This dataset contains all data collected for the paper by Hollanders et al (Caries Res. 2022 doi: 10.1159/000522589). The paper describes an in vitro study investigating the effect of occlusal loading on secondary caries lesion development. 64 human molars received standardized (4.0x4.2x3.0 mm) box preparations. Composite restorations were made using two different restorative materials (Clearfil AP-X and Clearfil Majesty E-S flow) with different elastic moduli. A cervical gap (100 µm) was present in all samples. Samples were exposed to simulated caries lesion development in a lactic acid solution (pH 4.8) for 8 weeks. 4 samples were sliced and measured after 5 weeks to check lesion development. Half of the samples were subjected to 90N cyclic loading. Lesion depth was measured using microradiography. A full description of the study protocol can be found in the Methodology file. The Codebook describes the variables used in the analysis. The full dataset used for analysis is available in the file “2021_Hollanders_edited_complete dataset.csv”. The raw measured data which were used to compose the edited dataset can be found in three different files: “2021_Hollanders_raw_enamel thickness.csv” contains the values in µm measured in Adobe photoshop for each of the samples. “2021_Hollanders_raw_gap width.csv” contains the values in µm measured in Adobe Photoshop. There are three values for each sample. The average of these was used in the edited dataset. “2021_Hollanders_raw_lesion depth mineral loss.csv” contains the raw mesurements from the WIM program for measuring lesion depth and mineral loss. These measurements were arranged per sample to create the edited dataset. The syntax file (“2021_Hollanders_syntax.R”) can be run from top to bottom and will produce first descriptive data output and, subsequently, write the results of the regression analyses to different files. Data analysis was carried out in the R environment for statistical computing (R Core Team, 2013. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/.) with the aid of packages “tidyverse”, “readxl”, “dplyr”, and “psych”.