The same original dataset by Tilton et al. is included in Dataset 3, RDFied in this work (accession numbers: GSE42066, GSE42067 and GSE42068). That makes it possible to reproduce for example the difference in perturbation, represented by the number of differentially expressed genes, observed between two cell lines. However, it is important to notice that the samples in Dataset 3 went through a gene expression analysis pipeline explained in Saarimaki et al. which despite having differences from the analysis of the original study, but should lead to similar results. This example demonstrates the potential of semantic integration in performing analysis and deriving insights across different datasets. Listing 2 shows a SPARQL query to retrieve the differentially expressed gene (Ensembl identifier) and average expression for a treatment (100 µg/ml)-control contrast of TiO2-nanobelts measured at 24h timepoint. The query is executed twice, once for each cell line contrast (using the GSE identifiers: GSE42067 and GSE42068 for cell lines SAE and THP-1 respectively), as shown in the Jupyter notebook documenting this process . Next, a Venn diagram was made to show the size and overlap between the two gene sets (Figure ). The results are comparable to the findings of Winkers et al. in Figure . where THP-1 cells treated with TiO2 nanobelts shows much higher number of perturbed genes in comparison to the SEA samples. This visualization shows the difference in perturbation of TiO2 between the two cell lines. First, the huge difference in set sizes indicate that TiO2 is much more perturbating in THP-1, while the small number of overlapping DEGs indicate the difference in patterns of perturbation between the two cell lines. The query results were obtained using two SPARQL queries, one for each GSE experiment which then were intersected using Python.