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https://github.com/kallewesterling/drag-data-1930s


https://github.com/kallewesterling/drag-data-1930s

data-analysis-python dataset digital-humanities historical-newspapers history network-analysis newspapers performing-arts

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README 

        
# Drag data in the 1930s



[![ci](https://github.com/kallewesterling/drag-data-1930s/actions/workflows/cy.yml/badge.svg)](https://github.com/kallewesterling/drag-data-1930s/actions/workflows/cy.yml)



A workflow will run and generate the visualization files in a different repository, so to see the final visualization, visit [this website](https://kallewesterling.github.io/drag-network/).



## Running on local machines



### Step 1. Clone the Correct Repository



This repository contains the source code but the most recent "compiled" `network-app` is located in the [`drag-network` repository](https://github.com/kallewesterling/drag-network). The easiest thing is therefore to 



```sh

$ git clone https://github.com/kallewesterling/drag-network

```



### Step 2. Processing dataset



Since you are cloning the `drag-network` repository, you do not need to process the dataset as it already comes with the latest updated one.



~~To run the analysis, clone this package and run in your terminal:~~



~~$ python generate-cooccurrence-data.py~~



### Step 3. Run server



Navigate into the cloned directory:



```sh

$ cd drag-network

```



Then open a local HTTP server:



```sh

$ python -m http.server

```



_Note that this will only work on Python 3._



## Who is the Researcher?



Kalle Westerling is a Ph.D. Candidate in Theatre and Performance at The Graduate Center, CUNY, where he works on a dissertation about the history and aesthetics of male-identified bodies in 20th-century burlesque and 21st-century boylesque. He is also the project manager for the NEH-funded project “Expanding Communities of Practice,” aimed at helping to create infrastructure for digital humanities across several higher education institutions across the U.S. [Read more about Kalle Westerling on his website.](https://westerling.nu/)



## What is this dataset?



The dataset was created in a combination of a manual and automatic process, where searches were performed across a number of databases, results collated and PDF files/images of scanned newspapers were presented to the researcher (see below), who then manually coded all of the data into a data row for each person who occurred on that particular data in that particular newspaper.



The dataset can be seen [here](https://docs.google.com/spreadsheets/d/1UlpFQ9WWA6_6X-RuMJ3vHdIbyqhCZ1VRYgcQYjXprAg/edit#gid=0).



The data was manually processed into each column of each row as follows.



Each row has some central data assigned to it, which includes:

- a date (in the format YYYY-MM-DD)

- a name of the performer

- a name of the venue

- if not venue is mentioned but a city is mentioned, that name is filled out as well

- a source



Optional data includes:

- If there is a revue name mentioned, it is also noted here.

- If there is a legal name mentioned for the given performer, the legal name is noted

- If there is an alleged age mentioned for the given performer, the alleged age (and consequentially, the assumed birth year) are noted

- ID number that identifies the source in the Entertainment Industry Magazine Archive (EIMA)

- How the source was found through a search in newspapers.com

- How the source was found through a search in Fulton archives

- How the source was found through a search in an already existing archive

- Edge comment, which refers to any comments on the source itself (meta)

- Whether the data point shall be excluded from the final visualization

- Any interesting quotes from source

- Any interesting comments on the performer

- Any interesting comments on the venue

- Any interesting comments on the city

- Any interesting comments on the revue



Cleaned up data includes:

- Name of the performer

- Name of the venue

- Name of the city

- Source