Training and onboarding initiatives in high energy physics experiments.

IF 2.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS

Frontiers in Big Data Pub Date : 2025-02-10 eCollection Date: 2025-01-01 DOI:10.3389/fdata.2025.1497622

Allison Reinsvold Hall, Nicole Skidmore, Gabriele Benelli, Ben Carlson, Claire David, Jonathan Davies, Wouter Deconinck, David DeMuth, Peter Elmer, Rocky Bala Garg, Stephan Hageböck, Killian Lieret, Valeriia Lukashenko, Sudhir Malik, Andy Morris, Heidi Schellman, Graeme A Stewart, Jason Veatch, Michel Hernandez Villanueva

{"title":"Training and onboarding initiatives in high energy physics experiments.","authors":"Allison Reinsvold Hall, Nicole Skidmore, Gabriele Benelli, Ben Carlson, Claire David, Jonathan Davies, Wouter Deconinck, David DeMuth, Peter Elmer, Rocky Bala Garg, Stephan Hageböck, Killian Lieret, Valeriia Lukashenko, Sudhir Malik, Andy Morris, Heidi Schellman, Graeme A Stewart, Jason Veatch, Michel Hernandez Villanueva","doi":"10.3389/fdata.2025.1497622","DOIUrl":null,"url":null,"abstract":"<p><p>In this article we document the current analysis software training and onboarding activities in several High Energy Physics (HEP) experiments: ATLAS, CMS, LHCb, Belle II and DUNE. Fast and efficient onboarding of new collaboration members is increasingly important for HEP experiments. With rapidly increasing data volumes and larger collaborations the analyses and consequently, the related software, become ever more complex. This necessitates structured onboarding and training. Recognizing this, a meeting series was held by the HEP Software Foundation (HSF) in 2022 for experiments to showcase their initiatives. Here we document and analyze these in an attempt to determine a set of key considerations for future HEP experiments.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"8 ","pages":"1497622"},"PeriodicalIF":2.4000,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11847898/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Big Data","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3389/fdata.2025.1497622","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}

引用次数: 0

Abstract

In this article we document the current analysis software training and onboarding activities in several High Energy Physics (HEP) experiments: ATLAS, CMS, LHCb, Belle II and DUNE. Fast and efficient onboarding of new collaboration members is increasingly important for HEP experiments. With rapidly increasing data volumes and larger collaborations the analyses and consequently, the related software, become ever more complex. This necessitates structured onboarding and training. Recognizing this, a meeting series was held by the HEP Software Foundation (HSF) in 2022 for experiments to showcase their initiatives. Here we document and analyze these in an attempt to determine a set of key considerations for future HEP experiments.

查看原文本刊更多论文

高能物理实验的培训和入职计划。

在本文中，我们记录了目前在几个高能物理（HEP）实验中：ATLAS， CMS, LHCb， Belle II和DUNE的分析软件培训和使用活动。对于HEP实验来说，快速高效的新合作成员的入职越来越重要。随着快速增长的数据量和更大的协作，分析和相关软件变得越来越复杂。这就需要结构化的入职和培训。意识到这一点，HEP软件基金会（HSF）于2022年举行了一系列会议，以展示他们的举措。在这里，我们记录和分析这些，试图确定未来HEP实验的一组关键考虑因素。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊