ArticlePDF Available


Virtual Research Environments are innovative, web-based, community-oriented, comprehensive, flexible, and secure working environments conceived to serve the needs of modern science. We overview the existing initiatives developing these environments by highlighting the major distinguishing features. We envisage a future where regardless of geographical location, scientists will be able to use their Web browsers to seamlessly access data, software, and processing resources that are managed by diverse systems in separate administration domains via Virtual Research Environments. We identify and discuss the major challenges that should be resolved to fully achieve the proposed vision, i.e., large-scale integration and interoperability, sustainability, and adoption.
Leonardo Candela*, Donatella Castelli, Pasquale Pagano
Istituto di Scienza e Tecnologie dellInformazione (ISTI) “Alessandro Faedo”, Italian National Research
Council (CNR), Via G. Moruzzi 1, 56124 Pisa, Italy
Email:, {donatella.castelli, pasquale.pagano}
Virtual Research Environments are innovative, web-based, community-oriented, comprehensive, flexible, and
secure working environments conceived to serve the needs of modern science. We overview the existing
initiatives developing these environments by highlighting the major distinguishing features. We envisage a future
where regardless of geographical location, scientists will be able to use their Web browsers to seamlessly access
data, software, and processing resources that are managed by diverse systems in separate administration
domains via Virtual Research Environments. We identify and discuss the major challenges that should be
resolved to fully achieve the proposed vision, i.e., large-scale integration and interoperability, sustainability,
and adoption.
Keywords: Virtual Research Environment, Scientific Gateway, Digital Library, Data Infrastructure
A recent study promoted by The Royal Society in cooperation with Elsevier reviewed the changing patterns of
science and scientific collaborations and confirmed that science is increasingly global, multipolar, and
networked (Llewellyn Smith, Borysiewicz, Casselton, Conway, Hassan, Leach, et al., 2011). This trend calls for
innovative, dynamic, and ubiquitous research supporting environments where scattered scientists can seamlessly
access data, software, and processing resources managed by diverse systems in separate administration domains
through their web browser.
Dependent on context, these environments are commonly referred to as either Virtual Research Environments
(Carusi & Reimer, 2010), Science Gateways (Wilkins-Diehr, 2007), Collaboratories (Wulf, 1993), Digital
Libraries (Candela, Castelli, & Pagano, 2011) or Inhabited Information Spaces (Snowdon, Churchill, & Frécon,
2004). These environments are among the goals that e-Infrastructures (e-Infrastructure Reflection Group, 2010)
and cyberinfrastructures (Cyberinfrastructure Council, 2007) are going to realise. A variety of systems and
services fall within the scope of these definitions, from ad-hoc portals with minimal access services to content
resources held in external repositories (lightweight integration to promote resource discovery) to
general-purpose management systems with advanced services defined over a wide range of resources (strong
integration to promote resource exploitation). In some cases, motivations and design (sharing, on-demand
resource provision, economies of scale) align with the principles of grid computing and its ecology of virtual
organizations (Foster & Kesselman, 1998) as well as with cloud computing (Foster, Zhao, Raicu, & Lu, 2008).
For the purposes of this paper, the term Virtual Research Environment (VRE) is used with a comprehensive
scope, i.e., it represents a concept overarching all the environments cited above and identifies a system with the
following distinguishing features: (i) it is a web-based working environment; (ii) it is tailored to serve the needs
of a community of practice (Lave & Wenger, 1991); (iii) it is expected to provide a community of practice with
the whole array of commodities needed to accomplish the community’s goal(s); (iv) it is open and flexible with
respect to the overall service offering and lifetime; and (v) it promotes fine-grained controlled sharing of both
intermediate and final research results by guaranteeing ownership, provenance and attribution.
The VREs’ characteristic of being a web based working environment is the most common one and usually that
which contributes to misuse of the term “VRE” itself. In many cases, ad-hoc portals implementing simple
catalogue facilities and completely missing the collaborative, dynamicity, and openness features discussed
above have been tagged with the “VRE” term (Allan, 2009). Allan explains how Web-based services should be
loosely combined into portals to provide a comprehensive infrastructure for the support of research across all
Data Science Journal, Volume 12, 10 August 2013
academic disciplines. He feels that “VRE” portals should not only provide an environment for housing, indexing,
and retrieving large data sets but also leverage Web 2.0 technologies (O'Reilly, 2005) and social networking
solutions (Wang, Carley, Zeng & Mao, 2007) to give researchers a comprehensive environment for collaboration
and resource discovery.
The VREs’ characteristic of being the framework expected to support communities of practice is what makes
VREs definition very heterogeneous and VREs implementation a challenging activity. “Community of practice”
is a term coined to capture an “activity system” that includes individuals who are united in action and in the
meaning that “action” has for them and for the larger collective. The communities of practice are “virtual”, i.e.,
they are not formal structures, such as departments or project teams. Instead, these communities exist in the
minds of their members and are glued together by the connections they have with each other as well as by their
specific shared problems or areas of interest. The generation of knowledge in communities of practice occurs
when people participate in problem solving and share the knowledge necessary to solve the problems (Wenger,
1998). Creating and supporting communities of practice as a strong alternative to building teams was an early
observation (Nirenberg, 1994). This is particularly true in science and scientific collaborations as confirmed by
the Royal Society study previously cited. It is evident that realising working environments for communities with
features for communities of practice is a challenging task: the service has to be guaranteed at a level of quality
of service although the requirements and needs are highly evolving and the membership is volatile.
The VREs’ characteristic of being the system that offers the whole array of needed commodities is another
aspect concurring to difficulties in defining VREs’ scope boundaries and enlarging realization challenges. The
larger the pool of expected commodities (both quantitatively and qualitatively) the bigger the effort needed to
implement the related VRE. It is quite common to describe a VRE’s commodities by decoupling the resources
managed through the VRE from the VRE’s services facilitating resources management. Resources range from
data sets, collections, storage facilities, and computing power to services realising specific utilities and research
objects. Research objects themselves evolve from traditional research outputs, such as papers and experimental
data, to living reports (Candela, Castelli, Pagano, & Simi, 2005; Candela, Akal, Avancini, Castelli, Fusco,
Guidetti, et al., 2007), executable research papers (Van Gorp & Mazanek, 2011; Nowakowski, Ciepiela,
Harężlak, Kocot, Kasztelnik, Bartyoski, et al., 2011), scientific workflows (De Roure, Goble, & Stevens, 2009),
and enhanced publications (Hoogerwerf, Lösch, Schirrwagen, Callaghan, Manghi, Iatropoulou, et al., 2013). In
addition to that, a VRE is required to offer a unified and sometimes virtualised view on a pool of resources that
might come from different “providers”.
The VREs’ characteristic of being open and flexible with respect to the overall service offering brings
development approaches into question. Traditional approaches, mainly based on from scratch development of
ad-hoc portals, are not sustainable in community-of-practice-oriented scenarios. There is a need for innovative
approaches aimed at promoting and maximising sharing and reuse of existing commodities to build and operate
a number of VREs. In the context of the DILIGENT, D4Science, D4Science-II triplet of EU projects an
approach that has been developed and deployed based on: (i) an infrastructure making available a rich pool of
resources including datasets, computing power, and hosting machines; (ii) a software framework offering
resource management facilities for a rich array of resources including software packages; (iii) a wizard-based
mechanism allowing users to characterize the VRE they are interested in; and (iv) automatic VRE deployment
facilities that acquire the constituents needed to satisfy the VRE specification by relying on the infrastructure
and software framework offering (Assante, Candela, Castelli, Frosini, Lelii, Manghi, et al., 2008). In the three
projects, the intended communities of practice ranged from humanities research to biodiversity. Moreover, this
initiative is among the first to rely on cloud technologies to implement VREs (Candela, Castelli, & Pagano,
Finally, the VREs’ characteristic of supporting fine-grained controlled sharing of both intermediate and final
research results while guaranteeing ownership, provenance, and attribution is somehow a consequence of the
scenarios VREs are going to serve. Many science users will not be willing to contribute unless mechanisms
guaranteeing their work are in place (De Roure, Goble, & Stevens, 2009). These mechanisms can be either
explicit, e.g., the visibility of a resource is defined by its creator/owner through a set of policies, or implicit, e.g.,
it is the framework implementing the VRE that injects provenance metadata in the research outputs.
Data Science Journal, Volume 12, 10 August 2013
In ten years, it is expected that the trend characterizing science and scientific collaborations discussed above
continues, thus becoming the “default” approach for scientific investigations as well as for any societal
collaboration-based activity. Virtual Research Environments will be integrated into standard practices and tools
used by communities of practice, thus becoming the “enabler” working environments for implementing
investigation and collaboration activities efficiently and effectively.
The creation and management of Virtual Research Environments will be a very straightforward process that
relies on specific services VRE Management Services built atop a “global virtual infrastructure” resulting
from the aggregation and interoperation of a number of existing infrastructures and systems. The VRE
Management Services will support the phases of VRE definition, deployment, and monitoring / maintenance.
The VRE definition phase will guide an authorised actor of an application domain in characterizing the expected
VRE service in very abstract terms, e.g., defining the policies and procedures governing the VRE community
building, defining the policies governing the VRE operation, identifying the datasets the VRE community is
willing to play with, describing the data types the VRE community is going to manage, and identifying the
facilities the VRE is requested to support. Which characterizations are allowed depends on the current offering
of the “global virtual infrastructure”, i.e., the “global virtual infrastructure” is actually playing the role of
“resources provider” and a VRE will be an application built by dynamically acquiring the needed constituents
from the overall offering. The quality of service of the resulting VRE is declared in its specification; thus it is
known a-priori and depends on the amount of resources spent to acquire the resources needed to realize the
The VRE deployment phase will be almost automatic. The Management Services will crunch the specification
of the expected VRE service including (i) the directives on the quality of service and (ii) the available budget to
identify the “optimal” set of resources to be acquired from the “global virtual infrastructure”. The Management
Services will take care of creating the application context changing this set of resources from a complex whole
into an integrated system.
The VRE monitoring / maintenance phase will require little direct human control. The Management Services
will take care of checking the state of the set of resources allocated to implement the VRE service. When needed,
they will perform corrective actions aiming at guaranteeing that the VRE service specification is satisfied, e.g.,
by dynamically acquiring new resources from the “global virtual infrastructure”.
The resulting Virtual Research Environment will be very flexible and customizable. Every single user can
simply define its own workflows workflows realizing a scientific investigation by combining existing
facilities without taking care of implementation details and computational resources acquisition. The
computational resources as well as the workflow constituents will be dynamically acquired and combined by the
Management Service, in accordance with the VRE specification.
Thus Virtual Research Environments creation and management will become a societal and organisational
process rather than a technological one.
There are three major issues to be resolved to realise the above vision as well as to implement sustainable
Virtual Research Environments: large scale integration and interoperability, sustainability, and adoption.
Because of their intrinsic nature, any Virtual Research Environment is built as a “collection” of existing systems
and resources; thus their developers have to deal with the entire stack of issues that go under the interoperability
umbrella. Interoperability is actually a multi-layered and context-specific concept, which encompasses different
levels along a multi-dimensional spectrum ranging from organisational to semantic and technological aspects.
From the VRE developers’ point of view it is fundamental to rely on a rich array of systems and resources
both in terms of variety and size that can be seamlessly accessed and combined in innovative ways to satisfy
the evolving needs of the community of practice. Part of the resources can be acquired and put in place from
scratch for specific purposes while other resources have necessarily to be acquired from existing systems either
because they are produced by those systems or for opportunistic reasons, e.g., economic ones. However, the
Data Science Journal, Volume 12, 10 August 2013
challenges affecting Virtual Research Environments are actually very broad and include those characterising
every aspect of a data infrastructure. In fact, Virtual Research Environments are at the higher level in a
conceptually layered architecture of a virtual and scattered system as they represent the application layer that is
built on top of one or more layers offering at least (i) raw resources (e.g., computing, storage, network, and
software resources), (ii) communication and authentication protocols, (iii) protocols for publication, discovery,
negotiation, monitoring, accounting, and payment of resources usage, and (iv) protocols allowing the definition
and management of groups of resources. In the context of a (global) research data infrastructure, the majority of
these challenges are expected to be assigned to the infrastructure itself, i.e., the infrastructure should take care of
putting in place a rich array of mechanisms enabling interoperability with existing systems conceptually acting
as resource providers to build a unified space of resources ranging from data sets, collections, storage
facilities, and computing power to services realising specific utilities and research objects. The richer the array
of interoperability mechanisms the infrastructure is equipped with, the larger the resources space and,
consequently, the domain of “VREs” that can be built.
Sustainability is definitely one of the major challenges affecting Virtual Research Environments development.
VREs require effort and money to be built and maintained according to the communities of practice needs. It is
a waste of effort and money building them without having a long term support although costs can be mitigated
by devising innovative development approaches eventually based on global virtual infrastructures”. As
proposed in (Carusi & Reimer, 2010), there are three key strategies for sustainability that might be put in place
either singly or in combinations: (i) acquire further funding from diverse research bodies; (ii) develop business
models aiming at self-sustainability; and (iii) rely on community support. However, given the volatile nature of
communities of practice the sustainability issue remains a challenging problem.
Although several Virtual Research Environments have been developed in various application domains and a
plethora of communities of practice are in action, the majority of these systems are not yet fully integrated into
standard practices, tools, and research protocols used by real life communities of practice. This reluctance to
migrate from traditional and consolidated research practices and facilities to the innovative ones promoted by
VREs is among the most difficult barriers affecting the entire VRE domain. As recognised by Carusi and Reimer
(2010), among the factors causing this issue are: (i) the lack of support of both technical (e.g., bug fixing and
further development of the VRE service) and instructional (e.g., training especially in early stages) nature; (ii)
the gap between the community of practice needs and the actual service implemented by the VRE; (iii) the
reliability of the technology (very often VREs are based on cutting edge and evolving technologies); (iv) legal,
ethical, and cultural issues (the willingness to “share” research outputs and participate in web based research
investigations might be nullified by fear for ownership and attribution); and (v) interdisciplinarity (differences in
“languages” and working practices are a need, a potentiality and an issue as well). The lack of community
uptake has cascading effects on the entire VRE research domain, in particular its impacts on sustainability.
Virtual Research Environments represent innovative working environments that aim at enhancing the
cooperation and collaboration among researchers in all modern research scenarios. They promote novel
approaches and facilitate global and timely sharing of research findings, expertise, and any research supporting
“asset” across organizational and operational boundaries and barriers. Because of these potentialities, their
development should be guided by a number of principles and best practices aiming at promoting efficiency and
effectiveness of the resulting services.
A rich array of resources and systems has been developed, and a lot of effort is currently spent in building
infrastructures all over the world including: (a) Internet infrastructures, e.g., GÉANT (, the
high-bandwidth Internet serving Europe research and education community, and Internet2 (,
the network designed to serve the US research and education community; (b) grid infrastructures, e.g.,
European Grid Infrastructure (, the European Grid Infrastructure built by federating a number of
mainly European providers, and Open Science Grid (, a grid infrastructure built by
bringing together computing and storage resources from computers and research communities in the US; (c)
data infrastructures, e.g., DataONE (, an infrastructure for supporting Earth observational
data mainly in US, Data Conservancy (, an infrastructure promoting scientific data
curation, OpenAIRE (, an infrastructure promoting the dissemination and sharing on open
Data Science Journal, Volume 12, 10 August 2013
access artifacts including data, and D4Science (, an Hybrid Data Infrastructure stemming
from a series of EU Funded projects and promoting the realisation of Virtual Research Environments (Candela,
Castelli, Pagano, 2012). Moreover, a lot of momentum has been gained by cloud technologies (Foster, Zhao,
Raicu, & Lu, 2008). All these efforts should be considered as building blocks for realising Virtual Research
Environments. However, to make this possible, services and resources that are aggregated and offered by such
infrastructures should, as much as possible, be independent of a specific application domain and designed for
reuse”. From scratch and ‘self-sustained’ approaches, e.g., approaches aimed at building the entire spectrum of
the needed resources without ‘outside’ assistance, should be discouraged and prevented because of their
intrinsic development costs and difficulties to deal with evolving scenarios. Actually, Virtual Research
Environments should be linked to existing infrastructures with both roles of consumer, i.e., VREs should benefit
from the services offered by these infrastructures, and provider, i.e., the resources produced in the context of the
VRE operation should contribute to the infrastructures offering.
Virtual Research Environments should be designed, since the beginning, to promote uptake, ensure usability,
and guarantee sustainability. These three aspects form a virtuous circle that, if properly managed, ensure the
success of a specific VRE. In reference to uptake, it is fundamental that the community served by the specific
VRE, although virtual and aggregated by the VRE itself, is provided with tools and facilities for managing and
maintaining the VRE services that have limited requirements with respect to community expertise. Moreover,
the conceivers of the VRE should plan how to engage the broader community of practice that can be served by
the VRE, e.g., it might be possible to build a core team that sustains the VRE itself in the medium and long term
by awareness raising, targeted training, and other engagement events tailored to attract and convince key
representatives of the community of practice. As regards usability, Virtual Research Environments building
should be mainly a community building process rather than a technology development process. This implies that
the focus should be primarily on using technology to identify and rationalise workflows, procedures, and
processes characterising a certain research scenario rather than having technology invading the research scenario
and distracting effort from its real needs. As far as sustainability is concerned, it is fundamental that the
resulting VRE service is conceived as a vital tool in the community of practice it is dedicated to. Moreover,
sustainability is further enhanced whenever the VRE is perceived as a useful tool in the context of larger
research initiatives and communities so to benefit from economies of scale, i.e., savings gained by an
incremental level of production, and economies of scope, i.e., savings gained by producing two or more distinct
goods when the costs of doing so is less than that of producing each of them separately.
The work reported has been partially supported by the GRDI2020 project (FP7 of the European Commission,
INFRA-2009.3, Contract No., 246682).
Allan, R. (2009) Virtual Research Environments: From Portals to Science Gateways. Oxford, UK: Chandos
Assante, M., Candela, L., Castelli, D., Frosini, L., Lelii, L., Manghi, P., et al. (2008) An Extensible Virtual
Digital Libraries Generator. Christensen-Dalsgaard, B., Castelli, D., Jurik, B. A., & Lippincott,J.(Eds.), 12th
European Conference on Research and Advanced Technology for Digital Libraries, ECDL 2008, Aarhus,
Denmark, September 14-19, volume 5173 of Lecture Notes in Computer Science, pp 122-134.
Blanke, T., Candela, L., Hedges, M., Priddy, M., & Simeoni, F. (2010) Deploying general-purpose virtual
research environments for humanities research. Phil. Trans. R. Soc. A 368, pp 3813-3828.
Candela, L., Akal, F., Avancini, H., Castelli, D., Fusco, L., Guidetti, V., et al. (2007) DILIGENT: integrating
Digital Library and Grid Technologies for a new Earth Observation Research Infrastructure. International
Journal on Digital Libraries 7 (1-2), pp 59-80.
Candela, L., Castelli, D., & Pagano, P. (2012) Managing Big Data through Hybrid Data Infrastructures. ERCIM
News (89), pp 37-38.
Data Science Journal, Volume 12, 10 August 2013
Candela, L., Castelli, D., & Pagano, P. (2011) History, Evolution and Impact of Digital Libraries. In Iglezakis, I.,
Synodinou, T.-E. , & Kapidakis, S. (Eds.), E-Publishing and Digital Libraries: Legal and Organizational Issues,
Candela, L., Castelli, D., & Pagano, P. (2010) Making Virtual Research Environments in the Cloud a Reality:
the gCube Approach. ERCIM News (83), pp 32-33.
Candela, L., Castelli, D., Pagano, P., & Simi, M. (2005) From Heterogeneous Information Spaces to Virtual
Documents. Digital Libraries: Implementing Strategies and Sharing Experiences, 8th International Conference
on Asian Digital Libraries, ICADL 2005, Bangkok, Thailand, December 12-15, 2005, Proceedings. Springer.
Carusi, A., & Reimer, T. (2010) Virtual Research Environment Collaborative Landscape Study. JISC.
Cyberinfrastructure Council. (2007) Cyberinfrastructure Vision for the 21st Century Discovery. National
Science Foundation.
Davies, S. (2011) Still Building the Memex. Communications of the ACM 54 (2), pp 80-88.
De Roure, D., Goble, C., & Stevens, R. (2009) The design and realisation of the myExperiment Virtual Research
Environment for social sharing of workflows. Future Generation Computer Systems (25), pp 561-567.
e-Infrastructure Reflection Group (2010) Blue Paper. E-IRG.
Foster, I. & Kesselman, C. (1998) The Grid: Blueprint for a New Computing Infrastructure. Morgan Kaufmann.
Foster, I., Zhao, Y., Raicu, I., & Lu, S. (2008) Cloud Computing and Grid Computing 360-Degree Compared. In
Grid Computing Environments Workshop, 2008. GCE ’08.
Hey, T., Tansley, S., & Tolle, K. (2009) The Fourth Paradigm - Data-intensive Scientific Discovery. Microsoft
Hoogerwerf, M., Lösch, M., Schirrwagen, J., Callaghan, S., Manghi, P., Iatropoulou, K., et al. (2013) Linking
Data and Publications: Towards a Cross-Disciplinary Approach. International Journal of Digital Curation 8 (1),
pp 244-254.
Lave, J. & Wenger, E. (1991) Situated Learning: Legitimate Peripheral Participation. New York, NY:
Cambridge University Press.
Llewellyn Smith, C., Borysiewicz, L., Casselton, L., Conway, G., Hassan, M., Leach, M., et al. (2011)
Knowledge, Networks and Nations: Global Scientific Collaboration in the 21st Century. The Royal Society. The
Royal Society.
Nirenberg, J. (1994) From team building to community building. National Productivity Review 14(1), pp 51-62.
Nowakowski, P., Ciepiela, E., Harężlak, D., Kocot, J., Kasztelnik, M., Bartyoski, T., et al. (2011) The Collage
Authoring Environment . Procedia Computer Science 4, pp 608-617.
O'Reilly, T. (2005) What Is Web 2.0 - Design Patterns and Business Models for the Next Generation of Software.
Snowdon, D. N., Churchill, E. F., & Frécon, E. (2004) Inhabited Information Spaces: Living with your Data.
London, UK: Springer-Verlag London Ltd.
Van Gorp, P. & Mazanek, S. (2011) SHARE: a web portal for creating and sharing executable research papers.
Procedia Computer Science 4, pp 589-597.
Wang, F.-Y., Carley, K., Zeng, D., & Mao, W. (2007) Social computing: From social informatics to social
intelligence. Intelligent Systems, IEEE 22(2), pp 79-83.
Data Science Journal, Volume 12, 10 August 2013
Wenger, E. (1998) Communities of Practice: Learning, Meaning and Identity. Cambridge, UK: Cambridge
University Press.
Wilkins-Diehr, N. (2007) Special Issue: Science Gateways - Common Community Interfaces to Grid Resources.
Concurrency and Computation: Practice and Experience 19 (6), pp 743-749.
Wulf, A. (1993) The collaboratory oppurtunity. Science 261, pp 854-855.
(Article history: Available online 30 July 2013)
Data Science Journal, Volume 12, 10 August 2013
... Following principles of technical interoperability, sustainability, security, and easy-to-use practices, the VRE is a fundamental tool for intuitive Research Infrastructures. The VRE places collaboration at the core of the experience, enabling the reuse of digital resources, promoting dialogue between different interlocutors, and strengthening scientific networks and communities [24][25][26]. Currently, we are implementing several information and communication technologies (ICT) tools to accomplish these goals and to strengthen the application of FAIR principles ( Figure 5): ...
... • My Folders-to store and organise the resources selected; • Search-connected with the discovery portal (search, visualise and select data); • Resources-selected resources and personal annotations; • Text editor-to write notes on the resources chosen; • Share-For sharing with other users the resources and personal notes on them, increasing the collaborative work and the community of practices [24][25][26]. The implementation of these tools required the organisation of focus groups and the execution of test sessions (e.g., usability, intelligibility). ...
... • Search-connected with the discovery portal (search, visualise and select data); • Resources-selected resources and personal annotations; •Text editor-to write notes on the resources chosen; •Share-For sharing with other users the resources and personal notes on them, increasing the collaborative work and the community of practices[24][25][26]. ...
Full-text available
The ROSSIO Infrastructure is developing a free and open-access platform for aggregating, organising, and connecting the digital resources in the Social Sciences, Arts and Humanities provided by Portuguese higher education and cultural institutions. This paper presents an overview of the ROSSIO Infrastructure, its main objectives, the institutions involved, and the services offered by the infrastructure’s aims through its platform—namely, a discovery portal, digital exhibitions, collections, and a virtual research environment. These services rely on a metadata-aggregation solution for bringing the digital objects’ metadata from the providing institutions into ROSSIO. The aggregated datasets are converted into linked data and undergo an enrichment process based on controlled vocabularies, which are developed and published by ROSSIO. The paper will describe this process, the applications involved, and how they interoperate. We will further reflect on how these services may enhance the dissemination of science, considering the FAIR principles.
... • Getty Vocabulary Program ontology. 16 The vocabularies employ properties of this ontology for further specifying types of agents and places through concepts in the ROSSIO Thesaurus. This allows, for example, to link Portugal in ROSSIO Places to the Countries concept in ROSSIO Thesaurus. ...
... OAI-PMH protocol), sustainability, security and easy-to-use practices, the VRE is an indispensable tool for intuitive Research Infrastructures. Its collaborative character is particularly relevant, enabling dialogue and cooperation between different interlocutors in the scientific community [16,17,18]. ...
... Science gateways (SGs) 37 , Virtual Research Environments (VREs) 5 and Virtual Laboratories (VLabs) are all terms used to indicate solutions aiming at providing a designated community with online research platform catering for an integrated access to resources (e.g. computing, software, data) of interest for the community 6,7 . ...
Full-text available
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.
... Other as repositories as collections of models, virtual research environments (VREs) are web-based information systems that provide a working environment for researchers, e.g., by including various tools for research (e.g., for analysis, comparison etc.) [633]. Specifically for cultural heritage research, a large number of VREs is available-often for specific communities like archaeology [634] or architectural history [635]. ...
Full-text available
Digital 3D modelling and visualization technologies have been widely applied to support research in the humanities since the 1980s. Since technological backgrounds, project opportunities, and methodological considerations for application are widely discussed in the literature, one of the next tasks is to validate these techniques within a wider scientific community and establish them in the culture of academic disciplines. This article resulted from a postdoctoral thesis and is intended to provide a comprehensive overview on the use of digital 3D technologies in the humanities with regards to (1) scenarios, user communities, and epistemic challenges; (2) technologies, UX design, and workflows; and (3) framework conditions as legislation, infrastructures, and teaching programs. Although the results are of relevance for 3D modelling in all humanities disciplines, the focus of our studies is on modelling of past architectural and cultural landscape objects via interpretative 3D reconstruction methods.
... The intent of this project is to propose a virtual software environment that allows the creation and the execution of FSPM models. As reported by Capuccini et al. (2019), the idea of on-demand Web-based working environments on virtual infrastructures was envisioned by Candela, Castelli & Pagano (2013). These working environments, dedicated to a community of practice, were originally referred to as Virtual Research Environments. ...
Full-text available
Functional-Structural Plant Models (FSPMs) are powerful tools to explore the complex interplays between plant growth, underlying physiological processes and the environment. Various modeling platforms dedicated to FSPMs have been developed with limited support for collaborative and distributed model design, reproducibility and dissemination. With the objective to alleviate these problems, we used the Jupyter project, an open-source computational notebook ecosystem, to create virtual modeling environments for plant models. These environments combined Python scientific modules, L-systems formalism, multidimensional arrays and 3D plant architecture visualization in Jupyter notebooks. As a case study, we present an application of such an environment by reimplementing V-Mango, a model of mango tree development and fruit production built on interrelated processes of architectural development and fruit growth that are affected by temporal, structural and environmental factors. This new implementation increased model modularity, with modules representing single processes and the workflows between them. The model modularity allowed us to run simulations for a subset of processes only, on simulated or empirical architectures. The exploration of carbohydrate source-sink relationships on a measured mango branch architecture illustrates this possibility. We also proposed solutions for visualization, distant distributed computation and parallel simulations of several independent mango trees during a growing season. The development of models on locations far from computational resources makes collaborative and distributed model design and implementation possible, and demonstrates the usefulness and efficiency of a customizable virtual modeling environment.
... The AGINFRA PLUS project has been set up to develop an innovative approach in Agri-food digital science practices aiming at overcoming the limitations stemming from the above settings by leveraging on existing e-Infrastructures and services. In particular, AGINFRA PLUS promotes the exploitation of Virtual Research Environments (VREs) [3] to provide designated communities with seamless access to the data, services, and facilities they need to perform their research tasks in a collaborative way. Such VREs are built by relying on an open and distributed platform (see Sec. II) providing a rich array of services supporting all the phases of an open science research lifecycle from data collection to data analytics and publication. ...
Full-text available
p>The enhancements in IT solutions and the open science movement are injecting changes in the practices dealing with data collection, collation, processing and 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, e.g. data of interest are heterogeneous and scattered across several repositories, the tools modellers 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” with datasets, processes, workflows. This paper presents the AGINFRA PLUS endeavour 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.</p
... Researchers with purely biological backgrounds often lack the coding skills or even the familiarity required for working with Command Line Interfaces [34]. Virtual Research Environments are web-based e-service platforms that are particularly useful for researchers lacking expertise and/or computing resources [35]. Another common issue is that most analyses include a great number of steps, with the software used in each of these having equally numerous dependencies. ...
Full-text available
High-performance computing (HPC) systems have become indispensable for modern marine research, providing support to an increasing number and diversity of users. Pairing with the impetus offered by high-throughput methods to key areas such as non-model organism studies, their operation continuously evolves to meet the corresponding computational challenges. Here, we present a Tier 2 (regional) HPC facility, operating for over a decade at the Institute of Marine Biology, Biotechnology, and Aquaculture of the Hellenic Centre for Marine Research in Greece. Strategic choices made in design and 2 0s and 1s in marine molecular research upgrades aimed to strike a balance between depth (the need for a few high-memory nodes) and breadth (a number of slimmer nodes), as dictated by the idiosyncrasy of the supported research. Qualitative computational requirement analysis of the latter revealed the diversity of marine fields, methods, and approaches adopted to translate data into knowledge. In addition, hardware and software architectures, usage statistics, policy, and user management aspects of the facility are presented. Drawing upon the last decade's experience from the different levels of operation of the Institute of Marine Biology, Biotechnology, and Aquaculture HPC facility, a number of lessons are presented; these have contributed to the facility's future directions in light of emerging distribution technologies (e.g., containers) and Research Infrastructure evolution. In combination with detailed knowledge of the facility usage and its upcoming upgrade, future collaborations in marine research and beyond are envisioned.
Research in the humanities increasingly depends on how information is structured and managed and how, on the basis of that information, new knowledge is produced. Additionally, participatory approaches, which often rely on web information systems as their supportive infrastructure, have made an impact on the most recent historiographical trends, in particular in the methodological framework of digital humanities. The aim of this paper was to produce, from an operational and implementation perspective, a review of software solutions frequently used to develop web information systems for research projects in humanities and cultural heritage, in order to provide an understanding of the various possibilities available and their positives and limitations, also based on different users’ requirements. An individual and comparative analysis of sixteen different application frameworks commonly used in these fields, either generic or developed for a specific research domain, has been carried out, considering their main functionalities, strengths, and weaknesses. The achieved results facilitate critical and reasoned decision-making among several available options, guiding the makers of those systems, both researcher(s) and developers(s), and providing them also with a common ground of terms and use cases to facilitate their necessary dialogue. Resumen La investigación en humanidades depende cada vez más de cómo se estructura y gestiona la información y de cómo, a partir de ella, se produce nuevo conocimiento. Además, los enfoques participativos, que a menudo utilizan los sistemas de información de la web como su infraestructura de soporte, han influido en las tendencias historiográficas más recientes, en particular en el marco metodológico de las humanidades digitales. El objetivo de este trabajo ha sido realizar, desde una perspectiva operativa y de implementación, una revisión de soluciones software comúnmente utilizadas para el desarrollo de sistemas de información web en proyectos de investigación en humanidades y patrimonio cultural, con el fin de proporcionar una visión de las distintas opciones disponibles, con sus aspectos positivos y sus limitaciones, también en función de diferentes necesidades a nivel usuario. Se ha llevado a cabo un análisis individual y comparativo de dieciséis paquetes software comúnmente utilizados en estos campos, ya sean genéricos o desarrollados para un dominio de investigación específico, considerando sus principales funciones, fortalezas y debilidades. Los resultados obtenidos facilitan una toma de decisiones crítica y razonada entre varias opciones disponibles, ofreciendo orientación a los creadores de esos sistemas, tanto investigadores como desarrolladores, y proporcionando también una base común de términos y casos de uso para facilitar su necesario diálogo.
Full-text available
Vessel tracking data help study the potential impact of fisheries on biodiversity and produce risk assessments. Existing workflows process vessel tracks to identify fishing activity and integrate information on species vulnerability. However, there are significant data integration challenges across the data sources needed for an integrated impact assessment due to heterogeneous nomenclatures, data accessibility issues, geographical and computational scalability of the processes, and confidentiality and transparency towards decision making authorities. This paper presents an Open Science data integration approach to use vessel tracking data in integrated impact assessments. Our approach combines heterogeneous knowledge sources from fisheries, biodiversity, and environmental observations to infer fishing activity and risks to potentially impacted species. An Open Science e-Infrastructure facilitates access to data sources and maximises the reproducibility of the results and the method's reusability across several application domains. Our method's quality is assessed through three case studies: The first demonstrates cross-dataset consistency by comparing the results obtained from two different vessel data sources. The second performs a temporal pattern analysis of fishing activity and potentially impacted species over time. The third assesses the potential impact of reduced fishing pressure on marine biodiversity and threatened species due to the 2020 COVID-19 lockdown in Italy. The method is meant to be integrated with other systems through its Open Science-oriented features and can rapidly use new sources of findable, accessible, interoperable, and reusable (FAIR) data. Other systems can use it to (i) classify vessel activity in data-limited scenarios, (ii) identify bycatch species (when catchability data are available), and (iii) study the effects of fisheries on habitats and populations’ growth.
Full-text available
We are now seeing governments and funding agencies looking at ways to increase the value and pace of scientific research through increased or open access to both data and publications. In this point of view article, we wish to look at another aspect of these twin revolutions, namely, how to enable developers, designers and researchers to build intuitive,multimodal, user-centric, scientific applications that can aid and enable scientific research.
Full-text available
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 textquoteleftby referencetextquoteright 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.
Full-text available
This study investigated international developments in Virtual Research Communities (VRCs) and to evaluate them in relation to the activities in the JISC’s VRE programme. The study examined programmes in a number of key countries along with significant projects and communities as well as some countries where developments on this front are just beginning. There has been a great deal of activity over the past few years in terms of prototype and demonstration systems moving into the mainstream of research practice. Notable trends are emerging as researchers increasingly apply collaborative systems to everyday research tasks.
Full-text available
Although many organizations have become team-oriented, few are harnessing their full power. When teams become self-managing, the next logical step is to build workplace community—a truly living organization. Workplace community will unleash the full potential of the workforce and help realize the only lasting competitive advantage: brainpower, imagination, and resourcefulness. this article discusses the process of bow teams can become a workplace community and refers to several companies supporting the effort, ft concludes wilh a summary of the critical aspects required to build workplace community.
Conference Paper
This presentation will set out the eScience agenda by explaining the current scientific data deluge and the case for a “Fourth Paradigm” for scientific exploration. Examples of data intensive science will be used to illustrate the explosion of data and the associated new challenges for data capture, curation, analysis, and sharing. The role of cloud computing, collaboration services, and research repositories will be discussed.
Prologue Part I. Practice: Introduction I 1. Meaning 2. Community 3. Learning 4. Boundary 5. Locality Coda I. Knowing in practice Part II. Identity: Introduction II 6. Identity in practice 7. Participation and non-participation 8. Modes of belonging 9. Identification and negotiability Coda II. Learning communities Conclusion: Introduction III 10. Learning architectures 11. Organizations 12. Education Epilogue.