
This data contains information that is too large to be included in our GitHub repository: https://github.com/slds-lmu/paper_2023_ci_for_ge.After downloading the data you need to first extract/decompress it.The files are:* results - Result datasets that need to be moved into the the GitHub repository in order to reproduce the analysis from the paper. Both the raw and processed datasets are contained.* figures-granular - additional figures on a very granular levelThe folders also contain READMEs describing the datasets and figures.If you want to work with the datasets (all in .rds format) but don't want to work in R, you can simply load them in R (using readRDS()) and then convert them e.g. to csv using the write.csv function.
Machine learning, Statistics and probability
Machine learning, Statistics and probability
| 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 |
