2025
Journal article
Open Access
Computing ecosystem risk hotspots: a mediterranean case study
Coro G., Pavirani L., Ellenbroek A.
In ecosystem management, risk assessment quantifies the probability and impact of events and informs on intervention priorities. Analytical models for risk assessment quantify the impact of natural and anthropogenic stressors on ecosystems. Traditional approaches evaluate single stressors, whereas complex models assess cumulative impacts of frequently interacting stressors and offer better accuracy at the expense of low cross-area re-applicability and long implementation times. We introduce a versatile, re-useable, and semi-automated workflow designed for big data-driven ecosystem risk assessment, utilising spatiotemporal data from open repositories. It allows for a flexible definition of the stressors on which the risk under analysis depends. By applying cluster analysis, the workflow identifies different patterns of stressor concurrency, while statistical analysis highlights clusters of stressors likely linked to elevated risk. Ultimately, it generates geospatial risk maps and identifies spatial risk hotspots. The workflow methodology is independent of the geographical area of the application. As a case study, we present risk assessments for the Mediterranean Sea, a region with intense anthropogenic pressures and significant climatic vulnerabilities. We used over 1.1 million open data from 2017 to 2021 and projections to 2050 under the RCP8.5 scenario (a high greenhouse gas emission scenario) at a 0.5°spatial resolution. Data included environmental, oceanographic, biodiversity variables, and manifest and hidden fishing effort distributions. Our workflow identified different types of high-risk hotspots, highlighting different concurrencies of habitat loss, overfishing, hidden fishing, and climate change stressors. High-risk hotspots concentrated in the Western Mediterranean, the Tyrrhenian Sea, the Adriatic Sea, the Strait of Sicily, the Aegean Sea, and eastern Turkey. Our results agreed with an alternative Fuzzy C-means-based method (with a 90% to 96% overlap over the years) and a Bayesian regression model (∼80% overlap). Our Mediterranean risk maps can facilitate the development of management and monitoring strategies, supporting the sustainable development and resilience of coastal zones, and can act as prior knowledge for ecosystem models and spatial plans.Source: ECOLOGICAL INFORMATICS, vol. 85
DOI: 10.1016/j.ecoinf.2024.102918
Project(s): EcoScope
Metrics:
Coro G., Pavirani L., Ellenbroek A.
In ecosystem management, risk assessment quantifies the probability and impact of events and informs on intervention priorities. Analytical models for risk assessment quantify the impact of natural and anthropogenic stressors on ecosystems. Traditional approaches evaluate single stressors, whereas complex models assess cumulative impacts of frequently interacting stressors and offer better accuracy at the expense of low cross-area re-applicability and long implementation times. We introduce a versatile, re-useable, and semi-automated workflow designed for big data-driven ecosystem risk assessment, utilising spatiotemporal data from open repositories. It allows for a flexible definition of the stressors on which the risk under analysis depends. By applying cluster analysis, the workflow identifies different patterns of stressor concurrency, while statistical analysis highlights clusters of stressors likely linked to elevated risk. Ultimately, it generates geospatial risk maps and identifies spatial risk hotspots. The workflow methodology is independent of the geographical area of the application. As a case study, we present risk assessments for the Mediterranean Sea, a region with intense anthropogenic pressures and significant climatic vulnerabilities. We used over 1.1 million open data from 2017 to 2021 and projections to 2050 under the RCP8.5 scenario (a high greenhouse gas emission scenario) at a 0.5°spatial resolution. Data included environmental, oceanographic, biodiversity variables, and manifest and hidden fishing effort distributions. Our workflow identified different types of high-risk hotspots, highlighting different concurrencies of habitat loss, overfishing, hidden fishing, and climate change stressors. High-risk hotspots concentrated in the Western Mediterranean, the Tyrrhenian Sea, the Adriatic Sea, the Strait of Sicily, the Aegean Sea, and eastern Turkey. Our results agreed with an alternative Fuzzy C-means-based method (with a 90% to 96% overlap over the years) and a Bayesian regression model (∼80% overlap). Our Mediterranean risk maps can facilitate the development of management and monitoring strategies, supporting the sustainable development and resilience of coastal zones, and can act as prior knowledge for ecosystem models and spatial plans.Source: ECOLOGICAL INFORMATICS, vol. 85
DOI: 10.1016/j.ecoinf.2024.102918
Project(s): EcoScope
Metrics:
See at:
Ecological Informatics 
| CNR IRIS | www.sciencedirect.com | CNR IRIS 
2025
Journal article
Open Access
Gap analysis on the biology of marine fishes across european seas
Kesner-Reyes K., Capuli E. C., Reyes Jr R. B., Jansalin J. G. M., Rius-Barile J., Bactong M., Daskalaki E., Manousi S., Ferrà Carmen, Scarcella G., Coro G., Ordines F., Celie L., Scotti M., Lambert C., Gal G., Palomares M. L., Tsikliras A. C., Dimarchopoulou D.
This review evaluates the current knowledge of essential biological traits (diet, fecundity, maturity, length-weight relationships, spawning, growth, lifespan, and natural mortality) of marine fishes across European and adjacent waters. These traits are crucial for ecosystem modeling and stock assessments. Using data from FishBase, the largest and most comprehensive database on fishes, a gap analysis was performed to identify areas of research focus and the corresponding gaps that require further study. Biological data coverage is strong in the Baltic and North Seas but moderate in the Adriatic, Aegean, Biscay, Celtic, Levantine, and western Mediterranean Seas. Well-documented species include the European conger (Conger conger), thornback ray (Raja clavata), and transparent goby (Aphia minuta) which are reported from all areas. The narrowest knowledge gaps concern length-weight relationships, followed by spawning and growth, while natural mortality and fecundity are the least studied biological characteristics. Regional variations exist, particularly for protected species. Future research should focus on filling gaps by addressing overlooked species (bycatch and discarded species) and traits such as natural mortality and fecundity, with special attention to vulnerable groups like sharks and rays. Expanding biological data coverage will reduce uncertainties in stock assessments and improve ecosystem models, two widely used tools for sustainable fisheries management and marine conservation.Source: REVIEWS IN FISHERIES SCIENCE & AQUACULTURE, pp. 1-22
DOI: 10.1080/23308249.2024.2446806
Project(s): EcoScope, EcoScope
Metrics:
Kesner-Reyes K., Capuli E. C., Reyes Jr R. B., Jansalin J. G. M., Rius-Barile J., Bactong M., Daskalaki E., Manousi S., Ferrà Carmen, Scarcella G., Coro G., Ordines F., Celie L., Scotti M., Lambert C., Gal G., Palomares M. L., Tsikliras A. C., Dimarchopoulou D.
This review evaluates the current knowledge of essential biological traits (diet, fecundity, maturity, length-weight relationships, spawning, growth, lifespan, and natural mortality) of marine fishes across European and adjacent waters. These traits are crucial for ecosystem modeling and stock assessments. Using data from FishBase, the largest and most comprehensive database on fishes, a gap analysis was performed to identify areas of research focus and the corresponding gaps that require further study. Biological data coverage is strong in the Baltic and North Seas but moderate in the Adriatic, Aegean, Biscay, Celtic, Levantine, and western Mediterranean Seas. Well-documented species include the European conger (Conger conger), thornback ray (Raja clavata), and transparent goby (Aphia minuta) which are reported from all areas. The narrowest knowledge gaps concern length-weight relationships, followed by spawning and growth, while natural mortality and fecundity are the least studied biological characteristics. Regional variations exist, particularly for protected species. Future research should focus on filling gaps by addressing overlooked species (bycatch and discarded species) and traits such as natural mortality and fecundity, with special attention to vulnerable groups like sharks and rays. Expanding biological data coverage will reduce uncertainties in stock assessments and improve ecosystem models, two widely used tools for sustainable fisheries management and marine conservation.Source: REVIEWS IN FISHERIES SCIENCE & AQUACULTURE, pp. 1-22
DOI: 10.1080/23308249.2024.2446806
Project(s): EcoScope
, EcoScope Metrics:
See at:
ZENODO | CNR IRIS | CNR IRIS | www.tandfonline.com
2025
Journal article
Open Access
Distributed environments for ocean forecasting: the role of cloud computing
Ciliberti S., Coro G.
Cloud computing offers an opportunity to innovate traditional methods for provisioning of scalable and measurable computed resources as needed by operational forecasting systems. It offers solutions for more flexible and adaptable computing architecture, for developing and running models, for managing and disseminating data to finally deploy services and applications. The review discussed on the key characteristic of cloud computing related on on-demand self-service, network access, resource pooling, elasticity and measured services. Additionally, it provides an overview of existing service models and deployments methods (e.g., private cloud, public cloud, community cloud, and hybrid cloud). A series of examples from the weather and ocean community is also briefly outlined, demonstrating how specific tasks can be mapped on specific cloud patterns and which methods are needed to be implemented depending on the specific adopted service model.Source: STATE OF THE PLANET, vol. 5 (issue 24)
DOI: 10.5194/sp-2024-37
Metrics:
Ciliberti S., Coro G.
Cloud computing offers an opportunity to innovate traditional methods for provisioning of scalable and measurable computed resources as needed by operational forecasting systems. It offers solutions for more flexible and adaptable computing architecture, for developing and running models, for managing and disseminating data to finally deploy services and applications. The review discussed on the key characteristic of cloud computing related on on-demand self-service, network access, resource pooling, elasticity and measured services. Additionally, it provides an overview of existing service models and deployments methods (e.g., private cloud, public cloud, community cloud, and hybrid cloud). A series of examples from the weather and ocean community is also briefly outlined, demonstrating how specific tasks can be mapped on specific cloud patterns and which methods are needed to be implemented depending on the specific adopted service model.Source: STATE OF THE PLANET, vol. 5 (issue 24)
DOI: 10.5194/sp-2024-37
Metrics:
See at:
doi.org | CNR IRIS | sp.copernicus.org | CNR IRIS | CNR IRIS | Copernicus Publications
2025
Journal article
Open Access
An open data collection of 3D tool and equipment models for neonatology
Bardelli S., Coro G., Scaramuzzo R. T., Ciantelli M., Cuttano A.
Virtual Simulation (VS) offers an elegant and effective solution to the current need for innovation in medical education, thanks to the possibility of creating low-cost, realistic training environments for repetitive practice without compromising patient safety. However, this training methodology is only adopted in some healthcare settings often because of the absence of free digital libraries of clinical assets and tools. The present technical note describes a data collection of 3D models representing crucial tools and equipment used in maternal and newborn care training. We used free-to-use photogrammetry and structure-from-motion software and computational platform for 3D object reconstruction to digitalize the physical clinical instruments typically used during maternal and newborn care. In particular, we acquired photographs of 34 physical objects and reconstructed them as 3D models. Additionally, we created a complete, navigable virtual training room containing the 3D models. Eventually, we published the 3D models and the virtual training room as an open-access data collection on Sketchfab (a free-to-use online digital platform for 3D model publication), from which all models can be freely downloaded and inspected through Web browsers, mobile applications, and Virtual and Augmented Reality devices. Our data collection and repeatable and cost-effective methodology open new opportunities to use VS for training through simulation in healthcare.Source: RESULTS IN ENGINEERING, vol. 25
DOI: 10.1016/j.rineng.2025.104236
Metrics:
Bardelli S., Coro G., Scaramuzzo R. T., Ciantelli M., Cuttano A.
Virtual Simulation (VS) offers an elegant and effective solution to the current need for innovation in medical education, thanks to the possibility of creating low-cost, realistic training environments for repetitive practice without compromising patient safety. However, this training methodology is only adopted in some healthcare settings often because of the absence of free digital libraries of clinical assets and tools. The present technical note describes a data collection of 3D models representing crucial tools and equipment used in maternal and newborn care training. We used free-to-use photogrammetry and structure-from-motion software and computational platform for 3D object reconstruction to digitalize the physical clinical instruments typically used during maternal and newborn care. In particular, we acquired photographs of 34 physical objects and reconstructed them as 3D models. Additionally, we created a complete, navigable virtual training room containing the 3D models. Eventually, we published the 3D models and the virtual training room as an open-access data collection on Sketchfab (a free-to-use online digital platform for 3D model publication), from which all models can be freely downloaded and inspected through Web browsers, mobile applications, and Virtual and Augmented Reality devices. Our data collection and repeatable and cost-effective methodology open new opportunities to use VS for training through simulation in healthcare.Source: RESULTS IN ENGINEERING, vol. 25
DOI: 10.1016/j.rineng.2025.104236
Metrics:
See at:
CNR IRIS | www.sciencedirect.com | CNR IRIS
2025
Journal article
Open Access
A FAIR and open geographic data collection for the Massaciuccoli Lake basin wetland in Italy
Vannini G. L., Bove P., Coro G.
The creation of a catalogue of geodata harmonised over time and space is essential for describing the status of ecosystem services in wetlands. In the present work, a specific methodology has been developed for the collection and generation of spatially and temporally harmonized geographic data to describe essential ecological and socio-economic charactesristics of the Massaciuccoli Lake basin (Tuscany, Italy), while providing a re-usable methodology for other areas. We developed a methodology, which we called ‘Geodata Layers Harmonization Methodology’ (GLHM), divided into four main phases: Geodata Census (GC), Geodata Selection (GS), Geodata Alignment (GA), and Geodata Publication (GP). The first phase, GC, involved a census of geodata made available online by public institutions, prioritizing those most relevant for describing ecosystem services, such as climatic, agro-environmental, pedo-geological, and biodiversity variables, with a preference for detailed data at the local level. The metadata of the collected geodata were organized into a structured tabular format. In the GS phase, geodata were selected based on a spatial resolution compatible with regional-scale ecological models (maximum 0.0005° ≈ 50 m), and a temporal coverage that could represent from remote past to far future scenarios. Geodata with partial spatial coverage or unsuitable for ecological models were excluded. Additionally, we evaluated the compliance of the geodata published on the websites of public institutions with the Findable-Accessible-Interoperable-Reusable (FAIR) principles through a newly developed scoring system. Based on this score, we selected only the data that exceeded a minimum FAIRness threshold. In the GA phase, the selected geodata were aligned semantically (i.e., by variable meaning), temporally, and spatially. Each geodata was georeferenced using the WGS84/EPSG:4326 reference system and clipped to the boundaries of the Massaciuccoli Lake basin. Raster data were resampled to achieve a uniform spatial resolution of 0.0005°. In the last phase, GP, the aligned geodata were published on public access repositories and services: The entire collection was organized as a QGIS project with legends and a metadata table associated. An Atlas was also produced, in PDF format, which visually represented the data and metadata. The geodata and their corresponding legends were exposed through Web Map Service (WMS) and Web feature Service (WFS) standards on a GeoServer instance and catalogued in a GeoNetwork instance, compliant with the ISO19139 standard and the INSPIRE European Directive. The collection contains 148 geo-datasets, representing 75 climatic, agro-environmental, pedo-geological, morphological, ecological, biological, and socio-economic information distributed across five temporal reference time frames: a remote past (1950–1980), a near past (1981–2015), the present (2016–2024), a near future (2025–2050), and a far future (2051–2100). Future projections are available under the Representative Concentration Pathways (RPC) 2.6, 4.5, and 8.5 to simulate low, medium, and high greenhouse gas concentration scenarios respectively. The present geodata collection is particularly useful for wetland monitoring, management and planning. It can easily be integrated with ecological models and predictive studies to analyse the effects of climate change and anthropogenic pressures on wetlands. The GLHM methodology is applicable to other ecological contexts to create standardised structured frameworks for evaluating the status of the biodiversity and the ecosystem services and the interplay between anthropic pressures and the ecosystem response.Source: DATA IN BRIEF, vol. 59
DOI: 10.1016/j.dib.2025.111303
Project(s): EcoScope
Metrics:
Vannini G. L., Bove P., Coro G.
The creation of a catalogue of geodata harmonised over time and space is essential for describing the status of ecosystem services in wetlands. In the present work, a specific methodology has been developed for the collection and generation of spatially and temporally harmonized geographic data to describe essential ecological and socio-economic charactesristics of the Massaciuccoli Lake basin (Tuscany, Italy), while providing a re-usable methodology for other areas. We developed a methodology, which we called ‘Geodata Layers Harmonization Methodology’ (GLHM), divided into four main phases: Geodata Census (GC), Geodata Selection (GS), Geodata Alignment (GA), and Geodata Publication (GP). The first phase, GC, involved a census of geodata made available online by public institutions, prioritizing those most relevant for describing ecosystem services, such as climatic, agro-environmental, pedo-geological, and biodiversity variables, with a preference for detailed data at the local level. The metadata of the collected geodata were organized into a structured tabular format. In the GS phase, geodata were selected based on a spatial resolution compatible with regional-scale ecological models (maximum 0.0005° ≈ 50 m), and a temporal coverage that could represent from remote past to far future scenarios. Geodata with partial spatial coverage or unsuitable for ecological models were excluded. Additionally, we evaluated the compliance of the geodata published on the websites of public institutions with the Findable-Accessible-Interoperable-Reusable (FAIR) principles through a newly developed scoring system. Based on this score, we selected only the data that exceeded a minimum FAIRness threshold. In the GA phase, the selected geodata were aligned semantically (i.e., by variable meaning), temporally, and spatially. Each geodata was georeferenced using the WGS84/EPSG:4326 reference system and clipped to the boundaries of the Massaciuccoli Lake basin. Raster data were resampled to achieve a uniform spatial resolution of 0.0005°. In the last phase, GP, the aligned geodata were published on public access repositories and services: The entire collection was organized as a QGIS project with legends and a metadata table associated. An Atlas was also produced, in PDF format, which visually represented the data and metadata. The geodata and their corresponding legends were exposed through Web Map Service (WMS) and Web feature Service (WFS) standards on a GeoServer instance and catalogued in a GeoNetwork instance, compliant with the ISO19139 standard and the INSPIRE European Directive. The collection contains 148 geo-datasets, representing 75 climatic, agro-environmental, pedo-geological, morphological, ecological, biological, and socio-economic information distributed across five temporal reference time frames: a remote past (1950–1980), a near past (1981–2015), the present (2016–2024), a near future (2025–2050), and a far future (2051–2100). Future projections are available under the Representative Concentration Pathways (RPC) 2.6, 4.5, and 8.5 to simulate low, medium, and high greenhouse gas concentration scenarios respectively. The present geodata collection is particularly useful for wetland monitoring, management and planning. It can easily be integrated with ecological models and predictive studies to analyse the effects of climate change and anthropogenic pressures on wetlands. The GLHM methodology is applicable to other ecological contexts to create standardised structured frameworks for evaluating the status of the biodiversity and the ecosystem services and the interplay between anthropic pressures and the ecosystem response.Source: DATA IN BRIEF, vol. 59
DOI: 10.1016/j.dib.2025.111303
Project(s): EcoScope
Metrics:
See at:
CNR IRIS | www.sciencedirect.com | CNR IRIS
2025
Journal article
Open Access
Phoné: an Initiative to develop a dataset for the automatic recognition of spoken Italian
Coro G., Cutugno F., Schettino L., Tanda E., Vietti A., Vitale V. N.
Large Language Models (LLM) have revolutionised natural language processing and its applications. However, high-performance LLMs require copious data and computing resources for their development and are rarely public. This also concerns Large Acoustic Models (LAM) for processing spoken language. The Phoné initiative seeks to build an open Italian speech dataset to advance Automatic Speech Recognition (ASR) systems and support public research. Spearheaded by institutions in Naples, Pisa, and Bolzano, the project gathers diverse Italian audio sources and applies advanced ASR architectures, including supervised and self-supervised models. This paper details Phoné’s dataset creation, ASR model evaluation, and ethical considerations, aiming to democratise access to Italian-language resources and foster innovation in ASR technologies.Source: ORAL ARCHIVES JOURNAL, vol. 1
DOI: 10.36253/oar-3340
Metrics:
Coro G., Cutugno F., Schettino L., Tanda E., Vietti A., Vitale V. N.
Large Language Models (LLM) have revolutionised natural language processing and its applications. However, high-performance LLMs require copious data and computing resources for their development and are rarely public. This also concerns Large Acoustic Models (LAM) for processing spoken language. The Phoné initiative seeks to build an open Italian speech dataset to advance Automatic Speech Recognition (ASR) systems and support public research. Spearheaded by institutions in Naples, Pisa, and Bolzano, the project gathers diverse Italian audio sources and applies advanced ASR architectures, including supervised and self-supervised models. This paper details Phoné’s dataset creation, ASR model evaluation, and ethical considerations, aiming to democratise access to Italian-language resources and foster innovation in ASR technologies.Source: ORAL ARCHIVES JOURNAL, vol. 1
DOI: 10.36253/oar-3340
Metrics:
See at:
CNR IRIS | riviste.fupress.net | CNR IRIS
2025
Other
Restricted
InfraScience research activity report 2024
Angioni S., Artini M., Assante M., Atzori C., Baglioni M., Bardi A., Bosio C., Bove P., Calanducci A., Candela L., Casini G., Castelli D., Cirillo R., Coro G., De Bonis M., Debole F., Dell'Amico A., Frosini L., Ibrahim Ahmed, La Bruzzo S., Lelii L., Manghi P., Mangiacrapa F., Mangione D., Mannocci A., Molinaro E., Oliviero A., Pagano P., Panichi G., Teresa M. T., Pavone G., Peccerillo B., Piccioli T., Procaccini M., Straccia U., Vannini G. L., Versienti L.
InfraScience is a research group within the Institute of Information Science and Technologies (ISTI) of the National Research Council of Italy (CNR), based in Pisa. This activity report outlines the group's research achievements and initiatives throughout 2024. InfraScience focused its efforts on key challenges in the areas of Data Infrastructures, e-Science, and Intelligent Systems, maintaining a strong synergy between research and development and a firm commitment to open science principles. In 2024, the group played a leading role in the development and evolution of two major Open Science infrastructures: D4Science and OpenAIRE. InfraScience researchers contributed significantly to the scientific community through the publication of peer-reviewed papers, active participation in EU-funded research projects, organization of international conferences and training activities, and engagement in various working groups and task forces. This report highlights these contributions and underscores the group's ongoing dedication to advancing open, collaborative, and impactful science.DOI: 10.32079/isti-ar-2025/001
Metrics:
Angioni S., Artini M., Assante M., Atzori C., Baglioni M., Bardi A., Bosio C., Bove P., Calanducci A., Candela L., Casini G., Castelli D., Cirillo R., Coro G., De Bonis M., Debole F., Dell'Amico A., Frosini L., Ibrahim Ahmed, La Bruzzo S., Lelii L., Manghi P., Mangiacrapa F., Mangione D., Mannocci A., Molinaro E., Oliviero A., Pagano P., Panichi G., Teresa M. T., Pavone G., Peccerillo B., Piccioli T., Procaccini M., Straccia U., Vannini G. L., Versienti L.
InfraScience is a research group within the Institute of Information Science and Technologies (ISTI) of the National Research Council of Italy (CNR), based in Pisa. This activity report outlines the group's research achievements and initiatives throughout 2024. InfraScience focused its efforts on key challenges in the areas of Data Infrastructures, e-Science, and Intelligent Systems, maintaining a strong synergy between research and development and a firm commitment to open science principles. In 2024, the group played a leading role in the development and evolution of two major Open Science infrastructures: D4Science and OpenAIRE. InfraScience researchers contributed significantly to the scientific community through the publication of peer-reviewed papers, active participation in EU-funded research projects, organization of international conferences and training activities, and engagement in various working groups and task forces. This report highlights these contributions and underscores the group's ongoing dedication to advancing open, collaborative, and impactful science.DOI: 10.32079/isti-ar-2025/001
Metrics:
2024
Journal article
Open Access
Exploring emergent syllables in end-to-end automatic speech recognizers through model explainability technique
Vitale Vincenzo Norman, Cutugno Francesco, Origlia Antonio, Coro Gianpaolo
Automatic speech recognition systems based on end-to-end models (E2E-ASRs) can achieve comparable performance to conventional ASR systems while reproducing all their essential parts automatically, from speech units to the language model. However, they hide the underlying perceptual processes modelled, if any, and they have lower adaptability to multiple application contexts, and, furthermore, they require powerful hardware and an extensive amount of training data. Model-explainability techniques can explore the internal dynamics of these ASR systems and possibly understand and explain the processes conducting to their decisions and outputs. Understanding these processes can help enhance ASR performance and reduce the required training data and hardware significantly. In this paper, we probe the internal dynamics of three E2E-ASRs pre-trained for English by building an acoustic-syllable boundary detector for Italian and Spanish based on the E2E-ASRs’ internal encoding layer outputs. We demonstrate that the shallower E2E-ASR layers spontaneously form a rhythmic component correlated with prominent syllables, central in human speech processing. This finding highlights a parallel between the analysed E2E-ASRs and human speech recognition. Our results contribute to the body of knowledge by providing a human-explainable insight into behaviours encoded in popular E2E-ASR systems.Source: NEURAL COMPUTING & APPLICATIONS, vol. 36, pp. 6875-6901
DOI: 10.1007/s00521-024-09435-1
Metrics:
Vitale Vincenzo Norman, Cutugno Francesco, Origlia Antonio, Coro Gianpaolo
Automatic speech recognition systems based on end-to-end models (E2E-ASRs) can achieve comparable performance to conventional ASR systems while reproducing all their essential parts automatically, from speech units to the language model. However, they hide the underlying perceptual processes modelled, if any, and they have lower adaptability to multiple application contexts, and, furthermore, they require powerful hardware and an extensive amount of training data. Model-explainability techniques can explore the internal dynamics of these ASR systems and possibly understand and explain the processes conducting to their decisions and outputs. Understanding these processes can help enhance ASR performance and reduce the required training data and hardware significantly. In this paper, we probe the internal dynamics of three E2E-ASRs pre-trained for English by building an acoustic-syllable boundary detector for Italian and Spanish based on the E2E-ASRs’ internal encoding layer outputs. We demonstrate that the shallower E2E-ASR layers spontaneously form a rhythmic component correlated with prominent syllables, central in human speech processing. This finding highlights a parallel between the analysed E2E-ASRs and human speech recognition. Our results contribute to the body of knowledge by providing a human-explainable insight into behaviours encoded in popular E2E-ASR systems.Source: NEURAL COMPUTING & APPLICATIONS, vol. 36, pp. 6875-6901
DOI: 10.1007/s00521-024-09435-1
Metrics:
See at:
Neural Computing and Applications | FEDOA - IRIS Università degli Studi Napoli Federico II | CNR IRIS | CNR IRIS
2024
Journal article
Open Access
An open science automatic workflow for multi-model species distribution estimation
Coro Gianpaolo, Sana Lorenzo, Bove Pasquale
Integrated Environmental Assessment systems and ecosystem models study the links between anthropogenic and climatic pressures on marine ecosystems and help understand how to manage the effects of the unsustainable exploitation of ocean resources. However, these models have long implementation times, data and model interoperability issues and require heterogeneous competencies. Therefore, they would benefit from simplification, automatisation, and enhanced integrability of the underlying models. Artificial Intelligence can help overcome several limitations by speeding up the modelling of crucial functional parts, e.g. estimating the environmental conditions fostering a species’ persistence and proliferation in an area (the species’ ecological niche) and, consequently, its geographical distribution. This paper presents a full-automatic workflow to estimate species’ distributions through statistical and machine learning models. It embeds four ecological niche models with complementary approaches, i.e. Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps. It automatically estimates the optimal model parametrisations and decision thresholds to distinguish between suitable- and unsuitable-habitat locations and combines the models within one ensemble model. Finally, it combines several ensemble models to produce a species richness map (biodiversity index). The software is open-source, Open Science compliant, and available as a Web Processing Service-standardised cloud computing service that enhances efficiency, integrability, cross-domain reusability, and experimental reproduction and repetition. We first assess workflow stability and sensitivity and then demonstrate effectiveness by producing a biodiversity index for the Mediterranean based on $$\sim $$1500 species data. Moreover, we predict the spread of the invasive Siganus rivulatus in the Mediterranean and its current and future overlap with the native Sarpa salpa under different climate change scenarios.Source: INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS
DOI: 10.1007/s41060-024-00517-w
Project(s): EcoScope, ITINERIS PNRR Italian project
Metrics:
Coro Gianpaolo, Sana Lorenzo, Bove Pasquale
Integrated Environmental Assessment systems and ecosystem models study the links between anthropogenic and climatic pressures on marine ecosystems and help understand how to manage the effects of the unsustainable exploitation of ocean resources. However, these models have long implementation times, data and model interoperability issues and require heterogeneous competencies. Therefore, they would benefit from simplification, automatisation, and enhanced integrability of the underlying models. Artificial Intelligence can help overcome several limitations by speeding up the modelling of crucial functional parts, e.g. estimating the environmental conditions fostering a species’ persistence and proliferation in an area (the species’ ecological niche) and, consequently, its geographical distribution. This paper presents a full-automatic workflow to estimate species’ distributions through statistical and machine learning models. It embeds four ecological niche models with complementary approaches, i.e. Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps. It automatically estimates the optimal model parametrisations and decision thresholds to distinguish between suitable- and unsuitable-habitat locations and combines the models within one ensemble model. Finally, it combines several ensemble models to produce a species richness map (biodiversity index). The software is open-source, Open Science compliant, and available as a Web Processing Service-standardised cloud computing service that enhances efficiency, integrability, cross-domain reusability, and experimental reproduction and repetition. We first assess workflow stability and sensitivity and then demonstrate effectiveness by producing a biodiversity index for the Mediterranean based on $$\sim $$1500 species data. Moreover, we predict the spread of the invasive Siganus rivulatus in the Mediterranean and its current and future overlap with the native Sarpa salpa under different climate change scenarios.Source: INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS
DOI: 10.1007/s41060-024-00517-w
Project(s): EcoScope
, ITINERIS PNRR Italian projectMetrics:
See at:
International Journal of Data Science and Analytics | CNR IRIS | CNR IRIS
2024
Dataset
Open Access
Interactive geographic catalog of environmental, geomorphologic, and socio-economic variables of the Massaciuccoli Lake basin in Tuscany, Italy
Vannini G. L., Coro G., Panichi G.
The catalogue is the result of a process of evaluation, selection and harmonization of environmental, geomorphological and socio-economic data. The data were acquired following the evaluation of FAIR and Open Access principles, from varied sources with heterogeneous spatio-temporal resolutions. The data were then transformed into spatiotemporally aligned datasets and described in a standardised form. The metadata are described in the ISO 19139 standard, in accordance with the INSPIRE directives. The visualisation application integrated in GeoNetwork allows a simple overlay of the data (on first viewing, 'layerExtentZoom' must be selected from the options of the first selected layer). All data are open access.Project(s): Integrated Environmental Research Infrastructures System
Vannini G. L., Coro G., Panichi G.
The catalogue is the result of a process of evaluation, selection and harmonization of environmental, geomorphological and socio-economic data. The data were acquired following the evaluation of FAIR and Open Access principles, from varied sources with heterogeneous spatio-temporal resolutions. The data were then transformed into spatiotemporally aligned datasets and described in a standardised form. The metadata are described in the ISO 19139 standard, in accordance with the INSPIRE directives. The visualisation application integrated in GeoNetwork allows a simple overlay of the data (on first viewing, 'layerExtentZoom' must be selected from the options of the first selected layer). All data are open access.Project(s): Integrated Environmental Research Infrastructures System
See at:
CNR IRIS | services.d4science.org | CNR IRIS
2024
Journal article
Open Access
A semantic knowledge graph of European mountain value chains
Bartalesi Lenzi V., Coro G., Lenzi E., Pratelli N., Pagano P., Moretti M., Brunori G.
The United Nations forecast a significant shift in global population distribution by 2050, with rural populations projected to decline. This decline will particularly challenge mountain areas' cultural heritage, well-being, and economic sustainability. Understanding the economic, environmental, and societal effects of rural population decline is particularly important in Europe, where mountainous regions are vital for supplying goods. The present paper describes a geospatially explicit semantic knowledge graph containing information on 454 European mountain value chains. It is the first large-size, structured collection of information on mountain value chains. Our graph, structured through ontology-based semantic modelling, offers representations of the value chains in the form of narratives. The graph was constructed semi-automatically from unstructured data provided by mountain-area expert scholars. It is accessible through a public repository and explorable through interactive Story Maps and a semantic Web service. Through semantic queries, we demonstrate that the graph allows for exploring territorial complexities and discovering new knowledge on mountain areas' environmental, societal, territory, and economic aspects that could help stem depopulation.Source: SCIENTIFIC DATA, vol. 11
DOI: 10.1038/s41597-024-03760-9
Project(s): Mountain Valorization through Interconnectedness and Green Growth
Metrics:
Bartalesi Lenzi V., Coro G., Lenzi E., Pratelli N., Pagano P., Moretti M., Brunori G.
The United Nations forecast a significant shift in global population distribution by 2050, with rural populations projected to decline. This decline will particularly challenge mountain areas' cultural heritage, well-being, and economic sustainability. Understanding the economic, environmental, and societal effects of rural population decline is particularly important in Europe, where mountainous regions are vital for supplying goods. The present paper describes a geospatially explicit semantic knowledge graph containing information on 454 European mountain value chains. It is the first large-size, structured collection of information on mountain value chains. Our graph, structured through ontology-based semantic modelling, offers representations of the value chains in the form of narratives. The graph was constructed semi-automatically from unstructured data provided by mountain-area expert scholars. It is accessible through a public repository and explorable through interactive Story Maps and a semantic Web service. Through semantic queries, we demonstrate that the graph allows for exploring territorial complexities and discovering new knowledge on mountain areas' environmental, societal, territory, and economic aspects that could help stem depopulation.Source: SCIENTIFIC DATA, vol. 11
DOI: 10.1038/s41597-024-03760-9
Project(s): Mountain Valorization through Interconnectedness and Green Growth
Metrics:
See at:
Scientific Data | Scientific Data | IRIS Cnr | IRIS Cnr | Software Heritage | GitHub | CNR IRIS
2024
Book
Open Access
Massaciuccoli Lake basin - Harmonized geographic atlas for ecosystem services assessment
Vannini G. L., Coro G.
The ‘Harmonized Geographic Atlas for Ecosystem Services Assessment’ for the Massaciuccoli Lake basin contains 148 cartographic tables, each corresponding to a geodataset, representing 75 variables across climatic, agro-environmental, pedo-geological, morphological, ecological, biological, and socio-economic domains. The data is distributed across five temporal intervals: remote past (1950–1980), recent past (1981–2015), present (2016–2024), near future (2050, with projections according to RCP 2.6, 4.5, and 8.5 scenarios), and far future (2100, based on the same RCP scenarios). Each cartographic table is accompanied by a geographic map and a description of the represented variable. The document also includes two indices: the ‘Analytical Index’, located at the end, and the ‘Table and Index of Cartographic Coverages’, positioned at the beginning of the Atlas, offering a summary of metadata related to all the represented variables. This Atlas provides an integrated and systematic view of the geospatial data of the Massaciuccoli Lake basin, making it a valuable resource for studying ecosystem services.DOI: 10.5281/zenodo.13912261
Project(s): Integrated Environmental Research Infrastructures System
Metrics:
Vannini G. L., Coro G.
The ‘Harmonized Geographic Atlas for Ecosystem Services Assessment’ for the Massaciuccoli Lake basin contains 148 cartographic tables, each corresponding to a geodataset, representing 75 variables across climatic, agro-environmental, pedo-geological, morphological, ecological, biological, and socio-economic domains. The data is distributed across five temporal intervals: remote past (1950–1980), recent past (1981–2015), present (2016–2024), near future (2050, with projections according to RCP 2.6, 4.5, and 8.5 scenarios), and far future (2100, based on the same RCP scenarios). Each cartographic table is accompanied by a geographic map and a description of the represented variable. The document also includes two indices: the ‘Analytical Index’, located at the end, and the ‘Table and Index of Cartographic Coverages’, positioned at the beginning of the Atlas, offering a summary of metadata related to all the represented variables. This Atlas provides an integrated and systematic view of the geospatial data of the Massaciuccoli Lake basin, making it a valuable resource for studying ecosystem services.DOI: 10.5281/zenodo.13912261
Project(s): Integrated Environmental Research Infrastructures System
Metrics:
2024
Dataset
Open Access
Massaciuccoli Lake basin in Tuscany, Italy. Datasets of 75 environmental, geomorphologic, and socio-economic variables associated from remote past to remote future
Vannini G. L., Coro G.
Collection of 148 datasets representing 75 environmental, geomorphologic, and socio-economic variables associated with the Massaciuccoli Lake basin in Tuscany, Italy. The data cover five temporal snapshots: remote past (1950-1980), recent past (1981-2015), present (2016.2024), near future (2050 under RCP4.5 and 8.5), and remote future (2100 under RCP4.5 and 8.5) Raster data have been harmonised and resampled at 0.0005° (~50 m) resolution. Vector data have been aligned and cut over the basin boundaries. A QGIS project using the WGS84 EPSG:4326 projection is included in the ZIP file. A metadata file (in MS Excel format) reports data content descriptions, the primary sources and their FAIRness levels. The dataset numbers are aligned to the table Id-column entries.DOI: 10.5281/zenodo.11243783
Project(s): Integrated Environmental Research Infrastructures System
Metrics:
Vannini G. L., Coro G.
Collection of 148 datasets representing 75 environmental, geomorphologic, and socio-economic variables associated with the Massaciuccoli Lake basin in Tuscany, Italy. The data cover five temporal snapshots: remote past (1950-1980), recent past (1981-2015), present (2016.2024), near future (2050 under RCP4.5 and 8.5), and remote future (2100 under RCP4.5 and 8.5) Raster data have been harmonised and resampled at 0.0005° (~50 m) resolution. Vector data have been aligned and cut over the basin boundaries. A QGIS project using the WGS84 EPSG:4326 projection is included in the ZIP file. A metadata file (in MS Excel format) reports data content descriptions, the primary sources and their FAIRness levels. The dataset numbers are aligned to the table Id-column entries.DOI: 10.5281/zenodo.11243783
Project(s): Integrated Environmental Research Infrastructures System
Metrics:
See at:
CNR IRIS | zenodo.org | CNR IRIS | CNR IRIS
2024
Journal article
Open Access
Promoting best practices in ocean forecasting through an operational readiness level
Alvarez Fanjul E., Ciliberti S., Pearlman J., Wilmer-Becker K., Bahurel P., Ardhuin F., Arnaud A., Azizzadenesheli K., Aznar R., Bell M., Bertino L., Behera S., Brassington G., Calewaert J. B., Capet A., Chassignet E., Ciavatta S., Cirano M., Clementi E., Cornacchia L., Cossarini G., Coro G., Corney S., Davidson F., Drevillon M., Drillet Y., Dussurget R., El Serafy G., Fearon G., Fennel K., Ford D., Le Galloudec O., Huang X., Lellouche J. M., Heimbach P., Hernez F., Hogan P., Hoteit I., Joseph S., Josey S., Le Traon P. -Y., Libralato S., Mancini M., Martin M., Matte P., Mcconnell T., Melet A., Miyazawa Y., Moore A. M., Novellino A., Òdonncha F., Porter A., Qiao F., Regan H., Robert-Jones J., Sanikommu S., Schiller A., Siddorn J., Sotillo M. G., Staneva J., Thomas-Courcoux C., Thupaki P., Tonani M., Garcia Valdecasas J. M., Veitch J., Von Schuckmann K., Wan L., Wilkin J., Zhong A., Zufic R.
Predicting the ocean state in a reliable and interoperable way, while ensuring high-quality products, requires forecasting systems that synergistically combine science-based methodologies with advanced technologies for timely, user-oriented solutions. Achieving this objective necessitates the adoption of best practices when implementing ocean forecasting services, resulting in the proper design of system components and the capacity to evolve through different levels of complexity. The vision of OceanPrediction Decade Collaborative Center, endorsed by the UN Decade of Ocean Science for Sustainable Development 2021-2030, is to support this challenge by developing a “predicted ocean based on a shared and coordinated global effort” and by working within a collaborative framework that encompasses worldwide expertise in ocean science and technology. To measure the capacity of ocean forecasting systems, the OceanPrediction Decade Collaborative Center proposes a novel approach based on the definition of an Operational Readiness Level (ORL). This approach is designed to guide and promote the adoption of best practices by qualifying and quantifying the overall operational status. Considering three identified operational categories - production, validation, and data dissemination - the proposed ORL is computed through a cumulative scoring system. This method is determined by fulfilling specific criteria, starting from a given base level and progressively advancing to higher levels. The goal of ORL and the computed scores per operational category is to support ocean forecasters in using and producing ocean data, information, and knowledge. This is achieved through systems that attain progressively higher levels of readiness, accessibility, and interoperability by adopting best practices that will be linked to the future design of standards and tools. This paper discusses examples of the application of this methodology, concluding on the advantages of its adoption as a reference tool to encourage and endorse services in joining common frameworks.Source: FRONTIERS IN MARINE SCIENCE, vol. 11
DOI: 10.3389/fmars.2024.1443284
Metrics:
Alvarez Fanjul E., Ciliberti S., Pearlman J., Wilmer-Becker K., Bahurel P., Ardhuin F., Arnaud A., Azizzadenesheli K., Aznar R., Bell M., Bertino L., Behera S., Brassington G., Calewaert J. B., Capet A., Chassignet E., Ciavatta S., Cirano M., Clementi E., Cornacchia L., Cossarini G., Coro G., Corney S., Davidson F., Drevillon M., Drillet Y., Dussurget R., El Serafy G., Fearon G., Fennel K., Ford D., Le Galloudec O., Huang X., Lellouche J. M., Heimbach P., Hernez F., Hogan P., Hoteit I., Joseph S., Josey S., Le Traon P. -Y., Libralato S., Mancini M., Martin M., Matte P., Mcconnell T., Melet A., Miyazawa Y., Moore A. M., Novellino A., Òdonncha F., Porter A., Qiao F., Regan H., Robert-Jones J., Sanikommu S., Schiller A., Siddorn J., Sotillo M. G., Staneva J., Thomas-Courcoux C., Thupaki P., Tonani M., Garcia Valdecasas J. M., Veitch J., Von Schuckmann K., Wan L., Wilkin J., Zhong A., Zufic R.
Predicting the ocean state in a reliable and interoperable way, while ensuring high-quality products, requires forecasting systems that synergistically combine science-based methodologies with advanced technologies for timely, user-oriented solutions. Achieving this objective necessitates the adoption of best practices when implementing ocean forecasting services, resulting in the proper design of system components and the capacity to evolve through different levels of complexity. The vision of OceanPrediction Decade Collaborative Center, endorsed by the UN Decade of Ocean Science for Sustainable Development 2021-2030, is to support this challenge by developing a “predicted ocean based on a shared and coordinated global effort” and by working within a collaborative framework that encompasses worldwide expertise in ocean science and technology. To measure the capacity of ocean forecasting systems, the OceanPrediction Decade Collaborative Center proposes a novel approach based on the definition of an Operational Readiness Level (ORL). This approach is designed to guide and promote the adoption of best practices by qualifying and quantifying the overall operational status. Considering three identified operational categories - production, validation, and data dissemination - the proposed ORL is computed through a cumulative scoring system. This method is determined by fulfilling specific criteria, starting from a given base level and progressively advancing to higher levels. The goal of ORL and the computed scores per operational category is to support ocean forecasters in using and producing ocean data, information, and knowledge. This is achieved through systems that attain progressively higher levels of readiness, accessibility, and interoperability by adopting best practices that will be linked to the future design of standards and tools. This paper discusses examples of the application of this methodology, concluding on the advantages of its adoption as a reference tool to encourage and endorse services in joining common frameworks.Source: FRONTIERS IN MARINE SCIENCE, vol. 11
DOI: 10.3389/fmars.2024.1443284
Metrics:
See at:
Frontiers in Marine Science | Frontiers in Marine Science | HAL-INSU | HAL-INSU | IRIS Cnr | IRIS Cnr | Hal | Hal | Hal | CNR IRIS
2024
Conference article
Restricted
New Tools for Geo-scientific Data Management in the Framework of the ITINERIS Project Leveraging D4Science e-Infrastructure Capabilities.
Gennaro S., Di Giuseppe P., Perrone E., Agostini S., Trumpy E., Assante M., Candela L., Pagano P., Procaccini M., Coro G., Provenzale A.
Open Science is a cultural movement based on transparency, inclusion, research integrity, collaboration, and cooperative work, promoting an enhancing approach to science. Benefits are expected from this approach, although doing open science can entail a contrast with several barriers, including: (i) cultural factors (e.g., the fear of the loss of control of the datasets); (ii) cost-base factors; and (iii) disincentive factors (Assante et al., 2019; 2023). The effectiveness of the Open Science approach of a project can be enhanced by using the D4Science infrastructure. This infrastructure promotes collaboration and cooperative work with Virtual Research Environments (VREs). As part of the ITINERIS Project, a comprehensive Italian Research Infrastructures (RDIs) hub in the geoscientific and environmental fields is under development, in which teams with a high level of trans-disciplinarity are working on the development of thematic VREs for topics that includes: (i) Critical Zone (CZ) VRE; (ii) Aquatic Biomass services (BIOMASS) VRE; (iii) Crops, Plants and Pests services (CPP VRE); (iv) Essential Variables (EV VRE); (v) Aerosol-biosphere (AERO VRE); (vi) Carbon Cycle services (CARBON VRE); (vii) Indicators and Impacts of Climate Change (CLIMA VRE); (viii) Downstream Effects of Environmental Change (DOWNSTREAM VRE); (ix) Isotope Database (ISOTOPE VRE). VREs are based on the D4Science infrastructure, and their development is based on the needs of the scientific communities and the specific stakeholders identified by the researcher. VREs are new eScience facilities that address scientifically and socially relevant topics, especially through the sharing of information and data produced. Research data and results products following international standards are managed and shared with the members of the VREs. In this context, these D4Science enabled VREs will become tools supporting the entire spectrum of the research lifecycle. Specifically, for data collection (i) the Collaborative Storage Framework promotes teamwork among users and offers a collaborative space to share digital objects. For data analytics, (ii) the Analytics Engine Framework equips VREs with Cloud Computing Platforms. For data publishing, (iii) the Publishing Framework facilitates the dissemination of research outcomes by means of the Metadata Catalogue and the Spatial Data Catalogue, which help organise and make research results available to the broader scientific community. Moreover, VREs are planned with a modular structure with semantic services for data discovery, harmonization and interoperability, and will contribute to share workflows, procedures and analysis tools which could be applied to analyse new datasets by the members of the VRE. Data from multiple sources, analysis and modelling tools will be integrated into the VREs, allowing users to gain insights into the problems at hand and add their data and analysis methods to respond to the changing scientific and practical needs.DOI: 10.3301/absgi.2024.02
Metrics:
Gennaro S., Di Giuseppe P., Perrone E., Agostini S., Trumpy E., Assante M., Candela L., Pagano P., Procaccini M., Coro G., Provenzale A.
Open Science is a cultural movement based on transparency, inclusion, research integrity, collaboration, and cooperative work, promoting an enhancing approach to science. Benefits are expected from this approach, although doing open science can entail a contrast with several barriers, including: (i) cultural factors (e.g., the fear of the loss of control of the datasets); (ii) cost-base factors; and (iii) disincentive factors (Assante et al., 2019; 2023). The effectiveness of the Open Science approach of a project can be enhanced by using the D4Science infrastructure. This infrastructure promotes collaboration and cooperative work with Virtual Research Environments (VREs). As part of the ITINERIS Project, a comprehensive Italian Research Infrastructures (RDIs) hub in the geoscientific and environmental fields is under development, in which teams with a high level of trans-disciplinarity are working on the development of thematic VREs for topics that includes: (i) Critical Zone (CZ) VRE; (ii) Aquatic Biomass services (BIOMASS) VRE; (iii) Crops, Plants and Pests services (CPP VRE); (iv) Essential Variables (EV VRE); (v) Aerosol-biosphere (AERO VRE); (vi) Carbon Cycle services (CARBON VRE); (vii) Indicators and Impacts of Climate Change (CLIMA VRE); (viii) Downstream Effects of Environmental Change (DOWNSTREAM VRE); (ix) Isotope Database (ISOTOPE VRE). VREs are based on the D4Science infrastructure, and their development is based on the needs of the scientific communities and the specific stakeholders identified by the researcher. VREs are new eScience facilities that address scientifically and socially relevant topics, especially through the sharing of information and data produced. Research data and results products following international standards are managed and shared with the members of the VREs. In this context, these D4Science enabled VREs will become tools supporting the entire spectrum of the research lifecycle. Specifically, for data collection (i) the Collaborative Storage Framework promotes teamwork among users and offers a collaborative space to share digital objects. For data analytics, (ii) the Analytics Engine Framework equips VREs with Cloud Computing Platforms. For data publishing, (iii) the Publishing Framework facilitates the dissemination of research outcomes by means of the Metadata Catalogue and the Spatial Data Catalogue, which help organise and make research results available to the broader scientific community. Moreover, VREs are planned with a modular structure with semantic services for data discovery, harmonization and interoperability, and will contribute to share workflows, procedures and analysis tools which could be applied to analyse new datasets by the members of the VRE. Data from multiple sources, analysis and modelling tools will be integrated into the VREs, allowing users to gain insights into the problems at hand and add their data and analysis methods to respond to the changing scientific and practical needs.DOI: 10.3301/absgi.2024.02
Metrics:
See at:
doi.org | CNR IRIS | CNR IRIS | www.socgeol.it
2024
Journal article
Open Access
Extracting Mediterranean hidden fishing hotspots through big data mining
Coro G., Pavirani L., Ellenbroek A.
Monitoring fishing activity is crucial for fisheries management and governments to ensure regulatory compliance and sustainable marine ecosystems. Analysing vessel movements provides insights into fishing dynamics, aiding decision-making. Additionally, measuring unmonitored fishing activity (hidden fishing) helps counteract the underestimation of fishing pressure. Big data analysis can reveal fishing patterns and hidden activities from vessel position and speed data, such as those transmitted by fleets carrying Automatic Identification Systems (AIS). We used an Open Science-compliant (reproducible, repeatable, and reusable) cloud computing-based big data analysis to estimate the manifest, total, and hidden fishing distributions of AIS-carrying vessels in the Mediterranean Sea from 2017 to 2022, processing about 1.6 billion vessel speed and position data. We estimated the principal hotspots of hidden fishing over the years and the potentially involved stocks from these data. We also assessed whether the hotspots corresponded to illegal fishing or AIS communication issues and concluded that most hotspots potentially corresponded to illegal fishing. Our manifest fishing distribution agreed with another produced through machine learning by the Global Fishing Watch. We developed a fast and reusable approach that can produce new information to help management authorities understand the extent of hidden fishing.Source: IEEE ACCESS, vol. 12, pp. 85465-85483
DOI: 10.1109/access.2024.3416389
Project(s): EcoScope, Integrated Environmental Research Infrastructures System
Metrics:
Coro G., Pavirani L., Ellenbroek A.
Monitoring fishing activity is crucial for fisheries management and governments to ensure regulatory compliance and sustainable marine ecosystems. Analysing vessel movements provides insights into fishing dynamics, aiding decision-making. Additionally, measuring unmonitored fishing activity (hidden fishing) helps counteract the underestimation of fishing pressure. Big data analysis can reveal fishing patterns and hidden activities from vessel position and speed data, such as those transmitted by fleets carrying Automatic Identification Systems (AIS). We used an Open Science-compliant (reproducible, repeatable, and reusable) cloud computing-based big data analysis to estimate the manifest, total, and hidden fishing distributions of AIS-carrying vessels in the Mediterranean Sea from 2017 to 2022, processing about 1.6 billion vessel speed and position data. We estimated the principal hotspots of hidden fishing over the years and the potentially involved stocks from these data. We also assessed whether the hotspots corresponded to illegal fishing or AIS communication issues and concluded that most hotspots potentially corresponded to illegal fishing. Our manifest fishing distribution agreed with another produced through machine learning by the Global Fishing Watch. We developed a fast and reusable approach that can produce new information to help management authorities understand the extent of hidden fishing.Source: IEEE ACCESS, vol. 12, pp. 85465-85483
DOI: 10.1109/access.2024.3416389
Project(s): EcoScope
, Integrated Environmental Research Infrastructures SystemMetrics:
See at:
IEEE Access | IEEE Access | IRIS Cnr | IRIS Cnr | CNR IRIS
2023
Journal article
Open Access
A self-training automatic infant-cry detector
Coro G, Bardelli S, Cuttano A, Scaramuzzo Rt, 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
DOI: 10.1007/s00521-022-08129-w
Project(s): EcoScope
Metrics:
Coro G, Bardelli S, Cuttano A, Scaramuzzo Rt, 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
DOI: 10.1007/s00521-022-08129-w
Project(s): EcoScope
Metrics:
See at:
CNR IRIS | link.springer.com | ISTI Repository | CNR IRIS | CNR IRIS
2023
Journal article
Open Access
Global-scale parameters for ecological models
Coro G, Bove P, Kesnerreyes 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, vol. 10 (issue 7)
DOI: 10.1038/s41597-022-01904-3
Project(s): EcoScope
Metrics:
Coro G, Bove P, Kesnerreyes 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, vol. 10 (issue 7)
DOI: 10.1038/s41597-022-01904-3
Project(s): EcoScope
Metrics:
See at:
CNR IRIS | ISTI Repository | www.nature.com | CNR IRIS
2023
Journal article
Open Access
From unstructured texts to semantic story maps
Bartalesi Lenzi 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), vol. 16 (issue 1), pp. 234-250
DOI: 10.1080/17538947.2023.2168774
Project(s): MOVING
Metrics:
Bartalesi Lenzi 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), vol. 16 (issue 1), pp. 234-250
DOI: 10.1080/17538947.2023.2168774
Project(s): MOVING
Metrics:
See at:
CNR IRIS | ISTI Repository | ISTI Repository | www.tandfonline.com | CNR IRIS | CNR IRIS
2023
Journal article
Open Access
A simple framework for the exploration of functional biodiversity
Froese R, Coro G, Palomares Mld, Bailly N, Scotti M, Froese T, Garilao C, Pauly D
Un cadre pour l'exploration de la biodiversité fonctionnelle. Les traits clés de la biodiversité fonctionnelle sont examinés pour 31 134 espèces de poissons. Ces traits sont : le poids corporel maximal, la productivité et le niveau trophique. Un nouveau cadre simple est présenté qui montre l'utilisation combinée de ces traits, dans des catégories ordinales, pour près de 90% des espèces de poissons existantes. La plupart des espèces sont étroitement regroupées le long d'un axe évolutif dans l'espace taille-productivité-trophique (espace SPT), allant de quelques espèces anciennes de grande taille ayant une productivité très faible à de nombreuses espèces de taille moyenne ayant une productivité élevée. Cet axe évolutif se retrouve chez les espèces marines et d'eau douce, ainsi que chez les espèces arctiques. L'objectif principal de cette étude est de démontrer l'utilité du nouveau cadre SPT pour comparer les modèles de biodiversité fonctionnelle dans les écosystèmes en fonction de la salinité, de la géographie ou du temps. En outre, le cadre SPT a été utilisé pour explorer les corrélations avec d'autres caractéristiques telles que la forme du corps, et pour afficher la position des espèces individuelles, représentées par des pictogrammes de la forme du corps et de l'habitat, dans l'espace SPT.Source: CYBIUM, pp. 1-16
DOI: 10.26028/cybium/2023-003
Metrics:
Froese R, Coro G, Palomares Mld, Bailly N, Scotti M, Froese T, Garilao C, Pauly D
Un cadre pour l'exploration de la biodiversité fonctionnelle. Les traits clés de la biodiversité fonctionnelle sont examinés pour 31 134 espèces de poissons. Ces traits sont : le poids corporel maximal, la productivité et le niveau trophique. Un nouveau cadre simple est présenté qui montre l'utilisation combinée de ces traits, dans des catégories ordinales, pour près de 90% des espèces de poissons existantes. La plupart des espèces sont étroitement regroupées le long d'un axe évolutif dans l'espace taille-productivité-trophique (espace SPT), allant de quelques espèces anciennes de grande taille ayant une productivité très faible à de nombreuses espèces de taille moyenne ayant une productivité élevée. Cet axe évolutif se retrouve chez les espèces marines et d'eau douce, ainsi que chez les espèces arctiques. L'objectif principal de cette étude est de démontrer l'utilité du nouveau cadre SPT pour comparer les modèles de biodiversité fonctionnelle dans les écosystèmes en fonction de la salinité, de la géographie ou du temps. En outre, le cadre SPT a été utilisé pour explorer les corrélations avec d'autres caractéristiques telles que la forme du corps, et pour afficher la position des espèces individuelles, représentées par des pictogrammes de la forme du corps et de l'habitat, dans l'espace SPT.Source: CYBIUM, pp. 1-16
DOI: 10.26028/cybium/2023-003
Metrics:
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