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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

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

Biomedical ontology alignment: an approach based on representation learning
2018 Prodromos Kolyvakis, Alexandros Kalousis, Barry Smith 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.

Improved Transition-Based Parsing by Modeling Characters instead of Words with LSTMs
2015 Miguel Ballesteros, Chris Dyer, Noah A. Smith

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

Data Security, Privacy, Availability and Integrity in Cloud Computing: Issues and Current Solutions
2016 Sultan Aldossary, William Allen

Multi-domain Neural Network Language Generation for Spoken Dialogue Systems
2016 Tsung-Hsien Wen, Milica Gasic, Nikola Mrksic et al.

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

Spatial Representation of the Workspace in Blind, Low Vision, and Sighted Human Participants
2018 Jacob S. Nelson, Irene A. Kuling, Monica Gori et al.

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