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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

Internet Access by People with Intellectual Disabilities: Inequalities and Opportunities
2013 Darren Chadwick, Caroline Wesson, Chris Fullwood

Contribution of Chronic Disease to the Burden of Disability
2011 Bart Klijs, Wilma J. Nusselder, Caspar W. Looman et al.

A synthesis of recent analyses of human resources for health requirements and labour market dynamics in high-income OECD countries
2016 Gail Tomblin Murphy, Stephen Birch, Adrian MacKenzie et al.

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.

Life cycle assessment of construction and renovation of sewer systems using a detailed inventory tool
2016 Serni Morera, Christian Remy, Joaquim Comas et al.

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

Spatial Modeling in Environmental and Public Health Research
2010 Michael Jerrett, Sara Gale, Caitlin Kontgis

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

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

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.