Glycans or carbohydrates represent a major class of biomolecules. They are also the observable result of the most common Post-Translational Modification, known to play a crucial role in the host-pathogen interactions as well as in signaling processes or disease states, among others. Glycoinformatics is the dedicated bioinformatics field supporting glycobiology and its experimental parts, including glycomics and glycoproteomics and any other glycan-oriented studies. The generated data and resulting information drive the discipline. Glycans molecules have tree structures with a characterized topology. This structural complexity induces glycoinformatics problems relative to their representation, encoding formats, processing, and interpretation. Furthermore, the lack of consensus in this respect significantly limits reproducibility and comparability in glycobiology. We suggest that common descriptions, application guidelines, and best practices are solutions to overcome these issues. Over the recent years, Findability, Accessibility, Interoperability, and Reusability (FAIR) principles and minimum information required for a glycomics experiment (MIRAGE) initiatives have driven and ruled data standardization processes. Subsequently, adopting these principles and guidelines improves reproducibility, interoperability, and data reuse, increasing its value.