whbrewer/smoke-pm

Daily 10km Smoke PM2.5 Predictions

SetGo-processed geospatial dataset

Dataset Structure

Data Splits

Split	Samples
train	51,434,138

Features

This dataset contains 2 features:

• date
• smokePM_pred

Usage

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("whbrewer/smoke-pm")

# Access splits
train_data = dataset["train"]

# Convert to pandas
df = dataset["train"].to_pandas()

Processing Details

This dataset was processed using SetGo (Scientific Metadata Readiness Toolkit), which transforms raw scientific data into AI-ready formats.

Preprocessing Applied

• ingest
• preprocess
• transform
• structure
• output

Normalization

Features have been normalized using z-score normalization.

Source & Attribution

• License: Creative Commons Attribution Share Alike 4.0 International
• Title: Daily 10km Smoke PM2.5 Predictions
• Authors: Marissa Childs, Jessica Li
• DOI: 10.7910/DVN/DJVMTV
• Source: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DJVMTV

This dataset requires attribution — you must give appropriate credit, provide a link to the license, and indicate if changes were made.

This dataset uses a share-alike license — derivative works must be distributed under the same license.

Citation

If you use this dataset, please cite:

@dataset{childs2024wildfirepm25,
  author       = {Childs, Marissa and Li, Jessica and Wen, Jeffrey and Heft-Neal, Sam and Driscoll, Anne and Wang, Sherrie and Gould, Carlos and Qiu, Minghao and Burney, Jennifer and Burke, Marshall},
  title        = {Daily local-level estimates of ambient wildfire smoke PM2.5 for the contiguous US},
  year         = {2024},
  version      = {V1},
  publisher    = {Harvard Dataverse},
  doi          = {10.7910/DVN/DJVMTV},
  url          = {https://doi.org/10.7910/DVN/DJVMTV},
  note         = {UNF:6:F8qjyHrmAWW3JxnlUNRSxQ==}
}