Abstract
In science, a key requirement for conducting a scientific experiment is that it must be reproducible, meaning that a different team should be able to carry out the same or similar experiment and obtain similar results. For this to be possible, it is important to maintain the traceability of the end-to-end workflow used to conduct the experiment. However, partners may be responsible for specific process segments in large projects involving multiple laboratories or collaborating partners. As a result, maintaining the provenance of results requires kee** track of datasets and computing tools at each step of the workflow in a cross-partner fashion. While centralized solutions exist for tracking scientific workflows, they can sometimes be met with distrust. Blockchain-based scientific workflow management can help overcome this issue by offering integrity, transparency, and accessibility. This chapter aims to provide a better understanding of blockchain uses for scientific workflow management. To do so, it examines the existing literature on the topic, discusses the associated opportunities and challenges, and highlights future research topics.
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Notes
- 1.
The Bloom filter is a probabilistic data structure that efficiently tests the membership of an element in a set, providing a compact representation with potential false positives but no false negatives [57].
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Henry, T., Tucci-Piergiovanni, S. (2024). Scientific Workflows Management with Blockchain: A Survey. In: El Madhoun, N., Dionysiou, I., Bertin, E. (eds) Blockchain and Smart-Contract Technologies for Innovative Applications. Springer, Cham. https://doi.org/10.1007/978-3-031-50028-2_5
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