BUSCADOR RECOLECTA

This dataset in an Excel file compares the wave of globalization before the outbreak of the Great Recession in 2007 with its alleged historical antecedent before the outbreak of World War One. We describe trends in trade and openness, estimate the gains from trade and investigate the proximate causes of the growth of openness. We argue that the conventional wisdom has to be revised. The first wave of globalization started around 1820 and culminated around 1870. In the next century, trade continued to grow, with the exception of the Great Depression, but openness and gains fluctuated widely. Growth resumed in the early 1970s. By 2007, the world was more open than a century earlier and its inhabitants gained from trade substantially more than their ancestors did. The current wave of globalization, in spite of some similarities with previous trends, has no historical antecedents. This dataset is related to the working paper "A tale of two globalizations : gains for trade and openness 1800-2010" by by Giovanni Federico and Antonio Tena Junguito, available on: http://hdl.handle.net/10016/22354

This dataset is supporting information to the paper pubished in the Working papers in Economic History series, n. 16-01., This dataset in an Excel file presents our data-base on world trade from 1800 to 1938. We have collected or estimated series of imports and exports, at current and constant (1913) prices and at current and at constant (1913) borders, for 149 polities. After a short review of the available series, we describe the methods for the construction of the data-base. We then deal with the criteria for the inclusion of polities, the representativeness of our series, the main types of sources, the procedures of deflation and, when necessary, of adjustments to 1913 borders. We discuss the details of the estimation of our polity series in Appendix B. Following Feinstein and Thomas (2001), we assess the reliability of our polity estimates. In the last two sections we present our trade series at current and 1913 borders and compare them with other available series. This dataset is related to the working paper "World trade, 1800-1938 : a new data-set" by Giovanni Federico and Antonio Tena Junguito, available on: http://hdl.handle.net/10016/22222.

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.

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.

A needle-plot (aka stem-plot or lollipop-plot) plots each data point as a big dot and adds a vertical line that makes it appear like a needle.

Machine-learning based approach to classify cancer driver genes into to Activating or Loss of Function roles for cancer gene development.

Javascript library to create interactive heatmaps within webpages.

The aim of SVGMap is helping in the visualisation of experimental data which are associated with some graphical representation. Thus SVGMap browser allows to generate images with colored areas corresponding to the chosen data and color scale./nThe data is represented as a table and is searchable. All data as well as the generated images/figures can be exported easily through the interface./nAdditionally the tool allows to manage (add, edit or delete) experiments and configure the front-end user search appearance such as the number of images to be displayed, the scale types to use and more.

This database contains information on the genes identified as drivers in Rubio-Perez and Tamborero et al. (2015). It contains information on driver identification at mutational, CNA and gene fusion level. Additional ancillary information about the role and major clonality of drivers is also included. A table is also provided with the list of datasets used for mutational driver identification.