# 11.10.2023 DDLS education and training
## 4th 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/ favourite candy
* Jessica Lindvall / she-her/ Head of Training/ SciLifeLab and NBIS/ Sura kryptoniter
* Johan Bengtsson-Palme / he-him / CellMolBio / Chalmers / chocolate
* Nicholas Pearce / hehim / CellMolBio / Sour cadillacs
* Avlant Nilsson / / Precision medicine / KI / Tyrkisk Peber
* Clemens Wittenbecher/Precision medicine/CUT
* Patrick Bryant//CellMolBio/SU/chocolate
* Cemal Erdem / he-him / Precision medicine / Umeå / All things chocolate
* Camila Consiglio / sheher / Infection Biology / Lund / chocolate
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## **AGENDA**
| Duration | Activity |
| -------- | -------- |
| 5 mins | Welcome, housekeeping, round the table introductions|
| 5 mins | Re-cap previous meetings|
| 10 mins | **Wei Ouyang et al** short description on the collaborative DDLS fellows course -Super-Resolution Microscopy and Analytics to Investigate Complex Cellular Processes and Diseases |
|20 mins | **Discussion and capture** - What knowledge and skills do your PhD students (group members) require? [Gdoc](https://docs.google.com/spreadsheets/d/18hSpGPnQvnyCeIeT-Ru0nu764Qi8mZWCqfIYgld33Rw/edit?usp=sharing) |
| 10 mins | **Break** |
| 20 mins | **Discussion and capture** - What courses could you co-create? (advanced courses as fellows) [Gdoc](https://docs.google.com/spreadsheets/d/18hSpGPnQvnyCeIeT-Ru0nu764Qi8mZWCqfIYgld33Rw/edit?usp=sharing) |
| 10 mins | **Action points** from the discussion and capture |
|10 mins | **Elin Kronander (NBIS)** short description of NBIS course catalogue (focus foundational topics)|
|10 mins | **Lars Eklund (Uppmax/NBIS)** short description of the Programming Formalism course|
| 10 mins | Next steps and wrap-up |
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# Re-cap previous meetings
## [Collaborative training opportunities - add ideas](https://docs.google.com/spreadsheets/d/18hSpGPnQvnyCeIeT-Ru0nu764Qi8mZWCqfIYgld33Rw/edit?usp=sharing)

## Outline Data-driven Life Science knowledge, skills, abilities

#### **Foundational topics**
* Data science and Bioinformatics
* Mathematics and (bio)statistics
* Open Science and FAIR
* Research Data Management
* ELSI/A and Data

### What knowledge and skills are required?
Identifying KSAs (knowledge, skills, abilities) i.e. content/topics/knowledge across foundational categories of what core knowledge is needed to be a Data-driven life scientist

### Mapping existing courses
*from the captured excel document [Training collection](https://docs.google.com/spreadsheets/d/18hSpGPnQvnyCeIeT-Ru0nu764Qi8mZWCqfIYgld33Rw/edit?usp=sharing)*

**Data science & Bioinformatics courses:**
* RaukR (NBIS intermediate/advanced R course/summer school): https://nbisweden.github.io/workshop-RaukR-2206/
* Workshop on Data visualization in R (NBIS/ELIXIR-SE course): (https://uppsala.instructure.com/courses/46547)
* Introduction to Python - with application to bioinformatics (NBIS course): (https://uppsala.instructure.com/courses/71521)
* Quick and clean: Data Science in Biology using Advanced Python (NBIS advanced Python course): (https://github.com/NBISweden/workshop-advanced-python)
* Basic Data Handling and Visualization with R (LU PhD course)
* GU and GU Core Facility courses: (https://www.gu.se/en/core-facilities/bioinformatics-and-data-center-bdc/education-and-training)
* Unix applied to genomic data
* R Programming
* Python for biologists
* Gene Expression Analysis Using R
* Bioinformatics I and II

**Mathematics and Biostatistics courses:**
* Introduction to Biostatistics and Machine Learning (NBIS course): (https://uppsala.instructure.com/courses/74597)
* Neural Networks and Deep Learning (NBIS course): (https://uppsala.instructure.com/courses/75565/pages/schedule)

**Open Science and FAIR courses:**
* Tools for Reproducible Research (NBIS course): (https://github.com/NBISweden/workshop-reproducible-research/)
* Snakemake bring-your-own-code (BYOC) workshop (NBIS course): (https://uppsala.instructure.com/courses/70024)

**Research Data Management courses:**
* Introduction to Data Management Practices (NBIS course): (https://uppsala.instructure.com/courses/48087/pages/introduction-to-data-management-practices)
* Open Science and FAIR (https://github.com/NBISweden/module-open-science-dm-practices)
* Data organisation practices (https://github.com/NBISweden/module-organising-data-dm-practices)
* Metadata (https://github.com/NBISweden/module-metadata-dm-practices)
* Data publication (https://github.com/NBISweden/module-data-publication-dm-practices)
* Cleaning tabular data with OpenRefine (https://github.com/NBISweden/module-openrefine-dm-practices)
* Introduction to scripted analysis with R (https://github.com/NBISweden/module-r-intro-dm-practices)
* Versioning of Data and Code using Git (https://github.com/NBISweden/module-versioning-dm-practices)
* Data Management Plans (https://github.com/NBISweden/module-dmp-dm-practices)

**ELSI/A and Data courses:**
* AI and law (LU MOOC) (https://www.coursera.org/learn/ai-law)
* AI, Business & the Future of Work (LU MOOC) (https://www.coursera.org/learn/ai-business-future-of-work)
* Artificial Intelligence: Ethical & Societal Challenges (LU MOOC) (https://www.coursera.org/learn/ai-ethics)
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# 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]
- 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

##### "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) [Gdoc](https://docs.google.com/spreadsheets/d/18hSpGPnQvnyCeIeT-Ru0nu764Qi8mZWCqfIYgld33Rw/edit#gid=1108957154)
### Notes

PUT YOUR NAMES DOWN AS "SIGNUP"

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

- Scientific Communication
- Nick

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# Open Discussions Notes:
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# Q&A:
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# Wrap up:
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## Feedback:

- How can these meetings be most effective?

## 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
27th of November 2023, 13:00 -- 15_00

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# Thank you for joining and see you next time!