About Us

Welcome to DSSRN

The Data, Systems and Society Research Network (DSSRN) is a collaborative research network focused on building a community of research scholars, and data infrastructure, to support internal collaborations and external partnerships, as well as sharing knowledge, tools and resources in the broad area of data, systems, and society.

The Network provides leadership across the University of Melbourne to facilitate the development of research initiatives, support collaborative research, and assist in forming research clusters in conjunction with suitable partners by planning and delivering targeted research events.

Problem Statement

With the increasing sophistication and ubiquity of data garnering technologies, the quantity, resolution, and scope of data that we can collect is growing at an incredible rate. This has led to the popularisation of the notion of "big data" analytics. Beyond the celebratory rhetoric which sees governments, industry and the academy invest in building enhanced data capacity, the true potential of having more data is highly dependent on format and context, behind which are hidden many domain-specific subtleties, constraints, risks and blind-spots.

Many fields that have typically been seen as "data-poor" are now becoming "data-rich", and face the prospect of being able to explore many new questions. With greater quantities, and new types of data available, there are new opportunities to borrow from methods and tools that have been developed in traditionally data-rich disciplines. However cross-disciplinary knowledge transfer is critical to building meaningful and appropriate capacities around data.

Methods cannot be borrowed from other domains and applied indiscriminately to any dataset; just because something can be computed does not mean that the result has meaning. Locked away in many disciplines is a wealth of knowledge, much of it implicit, which must be understood in order to conduct valid research. Big data doesn’t always translate to better or more objective results.

Accordingly, we need to move beyond the celebratory moment of "big data" and deal with issues of definitional ambiguity, methodological constraints, ethics, governance, sustainability and other domain-specific complexities that do not preclude fruitful cross-disciplinary collaborations, but which require nuanced understanding and consideration from all sides.

Our Mission

We need to bring experts from difference disciplines and domains together to identify and understand these issues, so that we can identify real opportunities for cross-disciplinary research. Our mission is to help these people to find each other, to critically evaluate the applicability of available tools, methods, and datasets, and to help them foster new and exciting collaborations to address (and define) the real problems of working with unprecedented data.