Firstly, each sub-dataset in the entire training dataset is singled out, in turn, as the testing sample followed by the remaining N-1 sub-datasets that constitute the new training dataset; this workflow continues until all of the sub-datasets in the complete training dataset are traversed.
In this way, all datasets and workflow components are clearly defined (e.g., metadata, parameters) and organized within their individual categories.
These variants were then collapsed to genes enriched for variants for each dataset (see workflow in Figure S4).
Using the human RNA-Seq dataset, this workflow produced a splice-junction database comprising approximately 33,000 splice-junction polypeptide entries.
Using the human RNA-Seq dataset, this workflow produced a SAP database comprising 6,168 SAP polypeptide entries, which was combined with the Ensembl reference proteome.
It improves support for metacomputing on Galaxy entities by providing higher-level functionality and allowing users to more easily create programs to explore, query and create Galaxy datasets and workflows.
The OBIS‐SEAMAP web site offers advanced tools including representation of oceanographic variables, spatially and temporarily scalable animation of tagged animal movements, expanded metadata search for acoustic datasets and online workflow for viewing and matching photo‐identification images.
This section performs experiments on some workflow datasets from different domains, and shows that a universal function to model workflow classification can be applied based on SFA and distributional semantics.
For example, developers can constrain all datasets within a workflow run to have a specific metadata value (for instance, specific genome assembly).
The panels for the datasets and the workflow display essentially the same files, but while the former provides a typical folder view, the latter shows the relationships between the files.
