Learning and teaching biological data science in the Bioconductor community
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arXiv
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| Autores principales: | , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| author | Drnevich, Jenny Tan, Frederick J. Almeida-Silva, Fabricio Castelo, Robert Culhane, Aedin C. Davis, Sean Doyle, Maria A. Geistlinger, Ludwig Ghazi, Andrew R. Holmes, Susan Lahti, Leo Mahmoud, Alexandru Nishida, Kozo Ramos, Marcel Rue-Albrecht, Kevin Shih, David J. H. Gatto, Laurent Soneson, Charlotte |
| author_facet | Drnevich, Jenny Tan, Frederick J. Almeida-Silva, Fabricio Castelo, Robert Culhane, Aedin C. Davis, Sean Doyle, Maria A. Geistlinger, Ludwig Ghazi, Andrew R. Holmes, Susan Lahti, Leo Mahmoud, Alexandru Nishida, Kozo Ramos, Marcel Rue-Albrecht, Kevin Shih, David J. H. Gatto, Laurent Soneson, Charlotte |
| contents | Modern biological research is increasingly data-intensive, leading to a growing demand for effective training in biological data science. In this article, we provide an overview of key resources and best practices available within the Bioconductor project - an open-source software community focused on omics data analysis. This guide serves as a valuable reference for both learners and educators in the field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_01351 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning and teaching biological data science in the Bioconductor community Drnevich, Jenny Tan, Frederick J. Almeida-Silva, Fabricio Castelo, Robert Culhane, Aedin C. Davis, Sean Doyle, Maria A. Geistlinger, Ludwig Ghazi, Andrew R. Holmes, Susan Lahti, Leo Mahmoud, Alexandru Nishida, Kozo Ramos, Marcel Rue-Albrecht, Kevin Shih, David J. H. Gatto, Laurent Soneson, Charlotte Computers and Society Other Quantitative Biology Applications 97K80 K.3.2 Modern biological research is increasingly data-intensive, leading to a growing demand for effective training in biological data science. In this article, we provide an overview of key resources and best practices available within the Bioconductor project - an open-source software community focused on omics data analysis. This guide serves as a valuable reference for both learners and educators in the field. |
| title | Learning and teaching biological data science in the Bioconductor community |
| topic | Computers and Society Other Quantitative Biology Applications 97K80 K.3.2 |
| url | https://arxiv.org/abs/2410.01351 |