We see container technology as key to achieving the reproducibility goals. Containers are widely used tools to address dependency challenges and to make complicated software tools more portable. Currently available container technologies, such as Docker and Singularity, improve dependency and configuration management i.e., portability of the application. However, container solutions have a steep learning curve and often require dedicated personnel for management.
The goal of the Reproducibility Infrastructure project is to assess the requirements of existing infrastructure, examine how containers can be seamlessly adopted as part of existing infrastructure, and determine to what extent can emerging containers can containers solve solve some reproducibility infrastructure issues.
An Approach for Open and Reproducible Hydrological Modeling using Sciunit and HydroShare. Choi, YoungDon and Goodall, Jonathan and Ahmad, Raza and Malik, Tanu and Tarboton, David , EGU General Assembly Conference Abstracts, 2021.
A taxonomy for reproducible and replicable research in environmental modelling. Essawy, B. T. Goodall, J. L. Voce, D. Morsy, M. M. Sadler, J. M. Choi, Y. D. Tarboton, D. G. Malik, T. , Environmental Modelling & Software, 2020. Paper
Leveraging Scientific Cyberinfrastructures to Achieve Computational Hydrologic Model Reproducibility. Sadler, J. Essawy, B. Goodall, J. Voce, D. CHOI, Y. Morsy, M. Yuan, Z. Malik, T. , AGU Fall Meeting Abstracts, 2018.
Integrating scientific cyberinfrastructures to improve reproducibility in computational hydrology: Example for HydroShare and GeoTrust. Essawy, B. T. Goodall, J. L. Zell, W. Voce, D. Morsy, M. M. Sadler, J. Yuan, Z. Malik, T. , Environmental Modelling & Software, 2018. Paper
Achieving Reproducible Computational Hydrologic Models by Integrating Scientific Cyberinfrastructures. Essawy, B. T. Goodall, J. L. Morsy, M. M. Zell, W. Sadler, J. Malik, T. Yuan, Z. Voce, D. , 9th International Congress on Environmental Modelling and Software, 2018.
GeoTrust Hub: A Platform For Sharing And Reproducing Geoscience Applications. Malik, T. Tarboton, D. G. Goodall, J. L. Choi, E. Bhatt, A. Peckham, S. D. Foster, I. That, D. T. Essawy, B. Yuan, Z. Dash, P. Fils, G. Gan, T. Fadugba, O. I. Saxena, A. Valentic, T. A. , AGU Fall Meeting Abstracts, 2017.
Cyberinfrastructure to Support Collaborative and Reproducible Computational Hydrologic Modeling. Goodall, J. L. Castronova, A. M. Bandaragoda, C. Morsy, M. M. Sadler, J. M. Essawy, B. Tarboton, D. G. Malik, T. Nijssen, B. Clark, M. P. Liu, Y. Wang, S. , AGU Fall Meeting Abstracts, 2017.
Challenges with Maintaining Legacy Software to Achieve Reproducible Computational Analyses: An Example for Hydrologic Modeling Data Processing Pipelines. Essawy, B. T. Goodall, J. L. Malik, T. Xu, H. Conway, M. Gil, Y. , iEMSs Conference, 2016.
This work is supported by the NSF through grant ICER-1928369, ICER-1639759, ICER-1661918, and ICER-1722152.
