Making new data combinations and collaborating with researchers from different disciplines are becoming essential ingredients of scientific research. These activities are increasingly contributing to solutions for multidisciplinary global problems, such as climate change and energy transition. Virtual Research Environments (VREs) can potentially support making data combinations and researcher collaborations by providing a multiplicity of data and services. Many VREs have been developed already and are used in specific research domains. However, there is a lack of insight into what is needed to develop a multidisciplinary VRE in comparison with monodisciplinary VREs. This is currently blocking the development of innovative multidisciplinary VREs. This study aims to investigate the requirements for building a multidisciplinary VRE and to study the key differences between monodisciplinary VREs and multidisciplinary VREs. Our study shows that comprehensive requirements in nine categories need to be fulfilled when designing a multidisciplinary VRE. Lack of considering many requirements and limit focus in monodisciplinary VREs hinder the wide use of current VREs in multidisciplinary research.@en

Governments and publicly-funded research organisations increasingly make research data available openly. Researchers can use this data in Virtual Research Environments (VREs) to conduct multidisciplinary data-driven research and to obtain new insights potentially for governmental policy-making. However, the requirements for such a VRE are not yet clear. The objective of this study is to elicit and define requirements for a multidisciplinary VRE that integrates Open Government Data (OGD) and open research data for public policy making. Based on a VRE case study, we elicit 13 VRE requirements related to data storage, data accessing, data curation and other aspects, and describe a use case of open data for governmental policy-making. Meeting the requirements results in a VRE that 1) overlays the existing e-Research Infrastructures to provide researchers with integrated open data from different domains, 2) offers OGD in combination with data from publicly-funded research, and 3) stimulates innovation and research collaboration.@en

Governments and publicly-funded research institutions increasingly open up data collected and created through research. One way to share and use data obtained through research is through Virtual Research Environments (VREs). Insights obtained through open data use in VREs can subsequently provide input for policy making. However, this process involves many privacy, security and trust issues both for VRE developers and for end-users. We still know very little about what strategies can be used to handle these security, privacy and trust issues. This 1,5 hour interactive workshop aims to discuss and refine strategies for handling privacy, security and trust issues of VREs and their users as developed in the European VRE4EIC project. The workshop will facilitate open discussions making use of the interactive Mentimeter tool to involve all participants in the sharing of practices and in sharing feedback on the strategies.@en

Current global trends push enterprises to be increasingly efficient and flexible, and at the same time compliant with legislation regarding privacy, security, and transparency. The latest IoT (Internet-of-Things) developments offer opportunities for enterprises but at the same time those developments lead to an increased complexity with regard to the underlying software, this in turn leading to new risks. Hence, more advanced modeling methods and techniques may be necessary, especially in the area of enterprise information systems (often featured currently by enterprise-aligned IoT-enabled software systems), such that both the enterprise needs are captured (and understood) and software features are specified accordingly. We need a common modeling ground for this, allowing us to properly align enterprise modeling and software specification. Such a common ground can be co-created by enterprise engineers and software engineers, featuring: (a) technologyindependent enterprise models rooted in social theories; (b) technology-specific software models rooted in computing paradigms. An approach is needed on top of that because such an integrated enterprise-software modeling requires to be greased by modeling guidelines and notations, such that adequate modeling generations and transformations are possible. This means that taking as input unstructured business information, we should be able to usefully apply a modeling and design process, such that we come through enterprise models and reach as far as the specification and implementation of software. We argue that an existing approach has those capabilities, namely the approach SDBC (Software Derived from Business Components). Hence, we adopt SDBC in the current research. Further, we have opted for an explicit consideration of context-awareness and privacy, claiming their relevance with regard to some current IoT-related demands (mentioned above). Nevertheless, it has not yet been extensively studied how SDBC can be used for modeling software systems with requirements on those properties. For this reason, we aim at enriching the SDBC-rooted enterprise-modeling-driven software specification, by weaving in contextawareness and privacy enforcement. We partially demonstrate our proposed way of modeling, by means of a case example featuring land border security.@en