Multi-Language Readability Score Dataset

This dataset provides readability metrics across 12 languages, collected as part of the content accessibility research for Facil.guide, a multilingual technology guide platform designed for senior users aged 65 and older. Readability scoring is central to the editorial workflow at Facil.guide, where every article must meet strict comprehension thresholds before publication. Each row represents a single language and includes the following measurements: Flesch-Kincaid readability index (adapted for the target language), Gunning Fog index, Coleman-Liau index, average sentence length in words, average word length in characters, number of sample articles analyzed, target grade level, achieved grade level after editorial revision, measured comprehension rate among readers aged 65 and older, and the average number of revision rounds required to reach the target. The 12 languages covered are English, Spanish, French, Portuguese, Italian, German, Dutch, Japanese, Simplified Chinese, Korean, Arabic, and Hindi. The core five languages for Facil.guide (English, Spanish, French, Portuguese, Italian) have the largest sample sizes (75-120 articles each), while additional languages represent exploratory analysis for potential future expansion. Key findings show that English consistently achieves the highest comprehension rates (78.2% among 65+ readers) with the fewest revision rounds (2.1), largely due to shorter average sentence length and the maturity of English-language readability formulas. Agglutinative and logographic languages (Japanese, Chinese, Korean) require significantly more editorial iteration to reach accessible grade levels. The full methodology, including how traditional readability formulas were adapted for non-Latin scripts, is documented on facil.guide/en/. This dataset is useful for researchers in computational linguistics, accessible content design, senior UX, and multilingual NLP.