
This is a dataset used as a training set for "AI4Life Microscopy Supervised Denoising Challenge 2025" It is based on subsets of two datasets: Guillaume Jacquemet. (2021). Noisy nuclei dataset for testing deep learning-based denoising tools. Zenodo. https://doi.org/10.5281/zenodo.5750174 Image set BBBC006v1 from the Broad Bioimage Benchmark Collection [Ljosa et al., Nature Methods, 2012]. https://bbbc.broadinstitute.org/BBBC006 For more details about the original datasets, please consult the links above. For the details about this subset and its organization, please see https://ai4life-mdc25.grand-challenge.org/leaderboard-3/AI4Life has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement number 101057970. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.
| 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 |
