Leveraging National Science Data Fabric Services to Train Data Scientists

We document an interactive half-day tutorial in which participants explore the advanced applications of National Science Data Fabric (NSDF) services and strategies for comprehensive scientific data analysis. Targeting researchers, students, developers, and scientists, the tutorial provides valuable...

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Vydané v:SC24-W: Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis s. 355 - 362
Hlavní autori: Taufer, Michela, Martinez, Heberth, Panta, Aashish, Olaya, Paula, Marquez, Jack, Gooch, Amy, Scorzelli, Giorgio, Pascucci, Valerio
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 17.11.2024
Predmet:
Data analysis
Data visualization
Fabrics
Geoscience
High performance computing
Memory
Pain
Surveys
Tutorials
Visual analytics
workforce development
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Popis
Shrnutí:We document an interactive half-day tutorial in which participants explore the advanced applications of National Science Data Fabric (NSDF) services and strategies for comprehensive scientific data analysis. Targeting researchers, students, developers, and scientists, the tutorial provides valuable insights into managing and analyzing large datasets, particularly those exceeding 100TB. Participants gain hands-on experience by constructing modular workflows, leveraging public and private data storage and streaming solutions, and deploying sophisticated visualization and analysis dashboards. The tutorial emphasizes NSDF's role in supporting visualization conference themes by providing scalable visualization and visual analytics solutions. Our tutorial includes an overview of NSDF's capabilities, addressing common data analysis challenges, and intermediate hands-on exercises using NSDF services for Earth science data. Advanced applications cover handling and visualizing massive datasets requiring high-resolution data management. By the end of the session, attendees have a deeper understanding of integrating NSDF services into their research workflows, enhancing data accessibility, sharing, and collaborative scientific discovery. Our tutorial aims to advance knowledge in data-intensive computing and empower participants to harness the full potential of NSDF in their respective fields.
DOI:10.1109/SCW63240.2024.00053