Human cells and tissues are shaped by their unique proteins, and by the post-translational modifications (PTMs) these proteins carry. Mass spectrometry (MS) based proteomics offers a sensitive and specific method to analyze these proteins and PTMs, offering the potential to uncover a comprehensive view of the proteomic landscape of a tissue especially as vast amounts of data accumulate in public proteomics repositories every year. However, this derivation of proteome landscapes is currently limited by computational approaches and a lack of sufficient metadata annotation. My project therefore focuses on providing an advanced and generalised proteome-based tissue prediction model as the fundamental proteome landscape mapper. By integrating sequential, structural and PTM data, its application potential will be expanded to different model organisms, differential analysis in disease, and low-input samples (single-cell and organoid data). Simultaneously, I will also actively invest in promoting metadata annotation for new data sets using my established lesSDRF tool, and by fostering community efforts. Moreover, I will develop automated tools for retroactive metadata annotation of existing public domain datasets, thus optimising metadata availability for all public proteomics data sets. Taken together, my efforts will allow researchers to re-use my tools and approaches for their own exploration of proteome landscapes, and to develop innovative ways to utilize public proteomics data.