This dataset is a toy example used to demonstrate the functionality of the glycoTraitR package (https://github.com/matsui-lab/glycoTraitR). The original glycoproteomics data are derived from the publication:“Glycoproteomics Landscape of Asymptomatic and Symptomatic Human Alzheimer’s Disease Brain.”DOI: 10.1016/j.mcpro.2022.100433Raw data are available at ProteomeXchange under the accession:PXD032219 (https://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD032219). For this example dataset, the raw files were re-searched using two state-of-the-art glycoproteomics engines: pGlyco3 (Nature Methods)DOI: 10.1038/s41592-021-01306-0 Glyco-Decipher (Nature Communications)DOI: 10.1038/s41467-022-29530-y These re-searched results are formatted and curated specifically to illustrate the workflow and analytical capabilities of glycoTraitR. meta_mcp_2022_100433.rds contains the metadata, including sample annotations required to reproduce the example workflow. Table_S1.xlsx summarizes the samples and identification statistics, including diagnosis, sample ID, file name, and the numbers of GPSMs, glycoproteins, and glycosites identified by both pGlyco3 and GlycoDecipher. pGlycoDB-GP-FDR-Pro-Quant-Site.txt contains the merged glycopeptide identification and quantification results generated by pGlyco3 across all samples. decipher_result.zip contains the GlycoDecipher output files for individual samples, packaged in a single archive.