Transforming Scientific Data Standardization with Large Language Models (LLMs)
Large Language Models (LLMs) to standardize scientific data, including data format standardization, automatic extraction of metadata, data annotation, data quality assessment, data cleaning, and documentation.
- scientific data
- standardization
- interoperability
- large language models
- data format
- metadata
- annotation
- data quality
- data cleaning
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Transforming Scientific Data Standardization and Interoperability with Large Language Models (LLMs) Veerasamy Ravi Ravichandran, PhD. NIDDK
Using Large Language Models (LLMs) to standardize scientific data Data Format Standardization: Parsing and Conversion of data Automatic extraction of Metadata Data Annotation: Semantic Annotation Entity Recognition Data Quality Assessment: Error Detection Data Cleaning Data Description and Documentation: Automatic Documentation Linked Data
Using Large Language Models (LLMs) to standardize scientific data Data Search and Discovery: Natural Language Queries Recommendation Systems Data Governance and Compliance: Privacy and Security Version Control Collaboration and Communication: Chatbots and Virtual Assistants Data Translation Continuous Improvement: Feedback Loops
The Advantages of LLMs in Standardizing Scientific Data Enhanced Efficiency Standardized Format Improved Quality Increased Consistency Enhanced Common Data Elements (CDEs) Standardized Metadata Standardized Ontologies and Vocabularies Knowledge-driven recommendations
