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Preparation of High-Quality Fermented Fish Product
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High-quality science requires high-quality open data infrastructure
Susanna-Assunta Sansone1, Patricia Cruse2, Mark Thorley3
1University of Oxford, Oxford e-Research Centre, Department of Engineering Sciences, Oxford OX1 1TQ, UK.
Scientific Data
|February 28, 2018
Summary
Numerous data resources exist for management and discovery. More are needed to support the FAIR principles of data Findability, Accessibility, Interoperability, and Reusability.
Area of Science:
- Data Science
- Information Science
- Digital Curation
Background:
- A wide array of resources currently exists to support data management, discovery, and reuse.
- However, there is a recognized need for enhanced data resources that specifically address key data management principles.
Purpose of the Study:
- To highlight the importance and need for data resources that facilitate the FAIR principles.
- To emphasize the critical role of data Findability, Accessibility, Interoperability, and Reusability in scientific advancement.
Main Methods:
- This study is a conceptual analysis and synthesis of existing literature on data management and FAIR principles.
- It involves reviewing current resource landscapes and identifying gaps in FAIR data support.
Main Results:
- The current landscape of data resources is diverse but often lacks explicit support for all FAIR principles.
- There is a clear demand for more integrated and accessible platforms that enable FAIR data practices.
Conclusions:
- Developing and promoting data resources that fully embody the FAIR principles is essential for maximizing data's value.
- Future efforts should focus on creating and improving resources that enhance data Findability, Accessibility, Interoperability, and Reusability.
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