00000cam 2200000zi 4500 9.954824 CaOODSP 20260916093024 m o d f cr cn||||||||| 250904t20252025onca ob f000 0 eng d 9780660787893 CaOODSP eng rda CaOODSP M183-2/9309E-PDF Brouard, Etienne, author. Quality assurance and quality control of till compositional data using Python / E. Brouard and P.M. Godbout. [Ottawa] : Natural Resources Canada = Ressources naturelles Canada, 2025. ©2025 1 online resource (21 pages) : colour illustrations. text txt rdacontent computer c rdamedia online resource cr rdacarrier Open file, 2816-7155 ; 9309e Issued also in French under title: Contrôle de la qualité et validation des données compositionnelles des tills à l'aide de Python. Includes bibliographical references (page 21). "Compositional data derived from tills play a pivotal role in geoscientific investigations, offering insights into geological processes, environmental conditions, and mineral exploration. However, ensuring the accuracy and reliability of these datasets is paramount for meaningful analyses and informed decision-making. To address this need, this report introduces a comprehensive suite of streamlined Quality Assurance and Quality Control (QA/QC) protocols tailored to till data. Designed to simplify QA/QC workflows—particularly for users with limited experience in coding or statistical analysis—these protocols combine simple procedures with user-friendly Python scripts. The protocols cover essential aspects of data quality assessment, including basic statistics, accuracy, precision, and variability, providing users with plots for visual interpretation and a systematic approach to evaluating the integrity and reliability of their datasets. The manuscript outlines key considerations for meticulous planning, proper data formatting, and the effective use of tools such as Microsoft Excel and Python. By following the outlined procedures and using the provided scripts, users can generate summary statistics, assess measurement accuracy, evaluate differences between routine samples and their analytical duplicates, and determine whether data variability is acceptable for mapping and statistical purposes"--Page 1. Drift Composition Data processing. Quality assurance. Quality control. Geological Survey of Canada, issuing body. Contrôle de la qualité et validation des données compositionnelles des tills à l'aide de Python / (CaOODSP)9.959174 Open file (Geological Survey of Canada) 2816-7155 ; 9309e. (CaOODSP)9.506878 PDF 601 KB https://publications.gc.ca/collections/collection_2026/rncan-nrcan/m183-2/M183-2-9309-eng.pdf Open file report HTML N/A https://doi.org/10.4095/ps1ht4x4vg Complete dataset