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 different algorithms of the "imbalanced-learn" toolbox are evaluated on a set of common dataset, which are more or less balanced. These benchmark have been proposed in Ding, Zejin, "Diversified Ensemble Classifiers for H ighly Imbalanced Data Learning and their Application in Bioinformatics." Dissertation, Georgia State University, (2011)

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of prostate cancer to facilitate its diagnosis. A 3mm slice fat-suppressed T2W fast spin-echo sequence (TR/TE/ETL: 3400 ms/85 ms/13) is used to acquire images in sagittal and oblique coronal planes, the latter planes being orientated perpendicular or parallel to the prostate PZ – rectal wall axis. Three-dimensional T2W fast spin-echo (TR/TE/ETL: 3600 ms}/143 ms/109, slice thickness: 1.25 mm) images are then acquired in an oblique axial plane.The nominal matrix and FoV of the 3D T2W fast spin-echo images are 320x256 and 280x240 mm2, respectively, thereby affording sub-millimetric pixel resolution within the imaging plane.\nDCE-MRI is performed using a fat suppressed 3D T$_1$ VIBE sequence (TR/TE/Flip angle: 3.25 ms/1.12 ms/10 degrees; Matrix: 256x192; FoV: 280x210 (with 75% rectangular FoV); slab of 16 partitions of 3.5 mm thickness; temporal resolution: 6 s/slab over approximately 5 min). A power injector (Medrad, Indianola, USA) is used to provide a bolus injection of Gd-DTPA (Dotarem, Guerbet, Roissy, France) at a dose of 0.2 ml Gd-DTPA/kg of body weight

Development and investigation of a multiparametric magnetic resonance CAD system for prostate cancer (CaP) detection. Data associated with chapter 6 of Guillaume Lemaitre's doctoral thesis

Multi-parametric magnetic resonance imaging (MRI) dataset for prostate cancer detection. The purpose of this dataset is to help at the development of computer-aided detection and diagnosis (CAD) system of prostate cancer (CaP)

Dades associades a l'article publicat: Lemaitre, G., Marti, R., Freixenet, J., Vilanova, J.C., Walker, P.M. i Meriaudeau, F. (2015). Computer-Aided Detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: A review. Computers in Biology and Medicine, vol. 60, p. 8-31. Disponible a https://doi.org/10.1016/j.compbiomed.2015.02.009, Instruccions per obrir els fitxers: cal concatenar els fitxers per crear el fitxer original. Dps es pot descomprimir amb tar al linux i amb 7zip al windows. Amb Linux console: cat file1 file2 file3 ... > file.tar.gz, descomprimir: tar -xzf file.tar.gz. I amb windows console: type file1 file2 file3 ... > file.tar.gz, descomprimir amb 7zip, Dataset available at I2CVB http://i2cvb.github.io/, Multi-parametric magnetic resonance imaging (MRI) dataset for prostate cancer detection. The purpose of this dataset is to help at the development of computer-aided detection and diagnosis (CAD) system of prostate cancer (CaP)

Instruccions per obrir els fitxers: cal concatenar els fitxers per crear el fitxer original. Dps es pot descomprimir amb tar al linux i amb 7zip al windows. Amb Linux console: cat file1 file2 file3 ... > file.tar.gz, descomprimir: tar -xzf file.tar.gz. I amb windows console: type file1 file2 file3 ... > file.tar.gz, descomprimir amb 7zip, Dades associades amb el capítol 6 de la següent tesi doctoral: Lemaître, Guillaume. “Computer-Aided Diagnosis for Prostate Cancer using Multi-Parametric Magnetic Resonance Imaging”. Tesi. Universitat de Girona i Université de Bourgogne Franche-Comté, 2016, Development and investigation of a multiparametric magnetic resonance CAD system for prostate cancer (CaP) detection. Data used for the pipeline developed in https://github.com/I2Cvb/mp-mri-prostate, Desenvolupament i investigació d'un sistema CAD de ressonància magnètica multiparamètrica per a la detecció del càncer de pròstata (CaP). Dades utilitzades per al pipeline desenvolupat a https://github.com/I2Cvb/mp-mri-prostate

Instruccions per obrir els fitxers: cal concatenar els fitxers per crear el fitxer original. Dps es pot descomprimir amb tar al linux i amb 7zip al windows. Amb Linux console: cat file1 file2 file3 ... > file.tar.gz, descomprimir: tar -xzf file.tar.gz. I amb windows console: type file1 file2 file3 ... > file.tar.gz, descomprimir amb 7zip, Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of prostate cancer to facilitate its diagnosis. A 3mm slice fat-suppressed T2W fast spin-echo sequence (TR/TE/ETL: 3400 ms/85 ms/13) is used to acquire images in sagittal and oblique coronal planes, the latter planes being orientated perpendicular or parallel to the prostate PZ – rectal wall axis. Three-dimensional T2W fast spin-echo (TR/TE/ETL: 3600 ms}/143 ms/109, slice thickness: 1.25 mm) images are then acquired in an oblique axial plane.The nominal matrix and FoV of the 3D T2W fast spin-echo images are 320x256 and 280x240 mm2, respectively, thereby affording sub-millimetric pixel resolution within the imaging plane.\nDCE-MRI is performed using a fat suppressed 3D T$_1$ VIBE sequence (TR/TE/Flip angle: 3.25 ms/1.12 ms/10 degrees; Matrix: 256x192; FoV: 280x210 (with 75% rectangular FoV); slab of 16 partitions of 3.5 mm thickness; temporal resolution: 6 s/slab over approximately 5 min). A power injector (Medrad, Indianola, USA) is used to provide a bolus injection of Gd-DTPA (Dotarem, Guerbet, Roissy, France) at a dose of 0.2 ml Gd-DTPA/kg of body weight

Instruccions per obrir els fitxers: cal concatenar els fitxers per crear el fitxer original. Dps es pot descomprimir amb tar al linux i amb 7zip al windows. Amb Linux console: cat file1 file2 file3 ... > file.tar.gz, descomprimir: tar -xzf file.tar.gz. I amb windows console: type file1 file2 file3 ... > file.tar.gz, descomprimir amb 7zip, The different algorithms of the "imbalanced-learn" toolbox are evaluated on a set of common dataset, which are more or less balanced. These benchmark have been proposed in Ding, Zejin, "Diversified Ensemble Classifiers for H ighly Imbalanced Data Learning and their Application in Bioinformatics." Dissertation, Georgia State University, (2011)