Ocean studies often require information from several sources, including bathymetry, temperature and fishing activity. Those datasets can use different spatial formats and scales, requiring researchers to write code that combines and organizes them. The team converted frequently used programming scripts into ready-made functions within oceandatr, a package developed in R, a widely used programming language for data analysis. According to Jason Flower, a project scientist at UC Santa Barbara’s Environmental Markets Lab, the goal is to help users obtain geospatial ocean data quickly and in the format they need.

The peer-reviewed software paper, published in Ecology and Evolution, describes two applications. In one, the researchers used ocean data to guide planning for marine protected areas. In the other, they used data to model and map where fishing activity was likely to occur. These examples illustrate only part of the package’s range: co-author Echelle Burns said oceandatr could be used in any marine-science project requiring spatial data. The paper was not designed to answer one specific research question, Burns said, but to support scientists who repeatedly performed the same data-pulling and organizing workflows.

One central feature is the standardization of data obtained from different sources. The bathymetry data accessed by oceandatr has a resolution of 15 arc seconds, a little less than 0.25 degree, while fishing-effort data is available at either 0.01 degree or 0.1 degree, depending on user preference. The package brings these differing formats into a consistent structure behind the scenes, allowing researchers to focus on analysis. Although the tool can access global datasets, it can also support work in smaller areas, including individual islands and coastal regions. Users can supply their own datasets, and oceandatr will save them at the appropriate spatial scale.

The developers describe oceandatr as an evolving tool rather than a finished product. Planned additions include daily sea-surface-temperature data alongside the average temperature information already available. Flower is also working to improve the package’s speed and efficiency for researchers using older or slower computers. The software is freely available through GitHub, and Burns said oceandatr is intended to remain a “living package” that is updated and refined as R packages and marine-data workflows continue to evolve.

Subject of Research: An open-source R package for retrieving and standardizing spatial ocean datasets

Article Title: A new tool makes mapping the ocean easier

Article References: A new tool makes mapping the ocean easier. Original publication (Bibliographic details unavailable.)

Image Credits: AI Generated

DOI: Not provided

Keywords: oceandatr, ocean mapping, R package, open-source software, geospatial data, bathymetry, sea-surface temperature, fishing activity, marine spatial planning, UC Santa Barbara, tool, makes

The initiative took place within Italy’s PCTO framework—Percorsi per le Competenze Trasversali e l’Orientamento, or Pathways for Transversal Skills and Career Guidance—a national program established by the 2015 education reform “La Buona Scuola” and recently redefined as FSL, School-Work Training. PCTO requires upper-secondary students to alternate classroom instruction with hands-on professional experiences, encouraging collaboration among schools, universities, and research institutions. In this case, the collaboration paired CNR researchers who study environmental processes with art teachers who guide students in painting, design, and multimedia production. The result was a roughly 34-hour pathway combining 16 hours of scientific instruction, 6 hours of field and laboratory activities, and 12 hours of creative workshops.

The scientific component was ambitious in scope. CNR researchers introduced the students to the Anthropocene and its environmental implications, geophysical methods for studying soil, subsoil, and groundwater, satellite monitoring of land, air, and water quality, urban growth and natural hazards in the Mediterranean basin, and the data platforms used for Earth Observation. The lessons were not purely theoretical. Students participated in a field measurement campaign, observing how environmental data are acquired in real time under expert supervision, and visited the institute’s geophysical laboratory and the CIAO Atmospheric Observatory, with particular attention to its ACTRIS–CCRES component dedicated to cloud remote sensing. The thematic breadth covered soil, subsoil, vegetation, water, and air, supporting what the authors describe as a holistic understanding of socio-ecological systems.

Despite the range of topics, the project converged on a single unifying medium: the satellite image. The researchers identified satellite imagery as an effective bridge between scientific observation and artistic expression, and the practical work progressively focused on its reinterpretation. Students learned basic data visualization and processing using freely accessible geospatial platforms, primarily Google Earth, alongside graphic-design software including Adobe Photoshop. They examined landscape interpretation and the critical reading of satellite products, and then searched independently for additional imagery, guided by the examples and data sources the researchers had presented. The creative brief addressed processes such as soil degradation, urban expansion, atmospheric pollution, biodiversity loss, and water quality.

The creative process unfolded in three distinct phases. First came scientific contextualization and visual observation, in which students studied satellite images of Earth to identify patterns, chromatic variations, textures, and signs of human activity. Second was conceptualization and re-elaboration, during which students selected visual elements from the scientific materials and reinterpreted them through experimentation, abstraction, and discipline-specific modes of representation. Third was technical execution, in which the selected elements were translated into completed works according to the methods of each artistic track. The project produced eleven completed artworks, all of which were examined in the study.

The comparison across the school’s three artistic tracks revealed something striking: identical scientific source materials generated fundamentally different modes of translation. Students in the Figurative Arts track—five students in total—produced mixed-media paintings combining acrylic paint with epoxy resin. The resin pouring technique created a glossy, three-dimensional “liquid glass” effect, with saturated colors forming random shapes that evoke the complex, corrosive textures left by environmental pollution. The acrylic’s quick-drying properties prevented it from mixing with the resin, allowing the transparent protective layer to deepen the colors beneath. The researchers characterize this approach as material and chromatic abstraction, retaining references to environmental processes while opening space for subjective interpretation.

The Architecture and Environment track, with eleven students, took a spatial and scalar approach. These students transformed landscape morphology, topography, and spatial patterns into graphic and material compositions, working with contour lines, landscape textures, and top-down views of buildings. Some works integrated top-down architectural views directly into landscape maps, transforming buildings from mere constructions into forms that occupy and modify the terrain’s visual composition. In a particularly inventive move, other works applied satellite image textures onto building façades, merging the macro-scale perspective of Earth Observation with the micro-scale of architectural detail—effectively turning façades into geographical canvases onto which planetary processes are projected. One work went further, coating contour-line representations with real building materials, literally giving physical substance to topography and symbolizing the fragmentation and re-composition of landscape through human intervention.

The Audiovisual and Multimedia track, also with eleven students, pursued digital and narrative re-composition. Using Adobe Photoshop and Adobe InDesign, they created photomontages through layering, color alteration, filtering, and re-composition, transforming recognizable landscape features from satellite imagery into digital narratives of environmental change. The original images served both as source material and as a metaphor for a changing planet. The researchers note that digital tools functioned here not merely as instruments of technical execution but as vehicles for conceptual exploration and environmental narration, with the resulting works including animations and audiovisual products depicting planetary movement and human impact.

The eleven works were publicly exhibited under the title “COSMOS CREATIVO: Artistic Transformations of Earth from Space” during the “SuperScienceMe—ReSearch is your Elevation” event of the European Researchers’ Night on 28 March 2025. By moving the artworks from the educational setting into a public exhibition, the pathway extended from artistic production into environmental communication, positioning the works as interfaces between environmental science, artistic expression, and public interpretation. The authors are careful to note that audience responses were not collected through a systematic research protocol and are not analyzed as study data, and that two brief student interviews included in a project documentation video are presented only as anecdotal micro-feedback rather than a structured assessment of the whole cohort.

The study’s broader significance lies in what it reveals about the science-to-art translation itself. The authors argue that the translation from science to art is not a uniform process: the characteristics of each artistic practice influence which elements of scientific representations are selected, emphasized, transformed, and communicated. Scientific representations are accountable to evidence and methodological transparency, whereas artistic practices can employ abstraction, metaphor, material transformation, and emotional resonance—and within the PCTO framework, these different representational logics were brought into interaction while retaining their disciplinary specificity. For educators designing similar activities, the implication is that the choice of artistic practice should be considered alongside the selection of scientific source materials and the intended environmental focus, since a common source image need not lead to a uniform output. The authors frame the pathway as a context-dependent framework to be adapted rather than directly replicated, noting that openly accessible Earth Observation datasets, including those from Copernicus data portals, and commonly available software could facilitate adoption elsewhere. Future implementations could extend the model through travelling exhibitions, citizen-science activities, immersive virtual and augmented reality technologies, and collaborations with organizations such as ESA and NASA—though the authors stress that systematic evaluations of learning outcomes and audience responses remain a necessary next step for this emerging intersection of remote sensing, creativity, and environmental education.

Subject of Research: A STEAM education project integrating satellite imagery and environmental data into art production within Italy's PCTO framework

Article Title: Through the Kaleidoscopic Lens of Art: a STEM education pathway within Italy’s PCTO framework

Article References: Coluzzi, R., Imbrenda, V., Fanti, L., Traino, W., De Carlo, M., Camardelli, V., Smilzo, A., Cordisco, M., Limone, G., Amato, L., Calamita, G., Ciancia, E., Gandolfi, I., Perrone, A., Lucente, S., & Giampaolo, V. (2026). Through the Kaleidoscopic Lens of Art: a STEM education pathway within Italy’s PCTO framework. Frontiers in Education, 11, Article 1922875. https://doi.org/10.3389/feduc.2026.1922875

Image Credits: AI Generated

DOI: 10.3389/feduc.2026.1922875

Keywords: STEAM education, satellite imagery, Earth observation, PCTO, environmental communication, remote sensing, art high school, interdisciplinary learning, geospatial data, environmental awareness, Italy, science-to-art

The article, written by Falko Buschke, David Patterson, and colleagues at the Global Ecosystems Atlas initiative, convened by the Group on Earth Observations (GEO) Secretariat in Geneva, together with collaborators at James Cook University in Australia, draws on an extensive audit of candidate datasets for the Atlas. As of January 2026, the team had compiled a catalogue of 343 ecosystem datasets that could, in principle, be combined—with full attribution to their owners—into the first comprehensive, standardised, and open global map of ecosystems. Yet nearly one-third of those datasets, 29.7 percent, provide no explicit guidance whatsoever on how the data may be reused.

That figure matters because of a legal default that most people never think about. Like all creative works, ecosystem maps remain under exclusive copyright unless clearly stated otherwise. In practical terms, this means that even when a dataset is freely downloadable from a public website, users must assume it is protected and cannot be reused without prior permission. Permission takes the form of either an explicit data licence or direct authorisation from the provider. A dataset that sits openly on the internet but carries no licence statement is, legally speaking, locked.

The consequences ripple far beyond academic inconvenience. The Global Ecosystems Atlas is designed to bring together existing ecosystem information from trusted sources—national authorities, nongovernmental organisations, and the peer-reviewed scientific literature—and to fill remaining gaps using classification models trained on expert-evaluated, point-based data. Local expertise remains fundamental to this approach: field surveys of species composition, annotations of ecosystem-class records, and manual refinements of historical maps all feed into the final product. Top-down global approaches, however sophisticated, have repeatedly proven resistant to producing accurate maps without this local validation. When a third of the underlying information cannot legally be integrated, decades of preceding investment in local knowledge are effectively stranded.

The audit revealed a licensing landscape that is fragmented in ways that impose real technical and legal costs. Beyond the unlicensed third of the catalogue, a further 20.4 percent of datasets are governed by custom licences—legal terms tailored to a specific dataset or institution. These range from institutional policies of national governments and large research organisations to personalised modifications of standard licences. Some data owners even offer different conditions to different users, permitting free use by individuals and small businesses while restricting large corporations above a certain size. For nonexperts, navigating such bespoke terms is daunting, and for project managers, custom licences introduce legal overheads to verify compliance. Worse, when custom licences embed copyleft conditions—requiring that any derivative work be shared under identical terms—they severely limit uptake in composite products.

Only 47.8 percent of the datasets in the catalogue are shared under Creative Commons licences, the standardised public instruments designed precisely to make reuse conditions clear. Within that open minority, the majority—39.6 percent of all datasets—require simple attribution (CC-BY), while just 4 percent restrict reuse to noncommercial purposes (CC-BY-NC). On the surface, that seems like a healthy majority of usable data. But the mathematics of aggregation is unforgiving: any derived product assembled from multiple datasets must be licensed according to the most stringent conditions of its constituent parts.

This ‘weakest link’ principle means that even a single restrictive dataset can contaminate an entire global product. Include one dataset carrying a noncommercial condition, and the whole composite is barred from commercial applications such as corporate biodiversity reporting—a rapidly growing use case as companies come under pressure to disclose nature-related risks. Add a no-derivatives condition, and the product cannot be combined with any other dataset at all. A share-alike condition constrains the licence of everything downstream and can conflict with datasets that carry slightly different terms. In a synthesis that might draw on hundreds of sources, a handful of ambiguous or restrictive licences can quietly determine what the final map is legally allowed to do.

It is tempting to conclude that all ecosystem data should simply be released under maximally open licences. The authors resist that blanket prescription, and for good reason. Biodiversity data can be regarded as social infrastructure, and respecting data sovereignty—particularly that of Indigenous peoples and marginalised communities—is essential for equitable and effective conservation outcomes. Selling commercial access to biodiversity information is also a legitimate way for custodians to recoup the costs of producing and maintaining it, which is only possible when commercial rights are protected. Open data advocacy that ignores these realities risks repeating the extractive patterns that conservation is trying to move beyond.

In their discussions with data owners—scientists, government officials, and NGO researchers—the team found that licensing is often an afterthought, with stakeholders receiving little guidance on how to select and apply licences that support their intended uses. Their response is a set of three practical recommendations. First, license ecosystem data explicitly: choosing an appropriate Creative Commons licence with a user-friendly selection tool and copying the licence information alongside the data—on the access website, or in a ReadMe or metadata file accompanying GIS files—can take minutes. Second, use standardised licences wherever possible, since Creative Commons terms are translated into many languages and widely understood, whereas custom licences often impose the same restrictions in inaccessible legal jargon. Third, when restrictions are genuinely necessary, build in mechanisms to navigate them legally. The World Database on Protected Areas offers a prominent model: it maintains a public version for noncommercial use alongside a licensed version, accessible through the Integrated Biodiversity Assessment Tool, for commercial purposes.

The authors emphasise that initiatives like the Global Ecosystems Atlas are not intended to replace existing ecosystem information but to build on it, adding value by bringing datasets together under consistent standards. That vision depends on recognising the effort and investment behind every existing map, and on making the legal terms of reuse as clear as the data themselves. Clear data licences, the article argues, are an essential though often overlooked requirement for bottom-up global ecosystem mapping. The alternative is a world where the raw material for understanding Earth’s ecosystems exists in abundance—yet remains legally invisible to the very efforts trying to assemble it.

Subject of Research: Data licensing barriers to integrating local ecosystem datasets into global ecosystem maps

Article Title: Ambiguous data licences undermine global ecosystem mapping efforts

Article References: Buschke, F., Patterson, D., Cresswell, B. J., Gros-Dubois, N., Lloyd, T. J., Gomersall, L., Young, A. R., Gevorgyan, Y., & Murray, N. J. (2026). Ambiguous data licences undermine global ecosystem mapping efforts. PLOS Ecosystems, 1(1), e0000017. https://doi.org/10.1371/journal.pesy.0000017

Image Credits: AI Generated

DOI: 10.1371/journal.pesy.0000017

Keywords: ecosystem mapping, data licensing, Global Ecosystems Atlas, Creative Commons, open data, biodiversity, data sovereignty, copyright, Group on Earth Observations, conservation, geospatial data, PLOS Ecosystems

In a study published in Earth Science Informatics, Alexander Cotrina-Sanchez and Michele Torresani of the Free University of Bozen-Bolzano, together with Leonel Corado of the Universidade de Évora, present GEDIMetrics, an open-source plugin for the widely used geographic information system QGIS. The tool integrates the entire GEDI processing chain, from discovering and downloading raw data files to filtering, clipping, and fusing four complementary data products into a single analysis-ready dataset, all through a graphical interface that requires no programming. The plugin is freely available through the official QGIS Plugin Repository and on GitHub under the GNU General Public License.

The technical challenge the plugin addresses is deceptively simple to describe but notoriously tedious in practice. GEDI’s laser footprints, roughly 25 meters in diameter and spaced about 60 meters apart along the satellite’s track, capture the vertical distribution of returned laser energy. From these waveforms, NASA derives several distinct products: Level 2A provides canopy height and ground elevation metrics, Level 2B characterizes vertical canopy structure including canopy cover, plant area index, and foliage height diversity, Level 4A delivers aboveground biomass density estimates from globally calibrated models, and Level 4C offers the Waveform Structural Complexity Index, a machine-learning-derived measure of three-dimensional canopy complexity. Each product, however, is distributed as independent HDF5 files organized by laser beam, and the products must be aligned using a unique identifier called the shot number before any joint analysis becomes possible.

Existing tools each solved only fragments of this puzzle. NASA’s EarthData Search portal handles discovery and download but performs no subsetting, filtering, or cross-product integration. Programming libraries such as rGEDI and pyGEDI offer flexible scripted processing but demand coding expertise. Command-line frameworks like GEDI-Pipeline introduced automation, yet none provided a graphical workflow suitable for non-programmers, and none supported automated multi-product merging. GEDIMetrics closes that gap with a five-stage pipeline: granule discovery through NASA’s Common Metadata Repository API, authenticated download from EarthData, variable extraction with quality filtering, spatial subsetting to a user-defined region, and footprint-level merging of the selected products, followed by export to standard geospatial formats such as GeoPackage and GeoParquet.

The engineering behind the plugin reflects careful attention to both performance and reproducibility. Spatial filtering proceeds in two stages, beginning with a fast bounding-box pre-filter and followed by precise point-in-polygon clipping using Shapely geometric operations, an approach that substantially reduces processing time for granules covering far more area than the target region. Because data availability can differ across products due to variations in temporal coverage, algorithm convergence, or quality criteria, the merging stage uses Level 2A as the base product and offers configurable join strategies: an inner join retains only footprints present in all selected products, guaranteeing cross-product consistency, while an outer join preserves maximum spatial coverage at the cost of null values. Output files embed metadata recording the full processing configuration, including selected products, temporal range, region geometry, quality thresholds, and plugin version, ensuring that every analysis can be reproduced exactly.

Quality filtering, a critical step for GEDI data, is handled through an intuitive interface. Users can set minimum thresholds for product-specific quality flags, exclude footprints degraded by cloud or atmospheric effects, restrict analysis to land surfaces, and apply a global minimum beam sensitivity threshold, defaulting to 0.90 following criteria established for broad-scale applications. The sensitivity threshold can be raised for particular ecosystems, for example to 0.96 for temperate forests or higher for dense tropical canopies where the laser signal struggles to penetrate to the ground. A logging panel reports the number of granules identified and the estimated data volume before download begins, allowing users to adjust their parameters before committing to lengthy retrievals.

To demonstrate the plugin’s capabilities, the team tested GEDIMetrics across two contrasting forest ecosystems of roughly 100 square kilometers each: a temperate mixed forest in northeastern France and a humid tropical rainforest in the Madre de Dios region of southeastern Peru, an area partially overlapping the Tambopata National Reserve where intact forest faces mounting pressure from illegal gold mining. Using data from March 2019 to April 2023 with default quality settings, the temperate site yielded 96 granules and 5,939 forested footprints, while the tropical site produced only 23 granules and 1,712 footprints, a disparity that reflects the International Space Station’s orbital geometry, which delivers more frequent coverage at mid-latitudes than near the equator.

The case study also revealed striking asymmetries in product-level data availability that cannot be inferred from footprint counts alone. Retention of the Level 2B canopy structure product was moderate at the temperate site, at 60.8 percent of forested footprints, but nearly absent at the tropical site, at just 0.9 percent. The biomass product showed the inverse pattern, with 7.2 percent retention in France versus 87.2 percent in Peru. Despite these differences, the merged datasets painted a coherent ecological picture: the tropical forest exhibited taller canopies, with a 98th-percentile height of 24.05 meters compared to 18.86 meters at the temperate site, higher aboveground biomass density of 157.49 versus 139.51 megagrams per hectare, and greater structural complexity. Most strikingly, footprints inside the Tambopata National Reserve showed consistently taller canopies, 38 percent higher biomass density, and higher structural complexity than those in the surrounding buffer zone, a quantifiable signature of the degradation pressure from gold mining at the reserve’s edges.

At the temperate site, the plugin’s integrated dataset captured seasonal phenology with unexpected clarity. Canopy cover and plant area index rose sharply during the April-to-October vegetative period, consistent with the flush of deciduous foliage, while canopy height remained essentially stable across seasons, confirming that height metrics are largely insensitive to phenological state. The most dramatic seasonal signal appeared in the median height percentile, which jumped from 1.27 meters in the leafless months to 6.36 meters during the growing season, reflecting the increased interception of laser energy by mid-canopy leaves. This ability to distinguish genuine structural change from seasonal foliage dynamics has direct implications for disturbance monitoring and carbon accounting.

The researchers are candid about current limitations. Processing is single-machine and sequential, which can make large regions slow to analyze, and the plugin does not yet correct GEDI’s footprint geolocation uncertainty of roughly 10 meters, though its modular design allows integration with external correction tools. Future development priorities include polygon-level summary statistics, diagnostics quantifying footprint loss at each filtering stage, in-plugin visualization of vertical foliage profiles, and a hybrid local-cloud workflow leveraging NASA’s evolving cloud-native archives. The team also envisions extending the plugin to ICESat-2 canopy height data, which would push coverage beyond GEDI’s latitudinal limits into boreal and high-latitude forests. For now, GEDIMetrics stands as a quietly transformative piece of software: by collapsing a multi-step, script-heavy workflow into a transparent graphical process inside the world’s most widely used open-source GIS, it puts the vertical architecture of the world’s forests within reach of anyone with a computer, a QGIS installation, and a free NASA EarthData account.

Subject of Research: An open-source QGIS plugin for accessing, filtering, and fusing NASA GEDI spaceborne LiDAR forest structure data products

Article Title: GEDIMetrics: a QGIS plugin for accessing and integrating multi-product GEDI spaceborne LiDAR data

Article References: Cotrina-Sanchez, A., Torresani, M., & Corado, L. (2026). GEDIMetrics: a QGIS plugin for accessing and integrating multi-product GEDI spaceborne LiDAR data. Earth Science Informatics, 19(11), Article 205. https://doi.org/10.1007/s12145-026-02254-z

Image Credits: AI Generated

DOI: 10.1007/s12145-026-02254-z

Keywords: GEDI, spaceborne LiDAR, QGIS, forest structure, aboveground biomass, canopy height, remote sensing, open-source software, geospatial data, NASA EarthData, tropical forest, forest carbon

The research team set out to answer a deceptively simple question: how are quantitative modelling approaches being used to support One Health in the context of agri-food systems, and how could they be better organized within governmental institutions? The question matters because the food system sits at the center of some of the most pressing health challenges of the century, from zoonotic disease emergence and antimicrobial resistance to foodborne illness and the nutritional consequences of environmental change. Public health authorities, the authors argue, cannot prioritize interventions or evaluate trade-offs across these domains without quantitative models that capture the dynamics of the animal-human-environment interface. Intuition and siloed expertise, however sophisticated, are not enough when a single policy decision ripples simultaneously through livestock production, ecosystems, and human diets.

To establish what governments are actually doing, the researchers first defined an operational scope of One Health by examining the One Health Joint Plan of Action, the strategic framework developed by the quadripartite alliance of the World Health Organization, the Food and Agriculture Organization, the World Organization on Animal Health, and the United Nations Environmental Programme. They then cross-referenced the plan’s proposed food-system activities against the publicly available One Health resources of the Group of 20 countries, the economies with the greatest institutional capacity to act. The result was sobering. The activities that governments have actually implemented cluster tightly around coordination, interoperability, and data sharing, and they concentrate on a short list of established threats: antimicrobial resistance, zoonoses, and vector-borne diseases. Broader food-system concerns, including nutrition, environmental sustainability, and the structural dynamics of agricultural production, remain largely outside the institutional One Health agenda.

That institutional narrowness, however, contrasts sharply with what is happening in the research literature. The team conducted a PRISMA-compliant scoping review of One Health quantitative modelling with relevance to the food system, covering publications from 2021 to 2026. The search yielded 129 relevant articles, a body of work that reveals how far methodological ambition has outrun governmental practice. Beyond the epidemiological data that one might expect, these studies drew on agricultural, geospatial, and ecological data streams to address food-system questions. Researchers were combining crop and livestock statistics with satellite-derived land cover information, wildlife distribution data, and environmental indicators, weaving together evidence from domains that traditionally have never shared a analytical framework, let alone a database.

The methodological landscape that emerged from the review is strikingly heterogeneous, and the authors identify three broad strategies that dominate the field. The first involves models that ingest heterogeneous data, fusing inputs of different types and resolutions into a single analytical structure. The second relies on mixed-effects approaches, statistical designs that explicitly account for variation at multiple levels, whether that means differences between farms, regions, or ecological zones, and can therefore represent the nested structure of real food systems rather than flattening it. The third and perhaps most consequential strategy is model integration: coupling separate models, each specialized for one domain, into combined frameworks capable of scenario analysis. A disease transmission model might be linked to an agricultural production model and an environmental impact model, allowing analysts to ask what happens to human health, animal welfare, and ecosystems under different policy futures simultaneously.

This is where the technical heart of the paper lies, and it is worth pausing on why integrated modelling is so difficult and so valuable. Each component model carries its own assumptions, spatial and temporal scales, and data requirements. An epidemiological model of pathogen spread may operate on daily time steps across networks of farms and markets, while an ecological model of land-use change may work in yearly increments across entire watersheds. Reconciling these scales, propagating uncertainty across model boundaries, and ensuring that outputs from one model are meaningful inputs to another are nontrivial engineering and statistical challenges. The fact that 129 recent studies attempt this integration at all signals a maturing field, but the review also makes clear that the approaches remain fragmented, with little standardization in how interdisciplinary collaboration is structured or how data infrastructures support it.

The conceptual contribution of the paper is a modular framework designed to operationalize modelling within One Health initiatives that involve the food system. Rather than prescribing a single monolithic model, the framework treats modelling as a set of interoperable modules that institutions can assemble according to their needs, connecting data acquisition, domain-specific models, and scenario evaluation in a flexible architecture. The design is explicitly aimed at supporting interdisciplinary collaboration between food-systems research and One Health practice, providing a common structure through which epidemiologists, agricultural scientists, ecologists, and data specialists can contribute components without needing to master one another’s entire methodological repertoire. For governmental institutions, the appeal is practical: a modular approach lowers the barrier to entry and allows capacity to be built incrementally rather than requiring an impossible upfront investment in a fully integrated system.

Underpinning all of this is a data infrastructure problem that the authors place at the center of their conclusions. The review found that genomic, geospatial, and agricultural data are the raw material of interdisciplinary One Health modelling, and that the availability and interoperability of these data streams largely determines what models can achieve. Genomic surveillance data allow pathogens to be traced across species boundaries and supply chains, revealing transmission pathways that would otherwise remain invisible. Geospatial data connect disease events to environmental drivers such as land conversion, water systems, and climate variables. Agricultural data anchor the models in the economic and biological realities of food production. Yet these data types are held by different ministries, agencies, and research communities, often in incompatible formats and under different governance regimes. The coordination and data-sharing activities that dominate current G20 One Health initiatives are, in this light, a necessary foundation, but the review suggests they must extend beyond the narrow disease-focused remit to support genuinely food-system-wide modelling.

The implications reach well beyond academic methodology. As climate change alters agricultural zones, as demand for animal protein reshapes livestock systems, and as antimicrobial resistance erodes the foundations of modern medicine, the trade-offs facing policymakers will become increasingly difficult to evaluate with single-domain analysis. A policy that reduces antimicrobial use in livestock may have consequences for animal welfare, farm economics, food prices, and ultimately human nutrition. A land-use change that boosts crop yields may expand habitat overlap between wildlife and humans, raising zoonotic spillover risk. Only quantitative models that span these domains can make such trade-offs explicit, and only institutions equipped with appropriate data infrastructure and collaborative frameworks can deploy those models where decisions are actually made. The gap between the 129 studies of the research frontier and the narrow institutional initiatives documented in the G20 review is therefore not merely an academic observation; it is a measure of how much analytical capacity remains untapped in exactly the places where it is most needed.

The study, funded by the German Federal Ministry of Research, Technology and Space under the WeAreOne project on public health synergies in the Anthropocene, arrives at a moment when One Health is being institutionalized worldwide, with national plans, dedicated agencies, and international frameworks proliferating. Its central message is a constructive tension: One Health is holistic in principle but narrow in practice, and quantitative modelling is both a symptom of that narrowness and a potential cure. By mapping the methodological state of the art, documenting the institutional shortfall, and proposing a modular framework grounded in genomic, geospatial, and agricultural data infrastructure, Burke and Risius have provided a concrete agenda for turning a unifying philosophy into an operational science. Whether governments seize that agenda will determine whether One Health remains a compelling slogan or becomes the analytical engine that the food systems of the twenty-first century urgently require.

Subject of Research: Interdisciplinary quantitative modelling for One Health applications in agri-food systems

Article Title: Interdisciplinary modelling approaches for One Health in agri-food systems: a scoping review and conceptual analysis

Article References: Burke, T. A., & Risius, A. (2026). Interdisciplinary modelling approaches for One Health in agri-food systems: a scoping review and conceptual analysis. BMC Public Health, 26(1), Article 2760. https://doi.org/10.1186/s12889-026-29666-2

Image Credits: AI Generated

DOI: 10.1186/s12889-026-29666-2

Keywords: One Health, agri-food systems, quantitative modelling, scoping review, antimicrobial resistance, zoonoses, food system, public health, geospatial data, genomic surveillance, interdisciplinary collaboration, G20

The exposome, a concept first articulated to complement the genome, encompasses the totality of environmental exposures an individual experiences across a lifetime. In principle, it is the environmental mirror of the genome: where genes catalog inherited potential, the exposome catalogs the accumulated external and internal pressures that regulate health and disease. In practice, however, measuring the exposome has proven far harder than sequencing DNA. Genomic and multiomic technologies now deliver extraordinarily deep characterizations of biological systems, but the ability to characterize the external environment with comparable depth and rigor remains comparatively limited. Questionnaires, the traditional workhorse of exposure assessment, capture only what participants remember, notice, and are willing to report, leaving vast swaths of the chemical, physical, and social environment unmeasured.

A new study by MacNell and colleagues, examined in the commentary by R. Keith Reeves of Duke University School of Medicine, demonstrates a practical route forward. The researchers systematically integrated multiple publicly available geospatial datasets within an exposome-wide association study, or ExWAS, framework. Their platform was the Personalized Environment and Genes Study, known as PEGS, a research cohort that has already proven unusually versatile. Using residential information from more than 7,500 participants, the team linked each participant’s location to environmental indicators derived from geospatial datasets and then evaluated associations between those indicators and a range of common disease phenotypes. The approach effectively turns a participant’s address into a dense, multidimensional exposure profile, one assembled at low cost from data that already exist.

The technical logic of the ExWAS framework deserves attention. Analogous to genome-wide association studies, which scan hundreds of thousands of genetic variants for statistical links to disease, an ExWAS scans a broad panel of environmental exposures for associations with health outcomes. The challenge has always been assembling a sufficiently rich panel of exposures. GIS-based linkage addresses this by drawing on satellite-derived air pollution estimates, proximity to hazardous sites, temperature surfaces, and other spatially referenced data layers, then assigning each participant values based on where they live. Because these datasets are public and standardized, the same exposure panel can be reconstructed for almost any cohort with residential addresses, enabling retrospective and prospective analyses across studies at relatively low cost.

One of the most significant findings highlighted in the commentary is that geospatial information does more than reproduce what questionnaires already capture. In the MacNell analysis, GIS-based measures identified associations that overlapped with prior PEGS findings, including relationships between environmental exposures and cardiovascular and metabolic phenotypes. Critically, the geospatial approach also surfaced potential associations with exposures that had not previously been captured by survey-based assessments. That combination, confirming known signals while revealing new ones, is exactly what a maturing exposure science should deliver, and it suggests that place-based data can genuinely complement rather than merely duplicate self-reported measures.

PEGS has become a proving ground for this kind of integrative work. Beyond the new GIS-linked ExWAS, the cohort has supported exposome-wide studies of common diseases, the development of polyexposure risk scores for type 2 diabetes, investigations of air-pollution mixtures and inflammatory skin disease, and studies that combine geographic exposures with genetic susceptibility to immune-mediated disease. A dedicated dataset resource now provides genomic, exposomic, and geospatial data together, allowing researchers to test how environmental and genetic risk factors interact. Earlier questionnaire-based ExWAS analyses in PEGS had already revealed both expected and novel risk factors associated with cardiovascular outcomes, establishing the statistical machinery that the geospatial extension now enriches.

The broader opportunity, as Reeves frames it, could extend well beyond any single association. A partly standardized geospatial exposome would provide an environmental data infrastructure applicable across cohorts, retrospective and prospective alike. Exposure questionnaires could be complemented and streamlined, while environmental measures could be updated dynamically as participants move or as new data layers become available. This is a subtle but important shift in study design: rather than freezing exposure history at enrollment, researchers could maintain living exposure profiles that track participants through time. In a mobile society, where people change neighborhoods, cities, and even countries, that flexibility addresses one of the most persistent weaknesses of static exposure assessment.

The scope of what can be measured geospatially is also expanding. Air pollution and hazardous waste sites were the natural first targets, since both have well-developed national datasets and established health links. But the commentary points toward a much wider canvas: extreme heat, water quality, healthcare access, and other environmental and social determinants of health could all be layered into a geospatial exposome. Each addition broadens the range of exposures captured and opens new hypotheses about how the places people live shape chronic disease. Heat exposure, for instance, intersects with cardiovascular strain; healthcare access intersects with disease management and outcomes; water quality intersects with a growing list of chemical exposures of concern. Integrating these layers transforms the address from a simple demographic variable into a rich exposure summary.

The end goal, according to the commentary, is for environmental exposure information to become a routine component of phenotyped cohorts, integrated alongside genomic, molecular, clinical, and social determinants of health data. In that vision, a participant in a large biobank would carry not only a genome sequence and a set of lab values but also a standardized, continuously updated environmental profile derived from where they have lived. Disease association studies could then interrogate genes and environment in a single unified framework, something that has been promised conceptually for years but has been difficult to deliver because environmental data lagged so far behind biological data in depth and standardization.

Challenges remain, of course. Geospatial datasets are proxies, not personal monitors, and they capture exposure at residential locations rather than wherever a person actually spends their day. Spatial resolution, temporal coverage, and the comparability of datasets across regions all impose limits on how finely the exposome can be resolved. Yet the direction of travel is clear. As public geospatial data grow in quality and coverage, and as frameworks like ExWAS provide disciplined ways to test thousands of exposures against thousands of health outcomes, the environment is moving from the margins of epidemiology toward its center. The commentary on the MacNell study makes the case that putting place into the exposome is no longer an aspiration but an operational reality, one that could reshape how scientists understand, and ultimately prevent, the diseases that define modern life.

Subject of Research: Integration of geographic information systems data into exposome-wide association studies for human health research

Article Title: Putting place into the exposome for human health. Comment on: ‘Applying geographic information systems data linkages for an exposome-wide association study in the Personalized Environment and Genes Study’

Article References: Reeves, R. K. (2026). Putting place into the exposome for human health. Comment on: ‘Applying geographic information systems data linkages for an exposome-wide association study in the Personalized Environment and Genes Study’. Journal of Exposure Science & Environmental Epidemiology. https://doi.org/10.1038/s41370-026-00980-6

Image Credits: AI Generated

DOI: 10.1038/s41370-026-00980-6

Keywords: exposome, GIS, ExWAS, PEGS study, environmental epidemiology, air pollution, geospatial data, cardiovascular disease, type 2 diabetes, exposure assessment, public health, genomics

The problem the researchers set out to solve is well known in the database community. When a new object is inserted into an R-tree and a node exceeds its capacity, the node must be split into two, and the way that split is performed determines how much the resulting rectangles overlap. Traditional splitting heuristics are static; they apply the same rules regardless of how the data are actually distributed. Over time, especially with frequent insertions and deletions, the tree becomes unbalanced, storage utilization drops, and query processing degrades. Machine learning approaches such as the R+ Learned Spatial Index and the reinforcement-learning-based RLR-tree have shown impressive gains, but they demand large training datasets, significant computational resources, and often struggle with irregular or highly skewed spatial distributions.

The Jordanian team, led by Rahmeh Ibrahim with co-authors Amer Al-Badarneh and Qasem Abu Al-Haija, chose a different path. Particle swarm optimization, or PSO, is a metaheuristic inspired by the collective behavior of bird flocks and fish schools. Each particle in the swarm represents a candidate solution and moves through the search space guided by its own best experience and the best solution found by the entire swarm. PSO requires no pre-training, involves few control parameters, and converges quickly, making it an attractive alternative to heavyweight machine learning models. In PSO-RT, each particle encodes a potential R-tree configuration, such as a strategy for splitting an overflowing node or merging an underutilized one.

The heart of the method is a fitness function that scores each candidate configuration by combining two objectives: the total overlap among minimum bounding rectangles and the height of the tree. Weighting coefficients balance the two goals, so the swarm searches for splits and merges that simultaneously minimize overlap and keep the tree shallow and balanced. When a new data point causes a node to overflow, PSO is invoked to evaluate multiple splitting strategies and select the one with the lowest fitness. When deletions leave a node below its minimum threshold, the swarm searches for the optimal merge. This dynamic integration means the index continuously adapts to the shape of the data rather than relying on fixed heuristics, a property the authors argue is essential for real-time applications such as autonomous navigation, environmental monitoring, and location-based services.

To test the framework, the researchers ran experiments on three datasets with very different characteristics. The first was a synthetic set of 1,000 two-dimensional points sampled uniformly within a defined bounding box, providing a controlled baseline. The second was drawn from OpenStreetMap, capturing points of interest, amenities, and landmarks whose clustered distributions mimic real urban geography. The third came from the U.S. Census Bureau’s TIGER/Line shapefiles, representing road intersections and building footprints with a mix of dense and sparse regions. All experiments were implemented in Python using the RTree, NumPy, GeoPandas, and OSMnx libraries on a standard Intel Core i7 machine with 16 gigabytes of RAM, and results were averaged over ten independent runs to ensure reliability.

The improvements were consistent across all three datasets. On the synthetic data, query response time fell from 15.2 milliseconds to 10.4 milliseconds, a 31.6 percent improvement, while total MBR overlap dropped from 1,500 to 870 square units, a 42.0 percent reduction. Tree height shrank from seven levels to five, and node utilization rose from 72.3 percent to 84.6 percent. The OpenStreetMap dataset, with its challenging clustered distribution, saw the largest gains: query time improved by 35.7 percent, overlap fell by 42.7 percent, tree height dropped from eight levels to six, and node utilization climbed from 68.9 percent to 82.4 percent. On the TIGER data, query response time improved by 33.3 percent and overlap by 41.7 percent, with node utilization rising from 70.2 percent to 83.7 percent.

There is, however, a trade-off. Because PSO must evaluate candidate configurations during structural changes, insertion and deletion times increased substantially, with overheads ranging from 67.9 percent to 75.8 percent compared with baseline R-trees. In the synthetic dataset, for example, insertion time rose from 4.5 milliseconds to 7.8 milliseconds and deletion time from 3.8 to 6.5 milliseconds. The authors argue this cost is acceptable in query-dominated workloads, where the payoff in faster spatial queries and better storage efficiency outweighs slower updates. They also note that the framework’s sensitivity to PSO parameters, such as swarm size and inertia weight, requires careful calibration, and they recommend future work on hybrid optimization techniques and adaptive parameter tuning to reduce update overhead.

Compared with competing approaches, PSO-RT occupies a distinctive middle ground. The R+ Learned Spatial Index excels at nearest-neighbor queries but relies on static learned patterns and does little about overlap or storage utilization. The RLR-tree, which models subtree selection and node splitting as Markov decision processes, achieves strong performance on datasets with up to 100 million objects but demands computationally intensive training and retraining. The Grid-R-tree reduces overlap through adaptive grid partitioning but struggles with the irregular distributions found in OpenStreetMap and TIGER data. PSO-RT, by contrast, requires no training, adapts in real time, and handles diverse distributions without specialized hardware or additional partitioning infrastructure.

The study also connects to a broader theoretical conversation. Recent research applying complex network theory to evolutionary computation has begun to explain the collective dynamics and convergence behavior of swarm-based algorithms from a network science perspective, lending theoretical weight to the empirical success of PSO in domains like spatial indexing. The authors suggest that such analytical tools could further illuminate why swarm intelligence works so well for structural optimization problems, and they point toward parallel and distributed implementations as a way to soften the update-time penalty while preserving query gains.

For the geospatial industry, the message is pragmatic: intelligent optimization does not have to mean expensive models. A lightweight, biologically inspired algorithm, applied at exactly the moments when an index structure changes, can deliver double-digit improvements in the metrics that matter most to spatial databases. As location data continues to explode from navigation apps, Internet of Things sensors, satellite imagery, and smart city infrastructure, techniques like PSO-RT may become essential tools for keeping the world’s spatial queries fast, scalable, and adaptive to a planet that never stops moving.

Subject of Research: Optimizing R-tree spatial indexing with an adaptive particle swarm optimization algorithm to improve geospatial query performance.

Article Title: Enhancing R tree spatial indexing using adaptive particle swarm optimization algorithm

Article References: Ibrahim, R., Al-Badarneh, A., & Abu Al-Haija, Q. (2026). Enhancing R tree spatial indexing using adaptive particle swarm optimization algorithm. Discover Informatics, 1(1), Article 4. https://doi.org/10.1007/s44564-026-00004-3

Image Credits: AI Generated

DOI: 10.1007/s44564-026-00004-3

Keywords: R-tree, spatial indexing, particle swarm optimization, geospatial data, minimum bounding rectangles, query performance, metaheuristics, spatial databases, node splitting, OpenStreetMap, TIGER dataset, tree balance