CLAIMED -- the open source framework for building coarse-grained operators for accelerated discovery in science

Autor:	Kienzler, Romeo, Khan, Rafflesia, Nilmeier, Jerome, Nesic, Ivan, Haddad, Ibrahim
Rok vydání:	2023
Předmět:	Computer Science - Artificial Intelligence Computer Science - Databases Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	In modern data-driven science, reproducibility and reusability are key challenges. Scientists are well skilled in the process from data to publication. Although some publication channels require source code and data to be made accessible, rerunning and verifying experiments is usually hard due to a lack of standards. Therefore, reusing existing scientific data processing code from state-of-the-art research is hard as well. This is why we introduce CLAIMED, which has a proven track record in scientific research for addressing the repeatability and reusability issues in modern data-driven science. CLAIMED is a framework to build reusable operators and scalable scientific workflows by supporting the scientist to draw from previous work by re-composing workflows from existing libraries of coarse-grained scientific operators. Although various implementations exist, CLAIMED is programming language, scientific library, and execution environment agnostic. Comment: Received IEEE OSS Award 2023 - https://conferences.computer.org/services/2023/symposia/oss.html
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2307.06824 Zobrazit plný text záznamu View this record from Arxiv