Evaluating the harmonisation potential of diverse cohort datasets.
Evaluating the harmonisation potential of diverse cohort datasets.
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DOI：
10.1007/s10654-023-00997-3
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发表时间：
2023-06
影响因子：
13.6
通讯作者：
Gallacher, John
中科院分区：
文献类型：
--
作者：
Bauermeister, Sarah;Phatak, Mukta;Sparks, Kelly;Sargent, Lana;Griswold, Michael;McHugh, Caitlin;Nalls, Mike;Young, Simon;Bauermeister, Joshua;Elliott, Paul;Steptoe, Andrew;Porteous, David;Dufouil, Carole;Gallacher, John
数据发现，即找到与分析相关的数据集的能力，增加了科学机会，提高了严谨性并加速了活动。数据的深度、广度、数量和可用性的快速增长为数据发现提供了前所未有的机遇和挑战。提高数据发现效率的一个潜在工具，特别是在多个数据集之间，是数据协调。一组124个变量，被确定为对神经退行性疾病具有广泛的兴趣，使用C-Surv数据模型进行协调。使用的协调策略是简单的校准，算法转换和标准化的Z分布。广泛使用的数据惯例，优化的包容性，而不是病因学的精度，被用作协调规则。协调方案适用于来自四个不同人群队列的数据。在数据集中发现的120个变量中，协调数据模式与队列特定数据模型之间的对应关系为111（93%）。对于其余的，协调是可能的，粒度损失很小。虽然协调不是一门精确的科学，但在数据集之间实现了足够的可比性，使数据发现的信息损失相对较小。这为进一步将协调扩展到更大的变量列表，将协调应用于更多数据集以及激励数据发现工具的开发提供了基础。在线版本包含补充材料，可通过10.1007/s10654-023-00997-3获得。
Data discovery, the ability to find datasets relevant to an analysis, increases scientific opportunity, improves rigour and accelerates activity. Rapid growth in the depth, breadth, quantity and availability of data provides unprecedented opportunities and challenges for data discovery. A potential tool for increasing the efficiency of data discovery, particularly across multiple datasets is data harmonisation.A set of 124 variables, identified as being of broad interest to neurodegeneration, were harmonised using the C-Surv data model. Harmonisation strategies used were simple calibration, algorithmic transformation and standardisation to the Z-distribution. Widely used data conventions, optimised for inclusiveness rather than aetiological precision, were used as harmonisation rules. The harmonisation scheme was applied to data from four diverse population cohorts.Of the 120 variables that were found in the datasets, correspondence between the harmonised data schema and cohort-specific data models was complete or close for 111 (93%). For the remainder, harmonisation was possible with a marginal a loss of granularity.Although harmonisation is not an exact science, sufficient comparability across datasets was achieved to enable data discovery with relatively little loss of informativeness. This provides a basis for further work extending harmonisation to a larger variable list, applying the harmonisation to further datasets, and incentivising the development of data discovery tools. The online version contains supplementary material available at 10.1007/s10654-023-00997-3.
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DOI：
10.1186/s13195-017-0288-0
发表时间：
2017-08-29
期刊：
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影响因子：
--
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影响因子：
7.7
作者：
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影响因子：
7.7
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影响因子：
13.6
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影响因子：
7.7
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通讯作者：
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