Data and R code for the paper Size normalizing planktonic Foraminifera abundance in the water column (https://doi.org/10.1002/lom3.10637) by Sonia Chaabane, Thibault de Garidel-Thoron, Xavier Giraud, Julie Meilland, Geert-Jan A. Brummer, Lukas Jonkers, P. Graham Mortyn, Mattia Greco, Nicolas Casajus, Olivier Sulpis, Michal Kucera, Azumi Kuroyanagi, Hélène Howa, Gregory Beaugrand, Ralf Schiebel The codes serve to generate a new normalization approach for estimating the abundance of planktonic Foraminifera (ind/m³) within the specified collection size fraction range. Data utilized in this study are sourced from the FORCIS database, containing records collected from the global ocean at various depths spanning the past century. A cumulative distribution across size fractions is identified and modeled using a Michaelis-Menten function. This modeling results in multiplication factors enabling the normalization of one fraction to any other size fraction equal to or larger than 100 µm. The resultant size normalization model is then tested across various depths and compared against a previous size normalization solution. Scripts written by Sonia Chaabane. DATA SOURCES FORCIS database: https://doi.org/10.5281/zenodo.7390791 DATA data_raw_from_excel.RDS CODES Code 1_Prepare the data.R: Reads the data and prepares it for analysis. Code 2_Data-model_training.R: Analyzes the data and builds the model. Code 2_MM_confidence interval_all oceans_depths_seasons.R: Analyzes the data and computes confidence intervals across all oceans, depths, and seasons. Code 3_Validation.R: Compares actual vs. estimated number concentrations. Code 4_Test with berger scheme.R: Compares actual vs. estimated number concentrations using Berger 1969 correction scheme. Code 5_Cross validation_Retailleau et al.R: Applies the FORCIS number concentration-size correction scheme on an independent dataset. Code 6_Retailleau et al. using berger approach.R: Compares actual vs. estimated number concentrations using Berger 1969 correction scheme from an independent dataset. function.R: Additional functions used in the analysis.