Bianca, That's a great report! Thanks for the detail about what was done and why! I think it is very useful.
-- bennet On Thu, Oct 26, 2017 at 8:44 AM, Bianca Peterson <[email protected]> wrote: > > Hi all, > > We are currently running a 16S metagenomics workshop at my institution > (North-West University, South Africa), and I combined various lessons from > the Carpentries and modified most of them according to our main (critical) > lesson's dataset that we were using for 16S analysis. We are covering the > following topics: spreadsheet organization, shell, HPC for 16S analyses > (using Shi7, NINJA and QIIME), R genomics and specific plots of 16S data in > R. > > The link to our website is > https://nwu-eresearch.github.io/2017-10-24-ARC-16S/ and the various lessons > are linked. > > We will write a blog post after the workshop, but here some notes (thus > far): > - We needed to extend the workshop to 3 full days to fit all the lessons and > decided on an additional half-day where they have the opportunity to work on > their own data, and also allow some extra time if we needed it for the > lessons. > - I made an R presentation of the spreadsheet organization Ecology lesson of > DC to guide one of our newly-trained instructors who was teaching for the > 1st time. The .html can be viewed in a browser, thus not needing RStudio to > view the presentation. This also cut back a few minutes, which we then used > for the shell lesson. > - At the last minute, I decided to switch from the extensive SWC shell > lesson to the shortened genomics one of DC (due to limited time allocated > for this lesson) and only modified the output according to our HPC. I used > the lesson as is, which was a blessing, since the instructor deleted some > .fastq files to demonstrate the rm command, and our 16S data was safe for > the next session. Feedback shows that they still need a little more time on > this lesson. > - One full day for 16S analyses was perfect - we finished in time even with > some troubleshooting along the way. This workflow was written by Tonya Ward > (Knights Lab | University of Minnesota) and all the required software was > installed on our HPC prior to the workshop. > - We just finished the R lesson (R genomics from DC modified according to > the metadata/mapping file that we used for the 16S analyses). Not all the > data in this mapping file is real - I made up some variables in order to do > plotting (this lesson can certainly be improved by adding more variables). > Time allocation seemed to be perfect, the pace was not fast at all and > almost everybody kept up with the instructor. > - In the next session, they will use output files generated by QIIME to make > a variety of visualizations in R. Another newly-trained instructor will be > teaching this, and he wrote the workflow for us. > > Any comments/suggestions/questions are welcome! I hope this is helpful in > some way. > > A detailed blog post will follow in the next week or two. > > Kind regards, > > Bianca > > On Mon, Aug 22, 2016 at 9:45 AM, Carlos Martinez Ortiz > <[email protected]> wrote: >> >> Hi Jon, >> >> >> The data carpentry ecology lessons do try to use the same (or very >> similar) data. So doing OpenRefine -> SQL -> SQL from Python, works very >> well. In my opinion, when you are able to run these lessons one after the >> other, it makes the workshop more coherent for the learners, but the lessons >> are still complete and can stand on their own. >> >> >> Cheers, >> >> Carlos >> >> >> ________________________________ >> From: Discuss <[email protected]> on behalf of >> Jon Pipitone <[email protected]> >> Sent: 20 August 2016 16:56:43 >> To: [email protected] >> Subject: [Discuss] Teaching lessons around a single example >> dataset/scenario >> >> Hi all, >> >> A few of us at the Centre for Addiction and Mental Health in Toronto >> have been teaching a series of Software Carpentry-like workshops[1] >> (actually some of them are exactly SWC workshops) over two weeks aimed >> more specifically at researchers in our organization. Much of what we >> teach is very introductory: what is programming, how to use the linux >> shell, a very basic intro to R focusing on statistics, a MATLAB and SPSS >> primer, using Photoshop, etc.. We do also get to a few more advanced >> topics in some workshops: e.g. doing fMRI analysis in python, using a >> compute cluster. >> >> We received feedback from learners and instructors that having more >> cohesion between the lessons would be really helpful to tie things >> together (currently we have a mixture of lessons with toy examples, more >> elaborate worked examples, and some with only descriptions/powerpoint), >> but it's disjointed: there isn't a theme or example dataset running >> through the workshops. >> >> Has anyone tried creating lessons for several different topics around a >> single example scenario? E.g. using Nelle's data from the Shell >> lectures[2] to also teach R, Python, Git, etc.. How has it worked out? >> Is there anything we should be wary of as we wander down this road? >> >> Thanks! >> Jon. >> >> [1] e.g. https://camh-scwg.github.io/compucool >> [2] http://swcarpentry.github.io/shell-novice/01-intro/ >> _______________________________________________ >> Discuss mailing list >> [email protected] >> http://lists.software-carpentry.org/listinfo/discuss >> >> _______________________________________________ >> Discuss mailing list >> [email protected] >> http://lists.software-carpentry.org/listinfo/discuss > > > > _______________________________________________ > Discuss mailing list > [email protected] > http://lists.software-carpentry.org/listinfo/discuss _______________________________________________ Discuss mailing list [email protected] http://lists.software-carpentry.org/listinfo/discuss
