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Computational models are complex scientific constructs that have become essential for us to better understand the world. Many models are valuable for peers within and beyond disciplinary boundaries. However, there are no widely agreed-upon standards for sharing models. This paper suggests 10 simple rules for you to both (i) ensure you share models in a way that is at least “good enough,” and (ii) enable others to lead the change towards better model-sharing practices. 

How to Position Your Gateway for Failure:The Ten Don’ts of Gateway DesignAbstractScience gateways are accelerators for science and education, providing user-friendly access to powerful computational resources and data analysis tools. Sustained science gateways frameworks such as Hubzero, Tapis, and Galaxy demonstrate the potential for gateways to revolutionize scientific exploration.However, despite initial promise, many gateway projects struggle to transition from prototypes to sustainable, long-term services. Well-intentioned, yet ultimately unsuccessful, gateways are part of the scientific landscape. This raises a critical question: what factors contribute to the demise of science gateways, and how can we avoid these pitfalls to ensure the success of future endeavors?This paper delves into the ten most common pitfalls that lead to science gateway failure. By analyzing these roadblocks, we aim to equip new and developing gateway initiatives with suggestions for long-term success. Our research draws on the collective experiences of numerous gateway projects.We identified critical areas where focused attention and strategic planning are essential. This knowledge will enable the development of good practices that nurture vibrant gateway communities and ensure the long-term sustainability of these valuable research tools. 

The adaptation of machine learning (ML) in scientific and medical research in recent years has heralded a new era of innovation, catalyzing breakthroughs that were once deemed unattainable. In this paper, we present the Machine Learning Hub (ML Hub) – a web application offering a single point of access to pre-trained ML models and datasets, catering to users across varying expertise levels. Built upon the NSF-funded Tapis v3 Application Programming Interface (API) and Tapis User Interface (TapisUI), the platform offers a user-friendly interface for model discovery, dataset exploration, and inference server deployment. 

Community growth is one of the cornerstones contributing to the sustainability of a science gateway. Achieving community growth requires careful planning and a multifaceted approach. The Science Gateways Community Institute (SGCI) and the Center of Excellence for Science Gateways (SGX3) offer services such as UX advice, sustainability training via the Focus Week, and an annual conference to support the science gateway community with developers and users. This panel will discuss four successful use cases – QUBES, MyGeoHub, CHEESE, and the Hawaii Behavioral Health Dashboard – where the teams utilized various SGCI/SGX3 services, which significantly contributed to their community growth. The discussion will highlight specific strategies and outcomes from these use cases, providing valuable insights into the effective practices that drive community engagement and sustainability in science gateways. Additionally, panelists will share lessons learned and good practices that can be applied to other science gateways seeking to enhance their community presence and impact. 

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VREs are predestined to support many aspects of FAIR because of their characteristics to provide a workspace for collaboration, sharing data and simulations and/or workflows. The FAIR for VRE Working Group has worked on a checklist to measure FAIRness in science gateways. This list considers how to address the complexity in regard to which target group is addressed – developers or users – and the granularity such as VREs as software frameworks, services, APIs, workflows, data and simulations. We assume that not only VREs as software frameworks are FAIR but that they also are FAIR-enabling for the digital objects they contain. The objective of this session will be how to recognize and incentivize that providers, developers and users are actively working towards FAIRness of digital objects. The idea for this session is to address this via badges. It probably makes sense to split the badges for the four principles Findable, Accessible, Interoperable and Reusable. There are many open questions beyond this granularity such as how to create badges, who gives such badges, what are the rules for the duration of badges? 

The Covid-19 pandemic has majorly influenced the research around smart city applications and data management. One big topic for Smart City Applications are the novel traffic patterns. Traffic is going back to values near pre-pandemic and adding further traffic via delivery services that arose during the pandemic and are here to stay. Fast data solutions, efficient software frameworks and sensors as well as novel concepts such as semantic advancements are needed to address such challenges. The minitrack presents semantic complex event processing for processing sensor data. The panel addresses current trends evolving from new traffic patterns in cities and rural areas as well as trends in data streaming.