{"status":"OK","data":{"id":15093,"identifier":"FK2/DRYP80","persistentUrl":"https://doi.org/10.60507/FK2/DRYP80","protocol":"doi","authority":"10.60507","separator":"/","publisher":"bonndata","publicationDate":"2026-01-08","storageIdentifier":"file://10.60507/FK2/DRYP80","metadataLanguage":"en","datasetType":"dataset","latestVersion":{"id":573,"datasetId":15093,"datasetPersistentId":"doi:10.60507/FK2/DRYP80","datasetType":"dataset","storageIdentifier":"file://10.60507/FK2/DRYP80","versionNumber":2,"internalVersionNumber":8,"versionMinorNumber":0,"versionState":"RELEASED","latestVersionPublishingState":"DRAFT","lastUpdateTime":"2026-01-26T15:28:35Z","releaseTime":"2026-01-26T15:28:35Z","createTime":"2026-01-26T08:35:09Z","publicationDate":"2026-01-08","citationDate":"2026-01-08","license":{"name":"MIT","uri":"https://mit-license.org/","iconUri":"/imglibs/logos/lic/mit.svg"},"fileAccessRequest":true,"metadataBlocks":{"citation":{"displayName":"Citation Metadata","name":"citation","fields":[{"typeName":"title","multiple":false,"typeClass":"primitive","value":"Implementation of the paper \"SCANNER+: Neighborhood-based self-enrichment approach for traffic speed prediction\""},{"typeName":"alternativeURL","multiple":false,"typeClass":"primitive","value":"https://github.com/D-Stiv/SCANNER_plus/releases/tag/v1.0.0"},{"typeName":"author","multiple":true,"typeClass":"compound","value":[{"authorName":{"typeName":"authorName","multiple":false,"typeClass":"primitive","value":"Gounoue, Steve"},"authorAffiliation":{"typeName":"authorAffiliation","multiple":false,"typeClass":"primitive","value":"Data Science and Intelligent Systems Group (DSIS), University of Bonn"},"authorIdentifierScheme":{"typeName":"authorIdentifierScheme","multiple":false,"typeClass":"controlledVocabulary","value":"ORCID"},"authorIdentifier":{"typeName":"authorIdentifier","multiple":false,"typeClass":"primitive","value":"https://orcid.org/0009-0003-4580-3173","expandedvalue":{"personName":"GOUNOUE GUIFFO, Steve Ludovic","@id":"https://orcid.org/0009-0003-4580-3173","scheme":"ORCID","@type":"https://schema.org/Person"}}}]},{"typeName":"datasetContact","multiple":true,"typeClass":"compound","value":[{"datasetContactName":{"typeName":"datasetContactName","multiple":false,"typeClass":"primitive","value":"Gounoue, Steve"},"datasetContactAffiliation":{"typeName":"datasetContactAffiliation","multiple":false,"typeClass":"primitive","value":"Data Science and Intelligent Systems Group (DSIS), University of Bonn"},"datasetContactEmail":{"typeName":"datasetContactEmail","multiple":false,"typeClass":"primitive","value":"steve.gounoue@cs.uni-bonn.de"}}]},{"typeName":"dsDescription","multiple":true,"typeClass":"compound","value":[{"dsDescriptionValue":{"typeName":"dsDescriptionValue","multiple":false,"typeClass":"primitive","value":"In this repository, you can find the code to train and evaluate SCANNER+, a novel neighborhood-based self-enrichment approach for traffic speed prediction. SCANNER+ learns effective node representations in dynamic road traffic settings. This work extends SCANNER, which utilizes correlation-based pattern detection and a self-enrichment mechanism."}}]},{"typeName":"subject","multiple":true,"typeClass":"controlledVocabulary","value":["Computer and Information Science","Engineering"]},{"typeName":"freeKeyword","multiple":true,"typeClass":"compound","value":[{"freeKeywordValue":{"typeName":"freeKeywordValue","multiple":false,"typeClass":"primitive","value":"Traffic speed prediction"}},{"freeKeywordValue":{"typeName":"freeKeywordValue","multiple":false,"typeClass":"primitive","value":"Spatio-temporal enrichment"}}]},{"typeName":"publication","multiple":true,"typeClass":"compound","value":[{"publicationRelationType":{"typeName":"publicationRelationType","multiple":false,"typeClass":"controlledVocabulary","value":"IsSupplementTo"},"publicationCitation":{"typeName":"publicationCitation","multiple":false,"typeClass":"primitive","value":"Steve Gounoue, Ran Yu, and Elena Demidova. 2026. SCANNER+: Neighborhood-based self-enrichment approach for traffic speed prediction. ACM Transactions on Spatial Algorithms and Systems"}},{"publicationRelationType":{"typeName":"publicationRelationType","multiple":false,"typeClass":"controlledVocabulary","value":"References"},"publicationCitation":{"typeName":"publicationCitation","multiple":false,"typeClass":"primitive","value":"Steve Gounoue, Ran Yu, and Elena Demidova. 2023. SCANNER: A Spatio-temporal Correlation and Neighborhood-based Feature Enrichment for Traffic Prediction. In Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems (SIGSPATIAL '23). Association for Computing Machinery, New York, NY, USA, Article 103, 1–4."},"publicationIDType":{"typeName":"publicationIDType","multiple":false,"typeClass":"controlledVocabulary","value":"doi"},"publicationIDNumber":{"typeName":"publicationIDNumber","multiple":false,"typeClass":"primitive","value":"10.1145/3589132.3625653"},"publicationURL":{"typeName":"publicationURL","multiple":false,"typeClass":"primitive","value":"https://doi.org/10.1145/3589132.3625653"}}]},{"typeName":"depositor","multiple":false,"typeClass":"primitive","value":"Gounoue Guiffo, Steve"},{"typeName":"dateOfDeposit","multiple":false,"typeClass":"primitive","value":"2026-01-06"},{"typeName":"relatedMaterial","multiple":true,"typeClass":"primitive","value":["SCANNER: A Spatio-temporal Correlation and Neighborhood-based Feature Enrichment for Traffic Prediction: https://github.com/D-Stiv/SCANNER"]}]},"codeMeta20":{"displayName":"Software Metadata (CodeMeta v2.0)","name":"codeMeta20","fields":[{"typeName":"codeVersion","multiple":false,"typeClass":"primitive","value":"v1.0.0"},{"typeName":"developmentStatus","multiple":false,"typeClass":"controlledVocabulary","value":"Inactive"},{"typeName":"codeRepository","multiple":true,"typeClass":"primitive","value":["https://github.com/D-Stiv/SCANNER_plus"]},{"typeName":"programmingLanguage","multiple":true,"typeClass":"primitive","value":["Python"]},{"typeName":"runtimePlatform","multiple":true,"typeClass":"primitive","value":["Python 3.8.30"]},{"typeName":"operatingSystem","multiple":true,"typeClass":"primitive","value":["Any 64-bit OS supporting Python and PyTorch (e.g., Linux, macOS, Windows)"]},{"typeName":"softwareRequirementsItem","multiple":true,"typeClass":"compound","value":[{"softwareRequirements":{"typeName":"softwareRequirements","multiple":false,"typeClass":"primitive","value":"PyTorch 1.9.0+cu111"}},{"softwareRequirements":{"typeName":"softwareRequirements","multiple":false,"typeClass":"primitive","value":"NumPy 1.23.5"}},{"softwareRequirements":{"typeName":"softwareRequirements","multiple":false,"typeClass":"primitive","value":"Pandas 2.2.3"}},{"softwareRequirements":{"typeName":"softwareRequirements","multiple":false,"typeClass":"primitive","value":"Scikit-learn 1.6.1"}},{"softwareRequirements":{"typeName":"softwareRequirements","multiple":false,"typeClass":"primitive","value":"Scipy 1.13.1"}}]},{"typeName":"readme","multiple":false,"typeClass":"primitive","value":"https://github.com/D-Stiv/SCANNER_plus/blob/master/README.md"}]}},"files":[{"label":".gitignore","restricted":false,"version":1,"datasetVersionId":573,"dataFile":{"id":15773,"persistentId":"doi:10.60507/FK2/DRYP80/A33WIR","pidURL":"https://doi.org/10.60507/FK2/DRYP80/A33WIR","filename":".gitignore","contentType":"text/plain","friendlyType":"Plain Text","filesize":3443,"storageIdentifier":"file://19bf972a31c-5001b27f5a69","rootDataFileId":-1,"md5":"be85f81e6cc8066629a0464226450c3c","checksum":{"type":"MD5","value":"be85f81e6cc8066629a0464226450c3c"},"tabularData":false,"creationDate":"2026-01-26","publicationDate":"2026-01-26","fileAccessRequest":true}},{"label":"LICENSE","restricted":false,"version":1,"datasetVersionId":573,"dataFile":{"id":15771,"persistentId":"doi:10.60507/FK2/DRYP80/T6S3OE","pidURL":"https://doi.org/10.60507/FK2/DRYP80/T6S3OE","filename":"LICENSE","contentType":"text/plain; charset=US-ASCII","friendlyType":"Plain Text","filesize":1063,"storageIdentifier":"file://19bf972ab02-7243d017f7aa","rootDataFileId":-1,"md5":"9166a54d6b45565ba0e1420c2de7fdf0","checksum":{"type":"MD5","value":"9166a54d6b45565ba0e1420c2de7fdf0"},"tabularData":false,"creationDate":"2026-01-26","publicationDate":"2026-01-26","fileAccessRequest":true}},{"label":"README.md","restricted":false,"version":1,"datasetVersionId":573,"dataFile":{"id":15767,"persistentId":"doi:10.60507/FK2/DRYP80/6OIJN2","pidURL":"https://doi.org/10.60507/FK2/DRYP80/6OIJN2","filename":"README.md","contentType":"text/markdown","friendlyType":"Markdown Text","filesize":4213,"storageIdentifier":"file://19bf972b913-6d1cdd3ae15a","rootDataFileId":-1,"md5":"e0d58f8633a84bea05fec828fc1e5f07","checksum":{"type":"MD5","value":"e0d58f8633a84bea05fec828fc1e5f07"},"tabularData":false,"creationDate":"2026-01-26","publicationDate":"2026-01-26","fileAccessRequest":true}},{"label":"config.py","restricted":false,"version":1,"datasetVersionId":573,"dataFile":{"id":15764,"persistentId":"doi:10.60507/FK2/DRYP80/LPTNMA","pidURL":"https://doi.org/10.60507/FK2/DRYP80/LPTNMA","filename":"config.py","contentType":"text/x-python","friendlyType":"Python Source Code","filesize":4979,"storageIdentifier":"file://19bf972a490-7447a5cd0750","rootDataFileId":-1,"md5":"0389a89900e28058294d79ee957393cf","checksum":{"type":"MD5","value":"0389a89900e28058294d79ee957393cf"},"tabularData":false,"creationDate":"2026-01-26","publicationDate":"2026-01-26","fileAccessRequest":true}},{"label":"correlations.py","restricted":false,"version":1,"datasetVersionId":573,"dataFile":{"id":15775,"persistentId":"doi:10.60507/FK2/DRYP80/WOXBUF","pidURL":"https://doi.org/10.60507/FK2/DRYP80/WOXBUF","filename":"correlations.py","contentType":"text/x-python","friendlyType":"Python Source Code","filesize":4196,"storageIdentifier":"file://19bf972aa22-eed559a1d5a5","rootDataFileId":-1,"md5":"3dafcd83c5833accf319e6363b0521c4","checksum":{"type":"MD5","value":"3dafcd83c5833accf319e6363b0521c4"},"tabularData":false,"creationDate":"2026-01-26","publicationDate":"2026-01-26","fileAccessRequest":true}},{"label":"loader.py","restricted":false,"version":1,"datasetVersionId":573,"dataFile":{"id":15772,"persistentId":"doi:10.60507/FK2/DRYP80/TANYCH","pidURL":"https://doi.org/10.60507/FK2/DRYP80/TANYCH","filename":"loader.py","contentType":"text/x-python","friendlyType":"Python Source Code","filesize":9823,"storageIdentifier":"file://19bf972ab4c-84449a882833","rootDataFileId":-1,"md5":"0bc47c5210de4b0479c71c968b9aadb9","checksum":{"type":"MD5","value":"0bc47c5210de4b0479c71c968b9aadb9"},"tabularData":false,"creationDate":"2026-01-26","publicationDate":"2026-01-26","fileAccessRequest":true}},{"label":"main.py","restricted":false,"version":1,"datasetVersionId":573,"dataFile":{"id":15765,"persistentId":"doi:10.60507/FK2/DRYP80/RACTZI","pidURL":"https://doi.org/10.60507/FK2/DRYP80/RACTZI","filename":"main.py","contentType":"text/x-python","friendlyType":"Python Source Code","filesize":2900,"storageIdentifier":"file://19bf972adc1-ae8518cc9c93","rootDataFileId":-1,"md5":"1d18549aaaf4c8b13ae9e2a6855f1099","checksum":{"type":"MD5","value":"1d18549aaaf4c8b13ae9e2a6855f1099"},"tabularData":false,"creationDate":"2026-01-26","publicationDate":"2026-01-26","fileAccessRequest":true}},{"label":"metrics.py","restricted":false,"version":1,"datasetVersionId":573,"dataFile":{"id":15769,"persistentId":"doi:10.60507/FK2/DRYP80/RYE7N9","pidURL":"https://doi.org/10.60507/FK2/DRYP80/RYE7N9","filename":"metrics.py","contentType":"text/x-python","friendlyType":"Python Source Code","filesize":3706,"storageIdentifier":"file://19bf972ae9e-95cad68785e9","rootDataFileId":-1,"md5":"821a8b8c9d075a387d5c5901b31c4b84","checksum":{"type":"MD5","value":"821a8b8c9d075a387d5c5901b31c4b84"},"tabularData":false,"creationDate":"2026-01-26","publicationDate":"2026-01-26","fileAccessRequest":true}},{"label":"models.py","restricted":false,"version":1,"datasetVersionId":573,"dataFile":{"id":15774,"persistentId":"doi:10.60507/FK2/DRYP80/BL8SYK","pidURL":"https://doi.org/10.60507/FK2/DRYP80/BL8SYK","filename":"models.py","contentType":"text/x-python","friendlyType":"Python Source Code","filesize":11955,"storageIdentifier":"file://19bf972af3e-01fdee816e30","rootDataFileId":-1,"md5":"c9ea92936a88e78c8e37215c261226da","checksum":{"type":"MD5","value":"c9ea92936a88e78c8e37215c261226da"},"tabularData":false,"creationDate":"2026-01-26","publicationDate":"2026-01-26","fileAccessRequest":true}},{"label":"requirements.txt","restricted":false,"version":1,"datasetVersionId":573,"dataFile":{"id":15768,"persistentId":"doi:10.60507/FK2/DRYP80/S6S5LN","pidURL":"https://doi.org/10.60507/FK2/DRYP80/S6S5LN","filename":"requirements.txt","contentType":"text/plain","friendlyType":"Plain Text","filesize":324,"storageIdentifier":"file://19bf972bedb-7396854407b8","rootDataFileId":-1,"md5":"b8e3464bca1e99800beec8e671e19f91","checksum":{"type":"MD5","value":"b8e3464bca1e99800beec8e671e19f91"},"tabularData":false,"creationDate":"2026-01-26","publicationDate":"2026-01-26","fileAccessRequest":true}},{"label":"stnorm.py","restricted":false,"version":1,"datasetVersionId":573,"dataFile":{"id":15763,"persistentId":"doi:10.60507/FK2/DRYP80/KAHIZK","pidURL":"https://doi.org/10.60507/FK2/DRYP80/KAHIZK","filename":"stnorm.py","contentType":"text/x-python","friendlyType":"Python Source Code","filesize":2130,"storageIdentifier":"file://19bf9711968-d90cbe10b5f2","rootDataFileId":-1,"md5":"504e2317ef971d65b290ab33cb6607dc","checksum":{"type":"MD5","value":"504e2317ef971d65b290ab33cb6607dc"},"tabularData":false,"creationDate":"2026-01-26","publicationDate":"2026-01-26","fileAccessRequest":true}},{"label":"trainer.py","restricted":false,"version":1,"datasetVersionId":573,"dataFile":{"id":15766,"persistentId":"doi:10.60507/FK2/DRYP80/9Y3ASY","pidURL":"https://doi.org/10.60507/FK2/DRYP80/9Y3ASY","filename":"trainer.py","contentType":"text/x-python","friendlyType":"Python Source Code","filesize":9761,"storageIdentifier":"file://19bf972bff4-3d08a311940a","rootDataFileId":-1,"md5":"8527d7647042d0aa37a228828ca31c4c","checksum":{"type":"MD5","value":"8527d7647042d0aa37a228828ca31c4c"},"tabularData":false,"creationDate":"2026-01-26","publicationDate":"2026-01-26","fileAccessRequest":true}},{"label":"util.py","restricted":false,"version":1,"datasetVersionId":573,"dataFile":{"id":15770,"persistentId":"doi:10.60507/FK2/DRYP80/OIP0J0","pidURL":"https://doi.org/10.60507/FK2/DRYP80/OIP0J0","filename":"util.py","contentType":"text/x-python","friendlyType":"Python Source Code","filesize":366,"storageIdentifier":"file://19bf972c227-600068d2b5d9","rootDataFileId":-1,"md5":"38ea4a944cc63023dbc079d1a8f428c6","checksum":{"type":"MD5","value":"38ea4a944cc63023dbc079d1a8f428c6"},"tabularData":false,"creationDate":"2026-01-26","publicationDate":"2026-01-26","fileAccessRequest":true}}]}}}