The Data Management and Analysis Core (DSC) will be responsible for performing and supporting biostatistical analyses and management of multidimensional and subject metadata sets generated in the OmiT- HIV program. A custom project will be created and maintained on the UCSF Data Library platform to store, secure, curate, and make accessible all raw and processed biological data and de-identified clinical data associated with the research program. We will interface with each project team for data ingestion and provide statistical and bioinformatic support to each. We will additionally perform multi-modal integrative analyses, including cross-species analysis, to answer questions within and across each project toward better understanding how vaccine regimens and metabolic conditions influence T-cell responses to and virologic outcomes of therapeutic vaccination. We will accomplish these goals with the following specific aims: Aim 1: Develop and maintain a centralized system of data management and sharing using the UCSF Data Library. - We will leverage the UCSF Data Library (UCDL) to facilitate the ingestion, curation, and secure storage of the diverse high-dimensional data types and metadata or de-identified clinical data generated across the projects. - We will apply shared data schemas per modality that store raw and processed data and annotations. - The UCDL supports web-based data sharing across all members of project and core teams as well as serves as a staging area for the transfer of all raw and processed data and metadata to ImmPort and other public databases. Aim 2: Provide statistical analysis and bioinformatic support for testing project hypotheses relevant to vaccine regimens, T-cell features, metabolomics, and rebound kinetics. - We will apply standardized and custom approaches to harmonize datasets for use by all teams. - We will work collaboratively with each project and core team to perform described analyses including dimensionality reduction, differential analysis, and association with outcomes. Aim 3: Integrate multi-modal data to develop a model of joint immune and metabolomic influence over vaccine responses and control over viremia in ATI. - We will leverage the complementary nature of these projects and state-of-the-art integrative analysis techniques, including cross-species analysis, to construct models relating multi-modal factors to longitudinal immune and virologic outcomes. - We will address the Bridging Hypotheses described in the Overall component.