The main aim of this project is to explore new innovative techniques to store, process, and analyze large volumes of data for phenotyping. Additionally, the project explores new ways of working, e.g., through hackathons. As the project is explorative, knowledge transfer is done through scientific publications, presentations at international conferences, and by organizing dedicated workshops for B4F partners.
Published results in this project are:
Kamphuis, C., P. Duenk, R.F. Veerkamp, B. Visser, G. Singh, A. Nigsch, R.M. de Mol, M.L.W.H. Broekhuijse. 2020. Machine learning to further improve the decision which boar ejaculates to process into artificial insemination doses. Theriogenology. 144: 112-121. https://doi.org/10.1016/j.theriogenology.2019.12.017
Schokker, D., I.N. Athanasiadis, B. Visser, R.F. Veerkamp, and C. Kamphuis. 2019. Using a Data Lake Stack in Animal Science. European Conference on Precision Livestock Farming (ECPLF), 26-29 August 2019, Cork, Ireland. In: Precision Livestock Farming ’19. B. O’Brien, D. Hennesey, and L. Shalloo (eds.), Fermoy Print and Design, p 140-144.
Schokker, D., I.N. Athanasiadis, B. Visser, R.F. Veerkamp, and C. Kamphuis. 2018. Using a data lake stack in animal science. Scientific symposium FAIR data science for green life sciences, December 12, Wageningen, The Netherlands. https://doi.org/10.18174/FAIRdata2018.16282