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handle: 1942/40475
The dataset is based on the paper: Hoang-Son Pham, Hanne Poelmans and Amr Ali-Eldin ‘’A metadata-based approach for research discipline prediction using machine learning techniques and distance metrics’’, IEEE Access (2023). The dataset includes: 1. a list of project metadata extracted from FRIS portal 2. a list of VODS disciplines 3. a distance matrix * Kindly refer to our paper for more details on the dataset. https://ieeexplore.ieee.org/document/10156853
Machine Learning, Metadata, Interdisciplinarity, Distance Metrics, Research Disciplines Prediction, Research Information Systems (RIS)
Machine Learning, Metadata, Interdisciplinarity, Distance Metrics, Research Disciplines Prediction, Research Information Systems (RIS)
| 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 | 30 | |
| downloads | 2 |

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