Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Overarching Narrative #7

Open
mhamilt opened this issue Nov 21, 2020 · 1 comment
Open

Overarching Narrative #7

mhamilt opened this issue Nov 21, 2020 · 1 comment

Comments

@mhamilt
Copy link

mhamilt commented Nov 21, 2020

One missing element that would do a lot to tie the lesson together is an Overarching Narrative. Something similar to Nelle's Pipeline in the Shell Novice.

There is a similar problem with the Python Inflammation lesson, seen in issue 887.

Though the dataset of EEBO is demonstrative, I think it could still be integrated into a story. This would avoid re-writing material and sourcing another data set.

@mhamilt
Copy link
Author

mhamilt commented Nov 21, 2020

Collecting together the examples and challenges of the lesson here is a potential framework for a narrative.

Narrative

A Researcher needs to collect together multiple data sets of Early English Books. Part of their research will require creating a visualisation to better demonstrate distribution of book title by author by place

At the same time they need to:

  • Fulfill Requests for book lists by specific authors in specific years?
  • provide a list of books to an archivist to try and fill in missing details for specific literature?
    • sermons in certain areas in certain years?

Given the nature of the data set, it highly suggestive that the Research is either

  • required to liaise with a Centre for Research Collections

OR

  • Works as an archivist, librarian or within some centre for historic cultural research.

Breakdown

As part of the narrative there should also be a breakdown

In order to to do this, the Researcher needs to:

  • gather the tools to with with the data (Lessons 1, 2)
  • look at data first, before they can work with it. (Lessons 2)
  • get some stats on the data (Lesson 2)
  • Double check the data is clean (Lesson 4)
  • Consolidate data sets (Lesson 5)
  • Filter data by some criteria (Lesson 3)
  • Automate this workflow across multiple criteria (Lesson 6)
  • Visualise Data (Lessons 7, 8)
  • Pass on this data set so their work is reproducible? (Lesson 9)

I have no background in this kind of research, so input from someone within this field should hopefully lend a little verisimilitude

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

1 participant