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The gCube System - Create users portlet -------------------------------------------------- Create users portlet allows a VRE-Manager to enter user's personal data and automatically register he/she in the portal. The portlet also sends a temporary password to the just registered user. This software is part of the gCube Framework (https://www.gcube-system.org/): an open-source software toolkit used for building and operating Hybrid Data Infrastructures enabling the dynamic deployment of Virtual Research Environments by favouring the realisation of reuse oriented policies. The projects leading to this software have received funding from a series of European Union programmes including: * the Sixth Framework Programme for Research and Technological Development - DILIGENT (grant no. 004260); * the Seventh Framework Programme for research, technological development and demonstration - D4Science (grant no. 212488), D4Science-II (grant no. 239019),ENVRI (grant no. 283465), EUBrazilOpenBio (grant no. 288754), iMarine (grant no. 283644); * the H2020 research and innovation programme - BlueBRIDGE (grant no. 675680), EGIEngage (grant no. 654142), ENVRIplus (grant no. 654182), Parthenos (grant no. 654119), SoBigData (grant no. 654024); Version -------------------------------------------------- 2.4.0-4.8.0-148779 (2017-12-01) Please see the file named "changelog.xml" in this directory for the release notes. Authors -------------------------------------------------- * Costantino Perciante at ISTI-CNR(Pisa), Istituto di Scienza e Tecnologie dell'Informazione "A. Faedo" - CNR, Pisa (Italy). Maintainers ----------- * Costantino Perciante at ISTI-CNR(Pisa), Istituto di Scienza e Tecnologie dell'Informazione "A. Faedo" - CNR, Pisa (Italy). Download information -------------------------------------------------- Source code is available from SVN: http://svn.research-infrastructures.eu/public/d4science/gcube/trunk/portlets/admin/create-users Binaries can be downloaded from the gCube website: https://www.gcube-system.org/ Installation -------------------------------------------------- Installation documentation is available on-line in the gCube Wiki: https://wiki.gcube-system.org/gcube/index.php Documentation -------------------------------------------------- Documentation is available on-line in the gCube Wiki: https://wiki.gcube-system.org/gcube/index.php https://wiki.gcube-system.org/gcube/index.php Support -------------------------------------------------- Bugs and support requests can be reported in the gCube issue tracking tool: https://support.d4science.org/projects/gcube/ Licensing -------------------------------------------------- This software is licensed under the terms you may find in the file named "LICENSE" in this directory.

Matlab code for the numerical simulations in: Silvia Ortín, Miguel C. Soriano, Ingo Fischer, Claudio R. Mirasso, and Apostolos Argyris, "Optical dendrites for spatio-temporal computing with few-mode fibers [Invited]", Opt. Mater. Express 12, 1907-1919 (2022)

We present here a Community Inversion Framework (CIF) to help rationalize development efforts and leverage the strengths of individual inversion systems into a comprehensive framework. The CIF is primarily a programming protocol to allow various inversion bricks to be exchanged among researchers. In practice, the ensemble of bricks makes a flexible, transparent and open-source python-based tool to estimate the fluxes of various GHGs both at global and regional scales. It will allow running different atmospheric transport models, different observation streams and different data assimilation approaches. This adaptability will allow a comprehensively assessment of uncertainty in a fully consistent framework. The present object contains all codes and documentation pages included in the Community Inversion Framework: pycif: the Python interface to the CIF model_sources: Fortran sources of main models in the CIF tests: pytest configuration bin: example of bash scripts to run the CIF docs: ReStructured Text documentation sources and HTML compiled pages (also published here)

The source code is used to display pneumococcal compendium: gene expression values, fold changes and correlation values

Tools for the assessment of earthquake damage and loss in real-time, developed within the RISE project. The Real-Time Loss Tools carry out rapid loss assessments (RLA) and event-based operational earthquake loss forecasts (OELF) incorporating probabilities of damage states based on structural health monitoring (SHM) methods, calculating cumulative damage, expected economic losses and human casualties (injuries and deaths) and updating the number of occupants in the buildings, by taking into account the time of the day of the earthquake as well as whether people are allowed back (due to inspection and repair times) and are able to do so (due to their own health status). The tools recursively call the OpenQuake engine and update the exposure model and other relevant input files. These tools are not an operational deployment but a research tool made openly available to the community. Full deliverable available here. Version v1.0 is the version used to run the files contained in https://doi.org/10.5281/zenodo.7784841 and whose results are presented and discussed in the aforementioned deliverable of the RISE project.

The gCube System - tabular-data-metadata-widget -------------------------------------------------- tabular-data-metadata-widget allows display metadata as accordion window This software is part of the gCube Framework (https://www.gcube-system.org/): an open-source software toolkit used for building and operating Hybrid Data Infrastructures enabling the dynamic deployment of Virtual Research Environments by favouring the realisation of reuse oriented policies. The projects leading to this software have received funding from a series of European Union programmes including: * the Sixth Framework Programme for Research and Technological Development - DILIGENT (grant no. 004260); * the Seventh Framework Programme for research, technological development and demonstration - D4Science (grant no. 212488), D4Science-II (grant no. 239019),ENVRI (grant no. 283465), EUBrazilOpenBio (grant no. 288754), iMarine (grant no. 283644); * the H2020 research and innovation programme - BlueBRIDGE (grant no. 675680), EGIEngage (grant no. 654142), ENVRIplus (grant no. 654182), Parthenos (grant no. 654119), SoBigData (grant no. 654024); Version -------------------------------------------------- 1.7.0-4.7.0-125840 (2017-10-13) Please see the file named "changelog.xml" in this directory for the release notes. Authors -------------------------------------------------- * Giancarlo Panichi (giancarlo.panichi-AT-isti.cnr.it), Istituto di Scienza e Tecnologie dell'Informazione "A. Faedo" CNR, Pisa IT Maintainers ----------- * Giancarlo Panichi (giancarlo.panichi-AT-isti.cnr.it), Istituto di Scienza e Tecnologie dell'Informazione "A. Faedo" CNR, Pisa IT Download information -------------------------------------------------- Source code is available from SVN: https://svn.research-infrastructures.eu/public/d4science/gcube/trunk/portlets/user/tabular-data-metadata-widget Binaries can be downloaded from the gCube website: https://www.gcube-system.org/ Installation -------------------------------------------------- Installation documentation is available on-line in the gCube Wiki: https://gcube.wiki.gcube-system.org/gcube/index.php/Tabular_Data_Manager Documentation -------------------------------------------------- Documentation is available on-line in the gCube Wiki: https://gcube.wiki.gcube-system.org/gcube/index.php/Tabular_Data_Manager Support -------------------------------------------------- Bugs and support requests can be reported in the gCube issue tracking tool: https://support.d4science.org/projects/gcube/ Licensing -------------------------------------------------- This software is licensed under the terms you may find in the file named "LICENSE" in this directory.

Code and materials for the paper: K. Zdybał, A. Parente, J. C. Sutherland - Improving reduced-order models through nonlinear decoding of projection-dependent outputs, Patterns, 2023 The bigger picture Large datasets are increasingly abundant in various scientific and engineering disciplines. Multiple physical variables are frequently gathered into one dataset, leading to high data dimensionality. Visualizing and understanding multivariate datasets, and building data-driven models based on the collected variables can be achieved through dimensionality reduction. However, in many reduction techniques to date, there is no guarantee that the reduced data representation will posses certain desired topological qualities. We show that the quality of reduced data representations can be significantly improved by informing data projections by target quantities of interest (QoIs), some of which are functions of the projection itself. The target QoIs are often known to researchers as variables that should be well represented on a projection. Those can include closure terms required in modeling, important physical variables other than the state variables, or class labels in the case of categorical data. Our approach of computing improved data representations can find application in all areas of science and engineering that aim to reduce the dimensionality of multivariate datasets, as well as in fundamental research of representation learning. This work can have particular relevance in efficient data visualization and in efficient modeling of dynamical systems with many degrees of freedom.