{"url":"/dataset/dtbm","name":"DTBM","full_name":"Digital Twin Benchmark Model","description_markdown":"**DTBM** is a benchmark dataset for Digital Twins that reflects these characteristics and look into the scaling challenges of different knowledge graph technologies.\r\n\r\nSource: [Scaling Knowledge Graphs for Automating AI of Digital Twins](https://arxiv.org/pdf/2210.14596v1.pdf)\r\n\r\nImage Source: [https://arxiv.org/pdf/2210.14596v1.pdf](https://arxiv.org/pdf/2210.14596v1.pdf)","description_withheld":null,"homepage":"https://github.com/ibm/digital-twin-benchmark-model","introduced_date":"2022-10-26","introduced_date_note":null,"introduced_by":{"paper":"/paper/scaling-knowledge-graphs-for-automating-ai-of","title":"Scaling Knowledge Graphs for Automating AI of Digital Twins","first_author":"Joern Ploennigs","url":null},"license":{"name":"MIT license","url":"https://github.com/IBM/digital-twin-benchmark-model/blob/main/LICENSE"},"modalities":[],"tasks":[{"name":"Knowledge Graphs","url":"/task/knowledge-graphs","datasets_with_task":"/datasets/task/knowledge-graphs"}],"languages":[],"variants":["DTBM"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}