11 million Euros (870K Euro for WUR). PHENET services will be demonstrated in eight use cases of varying scales, from landscape to plant and soil health.
• is a network of testing and validation infrastructures in Europe that aspires to support agri-food technology companies to perform near-product development of their AI and robotics solutions in real-world facilities. The overall aim is to close the gap between excellent research in these fields and actual products that support an efficient and sustainable agriculture, while meeting stringent usability and economic requirements of their end users. With 8 nodes in France, Germany, Italy, the Netherlands, Sweden, Poland, Austria and Belgium, and a total budget of 60 million Euro (30 million funded from Europe, and another 30 from national/own contributions) the network aspires to support the whole EU market for testing AI solutions in the agri-food sector. The Dutch node (a coalition of WR and WU) is brings together existing WUR infrastructures, including the Farm of the Future, Agro Food Robotics, WDCC, One Planet, ELSA labs, NPEC and the Dairy Campus. The Dutch node (10 million Euro – 8 WR, 2WU) is coordinated by Kees Lokhorst (WR). Sjaak Wolfert (WR) leads the work on ecosystem development and visibility across all nodes, and Ioannis Athanasiadis (WU) will be the chief AI officer for the whole consortium.

More info about research and education in data science and artifical intelligence :

Research engineers

The Research Engineers (RE) hired as part of the investment programme have been a catalyst for change. Unfortunately Christine Staiger, RE data management, has left her position at WUR to work in Utrecht University. This is due to the incapability for her to perform the work she envisioned through the current organisational position of RE’s within the organisation at FB-IT. The other RE, Nick Brummans has experienced the same difficulty but will be able to start work in a new team within FB-IT on the first of October. This team, Research Solutions, will have a different way of working than classic FB-IT teams, which will lead to greater effectiveness for Nick to do his work. It is thanks to the DS/AI investment that we are able to first hand experience these issues when deploying research engineers. We will further improve upon the organisational positioning of RE’s in the new Implementation Study, which will be presented in the WDCC Advisory Group on September 26^th.

Alliances

The new focus for data science alliances – data sharing has been presented and being applied within a few areas of WUR's involvement. With AI hub Oost WUR organized a workshop on AI and datasharing for agriculture and food, main audience: private sector companies interested in this topic. Several other large project have been granted.

The Eindhoven Wageningen Utrecht University and UMC Utrecht (EWUU) alliance has granted a new wave of seed funding for projects in the area of AI for Health and EWUU is heavily involved in shaping a new growth fund proposal on Preventive Health on Obesity. In this proposal data sharing for data science methods (including Digital Twinning) is a central topic.