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ORCIDDaniela Huppenkothen
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Journal Articles
- 2024
[j4]Jeanette Falk
, Alexander Nolte, Daniela Huppenkothen, Marion Weinzierl, Kiev Gama, Daniel Spikol, Erik J. Tollerud, Neil P. Chue Hong, Ines Knäpper, Linda Bailey Hayden:
The Future of Hackathon Research and Practice. IEEE Access 12: 133406-133425 (2024)- 2023
- [j3]Thomas E. Riley, Devarshi Choudhury, Tuomo E. Salmi, Serena Vinciguerra, Yves Kini, Bas Dorsman, Anna L. Watts, Daniela Huppenkothen, Sebastien Guillot:
X-PSI: A Python package for neutron star X-ray pulse simulation and inference. J. Open Source Softw. 8(82): 4977 (2023) - 2021
- [j2]Faryad Sahneh, Meghan A. Balk, Marina Kisley, Chi-Kwan Chan, Mercury Fox, Brian Nord, Eric Lyons, Tyson Lee Swetnam, Daniela Huppenkothen, Will Sutherland, Ramona L. Walls, Daven P. Quinn, Tonantzin Tarin, David S. LeBauer, David Ribes, Dunbar P. Birnie III, Carol Lushbough, Eric Carr, Grey Nearing, Jeremy Fischer, Kevin Tyle, Luis Carrasco, Meagan Lang, Peter W. Rose, Richard R. Rushforth, Samapriya Roy, Thomas Matheson, Tina Lee, C. Titus Brown, Tracy K. Teal, Monica Papes, Stephen G. Kobourov, Nirav C. Merchant:
Ten simple rules to cultivate transdisciplinary collaboration in data science. PLoS Comput. Biol. 17(5) (2021) - 2019
- [j1]Daniela Huppenkothen, Matteo Bachetti, Abigail Stevens, Simone Migliari, Paul Balm, Omar Hammad, Usman Mahmood Khan, Himanshu Mishra, Haroon Rashid, Swapnil Sharma, Evandro Martinez Ribeiro, Ricardo Vallés Blanco:
stingray: A modern Python library for spectral timing. J. Open Source Softw. 4(38): 1393 (2019)
Conference and Workshop Papers
- 2020
- [c1]Daniela Huppenkothen, Gwendolyn Eadie:
Teaching the Foundations of Machine Learning with Candy. Teaching ML 2020: 29-35
Informal and Other Publications
- 2024
- [i5]Francisco Ardevol Martinez, Michiel Min, Daniela Huppenkothen, Inga Kamp, Paul I. Palmer:
FlopPITy: Enabling self-consistent exoplanet atmospheric retrievals with machine learning. CoRR abs/2401.04168 (2024) - 2023
- [i4]Daniela Huppenkothen, Michelle Ntampaka, M. Ho, M. Fouesneau, Brian Nord, Joshua E. G. Peek, Mike Walmsley, John F. Wu, Camille Avestruz, T. Buck, M. Brescia, D. P. Finkbeiner, Andy D. Goulding, Tomasz Kacprzak, Peter Melchior, M. Pasquato, Nesar Ramachandra, Yuan-Sen Ting, G. van de Ven, S. Villar, V. A. Villar, E. Zinger:
Constructing Impactful Machine Learning Research for Astronomy: Best Practices for Researchers and Reviewers. CoRR abs/2310.12528 (2023) - 2022
- [i3]Jeanette Falk, Alexander Nolte, Daniela Huppenkothen, Marion Weinzierl, Kiev Gama, Daniel Spikol, Erik J. Tollerud, Neil P. Chue Hong, Ines Knäpper, Linda Bailey Hayden:
The Future of Hackathon Research and Practice. CoRR abs/2211.08963 (2022) - 2019
- [i2]Daniela Huppenkothen, Brian McFee, Laura Norén:
Entrofy Your Cohort: A Data Science Approach to Candidate Selection. CoRR abs/1905.03314 (2019) - 2017
- [i1]Daniela Huppenkothen, Anthony Arendt, David W. Hogg, Karthik Ram, Jake VanderPlas, Ariel Rokem:
Hack Weeks as a model for Data Science Education and Collaboration. CoRR abs/1711.00028 (2017)
Coauthor Index
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