This is the UK Carpentry Community space for the UK Carpentry instructors, helpers and workshop coordinators (or anyone involved in training tech to researchers in general and outside of the UK) to get to know each other better, update the commmunity about developments, discuss issues and ideas, and encourage collaboration.
During these meetings, we will be conforming to The Carpentries Code of Conduct.
Regular calls: 4th Monday of the month, 16:00-17:00 UK time (BST or GMT)
Meeting details are shared via local-uk mailing list and The Carpentries community calendar.
Community's HackMD workspace: https://hackmd.io/team/local-uk?nav=overview
Previous meetings: https://hackmd.io/V3ReKkEESzqyCNxWJdulOw#Meetings
Chairing rota: https://hackmd.io/@local-uk/rkPK1Si7F
Guest speaker sign up: https://hackmd.io/@local-uk/rkkzBTLOd
Time: 16:00 BST
Location: Zoom URL: https://zoom.us/j/95360073649
Chair: Mario Antonioletti
Timekeeper:
Notetaker: Aleks Nenadic
Name/pronoun if you like/ institution / optionally put "(checkout)" if you are here for the Instructor Training checkout:
Experiences with running different courses. Normally teaches shell, Python, Git and throws in a bit of HPC.
Once course per week: run so far: Unix Shell Extras, intro to Docker, Intro to Docker to data Scientists, ML with scikit still to run: DL with Keras, intermediate Git, SQL, Intro to Singularity.
Lesson links:
https://github.com/carpentries-incubator/shell-extras
cleaned up forked version: https://github.com/SCW-Aberystwyth/shell-extras
https://github.com/carpentries-incubator/docker-introduction
https://github.com/carpentries-incubator/introduction-to-conda-for-data-scientists
https://github.com/carpentries-incubator/machine-learning-novice-sklearn
https://github.com/carpentries-incubator/deep-learning-intro https://github.com/sa2c/git-demystified https://datacarpentry.org/sql-ecology-lesson/ https://github.com/carpentries-incubator/singularity-introduction
Shell lesson was hard work so added some extras (https://scw-aberystwyth.github.io/shell-extras/) - teaching ssh, file permission, understaning file transfer. A bit dated, Colin spent time working on it on a separate fork. Provided a PR to update the lesson, but not sure how active the lesson manitainers are.
Docker lesson good and nearly complete. ()
Anaconda lesson also good. Last episode on GPU a bit less conherent. ()
Covered a bunch of key skills people were requiring.
Hard to guess requirements and what people need.
Now has a long-running Google form where people can say what they are interested in and some times.
AI, ML and Advanced Computing CDT - another programme based on Carpentries. A bit more advanced and have some basic knowledge of shell, git. November: … January: Web APIs, using a project-based work, Docker, SQL April: Binder and reproducibility (ATI institute work adopted into the Carpentry lesson), Testing, CI June: Object Oriented Python - lesson developed at Swansea
Performant Numpy - https://github.com/edbennett/performant-numpy Web APIs - https://github.com/edbennett/web-novice Testing and CI - https://github.com/edbennett/python-testing-ci OO Python - https://github.com/sa2c/python-oop-novice Publishing your Data Analysis code (Binder) - https://github.com/edbennett/publishing-analysis based on https://github.com/alan-turing-institute/CompEnv-Ex1, https://github.com/alan-turing-institute/CompEnv-Ex2, https://github.com/alan-turing-institute/CompEnv-Ex3 and https://github.com/alan-turing-institute/CompEnv-Ex4
Toby Hodges: I helped at a Code Refinery workshop last week, and their Git material was very good: mostly covers branching, merging, exploring history, collaborating (everyone on one repository + everyone with their own fork) https://coderefinery.org/git-intro/ & https://coderefinery.org/git-collaborative/
Sarah Jaffa: 1 - Did you say you ran 1 course a WEEK!? Woah that's impressive, you must be exhausted! Is that all of your job at the moment or do you find time to work on other stuff as well as this? Colin: it was exausting. Tries to spread out courses a bit.
2 - What is your audience for these workshops? (Academic field/ career stage?) Life scicences people, a mix of PdH students, early career reseachers, not many academic staff as they have a clash with teaching
3 - Is each workshop a full day? Most of them are half-day, but varies.
Also totally agree that some beyond-beginner material is really valuable, I wish we could do more of this sort of stuff in UCL.
Lucia M: Paying 2-3 year PhD students to redevelop work stuff/methodology they do for their PhD into training matrials and teach. That training is open to other phD students or staff. Some materials may end up in Prorgamming Historian.
Aleks: Do they publish these materials? Do they get an official publication?
Aleks - you have an object oriented Python lesson, CI, etc … mini lessons. At the SSI we have been developing these intermediate software skills - we might want to link your courses as additional reading material.
You talked about you selected this course for the CDT - a training needs survey was made asking them basic questions to set the agenda for the year.
There is currently a call in NeRC for developing intermediate training needs. Have been asked to use material developed by the SSI. Wanted to put it out there if you want to reuse the SSI material then the SSI will be happy for that to take place.
Evgenij - as part of the Cirrus, an HPC platform at Edinburgh, upgrade we are thinking of upgrading the material. Looking for use cases from the social sciences and biological sciences. Want to have authentic examples within the domains - taking a view from the local machine to an HPC platform.
Toby has a project they would like to start working - create a catalogue of data sets well suited to teaching. Finding good data sets for teaching are out there but it would be good to indentify these. If you know such types of data sets then keep an eye open for when this project starts. Have a hack day to collaborate this - write ten simple sets.
Mario: Covid data but the fact that people are dying from this, Toby pointed out, may seem less suitable for teaching.
Lucia: election data and census data.
Want a brief description of any things that might trip you up with the data.
Provide 10 simple rules paper to let people know how to generate a good teaching data set for your purposes.
Colin: have an extended version of Nelle's pipeline from the laptop -> hpc. (https://github.com/SupercomputingWales/SCW-tutorial/blob/gh-pages/_episodes/07-optimising-for-parallel-processing.md)
Genomics lesson: https://github.com/fasrc/DataC-HPC-genomics https://datacarpentry.org/genomics-workshop/ https://datacarpentry.org/cloud-genomics/
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