This document serves as the definition of the SSHOC Reference Ontology (SSHOCro, v.1.0). SSHOCro proposes an ontological model and RDF schema to be used as a top-level ontology for organising knowledge and information found distributed across various primary sources of information in the Social Sciences and Humanities Open Cloud (SSHOC).

This report describes guidelines that can be applied for training specialized neural machine translation (NMT) systems aimed at translation in a narrow textual domain, namely the domain of social surveys, requiring a specialized MT model that is able to handle domain-specific terminology. The work presented in this report demonstrates how relatively low-resource in-domain corpora can be used to prepare these specialized models.

This report details the data collection process of audio data in an online survey and the subsequent preparation of speech-to-text transcripts. In addition, it assesses the data quality and usability of the collected data. This report is accompanied by the audio transcript data in excel (xlsx) format.

This text concerns the achievement of the MS19 “Consultation with SSH data producers completed”. SSHOCro will be a common meta level schema to be used as top level ontology for organizing knowledge and information found distributed across various resources of data in the SSH open cloud. This will be achieved by providing a common, agreed –upon, understanding of the concepts, entities and relationships holding between them, in order to enable knowledge sharing, information exchange and integration between heterogeneous sources.

Archaeologists, architects, engineers, materials specialists, teachers, curators, and restorers of cultural property, contribute to the daily knowledge and conservation of heritage artefacts. For many years, the development of digital technologies has produced important results in the collection, visualisation and indexing of digital resources.

This document describes the packaging and release of the CUNI machine translation (MT) systems trained within the Tensor2tensor framework for sequence-to-sequence learning that was developed for task T4.5. The MT backend, together with a simple command-line interface (CLI) is released separately from the models. The MT backend is released as a separate Docker image as a platform-independent solution.

This document describes the output of Task 4.1 of the SSHOC (Social Sciences and Humanities Open Cloud) project funded by the European Commission under Grant Agreement N° 823782.

A technical infrastructure called Web Panel Sample Service (WPSS) has been implemented following the specifications published in November 2919 as deliverable 4.11 "A sample management system for cross- national web survey".

Task 4.5 Social policy APIs for social surveys in the Social Sciences and Humanities Open Cloud project aims to demonstrate the application of social policy APIs in a social sciences survey infrastructure. A proof of concept was prepared by the Generations and Gender Programme and the WageIndicator Survey in the form of an experiment in which a Social Policy Module was integrated in the Dutch WageIndicator Survey. The experiment can be used as a template for future applications which aim to link social policy information with information of individuals or households.

This document is the deliverable D4.11 of the Horizon2020 project “Social Sciences & Humanities Open Cloud”). It reports on the experience with the automatic verification checks implemented during the development phase of the questionnaire for SHARE wave 9. It describes the outcomes of the exercise, and it points out the critical issues to be addressed for further development.

This report reviews potential mechanisms to integrate a Translation Memory (TM) solution into the Translation Management Tool (TMT), allowing large international surveys to improve their translation processes and deliver quicker and better quality translations. This will include further research into integration with other external CAT tools and the development of a stand-alone Translation Memory tool to which the TMT will be connected, followed by an evaluation and implementation of various TM matching algorithms and sharing of TMT data with partners via the TM solution.