Hutton et al. (2016) argued that computational hydrology can only be a proper science if the hydrological community makes sure that hydrological model studies are executed and presented in a reproducible manner. Hut, Drost and van de Giesen replied that to achieve this hydrologists should not “re-invent the water wheel” but rather use existing technology from other fields (such as containers and ESMValTool) and open interfaces (such as the Basic Model Interface, BMI) to do their computational science (Hut et al., 2017). With this paper and the associated release of the eWaterCycle platform and software package (available on Zenodo: https://doi.org/10.5281/zenodo.5119389, Verhoeven et al., 2022), we are putting our money where our mouth is and providing the hydrological community with a “FAIR by design” (FAIR meaning findable, accessible, interoperable, and reproducible) platform to do science.

The eWaterCycle platform separates the experiments done on the model from the model code. In eWaterCycle, hydrological models are accessed through a common interface (BMI) in Python and run inside of software containers. In this way all models are accessed in a similar manner facilitating easy switching of models, model comparison and model coupling. Currently the following models and model suites are available through eWaterCycle: PCR-GLOBWB 2.0, wflow, Hype, LISFLOOD, MARRMoT, and WALRUS While these models are written in different programming languages they can all be run and interacted with from the Jupyter notebook environment within eWaterCycle. Furthermore, the pre-processing of input data for these models has been streamlined by making use of ESMValTool. Forcing for the models available in eWaterCycle from well-known datasets such as ERA5 can be generated with a single line of code. To illustrate the type of research that eWaterCycle facilitates, this paper includes five case studies: from a simple “hello world” where only a hydrograph is generated to a complex coupling of models in different languages.

In this paper we stipulate the design choices made in building eWaterCycle and provide all the technical details to understand and work with the platform. For system administrators who want to install eWaterCycle on their infrastructure we offer a separate installation guide. For computational hydrologists that want to work with eWaterCycle we also provide a video explaining the platform from a user point of view (https://youtu.be/eE75dtIJ1lk, last access: 28 June 2022)​​​​​​​.

With the eWaterCycle platform we are providing the hydrological community with a platform to conduct their research that is fully compatible with the principles of both Open Science and FAIR science.@en

In 2016, Hutton et al. provided a commentary on the irreproducibility of computational hydrology due to complex methods and a lack of transparency of code and data used in many studies. Irreproducibility inhibits our growth as a community and our work quality because our ability to learn from one another is limited and errors can go unrealized. They provided several suggestions for improving research and reporting practices to adhere to FAIR - Findability, Accessibility, Interoperability, and Reusability - principles. The piece sparked discussion in the hydrology community and further suggestions and ideas to facilitate FAIRness (Hut et al., 2017; Melsen et al., 2017). Particularly, the community has highlighted the importance of communication, collaboration, and adhering to best practices in research software. Whether you study sustainability questions that require models from different fields of geosciences, economics, social sciences, or want to compare the river discharge predictions from your model to the predictions from another research groups model: running each other (hydrological) models is often a painstaking process. Recognising the need for hydrologist to not only have access to the software code of each others models, but also to be able to run these models without the tech-support of the researcher that made the model, we have built the eWaterCycle II platform. The goals for the eWaterCycle II project is to provide the hydrological community with tools that: Allow the use of a wide variety of models, written in different programming languages, without having to learn those languages. Have access to all the relevant datasets from the community (forcing, observations) Allow advanced use cases such as data assimilation and model coupling studies. Allow the sharing of models with the entire community, both for citing (DOIs) and re-use. Ultimately providing hydrologists with a toolset that allows them to run each other models, but also adept, couple, and in general tinker with models without the headache of having to delve into each other’s code. This fits nicely with water and climate change-related challenges: it allows for quicker solutions by combining research efforts from different fields. We will demonstrate (and make available to the community) the system we have built during the presentation.@en

Here, we describe our efforts to create a multi-scale and multi-physics framework that can be retargeted across different disciplines. Currently we have implemented our approach in the astrophysical domain, for which we developed AMUSE (github.com/amusecode/amuse ), and generalized this to the oceanographic and climate sciences, which led to the development of OMUSE (bitbucket.org/omuse ). The objective of this paper is to document the design choices that led to the successful implementation of these frameworks as well as the future challenges in applying this approach to other domains.@en

Methods that fuse multiple localization microscopy images of a single structure can improve signal-to-noise ratio and resolution, but they generally suffer from template bias or sensitivity to registration errors. We present a template-free particle-fusion approach based on an all-to-all registration that provides robustness against individual misregistrations and underlabeling. We achieved 3.3-nm Fourier ring correlation (FRC) image resolution by fusing 383 DNA origami nanostructures with 80% labeling density, and 5.0-nm resolution for structures with 30% labeling density.

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In this paper we present the Oceanographic Multipurpose Software Environment (OMUSE). OMUSE aims to provide a homogeneous environment for existing or newly developed numerical ocean simulation codes, simplifying their use and deployment. In this way, numerical experiments that combine ocean models representing different physics or spanning different ranges of physical scales can be easily designed. Rapid development of simulation models is made possible through the creation of simple high-level scripts. The low-level core of the abstraction in OMUSE is designed to deploy these simulations efficiently on heterogeneous high-performance computing resources. Cross-verification of simulation models with different codes and numerical methods is facilitated by the unified interface that OMUSE provides. Reproducibility in numerical experiments is fostered by allowing complex numerical experiments to be expressed in portable scripts that conform to a common OMUSE interface. Here, we present the design of OMUSE as well as the modules and model components currently included, which range from a simple conceptual quasi-geostrophic solver to the global circulation model POP (Parallel Ocean Program). The uniform access to the codes' simulation state and the extensive automation of data transfer and conversion operations aids the implementation of model couplings. We discuss the types of couplings that can be implemented using OMUSE. We also present example applications that demonstrate the straightforward model initialization and the concurrent use of data analysis tools on a running model. We give examples of multiscale and multiphysics simulations by embedding a regional ocean model into a global ocean model and by coupling a surface wave propagation model with a coastal circulation model.@en

We introduce the Oceanographic Multipurpose Software Environment ( OMUSE): an open source framework for oceanographic simulation codes developed at the IMAU (Utrecht) using coupling technology developed at Leiden Observatory (Leiden). OMUSE aims to provide a homogeneous environment for numerical ocean simulation codes, simplifying their use and deployment. Using OMUSE numerical experiments that combine ocean models representing different physics or spanning different ranges of physical scales can be easily designed. Here, we present the design of OMUSE as well as the modules and model components currently included, which range from a simple conceptual models to to full global circulation models such as POP. We discuss the types of the couplings that can be implemented using OMUSE and present examples of OMUSE applications, that demonstrate the efficient and relatively straightforward model initialisation and coupling possible with OMUSE.@en

This talk will give a brief introduction to OMUSE, the Oceanographic Multipurpose Software Environment, which is currently being developed. OMUSE is a Python framework that provides high-level object-oriented interfaces to existing or newly developed numerical ocean simulation codes, simplifying their use and development In this way, OMUSE facilitates the efficient design of numerical experiments that combine ocean models representing different physics or spanning different ranges of physical scales, for example coupling a global open ocean simulation with a regional coastal ocean model. OMUSE enables its users to write high-level Python scripts that describe simulations. The functionality provided by OMUSE takes care of the low-level integration with the code and deploying simulations on high-performance computing resources, allowing its users to focus on the physics of the simulation. We give an overview of the design of OMUSE and the modules and model components currently included. In particular, we will discuss the process of creating a new OMUSE interface to an existing code, and explain how OMUSE keeps track of the internal state of a running simulation. In addition, we will discuss the grid data types and grid remapping functionality that OMUSE provides. We also give an example of performing online data analysis on a running simulation, which is becoming increasingly important as models simulate a broader range of scales, generating large datasets that cannot be fully stored for offline analysis.@en