if we start making templates for these snakemake vidlets, we should outline what the necessary components are.
some of things I think we will need --
we want people to be in the right location with the right files (logged in or otherwise have shell access via binder) - so need to link to appropriate binder/and or include setup instructions. specifically,
have a text editor, nano, installed.
have installed the necessary software with conda
already know what the bioinformatics is (e.g. what is variant calling and what variant calling process we are using)
have technical prerequisite knowledge. for variant calling, this would include "have used the unix shell before".
have learning goals/motivations for each lesson. for snakemake,
"I want to learn how to automate"
"this specific lesson will teach me how to XXX YYY ZZZ"
provide informal formative assessment? surveys, knowledge confidence forms, etc.
for more advanced lessons, links to previous lessons and next lessons.
transcripts and links to appropriate places in the embedded videos.
short term I'd like to just build a site out by hand. once it reaches 5-10 vidlets, we can think about systematizing things... perhaps as below.
in terms of medium-term approaches for building site(s) - this could be an opportunity to YAML it up, too, in the sense that we could provide metadata files in support of standard components (see DCPPC use case library for one example where there is a lot of yaml underneath for linking use cases).
if we start making templates for these snakemake vidlets, we should outline what the necessary components are.
some of things I think we will need --
short term I'd like to just build a site out by hand. once it reaches 5-10 vidlets, we can think about systematizing things... perhaps as below.
in terms of medium-term approaches for building site(s) - this could be an opportunity to YAML it up, too, in the sense that we could provide metadata files in support of standard components (see DCPPC use case library for one example where there is a lot of yaml underneath for linking use cases).