What question did this study set out to answer?

To provide reusable libraries that facilitate scientific and numeric data processing, particularly for multidimensional data structures.

March 26, 2026Open Access

INAF.Libraries.Net.Data.Helpers TECHNICAL REPORT

Key Points

To provide reusable libraries that facilitate scientific and numeric data processing, particularly for multidimensional data structures.
Developed small libraries each with specific responsibilities;
Created domain types and low-level data access for matrices and qubes;
Implemented higher-level APIs for building and converting data;
Established parallel execution helpers for efficient processing.
The codebase supports efficient transformation of structured scientific datasets into matrix format;
Parallel helpers enhance the performance of CPU-bound operations;
Facilitates building data-analysis pipelines effectively.

Abstract

This solution provides a set of reusable .NET libraries for scientific and numeric data processing, with a focus on multidimensional data structures, matrix-oriented transformations, and parallel execution helpers. The codebase is organized into small libraries with separated responsibilities:- `INAF.Libraries.Net.Data`: domain types and low-level data access for matrices, qubes, headers, and typed result containers.- `INAF.Libraries.Net.Data.Helpers`: higher-level helper APIs for building arrays and matrices, and for converting `Qube` data into analysis-ready `Matrix` instances.- `INAF.Libraries.Net.Parallelization`: reusable parallel iteration and processor allocation helpers used by the other libraries.The solution is suitable as a foundation for data-analysis pipelines that need to read structured scientific datasets, transform them into matrix form, and execute CPU-bound operations efficiently

INAF.Libraries.Net.Data.Helpers TECHNICAL REPORT

Key Points

Abstract

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