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1. SLATE and the Mobility of Capability

   Gardner, R; Breen, J; Bryant, L; McKee, S (October 2017, Science Gateways 2017)

   SLATE (Services Layer at the Edge) is a new project that, when complete, will implement “cyberinfrastructure as code” by augmenting the canonical Science DMZ pattern with a generic, programmable, secure and trusted underlayment platform. This platform will host advanced container-centric services needed for higher-level capabilities such as data transfer nodes, software and data caches, workflow services and science gateway components. SLATE will use best-of-breed data center virtualization components, and where available, software defined networking, to enable distributed automation of deployment and service lifecycle management tasks by domain experts. As such it will simplify creation of scalable platforms that connect research teams, institutions and resources to accelerate science while reducing operational costs and development cycle times. Since SLATE will be designed to require only commodity components for its functional layers, its potential for building distributed systems should extend across all data center types and scales, thus enabling creation of ubiquitous, science-driven cyberinfrastructure. By providing automation and programmatic interfaces to distributed HPC backends and other cyberinfrastructure resources, SLATE will amplify the reach of science gateways and therefore the domain communities they support. 

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2. From Data to Software to Science with the Rubin Observatory LSST

   Breivik, Katelyn; Connolly, Andrew; Ford, Saavik; and, others (August 2022, https://arxiv.org/)

   The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) dataset will dramatically alter our understanding of the Universe, from the origins of the Solar System to the nature of dark matter and dark energy. Much of this research will depend on the existence of robust, tested, and scalable algorithms, software, and services. Identifying and developing such tools ahead of time has the potential to significantly accelerate the delivery of early science from LSST. Developing these collaboratively, and making them broadly available, can enable more inclusive and equitable collaboration on LSST science.iv To facilitate such opportunities, a community workshop entitled “From Data to Software to Science with the Rubin Observatory LSST” was organized by the LSST Interdisciplinary Network for Collaboration and Computing (LINCC) and partners, and held at the Flatiron Institute in New York, March 28-30th 2022. The workshop included over 50 in-person attendees invited from over 300 applications. It identified seven key software areas of need: (i) scalable cross-matching and distributed joining of catalogs, (ii) robust photometric redshift determination, (iii) software for determination of selection functions, (iv) frameworks for scalable time-series analyses, (v) services for image access and reprocessing at scale, (vi) object image access (cutouts) and analysis at scale, and (vii) scalable job execution systems. This white paper summarizes the discussions of this workshop. It considers the motivating science use cases, identified cross-cutting algorithms, software, and services, their high-level technical specifications, and the principles of inclusive collaborations needed to develop them. We provide it as a useful roadmap of needs, as well as to spur action and collaboration between groups and individuals looking to develop reusable software for early LSST science. 

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3. Building Scalable Analysis Infrastructure for ATLAS

   https://doi.org/10.1051/epjconf/202533701062  

   Bryant, Lincoln; Gardner_Jr, Robert William; Golnaraghi, Farnaz; Hu, Fengping; Jordan, David; Lancon, Eric Christian; Rosberg, Aidan; Stephen, Judith; Taylor, Ryan Paul; Vukotic, Ilija (October 2025, EPJ Web of Conferences)

   Szumlak, T; Rachwał, B; Dziurda, A; Schulz, M; vom_Bruch, D; Ellis, K; Hageboeck, S (Ed.)

   We explore the adoption of cloud-native tools and principles to forge flexible and scalable infrastructures, aimed at supporting analysis frameworks being developed for the ATLAS experiment in the High Luminosity Large Hadron Collider (HL-LHC) era. The project culminated in the creation of a federated platform, integrating Kubernetes clusters from various providers such as Tier-2 centers, Tier-3 centers, and from the IRIS-HEP Scalable Systems Laboratory, a National Science Foundation project. A unified interface was provided to streamline the management and scaling of containerized applications. Enhanced system scalability was achieved through integration with analysis facilities, enabling spillover of Jupyter/Binder notebooks and Dask workers to Tier-2 resources. We investigated flexible deployment options for a “stretched” (over the wide area network) cluster pattern, including a centralized “lights out management” model, remote administration of Kubernetes services, and a fully autonomous site-managed cluster approach, to accommodate varied operational and security requirements. The platform demonstrated its efficacy in multi-cluster demonstrators for low-latency analyses and advanced workflows with tools such as Coffea, ServiceX, Uproot and Dask, and RDataFrame, illustrating its ability to support various processing frameworks. The project also resulted in a robust user training infrastructure for ATLAS software and computing on-boarding events. 

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4. Open science 2.0: revolutionizing spatiotemporal data sharing and collaboration

   https://doi.org/10.1007/s43762-025-00165-1  

   Wang, Siqin; Huang, Xiao; Zhang, Mengxi; Bao, Shuming; Liu, Lingbo; Fu, Xiaokang; Zhang, Ting; Song, Yongze; Kedron, Peter; Wilson, John; et al (December 2025, Computational Urban Science)

   Abstract The Spatial Data Lab (SDL) project is a collaborative initiative by the Center for Geographic Analysis at Harvard University, KNIME, Future Data Lab, China Data Institute, and George Mason University. Co-sponsored by the NSF IUCRC Spatiotemporal Innovation Center, SDL aims to advance applied research in spatiotemporal studies across various domains such as business, environment, health, mobility, and more. The project focuses on developing an open-source infrastructure for data linkage, analysis, and collaboration. Key objectives include building spatiotemporal data services, a reproducible, replicable, and expandable (RRE) platform, and workflow-driven data analysis tools to support research case studies. Additionally, SDL promotes spatiotemporal data science training, cross-party collaboration, and the creation of geospatial tools that foster inclusivity, transparency, and ethical practices. Guided by an academic advisory committee of world-renowned scholars, the project is laying the foundation for a more open, effective, and robust scientific enterprise. 

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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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