Drawing inspiration from previously proposed metrics such as the U-index or the data-index, we exploit key SciLake-specific outputs from the value-added services—article segmentation, citance analysis and research artefact analysis—to enhance the notion and computation of impact for research objects like software and datasets. This approach  incorporates not only formal citations received by a given resource and/or the publication presenting it, but also informal mentions found in the body of other publications. Additionally, it accounts for the context of these mentions, such as the section where they appear, as well as their intent, to provide a more comprehensive understanding of impact.