Cross-Linguistic Data Formats, advancing data sharing and re-use in comparative linguistics

Area of Science:

• Linguistics
• Computational Linguistics
• Digital Humanities

Background:

• Increasing volume of digital language data presents challenges due to diverse formats.
• Lack of standardized formats hinders cross-linguistic data comparison and reuse.
• Existing data formats are not optimized for historical and typological language studies.

Purpose of the Study:

• To propose new data format standards for linguistic research.
• To facilitate the comparison and reuse of digital language data.
• To establish a flexible framework for incorporating various linguistic data types.

Main Methods:

• Development of standardized formats for word lists and structural datasets.
• Creation of a framework for integrating additional data types like parallel texts and dictionaries.
• Provision of a software package for data validation and manipulation.
• Establishment of a basic ontology for linking to broader frameworks.

Main Results:

• New specifications for Cross-Linguistic Data Formats (CLDF) have been developed.
• A software package supporting CLDF validation and manipulation is available.
• A foundational ontology and best practice examples are provided.
• The proposed standards address the need for interoperable linguistic data.

Conclusions:

• The CLDF initiative provides essential tools and standards for modern linguistic data.
• Standardized formats will significantly improve the accessibility and utility of linguistic datasets.
• This work supports advancements in historical and typological language comparison through enhanced data interoperability.