• Digital Humanities and Cultural Heritage
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WPSS stands for Web Panel Sample Service. It is a web application paired with the Qualtrics survey platform to meet the specific needs of high quality cross-national web surveys. WPSS enables: - a centralised management of survey fielding orchestration (publishing, sending invites and reminders), - and decentralised and privacy-compliant handling of panelist data. WPSS has been specified and developped within the framework of SSHOC, the Social Sciences and Humanities Open Cloud H2020, as part of the Work Package "Innovations in data production", led by ESS-ERIC. The software was developped by the CDSP's IT team at Sciences Po. Use cases can be found in the Social Sciences and Humanities but may extend to broader contexts as well. From October 2021 to June 2023, WPSS supports its first full-scale use case, within the frame of the European Social Survey for the web panel spinoff of ESS round 10, "Opinion Survey for (Country) "also known as the CRONOS-2 project.

En-De translation models, exported via TensorFlow Serving, available in the Lindat translation service (https://lindat.mff.cuni.cz/services/translation/). Models are compatible with Tensor2tensor version 1.6.6. For details about the model training (data, model hyper-parameters), please contact the archive maintainer. Evaluation on newstest2020 (BLEU): en->de: 25.9 de->en: 33.4 (Evaluated using multeval: https://github.com/jhclark/multeval)

ELMo language model (https://github.com/allenai/bilm-tf) used to produce contextual word embeddings, trained on entire Gigafida 2.0 corpus (https://viri.cjvt.si/gigafida/System/Impressum) for 10 epochs. 1,364,064 most common tokens were provided as vocabulary during the training. The model can also infer OOV words, since the neural network input is on the character level.

The pipeline code (written in groovy) models the entire deploy process of one or more gCube Services. The pipeline identifies the kind of component and invoke an ansible script in charge of deploy the component on the target VM, realizing in this way the "ContinuousDeployment". The pipeline can be started in three differnt ways: manually started, triggered by a JenkinsJob or triggered by a gCube Release Job. In this case, the pipeline accept a YAML input file that contains a list of jenkins job that will be deployed by the gCubeDeployer pipeline sequentially. The definition of the gCubeDeployer pipeline is mantained in this Git repository: https://code-repo.d4science.org/gCubeCI/gCubeBuilder

Il sistema "geoportal-data-entry-app", nasce nell'ambito del progetto europeo "ARIADNEPlus", in cui in collaborazione e per i referenti dell'Istituto centrale per l'Archeologia (ICA) e per il Ministero dei Beni Culturali, si è realizzato un ambiente di prototipazione per il Geoportale Nazionale per l'Archeologia (GNA). In particolare, il sistema permette agli attori che lo utilizzano, il Project management (data entry e gestione del ciclo di vita) di prodotti spazio-temporali basati su Use Case Descriptor (UCD) document che ne specificano il modello del documento, gestione, lifecycle, handler, etc. Pertanto il sistema permette di: (i) accedere e ricercare i prodotti pubblicati nel sistema, (ii) accedere al report di pubblicazione dei prodotti, visionare lo stato di pubblicazione (SUCCESS, WARNING, ERROR), etc., (iii) accedere alla loro visualizzazione su mappa, (iv) gestire il loro contenuto mediante il flusso (Workflow) di pubblicazione, (v) gestire le relazioni (collegamenti temporali e/o qualitativi) tra i prodotti. Il sistema in generale consente il data-entry di qualsivoglia prodotto avente caratteristiche spazio-temporali e non, i cui metadati sono specificati da un indice che specifica uno o più Metadata Profile e la struttura del JSON risultante. Un Metadata Profile è un documento XML-based di specifica di un "prodotto generico" (es. un dataset spazio-temporale) e dei suoi attributi, che indica la lista dei metadati che lo compongono e che possono essere di tipo descrittivo, spaziale, temporale, risorsa (es. file immagine), ecc..

The pipeline code (written in groovy) models the entire deploy process of one or more gCube Services. The pipeline identifies the kind of component and invoke an ansible script in charge of deploy the component on the target VM, realizing in this way the "ContinuousDeployment". The pipeline can be started in three differnt ways: manually started, triggered by a JenkinsJob or triggered by a gCube Release Job. In this case, the pipeline accept a YAML input file that contains a list of jenkins job that will be deployed by the gCubeDeployer pipeline sequentially. The definition of the gCubeDeployer pipeline is mantained in this Git repository: https://code-repo.d4science.org/gCubeCI/gCubeBuilder

In 2018 the IPERION-CH Grounds Database was presented to examine how the data produced through the scientific examination of historic painting preparation or grounds samples, from multiple institutions could be combined in a flexible digital form. Exploring the presentation of interrelated high resolution images, text, complex metadata and procedural documentation. The original main user interface is live, though password protected at this time. The SSHOC work aimed to make this data more FAIR so in addition to mapping it to a standard ontology, to increase interoperability, it has also been made available in the form of open linkable data combined with a SPARQL end-point. A draft version of this live data presentation can been found Here. A live version of the mapped data created with this software can be found at: https://rdf.ng-london.org.uk/sshoc

This artifact contains gCube commons library version declaration for dependency management Support Bugs and support requests can be reported in the gCube issue tracking tool: https://support.d4science.org/projects/gcube/ Changes in this version: Added gxREST DependencyAdded gxJRS DependencyImported jersey-bomAdded javax.ws.rs-api compatible with defined jersey version ──────────This software is part of the gCube Framework: an open-source software toolkit used for building and operating Hybrid Data Infrastructures. Find out more at https://www.gcube-system.org/.

tabular-data-csv-import-widget allows csv import from file and workspace Documentation Documentation is available on-line in the gCube Wiki: https://gcube.wiki.gcube-system.org/gcube/index.php/Tabular_Data_Manager Installation Installation 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/ Changes in this version: Updated to StorageHub [task #11879] ──────────This software is part of the gCube Framework: an open-source software toolkit used for building and operating Hybrid Data Infrastructures. Find out more at https://www.gcube-system.org/.

This is an interactive live modelling system which can automatically convert simple tab separated triples or JSON-LD (experimental) into graphical models using the mermaid library. General updates and improvements have been carried out, with the addition of pan and zoom functions for the displayed models, simple tooltips, alternative display labels for links, an alternative format to fix property labels to relationships lines and an update default instructions model. Instructions: Several worked examples, including some simple instructions relating to how this system works are included in the interactive web site: dynamic modelling, with all of the related code presented in GitHub.