Overall, the structure is organized around four different types of files. First, we have the instrument files containing the raw data from the calorimeter. This data is available in a simple text-based format such as CSV files (or, less preferred, XLSX files). Second, a metadata file is needed that contains information about the samples, such as the sample name, the amount of cement, water, and any additives used. Ideally, The metadata file is stored in a simple text-based format such as CSV. It is crucial to note that the metadata connects the instrument data with the experimental sample information, such as the amount of cement and water or the chemical nature and amount of an additive. To ensure the correct connection between the metadata and the instrument data, the sample names in the metadata file must correspond to the filenames of the calorimetry instrument .csv files. Third, the programmatic data analysis relies on scripts that aggregate and analyze the data. We use the Python package CaloCem to read and process the calorimetry data. Finally, the data analysis output can consist of different artifacts, such as diagrams and tables, and possibly others, such as CSV files with aggregated data or statistical models.