Mathematical Research Data Initiative - TA2: Scientific Computing (MaRDI)

Basic data for this project

Description

Driven by the needs and requirements of mathematical research as well as scientific disciplines using mathematics, the NFDI-consortium MaRDI (Mathematical Research Data Initiative) will develop and establish standards and services for mathematical research data. Mathematical research data ranges from databases of special functions and mathematical objects, aspects of scientific computing such as models and algorithms to statistical analysis of data with uncertainties. It is also widely used in other scientific disciplines due to the cross-disciplinary nature of mathematical methods. Mathematical research data is vast, complex and multifaceted. It emerges within mathematical sciences but also in other scientific areas such as physics, chemistry, life sciences and the Arts. Standardised formats, data interoperability and application programming interfaces need to be developed to ensure the ease of use of data across broad disciplines. With this in mind, MaRDI is being established as the consortia initiative of mathematical science. Its mission is to: develop a robust Mathematical Research Data Infrastructure that would be useful within mathematics and other disciplines as well as non-scientific fields. set standards and confirmable workflows for certified Mathematical Research Data and provide services to both the mathematical and wider scientific community. All of which is essential in creating and establishing collaborative platforms crucial for knowledge dissemination, quality control and scientific discourse. Within MaRDI, WWU Münster particularly contributes toTask Area 2,Scientific computing: Scientific computing is a cross-disciplinary topic bordering applied mathematics, computational sciences and engineering (CSE), as well as other scientific areas involving numerical computations like digital humanities or computational medicine. The principal data types involved are fixed precision real numbers that are prone to roundoff errors during copmutation. Beyond the data types found in engineering, such as input/output data of numerical software, in computational mathematics, and specifically in scientific computing, including the algorithms, implementations, procedural data, and their metadata descriptions are research data. Task Area 2 will focus on establishing knowledge graphs of numerical algorithms and open interfaces for their seamless interconnection in scientific computations. A benchmark framework employs both to asses their performance on a collection of reference data sets from their corresponding area of application, while following standardized workflows identified in collaboration with Task Area 4 and other consortia.