🤖 AI Summary
This study addresses the widespread lack of systematic training in instrumentation software and machine learning tools among early-career researchers in high-energy physics, a gap that significantly hinders their research efficiency and professional development. Focusing specifically on this cohort’s practical needs and deficiencies regarding open-source software and machine learning education, the project collected feedback from 174 early-career researchers through a structured survey and employed statistical analysis to evaluate the accessibility and quality of existing training programs. The findings reveal that approximately 70% of respondents have received no such training. These results provide empirical evidence to inform the design of targeted, effective training frameworks aimed at enhancing the computational and analytical competencies of young scientists in the field.
📝 Abstract
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.