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1. Enhancing Scientific Research with FAIR Digital Objects in the National Science Data Fabric

   https://doi.org/10.1109/MCSE.2024.3363828  

   Taufer, Michela; Martinez, Heberth; Luettgau, Jakob; Whitnah, Lauren; Scorzelli, Giorgio; Newell, Pania; Panta, Aashish; Bremer, Peer-Timo; Fils, Douglas; Kirkpatrick, Christine R; et al (September 2023, Computing in Science & Engineering)

   This perspective article presents the vision of combining findable, accessible, interoperable, and reusable (FAIR) Digital Objects with the National Science Data Fabric (NSDF) to enhance data accessibility, scientific discovery, and education. Integrating FAIR Digital Objects into the NSDF overcomes data access barriers and facilitates the extraction of machine-actionable metadata in alignment with FAIR principles. The article discusses examples of climate simulations and materials science workflows and establishes the groundwork for a dataflow design that prioritizes inclusivity, web-centricity, and a network-first approach to democratize data access and create opportunities for research and collaboration in the scientific community. 

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2. TemplateFlow: FAIR-sharing of multi-scale, multi-species brain models

   https://doi.org/10.1038/s41592-022-01681-2  

   Ciric, Rastko; Thompson, William H.; Lorenz, Romy; Goncalves, Mathias; MacNicol, Eilidh E.; Markiewicz, Christopher J.; Halchenko, Yaroslav O.; Ghosh, Satrajit S.; Gorgolewski, Krzysztof J.; Poldrack, Russell A.; et al (December 2022, Nature Methods)

   Abstract Reference anatomies of the brain (‘templates’) and corresponding atlases are the foundation for reporting standardized neuroimaging results. Currently, there is no registry of templates and atlases; therefore, the redistribution of these resources occurs either bundled within existing software or in ad hoc ways such as downloads from institutional sites and general-purpose data repositories. We introduce TemplateFlow as a publicly available framework for human and non-human brain models. The framework combines an open database with software for access, management, and vetting, allowing scientists to share their resources under FAIR—findable, accessible, interoperable, and reusable—principles. TemplateFlow enables multifaceted insights into brains across species, and supports multiverse analyses testing whether results generalize across standard references, scales, and in the long term, species. 

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3. Deep Ocean Early-Career Researchers Dive into Data Management Training

   https://doi.org/10.5670/oceanog.2025.110  

   Beaulieu, Stace; Smith, Leslie; Stocks, Karen (January 2025, Oceanography)

   Open access to scientific data is increasingly recognized as critical to fostering scientific progress, trustworthy and reproducible science, global information equity, and evidence-based policymaking. It requires scientists to not only share their data, but to share in such a way that the data have high utility for later users. The FAIR data principles define a set of characteristics for making data “findable, accessible, interoperable, and reusable” (Wilkinson et al., 2016). Training scientists, particularly early-career scientists, on these principles can improve the volume and quality of open science data. 

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4. Rolling Deck to Repository: Supporting the marine science community with data management services from academic research expeditions

   https://doi.org/10.3389/fmars.2022.1012756  

   Carbotte, Suzanne M.; O’Hara, Suzanne; Stocks, Karen; Clark, P Dru; Stolp, Laura; Smith, Shawn R.; Briggs, Kristen; Hudak, Rebecca; Miller, Emily; Olson, Chris J.; et al (December 2022, Frontiers in Marine Science)

   Direct observations of the oceans acquired on oceanographic research ships operated across the international community support fundamental research into the many disciplines of ocean science and provide essential information for monitoring the health of the oceans. A comprehensive knowledge base is needed to support the responsible stewardship of the oceans with easy access to all data acquired globally. In the United States, the multidisciplinary shipboard sensor data routinely acquired each year on the fleet of coastal, regional and global ranging vessels supporting academic marine research are managed by the Rolling Deck to Repository (R2R, rvdata.us) program. With over a decade of operations, the R2R program has developed a robust routinized system to transform diverse data contributions from different marine data providers into a standardized and comprehensive collection of global-ranging observations of marine atmosphere, ocean, seafloor and subseafloor properties that is openly available to the international research community. In this article we describe the elements and framework of the R2R program and the services provided. To manage all expeditions conducted annually, a fleet-wide approach has been developed using data distributions submitted from marine operators with a data management workflow designed to maximize automation of data curation. Other design goals are to improve the completeness and consistency of the data and metadata archived, to support data citability, provenance tracking and interoperable data access aligned with FAIR (findable, accessible, interoperable, reusable) recommendations, and to facilitate delivery of data from the fleet for global data syntheses. Findings from a collection-level review of changes in data acquisition practices and quality over the past decade are presented. Lessons learned from R2R operations are also discussed including the benefits of designing data curation around the routine practices of data providers, approaches for ensuring preservation of a more complete data collection with a high level of FAIRness, and the opportunities for homogenization of datasets from the fleet so that they can support the broadest re-use of data across a diverse user community. 

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5. FAIR principles in workflows: A GIScience workflow management system for reproducible and replicable studies

   https://doi.org/10.1016/j.jag.2025.104477  

   Hu, Tao; Liu, Taiping; Divyacharan_Jarugumalli, Venkat Sai; Cheng, Samuel; Deng, Chengbin (April 2025, International Journal of Applied Earth Observation and Geoinformation)

   Scientific workflow management systems (WfMS) provide a systematic way to streamline necessary processes in scientific research. The demand for FAIR (Findable, Accessible, Interoperable, and Reusable) workflows is increasing in the scientific community, particularly in GIScience, where data is not just an output but an integral part of iterative advanced processes. Traditional WfMS often lack the capability to ensure geospatial data and process transparency, leading to challenges in reproducibility and replicability of research findings. This paper proposes the conceptualization and development of FAIR-oriented GIScience WfMS, aiming to incorporate the FAIR principles into the entire lifecycle of geospatial data processing and analysis. To enhance the findability and accessibility of workflows, the WfMS utilizes Harvard Dataverse to share all workflow-related digital resources, organized into workflow datasets, nodes, and case studies. Each resource is assigned a unique DOI (Digital Object Identifier), ensuring easy access and discovery. More importantly, the WfMS complies with the Common Workflow Language (CWL) standard to guarantee interoperability and reproducibility of workflows. It also enables the integration of diverse tools and software, supporting complex analyses that require multiple processing steps. This paper demonstrates the prototype of the GIScience WfMS and illustrates two geospatial science case studies, reflecting its flexibility in selecting appropriate techniques for various datasets and research goals. The user-friendly workflow designer makes it accessible to users with different levels of technical expertise, promoting reusable, reproducible, and replicable GIScience studies. 

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