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1. Introducing the Non-Clinical Tomography Users Research Network (NoCTURN)

   Chase, M. (December 2023, Integrative and comparative biology)

   Digital publishing platforms and internet resources enable openness of access to scientific findings and data at scales never before realized. Unfortunately, researchers sometimes embrace lock-in systems for data generation and analysis out of necessity because meaningful alternatives do not exist. Scientific advances still take place when this occurs, but they become fragmented with discordant quality control, interoperability, reproducibility, and democratization of access. To maximize the value of these—often—publicly funded resources, disciplines are turning to FAIR Guiding Principles for data stewardship. FAIR (Findability, Accessibility, Interoperability, and Reuse) promotes the added value of widespread data sharing that is transparent, equitable, and inclusive. Here we present NoCTURN, an NSF-funded FAIR Open Science Research Coordination Network for computed tomography users. NoCTURN (the Non-clinical Computed Tomography Users Research Network) aims to address the fragmentation of tomography toolkits stemming from proprietary software, non-uniform metadata formats, and repeatability limits. In this presentation, we outline how we will achieve this aim together by 1) developing a community committed to information sharing; 2) coordinating data analysis, storage, and reporting requirements; 3) highlighting underrepresented voices in the field; 4) developing community standards inclusive of industry, research, education, and outreach stake-holders; and 5) modeling FAIR open science strategies for our colleagues and students. NoCTURN is recruiting undergraduates through established investigators from X-ray-, neutron-, and synchrotron-beam computed tomography communities—and we want to hear from you. 

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2. 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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3. The Non-Clinical Tomography Users Research Network (NoCTURN): Who We Are and What We Are Trying to Accomplish

   Maisano, J; Chase, M; Gignac, P; Gila, B; Stanley, E (July 2024, ICTMS)

   The Non-Clinical Tomography Users Research Network (NoCTURN) was established in 2022 to advance Findability, Accessibility, Interoperability, and Reuse (FAIR) and Open Science (OS) practices in the computed tomographic (CT) imaging community. CT specialists utilize a shared pipeline to create digital representations of real-world objects for research, education, and outreach, and we face a shared set of challenges and limitations imposed by siloing of current workflows, best practices, and expertise. Mirroring the U.S. National Science Foundation’s “10 Big Ideas” of Convergence Research (2016), and in consideration of the White House Office of Science and Technology Policy's Nelson Memorandum (2020), NoCTURN is leveraging input from a broad community of more than 100 CT educators, researchers, curators, and industry stakeholders to propose improvements to data handling, management, and sharing that cut across scientific disciplines and extend beyond. Our primary goal is to develop practical recommendations and tools that link today's CT data to tomorrow's CT discoveries. NoCTURN is working toward this goal by providing a platform to: 1) engage the international scientific CT community via participant recruitment from imaging facilities, academic departments and museums, and data repositories across the globe; 2) stimulate improvements for CT imaging and data management standards that focus on FAIR and OS principles; and 3) work directly with private companies that manufacture the hardware and software used in CT imaging, visualization, and analysis to find common ground in documentation and interoperability that better reflects the OS standards championed by federal funding agencies. The planned deliverables from this three-year grant include a ‘Rosetta Stone’ for CT terminology, an interactive world map of CT facilities, a guide to CT repositories, and ‘Good, Better, Best’ guidelines for metadata and long-term data management. We aim to reduce the barriers to entry that isolate individuals and research labs, and we anticipate that developing community standards and improving methodological reporting will enable long-term, systemic changes necessary to aid those at all levels of experience in furthering their access to and use of CT imaging. 

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4. VREs/Virtual Labs/Science Gateways: How could FAIRness badges for providers, developers and users of VREs look like?

   https://doi.org/10.5281/zenodo.13334253  

   Gesing, Sandra (January 2024, Zenodo)

   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? 

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5. FAIR Data and Services in Biodiversity Science and Geoscience

   https://doi.org/10.1162/dint_a_00034  

   Lannom, Larry; Koureas, Dimitris; Hardisty, Alex R. (January 2020, Data Intelligence)

   We examine the intersection of the FAIR principles (Findable, Accessible, Interoperable and Reusable), the challenges and opportunities presented by the aggregation of widely distributed and heterogeneous data about biological and geological specimens, and the use of the Digital Object Architecture (DOA) data model and components as an approach to solving those challenges that offers adherence to the FAIR principles as an integral characteristic. This approach will be prototyped in the Distributed System of Scientific Collections (DiSSCo) project, the pan-European Research Infrastructure which aims to unify over 110 natural science collections across 21 countries. We take each of the FAIR principles, discuss them as requirements in the creation of a seamless virtual collection of bio/geo specimen data, and map those requirements to Digital Object components and facilities such as persistent identification, extended data typing, and the use of an additional level of abstraction to normalize existing heterogeneous data structures. The FAIR principles inform and motivate the work and the DO Architecture provides the technical vision to create the seamless virtual collection vitally needed to address scientific questions of societal importance. 

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