{"id":20070351,"url":"https://github.com/kallewesterling/drag-data-1930s","last_synced_at":"2026-06-11T10:31:17.434Z","repository":{"id":44664491,"uuid":"276993390","full_name":"kallewesterling/drag-data-1930s","owner":"kallewesterling","description":null,"archived":false,"fork":false,"pushed_at":"2023-02-11T00:32:05.000Z","size":140786,"stargazers_count":0,"open_issues_count":21,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-03-02T11:48:37.452Z","etag":null,"topics":["data-analysis-python","dataset","digital-humanities","historical-newspapers","history","network-analysis","newspapers","performing-arts"],"latest_commit_sha":null,"homepage":"https://kallewesterling.github.io/drag-data-browser","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/kallewesterling.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2020-07-03T21:54:44.000Z","updated_at":"2021-11-30T17:20:44.000Z","dependencies_parsed_at":"2024-11-13T14:33:51.291Z","dependency_job_id":null,"html_url":"https://github.com/kallewesterling/drag-data-1930s","commit_stats":null,"previous_names":[],"tags_count":1,"template":false,"template_full_name":null,"purl":"pkg:github/kallewesterling/drag-data-1930s","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kallewesterling%2Fdrag-data-1930s","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kallewesterling%2Fdrag-data-1930s/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kallewesterling%2Fdrag-data-1930s/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kallewesterling%2Fdrag-data-1930s/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/kallewesterling","download_url":"https://codeload.github.com/kallewesterling/drag-data-1930s/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kallewesterling%2Fdrag-data-1930s/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":34195112,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-06-11T02:00:06.485Z","response_time":57,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-analysis-python","dataset","digital-humanities","historical-newspapers","history","network-analysis","newspapers","performing-arts"],"created_at":"2024-11-13T14:22:15.182Z","updated_at":"2026-06-11T10:31:17.414Z","avatar_url":"https://github.com/kallewesterling.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Drag data in the 1930s\n\n[![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)\n\nA 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/).\n\n## Running on local machines\n\n### Step 1. Clone the Correct Repository\n\nThis 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 \n\n```sh\n$ git clone https://github.com/kallewesterling/drag-network\n```\n\n### Step 2. Processing dataset\n\nSince you are cloning the `drag-network` repository, you do not need to process the dataset as it already comes with the latest updated one.\n\n~~To run the analysis, clone this package and run in your terminal:~~\n\n~~$ python generate-cooccurrence-data.py~~\n\n### Step 3. Run server\n\nNavigate into the cloned directory:\n\n```sh\n$ cd drag-network\n```\n\nThen open a local HTTP server:\n\n```sh\n$ python -m http.server\n```\n\n_Note that this will only work on Python 3._\n\n## Who is the Researcher?\n\nKalle 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/)\n\n## What is this dataset?\n\nThe 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.\n\nThe dataset can be seen [here](https://docs.google.com/spreadsheets/d/1UlpFQ9WWA6_6X-RuMJ3vHdIbyqhCZ1VRYgcQYjXprAg/edit#gid=0).\n\nThe data was manually processed into each column of each row as follows.\n\nEach row has some central data assigned to it, which includes:\n- a date (in the format YYYY-MM-DD)\n- a name of the performer\n- a name of the venue\n- if not venue is mentioned but a city is mentioned, that name is filled out as well\n- a source\n\nOptional data includes:\n- If there is a revue name mentioned, it is also noted here.\n- If there is a legal name mentioned for the given performer, the legal name is noted\n- If there is an alleged age mentioned for the given performer, the alleged age (and consequentially, the assumed birth year) are noted\n- ID number that identifies the source in the Entertainment Industry Magazine Archive (EIMA)\n- How the source was found through a search in newspapers.com\n- How the source was found through a search in Fulton archives\n- How the source was found through a search in an already existing archive\n- Edge comment, which refers to any comments on the source itself (meta)\n- Whether the data point shall be excluded from the final visualization\n- Any interesting quotes from source\n- Any interesting comments on the performer\n- Any interesting comments on the venue\n- Any interesting comments on the city\n- Any interesting comments on the revue\n\nCleaned up data includes:\n- Name of the performer\n- Name of the venue\n- Name of the city\n- Source\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkallewesterling%2Fdrag-data-1930s","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkallewesterling%2Fdrag-data-1930s","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkallewesterling%2Fdrag-data-1930s/lists"}