As communities increasingly aim to understand and demonstrate how their data repositories support the FAIR Principles, decision-makers need tools that provide a clear, actionable snapshot. This paper introduces FIP Check, a rubric-based tool for assessing FAIR Implementation Profiles (FIPs) through a structured assessment of FAIR Enabling Resources (FERs). Built on the FIP ontology, the FIP Check brings three key innovations to FAIR assessment. First, it enables granular and practical evaluation by using a FAIR Principle-aligned rubric to assess individual FERs. Second, it measures partial FAIRness through a progressive scoring scale that captures varying levels of FAIR alignment, offering constructive, context-aware feedback rather than purely binary results. Additionally, this tool complements existing FAIR assessment tools by focusing on FERs as the units of assessment rather than on entire repositories or datasets. Third, it ensures transparent and inspectable balance by embedding expert-informed assessments within a standardized rubric and making all scoring decisions directly visible. FIP Check was piloted across seven biomedical data repositories, testing its utility in identifying strengths and actionable gaps, and supporting more informed FAIR improvement efforts. It enables communities to assess their current practices and plan targeted enhancements. By providing detailed insights into how individual FERs contribute to FIP’s FAIR alignment, the FIP Check transforms the FAIR Principles from abstract ideals into practical guidance that support strategic alignment, foster shared understanding, and encourage repository owners to see their resources not only as providers of data, but as infrastructure components.

In cohort studies, systematic information management often receives limited attention in study protocols, resulting in delays, quality issues, and threats to data validity. This paper describes the data management process of a community-based cohort study, using the SEMILLA (Study of Environmental Exposure of Mothers and Infants Impacted by Large-Scale Agriculture) study conducted in Cayambe, Ecuador, as a case example, and highlights the challenges, adaptations, and lessons learned, with the aim of informing similar studies. The SEMILLA data management process was structured around three key responsibility areas: strategic management, technical coordination, and data administration. The process unfolded in two main stages: Preparatory, which involved iterative refinement of data collection instruments, definition of coding rules, platform adjustments and migration, continuous team training, and implementation of security and anonymization protocols; and Organization, which included the assignment of interviewers for field data entry, primary data cleaning, creation of additional variables to better describe the sample composition and operational conditions, and the production of technical documentation. This approach contributed to improving data entry consistency, reducing recurrent errors, and strengthening record traceability throughout the follow-up by means of operational monitoring procedures. Key lessons include the importance of establishing a data management protocol and involving a data manager from the study design phase, maintaining flexibility in selecting data collection platforms, ensuring proper assignment of interviewers for each instrument, automating quality control processes, and continuously generating technical and operational documentation. Collectively, these practices help preserve data quality and promote operational efficiency in longitudinal studies conducted in similar contexts.

Creating findable, accessible, interoperable, and reusable (FAIR) data and metadata is essential for researchers. Effective Research Data Management (RDM) is crucial for achieving this, as recognized by funding organizations. Both data FAIRness and RDM rely on well-structured and documented data, including organized storage in a clear folder structure. Establishing a suitable folder structure early on is beneficial, yet junior scientists often struggle due to uncertainty about their needs. Ready-made templates can help establish an initial structure and foster good RDM habits. While templates exist for small-scale projects, a comprehensive folder structure tailored to doctoral research is lacking. This paper presents a folder structure template (FST) designed for PhD candidates in life and natural sciences, encompassing locations for code, results, figures, manuscripts, background information, and administrative paperwork. The FST offers a practical solution for digital data organization, along with best practice RDM recommendations and metadata recording templates. By implementing a thoughtful structure early on and maintaining it consistently, researchers can manage their data more efficiently, leading to improved FAIR data outcomes and faster publication.

Data plays a fundamental role in advancing knowledge and driving research across all fields. Despite the abundance of data, several challenges still need to be addressed. These challenges include limited accessibility, heterogeneous data, lack of interconnections between associated topics, difficulty retrieving required information, semantic mismatches, and several other challenges. Ontologies provide a structured method to address these challenges effectively. This study addresses two central aspects of ontology research. First, it details the multidisciplinary ontology development process, highlighting the challenges, mitigation strategies, and impacts on domain data management. It then offers guidelines for beginners and individuals with a background in data management on effective engagement in ontology creation. Second, it introduces the Ontology for Multiscale Simulation methods (Onto-MS), constructed by following the guidelines from the first part. The ontology, developed in Web Ontology Language (OWL) using Protégé, integrates with other ontologies, such as the Elementary Multiperspective Material Ontology (EMMO), aligning this research with the Linked Data concept. A custom Python script was used to incorporate the ontology into an Electronic Laboratory Notebook (ELN), enabling the automatic creation of knowledge graphs and systematic data organization conforming to the ontology. This research successfully answers the fundamental questions in interdisciplinary or domain-level ontology development. Onto-MS provides a robust framework for organizing and linking data in multiscale simulations within computational materials science. Furthermore, ontology incorporation into an ELN simplifies its integration into data management practices. While ontology development is ongoing, the current version is functional and continuously refined with new insights and feedback.

People’s risk perceptions toward the COVID-19 pandemic have been well documented, but the understanding of how pandemic-related information elicits public health behaviours and how different cultural, geographic, and policy contexts influence them is still limited. This study aimed to identify similarities and differences among the UK, Japan, and Taiwan to empirically investigate how people evaluate COVID-19-related information when deciding whether to take risk-averse behaviour. It also explored whether subgroups exist among the public that share similar characteristics related to attitude change on COVID-19 situations. Comparative regional surveys incorporating identical conjoint experimental design were administered. A conjoint analysis conducted on each region’s data found that people in Japan and Taiwan decided whether to perform risk-averse behaviour based on the nationwide number of new daily infections, while those in the UK placed the most importance on the situation of direct contact via the presence of infections in their workplace or home. Statistical clustering using a finite mixture model revealed two distinctive sub-groups across each region: a risk-taking class with a low frequency of risk-averse behaviour, comprising predominantly young men and vaccine-hesitant individuals with low trust in governmental health policy; and a prudent class with a high frequency of risk-averse behaviour, comprising individuals with higher science literacy. The study’s findings can contribute to policymakers’ and medical experts’ deeper understanding of the relationship between information provision and behaviours related to a new infectious disease, as well as emphasise that data-driven analysis can be leveraged to gain deeper insights into complex societal behaviours.

Governmental, funder, and scholarly publisher mandates for FAIR and open data are pushing researchers to archive data with persistent identifiers in repositories and link datasets in journal articles. Data citations enable transparency in research and credit and impact metrics for data reuse. However, numerous adoption barriers still exist, including that bibliographic reference management software commonly used by researchers to ease the referencing process may not yet be equipped to handle datasets. This paper examines the readiness of commonly used reference management software to support researchers in importing bibliographic metadata for datasets and generating references that comply with leading practices for data citation. Using seven major reference managers and datasets sampled across 14 Earth, space, and environmental sciences repositories, we identify and analyze common errors in reference-manager-facilitated metadata capture, storage, and citation export, using quantitative content analysis to compare repository-provided recommended citations, reference manager results, and DataCite metadata records. We find that a majority of frequently used reference managers do not adequately support data citation, obstructing uptake of data citation by researchers and thereby limiting the growth of credit and incentives for data sharing and reuse. The range and scale of issues uncovered are broadly extensible and relevant to data citation across disciplines. We present actionable recommendations for reference manager, data repository, scholarly publisher, and researcher stakeholders for increasing the ease, efficiency, and accuracy of bibliographic management software-facilitated data citation.

Background: This paper explores strategies to enhance North-South collaboration to foster multi- and trans-disciplinary research in disaster risk reduction (DRR) and crisis management, emphasizing the role of open science, Artificial Intelligence (AI) and geoinformatics in developing a resilient and proactive approach to these challenges, as well as drawing on lessons learned from the COVID-19 pandemic, the paper highlights how collaboration and data sharing can enhance preparedness, response, and recovery in future crises. Methods: The study uses a mixed approach to gather and analyze information. Qualitative methods focus on gathering descriptive data through engaging with experts and a structured review. By investigating literature, a comprehensive strategic framework for minimizing disaster risk, and for crisis management, is developed to provide adequate guidance to policymakers on implementation strategies and the application of how to build the human, institutional, and technological capacity required for effective disaster and crisis management (DCM). The events were used to engage with experts to provide recommendations and opportunities to enhance crisis response, capacity development and policy. Findings: By presenting a comprehensive framework that bridges theoretical knowledge with practical application, our findings indicate that governments must address the challenges of DCM to minimize losses. The practices surrounding disaster recovery can provide opportunities for policymakers to reassess priorities and redirect resources toward building sustainable communities. Originality: There is no strategic framework that provides adequate guidance to policymakers on implementation strategies or the application of how to build the human, institutional and technological capacity required for effective DCM. This article develops a Decision Support Framework for DCM. We outline how open science promotes cross-disciplinary collaboration and the sharing of knowledge, while geoinformatics, AI enable real-time data analysis that supports proactive disaster preparedness, response, and recovery. This paper encompasses a strategic, comprehensive, capacity-building framework for achieving sustainable strengthening of North-South collaboration. Furthermore, it presents how to design the most efficient data policies in times of crisis and how we can use these tools to improve North-South cooperation in Africa’s DCM. Finally, it highlights challenges of implementation and presents solutions and opportunities for Africa.

This paper proposes a novel algorithm for converting XYZ Tiles (Slippy Maps) to World Grid Square tile for a given Z value by merging several XYZ Tiles and cutting them into several pieces of World Grid Square tiles. We propose a framework to analyze XYZ Tiles with World Grid Square statistics beyond different scientific disciplines. In other words, we can perform statistical analysis on cartographic images. This paper describes the conversion of XYZ-based map tiles into World Grid Square tiles. We will examine the size of the actual World Grid Square area and the World Grid Square tile made from XYZ Tiles. As an example of analysis, we calculate the number of people affected by the tsunami by combining Grid Square statistics created from the XYZ Tiles published using the Web API with existing Grid Square statistics. Finally, as a case study, we will calculate the number of people affected by the tsunami by combining the World Grid Square tile converted from the XYZ Tiles published using the API with existing World Grid Square statistics.

Background: Engineering sciences are essential for addressing contemporary technical, environmental, and economic challenges. Despite its data-intensive and interdisciplinary nature, the organization of Findable, Accessible, Interoperable, and Reusable (FAIR) scientific knowledge and data in this research field remains understudied. Engineers need infrastructures with services that support them in organizing FAIR scientific knowledge and data for communication and (re-)use. Aim: We explore the use of the Open Research Knowledge Graph (ORKG) as such an infrastructure by demonstrating how engineers can utilize the ORKG in innovative ways for communication and (re-)use. Method: For a use case from the Collaborative Research Center 1153 “Tailored Forming”, we collect, extract, and analyze scientific knowledge on 10 Tailored Forming Process Chains (TFPCs) from five publications in the ORKG. In particular, we semantically describe the TFPCs, i.a., regarding their steps, manufacturing methods, measurements, and results. The usefulness of the data extraction topics, their organization, and the relevance of the knowledge described is examined by an expert consultation with 21 experts. Results: Based on the described knowledge, we build and publish an ORKG comparison as a detailed overview for communication. Furthermore, we (re-)use the knowledge and answer eight competency questions asked by two domain experts. The validation shows a clear agreement of the 21 experts regarding the examined usefulness and relevance. Conclusions: Our use case shows that the ORKG as a ready-to-use infrastructure with services supports researchers, including engineers, in sustainably organizing FAIR scientific knowledge. The direct use of the ORKG by engineers is feasible, so the ORKG is a promising infrastructure for innovative ways of communicating and (re-)using FAIR scientific knowledge in engineering sciences, thus advancing this research field.

The European Integrated Data Archive (EIDA) is a data service within Observatories and Research Facilities for European Seismology (ORFEUS) which distributes seismological waveform data across, presently, 12 federated data centres, so-called EIDA nodes. EIDA is also integrated into the European Research Infrastructure EPOS (European Plate Observing System). The increasing need of quantifying the scientific impact of such Research Infrastructures at all levels of operations has led EIDA to develop a unified system able to provide global indicators for data delivery across all EIDA nodes. To achieve this goal, EIDA implemented a system where the numbers are collected and aggregated by each EIDA node and ingested in a centralised database, accessible through an online API. The successful implementation of the system stems from carefully identifying the relevant indicators for well-defined groups of users. The implementation combines simplicity and robustness whilst respecting the distributed design of EIDA. The architecture and implementation choices may be of interest to other multi-site data distribution research infrastructures that face the challenge of providing meaningful key performance indicators.