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Management of mathematical research software and data

Mathematical software is often written without applying reproducible workflows. This paper presents a workflow, based on iRODS, Docker, and GitLab, to aid mathematical researchers in improving such software and corresponding simulations, with minimal effort.

Published onApr 27, 2023
Management of mathematical research software and data


In the mathematical sciences, researchers increasingly turn to computational experiments, both when validating new theoretical results and when modeling physical processes. The software used for these experiments is often developed by researchers with temporary positions, whose main objective is producing publications. Once the code produces the required figures, then there is often little motivation to spend time documenting the code and ensuring long-term experiment reproducibility. In addition, external libraries change over time, endangering long-term reproducibility if software versions are not well-documented.

In a collaboration between the NUMA section at the Department of Computer Science, the Research Data Management Competence Centre, and the Research Coordination Office, a set of workflows is being developed, integrating iRODS, Docker, and GitLab, which automatically document the links between research results, code, and input data during the research process, with minimal effort from researchers themselves. The end goal is to ensure that computational experiments remain portable, well-documented and reproducible, both during the research process and years later.


Author biographies

Emil Løvbak (KU Leuven) acquired a master's degree in Mathematical Engineering from KU Leuven in 2017 and is concluding his PhD in the micro-Macro research group at the Department of Computer Science. His main research focus lies in stochastic optimization and particle-based simulation methods for kinetic equations, as well as the application of such methods to neutral particle codes with the end goal of fusion reactor design. During his PhD, he spent significant effort on introducing best-practice software development workflows, both in his own research and to other members of the micro-Macro group.

Mustafa Dikmen (KU Leuven) works as a research data engineer at the Research Data Management (RDM) team in ICTS. He provides support to users in the context of active data management by mainly focusing on workflow automations, solutions, and services with the ManGO platform/iRODS. His educational background includes electronics and public administration/international relations.

Naeem Muhammad (KU Leuven) holds a PhD degree in Software Engineering and is currently working as a Research Data Manager. He is involved in defining and implementing Research Data Management (RDM) workflows, implementation of best RDM practices, and translation of funders’ RDM/FAIR data requirements to actual implementations. He is actively involved in the communities around RDM, FAIR data, and open science.

Giovanni Samaey (KU Leuven) is a research professor in NUMA, Department of Computer Science. He focuses on the development and analysis of computational methods for large-scale simulation using hierarchies of mathematical models at different levels of resolution. In this context, he works on methods for uncertainty quantification, optimization, and Bayesian data assimilation. His work finds application in diverse domains, such as fusion energy, polymer physics, structural health monitoring, proteomics, and computational virology. He was a member of the Young Academy of Belgium (2013-2018) and is the coordinator of the EuroHPC project Time-X, on time-parallel time integration methods (2021-2024), and of the MSCA Joint Doctorates Network DATAHYKING (2023-2026) on kinetic and hyperbolic equations.

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