{ "edges": [ { "evidence": { "dataset": "german-ai-innovation-path", "field": "cordis_project_code_and_participant_pic", "sourceId": "cordis:101136607:participant:999580054", "url": "https://cordis.europa.eu/project/id/101136607" }, "evidenceClass": "exact", "from": "actor:cordis-pic:999580054", "id": "claim:funding:101136607:999580054", "relation": "participates_in", "to": "cordis:101136607" }, { "evidence": { "dataset": "german-ai-research-to-transfer-index", "field": "cordis_project_code", "sourceId": "101136607:output:09841d08c893a1cf962799bc", "url": "https://cordis.europa.eu/project/id/101136607" }, "evidenceClass": "exact", "from": "cordis:101136607", "id": "edge:project-output:101136607:09841d08c893a1cf962799bc", "relation": "has_observed_output", "to": "output:09841d08c893a1cf962799bc" }, { "evidence": { "dataset": "german-ai-research-to-transfer-index", "field": "cordis_project_code", "sourceId": "101136607:output:1b61a75ce3498cb33d6fa5ee", "url": "https://cordis.europa.eu/project/id/101136607" }, "evidenceClass": "exact", "from": "cordis:101136607", "id": "edge:project-output:101136607:1b61a75ce3498cb33d6fa5ee", "relation": "has_observed_output", "to": "output:1b61a75ce3498cb33d6fa5ee" }, { "evidence": { "dataset": "german-ai-research-to-transfer-index", "field": "cordis_project_code", "sourceId": "101136607:output:2cd9dca83b4a756150dc0704", "url": "https://cordis.europa.eu/project/id/101136607" }, "evidenceClass": "exact", "from": "cordis:101136607", "id": "edge:project-output:101136607:2cd9dca83b4a756150dc0704", "relation": "has_observed_output", "to": "output:2cd9dca83b4a756150dc0704" }, { "evidence": { "dataset": "german-ai-research-to-transfer-index", "field": "cordis_project_code", "sourceId": "101136607:output:39eac19f35f342aa603261b9", "url": "https://cordis.europa.eu/project/id/101136607" }, "evidenceClass": "exact", "from": "cordis:101136607", "id": "edge:project-output:101136607:39eac19f35f342aa603261b9", "relation": "has_observed_output", "to": "output:39eac19f35f342aa603261b9" }, { "evidence": { "dataset": "german-ai-research-to-transfer-index", "field": "cordis_project_code", "sourceId": "101136607:output:4160821793507467711d5807", "url": "https://cordis.europa.eu/project/id/101136607" }, "evidenceClass": "exact", "from": "cordis:101136607", "id": "edge:project-output:101136607:4160821793507467711d5807", "relation": "has_observed_output", "to": "output:4160821793507467711d5807" }, { "evidence": { "dataset": "german-ai-research-to-transfer-index", "field": "cordis_project_code", "sourceId": "101136607:output:5cb3d9df5b4e3c5beb0896b4", "url": "https://cordis.europa.eu/project/id/101136607" }, "evidenceClass": "exact", "from": "cordis:101136607", "id": "edge:project-output:101136607:5cb3d9df5b4e3c5beb0896b4", "relation": "has_observed_output", "to": "output:5cb3d9df5b4e3c5beb0896b4" }, { "evidence": { "dataset": "german-ai-research-to-transfer-index", "field": "cordis_project_code", "sourceId": "101136607:output:66871cef3d7f6e6e5fa3783f", "url": "https://cordis.europa.eu/project/id/101136607" }, "evidenceClass": "exact", "from": "cordis:101136607", "id": "edge:project-output:101136607:66871cef3d7f6e6e5fa3783f", "relation": "has_observed_output", "to": "output:66871cef3d7f6e6e5fa3783f" }, { "evidence": { "dataset": "german-ai-research-to-transfer-index", "field": "cordis_project_code", "sourceId": "101136607:output:69307741698e58481081cc1f", "url": "https://cordis.europa.eu/project/id/101136607" }, "evidenceClass": "exact", "from": "cordis:101136607", "id": "edge:project-output:101136607:69307741698e58481081cc1f", "relation": "has_observed_output", "to": "output:69307741698e58481081cc1f" }, { "evidence": { "dataset": "german-ai-research-to-transfer-index", "field": "cordis_project_code", "sourceId": "101136607:output:6f5ff64f05516fe5d06cfd2a", "url": "https://cordis.europa.eu/project/id/101136607" }, "evidenceClass": "exact", "from": "cordis:101136607", "id": "edge:project-output:101136607:6f5ff64f05516fe5d06cfd2a", "relation": "has_observed_output", "to": "output:6f5ff64f05516fe5d06cfd2a" }, { "evidence": { "dataset": "german-ai-research-to-transfer-index", "field": "cordis_project_code", "sourceId": "101136607:output:767fe2d490e4551f0af8b5ab", "url": "https://cordis.europa.eu/project/id/101136607" }, "evidenceClass": "exact", "from": "cordis:101136607", "id": "edge:project-output:101136607:767fe2d490e4551f0af8b5ab", "relation": "has_observed_output", "to": "output:767fe2d490e4551f0af8b5ab" }, { "evidence": { "dataset": "german-ai-research-to-transfer-index", "field": "cordis_project_code", "sourceId": "101136607:output:77ae5a53fe64f86881d73003", "url": "https://cordis.europa.eu/project/id/101136607" }, "evidenceClass": "exact", "from": "cordis:101136607", "id": "edge:project-output:101136607:77ae5a53fe64f86881d73003", "relation": "has_observed_output", "to": "output:77ae5a53fe64f86881d73003" }, { "evidence": { "dataset": "german-ai-research-to-transfer-index", "field": "cordis_project_code", "sourceId": "101136607:output:8706e31e1ded545295ceefbd", "url": "https://cordis.europa.eu/project/id/101136607" }, "evidenceClass": "exact", "from": "cordis:101136607", "id": "edge:project-output:101136607:8706e31e1ded545295ceefbd", "relation": "has_observed_output", "to": "output:8706e31e1ded545295ceefbd" }, { "evidence": { "dataset": "german-ai-research-to-transfer-index", "field": "cordis_project_code", "sourceId": "101136607:output:8b6b631fe234b5859346ed3e", "url": "https://cordis.europa.eu/project/id/101136607" }, "evidenceClass": "exact", "from": "cordis:101136607", "id": "edge:project-output:101136607:8b6b631fe234b5859346ed3e", "relation": "has_observed_output", "to": "output:8b6b631fe234b5859346ed3e" }, { "evidence": { "dataset": "german-ai-research-to-transfer-index", "field": "cordis_project_code", "sourceId": "101136607:output:8e74e50590e788a90ce9945c", "url": "https://cordis.europa.eu/project/id/101136607" }, "evidenceClass": "exact", "from": "cordis:101136607", "id": "edge:project-output:101136607:8e74e50590e788a90ce9945c", "relation": "has_observed_output", "to": "output:8e74e50590e788a90ce9945c" }, { "evidence": { "dataset": "german-ai-research-to-transfer-index", "field": "cordis_project_code", "sourceId": "101136607:output:ad2f4017a5dc8e3158075d04", "url": "https://cordis.europa.eu/project/id/101136607" }, "evidenceClass": "exact", "from": "cordis:101136607", "id": "edge:project-output:101136607:ad2f4017a5dc8e3158075d04", "relation": "has_observed_output", "to": "output:ad2f4017a5dc8e3158075d04" }, { "evidence": { "dataset": "german-ai-research-to-transfer-index", "field": "cordis_project_code", "sourceId": "101136607:output:bc526887f22c750cf6f7b726", "url": "https://cordis.europa.eu/project/id/101136607" }, "evidenceClass": "exact", "from": "cordis:101136607", "id": "edge:project-output:101136607:bc526887f22c750cf6f7b726", "relation": "has_observed_output", "to": "output:bc526887f22c750cf6f7b726" }, { "evidence": { "dataset": "german-ai-research-to-transfer-index", "field": "cordis_project_code", "sourceId": "101136607:output:c51098efcd0594d4245602e7", "url": "https://cordis.europa.eu/project/id/101136607" }, "evidenceClass": "exact", "from": "cordis:101136607", "id": "edge:project-output:101136607:c51098efcd0594d4245602e7", "relation": "has_observed_output", "to": "output:c51098efcd0594d4245602e7" }, { "evidence": { "dataset": "german-ai-research-to-transfer-index", "field": "cordis_project_code", "sourceId": "101136607:output:c7a48052134b328c14bea6d7", "url": "https://cordis.europa.eu/project/id/101136607" }, "evidenceClass": "exact", "from": "cordis:101136607", "id": "edge:project-output:101136607:c7a48052134b328c14bea6d7", "relation": "has_observed_output", "to": "output:c7a48052134b328c14bea6d7" }, { "evidence": { "dataset": "german-ai-research-to-transfer-index", "field": "cordis_project_code", "sourceId": "101136607:output:d07a9c22923d15e4e6e26cf8", "url": "https://cordis.europa.eu/project/id/101136607" }, "evidenceClass": "exact", "from": "cordis:101136607", "id": "edge:project-output:101136607:d07a9c22923d15e4e6e26cf8", "relation": "has_observed_output", "to": "output:d07a9c22923d15e4e6e26cf8" }, { "evidence": { "dataset": "german-ai-research-to-transfer-index", "field": "cordis_project_code", "sourceId": "101136607:output:d6c27375444d5c7e90ac9b2a", "url": "https://cordis.europa.eu/project/id/101136607" }, "evidenceClass": "exact", "from": "cordis:101136607", "id": "edge:project-output:101136607:d6c27375444d5c7e90ac9b2a", "relation": "has_observed_output", "to": "output:d6c27375444d5c7e90ac9b2a" }, { "evidence": { "dataset": "german-ai-research-to-transfer-index", "field": "cordis_project_code", "sourceId": "101136607:output:e00acf8402527991aef2cb0f", "url": "https://cordis.europa.eu/project/id/101136607" }, "evidenceClass": "exact", "from": "cordis:101136607", "id": "edge:project-output:101136607:e00acf8402527991aef2cb0f", "relation": "has_observed_output", "to": "output:e00acf8402527991aef2cb0f" }, { "evidence": { "dataset": "german-ai-research-to-transfer-index", "field": "cordis_project_code", "sourceId": "101136607:output:e0db476055c1531749eb27de", "url": "https://cordis.europa.eu/project/id/101136607" }, "evidenceClass": "exact", "from": "cordis:101136607", "id": "edge:project-output:101136607:e0db476055c1531749eb27de", "relation": "has_observed_output", "to": "output:e0db476055c1531749eb27de" }, { "evidence": { "dataset": "german-ai-research-to-transfer-index", "field": "cordis_project_code", "sourceId": "101136607:output:f5347f04133684848c9bcaa8", "url": "https://cordis.europa.eu/project/id/101136607" }, "evidenceClass": "exact", "from": "cordis:101136607", "id": "edge:project-output:101136607:f5347f04133684848c9bcaa8", "relation": "has_observed_output", "to": "output:f5347f04133684848c9bcaa8" } ], "entity": { "attributes": { "acronym": "CLARA", "code": "101136607", "endDate": "2030-10-31", "frameworkProgramme": "HORIZON", "funding": { "currency": "EUR", "germanNetEuContributionCents": "180150000", "participantEdgeCount": 1, "status": "linked" }, "knowledgeFlow": { "citingCountryCount": 75, "citingInstitutionCount": 1037, "distinctCitingWorkCount": 580, "firstObservedCitationDate": "2008-01-01", "lastObservedCitationDate": "2026-07-30", "observedCitationEdgeCount": 583, "status": "full_input_observation" }, "research": { "citationDataOutputCount": 44, "citedOutputCount": 23, "firstPublicationDate": "2023-07-19", "lastPublicationDate": "2026-03-26", "openAccessOutputCount": 43, "outputCount": 44, "status": "linked", "totalCitations": 422, "typeCounts": { "dataset": 6, "other": 0, "publication": 38, "software": 0 } }, "startDate": "2024-11-01" }, "description": "Center for Artificial Intelligence and Quantum Computing in System Brain Research", "edgeCount": 24, "id": "cordis:101136607", "indexable": true, "label": "CLARA · Center for Artificial Intelligence and Quantum Computing in System Brain Research", "links": [ { "kind": "official_source", "label": "CORDIS", "url": "https://cordis.europa.eu/project/id/101136607" }, { "kind": "local_dossier", "label": "KI-Innovationspfad", "url": "/tools/ki-innovationspfad/?project=101136607" } ], "slug": "project-cordis-101136607-10c276fb", "sourceRefs": [ { "dataset": "german-ai-innovation-path", "field": "cordis_project_id", "sourceId": "cordis:101136607", "url": "https://cordis.europa.eu/project/id/101136607" } ], "type": "project" }, "fingerprint": "sha256:8883b7e8ba6c3ed1dcf796434e1f726881aec8fce82b8e21e17beb3350918beb", "generatedAt": "2026-08-01T09:21:19.131Z", "neighbors": [ { "attributes": { "aliases": [ "BAYERISCHE AKADEMIE DER WISSENSCHAFTEN" ], "identifiers": [ { "scheme": "cordis-pic", "value": "999580054" } ], "identityBasis": "official_identifier", "kind": "organization", "lanes": [ "funding" ], "roles": [ "funding_participant" ] }, "edgeCount": 4, "id": "actor:cordis-pic:999580054", "indexable": true, "label": "BAYERISCHE AKADEMIE DER WISSENSCHAFTEN", "links": [ { "kind": "evidence_source", "label": "german-ai-innovation-path", "url": "https://cordis.europa.eu/project/id/101136607" }, { "kind": "evidence_source", "label": "german-ai-innovation-path", "url": "https://cordis.europa.eu/project/id/676580" }, { "kind": "evidence_source", "label": "german-ai-innovation-path", "url": "https://cordis.europa.eu/project/id/828826" }, { "kind": "evidence_source", "label": "german-ai-innovation-path", "url": "https://cordis.europa.eu/project/id/951732" } ], "slug": "actor-cordis-pic-999580054-8c24e658", "sourceRefs": [ { "dataset": "german-ai-innovation-path", "field": "official_identifier", "recordCount": 4, "sourceId": "cordis:101136607:participant:999580054", "url": "https://cordis.europa.eu/project/id/101136607" } ], "type": "actor" }, { "attributes": { "citationCount": 4, "isOpenAccess": true, "openAireId": "doi_dedup___::cd09cb040c80d69ffbf3c20f1ca6e58d", "outputType": "publication", "persistentIdentifiers": [ { "scheme": "doi", "value": "10.1021/acs.jcim.5c00611" }, { "scheme": "pmc", "value": "PMC12381856" }, { "scheme": "pmid", "value": "40768221" } ], "publicationDate": "2025-08-06" }, "edgeCount": 1, "id": "output:09841d08c893a1cf962799bc", "indexable": true, "label": "Decoding Protein Stabilization: Impact on Aggregation, Solubility, and Unfolding Mechanisms", "links": [ { "kind": "official_source", "label": "OpenAIRE", "url": "https://api.openaire.eu/graph/v3/research-products/doi_dedup___%3A%3Acd09cb040c80d69ffbf3c20f1ca6e58d" }, { "kind": "persistent_identifier", "label": "DOI 10.1021/acs.jcim.5c00611", "url": "https://doi.org/10.1021/acs.jcim.5c00611" } ], "slug": "output-09841d08c893a1cf962799bc-1c948467", "sourceRefs": [ { "dataset": "german-ai-research-to-transfer-index", "field": "openaire_research_product_id", "sourceId": "doi_dedup___::cd09cb040c80d69ffbf3c20f1ca6e58d", "url": "https://api.openaire.eu/graph/v3/research-products/doi_dedup___%3A%3Acd09cb040c80d69ffbf3c20f1ca6e58d" } ], "type": "output" }, { "attributes": { "citationCount": 0, "isOpenAccess": true, "openAireId": "doi_dedup___::060aaef16e5a8bfc63bded6ed16c1477", "outputType": "publication", "persistentIdentifiers": [ { "scheme": "doi", "value": "10.1117/1.jmi.12.5.054005" }, { "scheme": "pmc", "value": "PMC12543032" }, { "scheme": "pmid", "value": "41132780" } ], "publicationDate": "2025-10-16" }, "edgeCount": 1, "id": "output:1b61a75ce3498cb33d6fa5ee", "indexable": true, "label": "Benchmarking 3D generative autoencoders for pseudo-healthy reconstruction of brain 18F-fluorodeoxyglucose positron emission tomography", "links": [ { "kind": "official_source", "label": "OpenAIRE", "url": "https://api.openaire.eu/graph/v3/research-products/doi_dedup___%3A%3A060aaef16e5a8bfc63bded6ed16c1477" }, { "kind": "persistent_identifier", "label": "DOI 10.1117/1.jmi.12.5.054005", "url": "https://doi.org/10.1117/1.jmi.12.5.054005" } ], "slug": "output-1b61a75ce3498cb33d6fa5ee-35590271", "sourceRefs": [ { "dataset": "german-ai-research-to-transfer-index", "field": "openaire_research_product_id", "sourceId": "doi_dedup___::060aaef16e5a8bfc63bded6ed16c1477", "url": "https://api.openaire.eu/graph/v3/research-products/doi_dedup___%3A%3A060aaef16e5a8bfc63bded6ed16c1477" } ], "type": "output" }, { "attributes": { "citationCount": 0, "isOpenAccess": true, "openAireId": "doi_dedup___::45294e7f0a76f899c839483c4e28b545", "outputType": "publication", "persistentIdentifiers": [ { "scheme": "doi", "value": "10.1016/j.neurol.2025.07.011" }, { "scheme": "pmid", "value": "41238324" } ], "publicationDate": "2025-11-01" }, "edgeCount": 1, "id": "output:2cd9dca83b4a756150dc0704", "indexable": true, "label": "Artificial intelligence in presymptomatic neurological diseases: Bridging normal variation and prodromal signatures", "links": [ { "kind": "official_source", "label": "OpenAIRE", "url": "https://api.openaire.eu/graph/v3/research-products/doi_dedup___%3A%3A45294e7f0a76f899c839483c4e28b545" }, { "kind": "persistent_identifier", "label": "DOI 10.1016/j.neurol.2025.07.011", "url": "https://doi.org/10.1016/j.neurol.2025.07.011" } ], "slug": "output-2cd9dca83b4a756150dc0704-7d57d957", "sourceRefs": [ { "dataset": "german-ai-research-to-transfer-index", "field": "openaire_research_product_id", "sourceId": "doi_dedup___::45294e7f0a76f899c839483c4e28b545", "url": "https://api.openaire.eu/graph/v3/research-products/doi_dedup___%3A%3A45294e7f0a76f899c839483c4e28b545" } ], "type": "output" }, { "attributes": { "citationCount": 2, "isOpenAccess": true, "openAireId": "doi_dedup___::002c63b8c8efc35f3783e1cd09a1c4c1", "outputType": "publication", "persistentIdentifiers": [ { "scheme": "doi", "value": "10.3390/s25072253" }, { "scheme": "pmc", "value": "PMC11991245" }, { "scheme": "pmid", "value": "40218765" } ], "publicationDate": "2025-04-02" }, "edgeCount": 1, "id": "output:39eac19f35f342aa603261b9", "indexable": true, "label": "Electrocardiographic Discrimination of Long QT Syndrome Genotypes: A Comparative Analysis and Machine Learning Approach", "links": [ { "kind": "official_source", "label": "OpenAIRE", "url": "https://api.openaire.eu/graph/v3/research-products/doi_dedup___%3A%3A002c63b8c8efc35f3783e1cd09a1c4c1" }, { "kind": "persistent_identifier", "label": "DOI 10.3390/s25072253", "url": "https://doi.org/10.3390/s25072253" } ], "slug": "output-39eac19f35f342aa603261b9-e24d3e50", "sourceRefs": [ { "dataset": "german-ai-research-to-transfer-index", "field": "openaire_research_product_id", "sourceId": "doi_dedup___::002c63b8c8efc35f3783e1cd09a1c4c1", "url": "https://api.openaire.eu/graph/v3/research-products/doi_dedup___%3A%3A002c63b8c8efc35f3783e1cd09a1c4c1" } ], "type": "output" }, { "attributes": { "citationCount": 1, "isOpenAccess": false, "openAireId": "doi_________::242faa664d11a923beddfa64e4cc7b1c", "outputType": "publication", "persistentIdentifiers": [ { "scheme": "doi", "value": "10.1088/1741-2552/ae4455" } ], "publicationDate": "2026-03-10" }, "edgeCount": 1, "id": "output:4160821793507467711d5807", "indexable": true, "label": "EEG foundation models: a critical review of current progress and future directions", "links": [ { "kind": "official_source", "label": "OpenAIRE", "url": "https://api.openaire.eu/graph/v3/research-products/doi_________%3A%3A242faa664d11a923beddfa64e4cc7b1c" }, { "kind": "persistent_identifier", "label": "DOI 10.1088/1741-2552/ae4455", "url": "https://doi.org/10.1088/1741-2552/ae4455" } ], "slug": "output-4160821793507467711d5807-3b783580", "sourceRefs": [ { "dataset": "german-ai-research-to-transfer-index", "field": "openaire_research_product_id", "sourceId": "doi_________::242faa664d11a923beddfa64e4cc7b1c", "url": "https://api.openaire.eu/graph/v3/research-products/doi_________%3A%3A242faa664d11a923beddfa64e4cc7b1c" } ], "type": "output" }, { "attributes": { "citationCount": 5, "isOpenAccess": true, "openAireId": "doi_dedup___::08c605150b9e6ec9e9eb3a571c60774d", "outputType": "publication", "persistentIdentifiers": [ { "scheme": "arXiv", "value": "2307.10926" }, { "scheme": "doi", "value": "10.1016/j.media.2025.103565" }, { "scheme": "doi", "value": "10.48550/arxiv.2307.10926" }, { "scheme": "pmid", "value": "40367699" } ], "publicationDate": "2025-07-01" }, "edgeCount": 1, "id": "output:5cb3d9df5b4e3c5beb0896b4", "indexable": true, "label": "Confidence intervals for performance estimates in brain MRI segmentation", "links": [ { "kind": "official_source", "label": "OpenAIRE", "url": "https://api.openaire.eu/graph/v3/research-products/doi_dedup___%3A%3A08c605150b9e6ec9e9eb3a571c60774d" }, { "kind": "persistent_identifier", "label": "DOI 10.1016/j.media.2025.103565", "url": "https://doi.org/10.1016/j.media.2025.103565" }, { "kind": "persistent_identifier", "label": "DOI 10.48550/arxiv.2307.10926", "url": "https://doi.org/10.48550/arxiv.2307.10926" } ], "slug": "output-5cb3d9df5b4e3c5beb0896b4-6bea6fe5", "sourceRefs": [ { "dataset": "german-ai-research-to-transfer-index", "field": "openaire_research_product_id", "sourceId": "doi_dedup___::08c605150b9e6ec9e9eb3a571c60774d", "url": "https://api.openaire.eu/graph/v3/research-products/doi_dedup___%3A%3A08c605150b9e6ec9e9eb3a571c60774d" } ], "type": "output" }, { "attributes": { "citationCount": 0, "isOpenAccess": true, "openAireId": "doi_dedup___::2e5f837c8b10857af7b801f36e76ec13", "outputType": "publication", "persistentIdentifiers": [ { "scheme": "doi", "value": "10.1148/rycan.240446" }, { "scheme": "pmc", "value": "PMC12492439" }, { "scheme": "pmid", "value": "40970793" } ], "publicationDate": "2025-09-01" }, "edgeCount": 1, "id": "output:66871cef3d7f6e6e5fa3783f", "indexable": true, "label": "Automatic Segmentation of Primary Central Nervous System Lymphoma at Clinical Routine Postcontrast T1-weighted MRI", "links": [ { "kind": "official_source", "label": "OpenAIRE", "url": "https://api.openaire.eu/graph/v3/research-products/doi_dedup___%3A%3A2e5f837c8b10857af7b801f36e76ec13" }, { "kind": "persistent_identifier", "label": "DOI 10.1148/rycan.240446", "url": "https://doi.org/10.1148/rycan.240446" } ], "slug": "output-66871cef3d7f6e6e5fa3783f-2d19457a", "sourceRefs": [ { "dataset": "german-ai-research-to-transfer-index", "field": "openaire_research_product_id", "sourceId": "doi_dedup___::2e5f837c8b10857af7b801f36e76ec13", "url": "https://api.openaire.eu/graph/v3/research-products/doi_dedup___%3A%3A2e5f837c8b10857af7b801f36e76ec13" } ], "type": "output" }, { "attributes": { "citationCount": 4, "isOpenAccess": true, "openAireId": "doi_dedup___::5f47af32697b9bd7623376375544d746", "outputType": "publication", "persistentIdentifiers": [ { "scheme": "doi", "value": "10.1117/12.3044679" } ], "publicationDate": "2025-04-02" }, "edgeCount": 1, "id": "output:69307741698e58481081cc1f", "indexable": true, "label": "Comparing foundation models and nnU-Net for segmentation of primary brain lymphoma on clinical routine post-contrast T1-weighted MRI", "links": [ { "kind": "official_source", "label": "OpenAIRE", "url": "https://api.openaire.eu/graph/v3/research-products/doi_dedup___%3A%3A5f47af32697b9bd7623376375544d746" }, { "kind": "persistent_identifier", "label": "DOI 10.1117/12.3044679", "url": "https://doi.org/10.1117/12.3044679" } ], "slug": "output-69307741698e58481081cc1f-dfb30cbe", "sourceRefs": [ { "dataset": "german-ai-research-to-transfer-index", "field": "openaire_research_product_id", "sourceId": "doi_dedup___::5f47af32697b9bd7623376375544d746", "url": "https://api.openaire.eu/graph/v3/research-products/doi_dedup___%3A%3A5f47af32697b9bd7623376375544d746" } ], "type": "output" }, { "attributes": { "citationCount": 20, "isOpenAccess": true, "openAireId": "doi_dedup___::5ac45b6759ca8488b9f52932fe6d5a3d", "outputType": "publication", "persistentIdentifiers": [ { "scheme": "doi", "value": "10.1093/nar/gkaf399" }, { "scheme": "pmc", "value": "PMC12230698" }, { "scheme": "pmid", "value": "40337920" } ], "publicationDate": "2025-05-08" }, "edgeCount": 1, "id": "output:6f5ff64f05516fe5d06cfd2a", "indexable": true, "label": "Caver Web 2.0: analysis of tunnels and ligand transport in dynamic ensembles of proteins", "links": [ { "kind": "official_source", "label": "OpenAIRE", "url": "https://api.openaire.eu/graph/v3/research-products/doi_dedup___%3A%3A5ac45b6759ca8488b9f52932fe6d5a3d" }, { "kind": "persistent_identifier", "label": "DOI 10.1093/nar/gkaf399", "url": "https://doi.org/10.1093/nar/gkaf399" } ], "slug": "output-6f5ff64f05516fe5d06cfd2a-067fdaff", "sourceRefs": [ { "dataset": "german-ai-research-to-transfer-index", "field": "openaire_research_product_id", "sourceId": "doi_dedup___::5ac45b6759ca8488b9f52932fe6d5a3d", "url": "https://api.openaire.eu/graph/v3/research-products/doi_dedup___%3A%3A5ac45b6759ca8488b9f52932fe6d5a3d" } ], "type": "output" }, { "attributes": { "citationCount": 0, "isOpenAccess": true, "openAireId": "doi_dedup___::f1aa89e754d0adbd6c0c9a55a8268af2", "outputType": "publication", "persistentIdentifiers": [ { "scheme": "arXiv", "value": "2507.17486" }, { "scheme": "doi", "value": "10.1007/978-3-032-05472-2_25" }, { "scheme": "doi", "value": "10.48550/arxiv.2507.17486" } ], "publicationDate": "2025-09-25" }, "edgeCount": 1, "id": "output:767fe2d490e4551f0af8b5ab", "indexable": true, "label": "Unsupervised Anomaly Detection Using Bayesian Flow Networks: Application to Brain FDG PET in the Context of Alzheimer’s Disease", "links": [ { "kind": "official_source", "label": "OpenAIRE", "url": "https://api.openaire.eu/graph/v3/research-products/doi_dedup___%3A%3Af1aa89e754d0adbd6c0c9a55a8268af2" }, { "kind": "persistent_identifier", "label": "DOI 10.1007/978-3-032-05472-2_25", "url": "https://doi.org/10.1007/978-3-032-05472-2_25" }, { "kind": "persistent_identifier", "label": "DOI 10.48550/arxiv.2507.17486", "url": "https://doi.org/10.48550/arxiv.2507.17486" } ], "slug": "output-767fe2d490e4551f0af8b5ab-7253bdf8", "sourceRefs": [ { "dataset": "german-ai-research-to-transfer-index", "field": "openaire_research_product_id", "sourceId": "doi_dedup___::f1aa89e754d0adbd6c0c9a55a8268af2", "url": "https://api.openaire.eu/graph/v3/research-products/doi_dedup___%3A%3Af1aa89e754d0adbd6c0c9a55a8268af2" } ], "type": "output" }, { "attributes": { "citationCount": 0, "isOpenAccess": true, "openAireId": "doi_________::b0d152bfce2053ddd5daf029ecd2d451", "outputType": "publication", "persistentIdentifiers": [ { "scheme": "doi", "value": "10.64898/2026.03.24.713925" } ], "publicationDate": "2026-03-26" }, "edgeCount": 1, "id": "output:77ae5a53fe64f86881d73003", "indexable": true, "label": "Uncovering Functional Distant Mutations by Ultra-High-Throughput Screening of Dehalogenases", "links": [ { "kind": "official_source", "label": "OpenAIRE", "url": "https://api.openaire.eu/graph/v3/research-products/doi_________%3A%3Ab0d152bfce2053ddd5daf029ecd2d451" }, { "kind": "persistent_identifier", "label": "DOI 10.64898/2026.03.24.713925", "url": "https://doi.org/10.64898/2026.03.24.713925" } ], "slug": "output-77ae5a53fe64f86881d73003-e44b5199", "sourceRefs": [ { "dataset": "german-ai-research-to-transfer-index", "field": "openaire_research_product_id", "sourceId": "doi_________::b0d152bfce2053ddd5daf029ecd2d451", "url": "https://api.openaire.eu/graph/v3/research-products/doi_________%3A%3Ab0d152bfce2053ddd5daf029ecd2d451" } ], "type": "output" }, { "attributes": { "citationCount": 0, "isOpenAccess": true, "openAireId": "dedup_wf_002::b5bc4f37481636acfbcb1222e2735997", "outputType": "publication", "persistentIdentifiers": [], "publicationDate": "2026-01-01" }, "edgeCount": 1, "id": "output:8706e31e1ded545295ceefbd", "indexable": true, "label": "Unsupervised anomaly detection in brain FDG PET with deep generative models: An experimental analysis of model variability and mitigation strategies", "links": [ { "kind": "official_source", "label": "OpenAIRE", "url": "https://api.openaire.eu/graph/v3/research-products/dedup_wf_002%3A%3Ab5bc4f37481636acfbcb1222e2735997" } ], "slug": "output-8706e31e1ded545295ceefbd-6640d682", "sourceRefs": [ { "dataset": "german-ai-research-to-transfer-index", "field": "openaire_research_product_id", "sourceId": "dedup_wf_002::b5bc4f37481636acfbcb1222e2735997", "url": "https://api.openaire.eu/graph/v3/research-products/dedup_wf_002%3A%3Ab5bc4f37481636acfbcb1222e2735997" } ], "type": "output" }, { "attributes": { "citationCount": 0, "isOpenAccess": true, "openAireId": "doi_dedup___::34503f621cd0a2c876b7925948cc8546", "outputType": "publication", "persistentIdentifiers": [ { "scheme": "doi", "value": "10.1002/alz.70902" }, { "scheme": "pmc", "value": "PMC12689462" }, { "scheme": "pmid", "value": "41366786" } ], "publicationDate": "2025-12-01" }, "edgeCount": 1, "id": "output:8b6b631fe234b5859346ed3e", "indexable": true, "label": "Quantifying multimodal longitudinal brain changes in presymptomatic C9orf72 disease", "links": [ { "kind": "official_source", "label": "OpenAIRE", "url": "https://api.openaire.eu/graph/v3/research-products/doi_dedup___%3A%3A34503f621cd0a2c876b7925948cc8546" }, { "kind": "persistent_identifier", "label": "DOI 10.1002/alz.70902", "url": "https://doi.org/10.1002/alz.70902" } ], "slug": "output-8b6b631fe234b5859346ed3e-c609d986", "sourceRefs": [ { "dataset": "german-ai-research-to-transfer-index", "field": "openaire_research_product_id", "sourceId": "doi_dedup___::34503f621cd0a2c876b7925948cc8546", "url": "https://api.openaire.eu/graph/v3/research-products/doi_dedup___%3A%3A34503f621cd0a2c876b7925948cc8546" } ], "type": "output" }, { "attributes": { "citationCount": 7, "isOpenAccess": true, "openAireId": "doi_dedup___::dc7cceb76381e07f62f525dce100aec3", "outputType": "publication", "persistentIdentifiers": [ { "scheme": "doi", "value": "10.1038/s41598-025-96429-1" }, { "scheme": "pmc", "value": "PMC11994826" }, { "scheme": "pmid", "value": "40223138" } ], "publicationDate": "2025-04-13" }, "edgeCount": 1, "id": "output:8e74e50590e788a90ce9945c", "indexable": true, "label": "Optimized image segmentation using an improved reptile search algorithm with Gbest operator for multi-level thresholding", "links": [ { "kind": "official_source", "label": "OpenAIRE", "url": "https://api.openaire.eu/graph/v3/research-products/doi_dedup___%3A%3Adc7cceb76381e07f62f525dce100aec3" }, { "kind": "persistent_identifier", "label": "DOI 10.1038/s41598-025-96429-1", "url": "https://doi.org/10.1038/s41598-025-96429-1" } ], "slug": "output-8e74e50590e788a90ce9945c-2aa7bcc6", "sourceRefs": [ { "dataset": "german-ai-research-to-transfer-index", "field": "openaire_research_product_id", "sourceId": "doi_dedup___::dc7cceb76381e07f62f525dce100aec3", "url": "https://api.openaire.eu/graph/v3/research-products/doi_dedup___%3A%3Adc7cceb76381e07f62f525dce100aec3" } ], "type": "output" }, { "attributes": { "citationCount": 0, "isOpenAccess": true, "openAireId": "doi_________::ab042187896b526a2b43d450c112de54", "outputType": "publication", "persistentIdentifiers": [ { "scheme": "doi", "value": "10.64898/2026.01.04.697554" } ], "publicationDate": "2026-01-04" }, "edgeCount": 1, "id": "output:ad2f4017a5dc8e3158075d04", "indexable": true, "label": "Biochemical and Immunological Properties of Engineered Low-Immunogenic Staphylokinases for Next-Generation Thrombolytic Therapy", "links": [ { "kind": "official_source", "label": "OpenAIRE", "url": "https://api.openaire.eu/graph/v3/research-products/doi_________%3A%3Aab042187896b526a2b43d450c112de54" }, { "kind": "persistent_identifier", "label": "DOI 10.64898/2026.01.04.697554", "url": "https://doi.org/10.64898/2026.01.04.697554" } ], "slug": "output-ad2f4017a5dc8e3158075d04-dbcaa906", "sourceRefs": [ { "dataset": "german-ai-research-to-transfer-index", "field": "openaire_research_product_id", "sourceId": "doi_________::ab042187896b526a2b43d450c112de54", "url": "https://api.openaire.eu/graph/v3/research-products/doi_________%3A%3Aab042187896b526a2b43d450c112de54" } ], "type": "output" }, { "attributes": { "citationCount": 2, "isOpenAccess": true, "openAireId": "doi_dedup___::361ed1fb51eb2bc13517039166fb6a3a", "outputType": "publication", "persistentIdentifiers": [ { "scheme": "doi", "value": "10.1186/s13321-025-01005-4" }, { "scheme": "pmc", "value": "PMC12039276" }, { "scheme": "pmid", "value": "40296174" } ], "publicationDate": "2025-04-28" }, "edgeCount": 1, "id": "output:bc526887f22c750cf6f7b726", "indexable": true, "label": "Moldina: a fast and accurate search algorithm for simultaneous docking of multiple ligands", "links": [ { "kind": "official_source", "label": "OpenAIRE", "url": "https://api.openaire.eu/graph/v3/research-products/doi_dedup___%3A%3A361ed1fb51eb2bc13517039166fb6a3a" }, { "kind": "persistent_identifier", "label": "DOI 10.1186/s13321-025-01005-4", "url": "https://doi.org/10.1186/s13321-025-01005-4" } ], "slug": "output-bc526887f22c750cf6f7b726-73557ccc", "sourceRefs": [ { "dataset": "german-ai-research-to-transfer-index", "field": "openaire_research_product_id", "sourceId": "doi_dedup___::361ed1fb51eb2bc13517039166fb6a3a", "url": "https://api.openaire.eu/graph/v3/research-products/doi_dedup___%3A%3A361ed1fb51eb2bc13517039166fb6a3a" } ], "type": "output" }, { "attributes": { "citationCount": 0, "isOpenAccess": true, "openAireId": "dedup_wf_002::9e09ae82a263a3ea83790843d8fb4b91", "outputType": "publication", "persistentIdentifiers": [], "publicationDate": "2026-01-01" }, "edgeCount": 1, "id": "output:c51098efcd0594d4245602e7", "indexable": true, "label": "Comparing Longitudinal Preprocessing Pipelines for Brain Volume Consistency in T1-Weighted MRI Test-Retest Scans", "links": [ { "kind": "official_source", "label": "OpenAIRE", "url": "https://api.openaire.eu/graph/v3/research-products/dedup_wf_002%3A%3A9e09ae82a263a3ea83790843d8fb4b91" } ], "slug": "output-c51098efcd0594d4245602e7-5e40feb0", "sourceRefs": [ { "dataset": "german-ai-research-to-transfer-index", "field": "openaire_research_product_id", "sourceId": "dedup_wf_002::9e09ae82a263a3ea83790843d8fb4b91", "url": "https://api.openaire.eu/graph/v3/research-products/dedup_wf_002%3A%3A9e09ae82a263a3ea83790843d8fb4b91" } ], "type": "output" }, { "attributes": { "citationCount": 5, "isOpenAccess": true, "openAireId": "doi_dedup___::61cebc9317f09e2dbc01ccecd05c3094", "outputType": "publication", "persistentIdentifiers": [ { "scheme": "doi", "value": "10.1088/1741-2552/ade6aa" }, { "scheme": "pmc", "value": "PMC12247154" }, { "scheme": "pmid", "value": "40541229" } ], "publicationDate": "2025-07-11" }, "edgeCount": 1, "id": "output:c7a48052134b328c14bea6d7", "indexable": true, "label": "Improved spatial memory for physical versus virtual navigation", "links": [ { "kind": "official_source", "label": "OpenAIRE", "url": "https://api.openaire.eu/graph/v3/research-products/doi_dedup___%3A%3A61cebc9317f09e2dbc01ccecd05c3094" }, { "kind": "persistent_identifier", "label": "DOI 10.1088/1741-2552/ade6aa", "url": "https://doi.org/10.1088/1741-2552/ade6aa" } ], "slug": "output-c7a48052134b328c14bea6d7-ddf07b2c", "sourceRefs": [ { "dataset": "german-ai-research-to-transfer-index", "field": "openaire_research_product_id", "sourceId": "doi_dedup___::61cebc9317f09e2dbc01ccecd05c3094", "url": "https://api.openaire.eu/graph/v3/research-products/doi_dedup___%3A%3A61cebc9317f09e2dbc01ccecd05c3094" } ], "type": "output" }, { "attributes": { "citationCount": 0, "isOpenAccess": true, "openAireId": "doi_dedup___::6b020e85f4a1473b6e9cea25d475c5f7", "outputType": "publication", "persistentIdentifiers": [ { "scheme": "doi", "value": "10.1093/nar/gkaf1211" }, { "scheme": "pmc", "value": "PMC12807726" }, { "scheme": "pmid", "value": "41263104" } ], "publicationDate": "2025-11-20" }, "edgeCount": 1, "id": "output:d07a9c22923d15e4e6e26cf8", "indexable": true, "label": "FireProtDB 2.0: large-scale manually curated database of the protein stability data", "links": [ { "kind": "official_source", "label": "OpenAIRE", "url": "https://api.openaire.eu/graph/v3/research-products/doi_dedup___%3A%3A6b020e85f4a1473b6e9cea25d475c5f7" }, { "kind": "persistent_identifier", "label": "DOI 10.1093/nar/gkaf1211", "url": "https://doi.org/10.1093/nar/gkaf1211" } ], "slug": "output-d07a9c22923d15e4e6e26cf8-342c384f", "sourceRefs": [ { "dataset": "german-ai-research-to-transfer-index", "field": "openaire_research_product_id", "sourceId": "doi_dedup___::6b020e85f4a1473b6e9cea25d475c5f7", "url": "https://api.openaire.eu/graph/v3/research-products/doi_dedup___%3A%3A6b020e85f4a1473b6e9cea25d475c5f7" } ], "type": "output" }, { "attributes": { "citationCount": 4, "isOpenAccess": true, "openAireId": "doi_dedup___::9cb07b01f21ab4d514f5b94cd4b26d7a", "outputType": "publication", "persistentIdentifiers": [ { "scheme": "doi", "value": "10.3389/frai.2025.1601815" }, { "scheme": "pmc", "value": "PMC12247529" }, { "scheme": "pmid", "value": "40656161" } ], "publicationDate": "2025-06-27" }, "edgeCount": 1, "id": "output:d6c27375444d5c7e90ac9b2a", "indexable": true, "label": "A nnU-Net-based automatic segmentation of FCD type II lesions in 3D FLAIR MRI images", "links": [ { "kind": "official_source", "label": "OpenAIRE", "url": "https://api.openaire.eu/graph/v3/research-products/doi_dedup___%3A%3A9cb07b01f21ab4d514f5b94cd4b26d7a" }, { "kind": "persistent_identifier", "label": "DOI 10.3389/frai.2025.1601815", "url": "https://doi.org/10.3389/frai.2025.1601815" } ], "slug": "output-d6c27375444d5c7e90ac9b2a-6d168f4a", "sourceRefs": [ { "dataset": "german-ai-research-to-transfer-index", "field": "openaire_research_product_id", "sourceId": "doi_dedup___::9cb07b01f21ab4d514f5b94cd4b26d7a", "url": "https://api.openaire.eu/graph/v3/research-products/doi_dedup___%3A%3A9cb07b01f21ab4d514f5b94cd4b26d7a" } ], "type": "output" }, { "attributes": { "citationCount": 1, "isOpenAccess": true, "openAireId": "doi_dedup___::c22c58524266d9471fec0c3829b6c2af", "outputType": "publication", "persistentIdentifiers": [ { "scheme": "doi", "value": "10.1002/2211-5463.70132" }, { "scheme": "handle", "value": "1854/LU-01KAZZW3K0GVJHJ96PEPQ9B3HG" }, { "scheme": "pmid", "value": "41045044" } ], "publicationDate": "2025-10-04" }, "edgeCount": 1, "id": "output:e00acf8402527991aef2cb0f", "indexable": true, "label": "Thrombolytic proteins profiling: High‐throughput activity, selectivity, and resistance assays", "links": [ { "kind": "official_source", "label": "OpenAIRE", "url": "https://api.openaire.eu/graph/v3/research-products/doi_dedup___%3A%3Ac22c58524266d9471fec0c3829b6c2af" }, { "kind": "persistent_identifier", "label": "DOI 10.1002/2211-5463.70132", "url": "https://doi.org/10.1002/2211-5463.70132" } ], "slug": "output-e00acf8402527991aef2cb0f-b367702c", "sourceRefs": [ { "dataset": "german-ai-research-to-transfer-index", "field": "openaire_research_product_id", "sourceId": "doi_dedup___::c22c58524266d9471fec0c3829b6c2af", "url": "https://api.openaire.eu/graph/v3/research-products/doi_dedup___%3A%3Ac22c58524266d9471fec0c3829b6c2af" } ], "type": "output" }, { "attributes": { "citationCount": 2, "isOpenAccess": true, "openAireId": "doi_dedup___::1ae5e1f15695fbf2a5c5383ead532a50", "outputType": "publication", "persistentIdentifiers": [ { "scheme": "doi", "value": "10.1016/j.mlwa.2025.100818" } ], "publicationDate": "2026-03-01" }, "edgeCount": 1, "id": "output:e0db476055c1531749eb27de", "indexable": true, "label": "A hybrid DEA–fuzzy clustering approach for accurate reference set identification", "links": [ { "kind": "official_source", "label": "OpenAIRE", "url": "https://api.openaire.eu/graph/v3/research-products/doi_dedup___%3A%3A1ae5e1f15695fbf2a5c5383ead532a50" }, { "kind": "persistent_identifier", "label": "DOI 10.1016/j.mlwa.2025.100818", "url": "https://doi.org/10.1016/j.mlwa.2025.100818" } ], "slug": "output-e0db476055c1531749eb27de-443e04f5", "sourceRefs": [ { "dataset": "german-ai-research-to-transfer-index", "field": "openaire_research_product_id", "sourceId": "doi_dedup___::1ae5e1f15695fbf2a5c5383ead532a50", "url": "https://api.openaire.eu/graph/v3/research-products/doi_dedup___%3A%3A1ae5e1f15695fbf2a5c5383ead532a50" } ], "type": "output" }, { "attributes": { "citationCount": 1, "isOpenAccess": true, "openAireId": "doi_dedup___::99351b00d493bb805b68dc02c0ccbc97", "outputType": "publication", "persistentIdentifiers": [ { "scheme": "doi", "value": "10.3390/ijms26094138" }, { "scheme": "pmc", "value": "PMC12071533" }, { "scheme": "pmid", "value": "40362377" } ], "publicationDate": "2025-04-27" }, "edgeCount": 1, "id": "output:f5347f04133684848c9bcaa8", "indexable": true, "label": "Acridine-Based Chalcone 1C and ABC Transporters", "links": [ { "kind": "official_source", "label": "OpenAIRE", "url": "https://api.openaire.eu/graph/v3/research-products/doi_dedup___%3A%3A99351b00d493bb805b68dc02c0ccbc97" }, { "kind": "persistent_identifier", "label": "DOI 10.3390/ijms26094138", "url": "https://doi.org/10.3390/ijms26094138" } ], "slug": "output-f5347f04133684848c9bcaa8-e2d256f9", "sourceRefs": [ { "dataset": "german-ai-research-to-transfer-index", "field": "openaire_research_product_id", "sourceId": "doi_dedup___::99351b00d493bb805b68dc02c0ccbc97", "url": "https://api.openaire.eu/graph/v3/research-products/doi_dedup___%3A%3A99351b00d493bb805b68dc02c0ccbc97" } ], "type": "output" } ], "schemaVersion": "ki-evidenznetz.entity.v1" }