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Can citations tell us about a paper's reproducibility? A case study of machine learning papers
Rochana R. Obadage, Sarah M. Rajtmajer, Jian Wu
https://arxiv.org/abs/2405.03977

Can citations tell us about a paper's reproducibility? A case study of machine learning papers
The iterative character of work in machine learning (ML) and artificial intelligence (AI) and reliance on comparisons against benchmark datasets emphasize the importance of reproducibility in that literature. Yet, resource constraints and inadequate documentation can make running replications particularly challenging. Our work explores the potential of using downstream citation contexts as a signal of reproducibility. We introduce a sentiment analysis framework applied to citation contexts from…

On Using Agent-based Modeling and Simulation for Studying Blockchain Systems
\"Onder G\"urcan
https://arxiv.org/abs/2405.01574 https://

On Using Agent-based Modeling and Simulation for Studying Blockchain Systems
There is a need for a simulation framework, which is develop as a software using modern engineering approaches (e.g., modularity --i.e., model reuse--, testing, continuous development and continuous integration, automated management of builds, dependencies and documentation) and agile principles, (1) to make rapid prototyping of industrial cases and (2) to carry out their feasibility analysis in a realistic manner (i.e., to test hypothesis by simulating complex experiments involving large numbe…

Guidelines for the Creation of Analysis Ready Data
Harriette Phillips, Aiden Price, Owen Forbes, Claire Boulange, Kerrie Mengersen, Marketa Reeves, Rebecca Glauert
https://arxiv.org/abs/2403.08127

Guidelines for the Creation of Analysis Ready Data
Globally, there is an increased need for guidelines to produce high-quality data outputs for analysis. There is no framework currently exists providing guidelines for a comprehensive approach in producing analysis ready data (ARD). Through critically reviewing and summarising current literature, this paper proposes such guidelines for the creation of ARD. The guidelines proposed in this paper inform ten steps in the generation of ARD: ethics, project documentation, data governance, data managem…