Provenance in the context of workflows, both for the data they derive and for their
specification, is an essential component to allow for result reproducibility, sharing, and …

Reproducibility is widely considered to be an essential requirement of the scientific process.
However, a number of serious concerns have been raised recently, questioning whether …

An increasing number of researchers support reproducibility by including pointers to and
descriptions of datasets, software and methods in their publications. However, scientific …

Nowadays, more and more computer-based scientific experiments need to handle massive
amounts of data. Their data processing consists of multiple computational steps and …

Trust is an integral component in many kinds of human interaction, allowing people to act
under uncertainty and with the risk of negative consequences. For example, exchanging …

The problem of systematically capturing and managing provenance for computational tasks
has recently received significant attention because of its relevance to a wide range of …

Deep learning training typically starts with a random sampling initialization approach to set
the weights of trainable layers. Therefore, different and/or uncontrolled weight initialization …

Provenance, ie, the origin or source of something, is becoming an important concern, since it
offers the means to verify data products, to infer their quality, to analyse the processes that …

Modern scientific collaborations have opened up the opportunity to solve complex problems
that require both multidisciplinary expertise and large-scale computational experiments …

Abstract The first Provenance Challenge was set up in order to provide a forum for the
community to understand the capabilities of different provenance systems and the …