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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2509.09601 |
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| _version_ | 1866908533336834048 |
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| author | Delorme, Stéphane Mach, Leon Paszkiewicz, Hubert Ruiz, Richard |
| author_facet | Delorme, Stéphane Mach, Leon Paszkiewicz, Hubert Ruiz, Richard |
| contents | Extracting information from big data sets, both real and simulated, is a modern hallmark of the physical sciences. In practice, students face barriers to learning ``Big Data'' methods in undergraduate physics and astronomy curricula. As an attempt to alleviate some of these challenges, we present a simple, farm-to-table data analysis pipeline that can collect, process, and plot data from the 800k entries common to the arXiv preprint repository and the bibliographical database inSpireHEP. The pipeline employs contemporary research practices and can be implemented using open-sourced Python libraries common to undergraduate courses on Scientific Computing. To support the use such pipelines in classroom contexts, we make public an example implementation, authored by two undergraduate physics students, that runs on off-the-shelf laptops. For advanced students, we discuss applications of the pipeline, including for online DAQ monitoring and commercialization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_09601 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Are arXiv submissions on Wednesday better cited? Introducing Big Data methods in undergraduate courses on scientific computing Delorme, Stéphane Mach, Leon Paszkiewicz, Hubert Ruiz, Richard Physics Education High Energy Physics - Experiment Computational Physics Extracting information from big data sets, both real and simulated, is a modern hallmark of the physical sciences. In practice, students face barriers to learning ``Big Data'' methods in undergraduate physics and astronomy curricula. As an attempt to alleviate some of these challenges, we present a simple, farm-to-table data analysis pipeline that can collect, process, and plot data from the 800k entries common to the arXiv preprint repository and the bibliographical database inSpireHEP. The pipeline employs contemporary research practices and can be implemented using open-sourced Python libraries common to undergraduate courses on Scientific Computing. To support the use such pipelines in classroom contexts, we make public an example implementation, authored by two undergraduate physics students, that runs on off-the-shelf laptops. For advanced students, we discuss applications of the pipeline, including for online DAQ monitoring and commercialization. |
| title | Are arXiv submissions on Wednesday better cited? Introducing Big Data methods in undergraduate courses on scientific computing |
| topic | Physics Education High Energy Physics - Experiment Computational Physics |
| url | https://arxiv.org/abs/2509.09601 |