Facilitating the Sharing of Electrophysiology Data Analysis Results Through In-Depth Provenance Capture

Abstract

Scientific research demands reproducibility and transparency, particularly in data-intensive fields like electrophysiology. Electrophysiology data is typically analyzed using scripts that generate output files, including figures. Handling these results poses several challenges due to the complexity and iterative nature of the analysis process. These stem from the difficulty to discern the analysis steps, parameters, and data flow from the results, making knowledge transfer and findability challenging in collaborative settings. Provenance information tracks data lineage and processes applied to it, and provenance capture during the execution of an analysis script can address those challenges. We present Alpaca (Automated Lightweight Provenance Capture), a tool that captures fine-grained provenance information with minimal user intervention when running data analysis pipelines implemented in Python scripts. Alpaca records inputs, outputs, and function parameters and structures information according to the W3C PROV standard. We demonstrate the tool using a realistic use case involving multichannel local field potential recordings of a neurophysiological experiment, highlighting how the tool makes result details known in a standardized manner in order to address the challenges of the analysis process. Ultimately, using Alpaca will help to represent results according to the FAIR principles, which will improve research reproducibility and facilitate sharing the results of data analyses.Significance StatementSharing electrophysiology data analysis results is challenging, especially in collaborative environ­ments. The results can be understood and interpreted only with the accurate description of the individual analysis steps, the parameters and the data flow, which can be achieved by storing the results together with detailed provenance information. We implemented the Alpaca toolbox to capture provenance during the execution of Python scripts, a typical implementation in pipelines that analyze electrophysiology datasets. Alpaca provides an easy and lightweight solution to record the relevant details of the analysis, facilitating sharing the results.