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Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

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DataDesc: A framework for creating and sharing technical metadata for research software interfaces.

Patrick Kuckertz¹, Jan Göpfert^1,2, Oliver Karras³

¹Forschungszentrum Jülich GmbH, Institute of Climate and Energy Systems (ICE) - Jülich Systems Analysis (ICE-2), 52425 Jülich, Germany.

Patterns (New York, N.Y.)

|November 21, 2024

Summary

This summary is machine-generated.

Research software reuse is hampered by discoverability and compatibility issues. The DataDesc framework and machine-actionable metadata improve research software FAIRness (findability, accessibility, interoperability, reusability), boosting its impact.

Keywords:

FAIR RDM interface description machine actionable metadata schema research data management semantic software description software documentation software metadata software publication software reuse

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Area of Science:

Computer Science
Information Science
Scientific Computing

Background:

Research software reuse is crucial for efficiency and academic exchange.
Current challenges include software discoverability and compatibility, leading to redundant development.
This impedes scientific progress and community advancement.

Purpose of the Study:

Introduce the DataDesc (Data Description) framework to address research software reuse challenges.
Enable the description of software interface data models using machine-actionable metadata.
Enhance the FAIRness (Findability, Accessibility, Interoperability, Reusability) of research software.

Main Methods:

Developed the DataDesc framework with a specialized metadata schema.
Created an exchange format for software interface data models.
Introduced support tools for automated collection and publishing of software documentation.

Main Results:

The DataDesc framework provides machine-actionable metadata for software interfaces.
Automated documentation publishing increases software findability and accessibility.
Improved interoperability and reusability of research software are facilitated.

Conclusions:

The DataDesc framework significantly enhances the FAIRness of research software.
This approach promotes wider adoption and impact of research software.
Streamlined software development and scientific collaboration are key outcomes.