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.

LabInform ELN: A lightweight and flexible electronic laboratory notebook for academic research based on the open-source software DokuWiki

“…Bear in mind that no ELN will ever be a turn-key solution and that we need to first gain a thorough understanding of our own processes before we can start mapping them to a digital workflow. Furthermore, the LabInform ELN is but one component of a larger infrastructure for research data management employed by the authors, others being tools for metadata acquisition during data recording [73], a framework for reproducible data analysis [58], a local repository [54], and a LIMS [55]. The interplay of these different components will briefly be described and afterwards other, partly similar solutions mentioned.…”

“…Data processing and analysis should be automated as much as possible, and a gap-less automatically written protocol of each individual step including all implicit and explicit parameters is of particular importance. This has been implemented in the ASpecD framework [58] and packages based on it [59][60][61][62][63]. Already now the reporting capabilities of the ASpecD framework can be used to provide code snippets with graphical (or tabular) representations of the data analysis that are manually included into a labbook page (as seen in Fig.…”

“…This is possible using APIs allowing to access the ELN from the analysis framework. For an approach to a fully reproducible data analysis see the ASpecD framework [58] and packages based on it [59][60][61][62][63]. Other aspects sometimes covered by ELNs or LIMS are the management of measurement setups in the lab or even a more general project management, including proposals and publications.…”

“…The contents of such a labbook page are: (i) the basic metadata as key-value pairs that can be used to aggregate information contained therein in overview tables; (ii) a detailed log (with time) of the history of the measurement; (iii) some general comments; (iv) a first evaluation or analysis of the recorded data, typically in form of a graphical representation that could be automatically generated, e.g. using analysis tools [59][60][61][62] based on the ASpecD framework [58], these analyses serve as a quick overview, especially if one does not have a catalogue, and they could eventually be automatically included by the analysis tools via the XML-RPC interface provided by DokuWiki; (v) further plans; (vi) metadata for the measurement as a downloadable code block, e.g. an Infofile [58,59,73].…”

“…using analysis tools [59][60][61][62] based on the ASpecD framework [58], these analyses serve as a quick overview, especially if one does not have a catalogue, and they could eventually be automatically included by the analysis tools via the XML-RPC interface provided by DokuWiki; (v) further plans; (vi) metadata for the measurement as a downloadable code block, e.g. an Infofile [58,59,73].…”