Datasets:

catherpker
/

TRACE-TimeseriesRAG-Dataset

{
  "event_id": 1065296,
  "event_type": "Debris Flow",
  "state": "CALIFORNIA",
  "cz_name": "MADERA",
  "begin_date_time": "2023-01-10 21:11:00",
  "end_date_time": "2023-01-10 23:11:00",
  "narrative": "A strong low pressure system moved through central California...",
  "ts_dict_index": [12, 13, 14]
}

Field	Type	Description
`event_id`	int	Unique event identifier
`event_type`	string	Type of weather event (e.g., Debris Flow, Flood, Tornado)
`state`	string	U.S. state where the event occurred
`cz_name`	string	County/zone name
`begin_date_time`	string	Event start time (`YYYY-MM-DD HH:MM:SS`)
`end_date_time`	string	Event end time (`YYYY-MM-DD HH:MM:SS`)
`narrative`	string	Free-text description of the event
`ts_dict_index`	list[int]	Indices into `mmts.jsonl` for associated time series samples

Note: ts_dict_index values are 0-based line indices into mmts.jsonl, linking each event report to one or more nearby weather station time series.

`mmts.jsonl`

Each line is a JSON object representing a multimodal time series sample from a weather station:

{
  "id": "0",
  "station_id": "USW00025323",
  "latitude": 59.2428,
  "longitude": -135.5114,
  "temperature": [2.2, 1.7, 1.4, ...],
  "precipitation": [1.0, 0.5, 0.5, ...],
  "relative_humidity": [82.0, 85.0, 88.5, ...],
  "visibility": [12.88, 11.26, 6.44, ...],
  "wind_u": [-1.59, 2.25, -0.54, ...],
  "wind_v": [-0.61, 1.3, 3.05, ...],
  "sky_code": [8, 8, 8, ...],
  "DATE": ["2020-11-24T00:00:00", "2020-11-24T01:00:00", ...],
  "mode": "7day_hourly",
  "location": "HAINES BOROUGH,ALASKA",
  "description": {
    "DATE": "The past week from November 24 to November 30, 2020.",
    "location": "...",
    "temperature": "...",
    "precipitation": "...",
    "relative_humidity": "...",
    "visibility": "...",
    "wind_u": "...",
    "wind_v": "...",
    "sky_code": "...",
    "labels": "[Cold, Rainy, Cloudy, Windy]"
  }
}

Field	Type	Description
`id`	string	Row index (matches position in file, 0-based)
`station_id`	string	NOAA weather station identifier
`latitude` / `longitude`	float	Station coordinates
`temperature`	list[float]	Temperature readings (°C)
`precipitation`	list[float]	Precipitation (mm)
`relative_humidity`	list[float]	Relative humidity (%)
`visibility`	list[float]	Visibility (km)
`wind_u`	list[float]	Eastward wind component (m/s)
`wind_v`	list[float]	Northward wind component (m/s)
`sky_code`	list[int]	Sky cover code (0–8 oktas)
`DATE`	list[string]	ISO 8601 timestamps for each hourly reading
`mode`	string	Sampling mode (e.g., `7day_hourly` = 7-day window at hourly resolution)
`location`	string	Human-readable station location
`description`	dict	Natural language descriptions of each channel plus weather labels

Linking Events to Time Series

The ts_dict_index field in event_report.jsonl contains a list of line indices (0-based) pointing to rows in mmts.jsonl. These identify the weather station time series samples spatially and temporally associated with each event.

import json

with open("mmts.jsonl") as f:
    mmts = [json.loads(line) for line in f]

with open("event_report.jsonl") as f:
    for line in f:
        event = json.loads(line)
        related_ts = [mmts[i] for i in event["ts_dict_index"]]

Preprocessed Dataset

A preprocessed version of this dataset (formatted for model training and evaluation) is available for download: Google Drive Link

Citation

If you use this dataset, please cite:

@article{chen2025trace,
  title={Trace: Grounding time series in context for multimodal embedding and retrieval},
  author={Chen, Jialin and Zhao, Ziyu and Nurbek, Gaukhar and Feng, Aosong and Maatouk, Ali and Tassiulas, Leandros and Gao, Yifeng and Ying, Rex},
  journal={arXiv preprint arXiv:2506.09114},
  year={2025}
}