TY - GEN A1 - Diercks, Philipp A1 - Gläser, D. A1 - Unger, Jörg F. A1 - Crusoe, M. R. A1 - Huber, S. T1 - BAMresearch/NFDI4IngScientificWorkflowRequirements: Initial version N2 - Software-driven scientific workflows are often characterized by a complex interplay of various pieces of software executed in a particular order. The output of a computational step may serve as input to a subsequent computation, which requires them to be processed sequentially with a proper mapping of outputs to inputs. Other computations are independent of each other and can be executed in parallel. Thus, one of the main tasks of a workflow tool is a proper and efficient scheduling of the individual processing steps. Each processing step, just as the workflow itself, typically processes some input and produces output data. Apart from changing the input data to operate on, processing steps can usually be configured by a set of parameters to change their behavior. Moreover, the behavior of a processing step is determined by its source code and/or executable binaries/packages that are called within it. Beyond this, the computation environment not only has a significant influence on its behavior, but is also crucial in order for the processing step to work at all. The environment includes the versions of the interpreters or compilers, as well as all third-party libraries and packages that contribute to the computations carried out in a processing step. KW - Zenodo KW - 7790634 PY - 2023 DO - https://doi.org/10.5281/zenodo.7790633 PB - Zenodo CY - Geneva AN - OPUS4-59687 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Diercks, Philipp A1 - Gläser, D. A1 - Lünsdorf, O. A1 - Selzer, M. A1 - Flemisch, B. A1 - Unger, Jörg F. T1 - Evaluation of tools for describing, reproducing and reusing scientific workflows N2 - In the field of computational science and engineering, workflows often entail the application of various software, for instance, for simulation or pre- and postprocessing. Typically, these components have to be combined in arbitrarily complex workflows to address a specific research question. In order for peer researchers to understand, reproduce and (re)use the findings of a scientific publication, several challenges have to be addressed. For instance, the employed workflow has to be automated and information on all used software must be available for a reproduction of the results. Moreover, the results must be traceable and the workflow documented and readable to allow for external verification and greater trust. In this paper, existing workflow management systems (WfMSs) are discussed regarding their suitability for describing, reproducing and reusing scientific workflows. To this end, a set of general requirements for WfMSswere deduced from user stories that we deem relevant in the domain of computational science and engineering. On the basis of an exemplary workflow implementation, publicly hosted at GitHub (https:// this http URL), a selection of different WfMSs is compared with respect to these requirements, to support fellow scientists in identifying the WfMSs that best suit their requirements. KW - FAIR KW - Reproducibility, scientific workflow KW - Tool comparison KW - Workflow management PY - 2024 UR - https://arxiv.org/abs/2211.06429 DO - https://doi.org/10.48550/arXiv.2211.06429 PB - Arxiv; Cornell Tech CY - New York, NY AN - OPUS4-59804 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -