Enhancing Scientific Research with FAIR Digital Objects in the National Science Data Fabric

IF 1.8 4区计算机科学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computing in Science & Engineering Pub Date : 2024-04-18 DOI:10.1109/mcse.2024.3363828

Michela Taufer, Heberth Martinez, Jakob Luettgau, Lauren Whitnah, Giorgio Scorzelli, Pania Newell, Aashish Panta, Peer-Timo Bremer, Douglas Fils, Christine R. Kirkpatrick, Valerio Pascucci

引用次数: 0

Abstract

This perspective article presents the vision of combining findable, accessible, interoperable, and reusable (FAIR) Digital Objects with the National Science Data Fabric (NSDF) to enhance data accessibility, scientific discovery, and education. Integrating FAIR Digital Objects into the NSDF overcomes data access barriers and facilitates the extraction of machine-actionable metadata in alignment with FAIR principles. The article discusses examples of climate simulations and materials science workflows and establishes the groundwork for a dataflow design that prioritizes inclusivity, web-centricity, and a network-first approach to democratize data access and create opportunities for research and collaboration in the scientific community.

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利用国家科学数据结构中的 FAIR 数字对象加强科学研究

本视角文章介绍了将可查找、可访问、可互操作和可重用（FAIR）数字对象与国家科学数据架构（NSDF）相结合，以提高数据可访问性、科学发现和教育的愿景。将 FAIR 数字对象集成到 NSDF 中，可以克服数据访问障碍，并促进提取符合 FAIR 原则的机器可操作元数据。文章讨论了气候模拟和材料科学工作流的示例，并为数据流设计奠定了基础，该设计优先考虑包容性、以网络为中心和网络优先的方法，以实现数据访问的民主化，并为科学界的研究与合作创造机会。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computing in Science & Engineering 工程技术-计算机：跨学科应用

CiteScore

4.20

自引率

0.00%

发文量

审稿时长

6-12 weeks

期刊介绍： Physics, medicine, astronomy -- these and other hard sciences share a common need for efficient algorithms, system software, and computer architecture to address large computational problems. And yet, useful advances in computational techniques that could benefit many researchers are rarely shared. To meet that need, Computing in Science & Engineering presents scientific and computational contributions in a clear and accessible format. The computational and data-centric problems faced by scientists and engineers transcend disciplines. There is a need to share knowledge of algorithms, software, and architectures, and to transmit lessons-learned to a broad scientific audience. CiSE is a cross-disciplinary, international publication that meets this need by presenting contributions of high interest and educational value from a variety of fields, including—but not limited to—physics, biology, chemistry, and astronomy. CiSE emphasizes innovative applications in advanced computing, simulation, and analytics, among other cutting-edge techniques. CiSE publishes peer-reviewed research articles, and also runs departments spanning news and analyses, topical reviews, tutorials, case studies, and more.