Aloft data

There is a newer version of the record available.

Published October 8, 2025 | Version 1.0.0
Dataset Open

Dark Ecology Dataset: Measurements of Aerial Biomass in US Weather Radar: Vertical Profiles from 2020–2022

Authors/Creators

  • 1. ROR icon University of Massachusetts Amherst
  • 2. ROR icon Yale University
  • 3. ROR icon Inari Agriculture (United States)
  • 4. Meta AI
  • 5. ROR icon Research Institute for Nature and Forest
  • 6. ROR icon Cornell Lab of Ornithology
  • 7. EDMO icon Purdue University
  • 8. ROR icon Lund University
  • 9. ROR icon University of Illinois Urbana-Champaign

Contributors

Data curator:

  • 1. ROR icon University of Massachusetts Amherst

Description

The US NEXRAD radar network has monitored the aerosphere over the US and its territories since the 1990s and archived over 250 million files that contain a wealth of information about the movements of birds, bats, and insects. We extracted measurements of biological activity from all files in the NEXRAD archive.

This data record contains vertical profiles of aerial biomass from 2020 through 2022. Vertical profiles for other years and time series data products can be found in related works.

Please visit https://github.com/darkecology/darkeco-dataset to access the full dataset, including download links, documentation, and examples.

Files

nexrad-stations.csv

Files (29.0 GB)

Name Size Download all
md5:2b993b274e085043f38b7a7084a23557
19.0 kB Preview Download
md5:a906ed69473c4e25e9136b68ce2c576b
9.6 GB Download
md5:c7d69ec388b5dbaf7b9d923574dbb37d
9.7 GB Download
md5:8804a1f7fc5bc3c1aca82de5cf826462
9.7 GB Download

Additional details

Related works

Continues
Dataset: 10.5281/zenodo.13345209 (DOI)
Is documented by
Software: https://github.com/darkecology/darkeco-dataset (URL)
Is supplemented by
Dataset: 10.5281/zenodo.13345265 (DOI)

Funding

U.S. National Science Foundation
Collaborative Research: ABI Innovation: Dark Ecology: Deep Learning and Massive Gaussian Processes to Uncover Biological Signals in Weather Radar 1661259