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Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics

Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics

Published on: June 17, 2012

Semantic annotation of mutable data.

Robert A Morris¹, Lei Dou, James Hanken

¹Harvard University Herbaria, Cambridge, Massachusetts, United States of America ; Computer Science Department, University of Massachusetts, Boston, Massachusetts, United States of America.

|November 14, 2013

Summary

This summary is machine-generated.

Electronic annotation of scientific data enhances knowledge sharing and supports data quality control. New extensions to annotation ontologies enable fine-grained, interest-based distribution of annotations for improved data management.

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Area of Science:

Computer Science
Data Science
Information Science

Background:

Electronic annotation shares similarities with document annotation, serving as a framework for discourse and knowledge amplification.
However, electronic data annotation differs in its potential for shared content, supporting evidence, and consequent actions among network members.
Existing annotation systems often lack the granularity needed for effective data quality control.

Purpose of the Study:

To address limitations in current electronic data annotation systems for data quality control.
To propose extensions to an existing annotation ontology for improved data annotation granularity and distribution.
To design and deploy a platform for annotation services on distributed data networks, focusing on data quality control.

Main Methods:

Extending an existing annotation ontology with simple modifications.
Developing a framework for interest-based distribution of annotations.
Designing and deploying a platform for annotation services on distributed data networks.
Utilizing XML-Schema for modeling data annotations in a proof-of-concept.

Main Results:

The proposed ontology extensions facilitate fine-grained annotation of scientific data.
The developed platform supports annotation services overlaid on networks of distributed data.
The system enables interest-based distribution of annotations.
Initial applications focus on natural science collection metadata services and data quality control.

Conclusions:

Simple extensions to annotation ontologies can significantly improve data annotation capabilities.
The developed platform offers a robust solution for managing and controlling the quality of distributed scientific data.
This approach enhances collaborative research by enabling effective sharing and utilization of annotated data.