data-and-cloud-computing — Technology

Enhancing Scholarly Articles for Machine Learning Processes

Open-access scholarly articles hub ArXiv, based at Cornell University in New York, has shared its entire collection of 1.7 million research papers on Kaggle, a publicly-accessible machine learning training platform. Each article's dataset comprises details like:

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2025 September 13 . 3:15 AM

1 min read

Enhancing Scholarly Article Accessibility for Machine Learning Applications

Enhancing Scholarly Articles for Machine Learning Processes

In a significant move towards open access and data-driven research, ArXiv, the renowned digital repository of scholarly articles, has made its 1.7 million research articles available on Kaggle. This collaboration allows for the articles to be used as datasets, opening up a world of possibilities for data analysis and machine learning.

The articles, which are maintained by Cornell University in New York, are now accessible on Kaggle, a popular online platform for data science and machine learning competitions. This means that researchers, data scientists, and enthusiasts alike can utilise this data for trend analysis, creating algorithms that group scholarly papers by topic, and even improving search engines for scholarly papers.

Each article on Kaggle includes essential information such as the author, title, category, abstract, citations, and a link to the full-text PDF of the article. Moreover, the data includes the category of each article, the title, and the abstract of each article, providing a comprehensive resource for those looking to delve into specific research areas.

The first ArXiv research paper made available on Kaggle was authored by J Torrents, who published "New idtracker.ai: rethinking multi-animal tracking as a self-supervised contrastive representation" in 2025. This move not only makes the vast wealth of ArXiv's research accessible but also opens up opportunities for new discoveries and advancements in various fields.

With this collaboration, Kaggle users now have at their disposal a wealth of data that can be used to fuel their projects, from data science competitions to academic research. The potential applications are vast, and it is exciting to imagine the discoveries that may emerge from this open-access initiative.