# ACM Digital Library as a Gemini Notebook source > Publisher library for computing research — conference proceedings and journals. Many papers are open access, and those PDFs import directly. _Markdown version of https://gemini-notebook-hub.online/sources/acm-digital-library — for AI and LLM crawlers. Content is identical to the web page._ --- - Site: [dl.acm.org](https://dl.acm.org) - Category: AI & technology research - Access: free + paid Publisher library for computing research — conference proceedings and journals. Many papers are open access, and those PDFs import directly. It's the primary publisher archive for computing research, covering conferences and journals like CHI and SIGCOMM that a lot of computer science gets published in outside arXiv's reach, particularly in areas like human-computer interaction and databases. An increasing share of ACM content is open access, and those PDFs import directly without needing institutional access. Papers still behind ACM's paywall need a subscribing library to reach, so check open-access status before assuming a PDF is downloadable. ## Import into Gemini Notebook 1. Copy the link: https://dl.acm.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. - [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. - [OpenML](https://gemini-notebook-hub.online/sources/openml.md) — Shared machine learning datasets and benchmark task results with API access. Dataset description pages document provenance for reproducibility notebooks. - [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.