Abstract Virtual research environments are systems called to serve the needs of their designated communities of practice. Every community of practice is a group of people dynamically aggregated by the willingness to collaborate to address a given research question. The virtual research environment provides its users with seamless access to the resources of interest (namely, data and services) no matter what and where they are. Developing a virtual research environment thus to guarantee its uptake from the community of practice is a challenging task. In this article, we advocate how the co‐creation driven approach promoted by D4Science has proven to be effective. In particular, we present the co‐creation options supported, discuss how diverse communities of practice have exploited these options, and give some usage indicators on the created VREs.

Abstract The enhancements in IT solutions and the open science movement are injecting changes in the practices dealing with data collection, collation, processing, analytics, and publishing in all the domains, including agri‐food. However, in implementing these changes one of the major issues faced by the agri‐food researchers is the fragmentation of the “assets” to be exploited when performing research tasks, for example, data of interest are heterogeneous and scattered across several repositories, the tools modelers rely on are diverse and often make use of limited computing capacity, the publishing practices are various and rarely aim at making available the “whole story” including datasets, processes, and results. This paper presents the AGINFRA PLUS endeavor to overcome these limitations by providing researchers in three designated communities with Virtual Research Environments facilitating the use of the “assets” of interest and promote collaboration.

Summary 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. NLPHub adds Open Science‐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.

Summary 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 specialised 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.

Summary New Science paradigms have recently evolved to promote open publication of scientific findings as well as multi‐disciplinary collaborative approaches to scientific experimentation. These approaches can face modern scientific challenges but must deal with large quantities of data produced by industrial and scientific experiments. These data, so‐called Big Data, require to introduce new computer science systems to help scientists cooperate, extract information, and possibly produce new knowledge out of the data. E‐infrastructures are distributed computer systems that foster collaboration between users and can embed distributed and parallel processing systems to manage big data. However, in order to meet modern Science requirements, e‐Infrastructures impose several requirements to computational systems in turn, eg, being economically sustainable, managing community‐provided processes, using standard representations for processes and data, managing big data size and heterogeneous representations, supporting reproducible Science, collaborative experimentation, and cooperative online environments, managing security and privacy for data and services. In this paper, we present a cloud computing system (gCube DataMiner) that meets these requirements and operates in an e‐Infrastructure, while sharing characteristics with state‐of‐the‐art cloud computing systems. To this aim, DataMiner uses the web processing service standard of the open geospatial consortium and introduces features like collaborative experimental spaces, automatic installation of processes and services on top of a flexible and sustainable cloud computing architecture. We compare DataMiner with another mature cloud computing system and highlight the benefits our system brings, the new paradigms requirements it satisfies, and the applications that can be developed based on this system.