Sudhir Malik, Samuel Meehan, Kilian Lieret, Meirin Oan Evans, Michel H. Villanueva, Daniel S. Katz, Graeme A. Stewart, Peter Elmer, Sizar Aziz, Matthew Bellis, … 展开作者
Sudhir Malik, Samuel Meehan, Kilian Lieret, Meirin Oan Evans, Michel H. Villanueva, Daniel S. Katz, Graeme A. Stewart, Peter Elmer, Sizar Aziz, Matthew Bellis, Riccardo Maria Bianchi, Gianluca Bianco, Johan Sebastian Bonilla, Angela Burger, Jackson Burzynski, David Chamont, Matthew Feickert, Philipp Gadow, Bernhard Manfred Gruber, Daniel Guest, Stephan Hageboeck, Lukas Heinrich, Maximilian M. Horzela, Marc Huwiler, Clemens Lange, Konstantin Lehmann, Ke Li, Devdatta Majumder, Judita Mamužić, Kevin Nelson, Robin Newhouse, Emery Nibigira, Scarlet Norberg, Arturo Sánchez Pineda, Mason Proffitt, Brendan Regnery, Amber Roepe, Stefan Roiser, Henry Schreiner, Oksana Shadura, Giordon Stark, Stephen Nicholas Swatman, Savannah Thais, Andrea Valassi, Stefan Wunsch, David Yakobovitch, Siqi Yuan
备注：
For CHEP 2021 conference,sent for publication to CSBS Springer
Long term sustainability of the high energy physics (HEP) research software ecosystem is essential for the field. With upgrades and new facilities coming online throughout the 2020s this will only become increasingly relevant throughout this decade. Meeting this sustainability challenge requires a workforce with a combination of HEP domain knowledge and advanced software skills. The required software skills fall into three broad groups. The first is fundamental and generic software engineering (e.g. Unix, version control,C++, continuous integration). The second is knowledge of domain specific HEP packages and practices (e.g., the ROOT data format and analysis framework). The third is more advanced knowledge involving more specialized techniques. These include parallel programming, machine learning and data science tools, and techniques to preserve software projects at all scales. This paper dis-cusses the collective software training program in HEP and its activities led by the HEP Software Foundation (HSF) and the Institute for Research and Innovation in Software in HEP (IRIS-HEP). The program equips participants with an array of software skills that serve as ingredients from which solutions to the computing challenges of HEP can be formed. Beyond serving the community by ensuring that members are able to pursue research goals, this program serves individuals by providing intellectual capital and transferable skills that are becoming increasingly important to careers in the realm of software and computing, whether inside or outside HEP
Results of the analysis of a survey for young scientists on training quality in HEP instrumentation software and machine learning
University of Torino and INFN, Turin（都灵大学和意大利国家核物理研究所）；Italy（巴塞罗那高等物理研究所）；Institut de Física d’Altes Energies, Barcelona（根特大学）；Spain（波兰科学院亨利克·涅沃德尼茨基核物理研究所）；Ghent University, Ghent（热那亚大学和意大利国家核物理研究所）；Belgium（卡托维兹西里西亚大学）；Henryk Niewodniczanski Institute of Nuclear Physics Polish Academy of Sciences, Kraków；Poland；University of Genoa and INFN, Genoa；Italy；University of Silesia in Katowice, Katowice；Poland
Brown University（布朗大学）；Columbia University（哥伦比亚大学）；University of California San Diego（加州大学圣地亚哥分校）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；California Institute of Technology（加州理工学院）；Fermi National Accelerator Laboratory（费米国家加速器实验室）；Massachusetts Institute of Technology（麻省理工学院）；University of Washington（华盛顿大学）；Universität Hamburg（汉堡大学）；European Organization for Nuclear Research（欧洲核子研究组织）；Purdue University（普渡大学）；University of Puerto Rico Mayagüez（波多黎各大学马亚圭斯分校）；University of Colorado Boulder（科罗拉多大学博尔德分校）；SLAC National Accelerator Laboratory（SLAC国家加速器实验室）；Princeton University（普林斯顿大学）；Laboratório de Instrumentação e Física Experimental de Partículas (LIP)（粒子仪器与实验物理实验室）
CelloAI Benchmarks: Toward Repeatable Evaluation of AI Assistants
Brookhaven National Laboratory（布鲁克海文国家实验室）；Stony Brook University（石溪大学）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；Argonne National Laboratory（阿贡国家实验室）
University of California San Diego（加利福尼亚大学圣迭戈分校）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Argonne National Laboratory（阿贡国家实验室）；The University of Chicago（芝加哥大学）；Massachusetts Institute of Technology（麻省理工学院）；Halıcıoğlu Data Science Institute（哈勒哲奥卢数据科学研究所）
