DARIAH and the Benelux

International audienceDARIAH, the Digital Research Infrastructure for the Arts and Humanities, aims to enhance and support digitally-enabled research and teaching across the humanities and arts. By bringing together national activities from Member countries, DARIAH is able to offer a portfolio of services and activities centred around research communities. DARIAH was established as a European legal entity in August 2014 with 15 countries - Austria, Belgium, Croatia, Cyprus, Denmark, France, Germany, Greece, Ireland, Italy, Luxembourg, Malta, The Netherlands, Slovenia and Serbia – as Founding Members. This was an important step towards developing a research infrastructure for sharing and sustaining digital arts and humanities knowledge across Europe and beyond. Using the opportunity to present a poster at DH Benelux 2015 as a starting point, the authors would like to explore how DARIAH-BE, DARIAH-LU and DARIAH-NL could collaborate to both strengthen their participation in DARIAH within their individual countries and together as the Benelux region. Initial ideas include: a) increasing collaboration between researchers and infrastructure providers: taking advantage of the geographical proximity and language synergies to participate in shared activities e.g. joint research projects and training events, b) increasing funding opportunities: exploring regional possibilities for funding and establishing partnerships for European funding proposals and c) sharing DARIAH knowledge and experience: increasing understanding and identifying synergies between the DARIAH activities in each country. Through strengthening the collaboration between DARIAH activities in Belgium, Luxembourg and The Netherlands, we would like to facilitate maximum participation of digital humanities researchers in the Benelux region in DARIAH in order to take full advantage of the benefits of being part of the European DARIAH community

Connecting Data and Publications through e-Infrastructures

Gräf F, Hoogerwerf M, Lösch M, et al. Connecting Data and Publications through e-Infrastructures.; Accepted.The document reports results of the design, development and dissemination of “Subject-specific Pilots for Enhanced Publications” (T3.1). Being part of WP3 “Studies on practices and principles of OA”, the outcome of the task is twofold: (i) Development of three prototype applications to showcase how interconnected research information is being managed in different disciplines. (ii) Experiences and insights gained on a (potentially discipline-independent) implementation of systems capable of managing such linked artefacts that will inform the future development of the OpenAIRE infrastructure and its portal. This report is based on the paper “Linking Data and Publications: Toward a Cross-Disciplinary Approach” from the same authors. It was presented at the International Digital Curation Conference, Amsterdam 2013 and submitted to the International Journal of Digital Curation

Linking Data and Publications: Towards a Cross-Disciplinary Approach

Hoogerwerf M, Lösch M, Schirrwagen J, et al. Linking Data and Publications: Towards a Cross-Disciplinary Approach. International Journal of Digital Curation. 2013;8(1):244-254.In this paper, we tackle the challenge of linking scholarly information in multi-disciplinary research infrastructures. There is a trend towards linking publications with research data and other information, but, as it is still emerging, this is handled differently by various initiatives and disciplines. For OpenAIRE, a European cross-disciplinary publication infrastructure, this poses the challenge of supporting these heterogeneous practices. Hence, OpenAIRE wants to contribute to the development of a common approach for discipline-independent linking practices between publications, data, project information and researchers. To this end, we constructed two demonstrators to identify commonalities and differences. The results show the importance of stable and unique identifiers, and support a ‘by reference’ approach of interlinking research results. This approach allows discipline-specific research information to be managed independently in distributed systems and avoids redundant maintenance. Furthermore, it allows these disciplinary systems to manage the specialized structures of their contents themselves

PRELIDA D3.1 State of the art assessment on Linked Data and Digital Preservation

The state of the art of Linked Data technologies and standards and of Digital Preservation solutions, standards and technologies is presented, along with an analysis of the characteristics of Linked Data that make their preservation different from that of other digital resources (A consolidated version of the report will be published at the end of the project

Enhanced Publications : Linking Publications and Research Data in Digital Repositories

The main conclusion is that publishers and repositories have the building blocks and the tools, but in general do not use them to create an Enhanced Publication for all three information categories. Publisher and repositories should offer the service and tools to add research data, extra materials and post-publication data to the publications. Researchers should be responsible for the content

PersID - III: (IIIa) Current State and State of the Art & (IIIb) User Requirements

This report is one of five reports with the outcomes of the PersID project commissioned by Knowledge Exchange and SURFfoundation. PersID addresses technical, functional and policy aspects of a persistent identifier infrastructure

An open source machine learning framework for efficient and transparent systematic reviews

To help researchers conduct a systematic review or meta-analysis as efficiently and transparently as possible, we designed a tool to accelerate the step of screening titles and abstracts. For many tasks—including but not limited to systematic reviews and meta-analyses—the scientific literature needs to be checked systematically. Scholars and practitioners currently screen thousands of studies by hand to determine which studies to include in their review or meta-analysis. This is error prone and inefficient because of extremely imbalanced data: only a fraction of the screened studies is relevant. The future of systematic reviewing will be an interaction with machine learning algorithms to deal with the enormous increase of available text. We therefore developed an open source machine learning-aided pipeline applying active learning: ASReview. We demonstrate by means of simulation studies that active learning can yield far more efficient reviewing than manual reviewing while providing high quality. Furthermore, we describe the options of the free and open source research software and present the results from user experience tests. We invite the community to contribute to open source projects such as our own that provide measurable and reproducible improvements over current practice