Scientific Data Research Articles

Unlocking the transformative potential of data science in improving maternal, newborn and child health in Africa: a scoping review protocol

IntroductionApplication of data science in maternal, newborn, and child health (MNCH) across Africa is variable with limited documentation. Despite efforts to reduce preventable MNCH morbidity and mortality, progress remains slow....

Six provocations for metaverse datafication: an emergent cultural, technological, and scholarly phenomenon

ABSTRACT Although the ‘metaverse’ is still the feverish pipedream of tech companies and venture capitalists, it is also a powerful imaginary for channelling enormous resources towards deepening and extending ongoing processes of digitalization and datafication. It is thus likely that an increasing amount of human activity – both professional as well as leisure-related – will take place in metaversal spaces, and that the paradigm of ‘Big Data’ is about to be expanded with massive amounts of new and varied or multimodal data that capture even more (corporeal, sensorial, spatial, and temporal) information produced by and about people as well as their interactions as these unfold in (mixed) spaces over time. Much like the rise of big data, the emergence of metaversal data gives rise to important tensions and issues. From the perspective of critical data studies, media studies, and science and technology studies, this paper spotlights the role of data obtained about, in, and through metaverse technologies and environments as well as how this can be understood as both an intensification and extension of the datafication of social life, the quantification of research methodologies, and the exacerbation of social inequality. We discuss these issues through a series of six provocations that each address a distinct tension between metaverse data, the production of knowledge, research methodologies, and various social issues that are central to the datafication of contemporary societies.

Nigerian medicinal plants with potential anticancer activity—a review

Despite the fact that life expectancies are increasing and the burden of infectious diseases is decreasing, global cancer incidence rates are on the rise. Cancer outcome metrics are dismal for low- and middle-income countries (LMICs), including sub-Saharan Africa, where adequate resources and infrastructure for cancer care and control are lacking. Nigeria, the most populous country in Africa, exemplifies the miserable situation. However, the investigation of medicinal plants for better and safer anti-cancer drugs has now increased tremendously. While scientific evidence is emerging of the potential of some constituents of medicinal plants used in traditional medicine in Nigeria to have anti-cancer effects, there is now a critical need for platforms that integrate ethnomedicinal information on such plants with emerging scientific data on them, to support and accelerate the discovery and development of more efficacious and safer anti-cancer drugs and recipes. Thus, this review highlights the scientific evidence to date for the anti-cancer potential of plants commonly used in traditional medicine to treat cancers in Nigeria. Scientific databases such as PubMed, Science Direct, Scopus, Google Scholar, and Web of Science, as well as related sources, were searched to retrieve relevant information on anti-cancer medicinal plants. Ethnobotanical/ethnomedicinal details of the identified plants were then linked with the available scientific data on their anti-cancer potential, including the cytotoxicity to cancer and normal cells of the extracts and constituent compounds responsible for the activity. This annotated chronicle of Nigerian medicinal plants with potential anticancer activity is a great resource for all stakeholders in the prevention and management of cancers.

Can AI make scientific data more equitable?

Can AI make scientific data more equitable?

Frequency-adjusted borders ordinal forest: A novel tree ensemble method for ordinal prediction.

Ordinal responses commonly occur in psychology, e.g., through school grades or rating scales. Where traditionally parametric statistical models like the proportional odds model have been used, machine learning (ML) methods such as random forest (RF) are increasingly employed for ordinal prediction. With new developments in assessment and new data sources yielding increasing quantities of data in the psychological sciences, such ML approaches promise high predictive performance. As RF does not inherently account for ordinality, several extensions have been proposed. A promising approach lies in assigning optimized numeric scores to the ordinal response categories and using regression RF. However, these optimization procedures are computationally expensive and have been shown to yield only situational benefit. In this work, I propose Frequency-Adjusted Borders Ordinal Forest (fabOF), a novel tree ensemble method for ordinal prediction forgoing extensive optimization while offering improved predictive performance in simulation and an illustrative example of student performance. To aid interpretation, I additionally introduce a permutation variable importance measure for fabOF tailored towards ordinal prediction. When applied to the illustrative example, an interest in higher education, mother's education, and study time are identified as important predictors of student performance. The presented methodology is made available through an accompanying R package.

Obesity as the main factor of metabolic syndrome influencing on respiratory function

In the review, a generalized analysis of current scientific data explaining the physiological mechanisms of the influence of obesity on respiratory system is carried out. The multifactorial nature of the respiratory effect of obesity, including mechanical and inflammatory effects, is emphasized. The consequences of restrictive and obstructive changes in the biomechanics of respiration, changes in the topographic distribution of lung ventilation, mismatch of ventilation and perfusion, and a decrease in the efficiency of the respiratory muscles are considered. Elucidation of the central mechanisms of the respiratory action of proinflammatory mediators expressed by adipose tissue cells is recognized as a promising area of research. Special attention is paid to the action of leptin, which is the main regulator of metabolism and respiratory control in obesity. Its ability to modulate the central respiratory chemosensitive is discussed. It is assumed that an increase in pulmonary ventilation due to an increase in leptin production in obesity has a compensatory character and allows obese patients to maintain normocapnia despite an increase in mechanical load on respiration system. Whereas leptin resistance and suppressed hypercapnic ventilation response play a key role in the development of obesity–hypoventilation syndrome. It is concluded that it is necessary to further study the physiological mechanisms of the influence of obesity on the respiratory function in order to find new effective therapeutic methods for the treatment of diseases associated with obesity, which is the main factor in the development of metabolic syndrome.

Getting (ECO)Ready: Does EU Legislation Integrate Up-to-Date Scientific Data for Food Security and Biodiversity Preservation Under Climate Change?

European policies on climate change (CC), food security (FS), and biodiversity (BD) represent the EU’s commitment to a sustainable agri-food system, highlighting the interdependence between environmental health and food security. By analyzing key drivers and indicators, the present study evaluates the effectiveness of existing measures and identifies gaps in the policy framework. A Scoping Group activity facilitated dialogue between policymakers, industry, and farmer representatives to gather feedback and strengthen the data–policy link. The results highlight progress in areas such as promoting sustainable agriculture and biodiversity, while pointing out unresolved issues like the challenges faced by smallholder farmers. The study emphasizes the need for real-time monitoring tools and tailored solutions to address the complexities of the agri-food system. It also encourages the integration of emerging technologies, such as IoT and AI, to enhance the sustainability of agricultural practices. Ultimately, the findings call for a landscape-specific approach to maximize biodiversity gains, mitigate climate impacts, and ensure food security within the broader context of the EU’s ecological and socio-economic challenges.

Advancing tourism resilience and data science using multimodal data

ABSTRACT To address data selection bias in tourism research and objectively assess tourism resilience, this study uses the impact of COVID-19 on China’s tourism industry as a case. By using data from POI, UGC, nighttime lights, corporate revenue, and infections, the study develops a multimodal ‘city-enterprise-tourist’ data approach to analyze tourism resilience comprehensively. The multimodal approach disproves the assumed correlation between city size and tourism resilience during public crises while uncovering resilience differences within the tourism sector. As a result, the multimodal data method effectively avoids data selection bias and offers a detailed understanding of tourism resilience. The study highlights the importance of integrating diverse data sources and targeted resilience improvements in tourism resilience research. It provides a comprehensive framework for assessing the impact of public health crises on tourism, contributing to both data science and tourism resilience research.

Synergizing Federated AI Systems with Circular Economy Principles: A Framework for Sustainable and Resilient Data Science

The convergence of Federated Artificial Intelligence (AI) systems and Circular Economy (CE) principles marks a transformative approach to sustainable and resilient data science. Federated AI, with its decentralized architecture and emphasis on data privacy, seamlessly integrates with CE's objectives of maximizing resource efficiency, reducing waste, and fostering closed-loop systems. This paper proposes a novel framework that synergizes CE principles with Federated AI to address persistent challenges, including data siloing, scalability, energy efficiency, and environmental sustainability. The framework leverages adaptive learning models and resource-aware algorithms to optimize data- driven decision-making across applications such as smart city development, sustainable supply chain management, and renewable energy optimization. Through rigorous simulations and real-world case studies, the study demonstrates measurable improvements: a 20–30% increase in resource efficiency and a marked reduction in computational energy consumption compared to traditional centralized AI systems. These findings highlight the transformative potential of Federated AI in driving circular and sustainable ecosystems. The research also contributes to ethical AI discourse, providing actionable insights for policymakers, academics, and industry leaders to harmonize AI advancements with global sustainability imperatives.

Hyper-Personalization in Retail: Leveraging Data Science and Real-Time Streaming

Personalization has transformed from a luxury feature to a fundamental business necessity in today's digital retail landscape. This comprehensive article explores the evolution from traditional personalization to hyper-personalization, examining the retail sector's technological components, practical applications, and future implications. The article investigates how advanced technologies, including artificial intelligence, machine learning, and real-time data streaming, enable retailers to deliver highly individualized customer experiences. Through a detailed examination of implementation strategies, benefits, and impacts, this analysis demonstrates how retailers leverage sophisticated data science frameworks and dynamic experience customization to enhance customer engagement, improve operational efficiency, and drive business growth. The article particularly highlights the transformative impact of hyper-personalization in specific retail segments, such as apparel, while also exploring the quantifiable benefits for retailers and customers.

Antioxidant Capacity and Cardiovascular Benefits of Fruits and Vegetables: A Proposal for Comparative Scales

Fruits and vegetables are sources of natural nutraceuticals. They contain a variety of bioactive compounds such as vitamins, minerals, dietary fibers and other phytochemicals that contribute to their health-promoting properties and disease prevention. A wide variety of fruits and vegetables should be eaten to ensure that an individual’s diet includes a combination of phytonutraceuticals and to obtain all their health benefits. This study aimed to compare the antioxidant potential and cardiovascular benefits within a restricted sample of ten fruits and ten vegetables, previously reported as largely consumed in Portugal. With data available from the literature, antioxidant potential scales were established. Additionally, a set of seven criteria, including high antioxidant capacity (FRAP values above 1), presence of n-3 fatty acids, saturated fat, cholesterol, trans fatty acids, fiber and sodium was used to create comparative scales of their potential cardiovascular benefits. The main results showed that the fruits that simultaneously exhibited the highest antioxidant potential values and the highest cardiovascular potential benefit were lemon, grapes, and melon; among vegetables, the top rankings were found to be tomato and onion. These products have been recognized as interesting sources of natural nutraceuticals for the food and pharmaceutical industries. In the future, similar approaches are desirable to translate complex scientific data into practical, simple and user-friendly information for food literacy initiatives, including nutrition education materials, about the relative level of the potential cardiovascular benefits of a wide diversity of food products.

Automatic extraction of fine structural information in angle-resolved photoemission spectroscopy by multi-stage clustering algorithm

Unsupervised clustering method has shown strong capabilities in automatically categorizing the ARPES (ARPES: angle-resolved photoemission spectroscopy) spatial mapping dataset. However, there is still room for improvement in distinguishing subtle differences caused by different layers and substrates. Here, we propose a method called Multi-Stage Clustering Algorithm (MSCA). Using the K-means clustering results/metrics for real space in different energy-momentum windows as the input of the second round K-means clustering for momentum space, the energy-momentum windows that exhibit subtle inhomogeneity in real space will be highlighted. It recognizes different types of electronic structures both in real space and momentum space in spatially resolved ARPES dataset. This method can be used to capture the areas of interest, and is especially suitable for samples with complex band dispersions, and can be a practical tool to any high dimensional scientific data analysis.

Leveraging AI and data science to mitigate the respiratory health impacts of climate change in Africa: Organisation, costs, and challenges.

Leveraging AI and data science to mitigate the respiratory health impacts of climate change in Africa: Organisation, costs, and challenges.

Effectiveness of Ketamine As a Rescue Drug for Patients Experiencing Benzodiazepine-Resistant Status Epilepticus in the Prehospital Setting.

Accumulating basic science data, early clinical findings and various feasibility considerations have provided rationales for administering ketamine as a proposed rescue medication for midazolam-resistant status epilepticus (SE) in the logistically challenging prehospital environment. This report details the multiyear experience of paramedics managing midazolam-resistant SE following the introduction of a ketamine-rescue protocol. A 7-year, population-based, observational study was conducted to evaluate outcomes of patients treated with IV, intraosseous, intramuscular, or intranasal ketamine for SE despite sufficient midazolam dosings. Tracked outcomes included: 1) rapid/sustained termination of clinical seizures in adults while under paramedics' care; 2) corresponding evaluations in children/adolescents; 3) any concerning observations regarding need for assisted ventilation, intubation, or other active interventions post-ketamine; and 4) any identifiable associations between outcomes and circumstances, demographics, or medical history. Emergency response 9-1-1 system serving a large, diverse U.S. county (jurisdictional population, 961,000/1,769 sq miles). Those receiving ketamine from paramedics for persistent seizures. Adults and adolescents: 100 mg ketamine IV/intraosseous/intramuscular/intranasal; children: 1 mg/kg intramuscular/intranasal. Among 81 total cases, 57 involved adults (18-86 yr old) receiving the SE-midazolam + ketamine protocol. Ketamine rapidly terminated convulsions in 56 (98.2%) without recurrence during prehospital and hospital arrival phases. For approved reasons, paramedics administered ketamine directly (no midazolam) in eight adults and one child, terminating convulsions in every case. Among 15 childhood/adolescent cases treated per protocol, ketamine rapidly terminated SE activity in 11, but only mitigated it in four, including two retrospectively judged to involve nonseizure activity and two involving intranasal administration. Among all 81 ketamine-treated cases, there were no identifiable clinically significant complications attributable to ketamine, particularly the need for any additional active interventions. Ketamine appeared to be consistently effective in treating adults with ongoing out-of-hospital seizures that were resistant to sufficient dosings of midazolam. Similar results were observed in children/adolescents.

Geodl: An R package for geospatial deep learning semantic segmentation using torch and terra.

Convolutional neural network (CNN)-based deep learning (DL) methods have transformed the analysis of geospatial, Earth observation, and geophysical data due to their ability to model spatial context information at multiple scales. Such methods are especially applicable to pixel-level classification or semantic segmentation tasks. A variety of R packages have been developed for processing and analyzing geospatial data. However, there are currently no packages available for implementing geospatial DL in the R language and data science environment. This paper introduces the geodl R package, which supports pixel-level classification applied to a wide range of geospatial or Earth science data that can be represented as multidimensional arrays where each channel or band holds a predictor variable. geodl is built on the torch package, which supports the implementation of DL using the R and C++ languages without the need for installing a Python/PyTorch environment. This greatly simplifies the software environment needed to implement DL in R. Using geodl, geospatial raster-based data with varying numbers of bands, spatial resolutions, and coordinate reference systems are read and processed using the terra package, which makes use of C++ and allows for processing raster grids that are too large to fit into memory. Training loops are implemented with the luz package. The geodl package provides utility functions for creating raster masks or labels from vector-based geospatial data and image chips and associated masks from larger files and extents. It also defines a torch dataset subclass for geospatial data for use with torch dataloaders. UNet-based models are provided with a variety of optional ancillary modules or modifications. Common assessment metrics (i.e., overall accuracy, class-level recalls or producer's accuracies, class-level precisions or user's accuracies, and class-level F1-scores) are implemented along with a modified version of the unified focal loss framework, which allows for defining a variety of loss metrics using one consistent implementation and set of hyperparameters. Users can assess models using standard geospatial and remote sensing metrics and methods and use trained models to predict to large spatial extents. This paper introduces the geodl workflow, design philosophy, and goals for future development.

CNS-CLIP: Transforming a Neurosurgical Journal Into a Multimodal Medical Model.

Classical biomedical data science models are trained on a single modality and aimed at one specific task. However, the exponential increase in the size and capabilities of the foundation models inside and outside medicine shows a shift toward task-agnostic models using large-scale, often internet-based, data. Recent research into smaller foundation models trained on specific literature, such as programming textbooks, demonstrated that they can display capabilities similar to or superior to large generalist models, suggesting a potential middle ground between small task-specific and large foundation models. This study attempts to introduce a domain-specific multimodal model, Congress of Neurological Surgeons (CNS)-Contrastive Language-Image Pretraining (CLIP), developed for neurosurgical applications, leveraging data exclusively from Neurosurgery Publications. We constructed a multimodal data set of articles from Neurosurgery Publications through PDF data collection and figure-caption extraction using an artificial intelligence pipeline for quality control. Our final data set included 24 021 figure-caption pairs. We then developed a fine-tuning protocol for the OpenAI CLIP model. The model was evaluated on tasks including neurosurgical information retrieval, computed tomography imaging classification, and zero-shot ImageNet classification. CNS-CLIP demonstrated superior performance in neurosurgical information retrieval with a Top-1 accuracy of 24.56%, compared with 8.61% for the baseline. The average area under receiver operating characteristic across 6 neuroradiology tasks achieved by CNS-CLIP was 0.95, slightly superior to OpenAI's Contrastive Language-Image Pretraining at 0.94 and significantly outperforming a vanilla vision transformer at 0.62. In generalist classification, CNS-CLIP reached a Top-1 accuracy of 47.55%, a decrease from the baseline of 52.37%, demonstrating a catastrophic forgetting phenomenon. This study presents a pioneering effort in building a domain-specific multimodal model using data from a medical society publication. The results indicate that domain-specific models, while less globally versatile, can offer advantages in specialized contexts. This emphasizes the importance of using tailored data and domain-focused development in training foundation models in neurosurgery and general medicine.

Historical Bird Atlas and Contemporary Citizen Science Data Reveal Long‐Term Changes in Geographic Range of Kenyan Birds

ABSTRACTAimHistorical bird atlases provide comprehensive datasets for investigating long‐term changes in species' distribution. In the context of accelerating biodiversity loss, these datasets can lend critical insights into the state of bird distributions across broad spatio‐temporal scales and provide much‐needed information for impactful conservation. In Africa, the potential of atlas data to understand changes in avian populations remains largely untapped.LocationThis study mapped changes in national distribution patterns of 1088 bird species found in Kenya.MethodsTapping into one of the earliest atlas databases, this study compared Kenyan bird atlas data collected between 1970 and 1984 with recent citizen science data sourced from the Kenya Bird Map project and eBird to determine changes in ranges across 50 years. We produced maps displaying, for every 27 × 27 km square of the country, whether a species appeared, was present throughout both periods, or disappeared. We account for the change in data collection effort between the two periods by quantifying the confidence of the change for each square.ResultsThe maps produced for each species are publicly accessible through an interactive website: https://kenyabirdtrends.co.ke/. We found that related species tended to experience similar changes in their distribution ranges. The ranges of Palearctic migrants and scavengers declined drastically, while introduced birds experienced a significant range increase over the past 50 years.Main ConclusionsThis study demonstrates the potential of integrating recent citizen science data with historical atlas data to draw out the changes in range for all species at national level. The range contraction of Palearctic migrants and scavengers echoed corresponding drops in abundance at local, regional and global scales. These findings lend additional weight to the need for an increased conservation focus on migratory and scavenging birds in Kenya.

Bridging Data Communities: Interoperability through inclusive, cross-institutional collaboration

Objectives: To demonstrate how librarians can use engagement strategies to foster the exchange of knowledge and skills for data analysis and to build bridges between data communities. A second objective is to help student instructors to develop effective live-coding pedagogical practices and to gain practical experience in leading participatory workshop sessions. Methods: Librarians developed a low-barrier introductory peer-to-peer data science workshop series to support students seeking to develop coding, data analysis, and visualization skills, with a focus on Python and SQL. We guided undergraduate peer instructors in participatory live-coding pedagogy, organized practice sessions for instructors, and managed the scheduling, logistics, outreach, and hosting of the workshops. Results: In Fall 2023 sessions in the workshop series were delivered synchronously to over 100 participants, including students from our home institution and more than a dozen community colleges; one workshop was delivered twice—once in English, once in Spanish. Workshop recordings posted online have been viewed over 1000 times. Conclusions: We successfully identified strategies for building upon existing relationships and strengthening connections among diverse data communities; designing programs and outreach efforts to lower barriers to participation in data science; and fostering a culture of diversity, equity, and inclusion in data science knowledge sharing.

Mobile sensing system for phenotyping of forest seedlings in container nurseries

The primary objective of this study was to develop a universal, lightweight, and mobile system that seamlessly integrates the scanner with the construction of the horizontal spray boom at a forest nursery. This innovative approach holds tremendous potential for advancing the assessment and management of forest seedlings, providing valuable insights and streamlining nursery operations. Multichannel spectral devices mounted on irrigation booms can be used to significantly enhance the quality assessment of forest seedlings in container nurseries. Contrary to traditional and time-consuming approaches such as laboratory analysis or destructive sampling, an active canopy optical sensor operates independently of ambient light conditions and does not necessitate spectral reference readings. Mobile irrigation systems in nurseries are ideal for incorporating plant scanners owing to their exceptional repeatability, ensuring consistently high-quality results. In our study, we rigorously tested a set of four ACS 435 Holland Scientific active crop canopy sensor along with the Vega Plus C21 laser sensor, all strategically positioned on a sprinkler boom above the nursery production field from May to September 2022. The seamless integration of these devices with the GeoSCOUT X Holland Scientific data acquisition unit formed the foundation of our system. The primary objective of this study was to develop a universal, lightweight, and mobile system that seamlessly integrates the scanner with the construction of the horizontal spray boom at a forest nursery. This innovative approach holds tremendous potential for advancing the assessment and management of forest seedlings, providing valuable insights and streamlining nursery operations.

Coordination Complexes of Cobalt with Pyrazole-Based Ligands and Potential Applications

Transition metals are one of the central aspects of modern coordination chemistry research. In addition to being well-studied, cobalt is a unique transition metal due to its presence in vitamin B12. Pyrazole-based structures are common in nature and organic synthesis. Over the past decade, our research group has focused on synthesizing and characterizing novel cobalt, nickel, and copper organometallic complexes. To date, we have reported three cobalt complexes, five nickel complexes and one copper complex with pyrazole-derived ligands. Based on the current scientific data, we are optimistic about potential applications of our coordination complexes with cobalt and pyrazole-based ligands. In this work we review applications of cobalt complexes with pyrazole-based ligands described in recent literature to explore possible applications of compounds synthesized and characterized by our research group.