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1. Software Training in HEP

   Sudhir Malik, Samuel Meehan (February 2021, ArXivorg)

   null (Ed.)

   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 

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2. Software Training in HEP

   Sudhir Malik, Samuel Meehan (February 2021, ArXivorg)

   null (Ed.)

   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 

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3. Training the Next Generation of Seismologists: Delivering Research-Grade Software Education for Cloud and HPC Computing Through Diverse Training Modalities

   https://doi.org/10.1785/0220240413  

   Denolle, Marine A; Tape, Carl; Bozdağ, Ebru; Wang, Yinzhi; Waldhauser, Felix; Gabriel, Alice-Agnes; Braunmiller, Jochen; Chow, Bryant; Ding, Liang; Feng, Kuan-Fu; et al (June 2025, Seismological Research Letters)

   Abstract With the rise of data volume and computing power, seismological research requires more advanced skills in data processing, numerical methods, and parallel computing. We present the experience of conducting training workshops in various forms of delivery to support the adoption of large-scale high-performance computing (HPC) and cloud computing, advancing seismological research. The seismological foci were on earthquake source parameter estimation in catalogs, forward and adjoint wavefield simulations in 2D and 3D at local, regional, and global scales, earthquake dynamics, ambient noise seismology, and machine learning. This contribution describes the series of workshops delivered as part of research projects, the learning outcomes for participants, and lessons learned by the instructors. Our curriculum was grounded on open and reproducible science, large-scale scientific computing and data mining, and computing infrastructure (access and usage) for HPC and the cloud. We also describe the types of teaching materials that have proven beneficial to the instruction and the sustainability of the program. We propose guidelines to deliver future workshops on these topics. 

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4. Understanding the Nature and Evolution of Sustainability Mindset in First-Year Engineering Students

   Colon, Krystal; Rivera_Castro, Andrea K; Santiago_Roman, Aidsa I; Papadopoulos, C; Dika, Sandra L; Santiago, Nayda G; Alicea_Romero, Kaishmarie (June 2024, ASEE Conferences)

   This article reviews the notion of a Sustainability Mindset in comparison to sustainability competencies in the context of a study of a first year cohort of students beginning a minor in Sustainability Engineering at the University of Puerto Rico, Mayagüez. A framework of Knowledge (K), Skills (S), Attitudes (A), Behaviors (B), and Attitudes (A) is adopted to capture the students' developing mindsets broadly the context of Sustainability. A detailed open-form survey was administered after the first semester and was completed by 9/11 participants. Results show mature growth in the domain of sustainability (e.g., broad knowledge of the concept of sustainability to encompass the three 'pillars' of environmental, equity, and economic factors, and deep knowledge regarding ideas such as circular economy and earth systems cycles). Further growth is also demonstrated in academic and personal development (e.g., improved study skills, time management, and research skills). Future work will endeavor to continue observing the evolution of the mindset at the KSBA level and at finer levels of detail. 

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5. Investigating Data Access Models for ATLAS: A Case Study with FABRIC Across Borders and ServiceX

   https://doi.org/10.1051/epjconf/202533701117  

   Vukotic, Ilija; Hu, Fengping; Bryant, Lincoln; Gardner_Jr, Robert William; McKee, Shawn; Stephen, Judith; Jordan, David (October 2025, EPJ Web of Conferences)

   Szumlak, T; Rachwał, B; Dziurda, A; Schulz, M; vom_Bruch, D; Ellis, K; Hageboeck, S (Ed.)

   This study explores enhancements in analysis speed, WAN bandwidth efficiency, and data storage management through an innovative data access strategy. The proposed model introduces specialized ‘delivery’ services for data preprocessing, which include filtering and reformatting tasks executed on dedicated hardware located alongside the data repositories at CERN’s Tier-0, Tier-1, or Tier-2 facilities. Positioned near the source storage, these services are crucial for limiting redundant data transfers and focus on sending only vital data to distant analysis sites, aiming to optimize network and storage use at those sites. Within the scope of the NSF-funded FABRIC Across Borders (FAB) initiative, we assess this model using an “in-network, edge” computing cluster at CERN, outfitted with substantial processing capabilities (CPU, GPU, and advanced network interfaces). This edge computing cluster features dedicated network peering arrangements that link CERN Tier-0, the FABRIC experimental network, and an analysis center at the University of Chicago, creating a solid foundation for our research. Central to our infrastructure is ServiceX, an R&D software project under the Data Organization, Management, and Access (DOMA) group of the Institute for Research and Innovation in Software for High Energy Physics - IRIS-HEP. ServiceX is a scalable filtering and reformatting service, designed to operate within a Kubernetes environment and deliver output to an S3 object store at an analysis facility. Our study assesses the impact of server-side delivery services in augmenting the existing HEP computing model, particularly evaluating their possible integration within the broader WAN infrastructure. This model could empower Tier-1 and Tier-2 centers to become efficient data distribution nodes, enabling a more cost-effective way to disseminate data to analysis sites and object stores, thereby improving data access and efficiency. This research is experimental and serves as a demonstrator of the capabilities and improvements that such integrated computing models could offer in the HL-LHC era. 

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