Leveraging open science machine learning challenges for data constrained planetary mission instruments
- DOI
- 10.1093/rasti/rzae009
- Published
- 2024-01
- Container
- RAS Techniques and Instruments
- Publisher
- Oxford University Press (OUP)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1093/rasti/rzae009,
title = {Leveraging open science machine learning challenges for data constrained planetary mission instruments},
author = {Victoria Da Poian and Eric I Lyness and Jay Y Qi and Isha Shah and Greg Lipstein and P Doug Archer Jr. and Luoth Chou and Caroline Freissinet and Charles A Malespin and Amy C McAdam and Christine A Knudson and Bethany P Theiling and Sarah M Hörst},
year = {2024},
journal = {RAS Techniques and Instruments},
doi = {10.1093/rasti/rzae009},
url = {https://doi.org/10.1093/rasti/rzae009}
}RIS
TY - JOUR TI - Leveraging open science machine learning challenges for data constrained planetary mission instruments AU - Victoria Da Poian AU - Eric I Lyness AU - Jay Y Qi AU - Isha Shah AU - Greg Lipstein AU - P Doug Archer Jr. AU - Luoth Chou AU - Caroline Freissinet AU - Charles A Malespin AU - Amy C McAdam AU - Christine A Knudson AU - Bethany P Theiling AU - Sarah M Hörst PY - 2024 JO - RAS Techniques and Instruments DO - 10.1093/rasti/rzae009 UR - https://doi.org/10.1093/rasti/rzae009 ER -
APA
Poian, V. D., Lyness, E. I., Qi, J. Y., Shah, I., Lipstein, G., Jr., P. D. A., Chou, L., Freissinet, C., Malespin, C. A., McAdam, A. C., Knudson, C. A., Theiling, B. P., & Hörst, S. M. (2024). Leveraging open science machine learning challenges for data constrained planetary mission instruments. RAS Techniques and Instruments. https://doi.org/10.1093/rasti/rzae009
Source records
- crossref · retrieved 2026-09-25T06:50:54.545Z