Work

NetCDFaster: Optimizing NetCDF data querying and geo-visualization using high-performance machine learning

Year: 2025

Type: software-paper

Abstract: Extracting and visualizing multidimensional subsets from large NetCDF datasets is often repetitive, time-consuming, and technically demanding. Existing tools typically trade off between performance and usability, either optimizing data retrieval at the cost of user experience or providing user-friendly interfaces that struggle with performance issues. NetCDFaster bridges this gap by integrating a lightweight CatBoost classifier model into a web-based platform that automatically selects optimal data retrieval strategies. The benchmark results demonstrate that NetCDFaster reduces end-to-end retrieval times by more than 80%, allowing researchers to efficiently explore data subsets and visualize results. This significantly enhances the speed and effectiveness of scientific workflows involving NetCDF data.

Source: SoftwareX

DOI: 10.1016/j.softx.2025.102269

Authors: Zhenlei Song, Zhe Zhang, Alan Sussman, Yuhang Xie, Jorge Brenner, Jikun Liu

Institutions: Texas A&M University, University of Maryland, College Park, Gulf of America Coastal Ocean Observing System

Language: English

Topic: Scientific Computing and Data Management

Subfield: Information Systems and Management

Field: Decision Sciences

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $1,200

Funder: National Science Foundation