{"id":16634,"identifier":"JUELICH-DATA/5PB3GT","persistentUrl":"https://doi.org/10.26165/JUELICH-DATA/5PB3GT","protocol":"doi","authority":"10.26165","publisher":"Jülich DATA","publicationDate":"2024-10-09","storageIdentifier":"s3://10.26165/JUELICH-DATA/5PB3GT","datasetVersion":{"id":595,"datasetId":16634,"datasetPersistentId":"doi:10.26165/JUELICH-DATA/5PB3GT","storageIdentifier":"s3://10.26165/JUELICH-DATA/5PB3GT","versionNumber":1,"versionMinorNumber":1,"versionState":"RELEASED","lastUpdateTime":"2024-11-18T15:47:48Z","releaseTime":"2024-11-18T15:47:48Z","createTime":"2024-11-18T14:44:01Z","license":"CC0","termsOfUse":"CC0 Waiver","fileAccessRequest":false,"metadataBlocks":{"citation":{"displayName":"Citation Metadata","fields":[{"typeName":"title","multiple":false,"typeClass":"primitive","value":"SimCATS_GaAs_v1_random_variations_v2"},{"typeName":"author","multiple":true,"typeClass":"compound","value":[{"authorName":{"typeName":"authorName","multiple":false,"typeClass":"primitive","value":"Hader, Fabian"},"authorAffiliation":{"typeName":"authorAffiliation","multiple":false,"typeClass":"primitive","value":"ZEA-2"},"authorIdentifierScheme":{"typeName":"authorIdentifierScheme","multiple":false,"typeClass":"controlledVocabulary","value":"ORCID"},"authorIdentifier":{"typeName":"authorIdentifier","multiple":false,"typeClass":"primitive","value":"0000-0003-3490-3227"}},{"authorName":{"typeName":"authorName","multiple":false,"typeClass":"primitive","value":"Fuchs, Fabian"},"authorIdentifierScheme":{"typeName":"authorIdentifierScheme","multiple":false,"typeClass":"controlledVocabulary","value":"ORCID"},"authorIdentifier":{"typeName":"authorIdentifier","multiple":false,"typeClass":"primitive","value":"0009-0009-1060-6093"}},{"authorName":{"typeName":"authorName","multiple":false,"typeClass":"primitive","value":"Fleitmann, Sarah"},"authorAffiliation":{"typeName":"authorAffiliation","multiple":false,"typeClass":"primitive","value":"ZEA-2"},"authorIdentifierScheme":{"typeName":"authorIdentifierScheme","multiple":false,"typeClass":"controlledVocabulary","value":"ORCID"},"authorIdentifier":{"typeName":"authorIdentifier","multiple":false,"typeClass":"primitive","value":"0000-0002-7699-5708"}}]},{"typeName":"datasetContact","multiple":true,"typeClass":"compound","value":[{"datasetContactName":{"typeName":"datasetContactName","multiple":false,"typeClass":"primitive","value":"Hader, Fabian"},"datasetContactAffiliation":{"typeName":"datasetContactAffiliation","multiple":false,"typeClass":"primitive","value":"ZEA-2"},"datasetContactEmail":{"typeName":"datasetContactEmail","multiple":false,"typeClass":"primitive","value":"f.hader@fz-juelich.de"}}]},{"typeName":"dsDescription","multiple":true,"typeClass":"compound","value":[{"dsDescriptionValue":{"typeName":"dsDescriptionValue","multiple":false,"typeClass":"primitive","value":"

Dataset: SimCATS_GaAs_v1_random_variations_v2

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Simulated data from the geometric SimCATS model (GitHub Repository, Paper) for benchmarking of semiconductor quantum dot tuning algorithms.

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Generated using this Jupyter Notebook and used for the final evaluation in Automated Charge Transition Detection in Quantum Dot Charge Stability Diagrams.

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Key Facts

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Usage

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To load the data, e.g. for calculating metrics, please have a look at SimCATS-Datasets (GitHub Repository, ReadTheDocs). The dataset can be loaded as numpy arrays using the function load_dataset or as PyTorch Dataset class (for machine learning purposes) using the class SimcatsDataset.

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