Part of the arXMLiv project at the KWARC research group
The content of this Dataset is licensed to SIGMathLing members for research and tool development purposes.
Access is restricted to SIGMathLing members under the SIGMathLing Non-Disclosure-Agreement as for most arXiv articles, the right of distribution was only given (or assumed) to arXiv itself.
| subset ID | number of documents | size archived | size unpacked |
|---|---|---|---|
| no_problem | 112,088 | 5 GB | 37 GB |
| warning | 574,638 | 71 GB | 595 GB |
| error | 401,644 | 50 GB | 421 GB |
| subset file name | MD5 |
|---|---|
arXMLiv_08_2017_no_problem.zip |
036945755c7cc75ea1577cf04ca4fead |
arXMLiv_08_2017_warning.zip |
c0d5c1baf626225b48264510ac4c6bd5 |
arXMLiv_08_2017_error.zip |
2f4e60b993d85d30523b064c19e45733 |
This is a first public release of the arXMLiv dataset generated by the KWARC research group. It contains 1,088,370 HTML5 scientific documents from the arXiv.org preprint archive, converted from their respective TeX sources.
The dataset is segmented in 3 different subsets, each corresponding to a severity level of the LaTeXML software responsible for the HTML5 conversion.
no_problem set had no obvious challenges in conversion and is the safest, most reliable subsetwarning set covers a variety of minor issues, from mathematical expressions unparseable by the LaTeXML grammar, to missing LaTeX packages with no apparent use in the document. The vast majority of the documents should both have a good-looking rendering, as well as data consistency for e.g. NLP tasks.error set covers all conversions which successfully generated an HTML5 document, but had major issues during the conversion. Examples would range from unknown macros (due to limited LaTeX coverage), unexpected latex syntax, math/text mode mismatches, as well as real LaTeX errors from the original sources. This subset should be used with extra caution, though should still preserve overall data consistency and could be safely used for e.g. generating word embeddings.This version of the dataset has had minimal manual quality control, and we offer no additional warranty beyond the latexml severity reported.
We welcome community feedback on all of: data quality, representation issues, need for auxiliary resources (e.g. figures, token models), as well as organization and archival best practices. The conversion, build system, and data redistribution efforts are all ongoing projects at the KWARC research group.
A following release is planned for mid-2018, with an up-to-date arXiv dataset and community feedback incorporated. We anticipate annual dataset releases going forward.
The dataset should be referenced in all academic publications that present results
obtained with its help. The reference should contain the identifier arXMLiv:08.2017 in
the title, the author, year, a reference to SIGMathLing, and the URL of the resource
description page. For convenience, we supply some records for bibTeX and EndNote below. To
cite a particular part of the dataset use the subset identifiers in the ciation;
e.g. \cite[no_problem subset]{arXMLiv:08.2017} or just explain it in the text using the
concrete identifier.
@MISC{SML:arXMLiv:08.2017,
author = {Deyan Ginev},
title = {arXMLiv:08.2017 dataset, an HTML5 conversion of arXiv.org},
howpublished = {hosted at \url{https://sigmathling.kwarc.info/resources/arxmliv/}},
note = {SIGMathLing -- Special Interest Group on Math Linguistics},
year = {2018}
@online{SML:arXMLiv:08.2017,
author = {Deyan Ginev},
title = {arXMLiv:08.2017 dataset, an HTML5 conversion of arXiv.org},
url = {https://sigmathling.kwarc.info/resources/arxmliv/},
note = {SIGMathLing -- Special Interest Group on Math Linguistics},
year = {2018}
%0 Generic
%T arXMLiv:08.2017 dataset, an HTML5 conversion of arXiv.org
%A Ginev, Deyan
%D 2018
%I hosted at https://sigmathling.kwarc.info/resources/arxmliv/
%F SML:arXMLiv:08.2017b
%O SIGMathLing – Special Interest Group on Math Linguistics