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Text mining involves a set of processes that analyze text to extract high-quality information. Among its large number of applications, there are experiments that tackle big data challenges using complex system architectures. However, text mining approaches are neither easy to discover and use nor easily combinable by end-users. Furthermore, they should be contextualized within new approaches to science (eg, Open Science) that ensure longevity and reuse of methods and results. This article presents NLPHub, a distributed system that orchestrates and combines several state-of-the-art text mining services that recognize spatiotemporal events, keywords, and a large set of named entities. NLPHub adopts an Open Science approach, which fosters the reproducibility, repeatability, and reusability of methods and results, by using an e-Infrastructure supporting data-intensive Science.NLPHubaddsOpenScience-compliance to the connected services through the use of representational standards for services and computations. It also manages heterogeneous service access policies and enables collaboration and sharing facilities. This article reports a performance assessment based on an annotated corpus of named entities, which demonstrates that NLPHub can improve the performance of the single-integrated processes by cleverly combining their output.

The development of data processing and analytics tools is heavily driven by applications, which results in a great variety of software solutions, which often address specific needs. It is difficult to imagine a single solution that is universally suitable for all (or even most) application scenarios and contexts. This chapter describes the data analytics framework that has been designed and developed in the ENVRIplus project to be (a) suitable for serving the needs of researchers in several domains including environmental sciences, (b) open and extensible both with respect to the algorithms and methods it enables and the computing platforms it relies on to execute those algorithms and methods, and (c) open-science-friendly, i.e. it is capable of incorporating every algorithm and method integrated into the data processing framework as well as any computation resulting from the exploitation of integrated algorithms into a “research object” catering for citation, reproducibility, repeatability and provenance.

Sets of two dimensional images are insufficient to capture the development in time and space of three-dimensional structures. The 2D 'flattening' of photographs results in a significant loss of features especially if the photos were taken by one person. Automatically collecting and aligning photos in order to render 3D structures from 2D images without specialized equipment, is currently a complex process that requires specialist knowledge with often limited results. In this paper, an Open Science oriented workflow is proposed where an on-line file system is used to share photos of an object or an environment and to produce a virtual reality scene as a navigable 3D reconstruction that can be shared with other people. Our workflow is based on a distributed e-Infrastructure and overcomes common limitations of other approaches by having all the used technology integrated on the same platform and by not requiring specialist knowledge. A performance evaluation of the 3D reconstruction process embedded in the workflow is reported against a commercial software and an open-source software in terms of computational efficiency and reconstruction accuracy, and three marine science use cases are reported to show potential applications of the workflow.

The open science movement is promising to revolutionise the way science is conducted with the goal to make it more fair, solid and democratic. This revolution is destined to remain just a wish if it is not supported by changes in culture and practices as well as in enabling technologies. This paper describes the D4Science offerings to enact open science-friendly Virtual Research Environments. In particular, the paper describes how complete solutions suitable for realising open science practices can be achieved by integrating a social networking collaborative environment with a shared workspace, an open data analytics platform, and a catalogue enabling to effectively find, access and reuse every research artefact.

Important changes have characterised research and knowledge production in recent decades. These changes are associated with developments in information technologies and infrastructures. The processes characterising research and knowledge production are changing through the digitalization of science, the virtualisation of research communities and networks, the offering of underlying systems and services by infrastructures. This paper gives an overview of gCube, a software system promoting elastic and seamless access to research assets (data, services, computing) across the boundaries of institutions, disciplines and providers to favour collaboration-oriented research tasks. gCube's technology is primarily conceived to enable Hybrid Data Infrastructures facilitating the dynamic definition and operation of Virtual Research Environments. To this end, it offers a comprehensive set of data management commodities on various types of data and a rich array of "mediators" to interface well-established Infrastructures and Information Systems from various domains. Its effectiveness has been proved by operating the D4Science.org infrastructure and serving concrete, multidisciplinary, challenging, and large scale scenarios.