Principles Aligned Institutionally-Contextualised (PAI-C) RDM Training
This GitHub repository contains resources for an institution to contextualise a principles-based RDM training with its institution's research data management policies, processes and systems.
The adoption of PAI-C across institutions will contribute to a common baseline understanding of RDM...
Keywords: PAI-C, Training, Data Management
Principles Aligned Institutionally-Contextualised (PAI-C) RDM Training
https://github.com/Adrian-W-Chew/PAI-C-RDM-Training
https://dresa.org.au/materials/principles-aligned-institutionally-contextualised-pai-c-rdm-training
This GitHub repository contains resources for an institution to contextualise a principles-based RDM training with its institution's research data management policies, processes and systems.
The adoption of PAI-C across institutions will contribute to a common baseline understanding of RDM across institutions, which in turn will facilitate cross institutional management of data (e.g. when researchers move between institutions, and collaborate across institutions).
Dr Adrian W. Chew (w.l.chew@unsw.edu.au)
Dr Adrian W. Chew
Dr Adele Haythornthwaite
Brock Askey
Dr Jacky Cho
Dr Anesh Nair
Dr Kyle Hemming
Iftikhar Hayat
Joanna Dziedzic
Janice Chan
Kaitlyn Houston
Linlin Zhao
Caitlin Savage
Jessica Suna
Dr Emilia Decker
Sharron Stapleton
PAI-C, Training, Data Management
VOSON Lab Code Blog
The VOSON Lab Code Blog is a space to share methods, tips, examples and code. Blog posts provide techniques to construct and analyse networks from various API and other online data sources, using the VOSON open-source software and other R based packages.
Keywords: visualisation, Data analysis, data collections, R software, Social network analysis, social media data, Computational Social Science, quantitative, Text Analytics
Resource type: tutorial, other
VOSON Lab Code Blog
https://vosonlab.github.io/
https://dresa.org.au/materials/voson-lab-code-blog
The VOSON Lab Code Blog is a space to share methods, tips, examples and code. Blog posts provide techniques to construct and analyse networks from various API and other online data sources, using the VOSON open-source software and other R based packages.
robert.ackland@anu.edu.au
visualisation, Data analysis, data collections, R software, Social network analysis, social media data, Computational Social Science, quantitative, Text Analytics
researcher
support
phd
masters
Data Storytelling
Nowadays, more information created than our audience could possibly analyse on their own! A study by Stanford professor Chip Heath found that during the recall of speeches, 63% of people remember stories and how they made them feel, but only 5% remember a single statistic. So, you should convert...
Keywords: data storytelling, data visualisation
Data Storytelling
https://griffithunilibrary.github.io/data-storytelling/
https://dresa.org.au/materials/data-storytelling
Nowadays, more information created than our audience could possibly analyse on their own! A study by Stanford professor Chip Heath found that during the recall of speeches, 63% of people remember stories and how they made them feel, but only 5% remember a single statistic. So, you should convert your insights and discovery from data into stories to share with non-experts with a language they understand. But how?
This tutorial helps you construct stories that incite an emotional response and create meaning and understanding for the audience by applying data storytelling techniques.
m.yamaguchi@griffith.edu.au
a.miotto@griffith.edu.au
Masami Yamaguchi
Amanda Miotto
Brett Parker
data storytelling, data visualisation
support
masters
phd
researcher
Porting the multi-GPU SELF-Fluids code to HIPFort
In this presentation by Dr. Joseph Schoonover of Fluid Numerics LLC, Joe shares their experience with the porting process for SELF-Fluids from multi-GPU CUDA-Fortran to multi-GPU HIPFort.
The presentation covers the design principles and roadmap for SELF and the strategy to port from...
Keywords: AMD, GPUs, supercomputer, supercomputing
Resource type: presentation
Porting the multi-GPU SELF-Fluids code to HIPFort
https://docs.google.com/presentation/d/1JUwFkrHLx5_hgjxsix8h498_YqvFkkcefNYbu-DsHio/edit#slide=id.g10626504d53_0_0
https://dresa.org.au/materials/porting-the-multi-gpu-self-fluids-code-to-hipfort
In this presentation by Dr. Joseph Schoonover of Fluid Numerics LLC, Joe shares their experience with the porting process for SELF-Fluids from multi-GPU CUDA-Fortran to multi-GPU HIPFort.
The presentation covers the design principles and roadmap for SELF and the strategy to port from Nvidia-only platforms to AMD & Nvidia GPUs. Also discussed are the hurdles encountered along the way and considerations for developing multi-GPU accelerated applications in Fortran.
SELF is an object-oriented Fortran library that supports the implementation of Spectral Element Methods for solving partial differential equations. SELF-Fluids is an implementation of SELF that solves the compressible Navier Stokes equations on CPU only and GPU accelerated compute platforms using the Discontinuous Galerkin Spectral Element Method. The SELF API is designed based on the assumption that SEM developers and researchers need to be able to implement derivatives in 1-D and divergence, gradient, and curl in 2-D and 3-D on scalar, vector, and tensor functions using spectral collocation, continuous Galerkin, and discontinuous Galerkin spectral element methods.
The presentation discussion is placed in context of the Exascale era, where we're faced with a zoo of available compute hardware. Because of this, SELF routines provide support for GPU acceleration through AMD’s HIP and support for multi-core, multi-node, and multi-GPU platforms with MPI.
training@pawsey.org.au
Joe Schoonover
AMD, GPUs, supercomputer, supercomputing
Embracing new solutions for in-situ visualisation
This PPT was used by Jean Favre, senior visualisation software engineer at CSCS, the Swiss National Supercomputing Centre during his presentation at P'Con '21 (Pawsey's first PaCER Conference).
This material discusses the upcoming release of ParaView v5.10, a leading scientific visualisation...
Keywords: ParaView, GPUs, supercomputer, supercomputing, visualisation, data visualisation
Resource type: presentation
Embracing new solutions for in-situ visualisation
https://github.com/jfavre/InSitu/blob/master/InSitu-Revisited.pdf
https://dresa.org.au/materials/embracing-new-solutions-for-in-situ-visualisation
This PPT was used by Jean Favre, senior visualisation software engineer at CSCS, the Swiss National Supercomputing Centre during his presentation at P'Con '21 (Pawsey's first PaCER Conference).
This material discusses the upcoming release of ParaView v5.10, a leading scientific visualisation application. In this release ParaView consolidates its implementation of the Catalyst API, a specification developed for simulations and scientific data producers to analyse and visualise data in situ.
The material reviews some of the terminology and issues of different in-situ visualisation scenarios, then reviews early Data Adaptors for tight-coupling of simulations and visualisation solutions. This is followed by an introduction of Conduit, an intuitive model for describing hierarchical scientific data. Both ParaView-Catalyst and Ascent use Conduit’s Mesh Blueprint, a set of conventions to describe computational simulation meshes.
Finally, the materials present CSCS’ early experience in adopting ParaView-Catalyst and Ascent via two concrete examples of instrumentation of some proxy numerical applications.
training@pawsey.org.au
Jean Favre
ParaView, GPUs, supercomputer, supercomputing, visualisation, data visualisation