Entities and identities within the IPDLN: Are we a distinct discipline?

Wednesday 24 August, 12:30 – 13:30
The Great Hall, Room GH001, Ground Floor


Part of the conversation at the 2015 IPDLN meeting in St. Andrews centred on what differentiates us and our Network. We clearly have a strong sense of professional kinship, but also a desire for clarity on what binds us, what our shared purpose is, and how the IPDLN can best support all of us. This workshop is an opportunity to move one specific aspect of this conversation forward: Are we developing a new scientific discipline? If so, what is it, and what are its boundaries?


Based on the 2015 discussion and some further background work that has taken place in the interim, Population Data Science is one potential term for the discipline encompassing and distinguishing our work. The following ideas are put forward as discussion openers:

What is it?

Population Data Science is a multi-disciplinary field aimed at obtaining population-level insights by maximising the use of big data pertaining to the lives of individuals, and turning it into information to make a positive impact on citizens and society.

What does it do?

Population Data Science encompasses all activities that relate to privacy-sensitive big data that focuses mainly (though not exclusively) on individuals, including: data storage and management; architectures and infrastructures; legal and regulatory issues; privacy-protection methodologies; data and linkage quality; analytical advances; accessing distributed data; using big data; linking to emerging/complex data types; outcomes research; public involvement and engagement; capacity building; and delivering and measuring impact.

What does it use?

Population Data Science uses technology to develop infrastructures and analytical solutions to manage, curate and provide secure access to big data using proportionate control measures for data privacy and utility. It makes use of multiple sources and types of big data, in standalone or linked forms. This includes the use of structured micro-data, image, free-text, spatial, omic, wearable device, social media and other emerging data types.

Who is invloved?

Population Data Science includes professionals from multiple disciplines including computer science, information governance, systems architecture, data management, software development, data analytics, privacy-protection methodologies, data mining, research methodologies, epidemiology, statistics, public engagement, etc.


This will be an interactive session, exploring whether and how Population Data Science should be defined, the benefits and drawbacks of pursuing defining a discipline, what should be included and excluded from any definition, and what opportunities and responsibilities would be implied. We will leave the workshop with clear and explicit next steps.

Participants will be asked to suggest short definitions from their work area: this can be in free text, in the form of a Haiku or other short poem or illustration. Feel free to work on these in advance of the workshop! Prizes will go to the most popular of these submissions.

All are welcome!

For enquires about the workshop, please contact the Workshop Coordinators:

Kimberlyn McGrail
University of British Columbia


Kerina Jones
Swansea University