Concise monthly summary for 2025-09 focusing on features delivered, major fixes, impact, and skills demonstrated for the clessn/livre-outils repository.

June 2025 monthly summary for clessn/livre-outils: Delivered Chapter 8 Content and Formatting Enhancements for Translation Tools, focusing on editor configuration improvements (Quarto) from visual to source for authoring, readability improvements by centering the table comparing AI vs human translation, and integration of TERMIUM Plus and IATE into the main text to streamline presentation of translation resources. These changes enhance authoring efficiency and reader experience while consolidating translation resources.

April 2025 performance summary for clessn/datagotchi_federal_2024: Delivered a new Debate Impact Analysis Pipeline to quantify how debates influence voting probabilities and perceived candidate competence, enabling data-driven insights for events. The pipeline establishes the foundation for ongoing experimentation and measurement of debate effects, strengthening analytics support for event planning and candidate evaluation. No major bugs fixed this month; focus was on feature delivery and expanding analytics capabilities. Key technical emphasis included ETL/analytics pipeline design, integration with existing data workflows, and robust, version-controlled delivery.

March 2025 monthly update for clessn/datagotchi_federal_2024: Delivered robust RCI analytics enhancements and spatial prediction/mapping capabilities, with improved data quality and bilingual documentation. The work increased reliability of RCI scores, standardized outputs on a -100 to 100 scale, and enabled end-to-end riding mapping from survey data to riding IDs, supporting better policy analysis and stakeholder reporting.

February 2025: Delivered a focused spatial-data export workflow for electoral ridings and RTAs in clessn/datagotchi_federal_2024. Added an R script to process and export spatial data, producing two Excel files: (1) RTAs-to-electoral riding IDs with area coverage proportions, and (2) names of electoral ridings, designed for consumption by the Hugo application. This enhances data availability and accuracy for downstream analytics and the Hugo UI, enabling faster data refreshes with reproducible wrangling steps. No major bugs reported this month.

Month: 2024-12. Focused on delivering business-value features for the datagotchi federal dataset and improving data quality and reliability. Highlights include a new time-series visualization across clusters with corrected daily filtering, and substantial data cleaning/standardization for lifestyle variables in the federal 2024 dataset. These efforts improve time-based insights, data quality, reproducibility, and pipeline clarity, enabling more reliable analyses and faster decision-making.

November 2024 performance highlights: delivered targeted documentation and tutorial improvements in livre-outils, advanced visualizations in Chapter 6, and implemented a Bayesian prediction pipeline along with repository hygiene improvements in datagotchi_federal_2024. These efforts reduce onboarding time, improve data science pedagogy, enable faster daily predictions, and enhance repository maintainability.