In the manuscript "Assessing the credibility of quantitative information: a general framework" we propose a general process called the 7S Framework, which can be used to assess the credibility of information, whether measured, inferred or predicted. The 7S Framework integrates and generalises the credibility assessment approaches used for measured information in metrology, inferred information in statistics, and predicted information in computational science and engineering. In this supplementary material, we apply the 7S framework to seven fairly different use cases to demonstrate that the proposed framework is effective, sufficiently general, and capable of capturing all the subtle differences that the concept of credibility implies for these different kinds of information. The seven use cases are: 1. Measurement - Credibility of strain gauge measurements2. Biophysical prediction - Credibility of CT-based prediction of human bone deformation3. Biophysical prediction - Credibility of CT-based prediction of human bone strength4. Biophysical prediction - Credibility of CT-based prediction of risk of hip fracture5. Synthetic dataset – Virtual cohort for In Silico Trials of interventions to prevent hip fractures6. In Silico Trial - Credibility of a model to predict interventions’ efficacy on a clinical cohort7. Surrogate model - Credibility of an ML predictor to surrogate a biophysical model We propose the 7S Framework as a generalisation useful in the credibility assessments of complex in silico medicine scenarios such as in-silico augmented clinical trials, physics-informed machine learning predictors, or the use of synthetic datasets to overcome privacy limitations, train machine learning predictors, and run large-scale In Silico Trials.