The 2019 release to the arXMLiv data set has been published.
Details can be found on the corresponding [data set resource page](/resources/arxmliv-dataset-082019/) and [embeddings resource page](/resources/arxmliv-embeddings-082019).
The content of this data set is licensed to [SIGMathLing members](/member/) for research
and tool development purposes subject to the [SIGMathLing Non-Disclosure-Agreement](/nda/).
This is the third public release of the arXMLiv dataset generated by the [KWARC](https://kwarc.info/) research group. It contains 1,374,539 HTML5 scientific documents from the arXiv.org preprint archive, converted from their respective TeX sources. An 11% increase in available articles over the 08.2018 release.
The dataset is segmented in 4 subsets, corresponding to three severity levels of the HTML conversion.
- The `no_problem` set had no obvious challenges in conversion and is the safest, most reliable subset
- The `warning_1` and `warning_2` sets cover 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.
- The `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](http://kwarc.info).
### Citing this Resource
The dataset should be referenced in all academic publications that present results
obtained with its help. The reference should contain the identifier `arXMLiv:08.2019` 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:08.2019}` or just explain it in the text using the
concrete identifier.
#### pure bibTeX
```
@MISC{SML:arXMLiv:08.2019,
author = {Deyan Ginev},
title = {arXMLiv:08.2019 dataset, an HTML5 conversion of arXiv.org},
howpublished = {hosted at \url{https://sigmathling.kwarc.info/resources/arxmliv-dataset-082019/}},
note = {SIGMathLing -- Special Interest Group on Math Linguistics},
year = 2019}
```
#### bibTeX for the bibLaTeX package (preferred)
```
@online{SML:arXMLiv:08.2019,
author = {Deyan Ginev},
title = {arXMLiv:08.2019 dataset, an HTML5 conversion of arXiv.org},
Please cite the main dataset when using the word embeddings, as they are generated and distributed jointly. [Instructions here](/resources/arxmliv-dataset-082019/#citing-this-resource)
@@ -7,13 +7,13 @@ Recall that {{site.title}} maintains [a bouquet of services](services/); here we
### Resource Repositories
We have a [{{site.title}} group](http://gl.kwarc.info/SIGMathLing) on the [GitLab](https://en.wikipedia.org/wiki/GitLab) server [gl.kwarc.info](http://gl.kwarc.info), where we will start making repositories on.
We have a [{{site.title}} group](http://gl.kwarc.info/SIGMathLing) on the [GitLab](https://en.wikipedia.org/wiki/GitLab) server [gl.kwarc.info](http://gl.kwarc.info), where we have hosted a range of data repositories.
This allows us to use Git permissions for access control and the GitLab permission UI for management.
We estimate that for the first two years {{site.title}} will have below 25 members (reducing the traffic) and below 5 TB data sets.
We estimate that for the first two years (2017-2019) {{site.title}} will have below 25 members (reducing the traffic) and below 5 TB data sets.
gl.kwarc.info should be able to serve that given that most data sets will be served via [Git LFS](https://git-lfs.github.com/).
Should space or traffic become a problem for the KWARC servers to handle, we will try to raise money for a more scalable solution.
We will also have a close look at [Zenodo](http://zenodo.org) and see whether we can delegate hosting to them.
[Zenodo](http://zenodo.org) has officially turned down hosting the SIGMathLing resources due to the large volume of data, but we are open to exploring alternative providers - feel free to reach out!
### Standardizing Datasets and Resources
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...
@@ -26,7 +26,8 @@ We will need to develop standards for representing, classifying, describing, and
* an evaluation data set (gold standard)?
* what is the quality? f-measure,
* what is the license.
3.*Citation* The idea is to have a "landing page per resourcer that address all
3.*Identification*: we are looking into obtaining a DOI data identifier for each resource
4.*Citation* The idea is to have a "landing page per resourcer that address all
the points in 1. and 2. as well as the authors that can be cited. The landing page
should also have pre-made bibTeX (and possibly EndNote) entries to make citations
easier.
...
...
@@ -42,4 +43,3 @@ Currently, this is just a manually curated [page on the {{site.title}} web site]
### Math Analysis Blackboard
MK would like develop and publish an annotation schema (using the KAT schema as a starting point) and establish a math result triple store that manages all of these. Technical details are still open how best to do this, but Deyan is quite skeptical.
