An intelligent and cost-effective remote underwater video device for fish size monitoring
Coro G., Bjerregaard Walsh M.
Monitoring the size of key indicator species of fish is important to understand ecosystem functions, anthropogenic stress, and population dynamics. Standard methodologies gather data using underwater cameras, but are biased due to the use of baits, limited deployment time, and short field of view. Furthermore, they require experts to analyse long videos to search for species of interest, which is time consuming and expensive. This paper describes the Underwater Detector of Moving Object Size (UDMOS), a cost-effective computer vision system that records events of large fishes passing in front of a camera, using minimalistic hardware and power consumption. UDMOS can be deployed underwater, as an unbaited system, and is also offered as a free-to-use Web Service for batch video-processing. It embeds three different alternative large-object detection algorithms based on deep learning, unsupervised modelling, and motion detection, and can work both in shallow and deep waters with infrared or visible light.Source: Ecological informatics (Print) 63 (2021). doi:10.1016/j.ecoinf.2021.101311
DOI: 10.1016/j.ecoinf.2021.101311
See at:
ISTI Repository 
| CNR ExploRA 
| www.sciencedirect.com
Psycho-acoustics inspired automatic speech recognition
Coro G., Massoli F. V., Origlia A., Cutugno F.
Understanding the human spoken language recognition process is still a far scientific goal. Nowadays, commercial automatic speech recognisers (ASRs) achieve high performance at recognising clean speech, but their approaches are poorly related to human speech recognition. They commonly process the phonetic structure of speech while neglecting supra-segmental and syllabic tracts integral to human speech recognition. As a result, these ASRs achieve low performance on spontaneous speech and require enormous costs to build up phonetic and pronunciation models and catch the large variability of human speech. This paper presents a novel ASR that addresses these issues and questions conventional ASR approaches. It uses alternative acoustic models and an exhaustive decoding algorithm to process speech at a syllabic temporal scale (100-250 ms) through a multi-temporal approach inspired by psycho-acoustic studies. Performance comparison on the recognition of spoken Italian numbers (from 0 to 1 million) demonstrates that our approach is cost-effective, outperforms standard phonetic models, and reaches state-of-the-art performance.Source: Computers & electrical engineering (Print) 93 (2021). doi:10.1016/j.compeleceng.2021.107238
DOI: 10.1016/j.compeleceng.2021.107238
See at:
ISTI Repository | CNR ExploRA | www.sciencedirect.com
Data poor approach for the assessment of the main target species of rapido trawl fishery in Adriatic Sea
Armelloni E. N., Scanu M., Masnadi F., Coro G., Angelini S., Scarcella G.
Information on stock status is available only for a few of the species forming the catch assemblage of rapido fishery of the North-central Adriatic Sea (Mediterranean Sea). Species that are caught almost exclusively by this gear, either as target (such as Pectinidae) or accessory catches (such as flatfishes apart from the common sole), remain unassessed mainly due to the lack of data and biological information. Based on cluster analysis, the catch assemblage of this fishery was identified and assessed using CMSY model. The results of this data-poor methodology showed that, among the species analyzed, no one is sustainably exploited. The single-species CMSY results were used as input to an extension of the same model, to test the effect of four different harvest control rule (HCR) scenarios on the entire catch assemblage, through 15-years forecasts. The analysis showed that the percentage of the stocks that will reach Bmsy at the end of the projections will depend on the HCR applied. Forecasts showed that a reduction of 20% of fishing effort may permit to most of the target and accessory species of the rapido trawl fishery in the Adriatic Sea to recover to Bmsy levels within 15 years, also providing a slight increase in the expected catches.Source: Frontiers in Marine Science (2021). doi:10.3389/fmars.2021.552076
DOI: 10.3389/fmars.2021.552076
See at:
ISTI Repository | CNR ExploRA | www.frontiersin.org
An Open Science approach to infer fishing activity pressure on stocks and biodiversity from vessel tracking data
Coro G., Ellenbroek A., Pagano P.
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.Source: Ecological informatics (Print) 64 (2021). doi:10.1016/j.ecoinf.2021.101384
DOI: 10.1016/j.ecoinf.2021.101384
Project(s): Blue Cloud 
See at:
ISTI Repository | ISTI Repository | ISTI Repository | CNR ExploRA | www.sciencedirect.com
Exploring the status of the Indonesian deep demersal fishery using length-based stock assessments
Dimarchopoulou D., Mous P. J., Firmana E., Wibisono E., Coro G., Humphries A. T.
The deep demersal snapper-grouper fishery in Indonesia is a data-poor fisheries resource that provides food security and a source of income to millions globally. Owing to an ongoing crew-operated data recording system implemented in Indonesia since 2015, the stocks of this fishery can now be assessed using length-frequency data and updated life-history parameters. Here, we use two length-based methods, one that is fishery-specific and another that is more generalized, to assess the status of Indonesian stocks. Specifically, we develop a literature-based assessment method based on a patchwork of conventional approaches but tailored to the studied stocks, and compare it with a newly established and broadly applicable length-based Bayesian biomass estimation method (LBB). The methods were applied to 16 stocks from 4 Indonesian Fisheries Management Areas and were compared based on simulations, as well as the convergence of the resulting stock status classification and uncertainty of the results. Analyzing the effect of using the literature-based species/family-specific life-history parameter values for asymptotic length (Linf) and relative natural mortality (M/K) in LBB showed that different values do affect the estimated biomass indicator. Nevertheless, in more than half the cases, the stock status classification did not differ between the two methods, while LBB results became more reliable with narrower confidence limits. Simulations, as well as similar status indicators between the two models support the value of the literature-based approach as an assessment methodology for the Indonesian deep demersal fisheries. Narrower confidence ranges highlight the importance of using fishery-specific information when applying generalized stock assessment methods. While most catches had few immature fish, half of the assessed stocks were consistently shown to have low biomass, indicating that important Indonesian stocks are at high risk of overfishing.Source: Fisheries research 243 (2021). doi:10.1016/j.fishres.2021.106089
DOI: 10.1016/j.fishres.2021.106089
See at:
ISTI Repository | CNR ExploRA | www.sciencedirect.com
The role of technology and digital innovation in sustainability and decarbonization of the Blue Economy
Campana E. F., Ciappi E., Coro G.
The development of a sustainable technology for the Blue Economy (a new Blue Technology) sets out three core research objectives, reflecting key challenges to be tackled by the sea industries and scientific and technological communities: The fast development of doable decarbonization processes through development and demonstrati on of deployable, competitive, and sustainable technological solutions for energy transition (climate neutral blue economy), a sustainable exploitation and exploration of oceans, seas and coastal areas to provide new resources, from raw materials to products, including food (sustainable use and management of marine resources), and the development and exploitation of digital-based knowledge while accumulating data from new observation networks (persistent monitoring and digitalization of seas and oceans). To meet these operational objectives, different topics and related technologies need to be further developed. A possible list of disciplinary objecti ves is the following.Source: Bollettino di geofisica teorica ed applicata (Online) 62 (2021): 123–130.
See at:
ISTI Repository | CNR ExploRA | www3.inogs.it
Realising a science gateway for the Agri-food: the AGINFRAplus experience
Assante M., Boizet A., Candela L., Castelli D., Cirillo R., Coro G., Fernandez E., Filter M., Frosini L., Kakaletris G., Katsivelis P., Knapen R., Lelii L., Lokers R., Mangiacrapa F., Pagano P., Panichi G., Penev L., Sinibaldi F., Zervas 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.Source: 11th International Workshop on Science Gateways, Ljubljana, Slovenia, 12-14/06/2019
Project(s): AGINFRA PLUS
See at:
ceur-ws.org | ISTI Repository | CNR ExploRA
Predicting geographical suitability of geothermal power plants
Coro G., Trumpy E.
A large and increasing number of countries use geothermal energy as power source for domestic and industrial applications. Geothermal power plants produce energy out of this natural and renewable source in a sustainable way and contribute to reduce global warming. However, power plants effectiveness depends on the suitability of an area to geothermal energy production, which is a complex and unknown combination of many environmental factors. Nowadays, geothermal suitability assessments require invasive inspections, high costs, and legal permissions. Thus, having a global suitability map of geothermal sites as reference would be useful prior knowledge during assessments, and would help saving time and money. In this paper, the first suitability map of potential geothermal sites at global scale is presented. The map is the result of the application of data collection and preparation processes, and a Maximum Entropy model, to geospatial data potentially correlated with geothermal site suitability and geothermal plants operation. The reliability of our map is assessed against currently active and planned geothermal power plants. Our approach follows the Open Science paradigm that guarantees results reproduction and transparency, and allows stakeholders to reuse the produced standardised data, services, and Web interfaces in other experiments or to generate new maps at regional scale. Overall, our results can help scientists, industry operators, and policy makers in geothermal sites assessments. Also, our approach supports communication with citizens whose territories are involved in probing and assessments, in order to transparently inform them about the reasons driving the selection of their territory and the potential future benefits.Source: Journal of cleaner production (2020). doi:10.1016/j.jclepro.2020.121874
DOI: 10.1016/j.jclepro.2020.121874
See at:
Journal of Cleaner Production | ISTI Repository | CNR ExploRA | www.sciencedirect.com | Journal of Cleaner Production | Journal of Cleaner Production | Journal of Cleaner Production | Journal of Cleaner Production | Journal of Cleaner Production
Learning from the review of Estimating stock status from relative abundance and resilience
Froese R., Winker H., Coro G., Demirel N., Tsikliras A. C., Dimarchopoulou D., Scarcella G., Palomares M. L., Dureuil M., Pauly D.
This contribution presents the detailed responses to the peer-review of Froese et al. (2019) "Estimating stock status from relative abundance and resilience" (ICES J. Mar. Sci. 2019) which outlined a method called "AMSY" for inferring biomass trends for stocks for which only catch-per-unit-effort and limited ancillary ('priors') data are available. The responses emphasize that the required priors are legitimate and straightforward to obtain, thus, making AMSY a method of choice in data-sparse situations. This is also a good example of the role of peer-review in validating and improving science.Source: Marine and Freshwater Miscellanea II, edited by Pauly D., Ruiz-Leotaud V., pp. 111–124, 2020
See at:
ISTI Repository | CNR ExploRA | www.seaaroundus.org
A global-scale ecological niche model to predict SARS-CoV-2 coronavirus infection rate
Coro G.
COVID-19 pandemic is a global threat to human health and economy that requires urgent prevention and monitoring strategies. Several models are under study to control the disease spread and infection rate and to detect possible factors that might favour them, with a focus on understanding the correlation between the disease and specific geophysical parameters. However, the pandemic does not present evident environmental hindrances in the infected countries. Nevertheless, a lower rate of infections has been observed in some countries, which might be related to particular population and climatic conditions.
In this paper, infection rate of COVID-19 is modelled globally at a 0.5° resolution, using a Maximum Entropy-based Ecological Niche Model that identifies geographical areas potentially subject to a high infection rate. The model identifies locations that could favour infection rate due to their particular geophysical (surface air temperature, precipitation, and elevation) and human-related characteristics (CO2 and population density). It was trained by facilitating data from Italian provinces that have reported a high infection rate and subsequently tested using datasets from World countries' reports. Based on this model, a risk index was calculated to identify the potential World countries and regions that have a high risk of disease increment.
The distribution outputs foresee a high infection rate in many locations where real-world disease outbreaks have occurred, e.g. the Hubei province in China, and reports a high risk of disease increment in most World countries which have reported significant outbreaks (e.g. Western U.S.A.). Overall, the results suggest that a complex combination of the selected parameters might be of integral importance to understand the propagation of COVID-19 among human populations, particularly in Europe. The model and the data were distributed through Open-science Web services to maximise opportunities for re-usability regarding new data and new diseases, and also to enhance the transparency of the approach and results.Source: Ecological modelling 431 (2020). doi:10.1016/j.ecolmodel.2020.109187
DOI: 10.1016/j.ecolmodel.2020.109187
Project(s): EOSCpilot
See at:
Ecological Modelling | Europe PubMed Central | ISTI Repository | CNR ExploRA | www.sciencedirect.com | Ecological Modelling | Ecological Modelling | Ecological Modelling | Ecological Modelling | Ecological Modelling | Ecological Modelling | Ecological Modelling | Ecological Modelling
Data Processing and Analytics for Data-Centric Sciences
Candela L., Coro G., Lelii L., Panichi G., Pagano P.
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.Source: Towards Interoperable Research Infrastructures for Environmental and Earth Sciences. A Reference Model Guided Approach for Common Challenges., edited by Zhao Z.; Hellström M., pp. 176–191, 2020
DOI: 10.1007/978-3-030-52829-4_10
Project(s): ENVRI PLUS
See at:
link.springer.com | link.springer.com | ISTI Repository | CNR ExploRA | academic.microsoft.com | link.springer.com | link.springer.com | link.springer.com
NLPHub: an e-Infrastructure-based text mining hub
Coro G., Panichi G., Pagano P., Perrone E.
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.Source: Concurrency and computation (Online) (2020): e5986. doi:10.1002/cpe.5986
DOI: 10.1002/cpe.5986
Project(s): PARTHENOS
See at:
ISTI Repository | Concurrency and Computation Practice and Experience | Concurrency and Computation Practice and Experience | Concurrency and Computation Practice and Experience | Concurrency and Computation Practice and Experience | CNR ExploRA
Realizing virtual research environments for the agri-food community: the AGINFRA PLUS experience
Assante M., Boizet A., Candela L., Castelli D., Cirillo R., Coro G., Fernández E., Filter M., Frosini L., Georgiev T., Kakaletris G., Katsivelis P., Knapen R., Lelii L., Lokers R. M., Mangiacrapa F., Manouselis N., Pagano P., Panichi G., Penev L., Sinibaldi F.
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.Source: Concurrency and computation (Online) 33 (2020). doi:10.1002/cpe.6087
DOI: 10.1002/cpe.6087
Project(s): AGINFRA PLUS
See at:
ISTI Repository | Concurrency and Computation Practice and Experience | Concurrency and Computation Practice and Experience | Concurrency and Computation Practice and Experience | Concurrency and Computation Practice and Experience | CNR ExploRA | Concurrency and Computation Practice and Experience
Open science and artificial intelligence supporting blue growth
Coro G.
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 multi-disciplinary 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 onesSource: Environmental Engineering and Management Journal (Online) 19 (2020): 1719–1729.
See at:
ISTI Repository | CNR ExploRA | www.eemj.icpm.tuiasi.ro
Blue Cloud - D4.2: Blue Cloud VRE Common Facilities (Release 1)
Assante M., Candela L., Pagano P., Dell'Amico A., Coro G., Cirillo R., Frosini L., Lelii L., Lettere M., Mangiacrapa F., Panichi G., Sinibaldi F.
The Blue-Cloud project plans to pilot a cyber platform bringing together and providing access to multidisciplinary data from observations and models, analytical tools, and computing facilities essential to support research to understand better and manage the many aspects of ocean sustainability.
To achieve this goal, Blue-Cloud is developing, deploying, and operating the Blue-Cloud platform whose architecture consists of two families of components: (a) the Blue Cloud Data Discovery and Access service component to serve federated discovery and access to 'blue data' infrastructures; and (b) the Blue Cloud Virtual Research Environment (VRE) component to provide a Blue Cloud VRE as a federation of computing platforms and analytical services.
This deliverable presents the Blue Cloud Virtual Research Environment constituents by focusing on both new services and revised existing services that have been developed in the reporting period to serve the needs of the Blue Cloud community. In particular, this deliverable describes a total of 11 services and components. These services and components contribute functionalities to the Blue Cloud VRE Enabling Framework (Identity and Access Management, VRE Management), Collaborative framework (Workspace and Social Networking), Analytics Framework (Software and Algorithm Importer, Smart Executor), Publishing Framework (Catalogue Service) and improved support for RStudio, JupyterHub, ShinyProxy, and Docker Applications. The services are described below by reporting their design principles, architectures, and main features.
The deliverable also describes the procedures and approaches governing services and components released by highlighting how Gitea (as Git hosting service), Jenkins (as automation server), and Maven (as project management and comprehension tool) are used to guarantee continuous integration processes.
Services and components discussed in this deliverable contribute to 11 gCube open-source software system releases (from gCube 4.16 up to gCube 4.25.1) and are in the pipeline for the next ones. They have been used to develop and operate the Virtual Laboratories of the Blue Cloud gateway https://blue-cloud.d4science.org and its underlying infrastructure. At the time of this deliverable (November 2020), the gateway hosts a total of 8 VREs and VLabs, including five specifically conceived to support the co-development of some of the Blue-Cloud demonstrators (namely, the Aquaculture Atlas Generation for Demonstrator #5, the Blue-Cloud Lab for several demonstrators, the GRSF pre for Demonstrator #4, the Marine Environmental Indicators for Demonstrator #3, the Zoo-Phytoplankton EOV for Demonstrator #1). This gateway and its tools serve more than 400 users that (since January 2020) performed a total of more than 5000 working sessions, more than 1700 accesses to the Workspace, and more than 750 analytics tasks. These exploitation and uptake indicators are likely to grow in the coming months thanks to data updates and continued use, further development of existing VLabs, and finally, the creation of new ones.Source: Project Report, Blue Cloud, D4.2, 2020
Project(s): Blue Cloud
See at:
ISTI Repository | CNR ExploRA
Predicting the spread of COVID-19 through Marine Ecological Niche Models
Coro G.
Researchers from ISTI-CNR (Italy) used marine models, designed to monitor species habitats and invasions, to identify the countries with the highest risk of COVID-19 spread due to climatic and human factors. The model correctly identified most locations where large outbreaks were recorded, independent of population density and dynamics, and is a valuable source of information for smaller-scale population models.Source: ERCIM news online edition 124 (2020).
Project(s): EOSCsecretariat.eu
See at:
ercim-news.ercim.eu | ISTI Repository | CNR ExploRA
A multiscale statistical method to identify potential areas of hyporheic exchange for river restoration planning
Magliozzi C., Coro G., Grabowski R., Packman A. I., Krause S.
The hyporheic zone (HZ) is an area of interaction between surface and ground waters present in and around river beds. Bidirectional mixing within the HZ, termed hyporheic exchange flow (HEF), plays significant roles in nutrient transport, organic matter and biogeochemical processing in rivers. The functional importance of the HZ in river ecology and hydrology suggests that river managers should consider the HZ in their planning to help compromised systems recover. However, current river restoration planning tools do not take into account the HZ. This paper describes a novel multiscale, transferable method that combines existing environmental information at different spatial scales to identify areas with potentially significant HEF for use in restoration prioritization and planning. It uses a deductive approach that is suited for data-poor case studies, which is common for most rivers, given the very limited data on the spatial occurrence of areas of hyporheic exchange. Results on nine contrasting European rivers, demonstrate its potential to inform river management.Source: Environmental modelling & software 111 (2019): 311–323. doi:10.1016/j.envsoft.2018.09.006
DOI: 10.1016/j.envsoft.2018.09.006
Project(s): BlueBRIDGE , HypoTRAIN
See at:
Environmental Modelling & Software | Cranfield CERES | ISTI Repository | ZENODO | Environmental Modelling & Software | Environmental Modelling & Software | Environmental Modelling & Software | Environmental Modelling & Software | Environmental Modelling & Software | Environmental Modelling & Software | Environmental Modelling & Software | CNR ExploRA | Environmental Modelling & Software | Environmental Modelling & Software
Reconstructing 3D virtual environments within a collaborative e-infrastructure
Coro G., Palma M., Ellenbroek A., Panichi G., Nair T., Pagano P.
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.Source: Concurrency and Computation: Practice and Experience 31 (2019). doi:10.1002/cpe.5028
DOI: 10.1002/cpe.5028
Project(s): MERCES
See at:
ISTI Repository | Concurrency and Computation Practice and Experience | Concurrency and Computation Practice and Experience | Concurrency and Computation Practice and Experience | Concurrency and Computation Practice and Experience | Concurrency and Computation Practice and Experience | Concurrency and Computation Practice and Experience | Concurrency and Computation Practice and Experience | CNR ExploRA
The gCube system: delivering virtual research environments as-a-service
Assante M., Candela L., Castelli D., Cirillo R., Coro G., Frosini L., Lelii L., Mangiacrapa F., Marioli V., Pagano P., Panichi G., Perciante C., Sinibaldi F.
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.Source: Future generation computer systems 95 (2019): 445–453. doi:10.1016/j.future.2018.10.035
DOI: 10.1016/j.future.2018.10.035
Project(s): AGINFRA PLUS , BlueBRIDGE , ENVRI PLUS , EOSCpilot
See at:
ISTI Repository | Future Generation Computer Systems | Future Generation Computer Systems | Future Generation Computer Systems | Future Generation Computer Systems | Future Generation Computer Systems | CNR ExploRA | Future Generation Computer Systems | www.sciencedirect.com
Enacting open science by D4Science
Assante M., Candela L., Castelli D., Cirillo R., Coro G., Frosini L., Lelii L., Mangiacrapa F., Pagano P., Panichi G., Sinibaldi F.
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.Source: Future generation computer systems (2019): 555–563. doi:10.1016/j.future.2019.05.063
DOI: 10.1016/j.future.2019.05.063
Project(s): AGINFRA PLUS , BlueBRIDGE , ENVRI PLUS , EOSCpilot
See at:
ISTI Repository | Future Generation Computer Systems | Future Generation Computer Systems | Future Generation Computer Systems | Future Generation Computer Systems | Future Generation Computer Systems | CNR ExploRA | Future Generation Computer Systems