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| 001 | 1046208 | ||
| 005 | 20251024202103.0 | ||
| 024 | 7 | _ | |a 10.5281/ZENODO.16736244 |2 doi |
| 037 | _ | _ | |a FZJ-2025-03744 |
| 100 | 1 | _ | |a Köhler, Cristiano |0 P:(DE-Juel1)180365 |b 0 |e Corresponding author |u fzj |
| 111 | 2 | _ | |a 2nd Conference on Research Data Infrastructure (CoRDI) |g CoRDI 2025 |c Aachen |d 2025-08-26 - 2025-08-28 |w Germany |
| 245 | _ | _ | |a Supporting FAIR Principles in Data Analysis Through Semantically-Enriched Provenance |
| 260 | _ | _ | |c 2025 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
| 336 | 7 | _ | |a CONFERENCE_POSTER |2 ORCID |
| 336 | 7 | _ | |a Output Types/Conference Poster |2 DataCite |
| 336 | 7 | _ | |a Poster |b poster |m poster |0 PUB:(DE-HGF)24 |s 1761310213_11652 |2 PUB:(DE-HGF) |x After Call |
| 520 | _ | _ | |a Scripts that read input datasets and generate result files are frequently used to construct computational workflows for the analysis of neural activity data obtained by electrophysiology recordings [1]. The increased complexity of datasets due to recent advances in recording techniques are also associated with increased computational costs for executing those workflows and generating analysis results. Increasing the FAIR-ness [2] of electrophysiology data analysis results will promote the efficient sharing of results among collaborators or the research community. With increased findability, a collaborator can easily access specific results produced by complex analysis without rerunning costly computations. If results are more accessible, they are transparent and can be reused across platforms and organizations. The increased interoperability facilitates understanding specific analysis results despite their generation by heterogeneous workflows, improving collaboration and allowing the comparison of different analysis results. Finally, reusable results allow researchers to build on previous analyses, conserving resources and speeding scientific discovery without repeating complex computations. In this work, we investigate an approach for describing the results generated by electrophysiology data analysis workflows in order to increase the FAIR-ness of results. We aim to go beyond providing source codes and free-text descriptions to facilitate querying, introspection and reuse of the results by capturing and evaluating run-time provenance information. We highlight several challenges within the workflows that hinder the creation of such descriptions, including the iterative characteristics of conventional analysis scenarios, the absence of technical and semantic standardization, and the presence of distinct software implementations for existing analysis methods [3]. To address those challenges, we first implemented Alpaca (Automatic Lightweight Provenance Capture) as a framework to generate machine-readable descriptions of the workflow execution with minimal user intervention [4]. Alpaca produces a detailed provenance record of the atomic analysis steps represented by Python functions within workflow scripts, that are serialized together with analysis results using the W3C PROV standard [5]. Complementing the approach, the provenance information can be enriched with semantic information provided by ontologies. For workflows analyzing electrophysiolgy datasets with recorded neural activity, we implemented the Neuroelectrophysiology Analysis Ontology (NEAO) to provide a unified vocabulary to standardize the descriptions of the methods involved in the analysis of extracellular electrophysiology data [6]. We demonstrate how using NEAO to enrich the provenance captured by Alpaca helps in describing analysis results produced by complex real-world workflows for analyzing and comparing heterogeneous data based on Elephant [7] and Cobrawap [8]. We highlight how the approach facilitates obtaining insights on the results (e.g., using knowledge graphs), thereby promoting the FAIR principles and facilitating sharing. We also discuss extensions to other computational workflows (e.g., neural simulation) and how the proposed approach may help to also improve representing their results according to the FAIR principles. REFERENCES [1] M. Denker et al., “Reproducibility and efficiency in handling complex neurophysiological data,” Neuroforum, vol. 27, no. 1, pp. 27–34, Feb, 2021, doi: https://doi.org/10.1515/nf-2020-0041 [2] M. D. Wilkinson et al., “The FAIR Guiding Principles for scientific data management and stewardship,” Sci Data, vol. 3, p. 160018, Mar, 2016, doi: https://doi.org/10.1038/sdata.2016.18 [3] V. A. Unakafova and A. Gail, “Comparing Open-Source Toolboxes for Processing and Analysis of Spike and Local Field Potentials Data,” Front Neuroinform, vol. 13, p. 57, Jul, 2019, doi: https://doi.org/10.3389/fninf.2019.00057 [4] C. A. Köhler et al., “Facilitating the Sharing of Electrophysiology Data Analysis Results Through In-Depth Provenance Capture,” eNeuro, vol. 11, no. 6, p. ENEURO.0476-23.2024, May, 2024, doi: https://doi.org/10.1523/ENEURO.0476-23.2024 [5] P. Groth and L. Moreau. “An Overview of the PROV Family of Documents.” PROV-Overview. https://www.w3.org/TR/prov-overview (accessed on 28 April 2025) [6] C. A. Köhler, S. Grün, and M. Denker. “Improving data sharing and knowledge transfer via the Neuroelectrophysiology Analysis Ontology (NEAO),” arXiv:2412.05021, Dec, 2024, doi: https://doi.org/10.48550/arXiv.2412.05021 [7] R. Gutzen et al., “A modular and adaptable analysis pipeline to compare slow cerebral rhythms across heterogeneous datasets,” Cell Rep Methods, vol. 4, no. 1, p. 100681, Jan, 2024, doi: https://doi.org/10.1016/j.crmeth.2023.100681 |
| 536 | _ | _ | |a 5235 - Digitization of Neuroscience and User-Community Building (POF4-523) |0 G:(DE-HGF)POF4-5235 |c POF4-523 |f POF IV |x 0 |
| 536 | _ | _ | |a 5231 - Neuroscientific Foundations (POF4-523) |0 G:(DE-HGF)POF4-5231 |c POF4-523 |f POF IV |x 1 |
| 536 | _ | _ | |a HDS LEE - Helmholtz School for Data Science in Life, Earth and Energy (HDS LEE) (HDS-LEE-20190612) |0 G:(DE-Juel1)HDS-LEE-20190612 |c HDS-LEE-20190612 |x 2 |
| 536 | _ | _ | |a HBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539) |0 G:(EU-Grant)945539 |c 945539 |f H2020-SGA-FETFLAG-HBP-2019 |x 3 |
| 536 | _ | _ | |a EBRAINS 2.0 - EBRAINS 2.0: A Research Infrastructure to Advance Neuroscience and Brain Health (101147319) |0 G:(EU-Grant)101147319 |c 101147319 |f HORIZON-INFRA-2022-SERV-B-01 |x 4 |
| 536 | _ | _ | |a JL SMHB - Joint Lab Supercomputing and Modeling for the Human Brain (JL SMHB-2021-2027) |0 G:(DE-Juel1)JL SMHB-2021-2027 |c JL SMHB-2021-2027 |x 5 |
| 536 | _ | _ | |a Algorithms of Adaptive Behavior and their Neuronal Implementation in Health and Disease (iBehave-20220812) |0 G:(DE-Juel-1)iBehave-20220812 |c iBehave-20220812 |x 6 |
| 588 | _ | _ | |a Dataset connected to DataCite |
| 650 | _ | 7 | |a FAIR |2 Other |
| 650 | _ | 7 | |a electrophysiology |2 Other |
| 650 | _ | 7 | |a data analysis |2 Other |
| 650 | _ | 7 | |a computational workflow |2 Other |
| 650 | _ | 7 | |a Python |2 Other |
| 650 | _ | 7 | |a provenance |2 Other |
| 650 | _ | 7 | |a ontology |2 Other |
| 700 | 1 | _ | |a Grün, Sonja |0 P:(DE-Juel1)144168 |b 1 |u fzj |
| 700 | 1 | _ | |a Denker, Michael |0 P:(DE-Juel1)144807 |b 2 |
| 773 | _ | _ | |a 10.5281/ZENODO.16736244 |
| 856 | 4 | _ | |u https://doi.org/10.5281/zenodo.16736244 |
| 909 | C | O | |o oai:juser.fz-juelich.de:1046208 |p openaire |p VDB |p ec_fundedresources |
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| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Natural, Artificial and Cognitive Information Processing |1 G:(DE-HGF)POF4-520 |0 G:(DE-HGF)POF4-523 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Neuromorphic Computing and Network Dynamics |9 G:(DE-HGF)POF4-5235 |x 0 |
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| 914 | 1 | _ | |y 2025 |
| 920 | 1 | _ | |0 I:(DE-Juel1)IAS-6-20130828 |k IAS-6 |l Computational and Systems Neuroscience |x 0 |
| 920 | 1 | _ | |0 I:(DE-Juel1)INM-10-20170113 |k INM-10 |l Jara-Institut Brain structure-function relationships |x 1 |
| 980 | _ | _ | |a poster |
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| 980 | _ | _ | |a UNRESTRICTED |
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