An open API service providing mapping between scientific papers and software projects that are mentioned in them.

Papers: 10.1038/s41598-021-85639-y

https://doi.org/10.1038/s41598-021-85639-y

African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning

Cited by: 79
Author(s): Tomislav Hengl, Matthew A. E. Miller, Josip Križan, Keith D. Shepherd, Andrew Sila, Milan Kilibarda, Ognjen Antonijević, Luka Glušica, A. Dobermann, Stephan M. Haefele, Steve P. McGrath, Gifty E. Acquah, Jamie Collinson, Leandro Parente, Mohammadreza Sheykhmousa, Kazuki Saito, Jean‐Martial Johnson, Jordan Chamberlin, Francis B. T. Silatsa, Martin Yemefack, John Wendt, R.A. MacMillan, Ichsani Wheeler, Jonathan H. Crouch
Published: over 4 years ago

Software Mentions 9

cran: Cubist
Rule- And Instance-Based Regression Modeling
Papers that mentioned: 54
Very Likely Science (99)
cran: deepnet
Deep Learning Toolkit in R
Papers that mentioned: 6
Very Likely Science (85)
cran: mlr
Machine Learning in R
Papers that mentioned: 64
Very Likely Science (100)
cran: mlr3
Machine Learning in R - Next Generation
Papers that mentioned: 8
Very Likely Science (100)
cran: parallelMap
Unified Interface to Parallelization Back-Ends
Papers that mentioned: 4
Very Likely Science (75)
cran: SuperLearner
Super Learner Prediction
Papers that mentioned: 56
Very Likely Science (100)
pypi: deepnet
package for deep learning
Papers that mentioned: 6
Very Likely Science (75)
pypi: GDAL
GDAL: Geospatial Data Abstraction Library
Papers that mentioned: 71
Very Likely Science (90)
pypi: mlr
Linear regression utility with inference tests, residual analysis, outlier visualization, multicollinearity test, and other features
Papers that mentioned: 64
Likely Science (40)