The Social Science Research Infrastructure Network (SSRIN) is a collaborative initiative to tackle social science infrastructure challenges identified in the Decadal Plan for the Social Sciences in Australia, developed by the Academy of the Social Sciences in Australia (2024).

Objectives

SSRIN enhances social science research capacity in Australia by:

  • Improving discovery of, and access to, existing large-scale administrative data

  • Building the foundations for new integrated data assets that
    offer significantly expanded research potential

  • Improving critical individual research capabilities

  • Improving the ethical use of Indigenous administrative data

  • Building promising stakeholder networks for continued and innovative infrastructure projects

Project Lead

Prof Wojtek Tomaszewski
Institute for Social Science Research
The University of Queensland

Learn more about the project team

Project Partners

  • The University of Queensland

  • Australian Bureau of Statistics

  • National Centre of Healthy Ageing (Monash University)

  • The University of Western Australia

  • Australian National University

  • Life Course Centre

  • Australian Research Data Commons

The initiative is organised in five substantial components:

Integrated Data Usability

Health and Social Science Data Integration

Public Social Science Data

Guidelines for Users of Indigenous Data

Training and Capacity Building

Project principles

Expert-informed

We consult with technical and substantive experts in defining inputs and outputs

Collaborative

We work together, share information, experiences, learnings and responsibility

End-user informed

We systematically involve end-users of research infrastructure in specifying and testing outputs

Ethical

We strive to consistently operate
ethically with respect to stakeholders, team members and the public

Feasibility-driven

We reflect on constraints and opportunities, define achievable outputs and allocate resources prudently

Flexible

We adapt and adjust to evolving circumstances and learnings

Impact-driven

We seek significant impacts on research behaviours/processes and research potential

Self-reflective

We regularly monitor and reflect on our processes and decision-making