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Automated Construction of a Photocatalysis Dataset for Water-Splitting Applications
We present an automatically generated dataset of 15,755 records that were extracted from 47,357 papers. These records contain water-splitting activity in the presence of certain photocatalysts, along with additional information about the chemical reaction conditions under which this activity was rec...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10517137/ https://www.ncbi.nlm.nih.gov/pubmed/37739960 http://dx.doi.org/10.1038/s41597-023-02511-6 |
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author | Isazawa, Taketomo Cole, Jacqueline M. |
author_facet | Isazawa, Taketomo Cole, Jacqueline M. |
author_sort | Isazawa, Taketomo |
collection | PubMed |
description | We present an automatically generated dataset of 15,755 records that were extracted from 47,357 papers. These records contain water-splitting activity in the presence of certain photocatalysts, along with additional information about the chemical reaction conditions under which this activity was recorded. These conditions include any co-catalysts and additives that were present during water splitting, the length of time for which the photocatalytic experiment was conducted, and the type of light source used, including its wavelength. Despite the text extraction of such a wide range of chemical reaction attributes, the dataset afforded good precision (71.2%) and recall (36.3%). These figures-of-merit were calculated based on a random sample of open-access papers from the corpus. Mining such a complex set of attributes required the development of novel techniques in knowledge extraction and interdependency resolution, leveraging inter- and intra-sentence relations, which are also described in this paper. We present a new version (version 2.2) of the chemistry-aware text-mining toolkit ChemDataExtractor, in which these new techniques are included. |
format | Online Article Text |
id | pubmed-10517137 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-105171372023-09-24 Automated Construction of a Photocatalysis Dataset for Water-Splitting Applications Isazawa, Taketomo Cole, Jacqueline M. Sci Data Data Descriptor We present an automatically generated dataset of 15,755 records that were extracted from 47,357 papers. These records contain water-splitting activity in the presence of certain photocatalysts, along with additional information about the chemical reaction conditions under which this activity was recorded. These conditions include any co-catalysts and additives that were present during water splitting, the length of time for which the photocatalytic experiment was conducted, and the type of light source used, including its wavelength. Despite the text extraction of such a wide range of chemical reaction attributes, the dataset afforded good precision (71.2%) and recall (36.3%). These figures-of-merit were calculated based on a random sample of open-access papers from the corpus. Mining such a complex set of attributes required the development of novel techniques in knowledge extraction and interdependency resolution, leveraging inter- and intra-sentence relations, which are also described in this paper. We present a new version (version 2.2) of the chemistry-aware text-mining toolkit ChemDataExtractor, in which these new techniques are included. Nature Publishing Group UK 2023-09-22 /pmc/articles/PMC10517137/ /pubmed/37739960 http://dx.doi.org/10.1038/s41597-023-02511-6 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Data Descriptor Isazawa, Taketomo Cole, Jacqueline M. Automated Construction of a Photocatalysis Dataset for Water-Splitting Applications |
title | Automated Construction of a Photocatalysis Dataset for Water-Splitting Applications |
title_full | Automated Construction of a Photocatalysis Dataset for Water-Splitting Applications |
title_fullStr | Automated Construction of a Photocatalysis Dataset for Water-Splitting Applications |
title_full_unstemmed | Automated Construction of a Photocatalysis Dataset for Water-Splitting Applications |
title_short | Automated Construction of a Photocatalysis Dataset for Water-Splitting Applications |
title_sort | automated construction of a photocatalysis dataset for water-splitting applications |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10517137/ https://www.ncbi.nlm.nih.gov/pubmed/37739960 http://dx.doi.org/10.1038/s41597-023-02511-6 |
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