Ministry of Higher Education and Research

Computational reproducibility remains limited in psychological research, despite widespread norms for sharing data and analysis code. One reason is that reproducibility exists on a continuum, ranging from partial transparency—such as providing scripts or software version numbers—to fully executable research compendia that regenerate all results from raw code. In this article, we introduce Nix and the {rix} R package as a practical framework for achieving full computational reproducibility in simulation-based research. We provide a step-by-step tutorial demonstrating how {rix} can be used to define, build, and share isolated, project-specific software environments that precisely capture R versions, package dependencies, system libraries, and integrated development environments. We further illustrate this workflow by reproducing a complete manuscript using Quarto and the {apaquarto} extension, showing how analyses, figures, and text can be regenerated in a single, executable pipeline. Together, these tools lower the technical barrier to robust, end-to-end reproducibility and offer a scalable solution for simulation studies and methodological research in psychology and related fields.

Accurate calculation of gamma dose rates in medical and industrial facilities is a critical component of comprehensive dosimetry assessment. Usually, two complementary approaches are employed to this end: experimental measurements and Monte Carlo (MC) simulations, both of which have established themselves as powerful and reliable tools in radiation protection and dosimetry practice. Given the high computational cost of Monte Carlo simulations, artificial intelligence can offer a compelling and efficient alternative for predicting dose rate distributions. This study evaluates the capability of machine learning models to predict MC-calculated dose rates and to identify the optimal 60Co source arrangement for the upcoming replenishment. The replenishment scenario involves inserting six new 60Co pencil sources. Dose rate prediction was performed using FLUKA MC simulations, complemented by an Artificial Neural Network (ANN)-based predictive model. The ANN model demonstrated strong concordance with FLUKA MC results, with deviations consistently below 1%, and exhibited reliable predictive performance on previously unseen configurations. Based on the dose uniformity ratio and the coefficient of determination, configuration 3 was identified as the optimal arrangement (R2 = 0.986). The integration of machine learning with MC simulation proves highly effective, enabling rapid and accurate dose rate prediction around the 60Co source while substantially reducing computational time and CPU resource demands.

Watch VIDEO. Since 2018, the French Open Science Monitor (BSO) has assessed the effectiveness of the national public policy in open science. This steering tool, developed by the French Ministry of Higher Education and Research, the University of Lorraine and Inria, measures the evolution of open science in France using reliable, open and controlled data updated every year. The result is a website presenting different dashboards, tracking for example the ratio of open access scientific publications by year, discipline or publisher. Since its last release in March 2023, the BSO also tracks the production and openness of research datasets and software mentioned in scientific publications on a national scale. To ensure a realistic coverage, our platform relies on large-scale open source Deep Learning techniques applied to the full texts of publications with at least one co-author with a French affiliation. DataStet identifies every mention of datasets in scholarly publications, including implicit mentions of datasets and explicitly named datasets. SoftCite recognizes any software mentions in scientific publications, using as training data the Softcite Dataset. Dataset and software mentions are then characterized automatically as used, created and shared by the research work described in the scientific document. These characterizations can be cumulative. Among 1,608,839 publications from our corpus, we were able to analyze 655,954 of them with our tool DataStet. For this subset, we found 6,511,998 mentions of datasets characterized as used, 330,062 mentions characterized as created, and 78,178 mentions characterized as shared. With this methodology, the BSO can offer new indicators about the proportion of French publications mentioning the usage, creation and sharing of data, as well as the proportion of publications in France that include a "Data Availability Statement". Similar indicators are dedicated to code and software. In addition, these indicators are further broken down into disciplines, publishers and institutions. The project is addressing major technical and organizational challenges: to identify French datasets and software without reference registries as for publications, thanks to artificial intelligence; to produce relevant indicators for the different scientific communities. As an enabling technology to identify research datasets and software, deep learning plays a crucial role. This presentation will be an opportunity to present the latest results of the project, to detail the methodology, and finally to underline the reusability of the project results.