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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 digitalisation of science, the virtualisation of research communities and ne...
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Context 1
... of Virtual Research Environments, i.e. ready to use web-based working environ- ments specifically conceived to provide their designated community with the facilities (services, data, capacity) they need. Virtual research environments are expected to be used primarily via a plain web browser. A screenshot of a typical home of the GUI is in Fig. 3 where the user is provided with the list of VREs he/she is member of (right column), some statistics on his/her activity and a direct access to the workspace (left column), access to the catalogue and the messages and discussions occurring (central column). Fig. 3 show- cases a typical home page of a VRE with its own specific menu for ...
Context 2
... primarily via a plain web browser. A screenshot of a typical home of the GUI is in Fig. 3 where the user is provided with the list of VREs he/she is member of (right column), some statistics on his/her activity and a direct access to the workspace (left column), access to the catalogue and the messages and discussions occurring (central column). Fig. 3 show- cases a typical home page of a VRE with its own specific menu for accessing the facilities it offers. In addition to web GUIs, there are RESTful APIs for accessing and using VRE services in a programmatic way. 7 ...
Citations
... It builds up a user-shared semantic knowledge base that automatically interconnects all stories and seamlessly enables collaborative story building. Finally, it operates within an Open-Science oriented e-Infrastructure (D4Science [6]), which enables data and information sharing within communities of narrators, and adds multi-tenancy, multi-user, security, and access-control facilities [5,7,24]. Our system represents narratives as a network of spatiotemporal events related by semantic relations (part-of, temporal, spatial, and causal relations). ...
... They can embed cloud computing systems with standardized interfaces that manage large computations on Big Data while tracking computational parameters [21,24]. D4Science is a free-to-use fully-compliant Open Science-oriented e-Infrastructure [5], constituted by a network of hardware and software resources (e.g., Web services, databases, semantic knowledge bases) that enable remote user collaboration, data exchange, and collaborative data-intensive processing. D4Science can manage heterogeneous access policies to data payloads, catalogues, computational resources, and Web services. ...
The paper presents the Story Map Building and Visualizing Tool (SMBVT) that allows users to create story maps within a collaborative environment and a usable Web interface. It is entirely open-source and published as a free-to-use solution. It uses Semantic Web technologies in the back-end system to represent stories through a reference ontology for representing narratives. It builds up a user-shared semantic knowledge base that automatically interconnects all stories and seamlessly enables collaborative story building. Finally, it operates within an Open-Science oriented e-Infrastructure, which enables data and information sharing within communities of narrators, and adds multi-tenancy, multi-user, security, and access-control facilities. SMBVT represents narratives as a network of spatiotemporal events related by semantic relations and standardizes the event descriptions by assigning internationalized resource identifiers (IRIs) to the event components, i.e., the entities that take part in the event (e.g., persons, objects, places, concepts). The tool automatically saves the collected knowledge as a Web Ontology Language (OWL) graph and openly publishes it as Linked Open Data. This feature allows connecting the story events to other knowledge bases. To evaluate and demonstrate our tool, we used it to describe the Apuan Alps territory in Tuscany (Italy). Based on a user-test evaluation, we assessed the tool’s effectiveness at building story maps and the ability of the produced story to describe the territory beyond the map.
... The D4Science back end components are services (often frameworks on their own) organised in four main areas: (i) core services support- ing VREs management, resources management, authentication and authorisation; (ii) data space management services supporting the storage and management of various typologies of data, including files stored in diverse storage systems and geospatial data managed by a spatial data infrastructure (SDI) exploiting an array of orchestrated GeoNetworks 3 , THREDDS data servers 4 , and GeoServers 5 ; (iii) data analytics services supporting several options for data analytics, including the DataMiner proprietary platform with its integration facility [9], JupyterHub 6 , and a cluster of RStudio 7 instances; (iv) collaborative services implementing facilities enacting the collaboration among the members of a VRE, for example, by supporting communication and sharing. ...
... Often they are also looking for solutions to help them to minimise the "timeto-market", i.e., the time in which they can start using the VRE to support their specific activities. D4Science implements a VRE distribution model in which it hosts the whole application and provides it to users over the internet as a service [9]. The advantage of this design choice is that the actual management of the IT solution is in the hands of expert operators who manage it by providing reliable services, leveraging economies of scale, and using elastic approaches to scale. ...
Today, complex research challenges, often based on the analysis of a large amount of data, require multidisciplinary collaboration and appropriate communication and sharing of data, processes and outcomes. Technologies and large-scale infrastructures provide stakeholders with computing capacity and data services to perform unprecedented levels of data-driven scientific activities. This opens the way to science gateways and virtual research environments supporting researchers in scientific and educational activities. This article describes our extensive experience with the Virtual Research Environments (VRE) operated by the D4Science infrastructure. It presents how this infrastructure supports their development, their basic functionalities and how they are easily customised to serve the needs of specific user communities. It also describes how they are used in real contexts. The article concludes by reporting how VREs are now progressively used as valuable instruments to support open science and how this role might become more relevant in the future.
... 1) Manual Approach: Assante et al. employs a modulebased discovery approach [9] depicted in figure 1. The overall working process consists of three main phases. ...
... This paper describes how the co-creation approach to VRE development has been implemented and promoted by D4Science 10,11 . In particular, it documents the co-creation options enacting designated communities to actively contribute to the realisation of their envisaged VREs and discusses how diverse communities of practice have used these options. ...
... D4Science is an IT infrastructure specifically conceived to support the development and operation of VREs by the as-a-Service provisioning mode 10,11 . D4Science-based VREs are web-based, community-oriented, collaborative, user-friendly, open-scienceenabler working environments for scientists and practitioners willing to work together to perform a specific (research) task. ...
... Among the components each VRE offers, some basic ones are enacting VRE users to perform their tasks collaboratively 11 , namely: (a) a workspace component to organise and share any digital artefact of interest; (b) a social networking component to communicate with coworkers by posts and replies; (c) a data analytics platform to share and execute analytics methods; (d) a catalogue component to document and publish any worth sharing digital artefact. 1 depicts the service-oriented view of the D4Science architecture (for details, refer to previous works 10,11 ). Services are conceptually organised into three groups: front-end components -the D4Science part with which user interacts directly; backend components -the D4Science part implementing the business logic of the system; provided resources -the D4Science part providing front-end components and back-end components with resources (computing, storage, data, software) to use. ...
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 paper, 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.
... D4Science [27] is a virtual research environment hosting platform that supports federated authentication via the European Open Science Cloud (EOSC) and authorization attributebased access control. D4Science uses the gCube software framework [28], similar to SciGaP's use of Airavata and Custos. A key difference is that Custos emphasizes the management of credentials for connecting to external HPC and cloud resources, whereas D4Science uses computing resources internal to the D4Science infrastructure. ...
Science gateways are user-facing cyberinfrastruc-ture that provide researchers and educators with Web-basedaccess to scientific software, computing, and data resources.Managing user identities, accounts, and permissions are essentialtasks for science gateways, and gateways likewise must man-age secure connections between their middleware and remoteresources. The Custos project is an effort to build open sourcesoftware that can be operated as a multi-tenanted service thatprovides reliable implementations of common science gatewaycybersecurity needs, including federated authentication, iden-tity management, group and authorization management, andresource credential management. Custos aims further to provideintegrated solutions through these capabilities, delivering end-to-end support for several science gateway usage scenarios. Thispaper examines four deployment scenarios using Custos andassociated extensions beyond previously described work. Thefirst capability illustrated by these scenarios is the need forCustos to provide hierarchical tenant management that allowsmultiple gateway deployments to be federated together andalso to support consolidated, hosted science gateway platformservices. The second capability illustrated by these scenarios is theneed to support service accounts that can support non-browserapplications and agent applications that can act on behalf ofusers on edge resources. We illustrate how the latter can be builtusing Web security standards combined with Custos permissionmanagement mechanisms.
... D4Science is a deployed instance of the gCube 8 technology [4], a software conceived to facilitate the integration of web services, code, and applications as resources of different types in a common framework, which in turn enables the construction of Virtual Research Environments (VREs) [7] as combinations of such resources (Fig. 5). As there is no common framework that can be trusted enough, sustained enough, to convince resource providers that converging to it would be a worthwhile effort, D4Science implements a "system of systems." ...
This paper shows data science’s potential for disruptive innovation in science, industry, policy, and people’s lives. We present how data science impacts science and society at large in the coming years, including ethical problems in managing human behavior data and considering the quantitative expectations of data science economic impact. We introduce concepts such as open science and e-infrastructure as useful tools for supporting ethical data science and training new generations of data scientists. Finally, this work outlines SoBigData Research Infrastructure as an easy-to-access platform for executing complex data science processes. The services proposed by SoBigData are aimed at using data science to understand the complexity of our contemporary, globally interconnected society.
... They are meant to support unprecedented scales of international collaboration in science, both within and across 1 Istituto di Scienza e Tecnologie dell'Informazione "A. Faedo" Consiglio Nazionale delle Ricerche, Pisa, Italy 2 Dipartimento di Informatica, Università di Pisa, Pisa, Italy disciplines. Their aim is to realize a common environment where scientists can create, validate, assess, compare and share their digital results of science, such as research data, intended as scientific data produced by a scientific effort, and research methods, intended as digital computation-oriented elements resulting from their research; research methods are discipline-specific, as well as research data, but examples include software, services, tools, workflows, scripts, algorithms and protocols. ...
... as pivot [3] is used. The D4Science platform supports an advanced notion of Virtual Research Environments (VREs), intended as innovative, web-based, community-oriented, comprehensive, flexible and secure working environments conceived to serve the needs of nowaday scientific investigations [2,7]. The implementation and operation of such challenging and evolving working environments largely benefits from and complements the offering of research infrastructures. ...
Research e-infrastructures are “systems of systems,” patchworks of resources such as tools and services, which change over time to address the evolving needs of the scientific process. In such environments, researchers carry out their scientific process in terms of sequences of actions that mainly include invocation of web services, user interaction with web applications, user download and use of shared software libraries/tools. The resulting workflows are intended to generate new research products (articles, datasets, methods, etc.) out of existing ones. Sharing a digital and executable representation of such workflows with other scientists would enforce Open Science publishing principles of “reproducibility of science” and “transparent assessment of science.” This work presents HyWare, a language and execution platform capable of representing scientific processes in highly heterogeneous research e-infrastructures in terms of so-called hybrid workflows. Hybrid workflows can express sequences of “manually executable actions,” i.e., formal descriptions guiding users to repeat a reasoning, protocol or manual procedure, and “machine-executable actions,” i.e., encoding of the automated execution of one (or more) web services. An HyWare execution platform enables scientists to ( i ) create and share workflows out of a given action set (as defined by the users to match e-infrastructure needs) and ( ii ) execute hybrid workflows making sure input/output of the actions flow properly across manual and automated actions. The HyWare language and platform can be implemented as an extension of well-known workflow languages and platforms.
... An extensive assessment of the proposed approach is ongoing to evaluate its e ciency under di↵erent operational settings and implementation decisions. The ReLock approach is currently under testing in the context of the D4Science infrastructure [3,2]. This is a large scale infrastructure that because of the settings and operation context is called to support workflows and processes spanning across diverse services not necessarily conceived to work together. ...
... The path variable parameters are shown within {} and using capital letters. This convention will be used in the rest of the paper.2 The information regarding which username and URL save as bookmark or which URL retrieve are provided via GETURL parameters not shown in the table. ...
Service composition and supporting transactions across composed services are among the major challenges characterizing service-oriented computing. REpresentational State Transfer (REST) is one of the approaches used for implementing Web services that is gaining momentum thanks to its features making it suitable for cloud computing and microservices-based contexts. This paper introduces ReLock, a resilient RESTful transaction model introducing general purpose transactions on RESTful services by a layered approach and a two-phase locking mechanism not requesting any change to the RESTful services involved in a transaction.
... The VRE is hosted by the AGINFRA Gateway (Assante et al., 2019a), a science gateway developed by relying on the D4Science infrastructure (Assante et al., 2019c) and the gCube technology (Assante et al., 2019b). This VRE realizes a web-based working environment (Fig. 1a) providing its users with: (a) a shared workspace, for storing, organising and sharing any version of an artefact, including dataset and model implementation AMR -antimicrobial resistance. ...
The source of a foodborne disease outbreak (FBO) is often difficult to identify, especially in the early phase where interventions would be most efficient. In addition, data on FBOs are mostly scattered in different formats either in national databases and reports or within pathogen-specific or regional reporting networks, both of which are often only accessible to a selected number of individuals.
Here, we developed an international, open, shared and searchable data catalogue of past FBOs – the Global Food-source Identifier (GFI). GFI was developed with two objectives: a) to create a collaborative online community of FBO investigators, encouraging the international sharing of data in a harmonized, detailed and comparable manner and b) to support foodborne outbreak investigation worldwide by providing access to detailed records of past outbreaks, which can convey valuable insight into potential ‘risk foods’ of a detected pathogen. GFI is hosted within a Virtual Research Environment (VRE), which offers additional features to facilitate the collaboration between the outbreak investigators. These features allow document exchange, communication and data visualization and analysis between the VRE members.
Based on scientific literature on foodborne outbreaks and discussions within a working group, we selected a total of 46 attributes characterising the outbreak records to be included in the catalogue, aggregated under the four overarching categories causative agent, epidata, food source and report details. Detailed descriptions of the attributes in the catalogue and instructions for harmonized data reporting are available on a wiki page in the VRE. At the time of writing and public launch of GFI, the data catalogue was populated with records of 102 FBOs occurred in Denmark over a period of 12 years (2005–2016) and covering the most frequent pathogens and a broad range of typing methods.
The VRE features that enable data analysis, document sharing and communication between members were applied for the graphical representation of the records available in GFI, and for the sharing of results and script files within the VRE. The descriptive analysis included the relationship between the most frequent causative agents and outbreak food sources. Such results can support a risk-based food sampling strategy in the very beginning of a foodborne outbreak investigation.
The Global Food-source Identifier is a data catalogue specifically designed to host an international collection of FBO records reported in a detailed and harmonized manner. It is implemented in a virtual research environment that offers key features to facilitate and enhance the global collaboration and data sharing among FBO investigators. Once in active use by the international food safety community, we envisage that GFI will contribute to the success of FBO investigations worldwide.
... They foster Open Science by embedding their computational platforms within an e-Infrastructure (e-I), i.e. a network of hardware and software resources (e.g. Web services, machines, processors, databases etc.) allowing users residing at remote sites to collaborate and exchange information in a context of data-intensive Science (Andronico et al., 2011;Assante et al., 2019a). An e-I can make a distributed/parallel processing platform (e.g. a High-Performance Computing, a Cloud/Grid Computing system etc.) interoperate with a distributed storage system and other services to manipulate, publish, harmonise, visualise, and access data. ...
... This scenario is an open issue for OS-oriented e-Is. Some e-Is have introduced tools to help and accelerate e-I users to (i) standardise data, (ii) automatically import data from heterogeneous repositories, and (iii) make models available as standardised services (Assante et al., 2019a;Candela et al., 2015;Coro et al., 2018c). Other e-Is have proposed academic index calculation promoting open-access research (Thelwall and Kousha, 2015). ...
... These applications can benefit from OS products such as GIS services, ENM species maps, AI-assisted applications, and collaboration tools. For example, OS services have been used to endow scuba-divers with ENM GIS maps in an explored area, and with processes for underwater photo sharing that rebuild an explored area as a 3D model through photogrammetry (Coro et al., 2019a;Palma et al., 2018aPalma et al., , 2018b. Additionally, Web services have been built to offer Virtual Reality tools to explore these reconstructions (Calvi et al., 2017;. ...
The long-term EU strategy to support the sustainable growth of the marine and maritime sectors (Blue Growth) involves economic and ecological topics that call for new computer science systems to produce new knowledge after processing large amounts of data (Big Data), collected both at academic and industrial levels. Today, Artificial Intelligence (AI) can satisfy the Blue Growth strategy requirements by managing Big Data, but requires effective multidisciplinary interaction between scientists. In this context, new Science paradigms, like Open Science, are born to promote the creation of computational systems to process Big Data while supporting collaborative experimentation, multi-disciplinarity, and the re-use, repetition, and reproduction of experiments and results. AI can use Open Science systems by making domain and data experts cooperate both between them and with AI modellers. In this paper, we present examples of combined AI and Open Science-oriented applications in marine science. We explain the direct benefits these bring to the Blue Growth strategy and the indirect advantages deriving from their re-use in other applications than their originally intended ones.
