{ "edges": [ { "evidence": { "dataset": "german-ai-innovation-path", "field": "cordis_project_code_and_participant_pic", "sourceId": "cordis:101003876:participant:998692310", "url": "https://cordis.europa.eu/project/id/101003876" }, "evidenceClass": "exact", "from": "actor:cordis-pic:998692310", "id": "claim:funding:101003876:998692310", "relation": "participates_in", "to": "cordis:101003876" }, { "evidence": { "dataset": "german-ai-innovation-path", "field": "cordis_project_code_and_participant_pic", "sourceId": "cordis:101003876:participant:999507401", "url": "https://cordis.europa.eu/project/id/101003876" }, "evidenceClass": "exact", "from": "actor:cordis-pic:999507401", "id": "claim:funding:101003876:999507401", "relation": "participates_in", "to": "cordis:101003876" }, { "evidence": { "dataset": "german-ai-innovation-path", "field": "cordis_project_code_and_participant_pic", "sourceId": "cordis:101003876:participant:999840111", "url": "https://cordis.europa.eu/project/id/101003876" }, "evidenceClass": "exact", "from": "actor:cordis-pic:999840111", "id": "claim:funding:101003876:999840111", "relation": "participates_in", "to": "cordis:101003876" }, { "evidence": { "dataset": "german-ai-research-to-transfer-index", "field": "cordis_project_code", "sourceId": "101003876:output:0188a2aea60b10c908a414eb", "url": "https://cordis.europa.eu/project/id/101003876" }, "evidenceClass": "exact", "from": "cordis:101003876", "id": "edge:project-output:101003876:0188a2aea60b10c908a414eb", "relation": "has_observed_output", "to": "output:0188a2aea60b10c908a414eb" }, { "evidence": { "dataset": "german-ai-research-to-transfer-index", "field": "cordis_project_code", "sourceId": "101003876:output:1302f6fc304f5394f7f11688", "url": "https://cordis.europa.eu/project/id/101003876" }, "evidenceClass": "exact", "from": "cordis:101003876", "id": "edge:project-output:101003876:1302f6fc304f5394f7f11688", "relation": "has_observed_output", "to": "output:1302f6fc304f5394f7f11688" }, { "evidence": { "dataset": "german-ai-research-to-transfer-index", "field": "cordis_project_code", "sourceId": "101003876:output:56f75eb36ff62f02994a1226", "url": "https://cordis.europa.eu/project/id/101003876" }, "evidenceClass": "exact", "from": "cordis:101003876", "id": "edge:project-output:101003876:56f75eb36ff62f02994a1226", "relation": "has_observed_output", "to": "output:56f75eb36ff62f02994a1226" }, { "evidence": { "dataset": "german-ai-research-to-transfer-index", "field": "cordis_project_code", "sourceId": "101003876:output:bb2ffa92cdbfca60ef25c251", "url": "https://cordis.europa.eu/project/id/101003876" }, "evidenceClass": "exact", "from": "cordis:101003876", "id": "edge:project-output:101003876:bb2ffa92cdbfca60ef25c251", "relation": "has_observed_output", "to": "output:bb2ffa92cdbfca60ef25c251" }, { "evidence": { "dataset": "german-ai-research-to-transfer-index", "field": "cordis_project_code", "sourceId": "101003876:output:d7b9bf4dcd6359143adee006", "url": "https://cordis.europa.eu/project/id/101003876" }, "evidenceClass": "exact", "from": "cordis:101003876", "id": "edge:project-output:101003876:d7b9bf4dcd6359143adee006", "relation": "has_observed_output", "to": "output:d7b9bf4dcd6359143adee006" }, { "evidence": { "dataset": "german-ai-research-to-transfer-index", "field": "cordis_project_code", "sourceId": "101003876:output:e0dbf544dbcd081b1618e8c0", "url": "https://cordis.europa.eu/project/id/101003876" }, "evidenceClass": "exact", "from": "cordis:101003876", "id": "edge:project-output:101003876:e0dbf544dbcd081b1618e8c0", "relation": "has_observed_output", "to": "output:e0dbf544dbcd081b1618e8c0" } ], "entity": { "attributes": { "acronym": "CLINT", "code": "101003876", "endDate": "2025-10-31", "frameworkProgramme": "H2020", "funding": { "currency": "EUR", "germanNetEuContributionCents": "135175751", "participantEdgeCount": 3, "status": "linked" }, "knowledgeFlow": { "citingCountryCount": 10, "citingInstitutionCount": 25, "distinctCitingWorkCount": 8, "firstObservedCitationDate": "2025-10-25", "lastObservedCitationDate": "2026-07-14", "observedCitationEdgeCount": 8, "status": "bounded_observation" }, "research": { "citationDataOutputCount": 106, "citedOutputCount": 65, "firstPublicationDate": "2021-10-05", "lastPublicationDate": "2026-03-28", "openAccessOutputCount": 104, "outputCount": 106, "status": "linked", "totalCitations": 1403, "typeCounts": { "dataset": 6, "other": 3, "publication": 97, "software": 0 } }, "startDate": "2021-07-01" }, "description": "CLImate INTelligence: Extreme events detection, attribution and adaptation design using machine learning", "edgeCount": 9, "id": "cordis:101003876", "indexable": true, "label": "CLINT · CLImate INTelligence: Extreme events detection, attribution and adaptation design using machine learning", "links": [ { "kind": "official_source", "label": "CORDIS", "url": "https://cordis.europa.eu/project/id/101003876" }, { "kind": "local_dossier", "label": "KI-Innovationspfad", "url": "/tools/ki-innovationspfad/?project=101003876" } ], "slug": "project-cordis-101003876-9e7244fe", "sourceRefs": [ { "dataset": "german-ai-innovation-path", "field": "cordis_project_id", "sourceId": "cordis:101003876", "url": "https://cordis.europa.eu/project/id/101003876" } ], "type": "project" }, "fingerprint": "sha256:75ca1414d73391fba603fbaed3b260d8696d72c421cf5d23266736154c0de828", "generatedAt": "2026-07-21T08:59:15.288Z", "neighbors": [ { "attributes": { "aliases": [ "DEUTSCHES KLIMARECHENZENTRUM GMBH" ], "identifiers": [ { "scheme": "cordis-pic", "value": "998692310" } ], "identityBasis": "official_identifier", "kind": "organization", "lanes": [ "funding" ], "roles": [ "funding_participant" ] }, "edgeCount": 3, "id": "actor:cordis-pic:998692310", "indexable": true, "label": "DEUTSCHES KLIMARECHENZENTRUM GMBH", "links": [ { "kind": "evidence_source", "label": "german-ai-innovation-path", "url": "https://cordis.europa.eu/project/id/101003876" }, { "kind": "evidence_source", "label": "german-ai-innovation-path", "url": "https://cordis.europa.eu/project/id/101081383" }, { "kind": "evidence_source", "label": "german-ai-innovation-path", "url": "https://cordis.europa.eu/project/id/642963" } ], "slug": "actor-cordis-pic-998692310-0012afa7", "sourceRefs": [ { "dataset": "german-ai-innovation-path", "field": "official_identifier", "recordCount": 3, "sourceId": "cordis:101003876:participant:998692310", "url": "https://cordis.europa.eu/project/id/101003876" } ], "type": "actor" }, { "attributes": { "aliases": [ "HELMHOLTZ-ZENTRUM HEREON GMBH" ], "identifiers": [ { "scheme": "cordis-pic", "value": "999507401" } ], "identityBasis": "official_identifier", "kind": "organization", "lanes": [ "funding" ], "roles": [ "funding_participant" ] }, "edgeCount": 6, "id": "actor:cordis-pic:999507401", "indexable": true, "label": "HELMHOLTZ-ZENTRUM HEREON GMBH", "links": [ { "kind": "evidence_source", "label": "german-ai-innovation-path", "url": "https://cordis.europa.eu/project/id/101003876" }, { "kind": "evidence_source", "label": "german-ai-innovation-path", "url": "https://cordis.europa.eu/project/id/101073821" }, { "kind": "evidence_source", "label": "german-ai-innovation-path", "url": "https://cordis.europa.eu/project/id/101093293" }, { "kind": "evidence_source", "label": "german-ai-innovation-path", "url": "https://cordis.europa.eu/project/id/101112823" }, { "kind": "evidence_source", "label": "german-ai-innovation-path", "url": "https://cordis.europa.eu/project/id/101137787" }, { "kind": "evidence_source", "label": "german-ai-innovation-path", "url": "https://cordis.europa.eu/project/id/101167207" } ], "slug": "actor-cordis-pic-999507401-63eec1f1", "sourceRefs": [ { "dataset": "german-ai-innovation-path", "field": "official_identifier", "recordCount": 6, "sourceId": "cordis:101003876:participant:999507401", "url": "https://cordis.europa.eu/project/id/101003876" } ], "type": "actor" }, { "attributes": { "aliases": [ "JUSTUS-LIEBIG-UNIVERSITAET GIESSEN" ], "identifiers": [ { "scheme": "cordis-pic", "value": "999840111" } ], "identityBasis": "official_identifier", "kind": "organization", "lanes": [ "funding" ], "roles": [ "funding_participant" ] }, "edgeCount": 10, "id": "actor:cordis-pic:999840111", "indexable": true, "label": "JUSTUS-LIEBIG-UNIVERSITAET GIESSEN", "links": [ { "kind": "evidence_source", "label": "german-ai-innovation-path", "url": "https://cordis.europa.eu/project/id/101003876" }, { "kind": "evidence_source", "label": "german-ai-innovation-path", "url": "https://cordis.europa.eu/project/id/101039712" }, { "kind": "evidence_source", "label": "german-ai-innovation-path", "url": "https://cordis.europa.eu/project/id/101076057" }, { "kind": "evidence_source", "label": "german-ai-innovation-path", "url": "https://cordis.europa.eu/project/id/101117441" }, { "kind": "evidence_source", "label": "german-ai-innovation-path", "url": "https://cordis.europa.eu/project/id/101136462" }, { "kind": "evidence_source", "label": "german-ai-innovation-path", "url": "https://cordis.europa.eu/project/id/101166700" }, { "kind": "evidence_source", "label": "german-ai-innovation-path", "url": "https://cordis.europa.eu/project/id/682859" }, { "kind": "evidence_source", "label": "german-ai-innovation-path", "url": "https://cordis.europa.eu/project/id/765121" }, { "kind": "evidence_source", "label": "german-ai-innovation-path", "url": "https://cordis.europa.eu/project/id/793660" } ], "slug": "actor-cordis-pic-999840111-8e923b95", "sourceRefs": [ { "dataset": "german-ai-innovation-path", "field": "official_identifier", "recordCount": 9, "sourceId": "cordis:101003876:participant:999840111", "url": "https://cordis.europa.eu/project/id/101003876" } ], "type": "actor" }, { "attributes": { "citationCount": 1, "isOpenAccess": true, "openAireId": "doi_dedup___::6651cf0e4ec40591f3cb00c23ff0ba11", "outputType": "publication", "persistentIdentifiers": [ { "scheme": "doi", "value": "10.5194/nhess-26-955-2026" }, { "scheme": "handle", "value": "11585/1051036" }, { "scheme": "handle", "value": "1871.1/cdbac548-13ee-4509-a9af-9f179e0cc3bd" } ], "publicationDate": "2026-02-27" }, "edgeCount": 1, "id": "output:0188a2aea60b10c908a414eb", "indexable": true, "label": "Lessons learned in institutional preparedness and response during the 2022 European drought", "links": [ { "kind": "official_source", "label": "OpenAIRE", "url": "https://api.openaire.eu/graph/v3/research-products/doi_dedup___%3A%3A6651cf0e4ec40591f3cb00c23ff0ba11" }, { "kind": "persistent_identifier", "label": "DOI 10.5194/nhess-26-955-2026", "url": "https://doi.org/10.5194/nhess-26-955-2026" } ], "slug": "output-0188a2aea60b10c908a414eb-1fa40662", "sourceRefs": [ { "dataset": "german-ai-research-to-transfer-index", "field": "openaire_research_product_id", "sourceId": "doi_dedup___::6651cf0e4ec40591f3cb00c23ff0ba11", "url": "https://api.openaire.eu/graph/v3/research-products/doi_dedup___%3A%3A6651cf0e4ec40591f3cb00c23ff0ba11" } ], "type": "output" }, { "attributes": { "citationCount": 0, "isOpenAccess": false, "openAireId": "doi_dedup___::4d08aae54305aeb7b3b439aec576dfc8", "outputType": "publication", "persistentIdentifiers": [ { "scheme": "doi", "value": "10.1175/bams-d-25-0049.1" } ], "publicationDate": "2025-12-01" }, "edgeCount": 1, "id": "output:1302f6fc304f5394f7f11688", "indexable": true, "label": "A Multimethod Attribution Analysis of Spain’s 2024 Extreme Precipitation Event", "links": [ { "kind": "official_source", "label": "OpenAIRE", "url": "https://api.openaire.eu/graph/v3/research-products/doi_dedup___%3A%3A4d08aae54305aeb7b3b439aec576dfc8" }, { "kind": "persistent_identifier", "label": "DOI 10.1175/bams-d-25-0049.1", "url": "https://doi.org/10.1175/bams-d-25-0049.1" } ], "slug": "output-1302f6fc304f5394f7f11688-bdfb3975", "sourceRefs": [ { "dataset": "german-ai-research-to-transfer-index", "field": "openaire_research_product_id", "sourceId": "doi_dedup___::4d08aae54305aeb7b3b439aec576dfc8", "url": "https://api.openaire.eu/graph/v3/research-products/doi_dedup___%3A%3A4d08aae54305aeb7b3b439aec576dfc8" } ], "type": "output" }, { "attributes": { "citationCount": 0, "isOpenAccess": true, "openAireId": "doi_________::25ea7b3effecd8b4bffd78fbc2d6916a", "outputType": "publication", "persistentIdentifiers": [ { "scheme": "doi", "value": "10.1029/2025wr040147" } ], "publicationDate": "2025-12-01" }, "edgeCount": 1, "id": "output:56f75eb36ff62f02994a1226", "indexable": true, "label": "Parametric Insurance for Drought and Market Impacts Mitigation in the Hydropower Sector", "links": [ { "kind": "official_source", "label": "OpenAIRE", "url": "https://api.openaire.eu/graph/v3/research-products/doi_________%3A%3A25ea7b3effecd8b4bffd78fbc2d6916a" }, { "kind": "persistent_identifier", "label": "DOI 10.1029/2025wr040147", "url": "https://doi.org/10.1029/2025wr040147" } ], "slug": "output-56f75eb36ff62f02994a1226-bada6d50", "sourceRefs": [ { "dataset": "german-ai-research-to-transfer-index", "field": "openaire_research_product_id", "sourceId": "doi_________::25ea7b3effecd8b4bffd78fbc2d6916a", "url": "https://api.openaire.eu/graph/v3/research-products/doi_________%3A%3A25ea7b3effecd8b4bffd78fbc2d6916a" } ], "type": "output" }, { "attributes": { "citationCount": 0, "isOpenAccess": true, "openAireId": "doi_dedup___::eccfdea8dcb3cf66118191cd66c4ecf9", "outputType": "publication", "persistentIdentifiers": [ { "scheme": "doi", "value": "10.5194/egusphere-2026-43" } ], "publicationDate": "2026-02-13" }, "edgeCount": 1, "id": "output:bb2ffa92cdbfca60ef25c251", "indexable": true, "label": "Improving low and high flow simulations at once: An enhanced metric for hydrological model calibration", "links": [ { "kind": "official_source", "label": "OpenAIRE", "url": "https://api.openaire.eu/graph/v3/research-products/doi_dedup___%3A%3Aeccfdea8dcb3cf66118191cd66c4ecf9" }, { "kind": "persistent_identifier", "label": "DOI 10.5194/egusphere-2026-43", "url": "https://doi.org/10.5194/egusphere-2026-43" } ], "slug": "output-bb2ffa92cdbfca60ef25c251-2c299c2b", "sourceRefs": [ { "dataset": "german-ai-research-to-transfer-index", "field": "openaire_research_product_id", "sourceId": "doi_dedup___::eccfdea8dcb3cf66118191cd66c4ecf9", "url": "https://api.openaire.eu/graph/v3/research-products/doi_dedup___%3A%3Aeccfdea8dcb3cf66118191cd66c4ecf9" } ], "type": "output" }, { "attributes": { "citationCount": 0, "isOpenAccess": true, "openAireId": "doi_________::078e04e62461f96e987d1bdaeb34cf55", "outputType": "publication", "persistentIdentifiers": [ { "scheme": "doi", "value": "10.1021/acs.est.5c15991" } ], "publicationDate": "2026-03-28" }, "edgeCount": 1, "id": "output:d7b9bf4dcd6359143adee006", "indexable": true, "label": "Diving into AI? Exploring the Potential for AI to Tackle Complex Water Quality Challenges", "links": [ { "kind": "official_source", "label": "OpenAIRE", "url": "https://api.openaire.eu/graph/v3/research-products/doi_________%3A%3A078e04e62461f96e987d1bdaeb34cf55" }, { "kind": "persistent_identifier", "label": "DOI 10.1021/acs.est.5c15991", "url": "https://doi.org/10.1021/acs.est.5c15991" } ], "slug": "output-d7b9bf4dcd6359143adee006-a2eb5354", "sourceRefs": [ { "dataset": "german-ai-research-to-transfer-index", "field": "openaire_research_product_id", "sourceId": "doi_________::078e04e62461f96e987d1bdaeb34cf55", "url": "https://api.openaire.eu/graph/v3/research-products/doi_________%3A%3A078e04e62461f96e987d1bdaeb34cf55" } ], "type": "output" }, { "attributes": { "citationCount": 2, "isOpenAccess": true, "openAireId": "doi_dedup___::502f0ac171218621b181957ccfeb325b", "outputType": "publication", "persistentIdentifiers": [ { "scheme": "doi", "value": "10.1016/j.egyai.2025.100596" }, { "scheme": "handle", "value": "10396/33334" } ], "publicationDate": "2025-12-01" }, "edgeCount": 1, "id": "output:e0dbf544dbcd081b1618e8c0", "indexable": true, "label": "Enhancing wind speed prediction in wind farms through ordinal classification", "links": [ { "kind": "official_source", "label": "OpenAIRE", "url": "https://api.openaire.eu/graph/v3/research-products/doi_dedup___%3A%3A502f0ac171218621b181957ccfeb325b" }, { "kind": "persistent_identifier", "label": "DOI 10.1016/j.egyai.2025.100596", "url": "https://doi.org/10.1016/j.egyai.2025.100596" } ], "slug": "output-e0dbf544dbcd081b1618e8c0-316a3c8d", "sourceRefs": [ { "dataset": "german-ai-research-to-transfer-index", "field": "openaire_research_product_id", "sourceId": "doi_dedup___::502f0ac171218621b181957ccfeb325b", "url": "https://api.openaire.eu/graph/v3/research-products/doi_dedup___%3A%3A502f0ac171218621b181957ccfeb325b" } ], "type": "output" } ], "schemaVersion": "ki-evidenznetz.entity.v1" }