# Linguistic Data Consortium as a Gemini Notebook source > Distributes speech and text corpora for language research and model training. Access is membership-based; corpus documentation pages are freely viewable. _Markdown version of https://gemini-notebook-hub.online/sources/linguistic-data-consortium — for AI and LLM crawlers. Content is identical to the web page._ --- - Site: [ldc.upenn.edu](https://www.ldc.upenn.edu) - Category: AI & technology research - Access: free + paid Distributes speech and text corpora for language research and model training. Access is membership-based; corpus documentation pages are freely viewable. LDC has been the standard distributor of corpora used to train and benchmark NLP systems for decades — the Penn Treebank came through here — so its documentation pages describing how a corpus was collected and annotated carry real weight for a notebook about NLP methodology. Licensing a corpus itself requires paid membership, so LDC works better as a documentation and methodology source for a notebook than as a place to pull data files directly. ## Import into Gemini Notebook 1. Copy the link: https://www.ldc.upenn.edu 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. - [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.