FFMDFPA: A FAIRification Framework for Materials Data with No-Code Flexible Semi-Structured Parser and Application...

Area of Science:

• Materials Science
• Data Science
• Computational Science

Background:

• The FAIR Data Principles (Findability, Accessibility, Interoperability, Reusability) are crucial for advancing data-driven materials science.
• Existing software solutions for implementing FAIR principles, especially for standardizing heterogeneous data and enabling data access, are limited.
• Efficient utilization and reuse of materials data are hindered by challenges in data standardization and access.

Purpose of the Study:

• To introduce a novel FAIRification Framework for Materials Data (FFMDFPA) to overcome current limitations in making materials data FAIR.
• To provide a no-code, flexible solution for processing raw, semistructured data from various sources.
• To facilitate seamless data sharing and reuse through a standardized API.

Main Methods:

• Development of a FAIRification Framework for Materials Data (FFMDFPA) incorporating a no-code, template-based parser.
• Implementation of a flexible, semi-structured parser for extracting and transforming data from diverse text formats without requiring coding.
• Design of a standardized API with efficient query syntax for streamlined data access and sharing.

Main Results:

• FFMDFPA successfully extracts and transforms semistructured data from various text formats using a template-based parser.
• The framework offers flexibility for data manipulation without coding, enhancing usability.
• A standardized API facilitates efficient data querying and seamless sharing of materials data.

Conclusions:

• FFMDFPA provides a practical solution for FAIR data implementation in materials science, addressing the scarcity of appropriate software.
• The framework's no-code parser and standardized API promote efficient utilization and reuse of materials data.
• The data semantic manipulation techniques in FFMDFPA have broader implications for FAIRification of textual data beyond materials science.