Master Fellowship within the Project ELIXIR

q Would you like to work in computer programming in the context of data analysis and data management and a particular interest in phenotyping and molecular biology? Apply now for this exciting Master Fellowship in the scope of the project “Increasing Plant data findability and reuse beyond ELIXIR”. During the time of the project, you will have the opportunity to work on the Genomics of Plant Stress Unit, at ITQB NOVA, as part of | ELIXIR Portugal.

Seminar: Latest developments in the FAIR data ecosystem

Good data stewardship is central to allow addressing tomorrow’s sustainability questions today. I will give an overview on important factors to consider FAIR data stewardship and solutions to help with the development of a good data stewardship plan. Although, everybody nowadays claims to be FAIR, there is no real good definition yet what FAIR in practice means. To solve this, FAIR metrics are under development and this initiative will be briefly discussed. From my background as Plant Breeder, I will address where the FAIR data principles play a role in my research. Although many of the important tools to do so are available, adaptation of these in breeding research community is slow. However, this will need to change and to exemplify this, I will briefly discuss the ambitions and expected outcome of the H2020 projects TRADITOM and G2P-SOL, and end with illustrating the Farm Data Train concept.

Plant Phenotyping-Genotyping Data Management Workshop

Plant phenotyping research has gone through a data revolution with the automation of plant phenotyping platforms, making it critical to adopt good data management practices in order the exploit the torrent of data to its full potential. Concretely, data should be published in a Findable, Accessible, Interoperable and Reusable (FAIR) way in order to enable the integration of data from disparate sources and the discovery of new knowledge. Yet, this is one of the more challenging domains to standardize, as it is extremely heterogeneous in terms of experimental settings and types of data.