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- Kattge, Jens, et al.
(author)
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TRY plant trait database - enhanced coverage and open access
- 2020
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In: Global Change Biology. - : Wiley-Blackwell. - 1354-1013 .- 1365-2486. ; 26:1, s. 119-188
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Journal article (peer-reviewed)abstract
- Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.
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- Bennaceur, A., et al.
(author)
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Mechanisms for leveraging models at runtime in self-adaptive software
- 2014
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In: Lecture Notes in Computer Science: Dagstuhl Seminar 11481 on Models@run.time; ; 27 November 2011 through 2 December 2011. - Cham : Springer. - 9783319089140
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Conference paper (peer-reviewed)abstract
- Modern software systems are often required to adapt their behavior at runtime in order to maintain or enhance their utility in dynamic environments. Models at runtime research aims to provide suitable abstractions, techniques, and tools to manage the complexity of adapting software systems at runtime. In this chapter, we discuss challenges associated with developing mechanisms that leverage models at runtime to support runtime software adaptation. Specifically, we discuss challenges associated with developing effective mechanisms for supervising running systems, reasoning about and planning adaptations, maintaining consistency among multiple runtime models, and maintaining fidelity of runtime models with respect to the running system and its environment. We discuss related problems and state-of-the-art mechanisms, and identify open research challenges.
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| 7. |
- Broz, J., et al.
(author)
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Designing an evaluation methodology for the Living Labs of the ELABORATOR project
- 2024
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In: 2024 SMART CITIES SYMPOSIUM PRAGUE, SCSP. - : IEEE. - 9798350360967 - 9798350360950
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Conference paper (peer-reviewed)abstract
- Urban mobility challenges since the COVID-19 pandemic crisis have gained added attention, with a focus on inclusivity of vulnerable citizens, safety and affordability. The EU-funded ELABORATOR project started in June 2023, aiming to deliver co-designed interventions in the urban fabric of 12 European cities. This paper introduces the project, focusing on the co-designed procedure of evaluation methodology comprising the evaluation plan and providing unified metrics suitable for knowledge transfer with outputs to measure and assess the impact of the co-designed city interventions.
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| 8. |
- Bucchiarone, A., et al.
(author)
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What Is the Future of Modeling?
- 2021
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In: IEEE Software. - : IEEE Computer Society. - 0740-7459 .- 1937-4194. ; 38:2, s. 119-127
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Journal article (peer-reviewed)abstract
- Modeling languages and frameworks have been the key technology for advancing model-driven engineering (MDE) methods and tools. Many industrial and research tools have been realized and are used across many domains. Hence, we think it is the right time to define what should be the future of modeling technologies, especially the requirements for the next generation of modeling frameworks and languages.
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