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Connecting the CCA knowledge landscape through weADAPT 2. Connecting and visualising the CCA knowledge landscape through the Climate Connectivity Hub ## 1st sub-case study: Connecting the CCA knowledge landscape through weADAPT ### Vision & Ambitions The goals and tasks of this case study look as follows: 1) Improve weADAPT to support communities, planners, practitioners, students, researchers and policymakers in accessing and sharing climate-resilient adaptation information and strategies that inform local, national, regional, and global policy processes. 2) Break down silos and avoid redundancy, replication, and wasted resources while promoting collaboration, dialogue, and learning, by harnessing the wealth of existing knowledge across various portals, platforms, and publications, and providing guidance on how to connect this knowledge together. 3) Integrate weADAPT in EOSC. Ensure that climate adaptation knowledge is accessible within EOSC, increasing its usability for a broad range of stakeholders. ### Description Users cannot quickly and easily find information, which impedes communication, coordination and collaboration. Knowledge is increasingly online, but fragmented, unstructured and siloed so users find it hard to search and discover information that they may not know exists. weADAPT is a global online knowledge-sharing platform designed to improve access to climate adaptation information, widely used amongst researchers, practitioners and decision-makers. Knowledge on the platform is organized around +25 themes and networks, and the platform also uses keywords to enhance discoverability. This project aims to make climate adaptation knowledge more accessible, discoverable, and usable by developing improved taxonomies and knowledge graphs. We hope it will also improve communication and collaboration between different stakeholders. The project will involve extracting, curating and making climate and non-climate information available. Key sources include climate data, socio-economic scenarios, case studies, adaptation measures, among others) from popular knowledge portals, platforms and publications, all of which are necessary to support the development and implementation of adaptation planning. ### FAIR2Adapt Contribution As part of this F2A case study, we will use AI to analyse the harvested content and automatically construct taxonomies and knowledge graphs.These tool will: - Ensure resources are well-described to be more discoverable on weADAPT; - Facilitatie more intuitive and accessible navigation of climate knowledge on the platform; - Support the development of a a glossary with clearly defined terms. ### Lead Partner SEI ### External Stakeholders involved in CCA Knowledge producers, decision- and policy-makers, knowledge brokers, governmental agencies, citizens. ## 2nd sub-case study: Connecting and visualising the CCA knowledge landscape through the Climate Connectivity Hub ### Vision & Ambitions The goals and tasks of this case study look as follows: 1) Improve the Connectivity Hub to support communities, planners, practitioners, students, researchers and policymakers in accessing and sharing climate-resilient adaptation strategies that inform local, national, regional, and global policy processes. 2) Break down silos and avoid redundancy, replication, and wasted resources while promoting collaboration, dialogue, and learning, by harnessing the wealth of existing knowledge across various portals, platforms, and publications, and providing guidance on how to connect this knowledge together. 3) Integrate the Connectivity Hub in EOSC. ### Description #### Automated adaptation content harvesting and visualisation (Pan-European) Users cannot quickly and easily find information, which impedes communication, coordination and collaboration. Knowledge is increasingly online, but fragmented, unstructured and siloed so users find it hard to search and discover information that they may not know exists. Creating improved taxonomies or knowledge graphs for the area of climate resilience will enhance search capabilities, improve communication and collaboration, and promote a shared understanding. Extract, curate and make available climate and non-climate information (e.g., climate data, socio-economic scenarios, case studies, adaptation measures, among others) from popular knowledge portals, platforms and publications, specifically necessary to support the development and implementation of adaptation planning. ### FAIR2Adapt Contribution Use of AI to analyse the harvested content and automatically construct taxonomies and knowledge graphs to both visualise the content on the Connectivity Hub and ensure resources are more discoverable. 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https://w3id.org/ro-id/enrichment/fef078a6-5999-4731-b2fb-6f2ce1d17db5 https://w3id.org/ro-id/2a889d82-5b8c-48c2-820b-e8da468d64bf/ http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/wf4ever/ro#ResearchObject https://w3id.org/ro-id/2a889d82-5b8c-48c2-820b-e8da468d64bf/ http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/wf4ever/roevo#LiveRO https://w3id.org/ro-id/2a889d82-5b8c-48c2-820b-e8da468d64bf/ http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/Dataset https://w3id.org/ro-id/2a889d82-5b8c-48c2-820b-e8da468d64bf/ https://www.w3.org/ns/iana/link-relations/relation#cite-as Fouilloux, Anne, Anne Fouilloux, and Barbara Magagna. "Enriching weADAPT and the Climate Connectivity Hub." ROHub. 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http://www.w3.org/1999/02/22-rdf-syntax-ns#type https://w3id.org/ro/terms/earth-science#IPTC https://w3id.org/ro-id/enrichment/1a2154e6-f051-4d53-95d0-385739f60c6c https://w3id.org/ro/terms/earth-science#path Economy, business and finance/Business information/Business finance/Shareholder https://w3id.org/ro-id/enrichment/20e787a2-948e-41fe-a9ed-68320a53d448 http://schema.org/name knowledge graph https://w3id.org/ro-id/enrichment/20e787a2-948e-41fe-a9ed-68320a53d448 http://www.w3.org/1999/02/22-rdf-syntax-ns#type https://w3id.org/ro/terms/earth-science#Phrase https://w3id.org/ro-id/enrichment/20e787a2-948e-41fe-a9ed-68320a53d448 https://w3id.org/ro/terms/earth-science#normScore 14.555256064690026 https://w3id.org/ro-id/enrichment/20e787a2-948e-41fe-a9ed-68320a53d448 https://w3id.org/ro/terms/earth-science#score 5.4 https://w3id.org/ro-id/enrichment/28af7903-9fca-4b1d-8ebe-17ae684caeef http://schema.org/name artificial intelligence 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https://w3id.org/ro-id/enrichment/657c24df-30af-4109-a399-59fe1702d449 http://schema.org/name earth sciences https://w3id.org/ro-id/enrichment/657c24df-30af-4109-a399-59fe1702d449 http://www.w3.org/1999/02/22-rdf-syntax-ns#type https://w3id.org/ro/terms/earth-science#FieldOfResearch https://w3id.org/ro-id/enrichment/657c24df-30af-4109-a399-59fe1702d449 https://w3id.org/ro/terms/earth-science#normScore 100.0 https://w3id.org/ro-id/enrichment/657c24df-30af-4109-a399-59fe1702d449 https://w3id.org/ro/terms/earth-science#score 0.9551669955253601 https://w3id.org/ro-id/enrichment/6931c5f2-ee3a-4ebd-961c-458393680985 http://schema.org/name geology https://w3id.org/ro-id/enrichment/6931c5f2-ee3a-4ebd-961c-458393680985 http://www.w3.org/1999/02/22-rdf-syntax-ns#type https://w3id.org/ro/terms/earth-science#FieldOfResearch https://w3id.org/ro-id/enrichment/6931c5f2-ee3a-4ebd-961c-458393680985 https://w3id.org/ro/terms/earth-science#normScore 100.0 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