Making Open Science the new normal

Konstantinos Repanas1

1Policy Officer, Open Science | European Commission



The talk will target two enablers of Open Science policies: responsible research data management, in line with the FAIR principles, and the development of supporting infrastructure such as the European Open Science Cloud. Responsible research data management will be key in mainstreaming Open Science policies under the next framework programme, Horizon Europe, through mandatory data management plans for all projects that generate or collect research data, and by introducing data management considerations as an element on which applicants can be evaluated. The European Open Science Cloud is a vibrant and collaborative ecosystem on which strategic work continues to be done along many facets (FAIR, architecture, sustainability, landscape, rules of participation, and training and skills). The European Commission President, Ursula von der Leyen, set EOSC as a top-level political priority in her World Trade Forum intervention in Davos and the talk will describe how the European Commission is delivering on this priority, while ensuring the maximum community consultation and participation.


Kostas Repanas is a policy officer at the Open Science Unit, in the Directorate General Research and Innovation (DG RTD) of the European Commission. Kostas is mainly involved in the data and interoperability aspects of the European Open Science Cloud (EOSC), as well as in advancing the Open Science agenda of the Commission. He is a long-standing advocate for Open Access, Open Data and Open Science, having previously worked for EMBL Heidelberg, the Agency for Science, Technology and Research (A-STAR) in Singapore, and the EU ESFRI landmark project ELIXIR. He is a co-organiser of the RDA FAIR Data Maturity Model WG and a member of the EOSC FAIR WG. Kostas is also the co-founder of the Asian Open Access community (Asia OA) launched at the 7th RDA Plenary in Tokyo, in collaboration with the National Institute of Informatics (NII-Japan) and the Confederation of Open Access Repositories (COAR). He holds a PhD in Biochemistry and Crystallography from the Netherlands Cancer Institute (NKI) in Amsterdam and the Erasmus University of Rotterdam.

Studying supermassive black holes with the next-generation radio surveys

Ivy Wong1,2

1CSIRO Astronomy & Space Science, PO Box 1130, Bentley, WA 6102
2ICRAR-M468 UWA, 35 Stirling Hwy, Crawley, WA 6009



Supermassive black holes lurk in the heart of most if not all galaxies. As galaxies grow and evolve, so do these central black holes. Jets of relativistic charged particles (synchrotron emission) can be launched through the process of matter being accreted into the black holes. Over time, the jets fade and leave behind lobes of non-relativistic particles that remain at large distances from their originating galaxy. Such galaxies with radio jets and/or lobes are known as radio galaxies. Several fundamental questions such as: 1) the driving mechanisms that triggers jet emission; and 1) the location relative to the central black hole where the jet is launched, remain largely unanswered. Large samples are required to constrain our understanding of the formation and evolution of radio galaxies. Unfortunately, the traditional method for cataloguing radio galaxies is through visual inspection. In this talk, I will describe our foray into crowd-sourcing and citizen science through projects such as Galaxy Zoo and Radio Galaxy Zoo. While crowdsourcing provides a significant improvement in source classification efficiency, this improvement is still insufficient to handle the millions of sources that we expect from the upcoming Evolutionary Map of the Universe (EMU) survey using the Australian Square Kilometre Array Pathfinder (ASKAP) currently being commissioned in Western Australia. To this end, I will describe some of our research on developing automated classifiers based on a range of deep learning methods that have been trained on the initial results of the Radio Galaxy Zoo project.


Dr Ivy Wong is a radio astronomer and a CSIRO Science Leader working on massive data challenges in the era of the Square Kilometre Array at CSIRO Astronomy & Space Science in Perth, WA. Using large all-sky radio surveys, Ivy studies how galaxies form stars; how central supermassive black holes grow (AGN) and how AGN affect the star formation history and evolution of a galaxy. Ivy’s research interests also include non-traditional data analysis methods such as the exploration of citizen science and the potential applications of deep learning algorithms.
The next-generation radio telescopes begin to survey wider, deeper and further back in the Universe’s history, astronomers will enter the massive data era when traditional methods of analyses will be severely tested. Ivy obtained her PhD (Astrophysics) in 2008 from the University of Melbourne and has previously worked at Yale University, CSIRO and the International Centre Radio Astronomy Research (UWA).

‘Time to Science’ and the Role of Research Computing

Christine Kirkpatrick1

1San Diego Supercomputer Center



‘Time to science’ describes the time consumed by activities scientists engage in before they can get to work – research in their chosen domain. Rather than spending time on scientific inquiry, productive hours are consumed finding and getting data ready and setting up cloud environments. For research with at-scale complexity and experimental computer science and data science workloads, the time to science can be even greater. Too often, researchers are faced with scaling their inquiry down to resources within their current portfolio or technical capabilities. If the highest aim of research computing is to meet scientists at their level of inquiry and to come up with approaches that match these requirements, then some of this ‘time to science’ challenge must be unbundled by research computing professionals. To decrease ‘time to science’ research computing groups must borrow techniques and competencies from data stewardship, as well as troubleshooting and right sizing cloud architectures.


Christine Kirkpatrick oversees the San Diego Supercomputer Center’s (SDSC) Research Data Services division, which manages infrastructure, networking, and services for research projects of regional and national scope. Kirkpatrick is a recognized expert in the implementation of research computing services, with an emphasis on data science workloads, as well as operational cyberinfrastructure (CI) at scale.

Kirkpatrick founded and hosts the US GO FAIR Office at SDSC, is the Executive Director of the US National Data Service (NDS), and Co-PI and Deputy Director of the West Big Data Innovation Hub (WBDIH). She co-chairs the All (Big Data) Hub Infrastructure Working Group, is co-PI of the Open Storage Network, and PI of the EarthCube Office (ECO). Kirkpatrick received her master’s degree from the Jacobs School of Engineering at University of California San Diego. She serves on the Technical Advisory Board (TAB) for the Research Data Alliance (RDA), and the external Advisory Boards for the European Open Science Cloud (EOSC) Hub and EOSC Nordic.

Cloud Computing for Research: A Comparison of Data Storage Services

James Gallagher1


Cloud computing represents a collection of different technologies that offer great promise but with sprawling features and documentation that can test one’s patience. In this lecture we will examine some common ‘cloud computing myths’ and two kinds of commonly used cloud data stores – attachable, shareable file systems and web object stores. Different implementations from four cloud vendors (Amazon, Google, MicroSoft Azure, and OpenStack) will be compared. In addition, for web object stores, the increase in data throughput achieved by several optimisation strategies will be examined.

James is one of the developers of the Data Access Protocol (DAP) protocol and is a co-founder of  OPeNDAP. Before that he worked on the Distributed Oceanographic Data System (DODS) project, along with Peter Cornillon, Glenn Flierl and George Milkowski. The DODS project developed the initial versions of the DAP and it’s core software implementation. He assumed the role of OPeNDAP President in 2015.

A new paradigm for environmental prediction?

Charlie Ewan, Alberto Arribas1

1Met Office, Exter, Devon United Kingdom

During the last 70 years, solving physical equations on High Performance Computers has been the main workhorse of weather, climate and environmental research and prediction. It has been a fantastic ride, substantially improving our understanding and boosting our forecasting capabilities. However, Moore’s law is over, the energy cost of HPC is hardly affordable, and increasing the scalability of scientific numerical codes is extremely complex … Have we reached the end of the road? If so, what is next?
The talk will present ongoing work at the Met Office to answer those questions from a scientific/technical point of view (scalable platforms for data science, machine learning, natural language processing) and an organisational point of view (innovation, communities of practices, multi-disciplinary approaches).


Charlie Ewan
I am accountable for all aspects of Technology and for the core technology teams responsible for the delivery of services and technology projects. I work closely with colleagues in other areas of the organisation to maintain the overall technology enterprise architecture for which I am responsible. We operate a federated approach to IT delivery in areas such as supercomputing and observations to ensure that we have the depth and range of skills and experience to deliver a world-class capability in selected areas.

I have worked for the Met Office in a number of senior technology roles since 2008 and, prior to that, worked in the Business-to-Business online retail and distribution industry focussing on technology driven change. I have significant experience in organisational transformation in a range of businesses. As well as a number of consulting appointments, I worked within the Premier Farnell group of companies for over 10 years. I have run my own small technology company and started my career as an Electronics Engineer in the defence industry

Alberto Arribas
Professor Arribas is the Head of the Met Office Informatics Lab and an Associate Professor at the University of Exeter Institute for Data Science and AI. Alberto is also a Research Fellow at the Met Office and the Alan Turing Institute.

The Informatics Lab was founded to address the challenges derived from the complexity of data and science in environmental research. The Informatics Lab is the “Innovation and Technology R&D” department for the Met Office, with the mission of solving strategic problems for the organisation through a multi-disciplinary team.

Alberto is the author of over 50 peer reviewed publications, has developed world-leading prediction systems for weather and climate, served in expert committees for the USA Academy of Science and the World Meteorological Organisation, and won various awards. His areas of expertise include: Probabilistic forecasting for decision-making; R&D on emerging technologies; and strategic innovation.

Data + Community = Action

Erin Robinson1

1Earth Science Information Partners, Boulder USA


Science funders around the world both federal and private are spending millions of dollars on large scale, data-intensive collaborative science projects. After all, the wicked challenges facing us in all fields from energy to food security to climate mitigation strategies are not solved by a single person, institution or domain. They take all of us working together. This talk will explore the relationship between data-intensive science and collaborative community efforts like the Earth Science Information Partners (ESIP) and C3DIS move science forward. You will walk away with a better understanding of your role in this meeting and how to enhance your role in the collaborative projects you participate in. Together, if we are better collaborators, we will make data matter, more interoperable, actionable and move science beyond where we thought possible!




I work at the intersection of community informatics, Earth science and non-profit management. Over the last 10 years, I’ve honed an eclectic skill set both technical and managerial, creating communities and programs with lasting impact around science, data, and technology.

Passionate about fostering innovation through collaboration across diverse nodes, I am currently the Executive Director for the Earth Science Information Partners (ESIP). In this position, I facilitate collaboration among Earth science technology practitioners to expedite progress toward data interoperability.

In my free time I love to be outside – mountain biking, hiking and skiing. I can also be found in the kitchen cooking with my partner, Ted and hosting dinner parties for our friends.

Confessions of a Data Wrangler

Dr Carina Kemp1

1AARNet, Chatswood, Australia

The last 20 years have seen an explosion in technologies to enable data to be FAIR. Semantic Web and Web Services emerged as potential technologies in the early 2000s but how long has it taken for them to be accepted and used by Scientists on a daily basis. In the search for FAIR Data we need to understand the technical journey that a Scientific Domain has travelled with respect to it’s data. How does a domain’s technical history effect its ability to be agile and adapt to new emerging technologies.

Dr Carina Kemp has a PhD in Geophysics and worked as a Research Geophysicist and Consulting Geophysicist for the first 10 years of her career but for the last 10 years, Carina has worked in collaborating on the development of technologies to make Geophysical Data and Tools more useable for the wider community. This has led Carina to a career as a technologist.

This talk will follow the journey of Geophysical data from the perspective of a user attempting to make it more useful for herself and her colleagues.  How can this journey be used to understand the challenges in developing and adopting tools to enable FAIR data in other Scientific Domains?




Dr Kemp is the Director of eResearch for AARNet responsible for making the Network work the best it can for Research in Australia. This includes working with the Australian and International research community to find and implement tools that sit above the network to make technology and data research ready.

Previous to joining AARNet, Dr Kemp was the Chief Information Officer at Geoscience Australia, with responsibility for providing strategic leadership for setting enterprise ICT directions to enable science across the organization including the development of virtual laboratories, high performance computing applications and innovative data architecture to enable FAIR data.

Dr Kemp’s 20-year career has focused on the application of innovative technologies. Dr Kemp has worked in research, as a consultant and as a company geophysicist. Her enduring interests include Open Data, data analytics, machine learning, high performance computing, communication of science, bridging the gap between research and ICT, mentoring and promoting diversity in STEM fields.

Dr Kemp holds a number of qualifications including a Doctor of Philosophy from the University of Sydney  and a Bachelor of Science (Geology and Geophysics) from the University of New South Wales. Dr Kemp has a technical background in geology, physics, mathematics and computer engineering.

In her limited spare time, Dr Kemp keeps busy raising three beautiful children.

 Work Mobile: +61 427 596 962


Gravitational Wave Astronomy and the Big Data Challenge

Dr Kendall Ackley


The uniquely sensitive Laser Interferometric Gravitational-Wave Observatory (LIGO) facilities have begun routinely detecting signal traces from distant massive black hole and neutron star mergers, some of which happened hundreds of millions of years ago. Representing a multi-layered data analysis problem for real-time and offline analyses, with the aid of computing clusters around the world, successful attempts to extract minute gravitational wave signatures from detector noise have become reality.

On 17 August 2017, LIGO detected its first signal from less massive objects thought to be neutron stars, reinforced by the observation of a coincident weak gamma-ray burst by the Fermi satellite.  Neither instrument has good spatial resolution, and with LIGO being an all-sky instrument, the challenges for astronomers to find the single light-emitting source amongst billions of objects in the sky that is associated with a particular event is not to be understated. Thus began a race of astronomical facilities around the world to be the first to detect the electromagnetic counterpart signal of the event.

The fact that the source was detected within hours of the first alert on the first ever occasion established and validated the field of multi-messenger gravitational wave astronomy, which had been a growing initiative, practically overnight. I will give insights into how this feat was accomplished and, as we begin to build larger and more sensitive telescopes, how we plan to manage the massive in-flux of nightly data, and how we utilise machine-learning to help us accomplish the most data-intensive tasks in an automated fashion.

Delivering on the promise

Brendan Bouffler

AWS Research Cloud


The promise of the super-computing industry has always been that pioneering bleeding-edge technologies we build for the hyperscale filter down to become mainstream quickly enough to fuel exciting stories about how the phone in your pocket is as powerful as the world’s fastest machine from not so many years ago.

Whilst this is true of the hardware, there are very few scientists who’ll tell you that ease of use factors, capacity or  accessibility has moved in such leaps and bounds. Moreover, whilst collaboration with others around the globe brings a lot of benefits, there are other factors like data security and privacy that can frequently – and suddenly – demand a lot of attention.

To keep our pace of scientific discovery going, however, our most pressing task as a community is to give working scientists access to the infrastructure they need in a form that’s easy to use, is secure for their data and scalable to the limits of their ideas – not limited by their laptop’s memory capacity.

This means an inherently different approach to what has come before. We’ll survey some of the work going on around the world and leverage lessons learned (both good and bad) to help target what needs to be a very innovative approach to computing, not just business as usual with more bandwidth.

Machine learning bias and fairness

KJ Pittl



As an AI-first company, Google aims to develop the benefits of machine learning for everyone. Democratizing ML tools and platforms and building inclusive machine learning algorithms is crucial to make this work successfully.

Machine learning bias examples and fairness in practise, including  tools in the TensorFlow ecosystem to help understand models and combat problems with fairness.



AeRO is the industry association focused on eResearch in Australasia. We play a critical coordination role for our members, who are actively transforming research via Information Technology. Organisations join AeRO to advance their own capabilities and services, to collaborate and to network with peers. AeRO believes researchers and the sector significantly benefit from greater communication, coordination and sharing among the increasingly different and evolving service providers.

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