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Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
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生物医学科学数据素养的AI-Ready能力框架

Zhe Wang1, Zhi-Gang Wang1, Wen-Ya Zhao1

  • 1Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences; School of Basic Medicine, Peking Union Medical College, Beijing 100005, China.

Chinese medical sciences journal = Chung-kuo i hsueh k'o hsueh tsa chih
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此摘要是机器生成的。

数据素养对于高质量的生物医学研究和人工智能 (AI) 准备工作至关重要. 本研究提出了数据素养能力模型和分层培训策略,以应对数据治理和研究人员教育方面的挑战.

关键词:
人工智能已经准备好了.公平的原则 公平的原则能力框架 能力框架数据素养 数据素养科学数据管理科学数据管理

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科学领域:

  • 生物医学研究生物医学研究
  • 数据科学数据科学数据科学
  • 科学数据治理科学数据治理

背景情况:

  • 数据密集型研究需要增强数据素养,以提高数据质量和人工智能准备.
  • 生物医学数据的复杂性和隐私敏感性需要强大的数据管理技能.
  • 当前的科学数据治理面临着诸如不一致的标准,语义错位和合规意识差距等挑战.

研究的目的:

  • 为生物医学研究提出一个全面的,以生命周期为导向的数据素养能力模型.
  • 在数据素养中强调道德和监管意识.
  • 概述一个阶层培训战略,以推进数据素养教育.

主要方法:

  • 审查科学数据治理和政策的当前趋势.
  • 现有的数据素养框架与生物医学研究特点的整合.
  • 制定能力模型和分层培训战略.

主要成果:

  • 一个针对生物医学领域量身定制的拟议数据素养能力模型.
  • 确定数据治理和研究人员培训的关键挑战.
  • 一个分层的培训战略,为本科,研究生和专业研究人员.

结论:

  • 结构化培训和实践支持对于克服数据素养挑战至关重要.
  • 拟议的模型和战略可以提高数据质量,人工智能准备以及生物医学研究中的监管合规性.
  • 大学和研究机构可以利用这个框架来推进数据素养教育.