Jupyter Notebooks: Jupyter by Discipline

Yu, W., Carrasco Kind, M., & Brunner, R. (2017). Vizic: A Jupyter-based interactive visualization tool for astronomical catalogs. Astronomy and Computing, 20(C), 128–139.

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Nguyen, H., Case, D., & Rose, A. (2018). NGLview-interactive molecular graphics for Jupyter notebooks. Bioinformatics, 34(7), 1241–1242.

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Rule, A., Birmingham, A., Zuniga, C., Altintas, I., Huang, S., Knight, R., Moshiri, N., Nguyen, M., Rosenthal, S., Pérez, F., & Rose, P. (2019). Ten simple rules for writing and sharing computational analyses in Jupyter Notebooks. PLoS Computational Biology, 15(7), e1007007–e1007007.

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Pillay, C. (2020). Analyzing biological models and data sets using Jupyter notebooks as an alternate to laboratory‐based exercises during COVID‐19. Biochemistry and Molecular Biology Education, 48(5), 532–534. https://doi.org/10.1002/bmb.21443

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Menke, E. (2020). Series of Jupyter Notebooks using Python for an analytical chemistry course. Journal of Chemical Education, 97(10), 3899–3903.

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Rule, A., Birmingham, A., Zuniga, C., Altintas, I., Huang, S., Knight, R., Moshiri, N., Nguyen, M., Rosenthal, S., Pérez, F., & Rose, P. (2019). Ten simple rules for writing and sharing computational analyses in Jupyter Notebooks. PLoS Computational Biology, 15(7), e1007007–e1007007.

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Beg, M., Belin, J., Kluyver, T., Konovalov, A., Ragan-Kelley, M., Thiery, N., & Fangohr, H. (2021). Using Jupyter for reproducible scientific workflows. Computing in Science & Engineering, 1–1. https://doi.org/10.1109/MCSE.2021.3052101

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Varela, J., Wehn, N., Desmettre, S., & Korn, R. (2017). Real-Time Financial Risk Measurement of Dynamic Complex Portfolios with Python and PyOpenCL. 1–10.

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Toms, S., Crickard, P., & van Rees, E. (2018). Mastering Geospatial Analysis with Python: Explore GIS processing and learn to work with GeoDjango, CARTOframes and MapboxGL-Jupyter. In Mastering Geospatial Analysis with Python. Packt Publishing, Limited.

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Braun, N., Hauth, T., Pulvermacher, C., & Ritter, M. (2017). An Interactive and Comprehensive Working Environment for High-Energy Physics Software with Python and Jupyter Notebooks. Journal of Physics. Conference Series, 898(7), 72020–. https://doi.org/10.1088/1742-6596/898/7/072020

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Green, A., & Tanedo, P. (2019). DarkCapPy: Dark matter capture and annihilation. Computer Physics Communications, 242(C), 120–131. https://doi.org/10.1016/j.cpc.2019.04.010

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Wang, Z., & Ma’ayan, A. (2016). An open RNA-Seq data analysis pipeline tutorial with an example of reprocessing data from a recent Zika virus study [version 1; peer review: 3 approved]. F1000 Research, 5, 1574–1574. https://doi.org/10.12688/f1000research.9110.1

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Perkel, J. (2018). By Jupyter, it all makes sense. Nature (London), 563(7729), 145–146.

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Reades, J. (2020). Teaching on Jupyter. Region (Louvain-La-Neuve), 7(1), 21–34.

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Tyagi, H. (2020). Building COVID-19 interactive dashboard from Jupyter Notebooks. Available from Towards Data Science