FAIR Data Steward

Our ideal candidate for this position has experience with the FAIR principles and metadata schema developing to meet the requirements and standards set for and from the scientific domain as well as by the organization. You will work in a stimulating medical environment and will actively participate in an international network of experts who collaborate on best data stewarding practices for rare disease data.

The work focuses on the FAIRification of multi-omics data. It is now widely acknowledged that understanding the mechanisms underlying health and disease requires the concerted study of different molecular (-omics) levels (DNA, RNA, proteins, metabolites). Moreover, a transition from static simplified views to dynamic comprehensive views on molecular pathways encompassing (e.g. genomics, proteomics and metabolomics) is needed.

Currently, this is not simple nor scalable. There is an increasing need to combine -omics data from different sources, but the data and their associated metadata are not always FAIR. For that reason, the Netherlands X-omics Initiative has developed a multi-omics data infrastructure that facilitates FAIR-compliant multi-omics data storage and analysis.

To help make data FAIR, the consolidation of metadata across omics is necessary. In X-omics, the ISA metadata schema is used as a standard metadata schema to capture metadata about (-omics) experiments.

Would you like to contribute your expertise to design and develop a consolidated ISA metadata schema over multi omics? Please don’t hesitate to start your application.

Tasks and responsibilities

• Collaborate with X-omics omics researchers, align with Health-RI omics working group, and FAIRification stewards in Radboudumc to ensure FAIR data principles adherence.
• Receive formal training on FAIR principles through tutorials and mentoring by colleague data stewards.
• Represent the onboarding team at events related to -omics metadata.
• Familiarize with data management, including drafting data validation and management plans.
• Develop shared work processes and standards for FAIR data.
• Raise awareness at national, regional, domain, and local levels about the importance of FAIR data.
• Engage with clinicians and researchers, providing support and training in FAIR data management.
• Co-develop software, tools, services, standard operating procedures (SOPs), and policies for FAIR data capture and publication.
• Participate in national and international data stewardship discussion groups and initiatives.