Open Datasets and Tools: An overview for Hugging Face

Datasets range from small, curated tables to petabyte‑scale collections of images, audio or molecular structures. Open access to data is critical for reproducible research and enables practitioners outside of well‑funded labs to experiment with state‑of‑the‑art models.

In recent years, open‑data releases such as Yandex’s Yambda‑5B recommendation dataset and LAION’s 5B image-text dataset have spurred rapid innovation.

This overview explains the types of datasets you’ll encounter, highlights trusted repositories for finding data, reviews several notable new releases and tools, and offers practical tips for working with open data.

Let’s begin!

Overview of dataset types and trends

Broadly speaking, there are four types of datasets:

Structured

Highly organized and stored in a tabular format (rows and columns). Easily searched using SQL and ideal for quantitative tasks such as finance or reservation systems. Examples include spreadsheets, relational databases, and CSV files.

Unstructured

Data without a predefined schema, such as social-media posts, emails, images,and audio. It accounts for the majority of organizational data and often requires natural-language processing or computer-vision techniques.

Time-series

Sequences of observations indexed by time. They have a natural temporal ordering and are used in economics, weather forecasting, medicine, and countless other domains.

Geospatial

Data associated with coordinates relative to Earth. It includes vector and raster formats and is typically stored in geographic information systems (GIS). Applications span remote sensing, urban planning, and autonomous navigation.

These categories often overlap. For example, a GPS-tagged social-media post is both unstructured (text/image) and geospatial. Understanding the type of data informs how you store, preproces,s and model it.

Trends in open data

Just like open-source LLMs have witnessed rapid growth, we have seen several trends in the past few years in open datasets:

• Scale and multimodality. Datasets have grown from thousands of examples to billions. LAION‑5B contains 5.85 billion CLIP‑filtered image-text pairs was was 14 times larger than the previous LAION‑400M dataset. Yandex’s Yambda‑5B offers nearly 4.79 billion user-item interactions for music recommendation. Meta’s OMol25 and ODAC25 datasets deliver tens of millions of quantum‑chemistry calculations. Such a scale allows researchers to train models that generalize better but demands efficient tooling (discussed later).

• Emphasis on openness and reproducibility. Scientific journals and conferences increasingly require datasets and evaluation protocols to be released alongside papers. Public portals like Europe’s data.europa.eu aggregate millions of government datasets, while institutions such as LAION publish open multi‑modal datasets for community use.

• Cloud‑hosted repositories. Many open datasets live on cloud platforms (e.g., AWS Open Data) to enable streaming and on‑demand access. Hugging Face’s Datasets library provides a unified interface to load thousands of datasets from the Hub using streaming and memory‑mapping.

Where to find datasets

Below are several well‑maintained repositories that cater to different needs. All of them can be accessed programmatically via the Hugging Face Hub or downloaded directly. Many of these repositories can be browsed through the Hugging Face Hub. The datasets library supports loading datasets by their short name (e.g., load_dataset('squad')) and provides streaming downloads and caching.

Kaggle

A platform hosting thousands of datasets across domains, including finance, health, sports, NLP, computer vision, and more. Kaggle is widely used for competitions, tutorials, and research. It provides metadata, discussions, and kernels (code notebooks) for exploration.

UCI Machine Learning Repository

One of the oldest and most cited repositories for ML datasets. It offers well-curated, clean datasets primarily aimed at benchmarking algorithms. Especially popular for tabular/classical ML problems like classification, regression, and clustering.

Google Dataset Search

A search engine for datasets across the web. It aggregates datasets from publishers, repositories, and research organizations, making them discoverable in a single place. Useful for finding both niche and large-scale datasets.

AWS Open Data Registry

A collection of high-value, cloud-hosted datasets available for free access and analysis. Datasets cover genomics, climate, satellite imagery, and more. Advantage: hosted on AWS infrastructure, so they can be analyzed directly with cloud tools.

Spotlight on recent open datasets

Yambda‑5B (Yandex Music Multi‑Interactions Dataset)

Yandex released Yambda‑5B in 2025 as the largest open dataset for recommender‑system research. It contains 4.79 billion anonymized user-item interactions drawn from 1 million users and 9.39 million tracks. Interactions include implicit feedback (listens) and explicit feedback (likes, dislikes, removals).

Each record contains a user ID, item ID, timestamp and an is_organic flag indicating whether the interaction was organic or triggered by a recommendation. The data are stored in Parquet files and offered at three scales (50 M, 500 M, and 5 B events).

Several researchers and practitioners have praised Yambda. Aman Chadha (currently AWS GenAI leadership, previously Stanford AI, Apple) notes that “datasets like Yambda‑5B make the path smoother, bridging the gap between academic research and industry relevance.” Aixin Sun (NTU Singapore) expects it to become widely adopted in recommender‑system research but cautions that it’s tailored to a specific recommendation setting.

Data scientists from companies like Meta, Nextory and Flipkart highlight that previous benchmark datasets were either too small or unrealistic, whereas Yambda‑5B finally provides a web‑scale resource. These comments underscore both excitement and nuance in the community.

Talking of use cases, Yambda‑5B enables large‑scale recommender research at industry scale. It can be used to train sequence‑aware models (RNNs, Transformers) and to study cold‑start issues with audio embeddings.

LAION‑5B (Large‑Scale Multi‑Modal Dataset)

LAION’s 2022 release of LAION‑5B offers 5.85 billion CLIP‑filtered image-text pairs, 14 times larger than LAION‑400M. About 2.3 billion pairs are English, 2.2 billion come from 100+ other languages, and 1 billion consist of names or unassignable strings.

The dataset includes CLIP ViT‑L/14 embeddings, k‑nearest‑neighbor indices, a search demo and NSFW/watermark detection scores. It is designed for research on large multi‑modal models such as CLIP, DALL‑E and ALIGN.

The AI community welcomed LAION‑5B as a major step toward democratizing vision-language research. Its open nature has enabled independent teams to reproduce models like DALL‑E and to explore multilingual alignment. At the same time, researchers stress the need for careful filtering due to the dataset’s uncurated origins.

LAION‑5B allows training and evaluating vision-language models at unprecedented scale. It enables work on zero‑shot image classification, text‑to‑image generation and cross‑modal retrieval. The dataset is uncurated, meaning it contains duplicates and potentially disturbing content; the authors recommend using NSFW filters and caution that it is not intended for production

OMol25 (Open Molecules 2025)

Meta FAIR’s Open Molecules 2025 (OMol25) dataset addresses the shortage of high‑quality molecular data for training machine‑learning surrogates of quantum chemistry. The dataset offers 83 million unique molecular systems across 83 elements, capturing a wide range of intra‑ and intermolecular interactions, explicit solvation, variable charge/spin states and reactive structures. Systems include small molecules, biomolecules, metal complexes and electrolytes, with sizes up to 350 atoms.

OMol25 enables training of neural network potentials and force‑field models for tasks such as drug discovery, materials design, and reaction prediction. It dramatically expands the chemical diversity and system sizes available compared with earlier datasets.

ODAC25 (Open DAC 2025)

The Open DAC 2025 (ODAC25) dataset, released by Meta in August 2025, targets climate engineering. It contains nearly 70 million DFT single‑point calculations of CO₂, H₂O, N₂ and O₂ adsorption in 15,000 metal-organic frameworks (MOFs).

ODAC25 introduces chemical and configurational diversity through functionalized MOFs, high‑energy Grand Canonical Monte Carlo (GCMC) placements and synthetically generated frameworks. It also improves the accuracy of DFT calculations and the treatment of flexible MOFs compared with its predecessor ODAC23.

ODAC25 provides a comprehensive benchmark for designing sorbent materials for direct air capture. Researchers can use it to train models predicting adsorption energies and Henry’s law coefficients, accelerating the search for materials that capture CO₂ from humid air. Like OMol25, ODAC25 is specialized and requires domain knowledge.

Practical tips for working with datasets

Here are some best practices and practical tips you should care about when using open datasets: Check licensing and privacy. Review a dataset’s licensing terms and privacy considerations before use. For instance, Yambda anonymizes user interactions, while LAION provides NSFW detection scores and urges caution. Be sure the dataset’s license allows the intended use.

Use tooling to explore data. Hugging Face’s datasets library makes it easy to load and preprocess data. The library caches data on disk and uses Apache Arrow for fast columnar access. It also integrates with PyTorch and TensorFlow for training. You can stream large datasets and even work with compressed files:

• Leverage evaluation metrics. The datasets library ships with a suite of metrics (e.g., accuracy, ROUGE, BLEU) and supports custom metrics.

• Use indexing and search. For large datasets such as LAION‑5B, adding a FAISS or elastic‑search index enables efficient similarity search. The add_faiss_index method from datasets library can build such an index; LAION provides pre‑computed indices for convenience.

• Start small. If the full dataset is too large for your resources, work with smaller versions (e.g., Yambda‑50M or Yambda‑500M) or sample subsets using dataset streaming to avoid memory overload.

• Validate data quality. Perform sanity checks (duplicate removal, distribution analysis) before training. LAION‑5B is uncurated and may contain duplicates and noise; cleaning improves model performance.

Tools and libraries for datasets on Hugging Face

Here are a few tools you should know when working with datasets.

• datasets library. A core component of Hugging Face’s ecosystem, datasets provides a unified API to load, slice and stream datasets. It supports numerous data formats (CSV, JSON, text, Parquet) and automatically caches downloads. The library implements transformations (map, filter, shuffle), dataset concatenation and dataset indexing. It also includes performance metrics and integrates with transformers to supply datasets directly to model trainers.

• huggingface_hub. This Python library allows programmatic interaction with the Hugging Face Hub. You can authenticate, upload new datasets, create dataset cards and version data. It exposes functions like hf_hub_download for retrieving files without the datasets API and upload_folder for pushing large data. Coupled with Git LFS, it handles version control for multi‑gigabyte files.

• Data viewers and Spaces. The Hugging Face Hub offers a dataset viewer that lets you explore dataset samples online and a Spaces platform for building interactive demos. For example, LAION provides a search demo for LAION‑5B, and Yandex offers baseline evaluation scripts as Hugging Face Spaces. Deploying your own Space can make your dataset more approachable to the community.

Complementary libraries. When analyzing large data, libraries like Pandas, Polars or DuckDB can help with columnar operations. For recommender‑system research, frameworks such as RecBole and LensKit integrate well with datasets like Yambda. In the chemistry domain, TorchMD‑Net and SchNetPack provide neural network potentials that can consume OMol25 or ODAC25.

Conclusion

Open datasets form the backbone of modern AI research.

This overview has highlighted different kinds of data (structured, unstructured, time‑series and geospatial), outlined major trends such as the rise of billion‑scale multi‑modal datasets and cloud‑hosted data repositories, and introduced a handful of trusted sources for discovering datasets.

Recent releases, including Yandex’s Yambda‑5B for recommendation systems, LAION’s 5B image-text dataset, Meta’s OMol25 chemistry dataset and the ODAC25 sorbent dataset, demonstrate how open data can catalyze innovation across domains ranging from music streaming to quantum chemistry.

As you explore these resources, remember to check licensing and privacy, use appropriate tools for loading and indexing, and start with manageable subsets.

Hugging Face’s datasets and huggingface_hub libraries provide efficient pipelines for working with data at scale. By contributing to open‑data projects and sharing your own datasets, you help build an ecosystem that accelerates discovery and democratizes AI research.

Happy exploring!