Scientists, stakeholders and decision makers face trade-offs between adopting simple or complex approaches when modeling ecosystem services (ES). Complex approaches may be time- and data-intensive, making them more challenging to implement and difficult to scale, but can produce more accurate and locally specific results. In contrast, simple approaches allow for faster assessments but may sacrifice accuracy and credibility. The ARtificial Intelligence for Ecosystem Services (ARIES) modeling platform has endeavored to provide a spectrum of simple to complex ES models that are readily accessible to a broad range of users
This infographic video has been produced by BC3- Basque Centre for Climate Change to graphically illustrate an introduction TO k.LAB and Integrated Modelling for non-technical audiences. We present the Philosophy embracing the FAIR principles and the Technology behind a cutting edge modelling environment for Knowledge Integration. K.LAB is a web accessible software that uses the meaning of information, its semantics, to integrate knowledge through Artificial Intelligence and builds computational solutions to environmental, policy and sustainability problems. Through this modelling environment, researchers can contribute knowledge from their discipline and seamlessly connect it to the knowledge generated by others through the use of shared semantics. The result is a powerful tool where users can enter their queries and k.LAB is able to select the most appropriate knowledge available building for the first time new knowledge from the integration of the existing one.
Financiado por el Ministerio de Ciencia Innovación y Universidades y la Agencia Estatal de Investigación,
a través de la Convocatoria: Maria de Maeztu 2017 (BOE 21/10/2017) siendo la referencia de nuestro expediente: MDM-2017-0714
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