nasa-ml-pubs
Generates associated trend figure for a summary paper on machine learning in NASA represented science fields (doi: 10.3847/25c2cfeb.aa328727)
Science Score: 54.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
○.zenodo.json file
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✓DOI references
Found 2 DOI reference(s) in README -
✓Academic publication links
Links to: zenodo.org -
○Academic email domains
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (11.4%) to scientific vocabulary
Repository
Generates associated trend figure for a summary paper on machine learning in NASA represented science fields (doi: 10.3847/25c2cfeb.aa328727)
Basic Info
Statistics
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 4
Metadata Files
README.md
Machine Learning and Artificial Intelligence Publications in NASA Represented Scientific Fields
This is a collection of code that creates a preliminary analysis of the prevalence of machine learning in NASA science related literature. The original data source can be found at the Scopus literature database.
The figure here was first computed for Azari et al., 2021 and has since been updated. As based on this figure, it is estimated that currently (in 2024) planetary science has seen roughly half (60%) as many applications of machine learning methods as in Earth science (11.1% vs 18.1% of publications).
The search terms regarding fields were updated in 2024 to include: - artificial intelligence as a search term in addition to machine learning; the exact search terms can be found in the data files - an approximate estimate of uncertainties (via Poisson counting statistics) - expanded field definitions in recognition that scientists do not identify in papers as "planetary science" but are more likely to use "planetary geology" etc
If you use this figure please reference the published paper and specify the version of the repository used for generating the figure (Azari et al., 2021, v2024). Sample Bibtex of the paper is given below.
@article{Azari2021,
author = {Azari, A and Biersteker, J. B. and Dewey, R. M. and Doran, G. and Forsberg, E. J. and
Harris, C. D. K. and Kerner, H. R. and Skinner, K. A. and Smith, A. W. and Amini, R. and
Cambioni, S. and Da Poian, V. and Garton, T. M. and Himes, M. D. and Millholland, S. and Ruhunusiri, S.},
journal = {Bulletin of the AAS},
number = {4},
year = {2021},
publisher = {Bulletin of the AAS},
title = {Integrating Machine Learning for Planetary Science: {P}erspectives for the Next Decade},
volume = {53},
doi = {10.3847/25c2cfeb.aa328727}
}
Current Figure (version 2024)
Owner
- Name: Abby Azari
- Login: abbyazari
- Kind: user
- Company: University of California, Berkeley
- Website: abbyazari.github.io
- Repositories: 1
- Profile: https://github.com/abbyazari
Planetary & space scientist working on large-scale data analysis.
Citation (Citation.bib)
@article{Azari2021,
author = {Azari, A and Biersteker, J. B. and Dewey, R. M. and Doran, G. and Forsberg, E. J. and
Harris, C. D. K. and Kerner, H. R. and Skinner, K. A. and Smith, A. W. and Amini, R. and
Cambioni, S. and Da Poian, V. and Garton, T. M. and Himes, M. D. and Millholland, S. and Ruhunusiri, S.},
journal = {Bulletin of the AAS},
number = {4},
year = {2021},
publisher = {Bulletin of the AAS},
title = {Integrating Machine Learning for Planetary Science: {P}erspectives for the Next Decade},
volume = {53},
doi = {10.3847/25c2cfeb.aa328727}
}
GitHub Events
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- Create event: 1
- Release event: 1
- Issues event: 1
- Push event: 8
Last Year
- Create event: 1
- Release event: 1
- Issues event: 1
- Push event: 8