Virtual research environments co‐creation: The D4Science experience

Assante, Massimiliano; Candela, Leonardo; Castelli, Donatella; Cirillo, Roberto; Coro, Gianpaolo; Andrea, Dell'Amico,; Frosini, Luca; Lelii, Lucio; Lettere, Marco; Mangiacrapa, Francesco; Pagano, Pasquale; Panichi, Giancarlo; Piccioli, Tommaso; Sinibaldi, Fabio

doi:10.1002/cpe.6925

Cited by 8 publications

(2 citation statements)

References 47 publications

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“…One limitation of our current implementation is that the three sub-models are not integrated into an all-in-one offline process because DIVA is currently released as a notebook that can be hardly transformed into an automatic process. Our nextfuture plan is to transform DIVA into a Web service to facilitate its automatic integration with the other sub-models, which will require preparing specific cloud services and infrastructures (Assante et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

“…Another input was a bathymetry NetCDF file from the highresolution GEBCO-2020 dataset (GEBCO, 2020). To speed up processing, we executed the model on the D4Science cloud computing platform (Coro et al, 2015a;Candela et al, 2016;Coro et al, 2017;Assante et al, 2019;Assante et al, 2020) that freely offers the DIVA software for notebook development (Blue Cloud, 2022). The used notebooks and platform are linked in the Supplementary Material.…”

Section: Spatial Componentmentioning

confidence: 99%

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Filling Gaps in Trawl Surveys at Sea through Spatiotemporal and Environmental Modelling

et al. 2022

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

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Section: Discussionmentioning

confidence: 99%

Section: Spatial Componentmentioning

confidence: 99%

Filling Gaps in Trawl Surveys at Sea through Spatiotemporal and Environmental Modelling

et al. 2022

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An open science automatic workflow for multi-model species distribution estimation

Coro,

Sana,

Bove

2024

Int J Data Sci Anal

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Integrated Environmental Assessment systems and ecosystem models study the links between anthropogenic and climatic pressures on marine ecosystems and help understand how to manage the effects of the unsustainable exploitation of ocean resources. However, these models have long implementation times, data and model interoperability issues and require heterogeneous competencies. Therefore, they would benefit from simplification, automatisation, and enhanced integrability of the underlying models. Artificial Intelligence can help overcome several limitations by speeding up the modelling of crucial functional parts, e.g. estimating the environmental conditions fostering a species’ persistence and proliferation in an area (the species’ ecological niche) and, consequently, its geographical distribution. This paper presents a full-automatic workflow to estimate species’ distributions through statistical and machine learning models. It embeds four ecological niche models with complementary approaches, i.e. Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps. It automatically estimates the optimal model parametrisations and decision thresholds to distinguish between suitable- and unsuitable-habitat locations and combines the models within one ensemble model. Finally, it combines several ensemble models to produce a species richness map (biodiversity index). The software is open-source, Open Science compliant, and available as a Web Processing Service-standardised cloud computing service that enhances efficiency, integrability, cross-domain reusability, and experimental reproduction and repetition. We first assess workflow stability and sensitivity and then demonstrate effectiveness by producing a biodiversity index for the Mediterranean based on $$\sim $$ ∼ 1500 species data. Moreover, we predict the spread of the invasive Siganus rivulatus in the Mediterranean and its current and future overlap with the native Sarpa salpa under different climate change scenarios.

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D-VRE: From a Jupyter-enabled Private Research Environment to Decentralized Collaborative Research Ecosystem

Wang,

Tripathi,

Farshidi

et al. 2024

Blockchain: Research and Applications

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Virtual research environments co‐creation: The D4Science experience

Cited by 8 publications

References 47 publications

Filling Gaps in Trawl Surveys at Sea through Spatiotemporal and Environmental Modelling

Filling Gaps in Trawl Surveys at Sea through Spatiotemporal and Environmental Modelling

An open science automatic workflow for multi-model species distribution estimation

D-VRE: From a Jupyter-enabled Private Research Environment to Decentralized Collaborative Research Ecosystem

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