In our work, we solve the issues arisen in the transition from the TCGA data portal to the GDC one, providing genomic data and their associated clinical/biospecimen metadata in a standardized format, making both of them seamless, straightforward and easy to be used. We enhance GDC harmonized data by applying a state of the art data model for genomics, in order to uniform genomic and clinical/biospecimen data. We automatically standardize data by mapping them to such unique common schema, thereby supporting scientists in data integration and analyses [19; 20; 21]. We also integrate information extracted from external public databases, i.e., GENCODE [22], HGNC [23], miRBase [24] and NCBI genome annotations [25], enriching the content of the experimental data. This work is an evolution of another project, TCGA2 BED [26], which faced partially similar but much simpler issues, focusing on the old TCGA portal. Unlike TCGA2 BED, beside extending TCGA genomic data and standardize the format in which they are provided by the GDC, we integrate, normalize and make non-redundant their multiple metadata available with different representations; we do so by mapping them to a unique data model and widely exploiting the GDC APIs to interact with and extract GDC data. Our main contribution is the integrative representation of experimental and clinical/biospecimen data by applying the Genomic Data Model (GDM [27]); this then allows querying them, together with other data from multiple sources, uniformly and comprehensively through the GenoMetric Query Language (GMQL [28; 29]) directly on a new publicly available repository of standardized data. GDM consists of two parts, one describing processed datasets with a genomic region-based format, and one describing the metadata. For the former one, we map the content of GDC data to GDM, thereby transforming the experimental data of the GDC into a new data collection, which we denote as OpenGDC, harmonized and extended by linking with other public databases. For the latter one, the Clinical and Biospecimen Supplements (which are semi-structured, not part of a data model) are extracted and merged with all the information on clinical and biospecimen data available through the GDC APIs (which is structured and adheres to the GDC Data Model), and finally converted to the metadata format of GDM, used by OpenGDC. Other works have dealt with the problem of storing, retrieving and enhancing data of the GDC, almost all of them are focused on the TCGA program. Among them, we mention: (i) TCGA-assembler 2 [30], a software pipeline which allows downloading TCGA data from the GDC defining filtering criteria to merge the extracted data files of samples into a single data table, and finally to process them; (ii) The International Cancer Genome Consortium (ICGC [31]), which provides a data portal to characterize genomic abnormalities in different cancer types, including data from TCGA; (iii) The Seven Bridges Cancer Genomics Cloud (CGC [32]), which allows accessing data from public cancer genomic datasets (e.g., TCGA) and analyzing them in the cloud by using bioinformatics tools and workflows. All these works are of great interest and improve the access to GDC data; in particular they aggregate them, identify important genomic features, and analyze them with cloud computing resources. Moreover, there are several state-of-the-art tools to retrieve and analyze TCGA data, including some R packages like (i) TCGAbiolinks [33], which provides algorithms for data mining and analysis of cancer genomics, (ii) chiopportal [34], an open platform for interactively exploring multidimensional cancer genomics data sets in the context of clinical data and biologic pathways, (iii) Xena [35], an easy-to-use cancer genomics visualization tool for large public data resources of the GDC. Conversely, our approach is different, as it aims at facilitating the use of TCGA data of the GDC by providing it in a standardized and extended format, and enriched with multiple integrated metadata.