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Updated: May 21, 2026

Transcranial Direct Current Stimulation (tDCS) of Wernicke's and Broca's Areas in Studies of Language Learning and Word Acquisition
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DDLm: a new dictionary definition language.

Nick Spadaccini1, Sydney R Hall

  • 1School of Chemistry and Biochemistry, The University of Western Australia, Nedlands 6009, Australia. Nick.Spadaccini@uwa.edu.au

Journal of Chemical Information and Modeling
|June 26, 2012
PubMed
Summary
This summary is machine-generated.

A new dictionary definition language (DDLm) enhances the STAR File format for better electronic data exchange and archiving. DDLm enables domain-specific data dictionaries with rich typing, validation, and modularity for improved data sharing.

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Last Updated: May 21, 2026

Transcranial Direct Current Stimulation (tDCS) of Wernicke's and Broca's Areas in Studies of Language Learning and Word Acquisition
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Published on: July 13, 2019

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Published on: June 6, 2020

Area of Science:

  • Chemical Information Science
  • Data Management

Background:

  • Existing STAR File syntax has been extended to improve electronic data exchange and archiving.
  • The need for a structured approach to define data items within specific scientific domains is recognized.

Purpose of the Study:

  • To describe a dictionary definition language (DDLm) for defining STAR File data items.
  • To enable the creation of extensible and machine-parsable domain dictionaries.
  • To enhance data validation, evaluation, and definition refinement.

Main Methods:

  • Development of a dictionary definition language (DDLm) implemented in STAR.
  • Utilizing dREL for embedded methods for data validation and evaluation.
  • Designing DDLm for strong data typing, enumerations, ranges, and relationship keys.

Main Results:

  • DDLm allows for the creation of semantically rich and specific domain dictionaries.
  • The language supports modular definition and reuse of ontological components through dictionary imports.
  • Enhanced data validation and evaluation capabilities are integrated.

Conclusions:

  • DDLm significantly improves the structure and exchange of electronic data within scientific disciplines.
  • The modular and reusable nature of DDLm promotes data dictionary sharing across domains.
  • DDLm facilitates more robust and reliable data management practices.