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EOSC – University perspectives
Hugh Shanahan
School of Engineering, Physical and Mathematical Sciences
@hughshanahan
Hugh.Shanahan@rhul.ac.uk
Researcher profile – long tail
Focussing on researchers who
Work in smaller labs
Do moderate amount of computing
Generate moderate amount of data
Work with University support
EOSC could be enormously useful to them
2 23/04/2021
Technical specifications EOSC
3 23/04/2021
EOSC = AAI + Metadata + Clouds
4 23/04/2021
PaaS, Web-services remain important …
IaaS (VMs) remains a workhorse
10-15 years ago wholesale switch to AWS away from University
services
Getting started on AWS was straightforward (no 1,000 word application
process)
Personal bias – Notebooks (Jupyter) are increasingly the way
computation is shared.
EGI Notebook service
5 23/04/2021
AAI
Low impediment to
getting started
6 23/04/2021
Metadata
Little awareness of FAIR
principles
FAIRsFAIR survey in
European Higher Education
Metadata management and
discovery in place makes
advantages clearer.
Ease implementation
7 23/04/2021
Training and community
”To summarize the summary of the summary: people are a problem”
Douglas Adams, The Restaurant at the end of the Universe
8 23/04/2021
Time, Effort, Cost
Research software and databases depend on research communities
to maintain/develop/curate
Training is essential – pay people to do it right.
Building communities is hard work and takes time – pay people to do
it right.
Enthusiasm != Sustainability
9 23/04/2021
10 23/04/2021
Community of Practice in training
11 23/04/2021
FAIRsFAIR
CODATA-RDA schools for ECRs (Research Data Science skills)
Two weeks face to face -> Ten weeks online
Data Steward Instructor Training
Identifying that regional / (small) national level critical mass of Data Stewards
Data Steward principles but also pedagogy and network building
2020 - University of Manchester and UNA, Costa Rica
2021 – Flanders Open Science Board, NORF (Ireland)
12 23/04/2021

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EOSC - university perspectives

  • 1. EOSC – University perspectives Hugh Shanahan School of Engineering, Physical and Mathematical Sciences @hughshanahan Hugh.Shanahan@rhul.ac.uk
  • 2. Researcher profile – long tail Focussing on researchers who Work in smaller labs Do moderate amount of computing Generate moderate amount of data Work with University support EOSC could be enormously useful to them 2 23/04/2021
  • 4. EOSC = AAI + Metadata + Clouds 4 23/04/2021
  • 5. PaaS, Web-services remain important … IaaS (VMs) remains a workhorse 10-15 years ago wholesale switch to AWS away from University services Getting started on AWS was straightforward (no 1,000 word application process) Personal bias – Notebooks (Jupyter) are increasingly the way computation is shared. EGI Notebook service 5 23/04/2021
  • 6. AAI Low impediment to getting started 6 23/04/2021
  • 7. Metadata Little awareness of FAIR principles FAIRsFAIR survey in European Higher Education Metadata management and discovery in place makes advantages clearer. Ease implementation 7 23/04/2021
  • 8. Training and community ”To summarize the summary of the summary: people are a problem” Douglas Adams, The Restaurant at the end of the Universe 8 23/04/2021
  • 9. Time, Effort, Cost Research software and databases depend on research communities to maintain/develop/curate Training is essential – pay people to do it right. Building communities is hard work and takes time – pay people to do it right. Enthusiasm != Sustainability 9 23/04/2021
  • 11. Community of Practice in training 11 23/04/2021
  • 12. FAIRsFAIR CODATA-RDA schools for ECRs (Research Data Science skills) Two weeks face to face -> Ten weeks online Data Steward Instructor Training Identifying that regional / (small) national level critical mass of Data Stewards Data Steward principles but also pedagogy and network building 2020 - University of Manchester and UNA, Costa Rica 2021 – Flanders Open Science Board, NORF (Ireland) 12 23/04/2021