Protein (de)phosphorylation plays an important role in plants. To provide a robust foundation for subcellular phosphorylation signaling network analysis and kinase-substrate relationships, we performed a meta-analysis of 27 published and unpublished in-house mass spectrometry–based phospho-proteome data sets for Arabidopsis thaliana covering a range of processes, (non)photosynthetic tissue types, and cell cultures. This resulted in an assembly of 60,366 phospho-peptides matching to 8141 nonredundant proteins. Filtering the data for quality and consistency generated a set of medium and a set of high confidence phospho-proteins and their assigned phospho-sites. The relation between single and multiphosphorylated peptides is discussed. The distribution of p-proteins across cellular functions and subcellular compartments was determined and showed overrepresentation of protein kinases. Extensive differences in frequency of pY were found between individual studies due to proteomics and mass spectrometry workflows. Interestingly, pY was underrepresented in peroxisomes but overrepresented in mitochondria. Using motif-finding algorithms motif-x and MMFPh at high stringency, we identified compartmentalization of phosphorylation motifs likely reflecting localized kinase activity. The filtering of the data assembly improved signal/noise ratio for such motifs. Identified motifs were linked to kinases through (bioinformatic) enrichment analysis. This study also provides insight into the challenges/pitfalls of using large-scale phospho-proteomic data sets to nonexperts.