We present the MultiplEYE Text Corpus, a large-scale, document-level, multi-parallel resource designed to advance cross-linguistic research on reading and language processing. The corpus provides paragraph-level alignment for texts in 39 languages spanning seven language families and seven scripts. Unlike many existing multilingual corpora, a substantial number of documents were originally written in languages other than English, reducing English-centric bias and supporting more typologically diverse investigations. The texts are carefully selected to balance linguistic richness with experimental feasibility, particularly for eye-tracking-while-reading studies. Developed within a multi-lab initiative, the MultiplEYE Text Corpus follows unified translation, alignment, and experimental design guidelines to ensure cross-linguistic comparability. Its inclusion of texts varying in type and difficulty enables research on discourselevel processing, genre effects, and individual differences across a wide range of languages. The text corpus and accompanying metadata provide a robust foundation for multilingual psycholinguistic and computational modeling research. Data and materials are publicly available at https://doi.org/10.23668/psycharchives.21750.