The management of scientific data is a crucial aspect of modern data science. Four simple guiding principles combined under the FAIR data moniker define the current “gold standard” in data quality: the data has to be Findable by anyone, Accessible without barriers, Interoperable with other programs, and Reusable after analysis (Wilkinson et al., 2016). Yet, many scientific data formats do not conform to these principles. This is especially true for proprietary formats, often associated with expensive lab instrumentation. The yadg package helps to resolve this issue by parsing raw data files into a standardised, annotated and timestamped format readable by both humans and machines. Various raw data formats are supported, including chromatograms, electrochemical cycling protocols, reflection coefficient traces, spectroscopic data, and tabulated data. The parsed files include information about data provenance, units of measure, and experimental uncertainties by default. Finally, several common data processing steps, such as applying calibration functions, integration of chromatographic traces, or fitting of reflection coefficients, are available in yadg.