odMLtables

odMLtables converts and manages metadata based on the Open Metadata Markup Language (odML) by mapping hierarchical odML structures to and from tabular representations (e.g., xls, csv) to organize neurophysiological experiment metadata and support reproducibility.

Key Features:

• Hierarchical–tabular conversion: Converts odML hierarchical structures to table-like representations and back, enabling round-trip transformation between formats.
• Spreadsheet support: Exports and imports tabular metadata to and from spreadsheet formats such as xls and csv.
• Python API: Provides a Python API for programmatic conversion and manipulation of odML metadata.
• Tabular editing and manipulation: Enables collection, table-based editing, manipulation, visualization, and storage of metadata collections in tabular form.
• Flexible data model: Supports storage of arbitrary metadata within the odML data model to accommodate diverse experimental setups.
• Merging and subsetting: Allows merging of metadata collections and extraction of subsets from complex odML metadata sets.

Scientific Applications:

• Neurophysiological metadata organization: Organizes complex and heterogeneous metadata arising from neuroscience experiments.
• Reproducibility: Captures and structures comprehensive metadata to support scientific reproducibility of neurophysiological studies.
• Dataset exploration and reuse: Facilitates exploration and reuse of published datasets that provide odML-formatted metadata.

Methodology:

Implements programmatic conversion between tabular and hierarchical odML formats, with export/import to xls and csv and operations for merging and extracting subsets of metadata collections.