Tummers Simone Catharina Maria Wilhelmina, Hommersom Arjen, Bolman Catherine, Lechner Lilian, Bemelmans Roger
Open University of the Netherlands, Heerlen, the Netherlands.
Radboud University, Nijmegen, the Netherlands.
MethodsX. 2024 Dec 11;14:103104. doi: 10.1016/j.mex.2024.103104. eCollection 2025 Jun.
The analysis of complex mechanisms within population data, and within sub-populations, can be empowered by combining datasets, for example to gain more understanding of change processes of health-related behaviours. Because of the complexity of this kind of research, it is valuable to provide more specific guidelines for such analyses than given in standard data science methodologies. Thereto, we propose a generic procedure for applied data science research in which the data from multiple studies are included. Furthermore, we describe its steps and associated considerations in detail to guide other researchers. Moreover, we illustrate the application of the described steps in our proposed procedure (presented in the graphical abstract) by means of a case study, i.e., a physical activity (PA) intervention study, in which we provided new insights into PA change processes by analyzing an integrated dataset using Bayesian networks. The strengths of our proposed methodology are subsequently illustrated, by comparing this data science trajectories protocol to the classic CRISP-DM procedure. Finally, some possibilities to extend the methodology are discussed.-A detailed process description for multidisciplinary data science research on multiple studies.-Examples from a case study illustrate methodological key points.
通过合并数据集，可以增强对总体数据以及子群体内复杂机制的分析，例如，以更深入地了解与健康相关行为的变化过程。由于这类研究的复杂性，相较于标准数据科学方法所提供的指导，为这类分析提供更具体的指南很有价值。为此，我们提出了一种应用数据科学研究的通用程序，其中纳入了多项研究的数据。此外，我们详细描述了其步骤及相关注意事项，以指导其他研究人员。此外，我们通过一个案例研究，即一项身体活动（PA）干预研究，说明了我们所提出程序（在图形摘要中展示）中所述步骤的应用，在该研究中，我们通过使用贝叶斯网络分析综合数据集，对PA变化过程有了新的见解。随后，通过将此数据科学轨迹协议与经典的CRISP-DM程序进行比较，展示了我们所提出方法的优势。最后，讨论了扩展该方法的一些可能性。-针对多项研究的多学科数据科学研究的详细过程描述。-来自案例研究的示例说明了方法要点。
