• DARIAH EU
• Publicationsclose
• Open Access close
• English close
• NARCIS close

The European Holocaust Research Infrastructure (EHRI) started in October 2010 to build on a network that connects both people (Holocaust researchers, archivists, curators, librarians and digital humanists) and dispersed Holocaust source material and collections. EHRI’s aim is making sources visible in a systematic way in order to counteract the fragmentation of the sources and to reveal interconnections. EHRI focuses on Archive and collection descriptions, which are available through the EHRI Portal. EHRI is currently in its second phase and is on the ESFRI Roadmap2 for a more sustainable future. EHRI has developed a set of controlled vocabularies that serves both as a retrieval and cataloguing tool for the multilingual and highly heterogeneous data of the EHRI portal. These vocabularies were partly implemented in the first phase of the project. In the current phase of EHRI the vocabularies are in the process of quality improvement improve and enrich the existing terms, add new terms, disambiguate and remove the mistakes (deduplication, merging, adding multilingual labels, consistency checks, multiple parent relations, etc.) and increase their coverage. In the EHRI portal the subject terms are currently not available for the public, as they are used only for retrieval purposes.

This article describes how virtual research environments (VREs) offer new opportunities for researchers to analyse open data and to obtain new insights for policy making. Although various VRE-related initiatives are under development, there is a lack of insight into how VREs support collaborative open data analysis by researchers and how this might be improved, ultimately leading to input for policy making to solve societal issues. This article clarifies in which ways VREs support researchers in open data analysis. Seven cases presenting different modes of researcher support for open data analysis were investigated and compared. Four types of support were identified: 1) ‘Figure it out yourself', 2) ‘Leading users by the hand', 3) ‘Training to provide the basics' and 4) ‘Learning from peers'. The author provides recommendations to improve the support of researchers' open data analysis and to subsequently obtain new insights for policy making to solve societal challenges.

The social sciences and the humanities taken together contain a heterogeneous range of research disciplines. Almost all existing methods of research can be found within these two domains. Data handling (collecting, processing, selecting, preserving) and publication methods differ greatly. Attitudes in the field towards Open Access of publications as well to research data vary as well. It is not possible to cover the total fullness, and complexity, of all the disciplines within these two domains. Our observations will therefore be based upon a number of case studies. Taken together these case studies give a fairly representative picture of the domains, at least of the most common research environments. The main dividing line is between those disciplines creating empirical data, such as survey data in the social sciences and those, especially in the humanities, using existing source material, such as history or text studies. This source material can either be of an analogous or a digital nature. As will be shown in the case studies in many disciplines a mix of created and existing is often combined.

International audience

In this chapter I will describe what the CLARIN infrastructure is and how it can be used, with a focus on the Low Countries (and especially the Netherlands) part of the CLARIN infrastructure. I aim to explain how a Humanities researcher can use the CLARIN infrastructure. I describe the basic functionality that CLARIN aims to offer, including searching for data and software, applying software to data, and storing data and software resulting from research.

This study discusses evaluation methods for linguists to use when employing an automatically annotated treebank as a source of linguistic evidence. While treebanks are usually evaluated with a general measure over all the data, linguistic studies often focus on a particular construction or a group of structures. To judge the quality of linguistic evidence in this case, it would be beneficial to estimate annotation quality over all instances of a particular construction. I discuss the relative advantages and disadvantages of four approaches to this type of evaluation: manual evaluation of the results, manual evaluation of the text, falling back to simpler annotation and searching for particular instances of the construction. Furthermore, I illustrate the approaches using an example from Dutch linguistics, two-verb cluster constructions, and estimate precision and recall for this construction on a large automatically annotated treebank of Dutch. From this, I conclude that a combination of approaches on samples from the treebank can be used to estimate the accuracy of the annotation for the construction of interest. This allows researchers to make more definite linguistic claims on the basis of data from automatically annotated treebanks.

On the complexity of accessing and using AV data online

This paper describes the achievements of the H2020 project INDIGO-DataCloud. The project has provided e-infrastructures with tools, applications and cloud framework enhancements to manage the demanding requirements of scientific communities, either locally or through enhanced interfaces. The middleware developed allows to federate hybrid resources, to easily write, port and run scientific applications to the cloud. In particular, we have extended existing PaaS (Platform as a Service) solutions, allowing public and private e-infrastructures, including those provided by EGI, EUDAT, and Helix Nebula, to integrate their existing services and make them available through AAI services compliant with GEANT interfederation policies, thus guaranteeing transparency and trust in the provisioning of such services. Our middleware facilitates the execution of applications using containers on Cloud and Grid based infrastructures, as well as on HPC clusters. Our developments are freely downloadable as open source components, and are already being integrated into many scientific applications. INDIGO-Datacloud has been funded by the European Commision H2020 research and innovation program under grant agreement RIA 653549. Peer reviewed