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Curating Big Data Made Simple: Perspectives from Scientific Communities.

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This study introduces a cloud platform and data curation model to help scientists manage and collaborate on big data. It addresses challenges in big data curation for earth and environmental science communities.

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Area of Science:

  • Computer Science
  • Data Science
  • Environmental Science

Background:

  • The digital universe generates vast amounts of data, presenting both opportunities and challenges for scientific research.
  • Cloud platforms are crucial for enabling scientists to access, analyze, and collaborate on big data.
  • Existing attention on big data often overlooks the needs of the communities producing and utilizing this data.

Purpose of the Study:

  • To present the architecture and design of a cloud platform tailored for big data curation.
  • To introduce a data curation model used by earth and environmental scientists.
  • To discuss the motivation, challenges, and lessons learned in supporting scientific big data curation.

Main Methods:

  • Development of a cloud platform architecture designed for data-intensive research.
  • Implementation of a big data curation model for a community of earth and environmental scientists.
  • Analysis of challenges and lessons learned in supporting scientific data curation.

Main Results:

  • A functional cloud platform architecture designed to meet scientists' needs for data management and collaboration.
  • A practical big data curation model successfully adopted by an earth and environmental science community.
  • Insights into overcoming challenges in supporting scientists with big data curation.

Conclusions:

  • Cloud platforms and effective curation models are essential for harnessing the potential of big data in science.
  • Addressing the community dynamics and technical requirements is key to successful big data platform deployment.
  • The presented platform and model offer a viable solution for big data curation in scientific research.