开源模型用于开发数据和元数据标准
Ariel Rokem1,2, Vani Mandava2,3, Nicoleta Cristea2,4
1University of Washington, Department of Psychology, Seattle, WA, USA.
Patterns (New York, N.Y.)
|September 10, 2025
概括
开源软件 (OSS) 模型通过为机器学习和人工智能提供可适应的数据标准来加速科学发现. 支持这些OSS数据标准可以确保研究的可复制性和透明度.
科学领域:
- 跨学科的科学研究.
- 数据科学是数据科学.
- 计算科学是一种计算科学.
背景情况:
- 机器学习 (ML) 和人工智能 (AI) 需要汇总的科学数据.
- 开放数据原则对于研究可重复性和透明度至关重要.
- 现有的数据存储库正在增长,但互操作性挑战仍然存在.
研究的目的:
- 探索开源软件 (OSS) 模型在创建和调整数据标准方面所带来的好处.
- 解决数据标准中平衡灵活性和稳定的挑战.
- 确保OSS数据标准的可持续发展和认可.
主要方法:
- 综合来自不同科学领域的经验:天文学,高能物理,地球科学和神经科学.
- 分析开源软件 (OSS) 模型在数据标准创建和调整中的适用性.
- 考虑用户和开发人员对灵活性,稳定性和新计算范式的看法.
主要成果:
- 在开发和调整可互操作的数据标准方面,OSS模型提供了显著的优势.
- 关键问题包括平衡标准灵活性与稳定性以及采用新技术.
- 用户和开发者的观点对于成功实施和长期可持续性至关重要.
结论:
- OSS是创建和维护科学数据标准的可行和有益的模型.
- 建议包括支持和认可OSS数据标准和软件的发展.
- 与广泛采用的科学工具保持一致对于更广泛的影响和采用至关重要.
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