BUSCADOR RECOLECTA

The photographs of glasses containing wine were acquired by researchers with different smartphones equipped with high-resolution cameras (12 or 48 MP). Photographs were previously designed considering usual photographic parameters (see DATA-SPECIFIC INFORMATION for details).-- The data have not been processed., The DATASET compiles 1.945 files corresponding to individual images of glasses containing red wine. The photographs of glasses containing wine were acquired by researchers with different smartphones equipped with high-resolution cameras (12 or 48 MP). Photographs were previously designed considering usual photographic parameters. Each file name is unique and contains information of the parameters under which the photograph was taken., This study was supported by MCIN (Ministerio de Ciencia e Innovación)/AEI (Agencia Estatal de Investigación)/10 .13039 /501100011033through the projects PID2019-108851RB-C21 & PID2019-108851RB-C22. The authors would like to thank CSIC Interdisciplinary Thematic Platform (PTI+) Digital Science and Innovation., The DATASET compiles 1.945 files corresponding to individual images of glasses containing red wine. Each file name is unique and contains information of the parameters under which the photograph was taken (see DATA-SPECIFIC INFOR-MATION for details). For example: The file Rea_Rio_C_Bor_175_nd_nd_fr10_nd_nd_ar2 corresponds to an almost real image (Rea), taken in La Rioja (Rio), of a “crianza wine”(C), in a Bourgogne wine glass (Bor), with a volume of 175 mL (175), taken at none defined time (nd) and undefined lighting (nd), with a real back-ground (fr10) and without reference (nd) nor distance (nd) considerations, and upper angle (ar2)., Peer reviewed

The photographs of glasses containing wine were acquired by wine consumers with different smartphones equipped with high-resolution cameras (12 or 48 MP). Photographs were previously designed considering usual photographic parameters (see DATA-SPECIFIC INFORMATION for details).-- The data have not been processed., The DATASET compiles 229 files corresponding to individual images of glasses containing red wine. The photographs of glasses containing wine were acquired by wine consumers with different smartphones equipped with high-resolution cameras (12 or 48 MP). Each file name is unique and contains information of the parameters under which the photo-graph was taken., This study was supported by MCIN (Ministerio de Ciencia e Innovación)/AEI (Agencia Estatal de Investigación)/10 .13039 /501100011033through the projects PID2019-108851RB-C21 & PID2019-108851RB-C22. The authors would like to thank CSIC Interdisciplinary Thematic Platform (PTI+) Digital Science and Innovation., The DATASET compiles 229 files corresponding to individual images of glasses containing red wine. Each file name is unique and contains information of the parameters under which the photograph was taken (see DATA-SPECIFIC INFOR-MATION for details). For example: The file Vol_Mad_C_Bor_175_Cena_nd_nd_nd_nd_nd corresponds to a real image taken by a consumer(#Vol), taken in Madrid (Mad), of a “crianza wine”(C), in a Bourgogne wine glass (Bor), with a volume of 175 mL (175), taken at dinner (Cena) with undefined lighting (nd) and a random real background (nd), and without reference (nd) nor distance (nd), nor angle (nd) considerations., Peer reviewed

General information, experimental procedures, new compound characterization, crystallographic data, HPLC chromatograms, NMR spectra, DFT calculations, and STM data., Peer reviewed

The cancer bioMarkers database is curated and maintained by several clinical and scientific experts in the field of precision oncology supported by the European Union’s Horizon 2020 funding. This database is currently being integrated with knowledge databases of other institutions in a collaborative effort of the Global Alliance for Genomics and Health.

A tool aimed at the rational design of cancer gene panels. It estimates the cost-effectiveness of the designed panel on a cohort of tumors and provides reports on the importance of individual mutations for tumorigenesis or therapy.

Analyses somatic mutations in thousands of tumor genomes to identify cancer driver genes.

Gitools is a framework for analysis and visualization of multidimensional genomic data using interactive heat-maps.

Method to identify genomic regions, both coding and non-coding, bearing mutations with significant shift towards high functional impact across a cohort of tumos (FMbias), which are candidates to function as cancer drivers, through a local test.

Python tab files parsing and validating schema tools.

Onexus is a modular framework to manage the complete life cycle of data analyses. Data analyses follow these steps: analysis definition, analysis execution, results storing, results browsing and finally results publishing.