ScholaWrite: A Dataset of End-to-End Scholarly Writing Process

• URL: http://arxiv.org/abs/2502.02904v3
• Date: Mon, 17 Feb 2025 07:37:57 GMT
• Title: ScholaWrite: A Dataset of End-to-End Scholarly Writing Process
• Authors: Linghe Wang, Minhwa Lee, Ross Volkov, Luan Tuyen Chau, Dongyeop Kang,
• Abstract summary: ScholaWrite dataset is a first-of-its-kind keystroke corpus of an end-to-end scholarly writing process for complete manuscripts. Our dataset includes-based keystroke data from five preprints with nearly 62K total text changes and annotations across 4 months of paper writing.
• Score: 12.170448539143909
• License:
• Abstract: Writing is a cognitively demanding task involving continuous decision-making, heavy use of working memory, and frequent switching between multiple activities. Scholarly writing is particularly complex as it requires authors to coordinate many pieces of multiform knowledge. To fully understand writers' cognitive thought process, one should fully decode the end-to-end writing data (from individual ideas to final manuscript) and understand their complex cognitive mechanisms in scholarly writing. We introduce ScholaWrite dataset, a first-of-its-kind keystroke corpus of an end-to-end scholarly writing process for complete manuscripts, with thorough annotations of cognitive writing intentions behind each keystroke. Our dataset includes LaTeX-based keystroke data from five preprints with nearly 62K total text changes and annotations across 4 months of paper writing. ScholaWrite shows promising usability and applications (e.g., iterative self-writing), demonstrating the importance of collection of end-to-end writing data, rather than the final manuscript, for the development of future writing assistants to support the cognitive thinking process of scientists. Our de-identified data examples and code are available on our project page.

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