DOI: 10.3897/rio.12.e195997

Authors: Dragan Ivanovic, Grischa Fraumann, Jennifer Dusdal, Kim Holmberg, Vladimir Trajkovik, Houcemeddine Turki, Colin Layfield, Stevo Popovic, Haris Memisevic, Lidija Ivanovic, Tim Engels, Georgia Kapitsaki, Cristina Huidiu, Đilda Pečarić, Romain David, Rossana Morriello

Abstract: The open science paradigm, digitalisation, interdisciplinarity and internationalisation have significantly changed the research process, collaboration, dissemination and impact of scholarly work in the 21st century. Research impact assessment should include new metrics based on Web 2.0 channels suffering from the following issues: data quality (i.e. accuracy, coverage, comprehensiveness), heterogeneity of data sources and APIs and potential manipulation (i.e. metrics gaming). Although the Findable, Accessible, Interoperable and Reusable (FAIR) principles were designed for research data, they can also be applied to research impact metrics to increase their discoverability and reusability. The main aim of this European Cooperation in Science and Technology (COST) Action is to remove barriers for the wider adoption and reusability of metrics, based on Web 2.0 technologies, which are a significant and vital part of research ecosystems. These metrics can serve as the basis for enhanced research impact assessment and, thus, improve recognition of excellence and foster the further development of science and society. Although Scientometrics, based on Web 2.0, is a paradigm that is over 10 years old, it has not yet been widely adopted. Therefore, a plan or roadmap for transition to Scientometrics 2.0 is needed. This should include recommendations for overcoming the challenges associated with new research impact metrics, as well as frameworks for the evaluation of new metrics and data sources. These challenges include the heterogeneity and comprehensiveness of metrics data sources, the varying quality of metrics data, metrics data gaming etc. Due to the multifaceted nature of these challenges, the Action proposes to create synergies between all interested actors: researchers, research software engineers, librarians, representatives of metrics data providers and policy-makers.This article presents an edited version of the original funding proposal submitted to the COST Open Call 2021.

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DOI: 10.3897/rio.11.e179253

Authors: Ronny Gey, Daniel Mietchen, Oliver Karras, Tim Wittenborg, Moritz Schubotz, Jan Bumberger

Abstract: Across essentially all fields of research, many aspects of the respective research processes – whether experimental, theoretical, empirical or outright computational – are closely related to software. Yet the process of finding software that is directly suitable or at least a good starting point for a given research task is cumbersome.This project aims to develop a community-driven system that provides potential users of research software with a diversity of pathways towards actually finding software that closely matches their research needs if such software exists. Conversely, it will provide software developers with mechanisms to make their software findable for research-related tasks and it will highlight mismatches between software supply and demand for specific tasks.To this end, we will document how various stakeholders of the research landscape have been searching for – or stumbling upon – research software so far, identify variables associated with successful search outcomes and build workflows that assist in describing software and associated concepts in a standardised fashion. These descriptions will then be aligned across various sources of relevant information and integrated into Wikidata, the knowledge graph that anyone can edit and that already contains considerable breadth and depth of information related to research, software and their interactions.While keeping an eye on similar approaches to software discovery that might work in parts of the research ecosystem, existing Wikidata content and workflows will be reviewed and built upon. Additional documentation, tooling and workflows will be developed to enrich, expand, curate, query and explore this content, both for specific use cases and with ongoing engagement of the communities involved in research software, open data or collaborative curation. Within its three years, the project seeks to establish a dedicated community overseeing a well-documented and smoothly running infrastructure for software discovery and to devise a plan for how this can be sustained for the longer term.

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DOI: 10.3897/rio.11.e171656

Authors: Daniel Mietchen, Sheeba Samuel

Abstract: Computational notebooks like Jupyter have transformed scientific and educational workflows in computational fields by combining code, text, and visualizations. They have also become a popular mechanism to share computational workflows. However, ensuring their reproducibility remains a persistent challenge due to often insufficiently documented direct and indirect dependencies, missing data, and inconsistencies in execution environments. Existing datasets lack the multimodal, fine-grained structure needed for AI applications. FAIRJupyter4AI aims to address this gap by creating a large-scale, AI-ready corpus of Jupyter notebooks enriched with executable code, markdown, outputs, and structured annotations. The project integrates these into a hybrid knowledge graph (KG) that incorporates symbolic, statistical, and execution-based representations. Key objectives include: curating diverse notebooks (initially Python, later R, with provisions for additional languages); automating reproducibility testing; building a KG for cross-notebook queries; training AI models for tasks like error repair and notebook generation; and fostering community use via APIs and integration with community platforms like NFDI or Hugging Face.The project will be implemented using the infrastructure established by the NFDI Basic Service Jupyter4NFDI, in the upcoming Integration Phase of which (October 2025-September 2027) the applicants are actively involved. Its central JupyterHub provides cross-consortial and cross-institutional access to scalable computing and data resources and associated software stacks for both research and training purposes.The FAIRJupyter4AI work programme is structured around five interlinked work packages: (1) Data Collection & Curation, (2) Reproducibility Assessment, (3) Knowledge Graph Development (4) AI Model Training, and (5) Communication, Community & Sustainability. Key innovations include continuous updates and enrichment pipelines (avoiding static snapshots), unifying multimodal content for AI, and bridging reproducibility with AI. Building on prior work involving 27,000+ notebooks and the FAIR Jupyter Knowledge Graph, FAIRJupyter4AI will curate, annotate and release over 20,000 notebooks that are research-related and openly licensed. In addition, we will share a metadata corpus for 50,000 research-related notebooks, along with open-source tools, models, and associated documentation. By making Jupyter notebooks metadata FAIR, reusable, and machine-understandable, this project will set a new standard for reproducible and AI-enhanced computational science, and it will open up new opportunities for learning and teaching about computational reproducibility across multiple domains of research.

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DOI: 10.3897/rio.11.e163815

Authors: Gonzalo Bravo, Gregorio Bigatti, Mariana Lozada, Luke Thompson, Juan Livore, María Mendez, Lorena Arribas, Lino Bigatti, Tyler Christian, Erasmo Macaya, Edgardo Londoño-Cruz, Nicolas Moity, Juan Cruz-Motta, Augusto Flores, Gabriela Vélez-Rubio, Maria Palomo, Cesar Cordeiro, Franciane Pellizzari, Maritza Cárdenas-Calle, La Daana Kanhai, Ivonne Vivar Linares, Patricia Gil-Kodaka, Linsey Martinez, Pablo Sugliano, Agostina Trigo, Juan Zottola, Dulce Blanco, Matias Tricase, Nadia Bravo, Mariana Degrati, Camila Tavano Formigo, Frank Muller-Karger, Enrique Montes

Abstract: The Marine Biodiversity Observation Network Pole to Pole of the Americas (MBON Pole to Pole) brought together 30 participants from 10 countries in Patagonia, Argentina, to strengthen observing capacity of coastal biodiversity across the Americas. The network held a five-day workshop focused on three core components: standardized rocky intertidal photo-quadrat surveys, low-cost environmental DNA (eDNA) sampling, and affordable plankton imaging tools. Participants included researchers, park rangers, and conservation practitioners fostering a collaborative and inclusive environment. Key outcomes included field validation of protocols, identification of context-specific methodological adaptations (e.g., for low tidal amplitude areas), adoption of novel tools for monitoring marine life, and strategies for broader participation and data harmonization. The workshop highlighted the potential of simple, replicable methods to support long-term monitoring, and emphasized the value of shared protocols, tools, and open data for building a more connected and resilient regional observation network.

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DOI: 10.3897/rio.11.e155393

Authors: Elizabeth Adams, Natalie Raia, Saebyul Choe, Isaac Wink, Doug Curl

Abstract: State geological surveys create and steward valuable long-term earth and environmental science datasets and often serve as physical archives for material samples. Often funded directly through state legislatures, these agencies face varying degrees of support, nuanced regulations and public-serving missions that direct their research and day-to-day operations. Scientists at state geological surveys produce a range of outputs: datasets that may be stored internally, through an institutional repository or disseminated to broader community repositories and publications that may include both grey and peer-reviewed literature. This paper discusses a workshop held at the Kentucky Geological Survey to introduce researchers to data management, sharing and stewardship practices and to better understand obstacles to implementing such practices.

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DOI: 10.3897/rio.7.e68121

Authors: Daniel Mietchen, Lane Rasberry, Thais Morata, John Sadowski, Jeanette Novakovich, James Heilman

Abstract: In this era of information overload and misinformation, it is a challenge to rapidly translate evidence-based health information to the public. Wikipedia is a prominent global source of health information with high traffic, multilingual coverage, and acceptable quality control practices. Viewership data following the Ebola crisis and during the COVID-19 pandemic reveals that a significant number of web users located health guidance through Wikipedia and related projects, including its media repository Wikimedia Commons and structured data complement, Wikidata.The basic idea discussed in this paper is to increase and expedite health institutions' global reach to the general public, by developing a specific strategy to maximize the availability of focused content into Wikimedia’s public digital knowledge archives. It was conceptualized from the experiences of leading health organizations such as Cochrane, the World Health Organization (WHO) and other United Nations Organizations, Cancer Research UK, National Network of Libraries of Medicine, and Centers for Disease Control and Prevention (CDC)'s National Institute for Occupational Safety and Health (NIOSH). Each has customized strategies to integrate content in Wikipedia and evaluate responses.We propose the development of an interactive guide on the Wikipedia and Wikidata platforms to support health agencies, health professionals and communicators in quickly distributing key messages during crisis situations. The guide aims to cover basic features of Wikipedia, including adding key health messages to Wikipedia articles, citing expert sources to facilitate fact-checking, staging text for translation into multiple languages; automating metrics reporting; sharing non-text media; anticipating offline reuse of Wikipedia content in apps or virtual assistants; structuring data for querying and reuse through Wikidata, and profiling other flagship projects from major health organizations.In the first phase, we propose the development of a curriculum for the guide using information from prior case studies. In the second phase, the guide would be tested on select health-related topics as new case studies. In its third phase, the guide would be finalized and disseminated.

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DOI: 10.3897/rio.4.e32449

Authors: Katja Seltmann, Sara Lafia, Deborah Paul, Shelley James, David Bloom, Nelson Rios, Shari Ellis, Una Farrell, Jessica Utrup, Michael Yost, Edward Davis, Rob Emery, Gary Motz, Julien Kimmig, Vaughn Shirey, Emily Sandall, Daniel Park, Christopher Tyrrell, R. Sean Thackurdeen, Matthew Collins, Vincent O'Leary, Heather Prestridge, Christopher Evelyn, Ben Nyberg

Abstract: Georeferencing is the process of aligning a text description of a geographic location with a spatial location based on a geographic coordinate system. Training aids are commonly created around the georeferencing process to disseminate community standards and ideas, guide accurate georeferencing, inform users about new tools, and help users evaluate existing geospatial data. The Georeferencing for Research Use (GRU) workshop was implemented as a training aid that focused on the creation and research use of geospatial coordinates, and included both data researchers and data providers, to facilitate communication between the groups. The workshop included 23 participants with a wide background of expertise ranging from students (undergraduate and graduate), professors, researchers and educators, scientific data managers, natural history collections personnel, and spatial analyst specialists. The conversations and survey results from this workshop demonstrate that it is important to provide opportunities for biocollections data providers to interact directly with the researchers using the data they produce and vice versa.

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DOI: 10.3897/rio.4.e29773

Authors: Daisuke Tsugama

Abstract:

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DOI: 10.3897/rio.4.e25485

Authors: Álvaro Briz-Redón, Ángel Serrano-Aroca

Abstract: Programming a computer is an activity that can be very beneficial to undergraduate students in terms of improving their mental capabilities, collaborative attitudes and levels of engagement in learning. Despite the initial difficulties that typically arise when learning to program, there are several well-known strategies to overcome them, providing a very high benefit-cost ratio to most of the students. Moreover, the use of a programming language usually raises the interest of students to learn any specific concept, which has caused that many teachers around the world employ a programming language as a learning environment to treat almost every possible topic. Particularly, mathematics can be taught and learnt while using a suitable programming language. The R programming language is endowed with a wide range of capabilities that allow its use to learn different kind of concepts while programming. Therefore, complex subjects such as mathematics could be learnt with the help of this powerful programming language. In addition, since the R language provides numerous graphical functions, it could be very useful to acquire simultaneously basic plane geometry and programming knowledge at the undergraduate level. This paper describes the LearnGeom R package, a novel pedagogical tool, which contains multiple functions to learn geometry in R at different levels of difficulty, from the most basic geometric objects to high-complexity geometric constructions, while developing numerous programming skills.

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DOI: 10.3897/rio.3.e13088

Authors: Lyubomir Penev, Teodor Georgiev, Peter Geshev, Seyhan Demirov, Viktor Senderov, Iliyana Kuzmova, Iva Kostadinova, Slavena Peneva, Pavel Stoev

Abstract: The ARPHA-BioDiv Тoolbox for Scholarly Publishing and Dissemination of Biodiversity Data is a set of standards, guidelines, recommendations, tools, workflows, journals and services, based on the ARPHA Publishing Platform of Pensoft, designed to ease scholarly publishing of biodiversity and biodiversity-related data that are of primary interest to EU BON and GEO BON networks. ARPHA-BioDiv is based on the infrastructure, knowledge and exeprience gathered in the years-long research, development and publishing activities of Pensoft, upgraded with novel tools and workflows that resulted from the FP7 project EU BON.

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DOI: 10.3897/rio.2.e9113

Authors: Katja Heuer, Satrajit Ghosh, Amy Robinson Sterling, Roberto Toro

Abstract:

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DOI: 10.3897/rio.2.e8817

Authors: Arno Klein

Abstract: We will analyze variations in brain anatomy and create the first integrated software environment to extract patterns from brains and target differences related to inter-individual variability, pathology, development, or degeneration. We will evaluate how well these differences can help diagnose and predict treatment outcome for major depressive disorder, which affects millions of Americans, but our work is intended to be applied to any mental illness, such as Alzheimer’s disease, bipolar disorder, schizophrenia – indeed to analyze differences in brain anatomy between any two populations.

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DOI: 10.3897/rio.2.e8813

Authors: Arno Klein

Abstract:

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DOI: 10.3897/rio.2.e8814

Authors: Arno Klein

Abstract: Visual communication is ubiquitous, commanding our attention and commandeering our inattention. The presentation of information can take myriad visual forms, such as bar charts, scatter plots, network diagrams, and tables. These information graphics are attempts to map potentially large amounts of complex data to easily navigable visual form for rapid and accurate knowledge transfer. However, there is not yet a satisfactory formal methodology for selecting the most appropriate visualization method for a given set of data. A data taxonomy and novel visual taxonomy will be used to select visual stimuli from a database of acquired and newly generated information graphics. Oculomotor responses (eye tracking data) and task-based responses (mouse clicks or keyboard input) are recorded; performance on the latter is used to establish an expert subgroup. These results will be used to satisfy the three primary objectives of the proposed research, determining: how the choice of data visualization impacts oculomotor behavior and task performance, if this behavior is discriminable between experts and novices, and an empirically-based taxonomy of visualization based on the results of 1 and 2. Intellectual merit of the proposed activity The proposed research will create a novel taxonomy for and database of acquired and generated information graphics as well as an associated web application to search, organize, and compare entries in the database. Part of this research program is intended to establish the most comprehensive, manually annotated (and taxonomically classified) information graphics database in the world, for use by the public via a web interface. These images will be important for procuring stimuli for other kinds of perceptual and cognitive psychology experiments. The eye tracking and task performance results should help lead to a better understanding of how humans look at data, respond to the relationship between data structures and visual composition, and respond differentially to visualizations of different types. With respect to qualifications, the PI has a background in brain imaging research, image processing, and programming applications for generating graphs. Through his collaborator Dr. Ferrera of Columbia University, he has access to facilities and faculty specialized in eye tracking and psychophysics research. Collaborator Dr. Michelle Zhou, a research manager at IBM T. J. Watson Research Center, has years of experience in the areas of data and visual taxonomies, image databases, and automated generation of information graphics [Zhou and Feiner 1998, Zhou et al. 2002b, Zhou et al. 2002a]. Broader impacts of the proposed activity In addition to contributions the image taxonomy, database, and web application are intended to make to research, they will serve as a rich resource for teaching about the history and scope of visualization methods and design within and across disciplines, and for the general public with an interest in information graphics. The research will be conducted on subjects of varied background and race and will be broadly disseminated via websites in addition to publications. Additionally, defining a visual taxonomy will inform design choices made in information visualization. One implication of this research is a determination of how effective different visualization methods are at conveying information; this understanding will be of profound help to anyone interested in conveying information effectively in a graphical form.

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