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This archive provides scripts related to the following publication: V. Tollenaar, H. Zekollari, S. Lhermitte, D. Tax, V. Debaille, S. Goderis, P. Claeys, F. Pattyn, Unexplored Antarctic meteorite collection sites revealed through machine learning. Science Advances 8, eabj8138 (2022). DOI: 10.1126/sciadv.abj8138 Contact: Veronica Tollenaar, Veronica.Tollenaar@ulb.be Users should cite the original publication when using all or part of the code. About the scripts: it includes the code to generate all results presented in the corresponding publication, as well an additional, user-oriented variant of the code that allows to directly classify a defined set of positive and unlabeled data as positive and negative observations. Further information is provided in the README.md file. Datasets needed to perform the analyses are provided at https://zenodo.org/record/5749752, or at external locations indicated in the references section of the publication.
| 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 |
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