Instructions on how to become a Fellow

Applications open for the second cohort

Deadline: 10 July 2022 at 23:59 BST

Not sure yet? Join the introductory webinar

9 June 2022 at 14:00 BST

Register for the meeting

What are we looking for?

Eligibility criteria

  1. You are based in the UK or with a formal affiliation with a UK based institution or office.
  2. Your training plan must be primarily focused on improving UK capability in the area of Research Data Management (RDM) in the life or biomedical sciences.
  3. You are able to demonstrate practical expertise in your field of data management. Examples
  4. of this could include expertise in a data type (e.g. transcriptomic data, biomedical imaging) or a data community (e.g. plant science, health data)
  5. Your training should be accessible without charge to members of your research institute or community.
  6. Training should not be part of an existing degree course. 

Roles

Roles working with data , irrespective of career stage.

  • Researchers at all career stages, including PhD students
  • Data Stewards
  • Core support (Bio)informaticians
  • Technicians
  • Facility Operators
  • Research Software Engineers
  • IT Support Staff
  • Data Policy Officers
  • Anyone passionate about data management training in the life sciences

Our Fellows will come from a range of career stages, and work in areas relevant to the life sciences. We encourage applications from anyone who is eligible and is excited by the opportunity to develop and deliver data management training offerings in the life sciences.

Training activities

Your training plan must be primarily focused on improving UK capability in the area of Research Data Management (RDM) in the life or biomedical sciences.  The focus should be on RDM and not the analysis of data.  

We welcome applications from any discipline within the life or biomedical sciences, where examples include but are not limited to:

  • Basic data management and sharing skills used in research (e.g. for the bench scientist)
  • Knowledge of specialised data types (e.g. brain imaging, proteomics)
  • Knowledge of data management in specialised communities/roles (e.g. marine biology, biocuration)
  • Data management in large data-driven projects (e.g. clinical trials)
  • Data policy
  • Building IT infrastructure for secure data management and sharing
  • Data ‘readiness’ for Artificial Intelligence and Systems Biology