Case study
Paperswithcode Scraper
A scraper for Papers with Code that extracts each paper's title, description, date and category, along with links to its repository, PDF and dataset.
Overview
Collects machine learning paper metadata at scale: title, description, repository link, publication date, PDF link, dataset link and category, written out as CSV for analysis.
Public and archived.
Complexities tackled
Optional fields are the whole difficulty. Most papers have a repository; many do not. Some have a dataset link, some do not. A collector that assumes every field is present either crashes or silently emits blanks that later read as real absences. Distinguishing “this paper has no dataset” from “the collector failed to find one” matters as soon as anyone counts.
Categories are a taxonomy, not a string. Papers sit under multiple categories and the taxonomy is nested, so flattening it into a CSV column is a decision about what gets lost rather than a formatting detail.
Stack
Python with Requests and BeautifulSoup, CSV output.
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