This study aims to empower the expanding ELIXIR network, supporting members in growing their capacities is necessary in order to maintain the quality of ELIXIR training, services and resources. The Training Platform will continue to expand the Train-the-Trainer (TtT) framework (developed under EXCELERATE) to increase the skills of ELIXIR trainers as well as their number across Nodes. 

The focus of this task is TeSS, the ELIXIR Training Platform’s flagship resource and key reference training portal for the ELIXIR community. This is the principal channel of communication and aggregator of information about ELIXIR training events.

Gap analysis, training materials development and training delivery

The objectives of this task are to:

This study will build on the EXCELERATE (WP11) training grant.

WP1: Publication and adoption of the ELIXIR Training toolkit

This Work Package will draft a Training Handbook detailing all of the materials developed so far, aiming to provide a comprehensive reference resource for developing training capacity. 

To raise the quality and sustainability of research software

This study will promote the production, adopting, promoting and measuring information standards and best practices applied to software development life cycle. We have published four simple recommendations to encourage best practices in research software and the Top 10 metrics for life science software good practices. 

The next steps are to:

bio.tools provides persistent identifiers for 10,000+ verified tool descriptions and has attracted 20 new contributors / month over the last year. Through this Study we will: 

1. Engage scientific expertise including ELIXIR Communities and ELIXIR Nodes to improve the scientific quality and national representation.
2. Develop tooling for domain-specific bio.tools views for communities

Through a combination of

This study will support OpenEBench in the goal to become an observatory of bioinformatics software quality regarding openness including: 

• ELIXIR AAI
• software development and containerization best-practices
• FAIR data principles for reference datasets as well as the principles on Open Data, Open Source and Open Science.

WP1. A Core Benchmarking Service: 

Work Package 1 has the following aims: