The recently signed Australian BioCommons / ELIXIR Collaboration Strategy Agreement allows for unprecendented cooperation on bioinformatics infrastructure between Australia and the EU.

A/Prof. Andrew Lonie1, Dr Rhys Francis1, Dr Corinne Martin2, Dr Andrew Smith2, Dr Niklas Blomberg2, Dr Jeff Christinasen3

1Australian BioCommons (University of Melbourne), Carlton, Australia
2ELIXIR, Wellcome Genome Campus, United Kingdom
3Australian BioCommons (Queensland Cyber Infrastructure Foundation), St Lucia, Australia

 

In this presentation I will discuss the mutual benefits codified in the Strategy Agreement, and our operational approach to achieving them.

Introduction:

A new Collaboration Strategy between ELIXIR and the Australian BioCommons creates a cooperative plan to exploit international synergies between the two research infrastructures. This three-year collaboration will actively involve Australian BioCommons in many of the activities related to the European life science infrastructures.

Methods:

A number of common alignment areas have been identified for collaboration including the adoption of international standards in software platforms, workflows, tools and data (such as the Global Alliance for Genomics and Health (GA4GH)). Supporting global research communities (such as in metagenomics methods, biodiversity, de-novo genome assembly, phylogenomics, plant phenotyping-genotyping), and the delivery of federated solutions to human data preservation and research access are prime examples of why these partners have come together to formalise a Collaboration Strategy.

Results:

ELIXIR and the Australian BioCommons are both already heavily involved in methodological platform and tool collaboration — a leading example of a joint project of interest being Galaxy, the open, web-based platform for collaborative research. International collaboration on training and training materials in bioinformatics has also begun, with ELIXIR Training Platform partners participating in the Australian BioCommons Training Advisory Group. Such initiatives will continue to strengthen links between BioCommons and ELIXIR over the course of this agreement.

Conclusion:

The collaboration strategy between ELIXIR and the Australian BioCommons promises to identify our international synergies as we partner to tackle our shared challenges in biological research.


Biography:

I am Director of the Australian BioCommons and A/Prof in the Faculty of Medicine, Densitry and Health Sciences, and the School of Computing & Informatics Systems at the University of Melbourne.

Curating, Discovering, and Disseminating Research Elements Using iRODS

Mr David Fellinger1

1iRODS Consortium, Chapel Hill, United States

It is indisputable that we are in the age of “Big Data”. Data must be managed that has the attributes of variety, velocity, volume, and veracity. The attribute of velocity is growing as the global bandwidth increases.

The Integrated Rule-Oriented Data System (iRODS) is an open source technology initiative that has been developed to manage data from raw instrument readings through publication solving the challenges of curation and addressing secure collaboration.

Data management solutions of worldwide research institutions were presented at the recent iRODS User Group Meeting.

CyVerse in the US, provides a Discovery Environment (DE) for a research data repository with over 80,000 users with 5,690 participating academic institutions and 2,438 non-academic organizations.

In Europe, the EUDAT Collaborative Data Infrastructure (CDI) was formed to host the data of over 50 universities and research institutions.

In the Netherlands, SURF has built a Research Data Management (RDM) framework entirely based on iRODS to manage their consolidated data.

In Sweden, the Swedish National Infrastructure for Computing (SNIC) provides storage capacity and compute resources for the nation.

In the state of Victoria, Australia, the Department of Agriculture is capturing, managing, and analyzing data for “smart farms” in order to define new and more efficient farming methods.

These are just a few examples of the use of iRODS by institutions that take advantage of the features of data virtualization, data discovery, workflow automation, and secure collaboration that iRODS technology provides. These and more use cases will be presented.


Biography:

Dave Fellinger is a Data Management Technologist and Storage Scientist with the iRODS Consortium. He has over three decades of engineering experience including film systems, video processing devices, ASIC design and development, GaAs semiconductor manufacture, RAID and storage systems, and file systems.

In his role at the iRODS Consortium, Dave is working with users in research sites and high performance computer centers to confirm that a broad range of use cases can be fully addressed by the iRODS feature set.

He attended Carnegie Mellon University and holds patents in diverse areas of technology.

Benchmarking and improvement opportunities for data management practices in health research

Dr Michelle Krahe1, Julie Toohey2, Malcolm Wolski3, Professor Paul Scuffham4,5, Professor Sheena Reilly1,5

1Health Group, Griffith University, Gold Coast, Australia
2Library and Learning Services, Griffith University, Gold Coast, Australia
3eResearch Services, Griffith University, Nathan, Australia
4Centre for Applied Health Economics, Griffith University, Nathan, Australia
5Menzies Health Institute Queensland, Griffith University, Gold Coast, Australia

Introduction

Research Data Management (RDM) best practice is imperative to higher academic institutions involved in the development of training programs that support researchers. Therefore, understanding researcher RDM practices will help articulate planning strategies for services and support, and highlight areas for future investment and development. This study sought to understand the current RDM practices of health and medical researchers from an academic institution in Australia.

Methods

Participants were drawn from a research institute and invited to complete an online survey to about: RDM practices, data storage and retention, data sharing practices and RDM training and development.

Results

Overall, our evaluation indicates that RDM practices which varied greatly, are likely to be influenced by level of experience or RDM practices carried out within teams or by supervisors. Only 1 in 3 researchers had a data management plan, almost 70% sourced their data from surveys and 53% collected consent for specific data use. The majority (80%) collected data using personal storage devices and 65% stored their data on removable media. Willingness to share data with colleagues, and the public significantly increased after being published (p<0.05). Collaboration, advancing knowledge and public benefit were the top reasons for sharing data.

Conclusion

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.


Biography:

Bio to come

Getting from knowing to doing: The importance of data storage and preservation practices in research translation

Dr Michelle Krahe1, Malcolm Wolski2, Julie Toohey3, Professor Paul Scuffham4,5, Professor Sheena Reilly1,5

1Health Group, Griffith University, Gold Coast, Australia
2eResearch Services, Griffith University, Nathan, Australia
3Library and Learning Services, Griffith University, Gold Coast, Australia
4Centre for Applied Health Economics, Griffith University, Nathan, Australia
5Menzies Health Institute Queensland, Griffith University, Gold Coast, Australia

Introduction

Research is driven by and a generator of large and diverse amounts of data. Despite this, a substantial gap between the evidence generated and that which is translated into practice or policy still exists. We propose that since the ability to translate knowledge is dependent upon access to and integrity of quality data and information, the importance where data is recorded (stored) and how it is secured and preserved during the research process is important.

Methods

As part of a larger evaluation of the research data management (RDM) practices of health and biomedical researchers, here we explore data storage and preservation practices from one Australian academic institution. Participants were researchers actively involved in the production of digital data and were invited to complete an online survey about RDM.

Results

The results indicate that practices are variable and not harmonious with best-practice. The majority of research data is stored on personal devices during data creation (49%), analysis (55%) and preservation (50%) and predominantly on personal computers (73%, 69% and 43%), a USB stick (36%, 36% and 21%), or external hard drive (33%, 31% and 38%). This trend was similar for both identifiable and non-identifiable data.

Conclusion

The findings highlight that researchers are primarily using storage devices and employing preservation techniques that are limiting the ability to translate knowledge into action. If data cannot be meaningfully and contextually interpreted, then its potential may not be realised and opportunities for the translation of knowledge and open science will be lost.


Biography:

Bio to come

The core capabilities of digital transformation you can’t afford to ignore: a model to assess research teams

Malcolm Wolksi1, Dr Michelle Krahe2, Joanna Richardson3

1Digital Solutions, Griffith University, Nathan, Australia
2Health Group, Griffith University, Gold Coast, Australia
3Library and Learning Services, Griffith University, Nathan, Australia

Introduction

Within the higher education sector, there is a driving imperative to adapt to the new digital environment not only in areas involved in learning and teaching but also within research. While libraries play an important role in developing the digital literacy of researchers as individuals, little attention has been paid to the key digital skills and capabilities of the research team.

Methods

A critical review of digital transformation was undertaken from several broad areas (i.e. business, government and higher education) to identify suitable models or conceptual frameworks that could be applied to the research team environment. In addition, key elements that influence digital capability were identified.

Results

As there were no existing frameworks suitable for assessing the digital capability of research teams, a model was developed by the authors. This model included five key transformation dimensions (continual assessment, culture, leadership, technical integration, workforce) and five operational capabilities (information management, analytical techniques, process agility, technology infrastructure, governance maturity). We describe how implementing the digital capability model, library staff can make an assessment and tailor support to better meet the needs of researchers across their organisation.

Conclusion

An increasingly important goal of research organisations is the need for continuous

development and adaptation of digital capability. Here we describe the key elements of an evidence-based model of digital capability with capacity-building principles and structured reflection and action. Importantly, this will also require collaboration between service providers in institutions from libraries, IT, research offices, graduate schools, faculty training providers and increasingly national providers.


Biography:

Bio to come

Real-time traffic flow estimation based on Deep Learning using CCTV videos

Mrs Nilani Algiriyage1, Dr Raj  Prasanna1, Dr Kristin Stock2, Dr Emma Hudson-Doyle1, Prof David Johnston1

1Joint Centre for Disaster Research, Massey University, Wellington, New Zealand
2Institute of Natural and Mathematical Sciences, Massey University, Auckland, New Zealand

Identification of traffic flow is the first step that leads to the effective management of road traffic infrastructure and deployment of intelligent transportation systems. Nowadays, CCTV cameras are mounted in most of the places in cities, and they provide a massive amount of data in real-time. The data generated by CCTV cameras can be used as the foundation for accurate traffic flow estimation.

In this poster, we present our early work of a system estimating traffic flow in real-time using deep learning from CCTV video data. Therefore, a case is selected at one of the busiest roads in Christchurch Central Business District (CBD), New Zealand. CCTV video data is obtained from the New Zealand Transport Agency (NZTA). During the first stage, we have analysed around 150 frames, including more than 200 objects. You Only Look Once (Yolov3) algorithm is used to detect and classify vehicles. Also, we use Simple Online and Realtime Tracking (SORT) for vehicle tracking. Furthermore, a heuristic-based algorithm is introduced to count the vehicles by movement direction such as “left-lane” and “right-lane”. Our initial results show a mean absolute percentage error that is less than 12%.

Upon the completion of the system, city council authorities can use it to understand traffic flow patterns in real-time, make traffic predictions, understand anomalies, and make management decisions.


Biography:

Nilani is a PhD candidate at the Joint Centre for Disaster Research at Massey University, Wellington, New Zealand. She obtained her Masters Degree from Department of Computer Science and Engineering, University of Moratuwa in April 2015. Her Bachelor’s degree was in Management and Information Technology, graduating with a first class honors from University of Kelaniya in 2011.  Before she joined the Joint Centre for Disaster Research, she had been working as a lecturer in a government university, Sri Lanka.

The Future of Community Cloud and State Based Infrastructure Organisations

Mr Michael Boyle1

1Queensland University Of Technology (QUT), Kelvin Grove, Australia

Planned Approach – Facilitated Discussion

Target Audience – State based eResearch Infrastructure Organisations, National Grant Administering bodies (eg ARDC), eResearch groups in institutions,

Background – In the past three years we have seen the collapse and/or significant pressure on state based eResearch infrastructure organisations that, for many years, formed the foundation for the distribution of federal eResearch infrastructure grants. There has also been a turnover of experienced senior public servants in the science and infrastructure domains.  In the last 6 months we have seen a collapse in government revenue and a severe reduction in university revenue.  This situation is unlikely to change in the next two years.  Technology density and pricing has also evolved to a point where bulk purchase arrangements have limited benefit.

Problems/Discussion Points

– Does shared eResearch Infrastructure still deliver benefits?

– Where are state based eResearch Infrastructure bodies heading?

– Under current arrangements are benefits to institutions accumulating unevenly?

– What might a ‘better’ model look like?

– Where is the ‘bang for buck’ for government and institutions?

– Are domain relevant digital services more relevant than infrastructure?

Expected Outcomes

Commencement of a national discussion on the future of eResearch infrastructure and digital services futures that could inform future government funding lobbying.


Biography:

Michael Boyle is the Associate Director, Service Operations at QUT.  He is responsible for managing and maintaining the infrastructure and applications supporting the operations of Queensland’s second largest university.  This includes working in partnership with the eResearch group to deliver high performance computing and dedicated research data storage.  Previous to higher education Michael worked in technology executive roles in state government.  He is an alternate board member of QCIF.

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