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1. Scientific Data Annotation and Dissemination: Using the ‘Ike Wai Gatewayto Manage Research Data

   Cleveland, Sean B; Geis, Jennifel Jacobs (July 2020, Practice and Experience in Advanced Research Computing)

   . Granting agencies invest millions of dollars on the generation and analysis of data, making these products extremely valuable. However, without sufficient annotation of the methods used to collect and analyze the data, the ability to reproduce and reuse those products suffers. This lack of assurance of the quality and credibility of the data at the different stages in the research process essentially wastes much of the investment of time and funding and fails to drive research forward to the level of potential possible if everything was effectively annotated and disseminated to the wider research community. In order to address this issue for the Hawai'i Established Program to Stimulate Competitive Research (EPSCoR) project, a water science gateway was developed at the University of Hawai‘i (UH), called the ‘Ike Wai Gateway. In Hawaiian, ‘Ike means knowledge and Wai means water. The gateway supports research in hydrology and water management by providing tools to address questions of water sustainability in Hawai‘i. The gateway provides a framework for data acquisition, analysis, model integration, and display of data products. The gateway is intended to complement and integrate with the capabilities of the Consortium of Universities for the Advancement of Hydrologic Science's (CUAHSI) Hydroshare by providing sound data and metadata management capabilities for multi-domain field observations, analytical lab actions, and modeling outputs. Functionality provided by the gateway is supported by a subset of the CUAHSI’s Observations Data Model (ODM) delivered as centralized web based user interfaces and APIs supporting multi-domain data management, computation, analysis, and visualization tools to support reproducible science, modeling, data discovery, and decision support for the Hawai'i EPSCoR ‘Ike Wai research team and wider Hawai‘i hydrology community. By leveraging the Tapis platform, UH has constructed a gateway that ties data and advanced computing resources together to support diverse research domains including microbiology, geochemistry, geophysics, economics, and humanities, coupled with computational and modeling workflows delivered in a user friendly web interface with workflows for effectively annotating the project data and products. Disseminating results for the ‘Ike Wai project through the ‘Ike Wai data gateway and Hydroshare makes the research products accessible and reusable. 

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2. An open web‐based module developed to advance data‐driven hydrologic process learning

   https://doi.org/10.1002/hyp.14273  

   Lane, Belize; Garousi‐Nejad, Irene; Gallagher, Melissa A.; Tarboton, David G.; Habib, Emad (July 2021, Hydrological Processes)

   Abstract The era of ‘big data’ promises to provide new hydrologic insights, and open web‐based platforms are being developed and adopted by the hydrologic science community to harness these datasets and data services. This shift accompanies advances in hydrology education and the growth of web‐based hydrology learning modules, but their capacity to utilize emerging open platforms and data services to enhance student learning through data‐driven activities remains largely untapped. Given that generic equations may not easily translate into local or regional solutions, teaching students to explore how well models or equations work in particular settings or to answer specific problems using real data is essential. This article introduces an open web‐based module developed to advance data‐driven hydrologic process learning, targeting upper level undergraduate and early graduate students in hydrology and engineering. The module was developed and deployed on the HydroLearn open educational platform, which provides a formal pedagogical structure for developing effective problem‐based learning activities. We found that data‐driven learning activities utilizing collaborative open web platforms like CUAHSI HydroShare and JupyterHub to store and run computational notebooks allowed students to access and work with datasets for systems of personal interest and promoted critical evaluation of results and assumptions. Initial student feedback was generally positive, but also highlighted challenges including trouble‐shooting and future‐proofing difficulties and some resistance to programming and new software. Opportunities to further enhance hydrology learning include better articulating the benefits of coding and open web platforms upfront, incorporating additional user‐support tools, and focusing methods and questions on implementing and adapting notebooks to explore fundamental processes rather than tools and syntax. The profound shift in the field of hydrology toward big data, open data services and reproducible research practices requires hydrology instructors to rethink traditional content delivery and focus instruction on harnessing these datasets and practices in the preparation of future hydrologists and engineers. 

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3. A living tool for the continued exploration of microplastic toxicity

   https://doi.org/10.1186/s43591-022-00032-4  

   Thornton Hampton, Leah M.; Lowman, Heili; Coffin, Scott; Darin, Emily; De Frond, Hannah; Hermabessiere, Ludovic; Miller, Ezra; de Ruijter, Vera N.; Faltynkova, Andrea; Kotar, Syd; et al (December 2022, Microplastics and Nanoplastics)

   Abstract Throughout the past decade, many studies have reported adverse effects in biota following microplastic exposure. Yet, the field is still emerging as the current understanding of microplastic toxicity is limited. At the same time, recent legislative mandates have required environmental regulators to devise strategies to mitigate microplastic pollution and develop health-based thresholds for the protection of human and ecosystem health. The current publication rate also presents a unique challenge as scientists, environmental managers, and other communities may find it difficult to keep up with microplastic research as it rapidly evolves. At present, there is no tool that compiles and synthesizes the data from these studies to allow for visualization, interpretation, or analysis. Here, we present the Toxicity of Microplastics Explorer (ToMEx), an open access database and open source accompanying R Shiny web application that enables users to upload, search, visualize, and analyze microplastic toxicity data. Though ToMEx was originally created to facilitate the development of health-based thresholds to support California legislations, maintaining the database by the greater scientific community will be invaluable to furthering research and informing policies globally. The database and web applications may be accessed at https://microplastics.sccwrp.org/ . Graphical Abstract 

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4. Assessing the state of research data publication in hydrology: A perspective from the Consortium of Universities for the Advancement of Hydrologic Science, Incorporated

   https://doi.org/10.1002/wat2.1422  

   Horsburgh, Jeffery S.; Hooper, Richard P.; Bales, Jerad; Hedstrom, Margaret; Imker, Heidi J.; Lehnert, Kerstin A.; Shanley, Lea A.; Stall, Shelley (March 2020, WIREs Water)

   Abstract Many have argued that datasets resulting from scientific research should be part of the scholarly record as first class research products. Data sharing mandates from funding agencies and scientific journal publishers along with calls from the scientific community to better support transparency and reproducibility of scientific research have increased demand for tools and support for publishing datasets. Hydrology domain‐specific data publication services have been developed alongside more general purpose and even commercial data repositories. Prominent among these are the Hydrologic Information System (HIS) and HydroShare repositories developed by the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI). More broadly, however, multiple organizations have been involved in the practice of data publication in the hydrology domain, each having different roles that have shaped data publication and reuse. Bibliographic and archival approaches to data publication have been advanced, but both have limitations with respect to hydrologic data. Specific recommendations for improving data publication infrastructure, support, and practices to move beyond existing limitations and enable more effective data publication in support of scientific research in the hydrology domain include: improving support for journal article‐based data access and data citation, considering the workflow for data publication, enhancing support for reproducible science, encouraging publication of curated reference data collections, advancing interoperability standards for sharing data and metadata among repositories, developing partnerships with university libraries offering data services, and developing more specific data management plans. While presented in the context of CUAHSI's data repositories and experience, these recommendations are broadly applicable to other domains. This article is categorized under:Science of Water > Methods 

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5. A FAIR Resource Recommender System for Smart Open Scientific Inquiries

   https://doi.org/10.3390/app15158334  

   Sakib, Syed N; Rubaiat, Sajratul Y; Naha, Kallol; Rahman, Hasan H; Jamil, Hasan M (July 2025, Applied Sciences)

   A vast proportion of scientific data remains locked behind dynamic web interfaces, often called the deep web—inaccessible to conventional search engines and standard crawlers. This gap between data availability and machine usability hampers the goals of open science and automation. While registries like FAIRsharing offer structured metadata describing data standards, repositories, and policies aligned with the FAIR (Findable, Accessible, Interoperable, and Reusable) principles, they do not enable seamless, programmatic access to the underlying datasets. We present FAIRFind, a system designed to bridge this accessibility gap. FAIRFind autonomously discovers, interprets, and operationalizes access paths to biological databases on the deep web, regardless of their FAIR compliance. Central to our approach is the Deep Web Communication Protocol (DWCP), a resource description language that represents web forms, HyperText Markup Language (HTML) tables, and file-based data interfaces in a machine-actionable format. Leveraging large language models (LLMs), FAIRFind combines a specialized deep web crawler and web-form comprehension engine to transform passive web metadata into executable workflows. By indexing and embedding these workflows, FAIRFind enables natural language querying over diverse biological data sources and returns structured, source-resolved results. Evaluation across multiple open-source LLMs and database types demonstrates over 90% success in structured data extraction and high semantic retrieval accuracy. FAIRFind advances existing registries by turning linked resources from static references into actionable endpoints, laying a foundation for intelligent, autonomous data discovery across scientific domains. 

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