datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
ChartDQAadaption-charts-p2-gold
Adaption Charts P2 — Gold Chart-QA Dataset
A verified, quality-first chart question-answering dataset built for the
Adaption Labs AutoScientist Challenge (Part 2, Data Visualization track).
Two sources: a programmatically generated synthetic core
(correct-by-construction) and a hand-authored hardset built from real
public dashboards and reports.
At a glance
3803 rows total — 3705 synthetic + 98 hardset
7 chart types — bar, line, grouped_bar, stacked_bar, pie… See the full description on the dataset page: https://huggingface.co/datasets/vinod-anbalagan/adaption-charts-p2-gold.ChartMuseum
[NeurIPS 2025] ChartMuseum: Testing Visual Reasoning Capabilities of Large Vision-Language Models
Authors: Liyan Tang, Grace Kim, Xinyu Zhao, Thom Lake, Wenxuan Ding, Fangcong Yin, Prasann Singhal, Manya Wadhwa, Zeyu Leo Liu, Zayne Sprague, Ramya Namuduri, Bodun Hu, Juan Diego Rodriguez, Puyuan Peng, Greg Durrett
Leaderboard 🥇 | Paper 📃 | Code 💻
Overview
ChartMuseum is a chart question answering benchmark designed to evaluate reasoning capabilities of large… See the full description on the dataset page: https://huggingface.co/datasets/lytang/ChartMuseum.ChartQAChartX
ChartX & ChartVLM: A Versatile Benchmark and Foundation Model for Complicated Chart Reasoning
[ Related Paper ] [ Website ] [Models 🤗(Hugging Face)]
ChartX & ChartVLM
Recently, many versatile Multi-modal Large Language Models (MLLMs) have emerged continuously. However, their capacity to query information depicted in visual charts and engage in reasoning based on the queried contents remains under-explored. In this paper, to comprehensively and rigorously benchmark the ability… See the full description on the dataset page: https://huggingface.co/datasets/InternScience/ChartX.Chartographer
Chartographer
Chartographer is a chart reasoning dataset for evaluating whether
vision-language models answer chart questions through visual reasoning rather
than shortcuts or prior familiarity with a chart. Each chart-question family
contains an upstream original chart, a reconstructed chart, and ten
seed-controlled counterfactual variants with the same Chartographer chart_id
and question_id.
More details on the construction pipeline and evaluation protocol are available
in the… See the full description on the dataset page: https://huggingface.co/datasets/1fanj/Chartographer.gridline-chartqa
Adaption Charts P2 — Gold Chart-QA Dataset
A verified, quality-first chart question-answering dataset built for the
Adaption Labs AutoScientist Challenge (Part 2, Data Visualization track).
Two sources: a programmatically generated synthetic core
(correct-by-construction) and a hand-authored hardset built from real
public dashboards and reports.
At a glance
1415 rows total — 1317 synthetic + 98 hardset
7 chart types — bar, line, grouped_bar, stacked_bar, pie… See the full description on the dataset page: https://huggingface.co/datasets/vinod-anbalagan/gridline-chartqa.Chart-MRAG
Benchmarking Multimodal RAG through a Chart-based Document Question-Answering Generation Framework
Overview
Multimodal Retrieval-Augmented Generation (MRAG) enhances reasoning capabilities by integrating external knowledge. However, existing benchmarks primarily focus on simple image-text interactions, overlooking complex visual formats like charts that are prevalent in real-world applications. In this work, we introduce a novel task, Chart-based MRAG, to address this… See the full description on the dataset page: https://huggingface.co/datasets/ymyang/Chart-MRAG.chart-reasoning-verified
chart-reasoning-verified
Chart reasoning examples generated from an explicit latent representation.
The data, the question and the answer are computed before the chart is
drawn, so the image is a rendering of known ground truth rather than the
source of it. No model was asked to label anything.
Each row carries both a rendered chart and a text serialisation of the same
chart, so the set is usable for vision-language training and for text-only
language model training without… See the full description on the dataset page: https://huggingface.co/datasets/vinod-anbalagan/chart-reasoning-verified.ChartMuseum
gsarch/ChartMuseum
This dataset includes images and annotations with keys:
image, question, answer, reasoning_type, source, hash.
Splits
test: 1000 rows
dev: 162 rows
Images are embedded via the datasets.Image feature, so they are available
directly when loading the dataset with datasets.load_dataset("gsarch/ChartMuseum").