克服瘤研究中的数据管理挑战:从NHS,行业,技术创业和学术合作中吸取教训
Alicia-Marie Conway1, Matthew Concannon2, Steven Brown2
1Division of Cancer Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK; The Christie NHS Foundation Trust, Manchester, UK; Nucleic Acid Biomarker Team, Cancer Research UK, National Biomarker Centre, The University of Manchester, Manchester, UK.
概括
一个英国项目成功地使用数据湖来安全地存储和共享用于瘤研究的大规模基因组和多模式数据. 这种涉及合作和强有力的治理方法为未来精确瘤学举措提供了模板.
科学领域:
- 癌症学
- 基因组学
- 数据科学
背景情况:
- 推进瘤研究需要整合大规模的基因组和多模式数据.
- 数据存储,共享和治理方面的挑战阻碍了这些数据的有效利用.
- 一个跨领域的英国项目,旨在优化瘤学研究中的多模式数据使用.
研究的目的:
- 分享经验,并提供建议,以促进和优化多式联络数据在瘤研究中的使用.
- 解决大规模基因组数据集的数据存储,共享和治理方面的挑战.
- 通过合作和多方利益相关方的方式制定强有力的数据管理策略.
主要方法:
- 采用了涉及NHS信托,行业,初创企业和学术伙伴的协作方法.
- 选择了一个数据湖架构作为多种数据集的集中安全存储库.
- 数据存储,访问控制,所有权和信息治理等关键因素影响了解决方案的选择和实施.
主要成果:
- 数据湖促进了大规模基因组和临床数据的安全,合规和联合存储.
- 早期的利益相关者参与和明确的数据治理框架对于成功实施至关重要.
- 技术解决方案与不同合作伙伴的治理,安全和可访问性要求保持一致至关重要.
结论:
- 在瘤学中有效的多模式数据管理需要积极的规划,多方参与和强有力的治理.
- 数据湖模型为安全,协作基因组数据研究提供了可扩展和兼容的方法.
- 这种方法可以作为未来精确瘤学倡议的模板.
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