2017
Other
Open Access
Gibbs sampling with JAGS: behind the scenes
Coro G.
Gibbs sampling is a Bayesian inference technique that is used in various scientific domains to generate samples from a certain posterior probability density function, given experimental data. Several software implementations of Gibbs sampling exist, which generally adopt very different approaches, because it is not easy to make a Gibbs sampling implementation exactly correspond to the theoretical approach. In particular, these implementations may use different approximation algorithms to and solutions to sub-steps of the Gibbs sampling process. Scientists working in different domains often use Gibbs sampling software without knowing the details of the implementation. Nevertheless, it is our experience that understanding the implementation can be crucial to enhance the performance of a model, because a software configuration conceived to help the underlying implementation may end in better approximation of the estimated probabilities functions. JAGS (Just Another Gibbs Sampler) is a widely used open-source implementation of Gibbs sampling. Its installation and user's guide are accurate, but do not indicate how the software really implements Gibbs sampling and it is not easy to infer this information from the source code. The aim of this paper is to give a high-level overview of the JAGS algorithms and its extensions that implement Gibbs sampling. Our target reader is a scientist who may want to understand the basic concepts underlying Bayesian inference and Gibbs sampling and who want to be aware of what happens behind the scenes when building a model.
Coro G.
Gibbs sampling is a Bayesian inference technique that is used in various scientific domains to generate samples from a certain posterior probability density function, given experimental data. Several software implementations of Gibbs sampling exist, which generally adopt very different approaches, because it is not easy to make a Gibbs sampling implementation exactly correspond to the theoretical approach. In particular, these implementations may use different approximation algorithms to and solutions to sub-steps of the Gibbs sampling process. Scientists working in different domains often use Gibbs sampling software without knowing the details of the implementation. Nevertheless, it is our experience that understanding the implementation can be crucial to enhance the performance of a model, because a software configuration conceived to help the underlying implementation may end in better approximation of the estimated probabilities functions. JAGS (Just Another Gibbs Sampler) is a widely used open-source implementation of Gibbs sampling. Its installation and user's guide are accurate, but do not indicate how the software really implements Gibbs sampling and it is not easy to infer this information from the source code. The aim of this paper is to give a high-level overview of the JAGS algorithms and its extensions that implement Gibbs sampling. Our target reader is a scientist who may want to understand the basic concepts underlying Bayesian inference and Gibbs sampling and who want to be aware of what happens behind the scenes when building a model.
See at:
ISTI Repository 
| CNR ExploRA
2012
Software
Unknown
Generic worker executor library.
Coro G.
The Generic Worker Executor is a Java library used in the gCube framework. It can transform a gCube based e-Infrastructure into a distributed computational infrastructure. The GWEL can be installed on a gCube node and is able to execute scripts on the local machine and read commands from an Active Message Queue instance. The GWEL was developed by extending the interface of the gCube Executor component.Project(s): IMARINE
Coro G.
The Generic Worker Executor is a Java library used in the gCube framework. It can transform a gCube based e-Infrastructure into a distributed computational infrastructure. The GWEL can be installed on a gCube node and is able to execute scripts on the local machine and read commands from an Active Message Queue instance. The GWEL was developed by extending the interface of the gCube Executor component.Project(s): IMARINE
See at: CNR ExploRA | www.gcube-system.org
2012
Software
Unknown
Ecological Engine Library
Coro G.
Ecological Engine is a Java library which allows to develop and run algorithms for performing Ecological Modeling experiments. The term Ecological Modeling indicates a set of functionalities for managing complex phenomena in order, for example, to predict the impact of climate changes on biodiversity, prevent the spread of invasive species or disease, help in ecosystems conservation planning. The Ecological Engine allows to dynamically add new algorithms as plug-ins. The algorithms can be executed as sequential or multi-thread computations.Project(s): IMARINE
Coro G.
Ecological Engine is a Java library which allows to develop and run algorithms for performing Ecological Modeling experiments. The term Ecological Modeling indicates a set of functionalities for managing complex phenomena in order, for example, to predict the impact of climate changes on biodiversity, prevent the spread of invasive species or disease, help in ecosystems conservation planning. The Ecological Engine allows to dynamically add new algorithms as plug-ins. The algorithms can be executed as sequential or multi-thread computations.Project(s): IMARINE
See at: CNR ExploRA | www.gcube-system.org
2012
Software
Unknown
Environment Explorer Library
Coro G.
The Environment Explorer Library is a Java library for exploring environmental G.I.S. layers. It addresses geographic layers containing physical or chemical characteristics of the world, which can be stored on a Geo Server or on a Thredds Service. The library is able to retrieve the values of NetCDF files residing on Thredds associated to some coordinates, or to get the values of an environmental layer residing on a Geo Server. Furthermore it manages the indexing procedure of those elements on a Geo Network Server, from which it can retrieve information about files or layers locations. The library is part of the gCube framework.Project(s): IMARINE
Coro G.
The Environment Explorer Library is a Java library for exploring environmental G.I.S. layers. It addresses geographic layers containing physical or chemical characteristics of the world, which can be stored on a Geo Server or on a Thredds Service. The library is able to retrieve the values of NetCDF files residing on Thredds associated to some coordinates, or to get the values of an environmental layer residing on a Geo Server. Furthermore it manages the indexing procedure of those elements on a Geo Network Server, from which it can retrieve information about files or layers locations. The library is part of the gCube framework.Project(s): IMARINE
See at: CNR ExploRA | www.gcube-system.org
2012
Software
Unknown
Times Series Geo-Tools and Vessel Information analysis library
Coro G.
Time Series Geo Tools is a java library for transforming a Time Series of observations about fishery catch statistics into a geographical map to be published by a GeoServer software. Furthermore it is able to manage and perform classification on Vessels Activity in the sea and to produce maps for displaying their trajectories and for calculating the fishing monthly effort at 0.5 degree resolutionProject(s): D4SCIENCE-II
Coro G.
Time Series Geo Tools is a java library for transforming a Time Series of observations about fishery catch statistics into a geographical map to be published by a GeoServer software. Furthermore it is able to manage and perform classification on Vessels Activity in the sea and to produce maps for displaying their trajectories and for calculating the fishing monthly effort at 0.5 degree resolutionProject(s): D4SCIENCE-II
See at: CNR ExploRA | www.gcube-system.org
2012
Report
Open Access
Imarine D9.1 - Imarine Data management software
Coro G., De Faveri F., Lelii L.
This deliverable describes the novelties within the iMarine Data Management Software up to M6 (Apr. '12), which include: Data Access, Data Transfer, Assessment,Harmonization and Certification facilities.Source: Project report, iMarine, Deliverable D9.1, 2012
Project(s): IMARINE
Coro G., De Faveri F., Lelii L.
This deliverable describes the novelties within the iMarine Data Management Software up to M6 (Apr. '12), which include: Data Access, Data Transfer, Assessment,Harmonization and Certification facilities.Source: Project report, iMarine, Deliverable D9.1, 2012
Project(s): IMARINE
See at:
ISTI Repository | CNR ExploRA
2013
Report
Open Access
A Lightweight Guide on Gibbs Sampling and JAGS
Coro G.
In this document we give some insight about how Gibbs Sampling works and how the JAGS modelling framework implements it. The hope is that, once the reader will have understood these concepts, building a model to perform Bayesian Inference with JAGS should be much easier or, at least, the reader should be more aware of what happens behind the scenes. We assume the reader to have basic knowledge about probability, sucient to understand the dierence between a probability and a probability density.Source: ISTI Technical reports, 2013
Coro G.
In this document we give some insight about how Gibbs Sampling works and how the JAGS modelling framework implements it. The hope is that, once the reader will have understood these concepts, building a model to perform Bayesian Inference with JAGS should be much easier or, at least, the reader should be more aware of what happens behind the scenes. We assume the reader to have basic knowledge about probability, sucient to understand the dierence between a probability and a probability density.Source: ISTI Technical reports, 2013
See at:
ISTI Repository | CNR ExploRA
2012
Report
Open Access
iMarine D10.4 - iMarine Data Consumption Software (1.0)
Antoniadis A., Brito F., Coro G., Gerbesiotis J., Marketakis Y.
This document describes the novelties within the iMarine Data Consumption Software which were achieved from the 7th to the 12th month of the project and provide pointers to the documentation and artifacts of the related components.Source: Project report, iMarine, Deliverable D10.4, 2012
Project(s): IMARINE
Antoniadis A., Brito F., Coro G., Gerbesiotis J., Marketakis Y.
This document describes the novelties within the iMarine Data Consumption Software which were achieved from the 7th to the 12th month of the project and provide pointers to the documentation and artifacts of the related components.Source: Project report, iMarine, Deliverable D10.4, 2012
Project(s): IMARINE
See at:
ISTI Repository | CNR ExploRA
2014
Report
Open Access
iMarine - iMarine Data Consumption Software
Antoniadis A., Brito F., Coro G., Gerbesiotis J., Laskaris N., Marketakis Y.
The iMarine Data Management Software comprises a number of components and subsystems offering facilities for accessing, transferring and harmonising a rich array of data typologies. This document describes the novelties within the iMarine Data Management Software from M12 (Oct.'12) to M27 (Jan.'14). It complements D9.2 [10] which describes iMarine Data Management Software up to M11 (Sept.'12). This deliverable is intended for documentation purposes only. The actual deliverable is represented by the software artifacts and their accompanying documentation.Source: Project report, iMarine, Deliverable D10.5, 2014
Project(s): IMARINE
Antoniadis A., Brito F., Coro G., Gerbesiotis J., Laskaris N., Marketakis Y.
The iMarine Data Management Software comprises a number of components and subsystems offering facilities for accessing, transferring and harmonising a rich array of data typologies. This document describes the novelties within the iMarine Data Management Software from M12 (Oct.'12) to M27 (Jan.'14). It complements D9.2 [10] which describes iMarine Data Management Software up to M11 (Sept.'12). This deliverable is intended for documentation purposes only. The actual deliverable is represented by the software artifacts and their accompanying documentation.Source: Project report, iMarine, Deliverable D10.5, 2014
Project(s): IMARINE
See at:
ISTI Repository | CNR ExploRA
2011
Software
Unknown
Ecological Modelling Library for gCube VRE
Coro G.
The Ecological Modelling library supplies the gCube platform with algorithms for calculating the probability distributions of living species across the world. Data mining and machine learning algorithms as well as expert systems are included for performing implicit or explicit modelling.Project(s): D4SCIENCE-II
Coro G.
The Ecological Modelling library supplies the gCube platform with algorithms for calculating the probability distributions of living species across the world. Data mining and machine learning algorithms as well as expert systems are included for performing implicit or explicit modelling.Project(s): D4SCIENCE-II
See at: CNR ExploRA | www.gcube-system.org
2011
Software
Unknown
Statistical Tools for GCube Platform
Coro G.
The Statistical Library is a support library for the gCube platform, which supplies Data Mining functionalities for performing lexical comparisons among user's data as well as data analysis and prevision. Machine Learning algorithms are included with parallel processing procedures, in order to give support for statistical data management and classification.Project(s): D4SCIENCE-II
Coro G.
The Statistical Library is a support library for the gCube platform, which supplies Data Mining functionalities for performing lexical comparisons among user's data as well as data analysis and prevision. Machine Learning algorithms are included with parallel processing procedures, in order to give support for statistical data management and classification.Project(s): D4SCIENCE-II
See at: CNR ExploRA
2014
Journal article
Open Access
Revisiting safe biological limits in fisheries
Froese R., Coro G., Kleisner K., Demirel N.
Fisheries management reference points used for stocks in the Northeast Atlantic were investigated as to the appropriateness of their current levels based on three practical limits of exploitation in fisheries management: (i) the smallest sustainable size of the fished stock (SSBpa), (ii) the maximum sustainable rate of exploitation (Fmsy), and (iii) the age at maturity, i.e., the smallest body size of captured fish that still allows for individual reproduction. SSBpa is a widely used reference point for low population size. In 46% of the examined stocks, the official value for this reference point was found to be below the consensus estimates determined from three different methods. Additionally, the natural rate of mortality M is widely regarded as an upper limit for sustainable fishing pressure (Fmsy) that can produce the maximum sustainable yield (MSY). However, the official estimates of Fmsy exceeded the rate of natural mortality in 76% of the stocks. Finally, there is wide agreement that age at maturity is a lower limit for age at first capture. However, age at first capture was below maturity in 74% of the stocks. No official estimates of the stock size (SSBmsy) that can produce MSY are available for the Northeast Atlantic. However, an analysis of stocks from other areas confirmed that twice SSBpa provides a reasonable preliminary estimate. Using this proxy with Northeast Atlantic stock sizes in 2013 showed that 88% were below the level that can produce MSY. Also, 54% of the stocks were outside of safe biological limits and 12% were severely depleted. Fishing mortality in 2013 exceeded natural mortality in 72% of the stocks, including those that were severely depleted. These results point to the urgent need to re-assess fisheries reference points in the Northeast Atlantic in order to implement the regulations of the new European Common Fisheries Policy regarding sustainable fishing pressure, healthy stock sizes and adult age/size at first capture.Source: Fish and fisheries (Oxf., Online) (2014). doi:10.1111/faf.12102
DOI: 10.1111/faf.12102
Project(s): IMARINE, ECOKNOWS
Metrics:
Froese R., Coro G., Kleisner K., Demirel N.
Fisheries management reference points used for stocks in the Northeast Atlantic were investigated as to the appropriateness of their current levels based on three practical limits of exploitation in fisheries management: (i) the smallest sustainable size of the fished stock (SSBpa), (ii) the maximum sustainable rate of exploitation (Fmsy), and (iii) the age at maturity, i.e., the smallest body size of captured fish that still allows for individual reproduction. SSBpa is a widely used reference point for low population size. In 46% of the examined stocks, the official value for this reference point was found to be below the consensus estimates determined from three different methods. Additionally, the natural rate of mortality M is widely regarded as an upper limit for sustainable fishing pressure (Fmsy) that can produce the maximum sustainable yield (MSY). However, the official estimates of Fmsy exceeded the rate of natural mortality in 76% of the stocks. Finally, there is wide agreement that age at maturity is a lower limit for age at first capture. However, age at first capture was below maturity in 74% of the stocks. No official estimates of the stock size (SSBmsy) that can produce MSY are available for the Northeast Atlantic. However, an analysis of stocks from other areas confirmed that twice SSBpa provides a reasonable preliminary estimate. Using this proxy with Northeast Atlantic stock sizes in 2013 showed that 88% were below the level that can produce MSY. Also, 54% of the stocks were outside of safe biological limits and 12% were severely depleted. Fishing mortality in 2013 exceeded natural mortality in 72% of the stocks, including those that were severely depleted. These results point to the urgent need to re-assess fisheries reference points in the Northeast Atlantic in order to implement the regulations of the new European Common Fisheries Policy regarding sustainable fishing pressure, healthy stock sizes and adult age/size at first capture.Source: Fish and fisheries (Oxf., Online) (2014). doi:10.1111/faf.12102
DOI: 10.1111/faf.12102
Project(s): IMARINE
, ECOKNOWS Metrics:
See at:
Aperta - TÜBİTAK Açık Arşivi | OceanRep | Fish and Fisheries 
| CNR ExploRA
2017
Journal article
Open Access
Estimating fisheries reference points from catch and resilience
Froese R., Demirel N., Coro G., Kleisner K., Winker H.
This study presents a Monte Carlo method (CMSY) for estimating fisheries reference points from catch, resilience and qualitative stock status information on data-limited stocks. It also presents a Bayesian state-space implementation of the Schaefer production model (BSM), fitted to catch and biomass or catch-per-unit-of-effort (CPUE) data. Special emphasis was given to derive informative priors for productivity, unexploited stock size, catchability and biomass from population dynamics theory. Both models gave good predictions of the maximum intrinsic rate of population increase r, unexploited stock size k and maximum sustainable yield MSY when validated against simulated data with known parameter values. CMSY provided, in addition, reasonable predictions of relative biomass and exploitation rate. Both models were evaluated against 128 real stocks, where estimates of biomass were available from full stock assessments. BSM estimates of r, k and MSY were used as benchmarks for the respective CMSY estimates and were not significantly different in 76% of the stocks. A similar test against 28 data-limited stocks, where CPUE instead of biomass was available, showed that BSM and CMSY estimates of r, k and MSY were not significantly different in 89% of the stocks. Both CMSY and BSM combine the production model with a simple stock-recruitment model, accounting for reduced recruitment at severely depleted stock sizes.Source: Fish and fisheries (Oxf., Online) 18 (2017): 506–526. doi:10.1111/faf.12190
DOI: 10.1111/faf.12190
Project(s): BlueBRIDGE, ECOKNOWS
Metrics:
Froese R., Demirel N., Coro G., Kleisner K., Winker H.
This study presents a Monte Carlo method (CMSY) for estimating fisheries reference points from catch, resilience and qualitative stock status information on data-limited stocks. It also presents a Bayesian state-space implementation of the Schaefer production model (BSM), fitted to catch and biomass or catch-per-unit-of-effort (CPUE) data. Special emphasis was given to derive informative priors for productivity, unexploited stock size, catchability and biomass from population dynamics theory. Both models gave good predictions of the maximum intrinsic rate of population increase r, unexploited stock size k and maximum sustainable yield MSY when validated against simulated data with known parameter values. CMSY provided, in addition, reasonable predictions of relative biomass and exploitation rate. Both models were evaluated against 128 real stocks, where estimates of biomass were available from full stock assessments. BSM estimates of r, k and MSY were used as benchmarks for the respective CMSY estimates and were not significantly different in 76% of the stocks. A similar test against 28 data-limited stocks, where CPUE instead of biomass was available, showed that BSM and CMSY estimates of r, k and MSY were not significantly different in 89% of the stocks. Both CMSY and BSM combine the production model with a simple stock-recruitment model, accounting for reduced recruitment at severely depleted stock sizes.Source: Fish and fisheries (Oxf., Online) 18 (2017): 506–526. doi:10.1111/faf.12190
DOI: 10.1111/faf.12190
Project(s): BlueBRIDGE
, ECOKNOWS Metrics:
See at:
Aperta - TÜBİTAK Açık Arşivi | OceanRep | Fish and Fisheries | ISTI Repository | Fish and Fisheries | onlinelibrary.wiley.com | CNR ExploRA
2016
Report
Open Access
BlueBRIDGE - Blue skills activity: interim report
Barde J., Coro G., Davies A., Eleni P.
The goal of Blue Skills is to develop and deploy Virtual Research Environments (VREs) essential for boosting education and knowledge bridging between research and innovation in the area of protection and management of marine resources. In order to achieve this goal, plans have been laid for real courses, both with physical attendance and online courses, which have been, and continue to be implemented throughout the project span. The course programme is expected to evolve during the project lifetime, e.g. new courses to be supported can be constantly identified. The initial list has been produced in October 2015 (MS4 Blue Skills VRE Plan) and whenever a course to be supported is identified it is recorded through dedicated tickets. The up to date list of target courses is always available by an automatic report available at https://support.d4science.org/issues/684. Depending on the characteristics of each course either a new Virtual Laboratory can be created or one or more existing ones can be re-used to support the activity of course participants. In the reporting period, 15 courses took place with the support of 7 dedicated VLabs. These courses were attended by more than 335 participants in total.Source: Project report, BlueBRIDGE, Deliverable D8.1, 2016
Project(s): BlueBRIDGE
Barde J., Coro G., Davies A., Eleni P.
The goal of Blue Skills is to develop and deploy Virtual Research Environments (VREs) essential for boosting education and knowledge bridging between research and innovation in the area of protection and management of marine resources. In order to achieve this goal, plans have been laid for real courses, both with physical attendance and online courses, which have been, and continue to be implemented throughout the project span. The course programme is expected to evolve during the project lifetime, e.g. new courses to be supported can be constantly identified. The initial list has been produced in October 2015 (MS4 Blue Skills VRE Plan) and whenever a course to be supported is identified it is recorded through dedicated tickets. The up to date list of target courses is always available by an automatic report available at https://support.d4science.org/issues/684. Depending on the characteristics of each course either a new Virtual Laboratory can be created or one or more existing ones can be re-used to support the activity of course participants. In the reporting period, 15 courses took place with the support of 7 dedicated VLabs. These courses were attended by more than 335 participants in total.Source: Project report, BlueBRIDGE, Deliverable D8.1, 2016
Project(s): BlueBRIDGE
See at:
ISTI Repository | CNR ExploRA
2019
Journal article
Open Access
A multiscale statistical method to identify potential areas of hyporheic exchange for river restoration planning
Magliozzi C., Coro G., Grabowski R., Packman A. I., Krause S.
The hyporheic zone (HZ) is an area of interaction between surface and ground waters present in and around river beds. Bidirectional mixing within the HZ, termed hyporheic exchange flow (HEF), plays significant roles in nutrient transport, organic matter and biogeochemical processing in rivers. The functional importance of the HZ in river ecology and hydrology suggests that river managers should consider the HZ in their planning to help compromised systems recover. However, current river restoration planning tools do not take into account the HZ. This paper describes a novel multiscale, transferable method that combines existing environmental information at different spatial scales to identify areas with potentially significant HEF for use in restoration prioritization and planning. It uses a deductive approach that is suited for data-poor case studies, which is common for most rivers, given the very limited data on the spatial occurrence of areas of hyporheic exchange. Results on nine contrasting European rivers, demonstrate its potential to inform river management.Source: Environmental modelling & software 111 (2019): 311–323. doi:10.1016/j.envsoft.2018.09.006
DOI: 10.1016/j.envsoft.2018.09.006
Project(s): BlueBRIDGE, HypoTRAIN
Metrics:
Magliozzi C., Coro G., Grabowski R., Packman A. I., Krause S.
The hyporheic zone (HZ) is an area of interaction between surface and ground waters present in and around river beds. Bidirectional mixing within the HZ, termed hyporheic exchange flow (HEF), plays significant roles in nutrient transport, organic matter and biogeochemical processing in rivers. The functional importance of the HZ in river ecology and hydrology suggests that river managers should consider the HZ in their planning to help compromised systems recover. However, current river restoration planning tools do not take into account the HZ. This paper describes a novel multiscale, transferable method that combines existing environmental information at different spatial scales to identify areas with potentially significant HEF for use in restoration prioritization and planning. It uses a deductive approach that is suited for data-poor case studies, which is common for most rivers, given the very limited data on the spatial occurrence of areas of hyporheic exchange. Results on nine contrasting European rivers, demonstrate its potential to inform river management.Source: Environmental modelling & software 111 (2019): 311–323. doi:10.1016/j.envsoft.2018.09.006
DOI: 10.1016/j.envsoft.2018.09.006
Project(s): BlueBRIDGE
, HypoTRAIN Metrics:
See at:
Environmental Modelling & Software | Cranfield CERES | ISTI Repository | ZENODO | Environmental Modelling & Software | www.sciencedirect.com | CNR ExploRA
2020
Journal article
Open Access
A global-scale ecological niche model to predict SARS-CoV-2 coronavirus infection rate
Coro G.
COVID-19 pandemic is a global threat to human health and economy that requires urgent prevention and monitoring strategies. Several models are under study to control the disease spread and infection rate and to detect possible factors that might favour them, with a focus on understanding the correlation between the disease and specific geophysical parameters. However, the pandemic does not present evident environmental hindrances in the infected countries. Nevertheless, a lower rate of infections has been observed in some countries, which might be related to particular population and climatic conditions. In this paper, infection rate of COVID-19 is modelled globally at a 0.5° resolution, using a Maximum Entropy-based Ecological Niche Model that identifies geographical areas potentially subject to a high infection rate. The model identifies locations that could favour infection rate due to their particular geophysical (surface air temperature, precipitation, and elevation) and human-related characteristics (CO2 and population density). It was trained by facilitating data from Italian provinces that have reported a high infection rate and subsequently tested using datasets from World countries' reports. Based on this model, a risk index was calculated to identify the potential World countries and regions that have a high risk of disease increment. The distribution outputs foresee a high infection rate in many locations where real-world disease outbreaks have occurred, e.g. the Hubei province in China, and reports a high risk of disease increment in most World countries which have reported significant outbreaks (e.g. Western U.S.A.). Overall, the results suggest that a complex combination of the selected parameters might be of integral importance to understand the propagation of COVID-19 among human populations, particularly in Europe. The model and the data were distributed through Open-science Web services to maximise opportunities for re-usability regarding new data and new diseases, and also to enhance the transparency of the approach and results.Source: Ecological modelling 431 (2020). doi:10.1016/j.ecolmodel.2020.109187
DOI: 10.1016/j.ecolmodel.2020.109187
Project(s): EOSCpilot
Metrics:
Coro G.
COVID-19 pandemic is a global threat to human health and economy that requires urgent prevention and monitoring strategies. Several models are under study to control the disease spread and infection rate and to detect possible factors that might favour them, with a focus on understanding the correlation between the disease and specific geophysical parameters. However, the pandemic does not present evident environmental hindrances in the infected countries. Nevertheless, a lower rate of infections has been observed in some countries, which might be related to particular population and climatic conditions. In this paper, infection rate of COVID-19 is modelled globally at a 0.5° resolution, using a Maximum Entropy-based Ecological Niche Model that identifies geographical areas potentially subject to a high infection rate. The model identifies locations that could favour infection rate due to their particular geophysical (surface air temperature, precipitation, and elevation) and human-related characteristics (CO2 and population density). It was trained by facilitating data from Italian provinces that have reported a high infection rate and subsequently tested using datasets from World countries' reports. Based on this model, a risk index was calculated to identify the potential World countries and regions that have a high risk of disease increment. The distribution outputs foresee a high infection rate in many locations where real-world disease outbreaks have occurred, e.g. the Hubei province in China, and reports a high risk of disease increment in most World countries which have reported significant outbreaks (e.g. Western U.S.A.). Overall, the results suggest that a complex combination of the selected parameters might be of integral importance to understand the propagation of COVID-19 among human populations, particularly in Europe. The model and the data were distributed through Open-science Web services to maximise opportunities for re-usability regarding new data and new diseases, and also to enhance the transparency of the approach and results.Source: Ecological modelling 431 (2020). doi:10.1016/j.ecolmodel.2020.109187
DOI: 10.1016/j.ecolmodel.2020.109187
Project(s): EOSCpilot
Metrics:
See at:
Ecological Modelling | Ecological Modelling | ISTI Repository | www.sciencedirect.com | CNR ExploRA
2020
Journal article
Open Access
Open science and artificial intelligence supporting blue growth
Coro G.
The long-term EU strategy to support the sustainable growth of the marine and maritime sectors (Blue Growth) involves economic and ecological topics that call for new computer science systems to produce new knowledge after processing large amounts of data (Big Data), collected both at academic and industrial levels. Today, Artificial Intelligence (AI) can satisfy the Blue Growth strategy requirements by managing Big Data, but requires effective multi-disciplinary interaction between scientists. In this context, new Science paradigms, like Open Science, are born to promote the creation of computational systems to process Big Data while supporting collaborative experimentation, multi-disciplinarity, and the re-use, repetition, and reproduction of experiments and results. AI can use Open Science systems by making domain and data experts cooperate both between them and with AI modellers. In this paper, we present examples of combined AI and Open Science-oriented applications in marine science. We explain the direct benefits these bring to the Blue Growth strategy and the indirect advantages deriving from their re-use in other applications than their originally intended onesSource: Environmental Engineering and Management Journal (Online) 19 (2020): 1719–1729.
Coro G.
The long-term EU strategy to support the sustainable growth of the marine and maritime sectors (Blue Growth) involves economic and ecological topics that call for new computer science systems to produce new knowledge after processing large amounts of data (Big Data), collected both at academic and industrial levels. Today, Artificial Intelligence (AI) can satisfy the Blue Growth strategy requirements by managing Big Data, but requires effective multi-disciplinary interaction between scientists. In this context, new Science paradigms, like Open Science, are born to promote the creation of computational systems to process Big Data while supporting collaborative experimentation, multi-disciplinarity, and the re-use, repetition, and reproduction of experiments and results. AI can use Open Science systems by making domain and data experts cooperate both between them and with AI modellers. In this paper, we present examples of combined AI and Open Science-oriented applications in marine science. We explain the direct benefits these bring to the Blue Growth strategy and the indirect advantages deriving from their re-use in other applications than their originally intended onesSource: Environmental Engineering and Management Journal (Online) 19 (2020): 1719–1729.
See at:
ISTI Repository | www.eemj.icpm.tuiasi.ro | CNR ExploRA
2021
Journal article
Open Access
An intelligent and cost-effective remote underwater video device for fish size monitoring
Coro G., Bjerregaard Walsh M.
Monitoring the size of key indicator species of fish is important to understand ecosystem functions, anthropogenic stress, and population dynamics. Standard methodologies gather data using underwater cameras, but are biased due to the use of baits, limited deployment time, and short field of view. Furthermore, they require experts to analyse long videos to search for species of interest, which is time consuming and expensive. This paper describes the Underwater Detector of Moving Object Size (UDMOS), a cost-effective computer vision system that records events of large fishes passing in front of a camera, using minimalistic hardware and power consumption. UDMOS can be deployed underwater, as an unbaited system, and is also offered as a free-to-use Web Service for batch video-processing. It embeds three different alternative large-object detection algorithms based on deep learning, unsupervised modelling, and motion detection, and can work both in shallow and deep waters with infrared or visible light.Source: Ecological informatics (Print) 63 (2021). doi:10.1016/j.ecoinf.2021.101311
DOI: 10.1016/j.ecoinf.2021.101311
Metrics:
Coro G., Bjerregaard Walsh M.
Monitoring the size of key indicator species of fish is important to understand ecosystem functions, anthropogenic stress, and population dynamics. Standard methodologies gather data using underwater cameras, but are biased due to the use of baits, limited deployment time, and short field of view. Furthermore, they require experts to analyse long videos to search for species of interest, which is time consuming and expensive. This paper describes the Underwater Detector of Moving Object Size (UDMOS), a cost-effective computer vision system that records events of large fishes passing in front of a camera, using minimalistic hardware and power consumption. UDMOS can be deployed underwater, as an unbaited system, and is also offered as a free-to-use Web Service for batch video-processing. It embeds three different alternative large-object detection algorithms based on deep learning, unsupervised modelling, and motion detection, and can work both in shallow and deep waters with infrared or visible light.Source: Ecological informatics (Print) 63 (2021). doi:10.1016/j.ecoinf.2021.101311
DOI: 10.1016/j.ecoinf.2021.101311
Metrics:
See at:
ISTI Repository | www.sciencedirect.com | CNR ExploRA
2021
Journal article
Open Access
Exploring the status of the Indonesian deep demersal fishery using length-based stock assessments
Dimarchopoulou D., Mous P. J., Firmana E., Wibisono E., Coro G., Humphries A. T.
The deep demersal snapper-grouper fishery in Indonesia is a data-poor fisheries resource that provides food security and a source of income to millions globally. Owing to an ongoing crew-operated data recording system implemented in Indonesia since 2015, the stocks of this fishery can now be assessed using length-frequency data and updated life-history parameters. Here, we use two length-based methods, one that is fishery-specific and another that is more generalized, to assess the status of Indonesian stocks. Specifically, we develop a literature-based assessment method based on a patchwork of conventional approaches but tailored to the studied stocks, and compare it with a newly established and broadly applicable length-based Bayesian biomass estimation method (LBB). The methods were applied to 16 stocks from 4 Indonesian Fisheries Management Areas and were compared based on simulations, as well as the convergence of the resulting stock status classification and uncertainty of the results. Analyzing the effect of using the literature-based species/family-specific life-history parameter values for asymptotic length (Linf) and relative natural mortality (M/K) in LBB showed that different values do affect the estimated biomass indicator. Nevertheless, in more than half the cases, the stock status classification did not differ between the two methods, while LBB results became more reliable with narrower confidence limits. Simulations, as well as similar status indicators between the two models support the value of the literature-based approach as an assessment methodology for the Indonesian deep demersal fisheries. Narrower confidence ranges highlight the importance of using fishery-specific information when applying generalized stock assessment methods. While most catches had few immature fish, half of the assessed stocks were consistently shown to have low biomass, indicating that important Indonesian stocks are at high risk of overfishing.Source: Fisheries research 243 (2021). doi:10.1016/j.fishres.2021.106089
DOI: 10.1016/j.fishres.2021.106089
Metrics:
Dimarchopoulou D., Mous P. J., Firmana E., Wibisono E., Coro G., Humphries A. T.
The deep demersal snapper-grouper fishery in Indonesia is a data-poor fisheries resource that provides food security and a source of income to millions globally. Owing to an ongoing crew-operated data recording system implemented in Indonesia since 2015, the stocks of this fishery can now be assessed using length-frequency data and updated life-history parameters. Here, we use two length-based methods, one that is fishery-specific and another that is more generalized, to assess the status of Indonesian stocks. Specifically, we develop a literature-based assessment method based on a patchwork of conventional approaches but tailored to the studied stocks, and compare it with a newly established and broadly applicable length-based Bayesian biomass estimation method (LBB). The methods were applied to 16 stocks from 4 Indonesian Fisheries Management Areas and were compared based on simulations, as well as the convergence of the resulting stock status classification and uncertainty of the results. Analyzing the effect of using the literature-based species/family-specific life-history parameter values for asymptotic length (Linf) and relative natural mortality (M/K) in LBB showed that different values do affect the estimated biomass indicator. Nevertheless, in more than half the cases, the stock status classification did not differ between the two methods, while LBB results became more reliable with narrower confidence limits. Simulations, as well as similar status indicators between the two models support the value of the literature-based approach as an assessment methodology for the Indonesian deep demersal fisheries. Narrower confidence ranges highlight the importance of using fishery-specific information when applying generalized stock assessment methods. While most catches had few immature fish, half of the assessed stocks were consistently shown to have low biomass, indicating that important Indonesian stocks are at high risk of overfishing.Source: Fisheries research 243 (2021). doi:10.1016/j.fishres.2021.106089
DOI: 10.1016/j.fishres.2021.106089
Metrics:
See at:
ISTI Repository | www.sciencedirect.com | CNR ExploRA
2020
Journal article
Open Access
Predicting the spread of COVID-19 through Marine Ecological Niche Models
Coro G.
Researchers from ISTI-CNR (Italy) used marine models, designed to monitor species habitats and invasions, to identify the countries with the highest risk of COVID-19 spread due to climatic and human factors. The model correctly identified most locations where large outbreaks were recorded, independent of population density and dynamics, and is a valuable source of information for smaller-scale population models.Source: ERCIM news online edition 124 (2020).
Project(s): EOSCsecretariat.eu
Coro G.
Researchers from ISTI-CNR (Italy) used marine models, designed to monitor species habitats and invasions, to identify the countries with the highest risk of COVID-19 spread due to climatic and human factors. The model correctly identified most locations where large outbreaks were recorded, independent of population density and dynamics, and is a valuable source of information for smaller-scale population models.Source: ERCIM news online edition 124 (2020).
Project(s): EOSCsecretariat.eu
See at:
ercim-news.ercim.eu | ISTI Repository | CNR ExploRA