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

Bing He1, Zhuming Gong1, Maxim Avdeev2,3

  • 1School of Computer Engineering and Science, Shanghai University, Shanghai 20444, China.

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|August 7, 2023
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Summary
This summary is machine-generated.

This study introduces a FAIRification Framework for Materials Data (FFMDFPA) to address sparse software solutions for FAIR data principles. FFMDFPA enables efficient data extraction, standardization, and sharing for materials science.

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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.