Semi-automatic staging area for high-quality structured data extraction from scientific literature

Foppiano, Luca; Mato, Tomoya; Terashima, Kensei; Ortiz Suarez, Pedro; Tou, Taku; Sakai, Chikako; Wang, Wei-Sheng; Amagasa, Toshiyuki; Takano, Yoshihiko; Ishii, Masashi

doi:10.1080/27660400.2023.2286219

Title: Semi-automatic staging area for high-quality structured data extraction from scientific literature

Journal Article · 14 December 2023 · Science and Technology of Advanced Materials: Methods

DOI: https://doi.org/10.1080/27660400.2023.2286219 · OSTI ID:2217642

;

; Tou, Taku; Sakai, Chikako;

;

View Journal Article

Cite

Export

Save

Sponsoring Organization:: USDOE Office of Electricity (OE), Advanced Grid Research & Development. Power Systems Engineering Research

OSTI ID:: 2217642

Journal Information:: Science and Technology of Advanced Materials: Methods, Journal Name: Science and Technology of Advanced Materials: Methods Vol. 3 Journal Issue: 1; ISSN 2766-0400

Publisher:: Informa UK LimitedCopyright Statement

Country of Publication:: Country unknown/Code not available

Language:: English

References (14)

SuperMat: construction of a linked annotated dataset from superconductors-related publications Foppiano, Luca; Dieb, Sae; Suzuki, Akira Science and Technology of Advanced Materials: Methods, Vol. 1, Issue 1 https://doi.org/10.1080/27660400.2021.1918396	journal	January 2021
SciBERT: A Pretrained Language Model for Scientific Text Beltagy, Iz; Lo, Kyle; Cohan, Arman Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) https://doi.org/10.18653/v1/D19-1371	conference	January 2019
AFLOW: An automatic framework for high-throughput materials discovery Curtarolo, Stefano; Setyawan, Wahyu; Hart, Gus L. W. Computational Materials Science, Vol. 58 https://doi.org/10.1016/j.commatsci.2012.02.005	journal	June 2012
Machine learning modeling of superconducting critical temperature Stanev, Valentin; Oses, Corey; Kusne, A. Gilad npj Computational Materials, Vol. 4, Issue 1 https://doi.org/10.1038/s41524-018-0085-8	journal	June 2018
Materials Discovery With Machine Learning and Knowledge Discovery Oliveira, Osvaldo N.; Oliveira, Maria Cristina F. Frontiers in Chemistry, Vol. 10 https://doi.org/10.3389/fchem.2022.930369	journal	July 2022
Machine-learning approach for discovery of conventional superconductors Tran, Huan; Vu, Tuoc N. Physical Review Materials, Vol. 7, Issue 5 https://doi.org/10.1103/PhysRevMaterials.7.054805	journal	May 2023
Automatic extraction of materials and properties from superconductors scientific literature Foppiano, Luca; Castro, Pedro Baptista; Ortiz Suarez, Pedro Science and Technology of Advanced Materials: Methods, Vol. 3, Issue 1 https://doi.org/10.1080/27660400.2022.2153633	journal	January 2023
Structuring superconductor data with ontology: reproducing historical datasets as knowledge bases Ishii, Masashi; Sakamoto, Koichi Science and Technology of Advanced Materials: Methods, Vol. 3, Issue 1 https://doi.org/10.1080/27660400.2023.2223051	journal	June 2023
The PAULING FILE Project and Materials Platform for Data Science: From Big Data Toward Materials Genome Blokhin, Evgeny; Villars, Pierre Handbook of Materials Modeling https://doi.org/10.1007/978-3-319-42913-7_62-1	book	January 2018
Predicting new superconductors and their critical temperatures using machine learning Roter, B.; Dordevic, S. V. Physica C: Superconductivity and its Applications, Vol. 575 https://doi.org/10.1016/j.physc.2020.1353689	journal	August 2020
The NOMAD laboratory: from data sharing to artificial intelligence Draxl, Claudia; Scheffler, Matthias Journal of Physics: Materials, Vol. 2, Issue 3 https://doi.org/10.1088/2515-7639/ab13bb	journal	May 2019
Commentary: The Materials Project: A materials genome approach to accelerating materials innovation Jain, Anubhav; Ong, Shyue Ping; Hautier, Geoffroy APL Materials, Vol. 1, Issue 1 https://doi.org/10.1063/1.4812323	journal	July 2013
Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis Ong, Shyue Ping; Richards, William Davidson; Jain, Anubhav Computational Materials Science, Vol. 68 https://doi.org/10.1016/j.commatsci.2012.10.028	journal	February 2013
Text-mined dataset of inorganic materials synthesis recipes Kononova, Olga; Huo, Haoyan; He, Tanjin Scientific Data, Vol. 6, Issue 1 https://doi.org/10.1038/s41597-019-0224-1	journal	October 2019

Similar Records

Materials Synthesis Insights from Scientific Literature via Text Extraction and Machine Learning

Journal Article · 2017 · Chemistry of Materials · OSTI ID:1476572

Kim, Edward; Huang, Kevin; Saunders, Adam; +3 more

Unleashing the Power of Knowledge Extraction from Scientific Literature in Catalysis

Journal Article · 2022 · Journal of Chemical Information and Modeling · OSTI ID:1977907

Zhang, Yue; Wang, Cong; Soukaseum, Mya; +2 more

Challenges and Advances in Information Extraction from Scientific Literature: a Review

Journal Article · 2021 · JOM. Journal of the Minerals, Metals & Materials Society · OSTI ID:1869908

Hong, Zhi; Ward, Logan; Chard, Kyle; +2 more

Title: Semi-automatic staging area for high-quality structured data extraction from scientific literature

Citation Formats

References (14)

Similar Records

Related Subjects