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As part of an effort to seek sustainable support models for Open Access (OA) publishing, the University of Maryland, Baltimore (UMB), Health Sciences and Human Services Library’s (HSHSL’s) Scholarly Communications Committee developed an interactive dashboard to visualize university-wide OA publishing trends. Using publication data exported from Scopus and visualized in Microsoft Power BI, the dashboard displays five years of publishing trends by OA model, publisher, journal, school, and citation count. The dashboard is fully interactive, allowing users to filter results based on school, OA model, and year.  The design of the dashboard was iterative, with planning discussions taking place in Summer 2024, data model development and initial data collection in Fall 2024, refining of the visualization and data model in early Spring 2025, and the publication of the final dashboard to our website in April 2025. The dashboard continues to be refined and improved based on feedback from stakeholders, and the project team plans to incorporate data on publishing costs in Spring 2026.  The project was designed for sustainability and adaptability, with a documented workflow that will be easy for future committees to implement. This innovative, replicable approach supports informed decision-making around OA publishing and provides a model that can be adopted by other academic health sciences libraries. 

Drought or dryness occurs due to the accumulative effect of certain climatological and hydrological variables over a certain period. Droughts are studied through numerically computed simple or compound indices. Vegetation condition index (VCI) is used for observing the change in vegetation that causes agricultural drought. Since the land surface temperature has minimum influence from cloud contamination and humidity in the air, so the temperature condition index (TCI) is used for studying the temperature change. Dryness or wetness of soil is a major indicator for agriculture and hydrological drought and for that purpose, the index, soil moisture condition index (SMCI), is computed. The deviation of precipitation from normal is a major cause for meteorological droughts and for that purpose, precipitation condition index (PCI) is computed. The years when the indices escalated the dryness situation to severe and extreme are pointed out in this research. Furthermore, an interactive dashboard is generated in the Google Earth Engine (GEE) for users to compute the said indices using country boundary, time period, and ecological mask of their choice: Agriculture Drought Monitoring. Apart from global results, three case studies of droughts (2002 in Australia, 2013 in Brazil, and 2019 in Thailand) computed via the dashboard are discussed in detail in this research.

Visual communications (VC) play a crucial role in effectively conveying public health data to diverse audiences, including policymakers, healthcare professionals, and the general public. Although the U.S. government invests heavily in health data and data accessibility, health data are not entirely accessible or easily understood. This can be attributed to data sharing and visualization challenges. VC challenges have created public health information gaps which are compounded in emergencies such as the COVID-19 pandemic, potentially impacting poor health outcomes and increasing health inequities. To examine visualization tools and techniques effective for public health visual data communication. A scoping review was conducted to summarize the available evidence related to visualization techniques and tools for public health visual data communication as well as related principles and best practices. Original peer-reviewed articles published in English that involve visualization, user-centered design of visual public health applications/interfaces, visual analytics, infographics, or dashboards from PubMed database from 2020 to 2024 were included. Also, review articles, commentaries, editorials, posters, systematic and scoping articles were excluded from this review. In all, twenty-eight (28) studies were included. There were 25 different visualization techniques identified which included charts and graphs (e.g., bar charts, line charts, pie charts, bubble charts, box plots, scatter plots), maps (e.g., choropleth maps, hotspot maps, and heatmaps), and specialized visualizations (e.g., sunburst diagrams, alluvial plots, upset plots, circos). These visuals were displayed employing different programming and statistical tools and libraries like R, Python, Power BI, Tableau, ArcGIS, and custom web-based applications. The visuals measured different types of data accessibility, pattern and trends identification, association and relationships of univariate and bivariate data, as well as exploring multidimensional forms of health data. The visualizations were applied in different public health domains, such as HIV prevention and care, public health communication, interventions, surveillance, policy measures and decision-making, and improving health education. Dashboards and web-based tools combined with static visualizations like charts, maps, or specialized plots can help with data exploration, pattern recognition, and dissemination of health information. Effective communication of public health data promotes informed decision-making, creates awareness, and leads to improved and better health outcomes.

This study introduces the critical individual contribution coefficient (CR-ICC), a novel metric that evaluates player effectiveness in critical moments of the game. We analyzed 16,631 technical actions from the top eight teams across 77 sets of the 2019 FIVB Women’s Club World Championship, ensuring data quality through inter- and intra-observer reliability. Traditional variables such as points scored, attack and reception efficiency, and balance were examined. Python programming was utilized to calculate the values of CR-ICC, which consider the contextual variables of set period, score difference, competitive load, and opponent’s level. Akaike’s and Bayesian information criteria, along with Nagelkerke’s coefficient of determination, were employed. Binomial logistic regression and receiver operating characteristic curves estimated the probability of victory associated with each variable. Interactive dashboards were developed, enabling dynamic analysis and data visualization. Statistically significant differences were observed in all variables (p < 0.05), except for reception efficiency (p < 0.05), at both the team and individual player levels. At the team level, points scored, attack efficiency, and balance exhibited the highest predictive abilities, with CR-ICC also demonstrating a strong predicting ability. The proposed CR-ICC has remarkable potential as a strategic asset for coaches, enabling the identification of players who excel in critical moments of the game.

Background: Since the declaration of COVID-19 as a global pandemic by the World Health Organization, the disease has gained momentum with every passing day. Various private and government sectors of different countries allocated funding for research in multiple capacities. A significant portion of efforts has been devoted to information technology and service infrastructure development, including research on developing intelligent models and techniques for alerts, monitoring, early diagnosis, prevention, and other relevant services. As a result, many information resources have been created globally and are available for use. However, a defined structure to organize these resources into categories based on the nature and origin of the data are lacking. Objective: This study aims to organize COVID-19 information resources into a well-defined structure to facilitate the easy identification of a resource, tracking information workflows, and to provide a guide for a contextual dashboard design and development. Methods: A sequence of action research was performed that involved a review of COVID-19 efforts and initiatives on a global scale during the year 2020. Data were collected according to the defined structure of primary, secondary, and tertiary categories. Various techniques for descriptive statistical analysis were employed to gain insights into the data to help develop a conceptual framework to organize resources and track interactions between different resources. Results: Investigating diverse information at the primary, secondary, and tertiary levels enabled us to develop a conceptual framework for COVID-19–related efforts and initiatives. The framework of resource categorization provides a gateway to access global initiatives with enriched metadata, and assists users in tracking the workflow of tertiary, secondary, and primary resources with relationships between various fragments of information. The results demonstrated mapping initiatives at the tertiary level to secondary level and then to the primary level to reach firsthand data, research, and trials. Conclusions: Adopting the proposed three-level structure allows for a consistent organization and management of existing COVID-19 knowledge resources and provides a roadmap for classifying future resources. This study is one of the earliest studies to introduce an infrastructure for locating and placing the right information at the right place. By implementing the proposed framework according to the stated guidelines, this study allows for the development of applications such as interactive dashboards to facilitate the contextual identification and tracking of interdependent COVID-19 knowledge resources.

Background Despite a recent increase in the number of RNA-seq datasets investigating heart failure (HF), accessibility and usability remain critical issues for medical researchers. We address the need for an intuitive and interactive web application to explore the transcriptional signatures of heart failure with this work. Methods We reanalysed the Myocardial Applied Genomics Network RNA-seq dataset, one of the largest publicly available datasets of left ventricular RNA-seq samples from patients with dilated (DCM) or hypertrophic (HCM) cardiomyopathy, as well as unmatched non-failing hearts (NFD) from organ donors and patient characteristics that allowed us to model confounding factors. We analyse differential gene expression, associated pathway signatures and reconstruct signaling networks based on inferred transcription factor activities through integer linear programming. We additionally focus, for the first time, on differential RNA transcript isoform usage (DTU) changes and predict RNA-binding protein (RBP) to target transcript interactions using a Global test approach. We report results for all pairwise comparisons (DCM, HCM, NFD). Results Focusing on the DCM versus HCM contrast (DCMvsHCM), we identified 201 differentially expressed genes, some of which can be clearly associated with changes in ERK1 and ERK2 signaling. Interestingly, the signs of the predicted activity for these two kinases have been inferred to be opposite to each other: In the DCMvsHCM contrast, we predict ERK1 to be consistently less activated in DCM while ERK2 was more activated in DCM. In the DCMvsHCM contrast, we identified 149 differently used transcripts. One of the top candidates is the O-linked N-acetylglucosamine (GlcNAc) transferase (OGT), which catalyzes a common post-translational modification known for its role in heart arrhythmias and heart hypertrophy. Moreover, we reconstruct RBP – target interaction networks and showcase the examples of CPEB1, which is differentially expressed in the DCMvsHCM contrast. Conclusion Magnetique ( https://shiny.dieterichlab.org/app/magnetique ) is the first online application to provide an interactive view of the HF transcriptome at the RNA isoform level and to include transcription factor signaling and RBP:RNA interaction networks. The source code for both the analyses ( https://github.com/dieterich-lab/magnetiqueCode2022 ) and the web application ( https://github.com/AnnekathrinSilvia/magnetique ) is available to the public. We hope that our application will help users to uncover the molecular basis of heart failure.

Background Nigeria’s significant contribution to the global pool of zero-dose children persists despite ongoing immunisation investments. This coverage deficits in Kano and Lagos states serve as stark indicators of underlying structural and socio-economic obstacles hindering equitable immunisation access at the sub-national level. Methods To investigate these barriers, localised secondary data from various sources were collected including data from the State Routine Immunisation (RI) microplans, demographic and health survey data, supportive supervision data, immunisation coverage survey data, administrative data from health management information systems (HMIS), and vaccine security and logistics data to study the structural determinants of zero dose children and missed communities. This data was then visualised in an interactive geospatial dashboard deployed in Tableau to visualise and interpret key immunisation metrics. Using the Stochastic Partial Differential Equation (SPDE) approach in R Interface to Integrated Nested Laplace Approximations (R-INLA), a geostatistical model was developed to predict immunisation outcomes based on structural determinants identified from the literature. Results Findings show that in Kano state, urban health facilities are 39% (OR = 0.609, 95% CI: 0.406–0.906) less likely to have zero-dose children compared to rural ones with less significant impact of the type of settlement on under-immunisation (OR = 1.146. 95% CI: 0.724–1.869). In Lagos state, the distinction between urban and rural settings does not significantly impact zero-dose (OR = 2.117, 95% CI:0.473–10.001) or under-immunisation rates (OR = 1.136, 95% CI:0.300–4.722). Conclusion Localised triangulation analysis of data from the two states demonstrates complex interaction between different structural determinants of immunisation outcomes. These findings emphasise the need to prioritise localised analysis of available data and invest in capacity for data analysis at lower levels of the health system, to enable the use of available data for decision-making and action.

As occupational rehabilitation services are part of the public medical and health services in Hong Kong, work-injured workers are treated along with other patients and are not considered a high priority for occupational rehabilitation services. The idea of a work trial arrangement in the private market occurred to meet the need for a more coordinated occupational rehabilitation practice. However, there is no clear service standard in private occupational rehabilitation services nor concrete suggestions on how to offer rehabilitation plans to injured workers. Electronic Health Records (EHRs) data can provide a foundation for developing a model to improve this situation. This project aims at using a machine-learning-based approach to enhance the traditional prediction of disability duration and rehabilitation plans for work-related injury and illness. To help patients and therapists to understand the machine learning result, we also developed an interactive dashboard to visualize machine learning results. The outcome is promising. Using the variational autoencoder, our system performed better in predicting disability duration. We have around 30% improvement compared with the human prediction error. We also proposed further development to construct a better system to manage the work injury case.

Product development, especially in aerospace, has become more and more interconnected with its operational environment. In a constant changing world, the operational environment will be subjected to changes during the life cycle of the product. The operational environment will be affected by not only technical and non-technical perturbations, but also economical, managerial and regulatory decisions, thus requiring a more global product development approach. One way to try tackling such complex and intertwined problem advocates studying the envisioned product or system in the context of system of systems (SoS) engineering. SoSs are all around us, probably in any field of engineering, ranging from integrated transport systems, public infrastructure systems to modern homes equipped with sensors and smart appliances; from cities filling with autonomous vehicle to defence systems. Since also aerospace systems are certainly affected, this work will present a holistic approach to aerospace product development that tries spanning from needs to technology assessment. The proposed approach will be presented and analysed and key enablers and future research directions will be highlighted from an interdisciplinary point of view. Consideration of the surrounding world will require to look beyond classical engineering disciplines.

The paper describes the marketing mix modeling results for companies in nonfood retailing. The main objectives of the research are to demonstrate the viable way of making effective recommendations for optimizing the media strategy by modeling offline and online traffic to the stores based on econometric modeling and to develop a decision support system, which enhance the effective growth of business KPIs and an effective decision-making process. Econometric modeling, deeper data analysis, decision support were implemented on the data of one of the main retailers in Ukraine in a period before the full-scale Russian invasion. Estimating the impact of different communication channels on business results made basis for ROI calculations and optimization of media investments allocation among media channels by periods, video durations, type of advertising and with optimal weekly media pressure. ROMI calculation was based on the results of regression modeling, which estimate the level of traffic and sales generated by each media channel. The information-analytical decision support system based on an interactive dashboard has been developed for improvement of day-by-day business planning and management. The developed framework of regional strategy selection facilitates to the formation of a strategic vision on a regional scale and improves the quality of a regional media strategy.