[WIP] EThOS PhD thesis metadata analysis

[mhauru]

Summary

Copy the notebook from https://github.com/mhauru/EThOS-analysis/blob/master/analysis.ipynb, make minimal edits to make it run. This is a starting point of the PhD thesis metadata (EThOS) story.

List of changes proposed in this PR (pull-request)

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What should a reviewer concentrate their feedback on?

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• Everything looks ok?

Acknowledging contributors

• All contributors to this pull request are already named in the table of contributors in the README file.
• The following people should be added to the table of contributors in the README file:

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mhauru commented on 2022-04-14T10:47:21Z
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Camila: Rerun on latest data (https://bl.iro.bl.uk/concern/datasets/bb0b3ec4-4667-436a-8e6a-d2e8e5383726?locale=en)

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mhauru commented on 2022-04-14T10:47:22Z
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Camila: Check how many people have a full first name. If many have it, we could get gender data based on them, and analyse that.

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mhauru commented on 2022-04-14T10:47:23Z
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Camila: Check if qualification is always PhD.

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mhauru commented on 2022-04-14T10:47:23Z
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Camila/Markus: This plot needs improving. Some points, ideas:

• colours are ugly
• could bin by decade
• could rolling average by decade
• could colour Russell group unis, or Turing network unis
• the plot has some strange features that look like something is happening in the data, but it's more an artefact of the plotting

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mhauru commented on 2022-04-14T10:47:24Z
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Maybe put in one figure? Maybe rolling average to smooth?

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mhauru commented on 2022-04-14T10:47:25Z
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Keep buzzwords in this story, move all network analysis stuff to its own story.

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mhauru commented on 2022-04-14T10:47:26Z
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Try turning this into a figure.

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mhauru commented on 2022-04-14T10:47:26Z
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Present more nicely, less long list, more plotty

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mhauru commented on 2022-04-14T10:47:27Z
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This plot is hard to interpret.

[crangelsmith]

@mhauru and @crangelsmith met on the 14th of April and decided to turn this notebook into two stories into two parts.

First part:

• Descriptive stats and figures.
• Add an analysis on gender if possible
• First pass a title analysis using word counts.

Second part:

• Title analysis using networks (second part of this notebook).

The comments above help guide the curation of the stories. We will open a new PR with the first part story and keep this one open until the second story is ready for review.

[mhauru]

Todo list:

• Update data to latest versions
• Do gender analysis
• Remove everything that will be in part 2
• Make plots prettier
• Go through review comments in ReviewNB
• Clean up imports (once part 2 is gone)