ASpecD: A Modular Framework for the Analysis of Spectroscopic Data Focussing on Reproducibility and Good Scientific Practice**

Abstract

Reproducibility is at the heart of science. However, most published results usually lack the information necessary to be independently reproduced. Even more, most authors will not be able to reproduce the results from a few years ago due to lacking a gap‐less record of every processing and analysis step including all parameters involved. There is only one way to overcome this problem: developing robust tools for data analysis that, while maintaining a maximum of flexibility in their application, allow the user to perform advanced processing steps in a scientifically sound way. At the same time, the only viable approach for reproducible and traceable analysis is to relieve the user of the responsibility for logging all processing steps and their parameters. This can only be achieved by using a system that takes care of these crucial though often neglected tasks. Here, we present a solution to this problem: a framework for the analysis of spectroscopic data (ASpecD) written in the Python programming language that can be used without any actual programming needed. This framework is made available open‐source and free of charge and focusses on usability, small footprint and modularity while ensuring reproducibility and good scientific practice. Furthermore, we present a set of best practices and design rules for scientific software development and data analysis. Together, this empowers scientists to focus on their research minimising the need to implement complex software tools while ensuring full reproducibility. We anticipate this to have a major impact on reproducibility and good scientific practice, as we raise the awareness of their importance, summarise proven best practices and present a working user‐friendly software solution.

Towards more reproducible and FAIRer research data: documenting provenance during data acquisition using the Infofile format

“…Of course, documenting research data is relevant in other phases as well, particularly during processing and analysis. This is the realm of scientific workflow systems such as the ASpecD framework 36 . The FAIR principles 8 (Fig.…”

“…Automating data processing and analysis using scientific workflow systems such as the ASpecD framework 36 is another motivation to document research data. Given the documentation to be in form of machine-actionable metadata, the processing and analysis routines can gain a "semantic understanding" of the data.…”

“…Other aspects of reproducible research 45 such as a complete audit trail of data processing and analysis are out of scope, but dealt with, e.g. scientific workflow systems, such as the ASpecD framework 36 .…”

“…In any case, these key-value pairs allow for a "semantic understanding" by the routines for data processing and analysis. Combined with a scientific workflow system such as the ASpecD framework 36 , these machine-actionable metadata reveal their full potential, allowing to automate the processing and analysis pipelines to a great extend. Furthermore, the metadata should be long-lasting and as permanent as possible.…”

“…This requires a thorough understanding of the processes resulting in a mental model of a dataset that can only be achieved and created by means of sufficient experience. One possible implementation of this model, consisting of the recorded (mostly numeric) data and the accompanying, machine-actionable metadata, is the dataset in context of the AS-pecD framework 36 . The data model needs to be modular and expandable, another application for the open-closed principle 48 p. 57 mentioned above.…”