Spring 2026 Slides

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# .Large[Data Science for IR: Reproducibility]
## .small[Korbel School of International Studies]
## .small[bit.ly/spring-2026-du]
#### .tiny[Dr. Evangeline Reynolds | 2026-04-01 | DU, Image credit: 
Jeffrey Betts, Upsplash]

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Title slide

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# Data science in 2026...

## Fluent with code?

## Fluent with 'moves'

## Being able to express yourself about moves you've seen and performed, (or can imagine!) 
--
(be it in code or w/ LLM assistance)

## And being able to compose moves to create coherent, robust, reproducible data products.

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# Reproducibility

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## "Replication Crisis"

![](../images/clipboard-926906303.png)

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## "Many labs"  project

Klein, R. A. "Investigating Variation in Replicability: A 'Many Labs' Replication Project (vol 45, pg 142, 2014)." SOCIAL PSYCHOLOGY 50.3 (2019): 211-213.

![](../images/clipboard-2473160817.png)

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# Related: Reproducibility

Replicable -> new subjects, new data

Reproducible -> same underlying data, exact same conclusions drawn, following the researchers instructions

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![](../images/clipboard-2804641906.png)

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![](../images/clipboard-3411260092.png)

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## Reproducibility

- Exact record of steps in an analysis

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## Why bother?

- Transparency

- Precision in documentation of steps
  
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- Efficiency

- adding new data
    - making small modifications to analysis

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## Tools

- Literate programming (Donald Knuth)
  - Abundant annotation of code in plain language
  - Scripts with comment character (.R)

- Dynamic documents
    - Jupiter Notebooks
    - Sweave
    - **R markdown**

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# Dynamic Documents

Prose (plain text)

\+ code

\+ output

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