SciLaD is a novel, large-scale dataset of scientific language constructed entirely using open-source frameworks and publicly available data sources. It comprises a curated English split containing over 10 million scientific publications and a multilingual, unfiltered TEI XML split including more than 35 million publications. We also publish the extensible pipeline for generating SciLaD. The dataset construction and processing workflow demonstrates how open-source tools can enable large-scale, scientific data curation while maintaining high data quality. Finally, we pre-train a RoBERTa model on our dataset and evaluate it across a comprehensive set of benchmarks, achieving performance comparable to other scientific language models of similar size, validating the quality and utility of SciLaD. We publish the dataset and evaluation pipeline to promote reproducibility, transparency, and further research in natural scientific language processing and understanding, including scholarly document processing.
@article{arxiv.2512.11192,
title = {SciLaD: A Large-Scale, Transparent, Reproducible Dataset for Natural Scientific Language Processing},
author = {Luca Foppiano and Sotaro Takeshita and Pedro Ortiz Suarez and Ekaterina Borisova and Raia Abu Ahmad and Malte Ostendorff and Fabio Barth and Julian Moreno-Schneider and Georg Rehm},
journal= {arXiv preprint arXiv:2512.11192},
year = {2026}
}