
This work package aims to streamline the allocation of computational resources for metagenomic assemblies by analyzing input data characteristics and developing a machine learning model to predict resource requirements. To support this effort, EMBL-EBI has generously provided data on the computational demands—specifically peak memory usage—of assemblies generated using the MGnify pipeline.
Science, ELIXIR Nodes, MGnify, CoS, BFSP
Science, ELIXIR Nodes, MGnify, CoS, BFSP
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
