2023
Journal article
Open Access
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
Metrics:
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
Metrics:
See at:
link.springer.com 
| ISTI Repository | CNR ExploRA
2023
Journal article
Open Access
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
Metrics:
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
Metrics:
See at:
ISTI Repository | www.nature.com | CNR ExploRA
2023
Journal article
Open Access
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
Metrics:
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
Metrics:
See at:
ISTI Repository | ISTI Repository | www.tandfonline.com | CNR ExploRA
2023
Journal article
Open Access
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
Metrics:
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
Metrics:
See at:
ISTI Repository | sfi-cybium.fr | CNR ExploRA
2023
Journal article
Open Access
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:
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 | ISTI Repository | CNR ExploRA
2023
Journal article
Open Access
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
Metrics:
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
Metrics:
See at:
ISTI Repository | www.frontiersin.org | CNR ExploRA
2023
Journal article
Open Access
New developments in the analysis of catch time series as the basis for fish stock assessments: the CMSY++ method
Froese R., Winker H., Coro G., "deng" Palomares M-L., Tsikliras A. C., Dimarchopoulou D., Touloumis K., Demirel N., Vianna G. M. S., Scarcella G., Schijns R., Liang C., Pauly D.
Following an introduction to the nature of fisheries catches and their information content, a new development of CMSY, a data-limited stock assessment method for fishes and invertebrates, is presented. This new version, CMSY++, overcomes several of the deficiencies of CMSY, which itself improved upon the "Catch-MSY" method published by S. Martell and R. Froese in 2013. The catch-only application of CMSY++ uses a Bayesian implementation of a modified Schaefer model, which also allows the fitting of abundance indices should such information be available. In the absence of historical catch time series and abundance indices, CMSY++ depends strongly on the provision of appropriate and informative priors for plausible ranges of initial and final stock depletion. An Artificial Neural Network (ANN) now assists in selecting objective priors for relative stock size based on patterns in 400 catch time series used for training. Regarding the cross-validation of the ANN predictions, of the 400 real stocks used in the training of ANN, 94% of final relative biomass (B/k) Bayesian (BSM) estimates were within the approximate 95% confidence limits of the respective CMSY++ estimate. Also, the equilibrium catch-biomass relations of the modified Schaefer model are compared with those of alternative surplus-production and age-structured models, suggesting that the latter two can be strongly biased towards underestimating the biomass required to sustain catches at low abundance. Numerous independent applications demonstrate how CMSY++ can incorporate, in addition to the required catch time series, both abundance data and a wide variety of ancillary information. We stress, however, the caveats and pitfalls of naively using the built-in prior options, which should instead be evaluated case-by-case and ideally be replaced by independent prior knowledge.Source: Acta Ichthyologica et Piscatoria 53 (2023): 173–189. doi:10.3897/aiep.53.105910
DOI: 10.3897/aiep.53.105910
Project(s): EcoScope
Metrics:
Froese R., Winker H., Coro G., "deng" Palomares M-L., Tsikliras A. C., Dimarchopoulou D., Touloumis K., Demirel N., Vianna G. M. S., Scarcella G., Schijns R., Liang C., Pauly D.
Following an introduction to the nature of fisheries catches and their information content, a new development of CMSY, a data-limited stock assessment method for fishes and invertebrates, is presented. This new version, CMSY++, overcomes several of the deficiencies of CMSY, which itself improved upon the "Catch-MSY" method published by S. Martell and R. Froese in 2013. The catch-only application of CMSY++ uses a Bayesian implementation of a modified Schaefer model, which also allows the fitting of abundance indices should such information be available. In the absence of historical catch time series and abundance indices, CMSY++ depends strongly on the provision of appropriate and informative priors for plausible ranges of initial and final stock depletion. An Artificial Neural Network (ANN) now assists in selecting objective priors for relative stock size based on patterns in 400 catch time series used for training. Regarding the cross-validation of the ANN predictions, of the 400 real stocks used in the training of ANN, 94% of final relative biomass (B/k) Bayesian (BSM) estimates were within the approximate 95% confidence limits of the respective CMSY++ estimate. Also, the equilibrium catch-biomass relations of the modified Schaefer model are compared with those of alternative surplus-production and age-structured models, suggesting that the latter two can be strongly biased towards underestimating the biomass required to sustain catches at low abundance. Numerous independent applications demonstrate how CMSY++ can incorporate, in addition to the required catch time series, both abundance data and a wide variety of ancillary information. We stress, however, the caveats and pitfalls of naively using the built-in prior options, which should instead be evaluated case-by-case and ideally be replaced by independent prior knowledge.Source: Acta Ichthyologica et Piscatoria 53 (2023): 173–189. doi:10.3897/aiep.53.105910
DOI: 10.3897/aiep.53.105910
Project(s): EcoScope
Metrics:
See at:
aiep.pensoft.net | ISTI Repository | CNR ExploRA
2023
Journal article
Open Access
Evaluation of operational ocean forecasting systems from the perspective of the users and the experts
Ciliberti S. A., Alvarez F. E., Pearlman J., Wilmer-Becker K., Bahurel P., Ardhuin F., Arnaud A., Bell M., Berthou S., Bertino L., Capet A., Chassignet E., Ciavatta S., Cirano M., Clementi E., Cossarini G., Coro G., Corney S., Davidson F., Drevillon M., Drillet Y., Dussurget R., El Serafy G., Fennel K., Garcia Sotillo M., Heimbach P., Hernandez F., Hogan P., Hoteit I., Joseph S., Josey S., Le Traon P-Y., Libralato S., Mancini M., Matte P., Melet A., Miyazawa Y., Moore A. M., Novellino A., Porter A., Regan H., Romero L., Schiller A., Siddorn J., Staneva J., Thomas-Courcoux C., Tonani M., Garcia-Valdecasas J. M., Veitch J., Von Schuckmann K., Wan L., Wilkin J., Zufic R.
The Intergovernmental Oceanographic Commission (IOC) has an Ocean Decade Implementation Plan (UNESCO-IOC, 2021) that states seven outcomes required for the ocean we want, with the fourth outcome being "A predicted ocean where society understands and can respond to changing ocean conditions." To facilitate the achievement of this goal, the IOC has endorsed Mercator Ocean International to implement the Decade Collaborative Center (DCC) for OceanPrediction (https://www.mercator-ocean.eu/oceanprediction/, last access: 21 August 2023), which is a cross-cutting structure that will work to develop global-scale collaboration between Decade Actions related to ocean prediction. To have a predicted ocean, the OceanPrediction DCC understands that is critical to co-design ocean forecasting architecture that will permit different services to deliver as one and that could take advantage of the concept of digital twinning (European Union, 2022). This architecture will be designed to overcome the present-day limitations of our systems in terms of interoperability and tools sharing. This will translate into a new scenario for ocean forecasting, where more robust systems will be easier to implement thanks to a common set of agreed tools, standards, and best practices. This new architecture will serve as inspiration for the development targets of the different decadal actions related to ocean forecasting, such as ForeSea (https://oceanpredict.org/un-decade-of-ocean-science/foresea/, last access: 21 August 2023), DITTO (https://ditto-oceandecade.org/, last access: 21 August 2023), CoastPredict (https://www.coastpredict.org/, last access: 21 August 2023), Global Environment Monitoring System for the Ocean and Coasts (GEMS Ocean (https://www.unep.org/explore-topics/oceans-seas/what-we-do/ocean-and-coastal-observations, last access: 21 August 2023)), Ocean Best Practices (https://www.oceanbestpractices.org/, last access: 21 August 2023), and others. To develop this architecture, the OceanPrediction DCC has implemented the Ocean Forecasting Co-Design Team (OFCT), which is composed of 43 international experts on all of the different aspects of the ocean forecasting value chain. The first task of this group is to analyze the present status of ocean forecasting at a global level, in order to properly identify the existing gaps before moving into the design phase. One of the first steps in this process has been to explore the degree of satisfaction of both users and experts with respect to the existing ocean forecasting systems. This has been done by launching a series of surveys among the members of the OFCT and another one among the users of the forecasting services. This paper describes the findings derived from the analysis of these surveys. Section 2 introduces the surveys, while Sect. 3 presents the results. Section 4 establishes a discussion and identifies some conclusions as part of the outlook for future exploitation.Source: Science magazine's state of the planet 1-osr7 (2023). doi:10.5194/sp-1-osr7-2-2023
DOI: 10.5194/sp-1-osr7-2-2023
Metrics:
Ciliberti S. A., Alvarez F. E., Pearlman J., Wilmer-Becker K., Bahurel P., Ardhuin F., Arnaud A., Bell M., Berthou S., Bertino L., Capet A., Chassignet E., Ciavatta S., Cirano M., Clementi E., Cossarini G., Coro G., Corney S., Davidson F., Drevillon M., Drillet Y., Dussurget R., El Serafy G., Fennel K., Garcia Sotillo M., Heimbach P., Hernandez F., Hogan P., Hoteit I., Joseph S., Josey S., Le Traon P-Y., Libralato S., Mancini M., Matte P., Melet A., Miyazawa Y., Moore A. M., Novellino A., Porter A., Regan H., Romero L., Schiller A., Siddorn J., Staneva J., Thomas-Courcoux C., Tonani M., Garcia-Valdecasas J. M., Veitch J., Von Schuckmann K., Wan L., Wilkin J., Zufic R.
The Intergovernmental Oceanographic Commission (IOC) has an Ocean Decade Implementation Plan (UNESCO-IOC, 2021) that states seven outcomes required for the ocean we want, with the fourth outcome being "A predicted ocean where society understands and can respond to changing ocean conditions." To facilitate the achievement of this goal, the IOC has endorsed Mercator Ocean International to implement the Decade Collaborative Center (DCC) for OceanPrediction (https://www.mercator-ocean.eu/oceanprediction/, last access: 21 August 2023), which is a cross-cutting structure that will work to develop global-scale collaboration between Decade Actions related to ocean prediction. To have a predicted ocean, the OceanPrediction DCC understands that is critical to co-design ocean forecasting architecture that will permit different services to deliver as one and that could take advantage of the concept of digital twinning (European Union, 2022). This architecture will be designed to overcome the present-day limitations of our systems in terms of interoperability and tools sharing. This will translate into a new scenario for ocean forecasting, where more robust systems will be easier to implement thanks to a common set of agreed tools, standards, and best practices. This new architecture will serve as inspiration for the development targets of the different decadal actions related to ocean forecasting, such as ForeSea (https://oceanpredict.org/un-decade-of-ocean-science/foresea/, last access: 21 August 2023), DITTO (https://ditto-oceandecade.org/, last access: 21 August 2023), CoastPredict (https://www.coastpredict.org/, last access: 21 August 2023), Global Environment Monitoring System for the Ocean and Coasts (GEMS Ocean (https://www.unep.org/explore-topics/oceans-seas/what-we-do/ocean-and-coastal-observations, last access: 21 August 2023)), Ocean Best Practices (https://www.oceanbestpractices.org/, last access: 21 August 2023), and others. To develop this architecture, the OceanPrediction DCC has implemented the Ocean Forecasting Co-Design Team (OFCT), which is composed of 43 international experts on all of the different aspects of the ocean forecasting value chain. The first task of this group is to analyze the present status of ocean forecasting at a global level, in order to properly identify the existing gaps before moving into the design phase. One of the first steps in this process has been to explore the degree of satisfaction of both users and experts with respect to the existing ocean forecasting systems. This has been done by launching a series of surveys among the members of the OFCT and another one among the users of the forecasting services. This paper describes the findings derived from the analysis of these surveys. Section 2 introduces the surveys, while Sect. 3 presents the results. Section 4 establishes a discussion and identifies some conclusions as part of the outlook for future exploitation.Source: Science magazine's state of the planet 1-osr7 (2023). doi:10.5194/sp-1-osr7-2-2023
DOI: 10.5194/sp-1-osr7-2-2023
Metrics:
See at:
ISTI Repository | sp.copernicus.org | CNR ExploRA
2023
Journal article
Open Access
Scientific knowledge gaps on the biology of non-fish marine species across European Seas
Abucay L. R., Sorongon-Yap P., Kesner-Reyes K., Capuli E. C., Reyes Jr R. B., Daskalaki E., Ferrà Vega C., Scarcella G., Coro G., Ordines F., Dakalov G., Celie L., Scotti M., Gremillet D., Gal G., "deng" Palomares M. L., Dimarchopoulou D., Tsikliras A. C.
Available information and potential data gaps for non-fish marine organisms (cnidarians, crustaceans, echinoderms, molluscs, sponges, mammals, reptiles, and seabirds) covered by the global database SeaLifeBase were reviewed for eight marine ecosystems (Adriatic Sea, Aegean Sea, Baltic Sea, Bay of Biscay/Celtic Sea/Iberian Coast, Black Sea, North Sea, western Mediterranean Sea, Levantine Sea) across European Seas. The review of the SeaLifeBase dataset, which is based on published literature, analyzed information coverage for eight biological characteristics (diet, fecundity, maturity, length-weight relationships, spawning, growth, lifespan, and natural mortality). These characteristics are required for the development of ecosystem and ecological models to evaluate the status of marine resources and related fisheries. Our analyses revealed that information regarding these biological characteristics in the literature was far from complete across all studied areas. The level of available information was nonetheless reasonably good for sea turtles and moderate for marine mammals in some areas (Baltic Sea, Bay of Biscay/Celtic Sea/Iberian Coast, Black Sea, North Sea and western Mediterranean Sea). Further, seven of the areas have well-studied species in terms of information coverage for biological characteristics of some commercial species whereas threatened species are generally not well studied. Across areas, the most well-studied species are the cephalopod common cuttlefish (Sepia officinalis) and the crustacean Norway lobster (Nephrops norvegicus). Overall, the information gap is narrowest for length-weight relationships followed by growth and maturity, and widest for fecundity and natural mortality. Based on these insights, we provide recommendations to prioritize species with insufficient or missing biological data that are common across the studied marine ecosystems and to address data deficiencies.Source: Frontiers in Marine Science 10 (2023). doi:10.3389/fmars.2023.1198137
DOI: 10.3389/fmars.2023.1198137
Project(s): EcoScope
Metrics:
Abucay L. R., Sorongon-Yap P., Kesner-Reyes K., Capuli E. C., Reyes Jr R. B., Daskalaki E., Ferrà Vega C., Scarcella G., Coro G., Ordines F., Dakalov G., Celie L., Scotti M., Gremillet D., Gal G., "deng" Palomares M. L., Dimarchopoulou D., Tsikliras A. C.
Available information and potential data gaps for non-fish marine organisms (cnidarians, crustaceans, echinoderms, molluscs, sponges, mammals, reptiles, and seabirds) covered by the global database SeaLifeBase were reviewed for eight marine ecosystems (Adriatic Sea, Aegean Sea, Baltic Sea, Bay of Biscay/Celtic Sea/Iberian Coast, Black Sea, North Sea, western Mediterranean Sea, Levantine Sea) across European Seas. The review of the SeaLifeBase dataset, which is based on published literature, analyzed information coverage for eight biological characteristics (diet, fecundity, maturity, length-weight relationships, spawning, growth, lifespan, and natural mortality). These characteristics are required for the development of ecosystem and ecological models to evaluate the status of marine resources and related fisheries. Our analyses revealed that information regarding these biological characteristics in the literature was far from complete across all studied areas. The level of available information was nonetheless reasonably good for sea turtles and moderate for marine mammals in some areas (Baltic Sea, Bay of Biscay/Celtic Sea/Iberian Coast, Black Sea, North Sea and western Mediterranean Sea). Further, seven of the areas have well-studied species in terms of information coverage for biological characteristics of some commercial species whereas threatened species are generally not well studied. Across areas, the most well-studied species are the cephalopod common cuttlefish (Sepia officinalis) and the crustacean Norway lobster (Nephrops norvegicus). Overall, the information gap is narrowest for length-weight relationships followed by growth and maturity, and widest for fecundity and natural mortality. Based on these insights, we provide recommendations to prioritize species with insufficient or missing biological data that are common across the studied marine ecosystems and to address data deficiencies.Source: Frontiers in Marine Science 10 (2023). doi:10.3389/fmars.2023.1198137
DOI: 10.3389/fmars.2023.1198137
Project(s): EcoScope
Metrics:
See at:
ISTI Repository | www.frontiersin.org | ZENODO | CNR ExploRA
2023
Contribution to journal
Open Access
Editorial: Ecocentric fisheries management in European seas: data gaps, base models and initial assessments, volume I
Tsikliras A. C., Coro G., Daskalov G., Grémillet D., Scotti M., Sylaios G.
Source: Frontiers in Marine Science 10 (2023). doi:10.3389/fmars.2023.1295733
DOI: 10.3389/fmars.2023.1295733
Project(s): EcoScope
Metrics:
Tsikliras A. C., Coro G., Daskalov G., Grémillet D., Scotti M., Sylaios G.
Source: Frontiers in Marine Science 10 (2023). doi:10.3389/fmars.2023.1295733
DOI: 10.3389/fmars.2023.1295733
Project(s): EcoScope
Metrics:
See at:
ISTI Repository | www.frontiersin.org | ZENODO | CNR ExploRA
2023
Contribution to conference
Open Access
Towards digital twins of territories through semantic story maps
Bartalesi V., Coro G., Lenzi E., Pratelli N., Pagano P.
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 outlines how online story maps can fill the gap between a map and a territory in narratives to create a digital twin of different territories as inter-connected semantic storiesSource: BUILD-IT 2023, pp. 41–45, Rome, Italy, 19/10/2023-20/10/2023
Bartalesi V., Coro G., Lenzi E., Pratelli N., Pagano P.
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 outlines how online story maps can fill the gap between a map and a territory in narratives to create a digital twin of different territories as inter-connected semantic storiesSource: BUILD-IT 2023, pp. 41–45, Rome, Italy, 19/10/2023-20/10/2023
See at:
inm.cnr.it | ISTI Repository | CNR ExploRA
2023
Journal article
Open Access
Using semantic story maps to describe a territory beyond its map
Bartalesi V., Coro G., Lenzi E., Pratelli N., Pagano P., Felici F., Moretti M., Brunori G.
The paper presents the Story Map Building and Visualizing Tool (SMBVT) that allows users to create story maps within a collaborative environment and a usable Web interface. It is entirely open-source and published as a free-to-use solution. It uses Semantic Web technologies in the back-end system to represent stories through a reference ontology for representing narratives. It builds up a user-shared semantic knowledge base that automatically interconnects all stories and seamlessly enables collaborative story building. Finally, it operates within an Open-Science oriented e-Infrastructure, which enables data and information sharing within communities of narrators, and adds multi-tenancy, multi-user, security, and access-control facilities. SMBVT represents narratives as a network of spatiotemporal events related by semantic relations and standardizes the event descriptions by assigning internationalized resource identifiers (IRIs) to the event components, i.e., the entities that take part in the event (e.g., persons, objects, places, concepts). The tool automatically saves the collected knowledge as a Web Ontology Language (OWL) graph and openly publishes it as Linked Open Data. This feature allows connecting the story events to other knowledge bases. To evaluate and demonstrate our tool, we used it to describe the Apuan Alps territory in Tuscany (Italy). Based on a user-test evaluation, we assessed the tool's effectiveness at building story maps and the ability of the produced story to describe the territory beyond the map.Source: Semantic web (Online) 14 (2023): 1255–1272. doi:10.3233/SW-233485
DOI: 10.3233/sw-233485
Metrics:
Bartalesi V., Coro G., Lenzi E., Pratelli N., Pagano P., Felici F., Moretti M., Brunori G.
The paper presents the Story Map Building and Visualizing Tool (SMBVT) that allows users to create story maps within a collaborative environment and a usable Web interface. It is entirely open-source and published as a free-to-use solution. It uses Semantic Web technologies in the back-end system to represent stories through a reference ontology for representing narratives. It builds up a user-shared semantic knowledge base that automatically interconnects all stories and seamlessly enables collaborative story building. Finally, it operates within an Open-Science oriented e-Infrastructure, which enables data and information sharing within communities of narrators, and adds multi-tenancy, multi-user, security, and access-control facilities. SMBVT represents narratives as a network of spatiotemporal events related by semantic relations and standardizes the event descriptions by assigning internationalized resource identifiers (IRIs) to the event components, i.e., the entities that take part in the event (e.g., persons, objects, places, concepts). The tool automatically saves the collected knowledge as a Web Ontology Language (OWL) graph and openly publishes it as Linked Open Data. This feature allows connecting the story events to other knowledge bases. To evaluate and demonstrate our tool, we used it to describe the Apuan Alps territory in Tuscany (Italy). Based on a user-test evaluation, we assessed the tool's effectiveness at building story maps and the ability of the produced story to describe the territory beyond the map.Source: Semantic web (Online) 14 (2023): 1255–1272. doi:10.3233/SW-233485
DOI: 10.3233/sw-233485
Metrics:
See at:
content.iospress.com | ISTI Repository | ISTI Repository | CNR ExploRA
2023
Journal article
Open Access
An Open Science oriented Bayesian interpolation model for marine parameter observations
Coro G.
Ecological and ecosystem modellers frequently need to interpolate spatiotemporal observations of geophysical and environmental parameters over an analysed area. However, particularly in marine science, modellers with low expertise in oceanography and hydrodynamics can hardly use interpolation methods optimally. This paper introduces an Open Science oriented, open-source, scalable and efficient workflow for 2D marine environmental parameters. It combines a fast, efficient interpolation method with a Bayesian hierarchical model embedding the stationary advection-diffusion equation as a constraint. Our workflow fills the usability gap between interpolation software providers and the users' communities. It can run entirely automatically without requiring expert parametrization. It is also available on a cloud computing platform, with a Web Processing Service compliant interface, supporting collaboration, repeatability, reproducibility, and provenance tracking. We demonstrate that our workflow produces comparable results to a state-of-the-art model (frequently used in oceanography) in interpolating four environmental parameters at the global scale.Source: Environmental modelling & software (2023). doi:10.1016/j.envsoft.2023.105901
DOI: 10.1016/j.envsoft.2023.105901
Project(s): EcoScope
Metrics:
Coro G.
Ecological and ecosystem modellers frequently need to interpolate spatiotemporal observations of geophysical and environmental parameters over an analysed area. However, particularly in marine science, modellers with low expertise in oceanography and hydrodynamics can hardly use interpolation methods optimally. This paper introduces an Open Science oriented, open-source, scalable and efficient workflow for 2D marine environmental parameters. It combines a fast, efficient interpolation method with a Bayesian hierarchical model embedding the stationary advection-diffusion equation as a constraint. Our workflow fills the usability gap between interpolation software providers and the users' communities. It can run entirely automatically without requiring expert parametrization. It is also available on a cloud computing platform, with a Web Processing Service compliant interface, supporting collaboration, repeatability, reproducibility, and provenance tracking. We demonstrate that our workflow produces comparable results to a state-of-the-art model (frequently used in oceanography) in interpolating four environmental parameters at the global scale.Source: Environmental modelling & software (2023). doi:10.1016/j.envsoft.2023.105901
DOI: 10.1016/j.envsoft.2023.105901
Project(s): EcoScope
Metrics:
See at:
ISTI Repository | www.sciencedirect.com 
| CNR ExploRA
2023
Report
Unknown
InfraScience research activity report 2023
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 A. S. T., La Bruzzo S., Lelii L., Manghi P., Mangiacrapa F., Mangione D., Mannocci A., Molinaro E., Pagano P., Panichi G., Paratore M. T., Pavone G., Piccioli T., Sinibaldi F., Straccia U., Vannini G. L.
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 2023 to highlight the major results. In particular, the InfraScience group engaged in 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 2023 InfraScience members contributed to the publishing of several papers, to the research and development activities of several research projects (primarily funded by EU), to the organization of conferences and training events, to several working groups and task forces.Source: ISTI Annual Reports, 2023
DOI: 10.32079/isti-ar-2023/002
Project(s): Blue Cloud, EOSC Future , TAILOR
Metrics:
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 A. S. T., La Bruzzo S., Lelii L., Manghi P., Mangiacrapa F., Mangione D., Mannocci A., Molinaro E., Pagano P., Panichi G., Paratore M. T., Pavone G., Piccioli T., Sinibaldi F., Straccia U., Vannini G. L.
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 2023 to highlight the major results. In particular, the InfraScience group engaged in 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 2023 InfraScience members contributed to the publishing of several papers, to the research and development activities of several research projects (primarily funded by EU), to the organization of conferences and training events, to several working groups and task forces.Source: ISTI Annual Reports, 2023
DOI: 10.32079/isti-ar-2023/002
Project(s): Blue Cloud
, EOSC Future , TAILOR Metrics:
See at: CNR ExploRA
2023
Report
Open Access
Massaciuccoli Lake Basin - Atlas of agri-environmental spatial data
Vannini G. L., Coro G.
Atlas of agri-environmental spatial data developed in the context of the ITINERIS PNRR project. This is a cartographic collection of geospatial data with resolution lower than 50m for the Massaciuccoli Lake Basin.Source: ISTI Technical Report, ISTI-2023-TR/013, 2023
Vannini G. L., Coro G.
Atlas of agri-environmental spatial data developed in the context of the ITINERIS PNRR project. This is a cartographic collection of geospatial data with resolution lower than 50m for the Massaciuccoli Lake Basin.Source: ISTI Technical Report, ISTI-2023-TR/013, 2023
See at:
data.d4science.net | CNR ExploRA
2022
Journal article
Open Access
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:
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 | ISTI Repository | www.nature.com | CNR ExploRA
2022
Journal article
Open Access
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
Metrics:
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
Metrics:
See at:
dl.acm.org | ISTI Repository | CNR ExploRA
2022
Journal article
Open Access
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, Blue Cloud , EOSC-Pillar , SoBigData-PlusPlus
Metrics:
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
, Blue Cloud , EOSC-Pillar , SoBigData-PlusPlus Metrics:
See at:
ISTI Repository | onlinelibrary.wiley.com | CNR ExploRA
2022
Journal article
Open Access
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:
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 | link.springer.com | CNR ExploRA
2022
Journal article
Open Access
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:
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 | link.springer.com | CNR ExploRA