# VizNet: Towards A Large-Scale Visualization Learning and Benchmarking Repository

arXiv: https://arxiv.org/abs/1905.04616 · HTML: https://mlsanity.com/paper/1905.04616/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 May 2019.

## Built on by (most cited first, 28 of 28)

- [TABBIE: Pretrained Representations of Tabular Data](https://mlsanity.com/paper/2105.02584/) (2021)
- [GitTables: A Large-Scale Corpus of Relational Tables](https://mlsanity.com/paper/2106.07258/) (2021)
- [VisEval: A Benchmark for Data Visualization in the Era of Large Language Models](https://mlsanity.com/paper/2407.00981/) (2024)
- [Annotating Columns with Pre-trained Language Models](https://mlsanity.com/paper/2104.01785/) (2021)
- [CHORUS: Foundation Models for Unified Data Discovery and Exploration](https://mlsanity.com/paper/2306.09610/) (2023)
- [TabLib: A Dataset of 627M Tables with Context](https://mlsanity.com/paper/2310.07875/) (2023)
- [Generative AI for Visualization: State of the Art and Future Directions](https://mlsanity.com/paper/2404.18144/) (2024)
- [ArcheType: A Novel Framework for Open-Source Column Type Annotation using Large Language Models](https://mlsanity.com/paper/2310.18208/) (2023)
- [LakeBench: Benchmarks for Data Discovery over Data Lakes](https://mlsanity.com/paper/2307.04217/) (2023)
- [Mind the Data Gap: Bridging LLMs to Enterprise Data Integration](https://mlsanity.com/paper/2412.20331/) (2024)
- [SimVecVis: A Dataset for Enhancing MLLMs in Visualization Understanding](https://mlsanity.com/paper/2506.21319/) (2025)
- [Agnostic Visual Recommendation Systems: Open Challenges and Future Directions](https://mlsanity.com/paper/2302.00569/) (2023)
- [InfoDet: A Dataset for Infographic Element Detection](https://mlsanity.com/paper/2505.17473/) (2025)
- [Graph Neural Network Approach to Semantic Type Detection in Tables](https://mlsanity.com/paper/2405.00123/) (2024)
- [KGLink: A column type annotation method that combines knowledge graph and pre-trained language model](https://mlsanity.com/paper/2406.00318/) (2024)
- [Personalized Visualization Recommendation](https://mlsanity.com/paper/2102.06343/) (2021)
- [Making Multimodal LLMs Reliable Chart Data Extractors: A Benchmark and Training Framework](https://mlsanity.com/paper/2606.29808/) (2026)
- [Social influence leads to the formation of diverse local trends](https://mlsanity.com/paper/2108.07437/) (2021)
- [VizAI : Selecting Accurate Visualizations of Numerical Data](https://mlsanity.com/paper/2111.04190/) (2021)
- [An Image-based Typology for Visualization](https://mlsanity.com/paper/2403.05594/) (2024)
- [TabSketchFM: Sketch-based Tabular Representation Learning for Data Discovery over Data Lakes](https://mlsanity.com/paper/2407.01619/) (2024)
- [Robust LLM-based Column Type Annotation via Prompt Augmentation with LoRA Tuning](https://mlsanity.com/paper/2512.22742/) (2025)
- [DCoM: A Deep Column Mapper for Semantic Data Type Detection](https://mlsanity.com/paper/2106.12871/) (2021)
- [ASTA: Learning Analytical Semantics over Tables for Intelligent Data Analysis and Visualization](https://mlsanity.com/paper/2208.01043/) (2022)
- [Comprehending Semantic Types in JSON Data with Graph Neural Networks](https://mlsanity.com/paper/2307.12807/) (2023)
- [ModelTables: A Corpus of Tables about Models](https://mlsanity.com/paper/2512.16106/) (2025)
- [Glyph: A Multi-Strategy Agentic System for Column Description and Sensitivity-Ontology Tagging of Enterprise Data Catalogs](https://mlsanity.com/paper/2609.10430/) (2026)
- [An Explainable Header-Centric Framework for Large-Scale Semantic Table Interpretation and Data Quality Assessment](https://mlsanity.com/paper/2610.10541/) (2026)

## Abstract (arXiv)

Researchers currently rely on ad hoc datasets to train automated visualization tools and evaluate the effectiveness of visualization designs. These exemplars often lack the characteristics of real-world datasets, and their one-off nature makes it difficult to compare different techniques. In this paper, we present VizNet: a large-scale corpus of over 31 million datasets compiled from open data repositories and online visualization galleries. On average, these datasets comprise 17 records over 3 dimensions and across the corpus, we find 51% of the dimensions record categorical data, 44% quantitative, and only 5% temporal. VizNet provides the necessary common baseline for comparing visualization design techniques, and developing benchmark models and algorithms for automating visual analysis. To demonstrate VizNet's utility as a platform for conducting online crowdsourced experiments at scale, we replicate a prior study assessing the influence of user task and data distribution on visual encoding effectiveness, and extend it by considering an additional task: outlier detection. To contend with running such studies at scale, we demonstrate how a metric of perceptual effectiveness can be learned from experimental results, and show its predictive power across test datasets.