Data Fluency for Research


Work smarter, not harder. For researchers, this means knowing how to use, explore, interpret and visualise data in a meaningful way and effectively communicate your research and ideas. That’s what data fluency enables you to do.

There is an increasing need for all researchers and professionals to be able to understand and write code and interact in the digital enquiry space. Data and text mining, data visualisation and analysis, and programming for research are increasingly relevant across all disciplinary areas.

Data Fluency for Research is a cross-disciplinary initiative to develop researcher capability at Monash University. We’re building a community of practice made up of like-minded researchers who are passionate about learning, sharing and helping other researchers use digital research tools. Whether you’re a novice, expert, or somewhere in between, you’re welcome to join the community. It is an initiative of Monash Bioinformatics and the Library.

We aim to be self-sustaining, with everyone being able to learn from and teach others through:

Software and Data Carpentry workshops on a range of applications such as R and Python for coding, Tableau for data visualisation, among others
Software Carpentry instructor training
Community of Practice networking events and seminars
Weekly drop-in sessions, hackathon events, tech talks and more.  @resdatflu

Monash University Research Data Archive (MURDA) Sentencing

Monash University has its own Retention and Disposal Authority (RDA), based on Public Record Office of Victoria (PROV) “sentences”. Sentencing is the process of matching information held by the organisation to a specific class of a records authority. This helps determine the value of the information and how it should be
managed throughout its lifecycle.

MURDA is a cross-organisational initiative to appropriately re-house 235 (at present) discreet research collections, representing approximately 150TB of
data, into really cold storage or, when appropriate, destruction. We have identified that 57% of this data can safely be destroyed in 2018, representing a real cost saving for the University.

A goal is to be confident that research data with an assumed life-sentence are those really intended for long-term preservation, and they are described and stored appropriately.

Having established a base-line for 2018 destructions, we are now developing a roadmap for 2019 to 2023 destructions by establishing review dates on present