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Scientific research demands reproducibility and transparency, particularly indata-intensive fields like electrophysiology. Electrophysiology data istypically analyzed using scripts that generate output files, including figures.Handling these results poses several challenges due to the complexity andinteractivity of the analysis process. These stem from the difficulty todiscern the analysis steps, parameters, and data flow from the results, makingknowledge transfer and findability challenging in collaborative settings.Provenance information tracks data lineage and processes applied to it, andprovenance capture during the execution of an analysis script can address thosechallenges. We present Alpaca (Automated Lightweight Provenance Capture), atool that captures fine-grained provenance information with minimal userintervention when running data analysis pipelines implemented in Pythonscripts. Alpaca records inputs, outputs, and function parameters and structuresinformation according to the W3C PROV standard. We demonstrate the tool using arealistic use case involving multichannel local field potential recordings of aneurophysiological experiment, highlighting how the tool makes result detailsknown in a standardized manner in order to address the challenges of theanalysis process. Ultimately, using Alpaca will help to represent resultsaccording to the FAIR principles, which will improve research reproducibilityand facilitate sharing the results of data analyses.

Facilitating the sharing of electrophysiology data analysis results through in-depth provenance capture