Untargeted metabolomic data of some Indonesian seaweed species (Halymenia durvillei, Gracilaria gigas, Caulerpa racemosa, and Palmaria palmata)

Halymenia durvillei, Gracilaria gigas, Caulerpa racemosa , and Palmaria palmata . Samples were collected from various coastal regions in Indonesia and processed using a standardized extraction protocol. Each seaweed sample was freeze-dried, ground, and extracted with methanol at a 1:20 (w/v) ratio, followed by sonication, filtration, and concentration through rotary evaporation. The extracts were analyzed with Liquid Chromatography-High Resolution Mass Spectrometry (LC- HRMS) using a Thermo Scientific UHPLC Vanquish Tandem Q Exactive Plus Orbitrap HRMS system. A C18 column and gradient elution profile, along with electrospray ionization in negative mode, were employed during analysis. Mass spectral data were processed with Compound Discoverer 3.3.2 software, and metabolite annotation were performed via MzCloud and ChemSpider databases. The dataset includes LC-HRMS spectral profiles and annotated metabolite features from six replicates per seaweed species. Principal component analysis (PCA) and heatmap analysis were performed on to group seaweed species with similar characteristics. A total of 31 putative metabolites were annotated and are available for further investigation. The PCA revealed that the segregation of the seaweeds was explained by a total variation of 92.6%, with PC1 and PC2 contributing 63.4% and 29.2%, respectively. A heatmap also provides prominent distinctive metabolite profiles among the four seaweeds. Associated metadata includes sampling locations with GPS coordinates, taxonomic classification, sample preparation protocols, LC-HRMS acquisition parameters, and data processing details. The dataset is publicly accessible through Figshare (DOI: 10.6084/m9.figshare.28658303.v1). This dataset can be reused for comparative metabolomics, biomarker discovery, nutritional profiling, or marine biodiversity research. It also supports researchers in pharmacology, marine biology, and bioinformatics who aim to explore or verify metabolite profiles of marine macroalgae using standardized high-resolution analytical techniques.