A collaborative semantic-based provenance management platform for reproducibility

Samuel, Sheeba; König‐Ries, Birgitta

doi:10.7717/peerj-cs.921

Cited by 6 publications

(3 citation statements)

References 45 publications

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“…Recently, CAESAR (CollAborative Environment for Scientific Analysis with Reproducibility) was proposed as a solution for the end-to-end description of provenance in scientific experiments ( Samuel and König-Ries, 2022a ). The overarching goal of CAESAR is to capture, query, and visualize the complete path of a scientific experiment, from the design to the results, while providing interoperability.…”

Section: Discussionmentioning

confidence: 99%

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

Köhler,

Ulianych,

Grün

et al. 2024

eNeuro

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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.

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Section: Discussionmentioning

confidence: 99%

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

Köhler,

Ulianych,

Grün

et al. 2024

eNeuro

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show abstract

“…Recently, Samuel & König-Ries (2022) presented a collaborative framework for the management of scientific experiments in academic publications named CAESAR, which represents a collaborative environment for scientific analysis with reproducibility. It allows scientists to capture, manage, query, and visualize the complete path of scientific experiments by including both types of data: computational and non-computational.…”

Section: Related Workmentioning

confidence: 99%

“…The proposed framework was applied and tested on research projects in the microscopic area. The final product is anticipated to assist the scientific community to track the complete path of the provenance of the results described in scientific publications ( Samuel & König-Ries, 2022 ).…”

Section: Related Workmentioning

confidence: 99%

AI-SPedia: a novel ontology to evaluate the impact of research in the field of artificial intelligence

Maatouk

2022

PeerJ Computer Science

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Background Sharing knowledge such as resources, research results, and scholarly documents, is of key importance to improving collaboration between researchers worldwide. Research results from the field of artificial intelligence (AI) are vital to share because of the extensive applicability of AI to several other fields of research. This has led to a significant increase in the number of AI publications over the past decade. The metadata of AI publications, including bibliometrics and altmetrics indicators, can be accessed by searching familiar bibliographical databases such as Web of Science (WoS), which enables the impact of research to be evaluated and identify rising researchers and trending topics in the field of AI. Problem description In general, bibliographical databases have two limitations in terms of the type and form of metadata we aim to improve. First, most bibliographical databases, such as WoS, are more concerned with bibliometric indicators and do not offer a wide range of altmetric indicators to complement traditional bibliometric indicators. Second, the traditional format in which data is downloaded from bibliographical databases limits users to keyword-based searches without considering the semantics of the data. Proposed solution To overcome these limitations, we developed a repository, named AI-SPedia. The repository contains semantic knowledge of scientific publications concerned with AI and considers both the bibliometric and altmetric indicators. Moreover, it uses semantic web technology to produce and store data to enable semantic-based searches. Furthermore, we devised related competency questions to be answered by posing smart queries against the AI-SPedia datasets. Results The results revealed that AI-SPedia can evaluate the impact of AI research by exploiting knowledge that is not explicitly mentioned but extracted using the power of semantics. Moreover, a simple analysis was performed based on the answered questions to help make research policy decisions in the AI domain. The end product, AI-SPedia, is considered the first attempt to evaluate the impacts of AI scientific publications using both bibliometric and altmetric indicators and the power of semantic web technology.

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A modular ontology modeling approach to developing digital product passports to promote circular economy in the built environment

Kebede,

Moscati,

Tan

et al. 2024

Sustainable Production and Consumption

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A collaborative semantic-based provenance management platform for reproducibility

Cited by 6 publications

References 45 publications

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

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

AI-SPedia: a novel ontology to evaluate the impact of research in the field of artificial intelligence

A modular ontology modeling approach to developing digital product passports to promote circular economy in the built environment

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