# OpenML as a Gemini Notebook source > Shared machine learning datasets and benchmark task results with API access. Dataset description pages document provenance for reproducibility notebooks. _Markdown version of https://gemini-notebook-hub.online/sources/openml — for AI and LLM crawlers. Content is identical to the web page._ --- - Site: [openml.org](https://www.openml.org) - Category: AI & technology research - Access: free Shared machine learning datasets and benchmark task results with API access. Dataset description pages document provenance for reproducibility notebooks. OpenML hosts machine learning datasets alongside the benchmark results other researchers got running models against them, with an API for pulling data programmatically. A notebook on model reproducibility can cite a dataset's documented provenance and prior benchmark numbers instead of taking a paper's reported results on faith. Dataset quality and documentation vary a lot, since much of it is community-contributed rather than centrally curated. ## Import into Gemini Notebook 1. Copy the link: https://www.openml.org 2. In your notebook, click Add source → Website. 3. Paste the link and confirm — several links can be pasted at once, one per line. **Tip:** Conference proceedings and lab blogs publish full papers as PDFs or Website pages — those import cleanly and keep citations intact. ## More ai & technology research sources - [AAAI Conference on Artificial Intelligence](https://gemini-notebook-hub.online/sources/aaai-conference-on-artificial-intelligence.md) — Proceedings of a major annual AI conference covering the whole field. Individual paper PDFs from the proceedings work well as sources. - [ACM Digital Library](https://gemini-notebook-hub.online/sources/acm-digital-library.md) — Publisher library for computing research — conference proceedings and journals. Many papers are open access, and those PDFs import directly. - [AI Alignment Forum](https://gemini-notebook-hub.online/sources/ai-alignment-forum.md) — Long-form technical posts on AI safety and alignment research. Individual post URLs work well as Website sources. - [Distill](https://gemini-notebook-hub.online/sources/distill.md) — Interactive, peer-reviewed explainers of machine learning concepts. Dormant since 2021, but the articles import well as Website sources for ML study notebooks. - [Google DeepMind Research](https://gemini-notebook-hub.online/sources/google-deepmind-research.md) — Publications and technical blog posts from Google DeepMind. Blog write-ups often summarize a paper more digestibly than the PDF — add both as sources. - [ICLR (OpenReview)](https://gemini-notebook-hub.online/sources/iclr-openreview.md) — ICLR papers with their full peer-review threads on OpenReview. Reviews and author rebuttals add useful critical context alongside the paper PDF. - [IEEE Xplore](https://gemini-notebook-hub.online/sources/ieee-xplore.md) — Journals, conference proceedings, and standards in electrical engineering and computing. Abstracts are open; full PDFs need a subscription or purchase. - [Journal of Machine Learning Research (JMLR)](https://gemini-notebook-hub.online/sources/journal-of-machine-learning-research-jmlr.md) — Long-running open-access machine learning journal. Every paper is a free PDF with no paywall, making it easy to build ML literature notebooks. - [Linguistic Data Consortium](https://gemini-notebook-hub.online/sources/linguistic-data-consortium.md) — Distributes speech and text corpora for language research and model training. Access is membership-based; corpus documentation pages are freely viewable. - [MLCommons / MLPerf Benchmarks](https://gemini-notebook-hub.online/sources/mlcommons-mlperf-benchmarks.md) — Home of the MLPerf benchmarks for training and inference speed. Results pages and methodology docs anchor notebooks comparing AI hardware and systems. - [Nature Machine Intelligence](https://gemini-notebook-hub.online/sources/nature-machine-intelligence.md) — Peer-reviewed journal on machine learning and AI applications. A curated counterweight to preprints when building an AI literature notebook. - [NeurIPS Proceedings](https://gemini-notebook-hub.online/sources/neurips-proceedings.md) — Every paper from the top machine learning conference, back to 1987. Free PDFs make it easy to build a notebook around a research thread across years. - [OpenReview.net](https://gemini-notebook-hub.online/sources/openreview-net.md) — Papers plus their full peer-review threads for venues like ICLR. Importing reviews alongside the paper gives a notebook both sides of the argument. - [Perplexity](https://gemini-notebook-hub.online/sources/perplexity.md) — Answer engine that links every claim to a source. Handy for finding candidate URLs to add to a notebook — import the cited pages, not the AI summary itself. - [Proceedings of Machine Learning Research (PMLR)](https://gemini-notebook-hub.online/sources/proceedings-of-machine-learning-research-pmlr.md) — Free full-text papers from ICML, AISTATS, UAI and other ML venues. Direct PDF links with no paywall — easy bulk imports for ML literature reviews. - [The Gradient](https://gemini-notebook-hub.online/sources/the-gradient.md) — Long-form essays on AI research written for a technical but general audience. Articles pair well with the papers they discuss in the same notebook. - [Transactions on Machine Learning Research (TMLR)](https://gemini-notebook-hub.online/sources/transactions-on-machine-learning-research-tmlr.md) — Open-access ML journal with rolling submissions and public reviews. Each paper has a landing page plus a PDF you can pull straight into a notebook.