# OpenDataLab: Empowering General Artificial Intelligence with Open Datasets

arXiv: https://arxiv.org/abs/2407.13773 · HTML: https://mlsanity.com/paper/2407.13773/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 14 references that are in the map.
- First posted on arXiv 4 Jun 2024.

## Builds on (most cited first, 6 of 6)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts](https://mlsanity.com/paper/2102.08981/) (2021)
- [InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation](https://mlsanity.com/paper/2307.06942/) (2023)
- [WanJuan: A Comprehensive Multimodal Dataset for Advancing English and Chinese Large Models](https://mlsanity.com/paper/2308.10755/) (2023)
- [DSDL: Data Set Description Language for Bridging Modalities and Tasks in AI Data](https://mlsanity.com/paper/2405.18315/) (2024)

## Built on by (most cited first, 29 of 29)

- [OpenING: A Comprehensive Benchmark for Judging Open-ended Interleaved Image-Text Generation](https://mlsanity.com/paper/2411.18499/) (2024)
- [ANAH-v2: Scaling Analytical Hallucination Annotation of Large Language Models](https://mlsanity.com/paper/2407.04693/) (2024)
- [Consensus Entropy: Harnessing Multi-VLM Agreement for Self-Verifying and Self-Improving OCR](https://mlsanity.com/paper/2504.11101/) (2025)
- [Beyond "Not Novel Enough": Enriching Scholarly Critique with LLM-Assisted Feedback](https://mlsanity.com/paper/2508.10795/) (2025)
- [Eigen-1: Adaptive Multi-Agent Refinement with Monitor-Based RAG for Scientific Reasoning](https://mlsanity.com/paper/2509.21193/) (2025)
- [DECKBench: Benchmarking Multi-Agent Frameworks for Academic Slide Generation and Editing](https://mlsanity.com/paper/2602.13318/) (2026)
- [MSEarth: A Multimodal Benchmark for Earth Science Phenomenon Discovery with MLLMs](https://mlsanity.com/paper/2505.20740/) (2025)
- [EarthSE: A Benchmark for Evaluating Earth Scientific Exploration Capability of LLMs](https://mlsanity.com/paper/2505.17139/) (2025)
- [The Northeast Materials Database for Magnetic Materials](https://mlsanity.com/paper/2409.15675/) (2024)
- [ProMind-LLM: Proactive Mental Health Care via Causal Reasoning with Sensor Data](https://mlsanity.com/paper/2505.14038/) (2025)
- [Easy Dataset: A Unified and Extensible Framework for Synthesizing LLM Fine-Tuning Data from Unstructured Documents](https://mlsanity.com/paper/2507.04009/) (2025)
- [LegalOne: A Family of Foundation Models for Reliable Legal Reasoning](https://mlsanity.com/paper/2602.00642/) (2026)
- [SciDataCopilot: An Agentic Data Preparation Framework for AGI-driven Scientific Discovery](https://mlsanity.com/paper/2602.09132/) (2026)
- [Investigating Public Fine-Tuning Datasets: A Complex Review of Current Practices from a Construction Perspective](https://mlsanity.com/paper/2407.08475/) (2024)
- [DeepForm: Reasoning Large Language Model for Communication System Formulation](https://mlsanity.com/paper/2506.08551/) (2025)
- [VeritasFi: An Adaptable, Multi-tiered RAG Framework for Multi-modal Financial Question Answering](https://mlsanity.com/paper/2510.10828/) (2025)
- [Multi-task Learning for Identification of Porcelain in Song and Yuan Dynasties](https://mlsanity.com/paper/2503.14231/) (2025)
- [ResearchGPT: Benchmarking and Training LLMs for End-to-End Computer Science Research Workflows](https://mlsanity.com/paper/2510.20279/) (2025)
- [PCMind-2.1-Kaiyuan-2B Technical Report](https://mlsanity.com/paper/2512.07612/) (2025)
- [PodBench: A Comprehensive Benchmark for Instruction-Aware Audio-Oriented Podcast Script Generation](https://mlsanity.com/paper/2601.14903/) (2026)
- [Puro-2B: Poor Lab's Qwen2-1.5B Trained on RTX 5090 within 5090](https://mlsanity.com/paper/2608.27370/) (2026)
- [Automatic Task Detection and Heterogeneous LLM Speculative Decoding](https://mlsanity.com/paper/2505.08600/) (2025)
- [OCR-Quality: A Human-Annotated Dataset for OCR Quality Assessment](https://mlsanity.com/paper/2510.21774/) (2025)
- [ReCrit: Transition-Aware Reinforcement Learning for Scientific Critic Reasoning](https://mlsanity.com/paper/2605.18799/) (2026)
- [RiskNet: A large-scale dataset of AI risk incidents from news with alignment and multi-dimensional annotations](https://mlsanity.com/paper/2606.08376/) (2026)
- [A New Multi-Domain Benchmark for Micro-Action Recognition and Detection](https://mlsanity.com/paper/2606.14096/) (2026)
- [Artificial Intelligence and Innovation Ecosystem: Evolutionary Developments, Challenges, and Future Directions](https://mlsanity.com/paper/2607.24589/) (2026)
- [Towards Physics-Faithful Generation of Scientific Diagrams](https://mlsanity.com/paper/2608.13112/) (2026)
- [InternReviewer & InternAdvocate: Objective Reward and Evaluation for Agentic Reinforcement Learning in Peer Review and Rebuttal](https://mlsanity.com/paper/2608.28612/) (2026)

## Abstract (arXiv)

The advancement of artificial intelligence (AI) hinges on the quality and accessibility of data, yet the current fragmentation and variability of data sources hinder efficient data utilization. The dispersion of data sources and diversity of data formats often lead to inefficiencies in data retrieval and processing, significantly impeding the progress of AI research and applications. To address these challenges, this paper introduces OpenDataLab, a platform designed to bridge the gap between diverse data sources and the need for unified data processing. OpenDataLab integrates a wide range of open-source AI datasets and enhances data acquisition efficiency through intelligent querying and high-speed downloading services. The platform employs a next-generation AI Data Set Description Language (DSDL), which standardizes the representation of multimodal and multi-format data, improving interoperability and reusability. Additionally, OpenDataLab optimizes data processing through tools that complement DSDL. By integrating data with unified data descriptions and smart data toolchains, OpenDataLab can improve data preparation efficiency by 30%. We anticipate that OpenDataLab will significantly boost artificial general intelligence (AGI) research and facilitate advancements in related AI fields. For more detailed information, please visit the platform's official website: https://opendatalab.com.