We develop a new model for automatic extraction of reported measurement
values from the astrophysical literature, utilising modern Natural Language
Processing techniques. We use this model to extract measurements present in the
abstracts of the approximately 248,000 astrophysics articles from the arXiv
repository, yielding a database containing over 231,000 astrophysical numerical
measurements. Furthermore, we present an online interface (Numerical Atlas) to
allow users to query and explore this database, based on parameter names and
symbolic representations, and download the resulting datasets for their own
research uses. To illustrate potential use cases we then collect values for
nine different cosmological parameters using this tool. From these results we
can clearly observe the historical trends in the reported values of these
quantities over the past two decades, and see the impacts of landmark
publications on our understanding of cosmology.
Description
Towards Machine Learning-Based Meta-Studies: Applications to Cosmological Parameters
cite arxiv:2107.00665Comment: 23 pages, 14 figures. Submitted to Monthly Notices of the Royal Astronomical Society. Astronomical measurement database available at http://numericalatlas.cs.ucl.ac.uk/
%0 Generic
%1 crossland2021towards
%A Crossland, Tom
%A Stenetorp, Pontus
%A Kawata, Daisuke
%A Riedel, Sebastian
%A Kitching, Thomas D.
%A Deshpande, Anurag
%A Kimpson, Tom
%A Liew-Cain, Choong Ling
%A Pedersen, Christian
%A Piras, Davide
%A Sharma, Monu
%D 2021
%K library
%T Towards Machine Learning-Based Meta-Studies: Applications to
Cosmological Parameters
%U http://arxiv.org/abs/2107.00665
%X We develop a new model for automatic extraction of reported measurement
values from the astrophysical literature, utilising modern Natural Language
Processing techniques. We use this model to extract measurements present in the
abstracts of the approximately 248,000 astrophysics articles from the arXiv
repository, yielding a database containing over 231,000 astrophysical numerical
measurements. Furthermore, we present an online interface (Numerical Atlas) to
allow users to query and explore this database, based on parameter names and
symbolic representations, and download the resulting datasets for their own
research uses. To illustrate potential use cases we then collect values for
nine different cosmological parameters using this tool. From these results we
can clearly observe the historical trends in the reported values of these
quantities over the past two decades, and see the impacts of landmark
publications on our understanding of cosmology.
@misc{crossland2021towards,
abstract = {We develop a new model for automatic extraction of reported measurement
values from the astrophysical literature, utilising modern Natural Language
Processing techniques. We use this model to extract measurements present in the
abstracts of the approximately 248,000 astrophysics articles from the arXiv
repository, yielding a database containing over 231,000 astrophysical numerical
measurements. Furthermore, we present an online interface (Numerical Atlas) to
allow users to query and explore this database, based on parameter names and
symbolic representations, and download the resulting datasets for their own
research uses. To illustrate potential use cases we then collect values for
nine different cosmological parameters using this tool. From these results we
can clearly observe the historical trends in the reported values of these
quantities over the past two decades, and see the impacts of landmark
publications on our understanding of cosmology.},
added-at = {2021-07-05T06:46:28.000+0200},
author = {Crossland, Tom and Stenetorp, Pontus and Kawata, Daisuke and Riedel, Sebastian and Kitching, Thomas D. and Deshpande, Anurag and Kimpson, Tom and Liew-Cain, Choong Ling and Pedersen, Christian and Piras, Davide and Sharma, Monu},
biburl = {https://www.bibsonomy.org/bibtex/2dcbd18e7aed76ce04aa98aca15902fa3/gpkulkarni},
description = {Towards Machine Learning-Based Meta-Studies: Applications to Cosmological Parameters},
interhash = {fca8ff9d6d438058ba119db03d7dd24c},
intrahash = {dcbd18e7aed76ce04aa98aca15902fa3},
keywords = {library},
note = {cite arxiv:2107.00665Comment: 23 pages, 14 figures. Submitted to Monthly Notices of the Royal Astronomical Society. Astronomical measurement database available at http://numericalatlas.cs.ucl.ac.uk/},
timestamp = {2021-07-05T06:46:28.000+0200},
title = {Towards Machine Learning-Based Meta-Studies: Applications to
Cosmological Parameters},
url = {http://arxiv.org/abs/2107.00665},
year = 2021
}