Enhancing Data Management and Curation with AI: Toward FAIR and Scalable Neuroinformatics

The exponential growth of neuroscience data necessitates advanced methods for data management and curation. This Research Topic aims to explore how artificial intelligence (AI) can be leveraged to enhance the consistency, completeness, and usability of neuroscience datasets and metadata.







We welcome contributions that delve into:



• Automated Metadata Enrichment: AI-driven tools for extracting and standardizing metadata from diverse data sources, improving data discoverability and integration.



• Active Learning for Annotation: Implementing machine learning techniques that iteratively improve data annotations with minimal human intervention, increasing efficiency and accuracy.



• Natural Language Processing (NLP) for Experimental Documentation: Utilizing NLP to parse and structure information from scientific texts, protocols, and publications, facilitating data reuse and reproducibility.



• Standardized Pipelines for Schema-Based Data Validation: Developing AI-assisted workflows that ensure data compliance with established schemas and standards, promoting interoperability.



• Ontology Development and Integration: Creating and applying ontologies to unify terminology across datasets, enhancing semantic interoperability and data linkage.







By bringing together interdisciplinary research, this collection aims to advance the field of neuroinformatics and foster the development of AI-powered tools that streamline data management and curation processes.