OpenDreamKit final review (rehearsal day 1)

This pad is for taking notes during the first day of rehearsal for OpenDreamKit's Final review.

Having a glossary seems useful to reviewers. At both previous reviews we (Vincent) made one, see the Glossary folder on github.

Agenda Brainstorm

Agenda brainstorm was copied into the pad for Day 2 and is edited there.

WP presentations

Suggestions from our reviewers:

We need to understand not only the achievements of the project as a whole, but also to know whether this kind of project is viable in the future. Here are three suggestions:

  • Stick to high level presentations on work packages, without going too much into the details
  • Focuse the presentations more on problems encountered
  • explain what the future generation of project can learn from our experience

One of the reviewers further suggested:

The project was funded on the idea of creating a collaborative environment. Therefore, the success of the project should be measured against this objective. He therefore suggests to make a special focus on:

  • How to evaluate Jupyter Notebooks as system by which one can collaborate internally
  • Same question for external collaboration

Demos

WP2: Open Science paper

@defeo

https://github.com/fdslrm/EBLUP-NE

Binder-powered GitHub repo associated to a research paper

  • uses Sage, CVXPY, Scipy and R
  • external to OpenDreamKit!

Hančová, M., Vozáriková, G., Gajdoš, A., Hanč, J. (2019). Estimating variance components in time series linear regression models using empirical BLUPs and convex optimization, https://arxiv.org/abs/1905.07771, 2019.

WP4

3d visualisation

@minrk, Marcin

3D visualisation with k3d, ideally include micromagnetics example

Jupyter collaboration (2-3 minutes)

@minrk

  • nbdime diff
  • live collaboration in JupyterLab

Give a few words of context:

  • feature been there in cocalc for years
  • use the analogy with google docs / …
  • big deal: impact
  • big deal: required structural changes in the core design of JupyterLab

WP6 Data.MathHub.info (5 min; Katja & Michael)

  • Statistics
  • Demonstrate F. A. I. R.
  • citability? permanent URI
  • Discussion about options for long term preservation Software heritage for metadata / small data sets that can be put in version control ??? from EOSC??? for raw data preservation

Persistent Memoization (GAP/Sage; Alex/Steve)

NT: How long a demo would be proper fit here? NT: If just a short one, maybe I could fit this in "Balthazar's use case; need to find a nice example" both best after the WP6 talk.

High Performance Mathematical Computing with GAP (Steve Linton)

All rather technical, designed for "drop in" rather than presentation - HPCGAP - Meataxe64 (9 minutes) - SyntaxTree

Suggestions:

  • MeatAxe: Tell the story on one use case (e.g. around the Monster), with comparison before / after
  • HPCGap:
    • Draw a picture for block multiplication?
    • Explain Before ODK / After ODK
    • Good news: lots of intricate code, many users: high impact
    • Bad news: lots of intricate code, many users: hard to maintain backward compatibility

GAP usability

​​​​- WP4 stuff (Alex) -- demonstration of usability of GAP Jupyter notebooks. Maybe pick up one, leave the rest for the "drop in"?
​​​​    - Homophonic quotients
​​​​    - Semigroups
​​​​        - libsemigroups example
​​​​        - francy package example
​​​​    - Packages ecosystem
​​​​- Other demos (Alex)
​​​​    - Memoisation (WP6)
​​​​    - PackageManager (WP3)
​​​​    - Use for lecturing (with RISE extension)
​​​​    - unipoly (on Binder!)

Singular

  • Benchmarks of old Singular vs new Singular
  • Possibly rational functions?

WP3/WP5 integration Linbox in Sage

Balthazar wants to do modular rep theory of semigroups (Nicolas)

Cross WP demo

Demonstrates:

  • low and high level interfaces
  • Sage, GAP, Semigroups, …
  • widgets / explorer (hopefuly)
  • Sharing via binder, "deploying on a VRE" (thanks to packaging)

Use Case: Jupyter for Enterprise Training at Logilab (Olivier, 4 minutes?)

Scenario: Logilab trains 500 scientists yearly on scientific computing, with highly heterogeneous groups

Requirements:

  • Maintain a large collection of exercises
  • Foster autonomy of students, engaging them to explore at their own pace, with automatic feedback

Previous solution: slides + python in the console

Current solution: Deploye a Jupyter-based Virtual Environmnent:

  • JupyterHub server
  • Jupyter
  • Jupyter-training: open source home-grown solution to solve our needs, now shared with the world

Outcome: people are more engaged, autonomous, … They keep using the server after their session! We need to cut them!

Suggestions:

  • more impressive example
  • show an answer
  • show the author's perspective: what do the sources look like
  • demonstrate how this could be used for teaching mathematics
Select a repo