Andreou, Panayiotis ORCID: 0000-0002-6369-1094, Osmolovskiy, Artem, Hadjidemetriou, Panagiotis and Fdida, Serge (2024) Towards Trustworthy Experimental Replication in SLICES-RI. 2024 IFIP Networking Conference (IFIP Networking) . pp. 672-677.
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Official URL: https://doi.org/10.23919/ifipnetworking62109.2024....
Abstract
Replication is crucial for maintaining the credibility and integrity of scientific research and is one of Europe's key enablers for Open Science. Several challenges must be addressed to facilitate replication, including applying robust methodologies, holistic data sharing, and detailed data lineage and provenance to allow researchers to leverage insights and findings from prior investigations and introduce novel perspectives or solutions in their field. Research Infrastructures are an important catalyst towards addressing the replication “crisis” by enforcing data sharing by design/default, with appropriate protocols and procedures that provide visibility and transparency to the whole data journey, and compliance with national and international regulations. SLICES Research Infrastructure will construct one of Europe's most advanced scientific platforms in the field of digital sciences, promoting scientific research replication through sophisticated policies and services. SLICES-RI transcends traditional data sharing of common digital objects, such as datasets, services and tools, by introducing replication of complex digital objects, such as experimental workflows, which orchestrate advanced tools and services to perform sophisticated experiments in smart networks and systems. This paper presents the preliminary design of the SLICES-RI replication framework, providing insight into its internal structures and mechanisms, and demonstrating how experimental workflows can be replicated. The proposed SLICES Trustworthy Experimental Replication Framework (STEF) provides visibility into the experiment workflow and the underlying data journey, and demonstrates the potential integration of sophisticated indicators, such as explainability, interpretability, and safety, contributing to trustworthy experimentation.
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