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News: Check out our newer demo at Paper To HTML.

Welcome to SciA11y!

This is an experimental prototype created by Semantic Scholar. It provides access to 1.5M open access scientific documents in accessible HTML format. Our system uses machine learning techniques to extract the semantic content of scientific papers and formats it in HTML for easier reading. Because of our reliance on statistical machine learning techniques, some errors are inevitable. We will continue to improve upon our models and would love to hear your feedback in the meantime. The papers included in this demo come from a static dataset; all papers have CC (non-ND) licenses and were published in or before April 2020. More about this prototype...

You can also upload your own PDF, which we process and render in HTML for reading. You can try this functionality here.

Example papers

Application of acidic accelerator for production of pure hydrogen from NaBH4
2014 Wameath S. Abdul-Majeed, Muhammad T. Arslan, William B. Zimmerman

TurkPrime.com: A versatile crowdsourcing data acquisition platform for the behavioral sciences
2016 Leib Litman, Jonathan Robinson, Tzvi Abberbock

The global burden of congenital heart disease
2013 Julien IE Hoffman

Modulating proximal cell signaling by targeting Btk ameliorates humoral autoimmunity and end-organ disease in murine lupus
2012 Jack Hutcheson, Kamala Vanarsa, Anna Bashmakov et al.

Risk Factors and Preventions of Breast Cancer
2017 Yi-Sheng Sun, Zhao Zhao, Zhang-Nv Yang et al.

Deep Learning for Computer Vision: A Brief Review
2018 Athanasios Voulodimos, Nikolaos Doulamis, Anastasios Doulamis et al.

Development and Evaluation of a UAV-Photogrammetry System for Precise 3D Environmental Modeling
2015 Mozhdeh Shahbazi, Gunho Sohn, Jérôme Théau et al.

Gd(III) ion-chelated supramolecular assemblies composed of PGMA-based polycations for effective biomedical applications
2015 Yu Zhao, Shun Duan, Bingran Yu et al.

Responses of Marine Organisms to Climate Change across Oceans
2016 Elvira S. Poloczanska, Michael T. Burrows, Christopher J. Brown et al.

Assessing the utility of social media as a data source for flood risk management using a real‐time modelling framework
2017 L. Smith, Q. Liang, P. James et al.

Preprint

To find out more about how we created this prototype, please read our preprint. Accessible PDF available here.

Team

Feedback

Please address questions or feedback to Lucy Lu Wang or Jonathan Bragg.