--- pretty_name: MMTA-v1.0 language: - en license: cc-by-4.0 task_categories: - other size_categories: - 1K.py video-timeseries/{agibot,future_factories,holo_assist}/ .omnits.jsonl video_clips/ timeseries/ video-timeseries/{covla,cuhk_x_har}/ README.md download.py generate.py .py processing/ README.md requirements.txt download.py common/ ``` JSONL is UTF-8 with one JSON object per line. IDs are unique across manifests. The release supports image/time-series prediction and video/sensor classification. | Field | Meaning | |---|---| | `id` | Globally unique example identifier. | | `split` | Always `test`. | | `domain` | Source task domain, matching its parent directory. | | `variant` | `original`; `percha` denotes an alternative forecasting channel view. | | `task` | `classification` or `prediction`. | | `video_path` | Relative path to the cropped MP4 clip. | | `timeseries_path` | Relative path to the history CSV with a header. | | `prompt` | Instructions, textual evidence and inline historical sensor observations. | | `answer` | Reference option letter or a `` block. | | `video_meta` | Actual width, height, source FPS, frame count and duration. | | `ts_shape` | `[history_rows, signal_columns]`, excluding timestamp columns. | | `image_path`, `image_meta` | Ordered image paths and matching image dimensions for image tasks. | | `forecast_horizon`, `forecast_target_path` | Future horizon and channel reference target CSV; evaluation labels. | | `channel_index`, `channel_name`, `target_variable` | Selected forecast channel identifiers for channel views. | All paths are relative to the release root, including `sample/`. Every asset belongs to the same domain as its manifest. CSVs preserve ordered signal rows; time and task context remain in prompts. Sensor row counts need not equal video frame counts because sampling rates differ. Empty strings and `nan` denote missing observations. Preserve them with a missing-value mask. Do not replace them with zero. Reference option letters correspond to the choices in each prompt; they are task-specific, not a shared label vocabulary. Video metadata describes the actual encoded clip. Some source stream-copy clips contain adjacent keyframe context; filename intervals need not exactly equal the encoded frame count. See README.md for evaluation limits. Terra ordinary CSVs contain `timestamp,v0,v1,v2,v3,v4,v5,v6`, mapping to `lwd`, `precipitation`, `pres`, `relhumidity`, `spechumidity`, `temp`, `wind` in that order. Each channel CSV contains one selected signal column, while the prompt retains all seven historical variables. The instruction requests only `target_variable`; its answer and target CSV contain that one variable. Reference answers preserve source rounding, while target CSVs preserve full source precision. ## Evaluation limits - The supplied AgiBot split has 113/131 test examples matching training prompts, answers, sensor CSVs and video bytes. Nine duplicate pairs remain within its test set. Account for this overlap when reporting results. - Future Factories and HoloAssist preserve missing sensor observations as empty values or `nan`. Four Future Factories gripper-load channels are entirely missing. Preserve a missing-value mask when loading sensor data. - Some AgiBot and HoloAssist clips include adjacent keyframe context. `video_meta` describes the actual encoded files. - Terra geographic descriptions contain upstream generated text with factual errors. Its channel instructions request only the selected forecast variable, while all seven inline histories remain context. ## License and attribution Original MMTA contributions are released under **[Creative Commons Attribution 4.0 International (CC BY 4.0)](LICENSE)**. When sharing or adapting original MMTA material, credit **MMTA-v1.0**, link https://huggingface.co/datasets/OpenTSLab/MMTA-v1.0 and https://creativecommons.org/licenses/by/4.0/, and indicate any changes. The CC BY 4.0 declaration applies to rights held by MMTA contributors. Third-party source data retain the licenses below. Terra permits noncommercial use and sharing; AgiBot additionally requires share-alike for adaptations. Future Factories requires share-alike for adaptations. HoloAssist requires making its CDLA-Permissive-2.0 agreement available with shared data and modifications. The supplied Terra source review does not establish redistribution permission for its third-party satellite images. Source components retain their independent attribution and licenses. Inclusion does not imply endorsement of MMTA by their authors. | Component | Credit and source | Source license | |---|---|---| | Terra | Wei Chen, Xixuan Hao, Yuankai Wu and Yuxuan Liang; [original data card](https://huggingface.co/datasets/onedean/Terra), [project](https://github.com/CityMind-Lab/NeurIPS24-Terra), *Terra: A Multimodal Spatio-Temporal Dataset Spanning the Earth*, NeurIPS 2024. | [CC BY-NC 4.0](licenses/CC-BY-NC-4.0.txt); third-party satellite-image permission unverified. | | AgiBot | AgiBot World Team; [AgiBot World 2026](https://huggingface.co/datasets/agibot-world/AgiBotWorld2026), tasks `task_3641` and `task_3777`. | [CC BY-NC-SA 4.0](licenses/CC-BY-NC-SA-4.0.txt). | | Future Factories | Ramy Harik, Future Factories Lab, University of South Carolina; [FF — 2024 08 13 — Multi-Modal Dataset [3/3]](https://www.kaggle.com/datasets/ramyharik/ff-2024-08-13-multi-modal-dataset-33), version 1. | [CC BY-SA 4.0](licenses/CC-BY-SA-4.0.txt). | | HoloAssist | The HoloAssist dataset team; [official project](https://holoassist.github.io/), *HoloAssist: An Egocentric Human Interaction Dataset for Interactive AI Assistants in the Real World*, ICCV 2023. | [CDLA-Permissive-2.0](licenses/CDLA-Permissive-2.0.txt). | ### Modifications Source examples were selected into task-specific test splits. Videos were cropped; numerical histories and forecast targets were exported to CSV; task prompts and reference labels were assembled as JSONL. Asset references were normalized to internal paths relative to the dataset repository root. Terra supplies image-conditioned seven-variable forecasting and alternative channel views. Its 1,400 channel instructions were corrected to request the selected variable. Source numerical observations, reference labels and released split membership were preserved. Satellite images were recovered from existing test archives; image-owner attribution was not supplied in the available source material. AgiBot supplies anomaly classification with selected robot channels. Future Factories supplies paired-camera cycle videos and PLC histories. HoloAssist supplies coarse-action segments with synchronized sensors and correctness labels. The full benchmark also uses MIMIC, PixelRec, CoVLA, CUHK-X and SP500/FinMultiTime. Their data are absent from this subset because of source sharing terms and access requirements. The recipe directories above provide processing code for users who obtain those sources themselves. Source agreements remain applicable to locally downloaded data and generated records. Script origins and existing code notices are documented in [processing/CODE_PROVENANCE.md](processing/CODE_PROVENANCE.md) and [processing/provenance.json](processing/provenance.json); no new license is assigned to third-party code. Questions about this release can be posted in this repository's Discussions.