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OAGT is a paper topic dataset consisting of 6942930 records which comprise various scientific publication attributes like abstracts, titles, keywords, publication years, venues, etc. The last two fields of each record are the topic id from a taxonomy of 27 topics created from the entire collection and the 20 most significant topic words. Each dataset record (sample) is stored as a JSON line in the text file. The data is derived from OAG data collection (https://aminer.org/open-academic-graph) which was released under ODC-BY license. This data (OAGT Paper Topic Dataset) is released under CC-BY license (https://creativecommons.org/licenses/by/4.0/). If using it, please cite the following paper: Erion Çano, Benjamin Roth: Topic Segmentation of Research Article Collections. ArXiv 2022, CoRR abs/2205.11249, https://doi.org/10.48550/arXiv.2205.11249
research articles, topic segmentation, keyword generation, multitopic dataset, research resources
research articles, topic segmentation, keyword generation, multitopic dataset, research resources
| 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 |
| views | 98 | |
| downloads | 14 |

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