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Behera, B. P.; panda, B.
Preprints are freely accessible full scientific articles deposited in preprint servers and are made available within a few days of submission, enabling faster dissemination of research findings ahead of formal peer review. Preprints allow researchers to share scientific results early, are citable, and enable authors to receive feedback from a wider community of researchers before any peer review process. Despite their importance, the wider and faster adoption of preprints across the globe, especially in the Global South, has been slow. One reason is the absence of a comprehensive and centralized platform that allows monitoring preprint trends, tracking their usage over time across disciplines, and understanding how they foster collaboration and advance open science. Here, we present Preprint Commons, a dedicated and versatile database and analytical platform for large-scale preprint meta-analysis. Preprint Commons is built with nearly 350,000 life sciences preprints, each annotated with metadata, including that obtained using large language models, to provide trends, citation counts, and geographic distribution. Preprint Commons generates interactive visualizations and provides analysis of preprints, aiding in tracking the lifecycles of preprints and identifying emerging research trends across disciplines and geographies. Preprint Commons supports applications beyond basic analysis, including dynamic visualization, a robust API, and detailed documentation. The database and underlying data are openly accessible at https://preprintcommons.online.
Like herding 350k wild preprints into one searchable online rodeo, this new Preprint Commons platform unifies bioRxiv/medRxiv data for meta-researchers with quirky analytics tools that make tracking science trends feel like a delightful data playground.
Excited launch by Bibhu Prasad Behera (@Bibhu_prasad_01) and team, welcomed by open science enthusiasts for its utility
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CD4⁺ T cells confer transplantable rejuvenation via Rivers of telomeres
Lanna, A.; Valvo, S.; Dustin, M.; Rinaldi, F.
Using a GPT-5-driven autonomous lab to optimize the cost and titer of cell-free protein synthesis
Smith, A. A.; Wong, E. L.; Donovan, R. C.; Chapman, B. A.; Harry, R.; Tirandazi, P.; Kanigowska, P.; Gendreau, E. A.; Dahl, R. H.; Jastrzebski, M.; Cortez, J. E.; Bremner, C. J.; Hemuda, J. C. M.; Dooner, J.; Graves, I.; Karandikar, R.; Lionetti, C.; Christopher, K.; Consiglio, A. L.; Tran, A.; McCusker, W.; Nguyen, D. X.; Nunes da Silva, I. B.; Bautista-Ayala, A. R.; McNerney, M. P.; Atkins, S.; McDuffie, M.; Serber, W.; Barber, B. P.; Thanongsinh, T.; Nesson, A.; Lama, B.; Nichols, B.; LaFrance, C.; Nyima, T.; Byrn, A.; Thornhill, R.; Cai, B.; Ayala-Valdez, L.; Wong, A.; Che, A. J.; Thavaraj
A Single-Cell and Spatial 3D Multi-omic Atlas of Developing Human Basal Ganglia and Inhibitory Neurons
Heffel, M. G.; Xu, H.; Pastor-Alonso, O.; Li, X.; Baig, M. S.; Irfan Ghoor, R.; Li, R.; Kern, C.; Kum, J.; Zhang, Y.; Paino, J.; Tsai, M. J.; Tai, C.-Y.; Tucker, G.; Zhao, Z.; Hou, A.; von Behren, Z.; Bhade, M.; Li, S.; Sandoval, K.; Scholes, J.; Codrea, F.; Calimlim, J.; Liao, E. K.; Leung, G.; Kim, J.; Eskin, E.; Flint, J.; Cotter, J. A.; Pasaniuc, B.; Bintu, B.; Zhu, Q.; Mukamel, E. A.; Ernst, J.; Paredes, M. F.; Luo, C.
Prediction of transformative breakthroughs in biomedical research
Davis, M. T.; Busse, B. L.; Arabi, S.; Meyer, P.; Hoppe, T. A.; Meseroll, R. A.; Hutchins, B. I.; Willis, K. A.; Santangelo, G. M.