## 4 Data Integration  
Built-in security features of database management systems can limit access to the whole dataset of a federated facility, and security can be increased while using encryption. Some solutions can be applied, such as establishing a common variable format and standard, creating a unique identifier for all individuals in the cohorts, implementing security access to data and integrity constraints in the database management system, and making automated integration algorithms in the core module to synchronize or federate multiple heterogeneous data sources, to facilitate the integration of different datasets among various cohorts. There are three steps in data integration: (1) extraction of data and harmonization into a common format at a data provider site; (2) transfer of harmonized data to a data-collecting center for checking; and, (3) data load into a common database [2]. Harmonization is a systematic approach, which allows the integration of data collected in multiple studies or multiple sources. Sample size can be increased by pooling data from different cohort studies. Conversely, individual datasets may be comprised of variables that measure the same hypothesis in different ways, which hinders the efficacy of pooled datasets [29]. Variable harmonization can help to handle this problem. The federation facility might also be created without the need for harmonization (i.e., any cohorts that have some data (e.g., genotyping data) in one place and some data (e.g., phenotypic) in another or have a connection to national registries, etc.). For example, in the Genome of the Netherlands Project ([[http://www.nlgenome.nl/](http://www.nlgenome.nl/)]([http://www.nlgenome.nl/](http://www.nlgenome.nl/))), nine large Dutch biobanks (~35,000 samples) were imputed with the population-specific reference panel, an approach that led to the identification of a variant within the ABCA6 gene that is associated with cholesterol levels [30]. \begin{table}
\begin{tabular}{c c} \hline
**Step** & **Description** \\ \hline Data collection in cohort studies & Study data is obtained from self-completed paper-based/online questionnaires, biosample analysis, clinical assessments, linkage to administrative records, etc. \\ \hline Integration on cohorts & Remote access to aggregated data for statistical analysis is provided and data collected in multiple studies is integrated with the use of harmonization data tools (if needed) \\ \hline Mega-analyses, meta-analyses or integrative data analyses & Statistical tools for analysis of combined data are applied \\ \hline \end{tabular}
\end{table} 
Table 1: Main Steps of Federated Facility Process. The potential of harmonization can be evaluated with the studies' questionnaires, data dictionaries, and standard operating techniques. Harmonized datasets available on a server in every single research centers across Europe can be interlocked through a federated system to allow for integration and statistical analysis [7]. To co-analyze harmonized datasets, the Opal [31], Mica software [7], and the Data SHIELD package within the R environment are used to generate a federated infrastructure that allows for investigators to mutually analyze harmonized data while recollecting individual-level records within their corresponding host organizations [7]. The idea is to generate harmonized datasets on local servers in every single host organization that can be securely connected while using encrypted remote connections. Using a strong collaborative association among contributing centers, this approach can lead to effortless collateral analyses using globally harmonized research databases while permitting each study to maintain complete control over individual-level data [7]. Data harmonization is implemented in light of several factors, for instance, detailed or partial variable matching about the question asked/responded, the answer noted (value definition, value level, data type), the rate of measurement, the period of measurement, and missing values [29].