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1. Community Workflows to Advance Reproducibility in Hydrologic Modeling: Separating Model‐Agnostic and Model‐Specific Configuration Steps in Applications of Large‐Domain Hydrologic Models

   https://doi.org/10.1029/2021WR031753  

   Knoben, W. J. M.; Clark, M. P.; Bales, J.; Bennett, A.; Gharari, S.; Marsh, C. B.; Nijssen, B.; Pietroniro, A.; Spiteri, R. J.; Tang, G.; et al (November 2022, Water Resources Research)

   Abstract Despite the proliferation of computer‐based research on hydrology and water resources, such research is typically poorly reproducible. Published studies have low reproducibility due to incomplete availability of data and computer code, and a lack of documentation of workflow processes. This leads to a lack of transparency and efficiency because existing code can neither be quality controlled nor reused. Given the commonalities between existing process‐based hydrologic models in terms of their required input data and preprocessing steps, open sharing of code can lead to large efficiency gains for the modeling community. Here, we present a model configuration workflow that provides full reproducibility of the resulting model instantiations in a way that separates the model‐agnostic preprocessing of specific data sets from the model‐specific requirements that models impose on their input files. We use this workflow to create large‐domain (global and continental) and local configurations of the Structure for Unifying Multiple Modeling Alternatives (SUMMA) hydrologic model connected to the mizuRoute routing model. These examples show how a relatively complex model setup over a large domain can be organized in a reproducible and structured way that has the potential to accelerate advances in hydrologic modeling for the community as a whole. We provide a tentative blueprint of how community modeling initiatives can be built on top of workflows such as this. We term our workflow the “Community Workflows to Advance Reproducibility in Hydrologic Modeling” (CWARHM; pronounced “swarm”). 

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2. Brain image data processing using collaborative data workflows on Texera

   https://doi.org/10.3389/fncir.2024.1398884  

   Ding, Yunyan; Huang, Yicong; Gao, Pan; Thai, Andy; Chilaparasetti, Atchuth Naveen; Gopi, M; Xu, Xiangmin; Li, Chen (July 2024, Frontiers in Neural Circuits)

   In the realm of neuroscience, mapping the three-dimensional (3D) neural circuitry and architecture of the brain is important for advancing our understanding of neural circuit organization and function. This study presents a novel pipeline that transforms mouse brain samples into detailed 3D brain models using a collaborative data analytics platform called “Texera.” The user-friendly Texera platform allows for effective interdisciplinary collaboration between team members in neuroscience, computer vision, and data processing. Our pipeline utilizes the tile images from a serial two-photon tomography/TissueCyte system, then stitches tile images into brain section images, and constructs 3D whole-brain image datasets. The resulting 3D data supports downstream analyses, including 3D whole-brain registration, atlas-based segmentation, cell counting, and high-resolution volumetric visualization. Using this platform, we implemented specialized optimization methods and obtained significant performance enhancement in workflow operations. We expect the neuroscience community can adopt our approach for large-scale image-based data processing and analysis. 

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3. What Ails End-User Composition: A Cross-Domain Qualitative Study

   https://doi.org/10.1007/978-3-319-58735-6 5  

   Dwivedi, V.; Herbsleb, J.; Garlan, D. (January 2017, International Symposium on End User Development)

   Across many domains, end-users need to compose computational elements into novel configurations to perform their day-to-day tasks. End-user composition is a common programming activity performed by such end-users to accomplish this composition task. While there have been many studies on end-user programming, we still need a better understanding of activities involved in end-user composition and environments to support them. In this paper we report a qualitative study of four popular composition environments belonging to diverse application domains, including: Taverna workflow environment for life sciences, Loni Pipeline for brain imaging, SimMan3G for medical simulations and Kepler for scientific simulations. We interview end-users of these environments to explore their experiences while performing common compositions tasks. We use “Content Analysis” technique to analyze these interviews to explore what are the barriers to end-user composition in these domains. Furthermore, our findings show that there are some unique differences in the requirements of naive end-users vs. expert programmers. We believe that not only are these findings useful to improve the quality of end-user composition environments, but they can also help towards development of better end-user composition frameworks. 

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4. The Right Tool for the Job: Data-Centric Workflows in Vizier

   Oliver Kennedy, Boris Glavic (September 2022, Bulletin of the Technical Committee on Data Engineering)

   Sudeepa Roy and Jun Yang (Ed.)

   Data scientists use a wide variety of systems with a wide variety of user interfaces such as spreadsheets and notebooks for their data exploration, discovery, preprocessing, and analysis tasks. While this wide selection of tools offers data scientists the freedom to pick the right tool for each task, each of these tools has limitations (e.g., the lack of reproducibility of notebooks), data needs to be translated between tool-specific formats, and common functionality such as versioning, provenance, and dealing with data errors often has to be implemented for each system. We argue that rather than alternating between task-specific tools, a superior approach is to build multiple user-interfaces on top of a single incremental workflow / dataflow platform with built-in support for versioning, provenance, error & tracking, and data cleaning. We discuss Vizier, a notebook system that implements this approach, introduce the challenges that arose in building such a system, and highlight how our work on Vizier lead to novel research in uncertain data management and incremental execution of workflows. 

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5. Building Trust in Earth Science Findings through Data Traceability and Results Explainability

   https://doi.org/10.1109/TPDS.2022.3220539  

   Olaya, Paula; Kennedy, Dominic; Llamas, Ricardo; Valera, Leobardo; Vargas, Rodrigo; Lofstead, Jay; Taufer, Michela (January 2022, IEEE Transactions on Parallel and Distributed Systems)

   To trust findings in computational science, scientists need workflows that trace the data provenance and support results explainability. As workflows become more complex, tracing data provenance and explaining results become harder to achieve. In this paper, we propose a computational environment that automatically creates a workflow execution’s record trail and invisibly attaches it to the workflow’s output, enabling data traceability and results explainability. Our solution transforms existing container technology, includes tools for automatically annotating provenance metadata, and allows effective movement of data and metadata across the workflow execution. We demonstrate the capabilities of our environment with the study of SOMOSPIE, an earth science workflow. Through a suite of machine learning modeling techniques, this workflow predicts soil moisture values from the 27 km resolution satellite data down to higher resolutions necessary for policy making and precision agriculture. By running the workflow in our environment, we can identify the causes of different accuracy measurements for predicted soil moisture values in different resolutions of the input data and link different results to different machine learning methods used during the soil moisture downscaling, all without requiring scientists to know aspects of workflow design and implementation. 

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