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Masashi Ishii
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PoLyInfo Conceptual Schema Version 1.0
Description/Abstract:
'PoLyInfo Conceptual Schema' is a machine-readable schema necessary to express polymer knowledge using the concept classes, predicates, a...
Keyword:
Conceptual schema
,
PoLyInfo
,
Polymer chemistry knowledge
,
ShEx
, and
collection - PoLyInfo Knowledge
Resource Type:
Dataset
Data origin:
others
Author:
Masashi Ishii
and
Koichi Sakamoto
Date Uploaded:
27/03/2024
PoLyInfo Ontology Version 1.0
Description/Abstract:
This 'PoLyInfo Ontology' summarizes the concept classes, predicates, instances, etc. necessary for machine-readable representation of the...
Keyword:
BFO
,
Ontology
,
PoLyInfo
,
Polymer chemistry knowledge
, and
collection - PoLyInfo Knowledge
Resource Type:
Dataset
Data origin:
others
Author:
Masashi Ishii
and
Koichi Sakamoto
Date Uploaded:
25/03/2024
Automatic extraction of materials and properties from superconductors scientific literature
Description/Abstract:
The automatic extraction of materials and related properties from the scientific literature is gaining attention in data-driven materials...
Keyword:
Materials informatics
,
NLP
,
TDM
,
machine learning
, and
superconductors
Resource Type:
Journal
Author:
Luca Foppiano
,
Pedro Baptista Castro
,
Pedro Ortiz Suarez
,
Kensei Terashima
,
Yoshihiko Takano
, and
Masashi Ishii
Journal:
SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS: METHODS
Date Uploaded:
26/12/2023
SuperCon RDF Ver. 1.1
Description/Abstract:
"SuperCon RDF" is an RDF version of the datasheets published in this collection based on the SuperCon Ontology (https://doi.org/10.48505/...
Keyword:
Ontology
,
RDF
,
Semantic web
,
Super conductor
, and
SuperCon
Resource Type:
Dataset
Data origin:
other
Author:
Masashi Ishii
Date Uploaded:
12/07/2023
Date Modified:
01/08/2023
Structuring Superconductor Data with Ontology: Reproducing Historical Datasets as Knowledge
Description/Abstract:
Applying historical human-readable databases to recent data-driven science is a natural concept. However, this cannot be realized by simp...
Keyword:
RDF
,
SuperCon
,
knowledge base
,
ontology
, and
superconducting material
Resource Type:
Article
Author:
Masashi Ishii
and
Koichi Sakamoto
Journal:
Science and Technology of Advanced Materials: Methods
Date Uploaded:
04/07/2023
Date Modified:
04/07/2023
Integration of X-ray Absorption Fine Structure Databases for Data-Driven Materials Science
Description/Abstract:
With the aim of introducing data-driven science and establishing an infrastructure for making XAFS spectra findable and reusable, we have...
Keyword:
DOI
,
RDF
,
X-ray absorption fine structure
,
data integration
,
materials data repository
, and
metadata
Resource Type:
Article
Author:
Masashi Ishii
,
Kosuke Tanabe
,
Asahiko Matsuda
,
Hironori Ofuchi
,
Takahiro Matsumoto
,
Toyonari Yaji
,
Yasuhiro Inada
,
Hiroaki Nitani
,
Masao Kimura
, and
Kiyotaka Asakura
Journal:
Science and Technology of Advanced Materials: Methods
Date Uploaded:
08/06/2023
Date Modified:
07/06/2024
SuperCon RDF Ver. 1.0
Description/Abstract:
"SuperCon RDF" is an RDF version of the datasheets published in this collection based on the SuperCon Ontology (https://doi.org/10.48505/...
Keyword:
Ontology
,
RDF
,
SPARQL
,
Semantic Web
,
SuperCon
, and
Superconductors
Resource Type:
Dataset
Data origin:
informatics and data science
Author:
Masashi Ishii
Date Uploaded:
11/04/2023
Date Modified:
19/07/2023
SuperCon Ontology
Description/Abstract:
"SuperCon Ontology" is the basic conceptual structure of SuperCon RDF (https://doi.org/10.48505/nims.3872), which is published in this co...
Keyword:
Ontology
,
RDF
,
SPARQL
,
Semantic Web
,
SuperCon
, and
Superconductors
Resource Type:
Dataset
Data origin:
informatics and data science
Author:
Masashi Ishii
Date Uploaded:
11/04/2023
Automatic Extraction of Materials and Properties from Superconductors Scientific Literature
Description/Abstract:
The automatic extraction of materials and related properties from the scientific literature is gaining attention in data-driven materials...
Keyword:
tdm
,
machine learning
,
materials science
,
superconductors
, and
database
Resource Type:
Article
Author:
Foppiano Luca
,
Pedro Baptista de Castro
,
Pedro Ortiz Suarez
,
Kensei Terashima
,
Yoshihiko Takano
, and
Masashi Ishii
Date Uploaded:
11/10/2022
Date Modified:
08/11/2022
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Masashi Ishii
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