| Time | Module |
|---|---|
| 08:30 - 09:35 | Hello Quarto |
| 09:35 - 09:45 | Break |
| 09:45 - 10:15 | GitHub |
| 10:15 - 10:55 | Documents |
| 10:55 - 11:05 | Break |
| 11:05 - 11:50 | Documents |
Pre-course work - Open Science Tools HS23
Thank you for your interest in this course. Your course instructor Lars Schöbitz is looking forward to meet you.
We will meet in Room HG F 33.5 at the following times:
- Day 1: 17th October 2023 - 08:30 am to 12:30 pm CET
- Day 2: 24th October 2023 - 08:30 am to 12:30 pm CET
Please ensure you bring the following items to class:
- a laptop, connected to ETH wifi
- a browser installation on your laptop
Prior to Day 1, there is some pre-work for you to do. This pre-work will ensure that we can focus our time together on learning instead of setting up infrastructure.
What do I need to prepare before Day 1?
Prior to Day 1, please complete the following seven steps:
- Step 1: Get an account on GitHub and setup a GitHub Personal Access Token (PAT)
- Step 2: Register for an ORCID iD
- Step 3: Sign-up for Zotero and setup Zotero reference management software
- Step 4: Log into Posit Cloud and join the workspace for the course
- Step 5: Log into Element and join the room for our course
- Step 6: Fill out the pre-course survey by Friday, 13th October
- Step 7: Read Wilson et al. (2017) and prepare for a discussion
Day 1
Day 2
| Time | Module |
|---|---|
| 08:30 - 09:00 | Scholarly Writing |
| 09:00 - 09:45 | Presentations |
| 09:45 - 10:00 | Break |
| 10:00 - 10:45 | Websites |
| 10:45 - 11:00 | Publish |
| 11:00 - 11:15 | Break |
| 11:15 - 12:15 | Develop |
| 12:15 - 12:30 | Wrap-up |
Thanks!
Thank you for working through these steps. You will hear from us again a day before the start of the course.
Attribution
Content was re-used from a workshop hosted by Mine Çetinkaya-Rundel at the 2023 Symposium on Data Science and Statistics and stored at https://github.com/mine-cetinkaya-rundel/quarto-sdss. The original content is licensed under a Creative Commons Attribution 4.0 International License.
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Wilson, Greg, Jennifer Bryan, Karen Cranston, Justin Kitzes, Lex Nederbragt, and Tracy K. Teal. 2017. “Good Enough Practices in Scientific Computing.” PLOS Computational Biology 13 (6): e1005510. https://doi.org/10.1371/journal.pcbi.1005510.