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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.16293 |
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| _version_ | 1866911522054209536 |
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| author | Borca, Cecilia Peña, Javier Jiménez Marckx, David Niemiec, Malgorzata Norella, Elisabetta Spadaro Urbaniak, Marta |
| author_facet | Borca, Cecilia Peña, Javier Jiménez Marckx, David Niemiec, Malgorzata Norella, Elisabetta Spadaro Urbaniak, Marta |
| contents | A 2021 study by the ECFA Early-Career Researchers Panel revealed that 71% of 334 respondents used open-source software tools in their instrumentation work, yet 70% reported receiving no training for these tools. In response, the Software and Machine Learning for Instrumentation group was formed in the ECFA Early-Career Researchers Panel to assess the accessibility and quality of training programs in machine learning and software for early-career researchers in experimental and applied physics. This group launched a new survey, reaching 174 participants. This report summarises the survey results in detail, and is intended to serve as a guiding document to improve the training programs that are available to early-career researchers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_16293 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Results of the analysis of a survey for young scientists on training quality in HEP instrumentation software and machine learning Borca, Cecilia Peña, Javier Jiménez Marckx, David Niemiec, Malgorzata Norella, Elisabetta Spadaro Urbaniak, Marta High Energy Physics - Experiment Software Engineering A 2021 study by the ECFA Early-Career Researchers Panel revealed that 71% of 334 respondents used open-source software tools in their instrumentation work, yet 70% reported receiving no training for these tools. In response, the Software and Machine Learning for Instrumentation group was formed in the ECFA Early-Career Researchers Panel to assess the accessibility and quality of training programs in machine learning and software for early-career researchers in experimental and applied physics. This group launched a new survey, reaching 174 participants. This report summarises the survey results in detail, and is intended to serve as a guiding document to improve the training programs that are available to early-career researchers. |
| title | Results of the analysis of a survey for young scientists on training quality in HEP instrumentation software and machine learning |
| topic | High Energy Physics - Experiment Software Engineering |
| url | https://arxiv.org/abs/2603.16293 |