Treffer: An open software package for data reconciliation and gap filling in preparation of Water and Resource Recovery Facility Modeling.

High quality data is of crucial importance for model development: it provides a model input and is a prerequisite for model calibration and validation. Data reconciliation is often a very time-consuming task, so even when on-line data is available, the option is often chosen to synthetically generate data, losing a lot of information contained in the available data. This contribution showcases a Python™ package that allows a streamlined work-flow and provides possibilities for data analysis, validation and gap filling, with as main goals to use as much of the data as possible and to fill gaps in the data with a known reliability. This provides a means towards more data use and a more sound calibration and validation, while significantly reducing time spent on data reconciliation. The package is published and made openly available on GitHub. This avoids multiple implementations while being accessible to the community for suggested improvements. [ABSTRACT FROM AUTHOR]

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