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1. Jetstream2: Accelerating cloud computing via Jetstream

   https://doi.org/10.1145/3437359.3465565  

   Hancock, David Y.; Fischer, Jeremy; Lowe, John Michael; Snapp-Childs, Winona; Pierce, Marlon; Marru, Suresh; Coulter, J. Eric; Vaughn, Matthew; Beck, Brian; Merchant, Nirav; et al (July 2021, Proceedings of the 2021 Practice & Experience in Advanced Research Computing (PEARC) Conference)

   null (Ed.)

   Jetstream2 will be a category I production cloud resource that is part of the National Science Foundation’s Innovative HPC Program. The project’s aim is to accelerate science and engineering by providing “on-demand” programmable infrastructure built around a core system at Indiana University and four regional sites. Jetstream2 is an evolution of the Jetstream platform, which functions primarily as an Infrastructure-as-a-Service cloud. The lessons learned in cloud architecture, distributed storage, and container orchestration have inspired changes in both hardware and software for Jetstream2. These lessons have wide implications as institutions converge HPC and cloud technology while building on prior work when deploying their own cloud environments. Jetstream2’s next-generation hardware, robust open-source software, and enhanced virtualization will provide a significant platform to further cloud adoption within the US research and education communities. 

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2. Cyberinfrastructure deployments on public research clouds enable accessible Environmental Data Science education

   https://doi.org/10.1145/3569951.3597606  

   McIntosh, Tyler L; Verleye, Erick; Balch, Jennifer K; Cattau, Megan E; Ilangakoon, Nayani T; Korinek, Nathan; Nagy, R. Chelsea; Sanovia, James; Skidmore, Edwin; Swetnam, Tyson L; et al (July 2023, ACM)

   Modern science depends on computers, but not all scientists have access to the scale of computation they need. A digital divide separates scientists who accelerate their science using large cyberinfrastructure from those who do not, or who do not have access to the compute resources or learning opportunities to develop the skills needed. The exclusionary nature of the digital divide threatens equity and the future of innovation by leaving people out of the scientific process while over-amplifying the voices of a small group who have resources. However, there are potential solutions: recent advancements in public research cyberinfrastructure and resources developed during the open science revolution are providing tools that can help bridge this divide. These tools can enable access to fast and powerful computation with modest internet connections and personal computers. Here we contribute another resource for narrowing the digital divide: scalable virtual machines running on public cloud infrastructure. We describe the tools, infrastructure, and methods that enabled successful deployment of a reproducible and scalable cyberinfrastructure architecture for a collaborative data synthesis working group in February 2023. This platform enabled 45 scientists with varying data and compute skills to leverage 40,000 hours of compute time over a 4-day workshop. Our approach provides an open framework that can be replicated for educational and collaborative data synthesis experiences in any data- and compute-intensive discipline. 

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3. Diversifying the genomic data science research community

   https://doi.org/10.1101/gr.276496.121  

   The Genomic Data Science Community Network (July 2022, Genome Research)

   Over the past 20 years, the explosion of genomic data collection and the cloud computing revolution have made computational and data science research accessible to anyone with a web browser and an internet connection. However, students at institutions with limited resources have received relatively little exposure to curricula or professional development opportunities that lead to careers in genomic data science. To broaden participation in genomics research, the scientific community needs to support these programs in local education and research at underserved institutions (UIs). These include community colleges, historically Black colleges and universities, Hispanic-serving institutions, and tribal colleges and universities that support ethnically, racially, and socioeconomically underrepresented students in the United States. We have formed the Genomic Data Science Community Network to support students, faculty, and their networks to identify opportunities and broaden access to genomic data science. These opportunities include expanding access to infrastructure and data, providing UI faculty development opportunities, strengthening collaborations among faculty, recognizing UI teaching and research excellence, fostering student awareness, developing modular and open-source resources, expanding course-based undergraduate research experiences (CUREs), building curriculum, supporting student professional development and research, and removing financial barriers through funding programs and collaborator support. 

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4. The eWaterCycle platform for open and FAIR hydrological collaboration

   https://doi.org/10.5194/gmd-15-5371-2022  

   Hut, Rolf; Drost, Niels; van de Giesen, Nick; van Werkhoven, Ben; Abdollahi, Banafsheh; Aerts, Jerom; Albers, Thomas; Alidoost, Fakhereh; Andela, Bouwe; Camphuijsen, Jaro; et al (January 2022, Geoscientific Model Development)

   Abstract. 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. 

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5. CitSci.org & PPSR Core: Sharing biodiversity observations across platforms

   https://doi.org/10.3897/biss.5.75666  

   Budnicki, Brandon; Newman, Gregory (September 2021, Biodiversity Information Science and Standards)

   CitSci.org is a global citizen science software platform and support organization housed at Colorado State University. The mission of CitSci is to help people do high quality citizen science by amplifying impacts and outcomes. This platform hosts over one thousand projects and a diverse volunteer base that has amassed over one million observations of the natural world, focused on biodiversity and ecosystem sustainability. It is a custom platform built using open source components including: PostgreSQL, Symfony, Vue.js, with React Native for the mobile apps. CitSci sets itself apart from other Citizen Science platforms through the flexibility in the types of projects it supports rather than having a singular focus. This flexibility allows projects to define their own datasheets and methodologies. The diversity of programs we host motivated us to take a founding role in the design of the PPSR Core, a set of global, transdisciplinary data and metadata standards for use in Public Participation in Scientific Research (Citizen Science) projects. Through an international partnership between the Citizen Science Association, European Citizen Science Association, and Australian Citizen Science Association, the PPSR team and associated standards enable interoperability of citizen science projects, datasets, and observations. Here we share our experience over the past 10+ years of supporting biodiversity research both as developers of the CitSci.org platform and as stewards of, and contributors to, the PPSR Core standard. Specifically, we share details about: the origin, development, and informatics infrastructure for CitSci our support for biodiversity projects such as population and community surveys our experiences in platform interoperability through PPSR Core working with the Zooniverse, SciStarter, and CyberTracker data quality data sharing goals and use cases. the origin, development, and informatics infrastructure for CitSci our support for biodiversity projects such as population and community surveys our experiences in platform interoperability through PPSR Core working with the Zooniverse, SciStarter, and CyberTracker data quality data sharing goals and use cases. We conclude by sharing overall successes, limitations, and recommendations as they pertain to trust and rigor in citizen science data sharing and interoperability. As the scientific community moves forward, we show that Citizen Science is a key tool to enabling a systems-based approach to ecosystem problems. 

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