Datasets:

whbrewer
/

smoke-pm

license: cc-by-sa-4.0
task_categories:
  - tabular-classification
  - tabular-regression
tags:
  - setgo
  - domain:geospatial
  - ai-ready
size_categories:
  - n>10M

Daily 10km Smoke PM2.5 Predictions

SetGo-processed geospatial dataset

Dataset Description

Domain: geospatial
Source: /opt/drai/geospatial/smokePM2pt5_predictions_daily_10km_20060101-20201231.csv
Processed by: SetGo (Scientific Metadata Readiness Toolkit)

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==}
}