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2018 CODATA-RDA School of Research Data Science, ICTP, Trieste

Agenda / Homepage
When: 
Monday, August 6, 2018 - 14:48 to Friday, August 17, 2018 - 14:48
Description: 

The CODATA-RDA Research Data Science Summer School provides training in the foundational skills of Research Data Science.  Contemporary research – particularly when addressing the most significant, transdisciplinary research challenges – cannot be done effectively without a range of skills relating to data. This includes the principles and practice of Open Science and research data management and curation, the use of a range of data platforms and infrastructures, large scale analysis, statistics, visualisation and modelling techniques, software development and annotation and more. We define ‘Research Data Science’ as the ensemble of these skills.

Learning goal: 

The CODATA-RDA Schools of Research Data Science will:

  • address recognised need for Research Data Science skills across all disciplines;
  • follow an recognised and accredited curriculum that addresses foundational data skills required by all researchers;
  • provide a pathway from a broad foundational course through to more advanced and specialised courses or workshops;
  • be reproducible: all materials will be online with Open licences;
  • be scalable: emphasis will be placed on training trainers, building partnerships and developing an international network which makes it easy for schools to be run in many locations.

Trainers from the EOSC-hub and EOSCpilot projects will contribute to the event with a cloud computing tutorial that will feature services from the EOSC-hub catalogue and will demonstrate the power of Jupyter Notebooks and cloud computing for research data analysis.

Venue: 

Kastler Lecture Hall (AGH)

Via Grignano, 9 I - 34151 Trieste (Italy)

Format:

Target group:

Trainers: 
Gergely Sipos
Giuseppe La Rocca
Training materials: 
2018 CODATA-RDA School of Research Data Science
External training materials: 
https://github.com/enolfc/envri-school-notebooks

EOSC-hub receives funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 777536. 

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