Workflow scheduling is one of the challenging issues in emerging trends of the distributed
environment that focuses on satisfying various quality of service (QoS) constraints. The …

With the development of new experimental technologies, biologists are faced with an
avalanche of data to be computationally analyzed for scientific advancements and …

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

The Taverna workflow tool suite (http://www. taverna. org. uk) is designed to combine
distributed Web Services and/or local tools into complex analysis pipelines. These pipelines …

A few years after Google announced that their 'Knowledge Graph'would have allowed
searching for things, not strings, 1 knowledge graphs start entering information retrieval …

Scientific workflows consisting of a high number of interdependent tasks represent an
important class of complex scientific applications. Recently, a new type of serverless …

Scientific workflows are a popular mechanism for specifying and automating data-driven in
silico experiments. A significant aspect of their value lies in their potential to be reused. Once …

Background The automation of data analysis in the form of scientific workflows has become
a widely adopted practice in many fields of research. Computationally driven data-intensive …

Data curation is the process of making a dataset fit-for-use and archivable. It is critical to data-
intensive science because it makes complex data pipelines possible, studies reproducible …

Workflow engines are commonly used to orchestrate large-scale scientific computations
such as, but not limited to weather, climate, natural disasters, food safety, and territorial …