musmem-extract-distinct-athletes

To extract athlete names from semicolon-delimited MusMem files, run the bundled Python script to parse the input data, normalize the text, and output a tab-separated file of candidate name groups.

Grouping athlete name variants for data cleaning involves parsing MusMem result files, normalizing names, and generating a TSV of candidate groups that capture likely spelling variations for review before canonicalization.

Yes, you can filter MusMem contest results by year when extracting athlete names by applying optional minimum-year filters during the parsing process to restrict the dataset processed.

The output format for extracted MusMem athlete name groups is a tab-separated values (TSV) file that lists candidate variant groups alongside their appearance counts and source file paths.

Yes, the MusMem name extraction script supports multi-file processing, allowing you to compare and aggregate athlete names across one or more semicolon-delimited data files while automatically embedding source file paths for traceability.

You need to normalize athlete names before canonicalization to resolve text variations and inconsistencies in raw MusMem data, ensuring that identical athletes are accurately grouped together in the output TSV.