Automated Construction of a Photocatalysis Dataset for Water-Splitting Applications
Abstract 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 activit...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-09-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-023-02511-6 |
| _version_ | 1797578189487210496 |
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| author | Taketomo Isazawa Jacqueline M. Cole |
| author_facet | Taketomo Isazawa Jacqueline M. Cole |
| author_sort | Taketomo Isazawa |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-03-10T22:19:32Z |
| format | Article |
| id | doaj.art-9b542ee98bfa4f82a1eee274e6e89f36 |
| institution | Directory Open Access Journal |
| issn | 2052-4463 |
| language | English |
| last_indexed | 2024-03-10T22:19:32Z |
| publishDate | 2023-09-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj.art-9b542ee98bfa4f82a1eee274e6e89f362023-11-19T12:20:24ZengNature PortfolioScientific Data2052-44632023-09-0110111110.1038/s41597-023-02511-6Automated Construction of a Photocatalysis Dataset for Water-Splitting ApplicationsTaketomo Isazawa0Jacqueline M. Cole1Cavendish Laboratory, Department of Physics, University of CambridgeCavendish Laboratory, Department of Physics, University of CambridgeAbstract 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.https://doi.org/10.1038/s41597-023-02511-6 |
| spellingShingle | Taketomo Isazawa Jacqueline M. Cole Automated Construction of a Photocatalysis Dataset for Water-Splitting Applications Scientific Data |
| 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 |
| url | https://doi.org/10.1038/s41597-023-02511-6 |
| work_keys_str_mv | AT taketomoisazawa automatedconstructionofaphotocatalysisdatasetforwatersplittingapplications AT jacquelinemcole automatedconstructionofaphotocatalysisdatasetforwatersplittingapplications |