Data Engineering

Code on a black screen

The recent growth of data scientific methods into all academic disciplines has created an unprecedented demand for research computing support. This is particularly so in fields which may be regarded as “non-traditional” users of research computing techniques.

Moreover, the pervasiveness of digital methods across these disciplines offers the opportunity to accelerate the research translation pipeline by wrapping up intellectual property in innovative digital solutions.

Lancaster University has researchers who:

  • Need cross-departmental collaborations to develop data-scientific solutions for application-focused research questions [LMS];
  • Need to accelerate their impact by translating innovative new ideas into robust and reliable software solutions for others to use [Psychology, LUMS];
  • Wish to explore data-infrastructure techniques and solutions to accelerate their research [FASS, Mathematical Sciences].

In addition, Lancaster has a nascent Research Software Engineer community currently backed by N8CIR, but which needs to grow into a self-sustaining community of specialist
individuals filling the gap between corporate IT support (ISS) and the academic research community.

Aims

The recent growth of data scientific methods into all academic disciplines has created an unprecedented demand for research computing support. This is particularly so in fields which may be regarded as “non-traditional” users of research computing techniques. The pervasiveness of digital methods across campus offers the opportunity to explore how we can accelerate our research translation pipeline, wrapping up our research outputs in innovative digital solutions to benefit our target audiences.

The Data Engineering Theme supports researchers who:

  • Need cross-departmental collaborations to develop data-scientific solutions for application-focused research questions;
  • Need to accelerate their impact by translating innovative new ideas into robust and reliable software solutions for others to use;
  • Wish to explore data-infrastructure techniques and solutions to accelerate their research.

The Data Engineering Theme has a growing Research Software Engineer community currently backed by N8CIR, aiming to develop a self-sustaining community of specialist individuals to fill the gap between corporate IT support (ISS) and the academic research community.

Theme Functions

The proposed functions of the DSI Data Engineering Theme are:

Research

Helping researchers to achieve research reproducibility; promoting improved approaches to digital development, such that code becomes re-useable and; promoting data engineering as a route to increase research impact through translation and production of research ideas.

Knowledge Exchange

Providing a discussion platform “research software network” to encourage sharing of best data engineering practice; providing training in modern software development techniques and; raising awareness of, and providing KE in, the availability of new computing styles such as cloud, HPC, and virtualisation as solutions to technical barriers faced by researchers.

Research Software Engineering

Positioning the DSI as the lead organisation for a Lancaster RSE/RIE strategy; drawing RSE/RIEs together with the academic community, providing software development support for research projects and; facilitating career development pathways in research software engineering for RSE/RIEs and academics alike.

Research Software Engineering

Within the Data Science Institute, our aim is to improve the reproducibility and replicability of research by improving the reusability, sustainability and quality of research software developed across the University. We are currently funded by the N8CIR, and work closely with our partner institutions across N8 Research.

Research Software Engineering

Members

Christopher Jewell

Professor Christopher Jewell

Professor in Statistics

Bayesian and Computational Statistics, Biostatistics , CHICAS, DSI - Health, MARS: Mathematics for AI in Real-world Systems, STOR-i Centre for Doctoral Training