
doi: 10.5281/zenodo.16883619 , 10.5281/zenodo.16883563 , 10.5281/zenodo.19654811 , 10.5281/zenodo.17834540 , 10.5281/zenodo.14337953 , 10.5281/zenodo.14042894 , 10.5281/zenodo.6808546 , 10.5281/zenodo.17835465 , 10.5281/zenodo.19954489 , 10.5281/zenodo.14041859 , 10.5281/zenodo.16882603 , 10.5281/zenodo.14590487 , 10.5281/zenodo.20005090 , 10.5281/zenodo.15586596 , 10.5281/zenodo.14041980
doi: 10.5281/zenodo.16883619 , 10.5281/zenodo.16883563 , 10.5281/zenodo.19654811 , 10.5281/zenodo.17834540 , 10.5281/zenodo.14337953 , 10.5281/zenodo.14042894 , 10.5281/zenodo.6808546 , 10.5281/zenodo.17835465 , 10.5281/zenodo.19954489 , 10.5281/zenodo.14041859 , 10.5281/zenodo.16882603 , 10.5281/zenodo.14590487 , 10.5281/zenodo.20005090 , 10.5281/zenodo.15586596 , 10.5281/zenodo.14041980
The Super Resolution for Renewable Resource Data (sup3r) software uses generative adversarial networks to create synthetic high-resolution wind and solar spatiotemporal data from coarse low-resolution inputs.
If you use this software, please cite it using the metadata from this file.
tensorflow, climate-data, generative-adversarial-network, solar-energy, wind-energy, machine-learning, climate-change, deep-learning, renewable-energy
tensorflow, climate-data, generative-adversarial-network, solar-energy, wind-energy, machine-learning, climate-change, deep-learning, renewable-energy
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| 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 |
