AAAS and DataSeer pilot AI-generated MDAR checklists
The American Association for the Advancement of Science (AAAS), publisher of the Science family of journals, has announced an extension of its partnership with DataSeer through a new pilot programme focused on automating research reporting workflows.
The six-month collaboration will explore how artificial intelligence can be used to fully automate the generation of MDAR (Materials, Design, Analysis, and Reporting) checklists for manuscripts submitted to Science. The pilot will evaluate whether structured MDAR reports can be created directly from submitted manuscripts in an entirely automated process.
MDAR checklists are widely used to support transparency and reproducibility in scientific publishing, but their preparation and assessment can be resource-intensive for authors and editorial teams. The initiative will assess whether automation can reduce manual effort while maintaining alignment with Science’s editorial standards.
“Ensuring clear and consistent reporting is central to the integrity of the scientific record,” said Valda Vinson, Executive Editor of the Science journals. “This pilot will help us understand how AI-driven tools can support our editors and authors in meeting these expectations more efficiently.”
As part of the pilot, DataSeer will deploy its SnapShot technology to generate pre-filled MDAR reports based on manuscript content. These reports will then be reviewed by Science editorial staff to assess their accuracy, usefulness, and suitability for integration into existing editorial workflows.
“AAAS has very much paved the way for advancing transparency and rigour in research reporting,” said Tim Vines, Founder and CEO of DataSeer. “We’re excited to work together to evaluate how automated MDAR generation can support editorial teams while preserving the high standards expected of Science.”
The pilot is also expected to provide practical insight into how AI-supported workflows could contribute to broader efforts to strengthen research transparency, data sharing, and reproducibility across the scholarly publishing ecosystem.