生物信息学中的主观数据模型以及湿实验室和计算生物学家如何概念化数据
Yo Yehudi1,2, Lukas Hughes-Noehrer3, Carole Goble3
1Department of Computer Science, University of Manchester, Oxford Road, Manchester, M13 9PL, UK. yochannah.yehudi@postgrad.manchester.ac.uk.
Scientific data
|November 3, 2023
概括
生物学研究人员通常具有灵活的主观数据模型,这些数据模型随着环境的变化而变化,而不是严格地与计算技能联系在一起. 清晰的元数据对于准确的数据解释和避免猜测工作至关重要.
科学领域:
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
- 在生命科学中的数据科学生命科学中的数据科学
背景情况:
- 生物科学产生大型数据集 ("大数据"),需要计算工具进行分析.
- 研究人员使用计算工具的范围很大,从基本的通信到复杂的编码.
研究的目的:
- 调查生物学研究人员如何概念化数据,称为"主观数据模型".
- 了解计算体验与数据概念化之间的关系.
主要方法:
- 采访了22名具有不同生物和计算背景的参与者.
- 分析研究人员如何理解和映射数据概念的变化.
主要成果:
- 主观数据模型往往是流动和上下文依赖的,不总是与计算专业知识联系在一起.
- 研究人员并不总是将抽象数据概念映射到现实世界的文件实体.
- 某些数据标识符格式比其他格式更有助于理解.
结论:
- 软件接口应该优先考虑基于任务的设计,而不是专业背景.
- 背景元数据对于防止误解和减少对猜测的依赖至关重要.
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