Abstract
An essential aspect of scientific reproducibility is a coherent and complete acquisition of metadata along with the actual data of an experiment. The high degree of complexity and heterogeneity of neuroscience experiments requires a rigorous management of the associated metadata. The odML framework represents a solution to organize and store complex metadata digitally in a hierarchical format that is both human and machine readable. However, this hierarchical representation of metadata is difficult to handle when metadata entries need to be collected and edited manually during the daily routines of a laboratory. With odMLtables, we present an open-source software solution that enables users to collect, manipulate, visualize, and store metadata in tabular representations (in xls or csv format) by providing functionality to convert these tabular collections to the hierarchically structured metadata format odML, and to either extract or merge subsets of a complex metadata collection. With this, odMLtables bridges the gap between handling metadata in an intuitive way that integrates well with daily lab routines and commonly used software products on the one hand, and the implementation of a complete, well-defined metadata collection for the experiment in a standardized format on the other hand. We demonstrate usage scenarios of the odMLtables tools in common lab routines in the context of metadata acquisition and management, and show how the tool can assist in exploring published datasets that provide metadata in the odML format.
Highlights
In recent years, the workflows involved in conducting and analyzing neurophysiological experiments have become increasingly complex (e.g., Coles et al, 2008; Denker and Grün, 2016; Brochier et al, 2018)
We developed odMLtables as a Python package to complement the open metadata Markup Language (odML) framework in simplifying working with, and in particular manually editing, the metadata stored in the hierarchical odML format. odMLtables facilitates the integration of the odML framework into the experimental workflow by converting between hierarchical odML and tabular representations in xls or csv format
We presented the odMLtables software, which facilitates the use of the odML metadata format in everyday experimental and data analysis work
Summary
The workflows involved in conducting and analyzing neurophysiological experiments have become increasingly complex (e.g., Coles et al, 2008; Denker and Grün, 2016; Brochier et al, 2018). The combinations of all these factors results in many experimental laboratories frequently collecting metadata in flat tabular formats independent of an explicit, underlying hierarchical structure, using tools for generation and manipulation of tables that do not require programming expertise, are widely adopted, readily available and familiar to the experimenters For these purposes a flat tabular representation of the metadata appears to be suitable. At its core, odMLtables provides functions to convert between the odML format and the corresponding tabular representation which can be represented in the Microsoft Excel (xls) or the generic comma separated value (csv) format (Figure 2) Metadata converted to these tabular formats are accessible via widely used spreadsheet software (e.g., Microsoft Excel or LibreOffice Calc3), such that users are able to intuitively view and edit the metadata. We describe in detail the structure of the hierarchical and tabular metadata representations, the main capabilities of odMLtables illustrated by means of the GUI, and its internal architecture
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