Published January 11, 2018
| Version v1
Journal article
Open
A machine learning calibration model using random forests to improve sensor performance for lower-cost air quality monitoring
Authors/Creators
- 1. Carnegie Mellon University
- 2. SenSevere
Description
Dataset accompanying the Atmospheric Measurement Techniques journal article "A machine learning calibration model using random forests to improve sensor performance for lower-cost air quality monitoring" by Naomi Zimmerman et al. (2018).
Files
AMT-Zimmerman-Data Files.zip
Files
(27.5 MB)
| Name | Size | Download all |
|---|---|---|
|
md5:1be50f0172272acae941eb6960f8d0da
|
27.5 MB | Preview Download |
Additional details
Related works
- Is supplement to
- 10.5194/amt-11-291-2018 (DOI)