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2023 Journal article Open Access OPEN
A self-training automatic infant-cry detector
Coro G., Bardelli S., Cuttano A., Scaramuzzo R. T., Ciantelli M.
Infant cry is one of the first distinctive and informative life signals observed after birth. Neonatologists and automatic assistive systems can analyse infant cry to early-detect pathologies. These analyses extensively use reference expert-curated databases containing annotated infant-cry audio samples. However, these databases are not publicly accessible because of their sensitive data. Moreover, the recorded data can under-represent specific phenomena or the operational conditions required by other medical teams. Additionally, building these databases requires significant investments that few hospitals can afford. This paper describes an open-source workflow for infant-cry detection, which identifies audio segments containing high-quality infant-cry samples with no other overlapping audio events (e.g. machine noise or adult speech). It requires minimal training because it trains an LSTM-with-self-attention model on infant-cry samples automatically detected from the recorded audio through cluster analysis and HMM classification. The audio signal processing uses energy and intonation acoustic features from 100-ms segments to improve spectral robustness to noise. The workflow annotates the input audio with intervals containing infant-cry samples suited for populating a database for neonatological and early diagnosis studies. On 16 min of hospital phone-audio recordings, it reached sufficient infant-cry detection accuracy in 3 neonatal care environments (nursery--69%, sub-intensive--82%, intensive--77%) involving 20 infants subject to heterogeneous cry stimuli, and had substantial agreement with an expert's annotation. Our workflow is a cost-effective solution, particularly suited for a sub-intensive care environment, scalable to monitor from one to many infants. It allows a hospital to build and populate an extensive high-quality infant-cry database with a minimal investment.Source: Neural computing & applications (Print) (2023). doi:10.1007/s00521-022-08129-w
DOI: 10.1007/s00521-022-08129-w
Project(s): EcoScope via OpenAIRE
Metrics:


See at: link.springer.com Open Access | ISTI Repository Open Access | CNR ExploRA Open Access


2023 Journal article Open Access OPEN
Global-scale parameters for ecological models
Coro G., Bove P., Kesner-Reyes K.
This paper presents a collection of environmental, geophysical, and other marine-related data for marine ecological models and ecological-niche models. It consists of 2132 raster data for 58 distinct parameters at regional and global scales in the ESRI-GRID ASCII format. Most data originally belonged to open data owned by the authors of this article but residing on heterogeneous repositories with different formats and resolutions. Other data were specifically created for the present publication. The collection includes 565 data with global scale range; 154 at 0.5° resolution and 411 at 0.1° resolution; 196 data with annual temporal aggregation over ~10 key years between 1950 and 2100; 369 data with monthly aggregation at 0.1° resolution from January 2017 to ~May 2021 continuously. Data were also cut out on 8 European marine regions. The collection also includes forecasts for different future scenarios such as the Representative Concentration Pathways 2.6 (63 data), 4.5 (162 data), and 8.5 (162 data), and the A2 scenario of the Intergovernmental Panel on Climate Change (180 data).Source: Scientific data 10 (2023). doi:10.1038/s41597-022-01904-3
DOI: 10.1038/s41597-022-01904-3
Project(s): EcoScope via OpenAIRE
Metrics:


See at: ISTI Repository Open Access | CNR ExploRA Open Access | www.nature.com Open Access


2023 Journal article Open Access OPEN
From unstructured texts to semantic story maps
Bartalesi V., Coro G., Lenzi E., Pagano P., Pratelli N.
Digital maps greatly support storytelling about territories, especially when enriched with data describing cultural, societal, and ecological aspects, conveying emotional messages that describe the territory as a whole. Story maps are interactive online digital narratives that can describe a territory beyond its map by enriching the map with text, pictures, videos, and other multimedia information. This paper presents a semi-automatic workflow to produce story maps from textual documents containing territory data. An expert first assembles one territory-contextual document containing text and images. Then, automatic processes use natural language processing and Wikidata services to (i) extract key concepts (entities) and geospatial coordinates associated with the territory, (ii) assemble a logically-ordered sequence of enriched story-map events, and (iii) openly publish online story maps and an interoperable Linked Open Data semantic knowledge base for event exploration and inter-story correlation analyses. Our workflow uses an Open Science-oriented methodology to publish all processes and data. Through our workflow, we produced story maps for the value chains and territories of 23 rural European areas of 16 countries. Through numerical evaluation, we demonstrated that territory experts considered the story maps effective in describing their territories, and appropriate for communicating with citizens and stakeholders.Source: International journal of digital earth (Online) 16 (2023): 234–250. doi:10.1080/17538947.2023.2168774
DOI: 10.1080/17538947.2023.2168774
Project(s): MOVING via OpenAIRE
Metrics:


See at: ISTI Repository Open Access | ISTI Repository Open Access | CNR ExploRA Open Access | www.tandfonline.com Open Access


2023 Journal article Open Access OPEN
A simple framework for the exploration of functional biodiversity
Froese R., Coro G., Palomares M. L. D., Bailly N., Scotti M., Froese T., Garilao C., Pauly D.
Key traits of functional biodiversity are examined for 31,134 species of fishes. These traits are maximum body weight, productivity, and trophic level. A new, simple framework is presented that shows the combined usage of these traits, in ordinal categories, for close to 90% of extant species of fishes. Most species are clustered tightly along an evolutionary axis in size-productivity-trophic space (SPT-space) from few large, evolutionary old species with very low productivity to many medium-sized newly evolved species with high productivity, superseding Cope's rule of a within-lineages trend towards larger size and lower productivity. The across-lineages evolutionary axis is also found in the subsets of marine, freshwater, and Arctic species. Another notable prediction is the five-fold increase in top predators in Arctic waters in 2100, which could cause the extinction of endemic species. The main purpose of this study is to demonstrate the usefulness of the SPT-framework for comparing functional biodiversity patterns in ecosystems by salinity, geography or time. Also, the SPT-framework was used to explore correlations with other traits such as body shape, and to display the position of individual species, represented by pictograms of body shape and habitat, within SPT-space.Source: Cybium (2023): 1–16. doi:10.26028/cybium/2023-003
DOI: 10.26028/cybium/2023-003
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See at: ISTI Repository Open Access | CNR ExploRA Open Access | sfi-cybium.fr Open Access


2023 Journal article Open Access OPEN
An exploratory approach to data driven knowledge creation
Thanos C., Meghini C., Bartalesi V., Coro G.
This paper describes a new approach to knowledge creation that is instrumental for the emerging paradigm of data-intensive science. The proposed approach enables the acquisition of new insights from the data by exploiting existing relationships between diverse types of datasets acquired through various modalities. The value of data consistently improves when it can be linked to other data because linking multiple types of datasets allows creating novel data patterns within a scientific data space. These patterns enable the exploratory data analysis, an analysis strategy that emphasizes incremental and adaptive access to the datasets constituting a scientific data space while maintaining an open mind to alternative possibilities of data interconnectivity. A technology, the Linked Open data (LOD), was developed to enable the linking of datasets. We argue that the LOD technology presents several limitations that prevent the full exploitation of this technology to acquire new insights. In this paper, we outline a new approach that enables researchers to dynamically create data patterns in a research data space by exploiting explicit and implicit/hidden relationships between distributed research datasets. This dynamic creation of data patterns enables the exploratory data analysis strategy.Source: Journal of big data 10 (2023). doi:10.1186/s40537-023-00702-x
DOI: 10.1186/s40537-023-00702-x
Metrics:


See at: journalofbigdata.springeropen.com Open Access | ISTI Repository Open Access | CNR ExploRA Open Access


2023 Journal article Open Access OPEN
Estimating hidden fishing activity hotspots from vessel transmitted data
Coro G., Sana L., Ferrà C., Bove P., Scarcella G.
Monitoring fishery activity is essential for resource planning and guaranteeing fisheries sustainability. Large fishing vessels constantly and continuously communicate their positions via Automatic Identification System (AIS) or Vessel Monitoring Systems (VMSs). These systems can use radio or Global Positioning System (GPS) devices to transmit data. Processing and integrating these big data with other fisheries data allows for exploring the relations between socio-economic and ecosystem assets in marine areas, which is fundamental in fishery monitoring. In this context, estimating actual fishing activity from time series of AIS and VMS data would enhance the correct identification of fishing activity patterns and help assess regulations' effectiveness. However, these data might contain gaps because of technical issues such as limited coverage of the terrestrial receivers or saturated transmission bands. Other sources of data gaps are adverse meteorological conditions and voluntary switch-offs. Gaps may also include hidden (unreported) fishing activity whose quantification would improve actual fishing activity estimation. This paper presents a workflow for AIS/VMS big-data analysis that estimates potential unreported fishing activity hotspots in a marine area. The workflow uses a statistical spatial analysis over vessel speeds and coordinates and a multi-source data integration approach that can work on multiple areas and multiple analysis scales. Specifically, it (i) estimates fishing activity locations and rebuilds data gaps, (ii) estimates the potential unreported fishing hour distribution and the unreported-over-total ratio of fishing hours at a 0.01° spatial resolution, (iii) identifies potential unreported fishing activity hotspots, (iv) extracts the stocks involved in these hotspots (using global-scale repositories of stock and species observation data) and raises an alert about their possible endangered, threatened, and protected (ETP) status. The workflow is also a free-to-use Web Service running on an open science-compliant cloud computing platform with a Web Processing Service (WPS) standard interface, allowing efficient big data processing. As a study case, we focussed on the Adriatic Sea. We reconstructed the monthly reported and potential unreported trawling activity in 2019, using terrestrial AIS data with a 5-min sampling period, containing ~50 million records transmitted by ~1,600 vessels. The results highlight that the unreported fishing activity hotspots especially impacted Italian coasts and some forbidden and protected areas. The potential unreported activity involved 33 stocks, four of which were ETP species in the basin. The extracted information agreed with expert studies, and the estimated trawling patterns agreed with those produced by the Global Fishing Watch.Source: Frontiers in sustainable food systems On line 7 (2023). doi:10.3389/fsufs.2023.1152226
DOI: 10.3389/fsufs.2023.1152226
Project(s): EcoScope via OpenAIRE
Metrics:


See at: ISTI Repository Open Access | CNR ExploRA Open Access | www.frontiersin.org Open Access


2022 Journal article Open Access OPEN
COVID-19 lockdowns reveal the resilience of Adriatic Sea fisheries to forced fishing effort reduction
Coro G., Tassetti A. N., Armelloni E. N., Pulcinella J., Ferrà C., Sprovieri M., Trincardi F., Scarcella G.
The COVID-19 pandemic provides a major opportunity to study fishing effort dynamics and to assess the response of the industry to standard and remedial actions. Knowing a fishing fleet's capacity to compensate for effort reduction (i.e., its resilience) allows differentiating governmental regulations by fleet, i.e., imposing stronger restrictions on the more resilient and weaker restrictions on the less resilient. In the present research, the response of the main fishing fleets of the Adriatic Sea to fishing hour reduction from 2015 to 2020 was measured. Fleet activity per gear type was inferred from monthly Automatic Identification System data. Pattern recognition techniques were applied to study the fishing effort trends and barycentres by gear. The beneficial effects of the lockdowns on Adriatic endangered, threatened and protected (ETP) species were also estimated. Finally, fleet effort series were examined through a stock assessment model to demonstrate that every Adriatic fishing fleet generally behaves like a stock subject to significant stress, which was particularly highlighted by the pandemic. Our findings lend support to the notion that the Adriatic fleets can be compared to predators with medium-high resilience and a generally strong impact on ETP species.Source: Scientific reports (Nature Publishing Group) 12 (2022). doi:10.1038/s41598-022-05142-w
DOI: 10.1038/s41598-022-05142-w
Metrics:


See at: ISTI Repository Open Access | ISTI Repository Open Access | CNR ExploRA Open Access | www.nature.com Open Access


2022 Journal article Open Access OPEN
A high-resolution global-scale model for COVID-19 infection rate
Coro G., Bove P.
Several models have correlated COVID-19 spread with specific climatic, geophysical, and air pollution conditions , and early models had predicted the lowering of infection cases in Summer 2020. These approaches have been criticized for their coarse assumptions and because they could produce biases if used without considering dynamic factors such as human mobility and interaction. However, human mobility and interaction models alone have not been able to suggest more innovative recommendations than simple social distancing and lockdown, and would definitely need to include information about the base environmental suitability of a World area to COVID-19 spread. This scenario would benefit from a global-scale high-resolution environmental model that could be coupled with dynamic models for large-scale and regional analyses. This article presents a 0.1°high-resolution global-scale probability map of low and high-infection-rates of COVID-19 that uses annual-average surface air temperature, precipitation, and CO 2 as environmental parameters, and Italian provinces as training locations. A risk index calculated on this map correctly identifies 87% of the World countries that reported high infection rates in 2020 and 80% of the low and high infection-rate countries overall. Our model is meant to be used as an additional factor in other models for monthly weather and human mobility. It estimates the base environmental inertia that a geographical place opposes to COVID-19 when mobility restrictions are not in place and can support how much the monthly weather favors or penalizes infection increase. Its high resolution and extent make it consistently usable in global and regional-scale analyses, also thanks to the availability of our results as FAIR data and software as an Open Science-oriented Web service.Source: ACM transactions on spatial algorithms and systems (Online) 8 (2022). doi:10.1145/3494531
DOI: 10.1145/3494531
Project(s): EOSCsecretariat.eu via OpenAIRE
Metrics:


See at: dl.acm.org Open Access | CNR ExploRA Open Access


2022 Journal article Open Access OPEN
Virtual research environments co-creation: the D4Science experience
Assante M., Candela L., Castelli D., Cirillo R., Coro G., Dell'Amico A., Frosini L., Lelii L., Lettere M., Mangiacrapa F., Pagano P., Panichi G., Piccioli T., Sinibaldi F.
Virtual research environments are systems called to serve the needs of their designated communities of practice. Every community of practice is a group of people dynamically aggregated by the willingness to collaborate to address a given research question. The virtual research environment provides its users with seamless access to the resources of interest (namely, data and services) no matter what and where they are. Developing a virtual research environment thus to guarantee its uptake from the community of practice is a challenging task. In this article, we advocate how the co-creation driven approach promoted by D4Science has proven to be effective. In particular, we present the co-creation options supported, discuss how diverse communities of practice have exploited these options, and give some usage indicators on the created VREs.Source: Concurrency and computation (Online) (2022). doi:10.1002/cpe.6925
DOI: 10.1002/cpe.6925
Project(s): AGINFRA PLUS via OpenAIRE, Blue Cloud via OpenAIRE, EOSC-Pillar via OpenAIRE, SoBigData-PlusPlus via OpenAIRE
Metrics:


See at: ISTI Repository Open Access | onlinelibrary.wiley.com Restricted | CNR ExploRA Restricted


2022 Journal article Open Access OPEN
An exploratory approach to archaeological knowledge production
Thanos C., Meghini C., Bartalesi V., Coro G.
The current scientific context is characterized by intensive digitization of the research outcomes and by the creation of data infrastructures for the systematic publication of datasets and data services. Several relationships can exist among these outcomes. Some of them are explicit, e.g. the relationships of spatial or temporal similarity, whereas others are hidden, e.g. the relationship of causality. By materializing these hidden relationships through a linking mechanism, several patterns can be established. These knowledge patterns may lead to the discovery of information previously unknown. A new approach to knowledge production can emerge by following these patterns. This new approach is exploratory because by following these patterns, a researcher can get new insights into a research problem. In the paper, we report our effort to depict this new exploratory approach using Linked Data and Semantic Web technologies (RDF, OWL). As a use case, we apply our approach to the archaeological domain.Source: International journal on digital libraries (Internet) (2022). doi:10.1007/s00799-022-00324-3
DOI: 10.1007/s00799-022-00324-3
Metrics:


See at: ISTI Repository Open Access | link.springer.com Restricted | CNR ExploRA Restricted


2022 Journal article Open Access OPEN
Automatic detection of potentially ineffective verbal communication for training through simulation in neonatology
Coro G., Bardelli S., Cuttano A., Fossati N.
Training through simulation in neonatology relies on sophisticated simulation devices that give realistic feedback to trainees during simulated scenarios. It aims at training highly specialised medical teams in established operational skills, timely clinical manoeuvres, and successful synergy with other professionals. For effective teaching, it is essential to tailor simulation to trainees' emotional status and communication abilities (human factors), which in turn affect their interaction with the equipment, the environment, and the rest of the team. These factors are crucial to achieving optimal timing and cooperation during a clinical intervention, to the point that they can determine the success of a complex operation such as neonatal resuscitation. Ineffective teams perform in a slow and/or poorly coordinated way and therefore jeopardise positive outcomes. Expert trainers consider human factors as crucial as technical skills. In this context, new technology can help measure learning improvement by quantitatively analysing verbal communication within a medical team. For example, Artificial Intelligence models can work on audio recordings, and draw from extensive historical archives, to extract useful human-factor related information for the trainers. In this study, we present an automatic workflow that supports training through simulation in neonatology by automatically detecting dialogue segments of a simulation session with potentially ineffective communication between team members due to anger, stress, fear, or misunderstandings. Rather than working on audio transcriptions, the workflow analyses syllabic-scale (100-200 ms) spoken dialogue energy and intonation. It uses cluster analysis to identify potentially ineffective communication and extracts the most important related words after audio transcription. Performance is measured against a gold standard containing annotations of 79 minutes of audio recordings from neonatal simulations, in Italian, under different noise conditions (from 4.63 to 14.17 SNR). Our workflow achieves a detection accuracy of 64% and a fair agreement with the gold standard in a challenging context for a speech-processing system, where a commercial automatic speech recogniser reaches just a 9.37% sentence accuracy. The workflow also identifies viable words for trainers to conduct the debriefing session, and can be easily extended to other languages and applications in healthcare. We consider it a promising first step towards introducing new technology to support training through simulation centred on human factors.Source: Education and information technologies (Dordr., Online) (2022). doi:10.1007/s10639-022-11000-z
DOI: 10.1007/s10639-022-11000-z
Metrics:


See at: ISTI Repository Open Access | link.springer.com Restricted | CNR ExploRA Restricted


2022 Journal article Open Access OPEN
Habitat distribution change of commercial species in the Adriatic Sea during the COVID-19 pandemic
Coro G., Bove P., Ellenbroek A.
The COVID-19 pandemic has led to reduced anthropogenic pressure on ecosystems in several world areas, but resulting ecosystem responses in these areas have not been investigated. This paper presents an approach to make quick assessments of potential habitat changes in 2020 of eight marine species of commercial importance in the Adriatic Sea. Measurements from floating probes are interpolated through an advection-equation based model. The resulting distributions are then combined with species observations through an ecological niche model to estimate habitat distributions in the past years (2015-2018) at 0.1° spatial resolution. Habitat patterns over 2019 and 2020 are then extracted and explained in terms of specific environmental parameter changes. These changes are finally assessed for their potential dependency on climate change patterns and anthropogenic pressure change due to the pandemic. Our results demonstrate that the combined effect of climate change and the pandemic could have heterogeneous effects on habitat distributions: three species (Squilla mantis, Engraulis encrasicolus, and Solea solea) did not show significant niche distribution change; habitat suitability positively changed for Sepia officinalis, but negatively for Parapenaeus longirostris, due to increased temperature and decreasing dissolved oxygen (in the Adriatic) generally correlated with climate change; the combination of these trends with an average decrease in chlorophyll, probably due to the pandemic, extended the habitat distributions of Merluccius merluccius and Mullus barbatus but reduced Sardina pilchardus distribution. Although our results are based on approximated data and reliable at a macroscopic level, we present a very early insight of modifications that will possibly be observed years after the end of the pandemic when complete data will be available. Our approach is entirely based on Findable, Accessible, Interoperable, and Reusable (FAIR) data and is general enough to be used for other species and areas.Source: Ecological informatics (Print) (2022). doi:10.1016/j.ecoinf.2022.101675
DOI: 10.1016/j.ecoinf.2022.101675
Project(s): Blue Cloud via OpenAIRE
Metrics:


See at: ISTI Repository Open Access | CNR ExploRA Restricted


2022 Software Unknown
Story Map Building and Visualising Tool (SMBVT)
Lenzi E., Bartalesi V., Pratelli N., Coro G., Pagano P.
In the context of the MOVING (MOuntain Valorisation through INterconnectedness and Green growth) project, we released an open-source software - the MOVING Story Map Building and Visualization Tool (SMBVT) - that allows users to create and visualise story maps within a collaborative environment and using a user-friendly Web interface. The tool uses Semantic Web technologies and the Narrative Ontology to represent the stories of the MOVING mountain Value Chains. The MOVING community access SMBVT through The MOVING story map Virtual Research Environment and creates the events of the story. For each event, the user can add: a title, a textual description, start and end dates, the geographic coordinates, a media object (i.e. a video or image), notes, and digital objects. The tool takes Wikidata as reference KB and assigns Wikidata Internationalized Resource Identifiers (IRIs) to the story components (i.e. the entities that take part in an event). All the knowledge collected by SMBVT is stored in a JSON Postgres DB. When a story is completed, the tool automatically creates the corresponding visualisation using StoryMapJS library and makes available a corresponding URL that can be freely shared. Finally, SMBVT saves the collected knowledge as a Web Ontology Language (OWL) graph and publishes it as a Linked Open Data.Project(s): "CMG Collaborative Research": A Systematic Approach to Large Amplitude Internal Wave Dynamics: An Integrated Mathematical, Observational, and Remote Sensing Model, Blue Cloud via OpenAIRE, MOVING via OpenAIRE

See at: github.com | CNR ExploRA


2022 Journal article Open Access OPEN
Filling gaps in trawl surveys at sea through spatiotemporal and environmental modelling
Coro G., Bove P., Armelloni E. N., Masnadi F., Scanu M., Scarcella G.
International scientific fishery survey programmes systematically collect samples of target stocks' biomass and abundance and use them as the basis to estimate stock status in the framework of stock assessment models. The research surveys can also inform decision makers about Essential Fish Habitat conservation and help define harvest control rules based on direct observation of biomass at the sea. However, missed survey locations over the survey years are common in long-term programme data. Currently, modelling approaches to filling gaps in spatiotemporal survey data range from quickly applicable solutions to complex modelling. Most models require setting prior statistical assumptions on spatial distributions, assuming short-term temporal dependency between the data, and scarcely considering the environmental aspects that might have influenced stock presence in the missed locations. This paper proposes a statistical and machine learning based model to fill spatiotemporal gaps in survey data and produce robust estimates for stock assessment experts, decision makers, and regional fisheries management organizations. We apply our model to the SoleMon survey data in North-Central Adriatic Sea (Mediterranean Sea) for 4 stocks: Sepia officinalis, Solea solea, Squilla mantis, and Pecten jacobaeus. We reconstruct the biomass-index (i.e., biomass over the swept area) of 10 locations missed in 2020 (out of the 67 planned) because of several factors, including COVID-19 pandemic related restrictions. We evaluate model performance on 2019 data with respect to an alternative index that assumes biomass proportion consistency over time. Our model's novelty is that it combines three complementary components. A spatial component estimates stock biomass-index in the missed locations in one year, given the surveyed location's biomass-index distribution in the same year. A temporal component forecasts, for each missed survey location, biomass-index given the data history of that haul. An environmental component estimates a biomass-index weighting factor based on the environmental suitability of the haul area to species presence. Combining these components allows understanding the interplay between environmental-change drivers, stock presence, and fisheries. Our model formulation is general enough to be applied to other survey data with lower spatial homogeneity and more temporal gaps than the SoleMon dataset.Source: Frontiers in Marine Science 9 (2022). doi:10.3389/fmars.2022.919339
DOI: 10.3389/fmars.2022.919339
Project(s): EcoScope via OpenAIRE
Metrics:


See at: ISTI Repository Open Access | CNR ExploRA Open Access | www.frontiersin.org Open Access


2022 Journal article Open Access OPEN
The potential effects of COVID-19 lockdown and the following restrictions on the status of eight target stocks in the Adriatic Sea
Scarcella G., Angelini S., Armelloni E. N., Costantini I., De Felice A., Guicciardi S., Leonori I., Masnadi F., Scanu M., Coro G.
The COVID-19 pandemic had major impacts on the seafood supply chain, also reducing fishing activity. It is worth asking if the fish stocks in the Mediterranean Sea, which in most cases have been in overfishing conditions for many years, may have benefitted from the reduction in the fishing pressure. The present work is the first attempt to make a quantitative evaluation of the fishing effort reduction due to the COVID-19 pandemic and, consequently, its impact on Mediterranean fish stocks, focusing on Adriatic Sea subareas. Eight commercially exploited target stocks (common sole, common cuttlefish, spottail mantis shrimp, European hake, red mullet, anchovy, sardine, and deepwater pink shrimp) were evaluated with a surplus production model, separately fitting the data for each stock until 2019 and until 2020. Results for the 2019 and 2020 models in terms of biomass and fishing mortality were statistically compared with a bootstrap resampling technique to assess their statistical difference. Most of the stocks showed a small but significant improvement in terms of both biomass at sea and reduction in fishing mortality, except cuttlefish and pink shrimp, which showed a reduction in biomass at sea and an increase in fishing mortality (only for common cuttlefish). After reviewing the potential co-occurrence of environmental and management-related factors, we concluded that only in the case of the common sole can an effective biomass improvement related to the pandemic restrictions be detected, because it is the target of the only fishing fleet whose activity remained far lower than expectations for the entire 2020.Source: Frontiers in Marine Science (2022). doi:10.3389/fmars.2022.920974
DOI: 10.3389/fmars.2022.920974
Project(s): EcoScope via OpenAIRE
Metrics:


See at: ISTI Repository Open Access | CNR ExploRA Open Access | www.frontiersin.org Open Access


2022 Report Restricted
MOVING D3.3 - Tools for science-society-policy interfaces. Using semantic story maps to describe a territory beyond its map
Bartalesi V., Coro G., Lenzi E., Pratelli N., Pagano P.
Maps have always stimulated people's imagination and spatiotemporally supported storytelling; however, they cannot alone represent the life and emotions associated with the territories they describe. Story maps are an IT solution to enrich maps with such information. They are online applications enriched with multimedia and textual information that tell map-based stories. Current software for story map building is either commercial or requires advanced IT skills that make it hardly used by environmental experts. This deliverable describes the Story Map Building and Visualizing Tool (SBVMT), an open-source and free-to-use tool to build and publish story maps, which overcomes common drawbacks of other software by operating within the open-science e-Infrastructure (D4Science) used in the MOVING Project. SBVMT includes new features such as an ontology to represent story maps, Semantic Web technologies for data representation, automatic connection to Wikidata, secure multi-user collaboration in story building, and visualisation of the narrative either as a story map or a timeline. This deliverable shows how SBVMT can overcome the perceptual gap between territory and map. We evaluated its usability and effectiveness from both the point of view of experts building the story map and users interacting with it. Using SMBVT we created the story maps related to the MOVING selected regions. Furthermore, exploiting the semantic web technologies, we implemented several SPARQL queries that allow linking different stories and discovering new knowledge.Source: ISTI Research Report, MOVING, D3.3, 2022
Project(s): MOVING via OpenAIRE

See at: CNR ExploRA Restricted


2022 Report Open Access OPEN
InfraScience research activity report 2021
Artini M., Assante M., Atzori C., Baglioni M., Bardi A., Bove P., Candela L., Casini G., Castelli D., Cirillo R., Coro G., De Bonis M., Debole F., Dell'Amico A., Frosini L., La Bruzzo S., Lazzeri E., Lelii L., Manghi P., Mangiacrapa F., Mangione D., Mannocci A., Ottonello E., Pagano P., Panichi G., Pavone G., Piccioli T., Sinibaldi F., Straccia U.
InfraScience is a research group of the National Research Council of Italy - Institute of Information Science and Technologies (CNR - ISTI) based in Pisa, Italy. This report documents the research activity performed by this group in 2021 to highlight the major results. In particular, the InfraScience group confronted with research challenges characterising Data Infrastructures, eScience, and Intelligent Systems. The group activity is pursued by closely connecting research and development and by promoting and supporting open science. In fact, the group is leading the development of two large scale infrastructures for Open Science, i.e. D4Science and OpenAIRE. During 2021 InfraScience members contributed to the publishing of 25 papers, to the research and development activities of 18 research projects (15 funded by EU), to the organization of conferences and training events, to several working groups and task forces.Source: ISTI Annual report, 2022
DOI: 10.32079/isti-ar-2022/001
Project(s): ARIADNEplus via OpenAIRE, Blue Cloud via OpenAIRE, PerformFISH via OpenAIRE, EOSC-Pillar via OpenAIRE, DESIRA via OpenAIRE, EOSC Future via OpenAIRE, EOSCsecretariat.eu via OpenAIRE, EcoScope via OpenAIRE, RISIS 2 via OpenAIRE, OpenAIRE-Advance via OpenAIRE, OpenAIRE Nexus via OpenAIRE, SoBigData-PlusPlus via OpenAIRE
Metrics:


See at: ISTI Repository Open Access | CNR ExploRA Open Access


2022 Report Closed Access
D4Science activity report 2022
Assante M., Candela L., Castelli D., Cirillo R., Coro G., Dell'Amico A., Frosini L., Lelii L., Mangiacrapa F., Pagano P., Panichi G., Piccioli T., Sinibaldi F., Zoppi F.
D4Science is an IT infrastructure specifically conceived to support the development and operation of Virtual Research Environments by the as-a-Service provisioning mode. This report documents the activities performed in 2022 to develop this infrastructure and support several projects and exploitations.Source: ISTI Technical Report, ISTI-2022-TR/037, 2022
DOI: 10.32079/isti-tr-2022/037
Project(s): ARIADNEplus via OpenAIRE, Blue Cloud via OpenAIRE, EOSC-Pillar via OpenAIRE, DESIRA via OpenAIRE, SoBigData-PlusPlus via OpenAIRE
Metrics:


See at: CNR ExploRA Restricted


2022 Report Closed Access
InfraScience research activity report 2022
Artini M., Assante M., Atzori C., Baglioni M., Bardi A., Bove P., Candela L., Casini G., Castelli D., Cirillo R., Coro G., De Bonis M., Debole F., Dell'Amico A., Frosini L., La Bruzzo S., Lelii L., Manghi P., Mangiacrapa F., Mangione D., Mannocci A., Ottonello E., Pagano P., Panichi G., Pavone G., Piccioli T., Sinibaldi F., Straccia U., Zoppi F.
InfraScience is a research group of the National Research Council of Italy - Institute of Information Science and Technologies (CNR - ISTI) based in Pisa, Italy. This report documents the research activity performed by this group in 2022 to highlight the major results. In particular, the InfraScience group confronted with research challenges characterising Data Infrastructures, e-Science, and Intelligent Systems. The group activity is pursued by closely connecting research and development and by promoting and supporting open science. In fact, the group is leading the development of two large scale infrastructures for Open Science, i.e. D4Science and OpenAIRE. During 2022 InfraScience members contributed to the publishing of several papers, to the research and development activities of 18 research projects (15 funded by EU), to the organization of conferences and training events, to several working groups and task forces.Source: ISTI Annual reports, 2022
Project(s): ARIADNEplus via OpenAIRE, Blue Cloud via OpenAIRE, EOSC-Pillar via OpenAIRE, DESIRA via OpenAIRE, EOSC Future via OpenAIRE, RISIS 2 via OpenAIRE, TAILOR via OpenAIRE, SoBigData-PlusPlus via OpenAIRE

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2021 Journal article Open Access OPEN
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
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