Minimal sufficient information about the scientific workflows to create reproducible experiment

The reproducibility of an in-silico experiment is a great challenge because of the parallel and distributed environment and the complexity of the scientific workflows. In order to solve such problems on one hand provenance data has to be captured about the dataflow, the ancestry of the results and the environment of the execution, on the other hand description data has to be collected from the scientist and stored about the essential details, the types and samples of input/output data, and the operation of the experiment. The ultimate goal of our work is to propose a minimal dataset for recording and reporting scientific workflow based experiment, which will facilitate the reproducibility of such experiments, the public repositories and enable to share and reuse the scientific results. One part of the dataset can be filled in manually by the scientist, certain part can be filled in automatically by the system and other part can be filled in from provenance data.