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FAIR Data Infrastructure.

Janna Neumann1

  • 1Technische Informationsbibliothek, Hannover, Germany. janna.neumann@tib.eu.

Advances in Biochemical Engineering/Biotechnology
|January 29, 2022
PubMed
Summary
This summary is machine-generated.

This chapter explores research data management, focusing on metadata and FAIR data principles for effective data sharing and publication. It discusses open science challenges and outlines steps for building FAIR data infrastructures.

Keywords:
Data infrastructureData managementFAIR dataMetadataResearch data

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Area of Science:

  • Information Science
  • Data Science
  • Scholarly Communication

Background:

  • Research data management (RDM) is crucial for modern research.
  • Data publication and infrastructures are key components of RDM.
  • Open science initiatives necessitate robust data sharing practices.

Purpose of the Study:

  • To highlight the concept of research data management within data publication and infrastructures.
  • To focus on metadata and FAIR data principles for data sharing.
  • To discuss challenges and initial steps towards FAIR data infrastructures.

Main Methods:

  • Conceptual analysis of research data management.
  • Review of metadata standards and FAIR data principles.
  • Discussion of challenges in open science adoption.
  • Illustration of foundational steps for FAIR data infrastructures.

Main Results:

  • Metadata and FAIR data principles are central to effective data sharing.
  • Data repositories are essential components of data infrastructures.
  • Researchers and communities face challenges in adopting open science practices.

Conclusions:

  • Implementing FAIR data principles is vital for advancing open science.
  • Developing FAIR data infrastructures requires addressing researcher challenges.
  • This work provides a foundation for understanding and building FAIR data ecosystems.