Renku talks - 2021/2022

Renku Tea Talk Series

Do you want to familiarize yourself more with Renku and/or learn more about the new cool -upcoming- Renku features?

Join us (virtually) for our bi-weekly at tea time for new feature demos, project expositions, and use cases! (details & topics below).

This is also an opportunity to ask live questions/give feedback to people from the Renku team on a specific topic.

We welcome users with any level of Renku expertise!


Date : every other Friday (starting 5th of November 2021)
Time : 15h00-16h00 (tea time!)
Place : zoom

Topics (subject to change):

  1. 05.11 New Renku UI :tada:
  2. 19.11 Boost your digital classroom with Renku: fostering innovation in teaching and enhancing the learning experience.
  3. 03.12 Renku case study 1: A framework for open and continuous community benchmarking of bioinformatic tools
  4. 14.01 Renku CLI 1.0
  5. 11.02 Renku at BIOP/EPFL for Bioimaging
  6. 28.02 Renku case study 2: Smartsky

We thank you in advance for your time and interest and look forward to seeing many of you in these sessions!

Best,
The Renku Team Tea Talk Organizers

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Our next Renku talk:

  • Date: Friday 19th November 2021 at 15h00 CET
  • Speakers: @ksanao @tao.sun @gavin-k-lee
  • Place: zoom link
  • Topic: Boost your digital classroom with Renku: fostering innovation in teaching and enhancing the learning experience.

If you wish to add these events to your calendar, you can subscribe to the Renku tea talk calendar or use this ics file directly.

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:mega: In a few minutes we have our next Renku talk: :frog:

  • Date : Friday 3rd December 2021 at 15h00 CET
  • Speakers : Almut Lütge & Anthony Sonrel from UZH/SIB
  • Place : zoom link
  • Topic : “A framework for open and continuous community benchmarking of bioinformatic tools”.

Our next Renku talk will be postponed to after the holidays :christmas_tree: :star2: :frog:

Welcome back! A quick reminder that our first tea talk of the year is happening today at 15h00.

Here is a quick teaser:

Abstract: Rerun your experiment on a range of parameters? Train your model on that big dataset you always wanted using HPC? Create big pipelines that can be run as needed? If that sounds appealing to you, then you should join this talk about the Renku workflow functionality and other improvements.

Link to the talk.

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