Persistent identifiers for Instruments BoF_Presenting the cummunity current issues and developments in a global context

Siobhann McCafferty1

1ARDC

Much work has been done previously on identifiers and schema for sensors, sequencers, and research platforms in geoscience and genomics.  These areas have led in the development of methods for identifying instruments, instances, recording calibration and connecting metadata to instruments and infrastructure amongst others.

There is also growing awareness outside of these traditional areas about the benefits of uniquely identifying instruments. This has resulted in an alignment of direction and energies in recent years developing and building agreement on a schema and Persistent Identifier (PID) that can be used globally and across disciplines in the drive for better description, use metrics, richer metadata and promoting joined up e-research.

Recent events in this area include the publication of a draft schema and white paper by the Research Data Alliance (RDA) Persistent Identification Working Group (PIDInst), and, the establishment and growth of the Identifiers for Instruments in Australia and Aotearoa/New Zealand Community of Practice (i4iOz).

This BOF will introduce the context and current work in Instrumentation PIDs globally by bringing together representatives from the RDA PIDINST Working Group, the i4iOZ Community of Practice, recent schema adoption pilot projects from both and long-time community members working on the area.

This BOF aims to drive discussion and cooperation in Instrument Identifier development in Australasia and internationally by bringing together groups currently working in the area and providing a platform for discussion and cooperation.

 

Galaxy Australia and CloudStor (AARNet) – creating linkages between two national resources

Dr Nuwan Goonasekera2, Mr Simon Gladman2, Dr Frankie Stevens3, Dr Gareth Price1

1QCIF Facility for Advanced Bioinformatics, St Lucia, Australia
2University of Melbourne, Carlton, Australia
3AARNet, Milton, Australia

Introduction

Galaxy Australia, the life science platform that allows reproducible, transparent analyses through a web interface and Cloudstor, AARNet’s cloud research data storage platform, have brought users of these platforms together by enabling easy and secure data transfer options between these national platforms.

Methods

The “Import from Cloudstor” tool allows users of Galaxy Australia, after a simple configuration of their Cloudstor storage allocation, to navigate through their folders to import files, directories or multi-directories of files into their Galaxy History. Once their data has been analysed the ”Send data to Cloudstor” tool allows the user to securely transfer any files back to their Cloudstor allocation.

Results

Running on the latest version of Galaxy (20.05), researchers analysing data can now rely on  seamless interaction and movement of their data to / from their AARNet provided Cloudstor personal account.

Conclusion

The development of functionality to connect a long standing national platform (AARNet) with the rapidly growing user base of Galaxy Australia demonstrates the advantages of national platforms working to service integration, for the benefit of all Australian researchers.

This talk will highlight the functionality of both tools and the development and implementation path taken in deploying these enhancements to the Galaxy Australia platform.


Biography:

Dr Gareth Price is Head of Computational Biology at QCIF Facility for Advanced Bioinformatics. In this role Gareth manages the diverse spectrum of researcher lead questions involving genomic data, provides training in genomic data analysis, as well as leading Galaxy Australia (https://usegalaxy.org.au) as Platform Manager.

Gareth has 20 years’ experience as a Bioinformatician and Genomics Scientist.

Gareth’s view is that research is at its best when coupled with the most accurate, highest throughput and innovative technology and analysis modalities.

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.

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