## 1 Introduction
These waves of data collection give researchers an opportunity to get data regarding deviations in the measures of participants' exposure and outcome over time. The duration of follow-up waves of data can range from one to two years up to 20 to 30 years or even with longer post-baseline assessments. Missingness of the data can be related to study designs in which recurrent measures of exposure and outcome over time are needed. Specifically, the candidates might not available or might be too ill to participate, or they might refuse to respond to specific inquiries or could be deceased [13]. Thus, researchers often face missing data, which might introduce bias in the parameter estimations (for instance of risk calculation) as well as the loss of statistical power and accuracy [13]. Although further research is required to understand both the impact of missing data and the development of effective methodologies for their handling, there are approaches (discussed below) that may decrease the detrimental effect of missing information. There are several ways of storing and integrating data, such as warehouses, federations, data hotels, etc. For example, the Dutch Techcentre for Life sciences (DTL) is an innovative solution for data hotels. DTL keenly supports FAIR (Findable, Accessible, Interoperable, and Reusable) data stewardship of life science data, within its partnership and in close collaboration with its international partners [14]. The principles of FAIR data serve as an international guideline for high quality data stewardship. A federated facility is a meta-database system that is distributed across multiple locations. It allows making the data from different sources comparable and useful for analyses [15]. Its main difference between federated facility and registries and warehouses is that data management is carried out via a remote distributed request from one federated server (or database manager) to multiple sources. A federated facility allows for researchers to receive analytical insight from the pooled information of diverse datasets without having to move all the data to the main location, thus reducing the extent of data movement in the distribution of intermediate results, and maximizing the security of the local data in the distributed sources [16; 17]. Most of the data are analyzed close to where they are produced in a federated analytical model. To enable collaboration at scale, federated analytics permits the integration of intermediate outcomes of data analytics while the raw data remains in its locked-down site. When the integrated results are pooled and explored, a substantial amount of knowledge is acquired, and researchers managing a single-center database have the ability to compare their results with the findings that were derived by the analyses of federated pooled data. In this paper, we describe some methodological aspects that are related to the federated facility. Specifically, we review the methods of harmonization and analysis of multi-center datasets, focusing on the impact of missing data (as well as different approaches to deal with them) in cohorts. Thus, firstly, we aim to suggest a few examples of cohort studies and a data collection procedure for a cohort study, and, secondly, to offer approaches of harmonization and integrative data analysis over cohorts. To this end, we present different methods for handling missing data, such as complete case-analysis and multiple imputations. Finally, we offer a perspective on the future directions of this research area.