produced, whose storage and analysis can be very demanding [4; 5; 6]. More and more often researchers have to face big biological data [7; 8], frequently lacking integrated data models and accessible schema representations. Thus, storing, retrieving, integrating, comparing, and analyzing heterogeneous biomedical data becomes a major challenge. In cancer research, several organizations are involved in the collection, management and publication of genomic and clinical data. In particular, the Genomic Data Commons (GDC [9; 10]) is a recent initiative of the National Cancer Institute (NCI) with the aim of creating a unified system to promote the sharing of these data. The GDC supports several programs and defines bioinformatics pipelines: it provides Clinical/Biospecimen Supplements and genomic data harmonization procedures related to DNA-sequencing [11], RNA-sequencing [12; 13], miRNA-sequencing [14], Copy Number Variation [15] and DNA-methylation [16]. The processed data is publicly available through the GDC portal, which deals with different cancer programs; The Cancer Genome Atlas (TCGA) [17] is the most relevant project within the GDC, collecting genomic and clinical data of 33 different tumor types of over 11,000 patients [18]. TCGA data were available at its own portal until late 2016, but since early 2017 they were migrated to the new GDC portal, resulting in a major change of genomic and clinical/biospecimen formats and schema. In the GDC portal, experimental data (i.e., DNA-sequencing, RNA-sequencing, miRNA-sequencing, Copy Number Variation and DNA-methylation data) are produced from harmonization procedures applied on different analysis strategies, improving the quality of data available at the old TCGA portal. Indeed, the GDC provides a programmatic access to interact with these harmonized data through Application Programming Interfaces (APIs), e.g., to obtain aliquot Universal Unique Identifiers (UUIDs) that identify uniquely GDC experiments. The harmonization procedures provide standardized and comparable data, depending on the type of NGS experiment, regardless of the program where they were generated. For what concerns metadata, Clinical/Biospecimen Supplements were represented in an unstructured format in the TCGA portal; conversely, the GDC introduced a new structured data model (i.e., the GDC Data Model). The transition is however still incomplete: the GDC provides some relevant clinical/biospecimen information only in the old unstructured format and some other only or also in the new format. Correspondingly, the GDC exposes two different methods for retrieving clinical and biospecimen information. The first one is the direct download of Supplements from the GDC portal in XML format, which is semi-structured and does not adhere to a specific data model. The second one is through the GDC APIs, which allow downloading structured information according to the GDC Data Model and provide output in JSON format. These methods allow reaching two different materializations of the metadata, partly overlapping with each other. The GDC is proceeding with the migration from the first representation to the second one, importing and inserting the data contained in the first within the second. However, in this transitory phase (that has lasted for several months and will probably last for a long time), much of the information in the first model is not yet replicated to the second, and there is no single source that provides information from both models. In order to obtain a comprehensive representation of such information, it is therefore necessary to extract data using a pipeline that deals with model differences and identifies, manages and removes the overlapping information. The first contribution of our work is the design and development of such a pipeline, such that the clinical and biospecimen data (referred to as metadata) are represented with a common format.