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Your role: Research Software Engineer (RSE)


Research software engineers (RSE) in the life sciences design, develop and maintain software systems that help researchers manage their software and data. The RSE’s software tools and infrastructure are critical in enabling scientific research to be conducted effectively.

In this role, it is essential that you implement software systems that meet the needs and requirements of researchers. Software needs to be reliable, scalable, secure, well-documented, and easy to use.

You often work in research-intensive environments such as universities, research institutions, or government agencies. Collaboration with researchers, data scientists, data managers, and IT professionals is vital.

Data management responsibilities

As a research software engineer, your focus is on the liaison between researchers and people involved in IT infrastructure and services. You are responsible for the implementation of IT infrastructure solutions and the access to data and software.

In your role of research software engineer, you may need to:

  • Identify the requirements of and provide access to data infrastructure and tool landscape for researchers according to the research data management policies;
  • Advise and assist researchers on short and long term actions for data infrastructure and tools including (meta)data standards;
  • Ensure the compliance of the data infrastructure and tool landscape with codes of conduct and regulations;
  • Align the data infrastructure and tool landscape to the FAIR data principles and the principles of Open Science, and facilitate and support FAIR data;
  • Align the data infrastructure and tools management inside and outside the organisation, in close collaboration with the IT department;
  • Facilitate the availability of local data-infrastructure and tools for FAIR and long term archiving of data.

Data management guidance

RDMkit pages

  • The data organisation page helps with file naming, versioning and folder structures.
  • Data documentation, such as README files and metadata, helps to make data understandable and reusable.
  • The identifiers page gives advice on how to create and use identifiers. Machine actionability helps to automatically access and process research data.
  • Consider the best practices and technical solutions for data analysis.
  • Data protection helps you to make research data GDPR-compliant.
  • Data sensitivity helps you to identify sensitivity of different research data types.
  • Licensing gives advice on how to assign a licence to research data.
  • Consult the data transfer page for information about transferring large data files.
  • The data brokering page provides information on uploading data to repositories and metadata requirements for the process.
  • The data storage page helps to consider short and long-term storage, during and at the end of a project.

Other resources

  • FAIR Principles gives an overview of how to make data Findable, Accessible, Interoperable and Reusable (FAIR).
  • FAIR Cookbook gives step by step recipes for common data management tasks, including levels and indicators of FAIRness, technologies, tools and the standards available.
  • FAIRsharing is a portal where you can search for databases, standards and policies.
  • Learn from experts in the field in the RDNL & DCC Delivery RDM Services course.
  • Software Carpentry helps set up training in basic lab skills for research computing.
  • ELN Guide is a useful resource on Electronic Laboratory Notebooks (ELN).