[ { "@id": "https://w3id.org/np/RA8wGTUii5HPpv0KJrt5pATt4u_WQzusfwVma0nle64O0/Head", "@graph": [ { "@id": "https://w3id.org/np/RA8wGTUii5HPpv0KJrt5pATt4u_WQzusfwVma0nle64O0", "http://www.nanopub.org/nschema#hasAssertion": [ { "@id": "https://w3id.org/np/RA8wGTUii5HPpv0KJrt5pATt4u_WQzusfwVma0nle64O0/assertion" } ], "http://www.nanopub.org/nschema#hasProvenance": [ { "@id": "https://w3id.org/np/RA8wGTUii5HPpv0KJrt5pATt4u_WQzusfwVma0nle64O0/provenance" } ], "http://www.nanopub.org/nschema#hasPublicationInfo": [ { "@id": "https://w3id.org/np/RA8wGTUii5HPpv0KJrt5pATt4u_WQzusfwVma0nle64O0/pubinfo" } ], "@type": [ "http://www.nanopub.org/nschema#Nanopublication" ] } ] }, { "@id": "https://w3id.org/np/RA8wGTUii5HPpv0KJrt5pATt4u_WQzusfwVma0nle64O0/provenance", "@graph": [ { "@id": "https://w3id.org/np/RA8wGTUii5HPpv0KJrt5pATt4u_WQzusfwVma0nle64O0/assertion", "http://www.w3.org/ns/prov#wasDerivedFrom": [ { "@id": "https://api.rohub.org/api/ros/54524973-c104-4f19-aa26-b5986dadbd00/crate/download/ro-crate-metadata.json" } ] } ] }, { "@id": "https://w3id.org/np/RA8wGTUii5HPpv0KJrt5pATt4u_WQzusfwVma0nle64O0/pubinfo", "@graph": [ { "@id": "https://w3id.org/np/RA8wGTUii5HPpv0KJrt5pATt4u_WQzusfwVma0nle64O0", "http://purl.org/dc/terms/created": [ { "@value": "2026-03-03T16:17:16.349+01:00", "@type": "http://www.w3.org/2001/XMLSchema#dateTime" } ], "http://purl.org/dc/terms/creator": [ { "@id": "https://w3id.org/kpxl/gen/terms/RoCrateBot" } ], "http://purl.org/nanopub/x/introduces": [ { "@id": "https://w3id.org/ro-id/54524973-c104-4f19-aa26-b5986dadbd00/" } ], "@type": [ "http://purl.org/nanopub/x/RoCrateNanopub" ], "http://www.w3.org/2000/01/rdf-schema#label": [ { "@value": "Data used in the article \"Profiling the baseline performance and limits of machine learning models for adaptive immune receptor repertoire classification\"" } ] }, { "@id": "https://w3id.org/np/RA8wGTUii5HPpv0KJrt5pATt4u_WQzusfwVma0nle64O0/sig", "http://purl.org/nanopub/x/hasAlgorithm": [ { "@value": "RSA" } ], "http://purl.org/nanopub/x/hasPublicKey": [ { "@value": "MIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEAxszSDYX5tuCSkP7UiCtftYPFNQVTjgNu0I5fwdML2DLRDlp0xzmsQXRk8oHuvwGvG1aMjj6cpUqO+0rz2Sg/wvHOgUpkRH8VJXvmlkhafMLCMtUtk5JIx7e+fkzCby+fnmD7kMkGLrT+OaExWwEDmNlCAt0TPKcHSdwsjso2isXjtAsGevyCMke8ufnFYpjs746JES1eNzVnHnn2Kp/lqcm60GM+J8dLgRZp7fX0anW098xhKym6+xXFzqeju0vYRIHBPerv+r7skWxwk+a7Sd8msqVeYEv6NTqnyWvyWb6Yh8cvj04N6qm/T6C5FUPLQhzSaQgMVMU6yLqjPuu9DwIDAQAB" } ], "http://purl.org/nanopub/x/hasSignature": [ { "@value": "VGDfukCFenpmgasui3MYWUA4NirG7QshW/ozNox2MMdKQRFPY5tmJXSGHvGRRvJu8Y5HgcI3GbE8lzdNP0/GrQEgPRM0JdB7BGn+HCLOYZhsIIM6Ar01FpgPv4tJM+mEsotvtP1l4lr/eFZE/lTRs6AcbEA62MuMlIihVDGezb0mZzK3sDR3SbqKn5B/zgTG2QTcaJHiYtRFXoQ8C0pWnhUn6e5gjMduMBl6ePg1AlniQWEHLZqJkONZFRXIM1xTA0wWqSDuJzF2mjzgfZDBF+yHTV9y7Vd2YqBU7P5NVD0ZcwFRXiO6eBDgbqNQJJVlnWy6FD79zAz2NsJHbmGpkg==" } ], "http://purl.org/nanopub/x/hasSignatureTarget": [ { "@id": "https://w3id.org/np/RA8wGTUii5HPpv0KJrt5pATt4u_WQzusfwVma0nle64O0" } ], "http://purl.org/nanopub/x/signedBy": [ { "@id": "https://w3id.org/kpxl/gen/terms/RoCrateBot" } ] } ] }, { "@id": "https://w3id.org/np/RA8wGTUii5HPpv0KJrt5pATt4u_WQzusfwVma0nle64O0/assertion", "@graph": [ { "@id": "http://eurovoc.europa.eu/2919", "http://schema.org/description": [ { "@value": "" } ], "http://schema.org/name": [ { "@value": "Environmental research" } ], "@type": [ "http://schema.org/DefinedTerm" ] }, { "@id": "http://eurovoc.europa.eu/3941", "http://schema.org/description": [ { "@value": "" } ], "http://schema.org/name": [ { "@value": "Life sciences" } ], "@type": [ "http://schema.org/DefinedTerm" ] }, { "@id": "http://eurovoc.europa.eu/3946", "http://schema.org/description": [ { "@value": "" } ], "http://schema.org/name": [ { "@value": "Physical sciences" } ], "@type": [ "http://schema.org/DefinedTerm" ] }, { "@id": "http://w3id.org/ro-id/rohub/model#subject/-1038351181", "@type": [ "https://w3id.org/contentdesc#Expression" ], "http://www.w3.org/2004/02/skos/core#prefLabel": [ { "@value": "baseline performance" } ] }, { "@id": "http://w3id.org/ro-id/rohub/model#subject/100002570", "@type": [ "https://w3id.org/contentdesc#Concept" ], "http://www.w3.org/2004/02/skos/core#prefLabel": [ { "@value": "machine learning" } ] }, { "@id": "http://w3id.org/ro-id/rohub/model#subject/100042879", "@type": [ "https://w3id.org/contentdesc#Concept" ], "http://www.w3.org/2004/02/skos/core#prefLabel": [ { "@value": "data" } ] }, { "@id": "http://w3id.org/ro-id/rohub/model#subject/100191098", "@type": [ "https://w3id.org/contentdesc#Concept" ], "http://www.w3.org/2004/02/skos/core#prefLabel": [ { "@value": "article" } ] }, { "@id": "http://w3id.org/ro-id/rohub/model#subject/100208498", "@type": [ "https://w3id.org/contentdesc#Concept" ], "http://www.w3.org/2004/02/skos/core#prefLabel": [ { "@value": "receptor" } ] }, { "@id": "http://w3id.org/ro-id/rohub/model#subject/100810278", "@type": [ "https://w3id.org/contentdesc#Concept" ], "http://www.w3.org/2004/02/skos/core#prefLabel": [ { "@value": "baseline" } ] }, { "@id": "http://w3id.org/ro-id/rohub/model#subject/100918416", "@type": [ "https://w3id.org/contentdesc#Concept" ], "http://www.w3.org/2004/02/skos/core#prefLabel": [ { "@value": "training" } ] }, { "@id": "http://w3id.org/ro-id/rohub/model#subject/101744659", "@type": [ "https://w3id.org/contentdesc#Concept" ], "http://www.w3.org/2004/02/skos/core#prefLabel": [ { "@value": "category" } ] }, { "@id": "http://w3id.org/ro-id/rohub/model#subject/103643208", "@type": [ "https://w3id.org/contentdesc#Concept" ], "http://www.w3.org/2004/02/skos/core#prefLabel": [ { "@value": "implementation" } ] }, { "@id": "http://w3id.org/ro-id/rohub/model#subject/155156", "@type": [ "https://w3id.org/contentdesc#Concept" ], "http://www.w3.org/2004/02/skos/core#prefLabel": [ { "@value": "repertory" } ] }, { "@id": "http://w3id.org/ro-id/rohub/model#subject/2028277143", "@type": [ "https://w3id.org/contentdesc#Expression" ], "http://www.w3.org/2004/02/skos/core#prefLabel": [ { "@value": "model training" } ] }, { "@id": "http://w3id.org/ro-id/rohub/model#subject/2052885613", "@type": [ "https://w3id.org/contentdesc#Expression" ], "http://www.w3.org/2004/02/skos/core#prefLabel": [ { "@value": "machine learning model" } ] }, { "@id": "http://w3id.org/ro-id/rohub/model#subject/29992", "@type": [ "https://w3id.org/contentdesc#Concept" ], "http://www.w3.org/2004/02/skos/core#prefLabel": [ { "@value": "input file" } ] }, { "@id": "http://w3id.org/ro-id/rohub/model#subject/59446", "@type": [ "https://w3id.org/contentdesc#Concept" ], "http://www.w3.org/2004/02/skos/core#prefLabel": [ { "@value": "limit" } ] }, { "@id": "http://w3id.org/ro-id/rohub/model#subject/747853685", "@type": [ "https://w3id.org/contentdesc#Expression" ], "http://www.w3.org/2004/02/skos/core#prefLabel": [ { "@value": "repertoire classification" } ] }, { "@id": "http://w3id.org/ro-id/rohub/model#subject/927372996", "@type": [ "https://w3id.org/contentdesc#Expression" ], "http://www.w3.org/2004/02/skos/core#prefLabel": [ { "@value": "subsequent machine learning" } ] }, { "@id": "http://w3id.org/ro-id/rohub/model#subject/969116140", "@type": [ "https://w3id.org/contentdesc#Domain" ], "http://www.w3.org/2004/02/skos/core#prefLabel": [ { "@value": "journalism" } ] }, { "@id": "https://w3id.org/ro-id/54524973-c104-4f19-aa26-b5986dadbd00/#enrichment_service-account-enrichment", "http://schema.org/name": [ { "@value": "service-account-enrichment" } ], "@type": [ "http://xmlns.com/foaf/0.1/Agent" ] }, { "@id": "https://w3id.org/ro-id/54524973-c104-4f19-aa26-b5986dadbd00/", "http://schema.org/about": [ { "@id": "http://eurovoc.europa.eu/2919" }, { "@id": "http://eurovoc.europa.eu/3941" }, { "@id": "http://eurovoc.europa.eu/3946" } ], "http://schema.org/author": [ { "@id": "mailto:chakravarthi.kanduri@rohub.com" } ], "http://schema.org/contentSize": [ { "@value": "8418", "@type": "http://www.w3.org/2001/XMLSchema#integer" } ], "http://schema.org/contentUrl": [ { "@value": "https://api.rohub.org/api/ros/54524973-c104-4f19-aa26-b5986dadbd00/crate/download/" } ], "http://schema.org/creator": [ { "@id": "mailto:georgehadib@gmail.com" } ], "http://schema.org/dateCreated": [ { "@value": "2022-03-22 01:00:27.959882+00:00" } ], "http://schema.org/dateModified": [ { "@value": "2025-03-05 00:50:09.143568+00:00" } ], "http://schema.org/datePublished": [ { "@value": "2022-03-22 01:00:27.959882+00:00" } ], "http://schema.org/description": [ { "@value": "All the input data required for simulating the immune state-associated signal and subsequent machine learning model training used in the article \"Profiling the baseline performance and limits of machine learning models for adaptive immune receptor repertoire classification\"" } ], "http://schema.org/encodingFormat": [ { "@value": "application/ld+json" } ], "http://schema.org/hasPart": [ { "@id": "https://w3id.org/ro-id/54524973-c104-4f19-aa26-b5986dadbd00/folders/294fc6ed-ef31-4840-b16b-2b353524a762" }, { "@id": "https://w3id.org/ro-id/54524973-c104-4f19-aa26-b5986dadbd00/folders/40dd6a60-4a65-49cc-b9c6-1a38c2f52828" }, { "@id": "https://w3id.org/ro-id/54524973-c104-4f19-aa26-b5986dadbd00/folders/99d3170c-f84c-483b-9829-e4ed8923425b" }, { "@id": "https://w3id.org/ro-id/54524973-c104-4f19-aa26-b5986dadbd00/folders/fac8ebb0-c06a-4723-9cf9-f4df3cf613b6" } ], "http://schema.org/identifier": [ { "@value": "https://w3id.org/ro-id/54524973-c104-4f19-aa26-b5986dadbd00" } ], "http://schema.org/license": [ { "@id": "https://choosealicense.com/no-permission/" } ], "http://schema.org/name": [ { "@value": "Data used in the article \"Profiling the baseline performance and limits of machine learning models for adaptive immune receptor repertoire classification\"" } ], "http://w3id.org/ro-id/rohub/model#creation_mode": [ { "@value": "MANUAL" } ], "@type": [ "http://purl.org/wf4ever/ro#ResearchObject", "http://purl.org/wf4ever/roevo#LiveRO", "http://schema.org/Dataset", "http://w3id.org/ro/earth-science#DataResearchObject", "https://w3id.org/ro/terms/earth-science#DataResearchObject" ], "https://www.w3.org/ns/iana/link-relations/relation#cite-as": [ { "@value": "Chakravarthi Kanduri. \"Data used in the article \"Profiling the baseline performance and limits of machine learning models for adaptive immune receptor repertoire classification\".\" ROHub. Mar 22 ,2022. https://w3id.org/ro-id/54524973-c104-4f19-aa26-b5986dadbd00." } ] }, { "@id": "mailto:chakravarthi.kanduri@rohub.com", "http://schema.org/email": [ { "@value": "chakravarthi.kanduri@rohub.com" } ], "http://schema.org/name": [ { "@value": "Chakravarthi Kanduri" } ], "@type": [ "http://xmlns.com/foaf/0.1/Agent" ] }, { "@id": "mailto:georgehadib@gmail.com", "http://schema.org/name": [ { "@value": "Geo H." } ], "@type": [ "http://xmlns.com/foaf/0.1/Agent" ] }, { "@id": "https://w3id.org/ro-id/54524973-c104-4f19-aa26-b5986dadbd00/folders/294fc6ed-ef31-4840-b16b-2b353524a762", "http://schema.org/name": [ { "@value": "metadata" } ], "@type": [ "http://purl.org/wf4ever/wf4ever#Folder", "http://schema.org/Dataset" ] }, { "@id": "https://w3id.org/ro-id/54524973-c104-4f19-aa26-b5986dadbd00/folders/40dd6a60-4a65-49cc-b9c6-1a38c2f52828", "http://schema.org/hasPart": [ { "@id": "https://w3id.org/ro-id/54524973-c104-4f19-aa26-b5986dadbd00/resources/9d33e2ab-889c-4510-8dd5-c383f6742d2f" } ], "http://schema.org/name": [ { "@value": "biblio" } ], "@type": [ "http://purl.org/wf4ever/wf4ever#Folder", "http://schema.org/Dataset" ] }, { "@id": "https://w3id.org/ro-id/54524973-c104-4f19-aa26-b5986dadbd00/folders/99d3170c-f84c-483b-9829-e4ed8923425b", "http://schema.org/hasPart": [ { "@id": "https://w3id.org/ro-id/54524973-c104-4f19-aa26-b5986dadbd00/resources/cd78d36f-9384-465d-ba29-51a6598889e2" } ], "http://schema.org/name": [ { "@value": "data" } ], "@type": [ "http://purl.org/wf4ever/wf4ever#Folder", "http://schema.org/Dataset" ] }, { "@id": "https://w3id.org/ro-id/54524973-c104-4f19-aa26-b5986dadbd00/folders/fac8ebb0-c06a-4723-9cf9-f4df3cf613b6", "http://schema.org/name": [ { "@value": "raw data" } ], "@type": [ "http://purl.org/wf4ever/wf4ever#Folder", "http://schema.org/Dataset" ] }, { "@id": "https://w3id.org/ro-id/54524973-c104-4f19-aa26-b5986dadbd00/resources/9d33e2ab-889c-4510-8dd5-c383f6742d2f", "http://schema.org/author": [ { "@id": "mailto:georgehadib@gmail.com" } ], "http://schema.org/contentUrl": [ { "@value": "https://www.biorxiv.org/content/10.1101/2021.05.23.445346v1" } ], "http://schema.org/creator": [ { "@id": "mailto:georgehadib@gmail.com" } ], "http://schema.org/dateCreated": [ { "@value": "2022-03-22 01:00:41.856083+00:00" } ], "http://schema.org/dateModified": [ { "@value": "2022-03-22 01:00:41.967825+00:00" } ], "http://schema.org/license": [ { "@id": "https://choosealicense.com/no-permission/" } ], "http://schema.org/name": [ { "@value": "https://www.biorxiv.org/content/10.1101/2021.05.23.445346v1" } ], "http://schema.org/sdDatePublished": [ { "@value": "2022-03-22 01:00:41.856083+00:00" } ], "@type": [ "http://purl.org/dc/terms/BibliographicResource", "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject" ] }, { "@id": "https://w3id.org/ro-id/54524973-c104-4f19-aa26-b5986dadbd00/resources/cd78d36f-9384-465d-ba29-51a6598889e2", "http://purl.org/dc/terms/bibliographicCitation": [ { "@value": "Kanduri, C. (2021).Data used in the article \"Profiling the baseline performance and limits of machine learning models for adaptive immune receptor repertoire classification\" [Data set]. Norstore. https://doi.org/10.11582/2021.00064" } ], "http://purl.org/dc/terms/rightsHolder": [ { "@value": "Chakravarthi Kanduri" } ], "http://purl.org/dc/terms/type": [ { "@value": "Model" } ], "http://schema.org/author": [ { "@id": "mailto:georgehadib@gmail.com" } ], "http://schema.org/contentUrl": [ { "@value": "https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00064" } ], "http://schema.org/creator": [ { "@id": "mailto:georgehadib@gmail.com" } ], "http://schema.org/dateCreated": [ { "@value": "None" } ], "http://schema.org/dateModified": [ { "@value": "2022-03-22 01:00:45.689738+00:00" } ], "http://schema.org/description": [ { "@value": "All the input data required for simulating the immune state-associated signal and subsequent machine learning model training used in the article \"Profiling the baseline performance and limits of machine learning models for adaptive immune receptor repertoire classification\"" } ], "http://schema.org/license": [ { "@id": "https://choosealicense.com/no-permission/" } ], "http://schema.org/name": [ { "@value": "Data used in the article \"Profiling the baseline performance and limits of machine learning models for adaptive immune receptor repertoire classification\"" } ], "http://schema.org/sdDatePublished": [ { "@value": "None" } ], "@type": [ "http://purl.org/wf4ever/wf4ever#Dataset", "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject" ], "https://schema.org/maintainer": [ { "@value": "Chakravarthi Kanduri" } ] }, { "@id": "https://w3id.org/ro-id/54524973-c104-4f19-aa26-b5986dadbd00/ro-crate-metadata.json", "http://purl.org/dc/terms/conformsTo": [ { "@id": "https://w3id.org/ro/crate/1.1" } ], "http://schema.org/about": [ { "@id": "https://w3id.org/ro-id/54524973-c104-4f19-aa26-b5986dadbd00/" } ], "@type": [ "http://schema.org/CreativeWork" ] } ] } ]