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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2410.09055 |
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| _version_ | 1866914970408583168 |
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| author | Janssens, Bram Pappalardo, Luca De Bock, Jelle Bogaert, Matthias Verstockt, Steven |
| author_facet | Janssens, Bram Pappalardo, Luca De Bock, Jelle Bogaert, Matthias Verstockt, Steven |
| contents | The field of cycling analytics has only recently started to develop due to limited access to open data sources. Accordingly, research and data sources are very divergent, with large differences in information used across studies. To improve this, and facilitate further research in the field, we propose the publication of a data set which links thousands of professional race results from the period 2017-2023 to detailed geographic information about the courses, an essential aspect in road cycling analytics. Initial use cases are proposed, showcasing the usefulness in linking these two data sources. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_09055 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Geospatial Road Cycling Race Results Data Set Janssens, Bram Pappalardo, Luca De Bock, Jelle Bogaert, Matthias Verstockt, Steven Computers and Society Machine Learning The field of cycling analytics has only recently started to develop due to limited access to open data sources. Accordingly, research and data sources are very divergent, with large differences in information used across studies. To improve this, and facilitate further research in the field, we propose the publication of a data set which links thousands of professional race results from the period 2017-2023 to detailed geographic information about the courses, an essential aspect in road cycling analytics. Initial use cases are proposed, showcasing the usefulness in linking these two data sources. |
| title | Geospatial Road Cycling Race Results Data Set |
| topic | Computers and Society Machine Learning |
| url | https://arxiv.org/abs/2410.09055 |