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This Data Management Plan (DMP) describes the handling of data generated within the research project focused on the development and evaluation of synbiotic ingredients and delivery systems based on locally available plant raw materials. The project investigates: the production of resistant starch fractions (RS2 and RS3) from Latvian potato varieties; the extraction and characterisation of xylooligosaccharides (XOS) from barley straw; the in vitro assessment of these prebiotics on the growth and activity of probiotic bacteria (Bifidobacterium and Lactobacillus spp.); the development of synbiotic microcapsules and evaluation of their physicochemical and microbiological stability. The DMP outlines procedures for data collection, processing, quality assurance, storage, documentation, sharing, and long-term preservation to ensure data integrity, security, and compliance with ethical requirements and the FAIR principles. Research data will include experimental and process metadata (e.g., raw material characteristics, processing parameters, sample identifiers), analytical outputs (e.g., resistant starch quantification results, HPLC chromatograms and quantified XOS profiles), microbiological fermentation datasets (e.g., CFU counts, growth curves, pH measurements), and microencapsulation characterisation data (e.g., capsule properties, microscopy images, and stability test results). The project is implemented by the Institute of Agricultural Resources and Economics in collaboration with national and international partners (including the Latvia University of Life Sciences and Technologies and the University of Oviedo), combining expertise in bioprocessing, analytical chemistry, microbiology, and food technology. The DMP specifies that all data will be managed in accordance with applicable legislation and institutional policies. As the project does not involve human participants, personal data processing is not expected; any incidental personal data (e.g., administrative contact details) will be handled under GDPR and national requirements. At the end of the project, the datasets underpinning publications and key results will be curated, documented with appropriate metadata and readme files, and deposited in a trusted research data repository that supports persistent identifiers (e.g., DOI) and FAIR-aligned access conditions (e.g., DataverseLV or an equivalent institutional/general repository). Where results have commercialisation or intellectual property potential, access may be provided under justified restrictions (e.g., embargo or controlled access), while still ensuring sufficient transparency for research verification and reuse.