AI Dataset2026
Kunal Handa, Miranda Zhang +22Anthropic, Social and Language Technologies (SALT) Lab, Stanford University +2
Provides aggregated, privacy-preserving cluster outputs from three external research teams' analyses of ~250k Claude/Claude Code conversations; includes per-team CSVs (Stanford, Oxford, METR) for studying human–AI collaboration and model behavior without raw conversations.
AI Dataset2026
Provides image-level tag correction instructions for a Danbooru anime-image tagging corpus, listing per-post tags to add or remove. Contains 1.74M normalized correction rows (snapshot 2026-08-30); it's a corrections manifest (no images) intended to be applied to existing metadata.
AI Dataset2026
Provides 997 chain-of-thought cybersecurity reasoning records distilled from the Kimi K3 model, each with an explicit <think> trace and a technical resolution or structured tool invocation. Includes verified tool-call objects, diffs, cross-domain coverage, and token-level metadata for fine-tuning and evaluating reasoning models.
AI Dataset2026
A living, crowdsourced dataset of hard-to-translate examples (text, images, audio, video) paired with handcrafted verification rules that flag concrete MT failures. LTBv1 contains 3,456 peer-reviewed examples across many language pairs and accepts ongoing contributions.
AI Dataset2026
Benchmark for typed probabilistic decisions: given one shared state, a model answers multiple typed questions at once and returns full probability distributions (noul/choice/score). Contains four workflows with train/test splits and soft gold labels from teacher samples, designed to evaluate accuracy, calibration, and latency trade-offs.
AI Dataset2026
Provides a complete benchmark and training release for native audio‑visual dialogue: 2,800 synthesized audio‑visual dialogues, tiered rubrics, scoring code, ~28GB of 1080p media, and an RL reward recipe to evaluate and train omni models that take video+audio and return text.
AI Dataset2026
Maps a state and question to typed probabilistic decisions (choice distributions, yes/no probabilities, or scored/ordinal outputs) across controlled synthetic tasks. Offers multiple frozen configs with train/calibration/validation/test/OOD splits, Parquet + raw JSONL exports, and reproducible manifests and provenance.
AI Dataset2026
Provides 5.5K+ self-contained data-analysis RL tasks: each row bundles a real tabular dataset, a question, and a deterministically-gradable gold answer. Verified from jupyter-agent notebooks; splits for training, held-out testing, and quick eval; intended for prompting, fine-tuning, and agent RL.
AI Dataset2026
Provides 150,000 source‑grounded decision examples for training models that pick options, judge yes/no propositions, or assign ordered scores. Each row pairs a 'state' with JEV-style typed questions (CHOICE/NOUL/SCORE); multiple configs and train/test splits are included, license mixed/unknown.
AI Dataset2026
A 180,000-row labeled dataset of agent decision steps for training fast decision models that choose whether to call a tool, which tool to pick, and whether arguments are complete. Provides leakage-safe splits, typed questions, canonical state/request fields, and ready-to-use Parquet format for low-latency routers.
AI Dataset2026
Aggregated, screened corpus of 55,050 normalized Indian public-information text bodies and 65,209 source records for retrieval and question-answering. Exports include deduplicated CSV/Parquet with provenance, topic labels, extraction quality flags and a private SQLite backup.
AI Dataset2026
Provides a monthly Parquet snapshot of ~5.6 billion public TikTok videos (2014–Oct 2026), including captions, hashtags, sounds, engagement metrics and TikTok Shop links. Designed for large-scale querying (DuckDB/Pandas/Polars); licensed CC BY-NC 4.0 for research and personal use.