科学的科学科学公平的数据生态系统
Jian Qin1, Sarah Bratt2, Jeff Hemsley1
1Syracuse University, USA.
概括
本研究介绍了科学科学研究的 FAIR 数据生态系统中的自动化研究工作流 (ARW). 它指导研究人员自动化数据科学项目以获得更广泛的适用性.
科学领域:
- 信息科学 信息科学 信息科学
- 计算社会科学 计算社会科学
- 科学的科学科学研究研究的科学.
背景情况:
- 开发强大的数据生态系统对于科学研究的科学进步至关重要.
- 当前的数据挑战阻碍了这个领域的高效研究过程.
- 集成自动化工作流是克服这些数据障碍的关键.
研究的目的:
- 在FAIR数据生态系统中概念上讨论自动化研究工作流程 (ARW).
- 为了说明FAIR数据生态系统设计人员和开发人员的特点和期望.
- 为科学科学领域及其他领域的研究工作流程自动化提供指南.
主要方法:
- 基于信息科学和技术视角的概念讨论.
- 数据问题在科学科学研究中的案例研究.
- 从长达十年的GenBank元数据工作流程数据科学项目中得出结论.
主要成果:
- 确定了设计FAIR数据生态系统的关键特征和预期.
- 证明了ARWs在FAIR数据生态系统框架中的整合.
- 建立了一个基础,使ARW在各个研究领域的应用得到普遍化.
结论:
- 自动化研究工作流对于构建有效的FAIR数据生态系统至关重要.
- 拟议的框架提高了数据自动化在科学研究中的通用性.
- 研究人员可以利用本指南在他们的数据科学项目中实施自动化工作流程.
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