The Australian National Medical Research Data Storage Facility - - PowerPoint PPT Presentation

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The Australian National Medical Research Data Storage Facility - - PowerPoint PPT Presentation

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#THETA2015 The Australian National Medical Research Data Storage Facility med.data.edu.au Jeff Christiansen, Mohammad Islam, Derek Van Dyk, Leonie Hellmers and Ian Gibson This work is licensed under a Crea ve

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This work is licensed under a Crea ve Commons A ribu on 4.0 Interna onal License.

  • #THETA2015

The Australian National Medical Research Data Storage Facility med.data.edu.au

Jeff Christiansen, Mohammad Islam, Derek Van Dyk, Leonie Hellmers and Ian Gibson

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med.data.edu.au

  • What is it?
  • What are the drivers?
  • Where did it come from?
  • Who has signed up?
  • Data Types
  • Why does it matter?
  • What will it do and how will it work?
  • What’s its status?
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What is it?

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What is it?

  • a National Facility to provide:
  • Highly secure petabyte-scale data storage
  • Related high-speed networked and secure

computational services

for Australian medical and health research

  • rganisations
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What are the drivers?

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What are the drivers?

  • 1. Strategic reviews

have pointed to an increased requirement to consolidate, aggregate and collaborate to maximise research

  • utcomes

www.mckeonreview.org.au/

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What are the drivers?

www.mckeonreview.org.au/

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What are the drivers?

www.mckeonreview.org.au/

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What are the drivers?

www.mckeonreview.org.au/

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What are the drivers?

  • 2. Fast growth of the volume of medical

research data

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What are the drivers?

  • 2. Fast growth of the volume of medical

research data – e.g. imaging

Jeff Lichtman/Harvard University, CC BY-NC-ND Dan Vogel, CC BY-2.0

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What are the drivers?

  • 2. Fast growth of the volume of medical

research data – e.g. genomic

Guy Cochrane, EBI

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What are the drivers?

  • 2. Fast growth of the volume of medical

research data

Illumina HiSeq XTEN

~USD 1000 per genome 0.6 terabases of sequence per day 3-4 terabytes of data per day 10 machines @ the Kinghorn Centre for Clinical Genomics (Garvan Institute)

www.businesswire.com

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What are the drivers?

  • 3. The rise of personalised medicine
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What are the drivers?

  • 3. The rise of personalised medicine

UNIVERSITY

CLINICAL and ADMINISTRATIVE RECORDS RESEARCH DATA

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Where did it come from?

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Where did it come from?

  • Funded through RDSI, RDS and ANDS

projects

  • All part of the NCRIS program
  • Over $1 billion over past few years
  • $150M planned for 2015-16
  • Longer term planning underway
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Where did it come from?

  • RDSI: established 8 large data storage

facilities nationally

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Where did it come from?

  • RDSI: established 8 large data storage

facilities nationally

  • Intended to store research data of value for

future research

  • Total storage ~50 petabytes
  • Large Collections Program
  • Medical, Imaging, Astronomy, Ecology, Genomics
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Where did it come from?

  • RDS: making RDSI storage sustainable.
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Where did it come from?

  • RDS: making RDSI storage sustainable.
  • optimising and maturing the RDSI capability

for 9 data-intensive research communities:

Medical, Climate, Genomics, Humanities, Astronomy, Ecology, Marine, Geoscience, Imaging.

  • integrate rapidly expanding data holdings with

the cloud and supercomputing infrastructure to provide a truly high-performance data using capability

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Where did it come from?

RDSI/RDS Medical

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Where did it come from?

  • ANDS: ensuring research data is:
  • Managed
  • Connected
  • Findable
  • Reusable
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Where did it come from?

  • ANDS: ensuring research data is:
  • Managed
  • Connected
  • Findable
  • Reusable
  • Data Management for all users of RDSI
  • Incl. independent Medical Research Institutes
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Who has signed up?

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Who has signed up?

  • 31 letters of support

including 20 Medical Research Institutes

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Who has signed up?

The Nold Laboratory Rapid Autopsy in Melanoma Consortium Melbourne Genomics Health Alliance

Melbourne Brain Centre Imaging Unit Melbourne femur collection Health and Biomedical Informatics Centre

53 organisations

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Data types

  • Imaging
  • MRI, CT, PET, Xray, histology…
  • Clinical
  • Surveys, pathology, video assessments, patient data,

longitudinal studies, clinical trials, eHealth, EEG, anatomy/morphology, personalised medicine, drug discovery/therapeutics…

  • “Omics”
  • Genomics, Transcriptomics, Proteomics…
  • Biobanking
  • Computational modelling
  • Health economics
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Why does it matter?

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Why does it matter?

  • Compliance and data security/privacy
  • Efficiency and cost effectiveness
  • Supporting translational impact
  • Enabling new research methods and
  • utcomes
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What will it do?

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What will it do?

  • Store data

– securely, including identifiable data

  • Describe data

– at collection and item level

  • Find data

– at collection level

  • Share data

– under appropriate conditions

  • Use data

– via a number of associated tools (TBD) – including secure options

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What will it do?

  • Store data

– securely, including identifiable (sensitive) data

  • Describe data

– at collection and item level

  • Share data

– only under appropriate conditions

  • Find data

– at collection level

  • Use data

– via a number of associated tools (TBD) – to include highly secure options

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How will it work?

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How will it work?

  • Each node is an

independent entity

  • Storage + (HPC, Cloud)
  • Connected via AARNet
  • Data custodian enters into

data storage agreement with node of their choice – typically the local one

  • Data custodians retain control over access
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How will it work?

  • Governance: Advisory Board (current)
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What’s its status?

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What’s its status?

  • Project team established
  • Advisory Board established
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What’s its status?

The Nold Laboratory Rapid Autopsy in Melanoma Consortium Melbourne Genomics Health Alliance

Melbourne Brain Centre Imaging Unit Melbourne femur collection Health and Biomedical Informatics Centre

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What’s its status?

  • 80 foundation data collections identified
  • 4 PB
  • 80 technical analyses underway
  • 53 organisations across 4 states
  • Security
  • Access control
  • Use cases
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What happens next?

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What happens next?

  • Signing up more organisations
  • Establish roadmap of services

– data, metadata standards, data management and curation, APIs, analytics

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What happens next?

  • Signing up more organisations
  • Establish roadmap of services

– data, metadata standards, data management and curation, APIs, analytics

  • Opportunity to align access methods

across consenting collections

  • Conducting research across

aggregated collections

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Thanks

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med.data.edu.au

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