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Data Shepherding in Nanotechnology. The Initiation.

Irini Furxhi1,2, Athanasios Arvanitis3, Finbarr Murphy1,2

  • 1Transgero Limited, Cullinagh, Newcastle West, V42V384 Limerick, Ireland.

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Summary
This summary is machine-generated.

This paper outlines a practical roadmap for creating a Data Management Plan (DMP) and implementing data FAIRification in research projects. It details a systematic approach for data creators and analysts to ensure data accessibility and reusability.

Keywords:
FAIRdata managementdata shepherddata stewardshipnanotechnology

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Area of Science:

  • Nanotechnology research
  • Data science
  • Scientific data management

Background:

  • Effective data management is crucial for multidisciplinary research projects like Horizon 2020's ASINA project.
  • FAIRification (Findable, Accessible, Interoperable, Reusable) is essential for maximizing data value.
  • Standardized approaches are needed for data management and FAIRification.

Purpose of the Study:

  • To describe a pragmatic approach for initiating, designing, and implementing a Data Management Plan (DMP) and data FAIRification process.
  • To provide a roadmap for managing and FAIRifying data in complex, multidisciplinary research projects.
  • To demonstrate a systematic method for data and metadata capture and integration into repositories.

Main Methods:

  • Utilized a generic questionnaire to capture primary data and metadata from consortium partners (data creators and analysts).
  • Engaged in an interactive process with external FAIR data initiatives for guidance on data/metadata capturing and repository integration.
  • Developed case-specific templates through internal communication among project partners.

Main Results:

  • Established a systematic conceptualization and visualization process for data FAIRification.
  • Successfully captured preliminary data and FAIRification templates.
  • Initiated an inner-communication process leading to the development of tailored data management templates.

Conclusions:

  • The described approach provides a valuable roadmap for data creators, analysts, stewards, and shepherds in managing and FAIRifying data.
  • The methodology facilitates systematic data management and FAIRification, applicable to various research projects.
  • The ASINA project's experience offers practical insights into overcoming challenges in data shepherding.