Metadata for Scientific Experiment Reporting: A Case Study in Metal-Organic Frameworks

TL;DR

This paper develops a metadata framework and schema for reporting Metal-Organic Framework synthesis experiments, enhancing reproducibility and discovery aligned with FAIR principles, through NLP extraction and expert classification.

Contribution

It introduces a novel metadata schema and a methodological framework for documenting MOF synthesis experiments to improve reproducibility and data sharing.

Findings

A metadata schema with nine entities and two relationships for MOF experiments

A growing database of structured MOF synthesis reports

Effective NLP and expert classification for metadata extraction

Abstract

Research methods and procedures are core aspects of the research process. Metadata focused on these components is critical to supporting the FAIR principles, particularly reproducibility. The research reported on in this paper presents a methodological framework for metadata documentation supporting the reproducibility of research producing Metal Organic Frameworks (MOFs). The MOF case study involved natural language processing to extract key synthesis experiment information from a corpus of research literature. Following, a classification activity was performed by domain experts to identify entity-relation pairs. Results include: 1) a research framework for metadata design, 2) a metadata schema that includes nine entities and two relationships for reporting MOF synthesis experiments, and 3) a growing database of MOF synthesis reports structured by our metadata scheme. The metadata schema is intended to support discovery and reproducibility of metal-organic framework research and the FAIR principles. The paper provides background information, identifies the research goals and objectives, research design, results, a discussion, and the conclusion.