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Related Experiment Video

Updated: Mar 17, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

Publishing FAIR Data: An Exemplar Methodology Utilizing PHI-Base.

Alejandro Rodríguez-Iglesias¹, Alejandro Rodríguez-González², Alistair G Irvine³

¹Center for Plant Biotechnology and Genomics, Universidad Politécnica de Madrid Madrid, Spain.

Frontiers in Plant Science

|July 20, 2016

Summary

This summary is machine-generated.

The Pathogen-Host Interaction Database (PHI-base) was migrated to FAIR data principles, enhancing plant science research accessibility. This transformation improves data findability, accessibility, interoperability, and reusability for researchers.

Keywords:

FAIR data Linked Data PHI-base Pathogen-Host Interactions SPARQL Semantic PHI-base Semantic Web data integration

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Area of Science:

Plant pathology
Bioinformatics
Data science

Background:

Pathogen-host interaction data is crucial for understanding disease mechanisms.
Plant sciences face challenges with dispersed genetic and phenotypic data across diverse species.
The FAIR data principles (Findable, Accessible, Interoperable, Reusable) are essential for scholarly data publishing.

Purpose of the Study:

To migrate the Pathogen-Host Interaction Database (PHI-base) to conform to FAIR data principles.
To detail the technical and architectural decisions and migration pathway for PHI-base.
To demonstrate the value-added benefits and utility of FAIR data transformation for integrative research questions.

Main Methods:

Database migration to a FAIR-compliant format.
Architectural and technical decision-making for data standardization.
Implementation of features beyond typical life science Linked Data resources.
Testing the FAIR data through integrative research questions.

Main Results:

PHI-base successfully migrated to a FAIR-compliant format.
Simultaneous addressing of multiple FAIR principles through careful design.
Data made FAIR for both human and machine access with minimal duplication.
Demonstrated value-added through integrative questions previously unanswerable.

Conclusions:

Migrating PHI-base to FAIR principles significantly enhances its utility for plant science research.
FAIR data publishing requires more than reformatting, necessitating advanced features.
Explicit provenance information increases data reuse and citation rates.
FAIR data facilitates novel integrative analyses in pathogen-host interactions.