# 27.11.2023 DDLS education and training
## 5th meeting with DDLS fellows

## **Welcome**
## **Code of Conduct reminder**
* Be respectful, honest, inclusive, accommodating, appreciative, and open to learning from everyone else.
* Do not attack, demean, disrupt, harass, or threaten others or encourage such behavior.
* Be patient, allow others to speak, and use the zoom reactions & chat if you would like to voice something.
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## **Links & Resources**

**Zoom link** https://www.google.com/url?q=https://stockholmuniversity.zoom.us/j/67177687102&sa=D&source=calendar&ust=1697366929408086&usg=AOvVaw1xnAe0eGp-Ew4HMhh9ACCL
**Miro board** https://miro.com/app/board/uXjVPZVPbOU=/?share_link_id=7147612421
**Training collection** https://docs.google.com/spreadsheets/d/18hSpGPnQvnyCeIeT-Ru0nu764Qi8mZWCqfIYgld33Rw/edit?usp=sharing
**NBIS course catalogue** (https://uppsala.instructure.com/courses/48087/pages/nbis-training-catalogue)
**ELIXIR TeSS (European training event catalogue)** (https://tess.elixir-europe.org/)
**SciLifeLab training events** (https://www.scilifelab.se/events/#calendar)
*email ([e-mail]) if you like to add a course/event to the SciLifeLab calendar*
**GU and GU core facility training** (https://www.gu.se/en/core-facilities/bioinformatics-and-data-center-bdc/education-and-training)
**Glittr.org: Github repository for Bioinformatics training materials** (https://glittr.org/?per_page=25&sort_by=stargazers&sort_direction=desc)
**SIB (Swiss Insitute for Bioinformatics): e-learning collection** (https://www.sib.swiss/training/e-learning)

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## Rollcall:
🗣 Name/🐸 pronouns/✍Reseach Area/🏼 Affiliation/
* Nina Norgren / she-her/ Training Manager / SciLifeLab and NBIS
* Patrick Bryant/ / cell mol bio / SU+SciLifeLab Solna
* Luisa Hugerth / she / Infection-epidemiology / UU
* Cemal Erdem / he-him / Precision medicine / UmU
* Camila Consiglio /she-her / Infection-epi / LU\
* Jacob Vogel
* Abhishek Niroula / he-him / precision medicine / GU
* Tobias Andermann / he-him / Evolution and Biodiversity / UU
* Nicholas (Nick) Pearce
* Wei Ouyang
* Clemens Wittenbecher
*
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## **AGENDA**
| Duration | Activity |
| -------- | -------- |
| 5 mins | Welcome and introductions|
| 10 mins | Re-cap previous meetings|
| 10 mins | **What is new?** Any new courses you are setting up? Current courses or trainings that could be DDLS branded? |
|30 mins | **Discussion** - How can we best capitalize on your knowledge in a way that benefits both the community and you? And how can we support you in that? |
| 10 mins | **Action points** from the discussion |
| 10 mins | Next steps and wrap-up |
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# Re-cap previous meetings

# Discussion and capture
## What knowledge and skills do your PhD students (group members) require? [Gdoc](https://docs.google.com/spreadsheets/d/18hSpGPnQvnyCeIeT-Ru0nu764Qi8mZWCqfIYgld33Rw/edit#gid=1108957154)
### Notes

#### Nick's "two cents"

##### Foundational Courses -- "Primers"
- Basic Python
- Introduction to programming concepts
- Basic plotting (overlap with visualisation)
- [Do in their own time? / Offline]

- Reproducible coding in R
- One-week courses for people from different backgrounds (Catchup Courses for "Outsiders")
- Bioinformatics in one week
- Systems Biology in one week
- XXX on one week
- Machine Learning for Biologists
- A primer for understanding an ML talk
- What defines ML, AI?
- Types: Supervised, unsupervised, etc.

##### Application Driven Python Courses:
- Data Wrangling in Python
- Numpy & Pandas
- Handing large analyses
- Approaches for exploratory data analysis
- Large Analyses in Python
- Parallelisation patterns
- Checkpointing
- Containerisation (Is NBIS course already)
- Software Development in Python
- Sustainable coding/design patterns
- Version Control
- Use of ChatGPT to power testing and documentation
- CI/CD
- Automated documentation (e.g. Sphinx)
- Code patterns
- FAIR by design
- Creating and managing complex workflows
- Neural Networks in Python
- ...
- GPU programming in Python (Berzelius)
- Advanced Python
- GPU programming
- Checkpointing
- Creating Web(server) resources in Python
- Webserver "theory" (components, etc)
- Good code patterns to follow (modular, etc, MVC)
- Making complex results pages in webservers (Javascript)
- Processing on remote compute resoruces and collection of results
- ChatGPT for life sciences and prompt engineering (Wei)

##### "Theory" courses

- Gold standards for publishing
- Publishing FAIR data
- Publishing open-source code (licensing)
- Publishing reproducible analyses
- Scientific Communication
- Currently awful basically everywhere
- Theory of communication
- How to give a talk (how to plan, construct and present)
- Scientific Visualisation: theory + practice (in Python)
- Currently awful basically everywhere
- Concepts & Theory for communicating scienfitic results
- How to make good plots
- FAIR principles in practice
- Currently awful basically everywhere
- "The reproducibility crisis"
- Reusable code and reproducibile analysis
- Publishing the analysis with the data
- Data repositories

## What courses could you co-create? (advanced courses as fellows)

PUT YOUR NAMES DOWN AS "SIGNUP"

- Data interrogation, visualisation and exploration (Data wrangling-adjacent)
- Nick
- Juliette

- Scientific Communication
- Nick

- DDLS (https://ddls.aicell.io/)
- Wei

- Python data science intro
- Tobias

- Neural Networks for application in Life Sciences (also a component of Wei's DDLS course)
- Tobias

- Accessing publicly available data via API (focus on Biodiversity and Evolution)
- Tobias

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# Open Discussions Notes:

What university courses do you recommend your PhD students to take?
There are some money for onsite courses in the research school
Tobias suggestion: list of what each DDLS fellow would be able to teach. Management collates and groups. Fellows put together some videos and exercises.
Train-the-Trainer course with follow up discussion about training needed. TtT with flavour of data driven programming training. Maybe at DDLS/SciLifeLab fellow retreat.
Decide on groups of topics: programming, soft skills, Ml, etc
Template from TH to be given feedback on from fellows
TH suggests 2-3 different ways forward (templates, formats, etc), to be presented and discussed at this meeting.
Clearer timeline for when they should get involved. Research school starts fall 2024, first course 2025.
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# Q&A:
:::success
❓ *Please add any questions you might have during the course of the session here:*
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# Wrap up:
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## Feedback:

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## Action points:
- Join our [Slack: ](https://scilifelab.slack.com/?redir=%2Farchives%2FC041VD1TL5T) channel: #ddls-education-training
- Continue to fill the [Training Collection](https://docs.google.com/spreadsheets/d/18hSpGPnQvnyCeIeT-Ru0nu764Qi8mZWCqfIYgld33Rw/edit?usp=sharing) Google excel
- Spread the survey to your local HEI and network '[Training need assessment (data-driven life science)](https://forms.gle/s86Ybzqt8er3EupT7)

## Next meeting

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# Thank you for joining and see you next time!