Figure In this Python code snippet for a Jupyter Notebook cell, the imported SpectraFitNotebook object is used to fit HERFD-XAS data of complex 1 to a fitting model. Initializing the plugin involves setting various parameters, such as the data to be analyzed, the columns to use for the x and y data, and the desired parent-output file name. To begin, the relevant energy range for the fit is selected by filtering the data using the `pandas.DataFrame.query` method. Next, the `x_column` and `y_column` are set to the appropriate columns from the filtered data, which in this example is the spectrum of complex 1. Finally, the `fname` parameter is set as the desired parent-output file name, and the `description` parameter is used to provide valuable metadata about the fit, including references, project name, and description, tags, and authors. Additional details can be included using the `meta_data` feature, which provides context beyond the information already given by the `DescriptionAPI` object, respectively, class. This can encompass details such as the sample, detection, endstation, calibration, or even a "Notice to the Reader" to guide the use of the data for scientific purposes. The notice can also be found at the end of the manuscript and serves to guide the data's use for scientific purposes. In general, the metadata plays a crucial role in providing context for future analysis and keeping track of the data's suitability. In summary, SpectraFitNotebook object should support the user in organizing the digital homework, which is defined by the data itself and the description of the data, meaning meta-data. Adapted from Ref. ; Copyright 2023 America Chemical Society (ACS).