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When using this dataset, please cite the accompanying preprint:\n\n<blockquote>\nvan der Kuil, T., Westerbeek, E., de Bruin, J., Fang, Q., &amp; van de Schoot, R. (2026). SYNERGY+: A Large-Scale, Curated Benchmark Dataset of Expert Screening Decisions for Systematic Reviews. arXiv preprint. <a href=\"https://arxiv.org/abs/XXXX.XXXXX\">https://arxiv.org/abs/XXXX.XXXXX</a>\n</blockquote>\n\nThe preprint contains a full description of what is in the dataset, an overview of steps taken, and the processes followed. For a quick overview: this new version now covers 119 systematic reviews, bringing the total number of records to 494.203. In total 168.948 are classified as open-access by OpenAlex and used in the official benchmark. We have gathered, checked and thoroughly cleaned all the abstracts.\n<br><br>\nSimilar to SYNERGY, this dataset can be used in various fields and for several types of research. But with the addition of more and cleaner records, the options for answering more specific questions will increase. As with the earlier version of SYNERGY, the dataset contains a large number of variables for each record, provided by OpenAlex. \nAdditionaly, we have gathered and extracted information on each of the included reviews. This data can be found in <i>reviews.csv</i>, along with an explanation about the data in <i>codebook.pdf</i>.\n<br><br>\nWe thank the authors of all systematic reviews included for publishing their data, please see <a href=\"https://github.com/asreview/synergy-dataset/blob/master/ATTRIBUTION.md\">this page</a> for a full overview.\n<br><br>\nThe easiest way to get the SYNERGY+ dataset is via the synergy-dataset Python package. Please read <a href=\"https://github.com/asreview/synergy-dataset\">this page</a> for how to do that."},"dsDescriptionDate":{"typeName":"dsDescriptionDate","multiple":false,"typeClass":"primitive","value":"2026-05-28"}}]},{"typeName":"subject","multiple":true,"typeClass":"controlledVocabulary","value":["Computer and Information Science","Engineering","Medicine, Health and Life Sciences","Social Sciences"]},{"typeName":"keyword","multiple":true,"typeClass":"compound","value":[{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"systematic-review"}},{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"machine learning"}},{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"natural language processing"}},{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"graph"}},{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"prisma"}}]},{"typeName":"publication","multiple":true,"typeClass":"compound","value":[{"publicationRelationType":{"typeName":"publicationRelationType","multiple":false,"typeClass":"controlledVocabulary","value":"IsSupplementTo"},"publicationCitation":{"typeName":"publicationCitation","multiple":false,"typeClass":"primitive","value":"van der Kuil, T., Westerbeek, E., de Bruin, J., Fang, Q., &amp; van de Schoot, R. 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