
The code that creates these correlated-k files is located here in PICASO. In short we use the double-gauss correlated-k method to compute these. The structure of the python file can be seen in the code provided. The code snippet provided for reproducibility relies on our full database of high resolution line-by-line opacities. Lack of access will hinder a user to fully reproduce these files. Our full database of high resolution opacities are in the process of being made downloadable via science.data.nasa.gov/opacities . Stay tuned! Once downloaded, they should be placed in `$picaso_refdata/opacities/resortrebin` in accordance with this tutorial. For a full descritpion of what line data was used to compute these see Mang et al. 2025. We recommend downloading the all the files
| 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). | 1 | |
| 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. | Top 10% | |
| 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 |
