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The Characterisation Virtual Laboratory.md
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The Characterisation Virtual Laboratory
5 June 2019
Paula Andrea Martinez

The Characterisation Virtual Laboratory

Paula Andrea Martinez Characterisation Training Coordinator

http://tiny.cc/CVL_Community 5 June 2019

![CC by 4 International](https://codimd.s3.shivering-isles.com/demo/uploads/upload_2026ab2eb535bbaff4917cf6f8c6d249.png =100x)

The Characterisation Virtual Laboratory

infrastructure + working group + training

"Nationally funded software infrastructure collaboration to make scientific tools, available freely and cloud-ready". Goscinski et al., 2014 https://doi.org/10.3389/fninf.2014.00030

*

Who is this for?

The Characterisation Virtual Laboratory supports various research domains

  • Microscopy
  • Neuroimaging
  • Structural Biology

What does the CVL offers to you?

  • Processing power - extra RAM + cores
  • Virtual desktop
  • Software tools 300+ and versioning

MASSIVE

The CVL is operated by MASSIVE Multi-modal Australian Sciences Imaging and Visualisation Environment

The MASSIVE Facility provides high performance computing (HPC) infrastructure called M3

MASSIVE lives at Monash University

CVL Partners

  • Microscopy Australia
  • ANSTO
  • NIF
  • Monash University, University of Sydney, The University of Queensland, University of Western Australia and University of Wollongong
  • ARDC supported by the Australian Government through (NCRIS)

CVL Partners

  • Microscopy Australia
  • Australian Nuclear Sciences and Technology Organisation ANSTO
  • National Image Facility NIF
  • Monash University, University of Sydney, The University of Queensland, University of Western Australia and University of Wollongong
  • Australina Research Data Commons ARDC supported by the Australian Government through the National Collaborative Research Infrastructure (NCRIS)

C-DeVL

C-DeVL

Characterisation Data enhanced Virtual Laboratory project is funded by The ARDC Leveraging data skills and engagement FAIR and Training

What is FAIR?

Findable, Accessible, Interoperable, and Reusable

The application of FAIR principles ensures that anyone can find, read, use and reuse research data and research outputs. https://www.go-fair.org/fair-principles/

C-DeVL, How do we do this?

National training program

  • Build a community to enchance research collaborations
  • Promote and enhance best data management practices

Resources

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