Good practices for quantitative bias analysis

Good Architecture = Good (ADL + Practices)

In order to ensure the quality of their software development, companies incorporate best practices from recognized repositories or from their own experiences. These best practices are often described in software quality manuals which does not guarantee their implementation. In this paper, we propose a framework for the implementation of best practices concerning the design of the software archi...

Good Practices for Reproducible Research

Multi-core architectures comprising several GPUs have become mainstream in the eld of High-Performance Computing. However, obtaining the maximum performance of such heterogeneous machines is challenging as it requires to carefully o oad computations and manage data movements between the di erent processing units. The most promising and successful approaches so far rely on task-based runtimes th...

Good Practices in Development Communication Good Practices in Development Communication

A Guide to Implementing Quantitative Bias Analysis

Estimates of association from nonrandomized epidemiologic studies are susceptible to two types of error: random error and systematic error. Random error, or sampling error, is often called chance, and decreases toward zero as the sample size increases and the data are more efficiently distributed in the categories of the adjustment variables. The amount of random error in an estimate of associa...

A comparative study of quantitative mapping methods for bias correction of ERA5 reanalysis precipitation data

This study evaluates the ability of different quantitative mapping (QM) methods as a bias correction technique for ERA5 reanalysis precipitation data. Climate type and geographical location can affect the performance of the bias correction method due to differences in precipitation characteristics. For this purpose, ERA5 reanalysis precipitation data for the years 1989-2019 for 10 selected syno...