Hacky Hour: Demographics, Building Communities and Sharing Local Expertise

Amanda Miotto1, Nick Hamilton2

1Griffith University/QCIF, Brisbane, Australia, a.miotto@griffith.edu.au
2University of Queensland, St Lucia, Australia



Researchers starting their journey through data science often have an ambiguous path to follow. While online data science classes are plentiful, it can be challenging for those researchers, who have often never seen programming code before, to know where to start or how apply methods to their own data.

In Queensland, many of the universities, including Griffith, UQ, QUT USQ and JCU, have been supporting these researchers by running ‘Hacky Hours’; an open session where researchers can meet research software engineers and other researchers doing similar work to share knowledge, ask questions freely and come together to work on projects in a friendly environment.

Hacky hour groups are often successfully paired with workshops such as Software Carpentry to compliment learning after an initial introduction to a programming language or skill. This is also a good segway into discussions about practical reproducible practices (such as version control with Git and naming conventions) and data management (such as backups and data sensitivity).

These community building groups connect researchers often left in silos and expand expertise in the university. People come along both looking for help and offering to volunteer their time. Some clients even come along to socialize and meet others in their universities.

Hacky Hour communities have also been a way to connect with the wider research and technical communities, providing links to relevant meetups, hackathons, workshops offered outside their university and national resources such as NeCTAR cloud compute and virtual labs, local High Performance Computing (HPC) and other NCRIS activities. As many researchers can operate in isolated silos, this can often be the first time clients learn about these resources and initiatives. This also leads to attendees becoming involved with the wider community and building networks nationally.

The coordinators of Hacky Hours also work together to build a larger community. Coordinators share ideas for events, resources that are relevant and lessons learnt. This has been collated into a ‘Hacky Hours’ handbook available here: https://github.com/amandamiotto/HackyHourHandbook

Over the past three years, demographics have been collected across Queensland to show the diversity of the audience and the inquiries. Trends are studied to shape more targeted approaches. For example, UQ now hosts monthly bioinformatics specific Hacky Hours. The poster submitted highlights demographics and trends in these sessions


Amanda Miotto is an eResearch Senior Analyst for Griffith University and QCIF. She started off in the field of Bioinformatics and learnt to appreciate the beauty of science before discovering the joys of coding. As well as working on platforms around HPC, microscopy & scientific database portals, she is also heavily involved in Software Carpentry, Hacky Hours and was the main organizer for Research Bazaar Brisbane 2018.

HPC Software Image Test

Gerard Kennedy1, Ahmed Shamsul Arefin2, Steve McMahon3

1Research School of Engineering, ANU, Canberra, Australia
2Scientific Computing, IM&T, CSIRO Canberra, Australia
3IM&T, DST Group Canberra, Australia



In this work, we present a Software Image Test (SIT) tool that can test the software image of a node or nodes in a HPC cluster system. The script comprises of a collection of BATS tests that run in an automated SLURM job and the outcomes are sent to the executing user via email. The results help to decide if the software image is ready for rolling on the production cluster.


BATS (Bash Automated Testing System) [1] is a TAP (Test Anything Protocol) [2] compliant testing framework for Bash. It provides a simple way to verify the functionality of the executing programs. The test uses BATS files, which are essentially Bash scripts with special syntax for defining the test cases. If every command in a test case exits with a 0 status code (success) the test is considered as passed. See an example run below:

The TAP has implementations in C, C++, Python, PHP, Perl, Java, JavaScript, and others. We have chosen the Bash version due to its simplicity and matching skillsets available across our teams.

Figure 1: BATS tests developed for the HPC Software Image Testing.

With the syntax demonstrated above, we have developed a number of BATS tests (see Figure 1):

  • nvidia.bats: This script contains tests for the node GPUs. It uses Nvidia Validation Suite [3], where the outcomes help to quickly check the CUDA configuration/ setup, ECC enablement, etc. It runs Deployment, Memory, and PCIe/Bandwidth tests, giving a quick overview of the main components of the GPUs.
  • intel.bats: This test runs the Intel Cluster Checker [4]. This package requires ‘config.xml’, ‘packagelist.head’, ‘packagelist.node’ and ‘nodelist’ files setup to execute successfully. The ‘Config.xml’ determines the modules that will be tested, and can be altered if the user wishes. Some examples of the modules tested: ping, ssh, infiniband, mpi_local, packages (uses packagelists), storage, etc. This test requires multiple nodes to run on.
  • benchmark.bats: This test runs the Intel MPI Benchmark [5], which helps to ensure that MPI has been correctly configured on the node(s) in question. This test requires multiple nodes to run on.
  • apoa.bats: This test uses the NAMD [6] ApoA1 simulation and tests OpenMP, MPI, CUDA, etc. configurations.

Further to these tests, we have developed scripts a few more essential tests, e.g., checking the storage mounts, SLURM partitions, ssh host-keys, etc.


In order to execute the SIT script, user must provide a valid set of input arguments. The possible input arguments are; Partition: The SIT script runs as a batch job, therefore the user needs define the partition in which the node or the set of nodes are located. If the nodes that we wish to test are spread across multiple partitions, need to enter the partitions as a comma separated list. Node(s): The user can input as many nodes as they wish.

Here are four examples of valid initialization commands and input argument combinations:


The SIT sends an email to the executing user as shown in the Figure 2. As the tests outcomes are sent as an email, users do not need to wait on the console. Based on the results, we further tune the software image as required.

Figure 2: SIT outcomes are sent as an email when the job is finished.

Conclusions and future works

We have devised a TAP-based tool that can quickly check the suitability of a software image before rolling it onto the production cluster nodes. The script as demonstrated above is simple, but robust enough to accommodate as many factors we wish to test. Our future plan includes to create a GUI, possibly web version where user add/remove tests and get outcomes visually. We are also aiming to use the Nvidia’s DCGM tool which has recently replaced the validation suit.


  1. Stephenson, S., “BATS”,  https://github.com/sstephenson/bats
  2. Test Anything Protocol  http://testanything.org/
  3. Nvidia Validation Suit  http://docs.nvidia.com/deploy/nvvs-user-guide/index.html
  4. Intel cluster checker https://software.intel.com/en-us/intel-cluster-checker
  5. Intel MPI Benchmark  https://software.intel.com/en-us/articles/intel-mpi-benchmarks
  6. NAMD https://www.ks.uiuc.edu/Research/namd/


Gerard Kennedy: Gerard is working as a Research Assistant at the Research School of Engineering, ANU and the Australian Centre for Robotic Vision. He is developing an asparagus-picking robot and involved with the robot’s perception system, which includes areas such as camera calibration, image segmentation and 3D reconstruction. He has a B.E in Mechatronics, Robotics and Systems Engineering from the Australian National University.

Ahmed Arefin: Ahmed works within the High Performance Computing Systems Team at the Scientific Computing, IM&T, CSIRO. He has done his PhD and Postdoc in the area of HPC & parallel data mining from the University of Newcastle and he published articles in PLOS ONE and Springer journals and IEEE sponsored conference proceedings. His primary research interest focuses on the application of high performance computing in data mining, graphs/networks and visualization.

Steve McMahon: Steve McMahon is an IT professional with a strong background in science, software development and IT service delivery. He understands the IT infrastructure needs of scientists and has worked with many. He has worked on negotiating, designing and establishing IT infrastructure for several large scale science projects. He has done major software development in the fields of computational fluid dynamics and biophysics simulation. He was integral in planning and implementing a broad range of data services for the federally funded Australian Research Collaboration Service (ARCS).  Steve is currently working as the Engineering Manager for HPC and Computational Sciences at the DST Group.

The Climate Data Enhanced Virtual Laboratory (Climate DEVL): Enhancing climate research capabilities in Australia

Kate Snow1, Clare Richards2, Aurel Moise3, Claire Trenham4, Paola Petrelli5, Chris Allen6, Matthew Nethery7, Sean Pringle8, Scott Wales9, Ben Evans10

1Australian National University, Canberra, Australia, kate.snow@anu.edu.au
2Australian National University, Canberra, Australia, clare.richards@anu.edu.au
3Bureau of Meteorology, Melbourne, Australia, aurel.moise@bom.gov.au
4Commonwealth Scientific and Industrial Research Organisation (CSIRO), Aspendale, Australia, claire.trenham@csiro.au
5University of Tasmania and ARC Centre of Excellence for Climate Extremes, Hobart, Australia, paola.petrelli@utas.edu.au
6Australian National University, Canberra, Australia, chris.allen@anu.edu.au
7Australian National University, Canberra, Australia, matthew.nethery@anu.edu.au
8Australian National University, Canberra, Australia, sean.pringle@anu.edu.au
9University of Melbourne and ARC Centre of Excellence for Climate Extremes, Melbourne, Australia, scott.wales@unimelb.edu.au
10Australian National University, Canberra, Australia, ben.evans@anu.edu.au


A major focus of the Australian climate research community currently is the preparation for and contribution to the World Climate Research Programme (WCRP) Coupled Model Intercomparison Project phase 6 (CMIP6). CMIP6 is an internationally coordinated research activity that provides climate model output from a series of carefully designed and targeted experiments. The analysis of CMIP6 data will form the basis for assessments by the Intergovernmental Panel on Climate Change (IPCC) and inform policy- and decision-makers around the world.

For Australia, CMIP6 will underpin research into historical climate variability as well as future projections research into the timing, extent and consequences of climate change and extreme events. This work may be used to assist Australian government, business, agriculture and industry to manage climate risks and opportunities related to climate variability, change and extremes.

Climate research is computationally-demanding and requires data-intensive High Performance Computing (HPC). More than 20 PBytes of CMIP6 data are expected globally, the largest collection of climate data ever produced, of which a substantial portion will be made available and analysed at NCI. The complexity and volume of CMIP6 means that data management is an impossible task without a national infrastructure approach and deeply collaborative effort, and NCI is an essential component in realising climate research in Australia. The Climate DEVL addresses the software- and data-management aspects of these needs, while NCI and the leaders from the Climate community work to secure funding for sufficient data storage infrastructure needed for the CMIP6 endeavour.

The Climate Data Enhanced Virtual Laboratory (DEVL) has focused on some key components of the infrastructure to manage this massive data archive and make accessible for CMIP6-based research in Australia. It builds on previous Australian e-infrastructure programs, the Climate & Weather Science Lab, and the National Earth Systems Data Collection and Data Services programs. It also supports NCI’s leading role in international collaborations, most notably the Earth Systems Grid Federation (ESGF) that provides the international federated capability for CMIP data. The value of this work over a long time has required the funding from various parties including other NCRIS funding programs ANDS, RDS, and NeCTAR NCRIS programs. This infrastructure directly supported other major investments from government-funded research from CAWCR (Collaboration for Australian Weather and Climate Research), NESP (National Environmental Science Program) and the ARC CoE for Climate System Science (ARCCSS) and ARC CoE for Climate Extremes (CLEX).

The Climate data at NCI is provided using the principles of FAIR: Findable, Accessible, Interoperable and Reusable. Providing a FAIR data service for such a large and complex data collection exposes significant data management challenges. NCI’s Data Quality Strategy (DQS) delivers data curation practices that permit FAIR standards and interdisciplinary data availability. This service permit streamlined access and analysis of CMIP6 data, enabling efficient state-of-the-art climate science research to be undertaken.

The unique challenges of the CMIP in both size and complexity has required new services to be developed and then made available as well managed operational services. The Climate DEVL has defined and developed the mechanisms for improved accessibility and usability of the data. One example is the need to find what data is available at NCI for use in analysis. This need has been addressed through the NCI’s Metadata Attribute Search (MAS). MAS provides consistent access to the information contained in the climate data collections by harvesting the metadata within the millions of self-describing files that constitute the CMIP data collection. The MAS also underpins a python-based API called CleF, developed by ARCCSS/CLEX, which provides command line search tools for accessing this data. CleF provides researchers with an easy interface to use the ESGF search API to discover what CMIP data has been published that match their specified requirements (experiment, variable, etc.) but is not yet available at NCI. The tool will be extended to enable users to then submit a data download request to add to the NCI CMIP6 replica service.

Another aspect of the Climate DEVL has been to focus a community approach to define the highest priority CMIP6 data needing to be replicated in Australia for local analysis, to permit timely development and publication of scientific research papers analysing the CMIP6 data as it becomes available. The DEVL also supports the evaluation of various model analysis tools, which provides an opportunity for the community to develop standardised workflows for data analysis contributing to the aforementioned research papers.

The Climate DEVL also provides a home for coordinating the ongoing development and availability of training materials necessary for a streamlined user experience. The extensive knowledge and interdisciplinary topics that span CMIP mean that effective training is needed, including face-to-face tutorials, online self-paced learning materials, and trainer training. The combined effort of NCI, CLEX, CSIRO and BoM permit such collaborative training efforts to benefit the entire Australian climate science community.


Dr Kate Snow: I began at the National Computational Infrastructure (NCI) at the Australian National University in November 2017 as a Research Data Management Specialist. Prior to my position at NCI I completed a PhD in physical oceanography at the ANU and a two-year post-doc position researching Antarctic ice-sheet dynamics at Edinburgh University, Scotland. I am able to apply my research skills form the climate sciences at NCI to help inform data management practices to benefit climate research in Australia. My current role focuses on aiding in providing the support, tools and infrastructure to manage the Coupled Model Intercomparison Project phase 6 (CMIP6) to help provide Australian climate scientists with the capabilities to undertake state-of-the-art climate science.

The Curtin Institute for Computation – meeting the increasing demand for research software and computing skills across all faculties

Rebecca Lange1, CIC data scientist team2
1Curtin Institute for Computation, Curtin University, Perth, Australia, rebecca.lange@curtin.edu.au
2Curtin Institute for Computation, Curtin University, Perth, Australia, curtinic@curtin.edu.au



In the era of ever growing data and interconnectivity, computation fundamentally underpins the majority of internationally competitive research across all fields and disciplines. As the demand for computational skills has grown, so too has the need for dedicated support for the research community. The Curtin Institute for Computation (CIC) was therefore established to meet this increasing demand at Curtin University.

The CIC is a truly multidisciplinary institute, inspiring and fostering collaboration across computer science, engineering, sciences, business, social sciences and the humanities. It has five themes; big data analytics, simulation, modelling and optimisation, visualisation, and education.

While the CIC is a virtual institute, it has a core team of data scientists who assist Curtin University researchers across all fields with their computational modelling, data analytics, and visualisation problems. Furthermore, the CIC data scientists are actively involved in creating opportunities for researchers to network and share ideas, and they develop and oversee computational training offered by the institute.

In this e-poster we provide an overview of the structure of the CIC and its achievements since the core data science team became operational in 2016. Furthermore, the poster will offer the opportunity to explore several case studies from across the institute, highlighting the need for, and success of, a central data scientist team supporting researchers from all fields.


Rebecca Lange received her PhD in astronomy from the International Centre for Radio Astronomy Research at the University of Western Australia.

Before Rebecca moved to Australia she studied Astronomy and Physics at Nottingham Trent University where she also worked as a research assistant in scientific imaging for art conservation and archaeology. Her work there included the development and testing of instruments and software for imaging and spectroscopy as well as the organisation and supervision of field trips, which often required liaising with art curators and conservators.

Throughout her studies and research Rebecca has gained extensive programming as well as data analytics and visualisation experience in various programming languages.

Currently she is working as a data scientist for the Curtin Institute for Computation where she helps researchers by providing data analytics and computational support and training.

Research data: To share or not to share?

Michelle Krahe1, Julie Toohey2, Malcolm Wolski3, Paul Scuffham4, Sheena Reilly5

1Health Group, Griffith University, Gold Coast, Australia, m.krahe@griffith.edu.au
2Library and Learning Services, Griffith University, Gold Coast, Australia, Julie.Toohey@griffith.edu.au
3eResearch Services, Griffith University, Nathan, Australia, m.wolski@griffith.edu.au
4Menzies Health Institute Queensland, Griffith University, Gold Coast, Australia, p.scuffham@griffith.edu.au
5Health Group, Griffith University, Gold Coast, Australia, s.reilly@griffith.edu.au



It is well accepted that data sharing facilitates the progress of research and is vital towards science that is open; where data is easily accessible, intelligible, reproducible, replicable, and verifiable. Despite the extensive benefits of data sharing, it is yet to become common practice among health and medical researchers. Evidence-based interventions that aim to encourage sharing and reuse of research data are lacking [1, 2].

In this study, we assess the current practices, and identify barriers and enablers of data sharing behaviours of health and medical researchers using the theoretical domains framework (TDF) and the COM-B model (capability, opportunity, motivation and behaviour). Outcomes from this study will provide a systematic and theoretically-based approach to designing interventions to promote data sharing practices among health and medical researchers.


This project employed a cross-sectional, observational study design and sampling technique. Data was drawn from a survey designed by the investigators to evaluate research data management (RDM) practices of health and medical researchers. The main outcome measures were derived from questions about researcher’s current data sharing practice, willingness and responses to statements designed to explore aspects of data sharing behaviours.

Participants were drawn from a research institute at Griffith University, Australia and invited by a number of internal broadcast emails to complete an anonymous online survey. Participation in the study was voluntary and approved by the Human Research Ethics Committee (GU/HREC#2017/457) of Griffith University.

Data were mapped onto the TDF domains and the COM-B model [3, 4]. This process identified emerging themes related to enablers and barriers to data sharing and characterisation of behavioural functions that can be targeted by specific interventions.


In total, responses from sixty-five researchers were included in the analysis. The majority were members of academic staff (69%); 25% were research fellows or research assistants and the remaining were adjunct staff (6%). All researchers are affiliated with the university Health School.

Data Sharing Behaviours

Of the 38% of researchers that had ever shared research data, 67% had done so after the research was published. The most common way to share data was as supplementary journal material, at conferences, or in institutional/discipline specific repositories. Only 10% of researchers had shared data publically (i.e. repositories or open access platforms), motivated by: funding or journal requirements (70%), to increase the impact/visibility of the research (40%), or for public benefit (40%). Researcher’s attitudes (i.e. willingness) to sharing data was positive but influenced by: (i) who they were asked to share with, and (ii) whether the research had been published.

Enablers and Barriers to Sharing

Themes that were mapped directly to the TDF domains and COM-B model of behaviour change (Table 1). Four enablers and six barriers were identified and is further explored using qualitative analysis.

Intervention Functions to Change Behaviours

Using the Behavior Change Wheel (BCW) we have identified the most effective intervention functions to promote the enablers and mitigate the barriers of researcher’s data sharing behaviours. For example, to address the behaviour associated with psychological capability (i.e. researchers who lack an understanding of the data sharing process, are less inclined to share their data), the BCW identifies education, training or enablement interventions as the most effective.

Table 1. Mapping of TDF domains to COM-B behavioural dimensions and behaviour statements.

TDF Domain COM-B Dimension Behaviour Statement
Knowledge Psychological Capability 1)     Researchers, who lack an understanding of the data sharing process, are less inclined to share their data.
Skills Physical Capability x  The majority of researchers do not know how to share their data, where to share their data, or whom they should share.
Professional role and identity Automatic Motivation √  If guaranteed credit for its use and/or it increased the impact / visibility of their research, researchers would be more willing to share their data.
Environmental context and resources Physical Opportunity x  Researchers lack time and resources to prepare their data for sharing.
√  Researchers are more likely to share data if it is a funding, institutional or journal requirement.
Social influences Social Opportunity √ Researchers would share their data if they knew it had public or patient benefit.
Beliefs about capability Reflective Motivation x  Most researchers do not know whether it is their responsibility to share data.
Beliefs about consequence x  Researchers want to protect the confidentiality of their data and are concerned about the ethics of data sharing.
√   If researcher’s trust the person requesting the data, they are more likely to share it
x  Researchers are concerned about their research or IP being stolen, misinterpreted or misused.

A cross represents a barrier and a tick represents an enabler.


A wide range of barriers and enablers were identified which influences researcher’s capability, opportunity and motivation to engage in data sharing practices. This study provides a theoretical starting point in making a behavioural diagnosis and the results will be used to inform the development of interventions designed to increase data sharing practices among health and medical researchers.


  1. Rowhani-Farid A, Allen M, Barnett A.G (2017) What incentives increase data sharing in health and medical research? A systematic review. Research Integrity and Peer Review 2(4). https://doi.org/10.1186/s41073-017-0028-9.
  2. Fecher B, Friesike S, Hebing M (2015) What Drives Academic Data Sharing? PLoS ONE 10(2):e0118053. https://doi.org/10.1371/journal.pone.0118053.
  3. Cane J, O’Connor D, Michie S (2012) Validation of the theoretical domains framework for use in behavior change and implementation research. Implementation Science 7(37).
  4. Michie S, van Stralen M, West R (2011) The behavior change wheel: A new method for characterizing and designing behavior change interventions 6(42).


Dr Michelle Krahe is a research professional with a passion for strategy, development and innovation in health. She is a Senior Research Fellow within the Health Executive at Griffith University and a Visiting Research Fellow with Gold Coast University Hospital. Michelle is responsible for the development and management of research initiatives for the Pro Vice Chancellor (Health) and has over 12 years’ experience in clinical research, working in academia, health services and research institutes.

Create, organise, keep and find: Data management practices of health and medical researchers

Dr Michelle Krahe1, Ms Julie Toohey2, Mr Malcolm Wolski3, Professor Paul Scuffham4, Professor Sheena Reilly1

1Health Group, Griffith University, Gold Coast, Australia, m.krahe@griffith.edu.au
2Library and Learning Services, Griffith University, Gold Coast, Australia, Julie.Toohey@griffith.edu.au
3eResearch Services, Griffith University, Nathan, Australia, m.wolski@griffith.edu.au
4Menzies Health Institute Queensland, Griffith University, Gold Coast, Australia, p.scuffham@griffith.edu.au
5Health Group, Griffith University, Gold Coast, Australia, s.reilly@griffith.edu.au



Research data is a highly valuable resource, requiring much time and money to be produced and often having a significant value beyond its original use. Therefore, caring for research data is essential to its integrity. Research data management (RDM) is part of every research process and concerns the organisation of data, from its entry to the research cycle through to the dissemination and archiving of valuable results. This includes: how you create data and plan for its use, organise its structure and maintain integrity, keep it stored securely and well preserved, and ensure others can find, access, integrate and reuse the data.

RDM best practice is of particular interest to higher academic institutions involved in the development of training programs that support researchers [1, 2]. Therefore, understanding researcher practices will help articulate planning strategies for Institutional services and support, and outline essential areas for future investment in data management. The evidence to date identifies the complexities in developing RDM programs. This project sought to understand the current RDM practices of health and medical researchers from an academic institution in Australia. The results of this evaluation will assist in designing interventions for future RDM skills training and development, process and policy and support services.


This project employed a cross-sectional, observational study design and sampling technique. Participants were drawn from a research institute at Griffith University, Australia and invited by a number of internal broadcast emails to complete an anonymous online survey.

A 37-item survey was constructed based upon an iterative process between the study investigators, in consultation with research leaders, and a review of the published literature. Survey questions included five key categories: (i) researcher characteristics; (ii) RDM practices; (iii) data storage and retention; (iv) data sharing practices; and (v) RDM training and development. Participation in the study was voluntary and approved by the Human Research Ethics Committee (GU/HREC#2017/457) of Griffith University.


A convenience sample of 81 members of a research institute (68 academic staff; 13 post-graduate students). Our evaluation indicated that RDM practices varied greatly, which is likely to be influenced by the researcher’s level of experience or the RDM practices carried out within their teams or by their supervisors. A selection of results are described below.

Create: Only 30% of researchers used data management plans (DMP). The top reason for having a DMP was: ‘it is good research practice’, and the top reason for not having a DMP was: ‘I’m unsure of what a DMP is’.

Organise: Data was typically sourced from surveys, interviews and experimental studies and the majority of respondents indicated that either the university, their research team or they themselves owned the data. Most researchers (86%) were responsible for the day-to-day management of the data, while others (42%) indicated that they had a designated person, such as a research manager of research assistant. Planning for the reuse of data was explored through patient consent and data format collected in clinical research (Figure 1).

Figure 1. The level of consent and data format collected by health and medical researchers. *according to the NHMRC National Statement of Ethical Conduct in Human Research

Keep: The storage of research data at three key time points is illustrated in Figure 2 and indicates that personal devices; in particular removable media such as USB and hard drives, are the most popular storage solution for research data.

Figure 2. The data storage practices of health and medical researchers at three time points (T1: Create – during data collection; T2: Organise – during data analysis and write-up; T3: Keep – long-term storage and archiving), categorised by storage facilities (personal, institutional, external).

Find and Share: The majority of respondents (65%) had never used data from existing datasets, databases and/or repositories, only 35% had ever shared their data outside the research project and only 16% had made their data publically available. The barriers and facilitators of data sharing practices is explored in more detail using a theoretical framework for behavior.


Evaluating the data management practices of health and medical researchers, contextualised by tasks associated with the research data lifecycle, is effective in informing RDM services and support. This study recognises that targeted institutional strategies will strengthen researcher capacity, instill good research practice, and overall improve health informatics and research data quality. Acknowledging this gap in practice is especially important given that national investment primarily focuses on down-stream activities, such as building sophisticated data storage and access facilities, e-research tools, and high-performance supercomputing.


  • Surkis, A., et al., Data day to day: Building a community of expertise to address data skills gaps in an academic medical center. J Med Libr Assoc, 2017. 105(2): p. 185-191.
  • Whitmire, A.L., M. Boock, and S.C. Sutton, Variability in academic research data management practices: implications for data services development from a faculty survey. Program, 2015. 49(4): p. 382-407.


Dr Michelle Krahe is a research professional with a passion for strategy, development and innovation in health. She is a Senior Research Fellow within the Health Executive at Griffith University and a Visiting Research Fellow with Gold Coast University Hospital. Michelle is responsible for the development and management of research initiatives for the Pro Vice Chancellor (Health) and has over 12 years’ experience in clinical research, working in academia, health services and research institutes.

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