Journal Article

FZJ-2024-03334

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Facilitating the Sharing of Electrophysiology Data Analysis Results Through In-Depth Provenance Capture

Köhler, C. (Corresponding author)FZJ*RWTH* ; Ulianych, D.FZJ* ; Grün, S.FZJ*RWTH* ; Decker, S.RWTH* ; Denker, M.FZJ*

2024
Society for Neuroscience Washington, DC

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Please use a persistent id in citations: doi:10.1523/ENEURO.0476-23.2024  doi:10.34734/FZJ-2024-03334

Abstract: Scientific research demands reproducibility and transparency, particularly in data-intensive fields like electrophysiology. Electrophysiology data are 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.

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Contributing Institute(s):

1. Computational and Systems Neuroscience (IAS-6)
2. Jara-Institut Brain structure-function relationships (INM-10)

Research Program(s):

1. 5235 - Digitization of Neuroscience and User-Community Building (POF4-523) (POF4-523)
2. HBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539) (945539)
3. HBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907) (785907)
4. HDS LEE - Helmholtz School for Data Science in Life, Earth and Energy (HDS LEE) (HDS-LEE-20190612) (HDS-LEE-20190612)
5. Algorithms of Adaptive Behavior and their Neuronal Implementation in Health and Disease (iBehave-20220812) (iBehave-20220812)
6. JL SMHB - Joint Lab Supercomputing and Modeling for the Human Brain (JL SMHB-2021-2027) (JL SMHB-2021-2027)
7. HAF - Helmholtz Analytics Framework (ZT-I-0003) (ZT-I-0003)
8. DFG project 491111487 - Open-Access-Publikationskosten / 2022 - 2024 / Forschungszentrum Jülich (OAPKFZJ) (491111487) (491111487)

Appears in the scientific report 2024

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Database coverage:
; ; ; ; Article Processing Charges ; BIOSIS Previews ; Biological Abstracts ; Clarivate Analytics Master Journal List ; Current Contents - Life Sciences ; DOAJ Seal ; Essential Science Indicators ; Fees ; IF < 5 ; JCR ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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