README.md

---
pretty_name: MMTA-v1.0
language:
- en
license: cc-by-4.0
task_categories:
- other
size_categories:
- 1K<n<10K
tags:
- benchmark
- multimodal
- time-series
- image
- video
- text
- classification
- forecasting
configs:
- config_name: terra
  data_files:
  - split: test
    path: sample/image-timeseries/terra/terra.omnits.jsonl
- config_name: terra_per_channel
  data_files:
  - split: test
    path: sample/image-timeseries/terra/terra.percha.omnits.jsonl
- config_name: agibot
  data_files:
  - split: test
    path: sample/video-timeseries/agibot/agibot.omnits.jsonl
- config_name: future_factories
  data_files:
  - split: test
    path: sample/video-timeseries/future_factories/future_factories.omnits.jsonl
- config_name: holo_assist
  data_files:
  - split: test
    path: sample/video-timeseries/holo_assist/holo_assist.omnits.jsonl
---

# MMTA-v1.0

MMTA evaluates multimodal temporal analysis using images, videos, text and
time-series observations. This repository contains a distributable test subset with
**1,322 original test records** and **1,400 alternative Terra channel views**.
All five configurations use the `test` split. It also includes **processing
recipes for six additional task domains** whose source data are not redistributed.

| Configuration / recipe | Records | Task |
|---|---:|---|
| `terra` | 200 | Image-conditioned forecasting of seven variables |
| `terra_per_channel` | 1,400 | Forecasting one selected variable per Terra view |
| `agibot` | 131 | Robot anomaly classification |
| `future_factories` | 46 | Industrial missing-part classification |
| `holo_assist` | 945 | Action-correctness classification |
| [`mimic_death`](sample/image-timeseries/mimic_death/README.md) | Code only; generate locally | Image-conditioned in-hospital mortality classification |
| [`mimic_disch`](sample/image-timeseries/mimic_disch/README.md) | Code only; generate locally | Image-conditioned discharge-within-48-hours classification |
| [`pixelrec`](sample/image-timeseries/pixelrec/README.md) | Code only; generate locally | Cover-image-conditioned interaction forecasting |
| [`sp500`](sample/image-timeseries/sp500/README.md) | Code only; generate locally | Financial forecasting with charts and dated news |
| [`covla`](sample/video-timeseries/covla/README.md) | Code only; generate locally | Video-conditioned braking-reason classification |
| [`cuhk_x_har`](sample/video-timeseries/cuhk_x_har/README.md) | Code only; generate locally | Video/IMU human activity classification |

Rows labeled **Code only; generate locally** include processing scripts and
instructions. Obtain their source data through the upstream providers and build
records locally; source data and generated records are not redistributed here.
The five rows with numerical record counts are the available Hugging Face
`load_dataset` configurations.

The Terra channel views reuse the same 200 original instances and images.
They are alternative evaluation views and should not be counted as additional
independent original examples. The released records cover four source domains.
The additional recipe directories contain code and instructions for local
reconstruction; their source data and generated task records are not included
and do not increase the record counts above.

The 6,393 referenced assets occupy approximately **11.65 GiB**: 1,149 images,
1,122 video clips, 2,722 history CSVs and 1,400 forecast target CSVs.

## Load a configuration

```python
from datasets import load_dataset

test = load_dataset("OpenTSLab/MMTA-v1.0", "terra", split="test")
record = test[0]
```

The same call supports `terra_per_channel`, `agibot`, `future_factories` and
`holo_assist`. The dataset viewer displays JSONL records. Media and sensor fields
contain relative file paths; download a snapshot to open those assets:

```python
import csv
import json
from pathlib import Path
from huggingface_hub import snapshot_download

root = Path(snapshot_download("OpenTSLab/MMTA-v1.0", repo_type="dataset"))
with (root / "sample/image-timeseries/terra/terra.omnits.jsonl").open(
    encoding="utf-8"
) as handle:
    record = json.loads(next(handle))

images = [root / path for path in record["image_path"]]
with (root / record["timeseries_path"]).open(encoding="utf-8", newline="") as handle:
    history = list(csv.DictReader(handle))
```

Resolve every released record path relative to the **repository root**. The directory
`sample/` contains both released test data and the recipe-only directories
listed below. `answer` and `forecast_target_path`
are evaluation references; exclude them from model inputs.

## Processing recipes for sources that are not redistributed

The following directories provide download entry points, source-processing
scripts and local generation instructions. Because of upstream sharing terms
and access requirements, this repository distributes **processing code only**
for these domains. It includes no source datasets, images, videos, patient
records or generated benchmark records for them.

| Recipe directory | Local task | Source access and instructions |
|---|---|---|
| `sample/image-timeseries/mimic_death/` | In-hospital mortality classification | [MIMIC mortality instructions](sample/image-timeseries/mimic_death/README.md); obtain the required MIMIC tables, notes and chest images through your own approved PhysioNet account. |
| `sample/image-timeseries/mimic_disch/` | Discharge-within-48-hours classification | [MIMIC discharge instructions](sample/image-timeseries/mimic_disch/README.md); the same credentialed source access is required. |
| `sample/image-timeseries/pixelrec/` | Cover-image-conditioned interaction forecasting | [PixelRec instructions](sample/image-timeseries/pixelrec/README.md); obtain interactions, item metadata and covers from the upstream project. |
| `sample/image-timeseries/sp500/` | Financial forecasting with charts and dated news | [SP500 / FinMultiTime instructions](sample/image-timeseries/sp500/README.md); obtain the upstream prices, news and charts. |
| `sample/video-timeseries/covla/` | Driving/braking classification | [CoVLA instructions](sample/video-timeseries/covla/README.md); complete upstream Hugging Face access requirements and obtain states, front-car signals and videos. |
| `sample/video-timeseries/cuhk_x_har/` | Human activity classification | [CUHK-X HAR instructions](sample/video-timeseries/cuhk_x_har/README.md); complete the source data-use agreement and obtain the matching IMU and skeleton archives. |

Use your own upstream account and follow each source's current terms. These
scripts do not grant access to restricted datasets or permission to share them
or their derivatives. Download and unpack sources locally, then run the
corresponding `generate.py` to build task prompts and image/video/CSV records.
Shared download and conversion dependencies are included in `processing/`;
see the [setup instructions](processing/README.md).

For the commands in the per-domain READMEs, run from the directory containing
your downloaded `MMTA-v1.0` folder:

```bash
python -m pip install -r MMTA-v1.0/processing/requirements.txt

# Inspect an upstream download plan without downloading data.
python -B MMTA-v1.0/sample/image-timeseries/pixelrec/download.py \
  --output local_sources/pixelrec --dry-run

# Inspect the inputs accepted by the local task builder.
python -B MMTA-v1.0/sample/image-timeseries/pixelrec/generate.py --help
```

Install `ffmpeg` and `ffprobe` for video processing, and `wget` for credentialed
PhysioNet downloads. Actual downloads require `--accept-terms` after completing
the upstream requirements; a dry run requires no source access. Keep
`local_sources/` and generated `local_work/` directories outside `MMTA-v1.0/`.
Locally generated record paths are relative to the generated domain directory,
as described in each recipe README; released record paths remain relative to
this repository root.

These six recipes are not Hugging Face dataset configurations. `load_dataset`
loads the five released configurations shown above; `snapshot_download`
also retrieves the processing scripts. Locally reconstructed records are not
guaranteed to reproduce the exact original benchmark selection, since available
upstream versions and files may differ.

## Files and format

```text
sample/
  image-timeseries/terra/
    terra.omnits.jsonl
    terra.percha.omnits.jsonl
    images/
    timeseries/
    forecast_targets/
  image-timeseries/{mimic_death,mimic_disch,pixelrec,sp500}/
    README.md
    download.py
    generate.py
    <source processing scripts>.py
  video-timeseries/{agibot,future_factories,holo_assist}/
    <domain>.omnits.jsonl
    video_clips/
    timeseries/
  video-timeseries/{covla,cuhk_x_har}/
    README.md
    download.py
    generate.py
    <source processing scripts>.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 `<forecast>` 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.