AI Training Datasets

20,000 previously public ranked games collected from the top 20 teams. Replay bytes are unchanged; team and bot labels already present in public gameplay remain present. This collection does not contain private experimental matches, source archives, account responses, credentials or collection indexes. Each TAR contains gzip-compressed.replay files. Extract an archive with tar -xf replays/shard-0000.tar. For the Battlecode visualiser, decompress a replay to a separate file: gzip -dc replays/GAMEID.replay > view.replay, then open view.replay at https://game.battlecode.au/visualiser. manifest.json records archive sizes, SHA256 hashes and game counts. Archive names are unique across the…

This dataset was created using LeRobot. Part B (a white disc with a hub and a bar) is found with the wrist camera, picked from the plate and seated into fixture A. Universal Robots UR7e, Robotiq gripper, wrist-mounted Intel RealSense D405. Collected autonomously during a continuous 48-hour run that started on 2026-10-10, to cover the lighting changes of day and night. This repository is one part of that run: 101 episodes, 65387 frames at 30 fps. - State and action (16 values each): 6 joint angles, TCP position, TCP orientation (6D), gripper position. Also recorded: joint velocity, torque and current, TCP force, raw force/torque wrench, gripper effort, capture time. - The wrist camera is…

This dataset was created using LeRobot. Part B (a white disc with a hub and a bar) is found with the wrist camera, picked from the plate and seated into fixture A. Universal Robots UR7e, Robotiq gripper, wrist-mounted Intel RealSense D405. Collected autonomously during a continuous 48-hour run that started on 2026-10-10, to cover the lighting changes of day and night. This repository is one part of that run: 100 episodes, 64553 frames at 30 fps. - State and action (16 values each): 6 joint angles, TCP position, TCP orientation (6D), gripper position. Also recorded: joint velocity, torque and current, TCP force, raw force/torque wrench, gripper effort, capture time. - The wrist camera is…

Data and results for Multi-Agent Prompting with In-Context Feedback for Cross-Modal Urban Event Chain Prediction (Sensors, MDPI). Code: github.com/bibekgupta3333/urban-event-chain-prediction-multi-agent-prompting The nine -bedrock- run directories are the model grid the paper reports: 5 configurations (singleagent, multiagent, noisysingle, multifeedback, multinofeedback) × expert prompt level × 3 cities, 85 test chains per configuration. Bedrock exposes no seed, so these runs are not byte-reproducible. - Traffic data are municipal traffic-speed feeds (probe-derived segment estimates, daily) — Chicago Traffic Tracker (sxs8-h27x), NYC Real-Time Traffic Speed (i4gi-tjb9), SF via DataSF. They…

Cached inputs for UCPE-conditioned Cosmos student distillation, with a short 33-frame GT seed and causal 81-frame target clips. Contains 9,188 DL3DV and 7,993 RealEstate10K scenes, 54,643 target clips, and a fixed 5+5-scene validation split. The bundle contains video VAE latents, rendered GS VAE features/masks, metric camera trajectories/intrinsics, projected text contexts, the matching projection, and provenance. Raw RGB videos and model weights are not included. Read shortseed33-v1-20261007/index.json and require complete: true and all 120 shards. A repository containing some shards is not a completed dataset. The full extracted payload is 499,974,383,360 bytes (~500 GB / 465.64 GiB)…

Dataset · Text to speech

uz-speech-sidon-24k

Aslkhon Khoshimkhujaev

Узбекская речь для обучения TTS: ~690 часов, 24 kHz, с транскриптами. Две версии аудио с одинаковыми id. Доступ — по заявке, одобряется вручную. Подробное описание — в DETAILS.md (доступно после одобрения).

Model-assisted research draft. The dataset contains 20,000 selected items from Telegram and YouTube, with original text/captions and selected original recording files. Labels have not received independent human validation. All acceptance, reuse and review flags retain their recorded values. Each sentiment has 5,000 items: positive, neutral, negative, and stronglynegative. Sources cover 200 Telegram channels, 200 public Telegram groups, and 57 YouTube channels. This is a selected, balanced sample; these counts do not estimate sentiment prevalence in the source population. The language labels are uzLatn (6,000), uzCyrl (2,388), uzrumixed (3,000), ru (4,000), spoken uz (4,541), en (65), and…

Dataset · Visual question answering

vl-checklist

Tri Lai

This repository hosts the Parquet edition of VL-CheckList (EMNLP 2022). - imageshake2019: Sharded image pool containing binary image bytes. - relation, location, size: Evaluation test cases with POS/NEG prompt pairs.