Data Gaps Research Articles

Increasing phosphorus loss despite widespread concentration decline in US rivers

The loss of phosphorous (P) from the land to aquatic systems has polluted waters and threatened food production worldwide. Systematic trend analysis of P, a nonrenewable resource, has been challenging, primarily due to sparse and inconsistent historical data. Here, we leveraged intensive hydrometeorological data and the recent renaissance of deep learning approaches to fill data gaps and reconstruct temporal trends. We trained a multitask long short-term memory model for total P (TP) using data from 430 rivers across the contiguous United States (CONUS). Trend analysis of reconstructed daily records (1980–2019) shows widespread decline in concentrations, with declining, increasing, and insignificantly changing trends in 60%, 28%, and 12% of the rivers, respectively. Concentrations in urban rivers have declined the most despite rising urban population in the past decades; concentrations in agricultural rivers however have mostly increased, suggesting not-as-effective controls of nonpoint sources in agriculture lands compared to point sources in cities. TP loss, calculated as fluxes by multiplying concentration and discharge, however exhibited an overall increasing rate of 6.5% per decade at the CONUS scale over the past 40 y, largely due to increasing river discharge. Results highlight the challenge of reducing TP loss that is complicated by changing river discharge in a warming climate.

Advancing an integrated understanding of land–ocean connections in shaping the marine ecosystems of coastal temperate rainforest ecoregions

AbstractLand and ocean ecosystems are strongly connected and mutually interactive. As climate changes and other anthropogenic stressors intensify, the complex pathways that link these systems will strengthen or weaken in ways that are currently beyond reliable prediction. In this review we offer a framework of land–ocean couplings and their role in shaping marine ecosystems in coastal temperate rainforest (CTR) ecoregions, where high freshwater and materials flux result in particularly strong land–ocean connections. Using the largest contiguous expanse of CTR on Earth—the Northeast Pacific CTR (NPCTR)—as a case study, we integrate current understanding of the spatial and temporal scales of interacting processes across the land–ocean continuum, and examine how these processes structure and are defining features of marine ecosystems from nearshore to offshore domains. We look ahead to the potential effects of climate and other anthropogenic changes on the coupled land–ocean meta‐ecosystem. Finally, we review key data gaps and provide research recommendations for an integrated, transdisciplinary approach with the intent to guide future evaluations of and management recommendations for ongoing impacts to marine ecosystems of the NPCTR and other CTRs globally. In the light of extreme events including heatwaves, fire, and flooding, which are occurring almost annually, this integrative agenda is not only necessary but urgent.

Review of landslide inventories for Nepal between 2010 and 2021 reveals data gaps in global landslide hotspot

AbstractA review of landslide inventories provides an essential assessment of the state of knowledge around landslide hazard and can guide the focus of future studies. This is especially true in Nepal, which is highly prone to landslides, but lacks a comprehensive overview of landslide occurrence nationally. Here, we compile a database of 117 landslide inventories for Nepal released between 2010 and 2021. We review how these existing inventories shape our understanding of landsliding in Nepal and discuss how future research efforts could mitigate current challenges. We find that 40% of the country was only manually mapped once across the study period, and, crucially, these areas did not always correspond with areas of low landslide susceptibility. Instead, existing landslide inventories typically focus on specific areas, such as the region affected by the 2015 Gorkha Earthquake and major highway corridors. We also extrapolated the individual inventory characteristics from within this unique database to infer a national-scale areal density of 0.05 landslides per km2, equating to 6000 landslides across the country. This extrapolated value provides a baseline for future national-scale studies, especially for inventories created through automated mapping approaches. Our review highlights the importance of expanding the footprint of landslide inventories in Nepal to include regions with low mapping coverage and the need for inventories to be openly available, with clear protocols to enable inter-comparison. Whilst our review has focused on Nepal, these findings are likely to be relevant in other landslide-prone countries and our recommendations are intended to be applicable elsewhere.

A novel linetype recognition algorithm based on track geometry detection data

Abstract The detection and recognition of track geometry are crucial for ensuring railway transportation safety and maintenance efficiency. Traditional linetype recognition algorithms struggle to effectively handle the complexities of variable track geometry and noise interference. To address these challenges, a novel linetype recognition algorithm (NLRA) is proposed, which thoroughly analyzes the characteristics of track curvature across straight lines, transition curves, and circular segments. By employing partial differential equations for data preprocessing, NLRA effectively filters out noise. Utilizing curve design parameters and integrating line anti-noise recognition with multi-segment line fitting techniques, the algorithm accurately identifies each line segment and incorporates a single-point recognition method for data gaps. Experimental results demonstrate that NLRA outperforms traditional algorithms in terms of real-time performance, recognition accuracy, anti-interference capability, and noise handling. Notably, NLRA enhances accuracy by at least 35.1% and recall by at least 17.4% on selected typical routes. This work underscores the algorithm's potential to provide efficient and accurate tools for track maintenance, facilitate advancements in track detection software, and significantly contribute to railway transportation safety.

Mapping the ionosphere with millions of phones.

The ionosphere is a layer of weakly ionized plasma bathed in Earth's geomagnetic field extending about 50-1,500 kilometres above Earth1. The ionospheric total electron content varies in response to Earth's space environment, interfering with Global Satellite Navigation System (GNSS) signals, resulting in one of the largest sources of error for position, navigation and timing services2. Networks of high-quality ground-based GNSS stations provide maps of ionospheric total electron content to correct these errors, but large spatiotemporal gaps in data from these stations mean that these maps may contain errors3. Here we demonstrate that a distributed network of noisy sensors-in the form of millions of Android phones-can fill in many of these gaps and double the measurement coverage, providing an accurate picture of the ionosphere in areas of the world underserved by conventional infrastructure. Using smartphone measurements, we resolve features such as plasma bubbles over India and South America, solar-storm-enhanced density over North America and a mid-latitude ionospheric trough over Europe. We also show that the resulting ionosphere maps can improve location accuracy, which is our primary aim. This work demonstrates the potential of using a large distributed network of smartphones as a powerful scientific instrument for monitoring Earth.

Curbing the crises, gauging the gap: measuring the nexus between gender and the environment

Abstract Women and men interact with the environment differently. Although data on the gender and environment nexus is only recently starting to become more widely available, existing evidence shows that, in many countries, women and men’s livelihoods, roles at home, and decision-making actions are widely different, and thus so are their contributions to environmental conservation and degradation. Simiarly, climate change, pollution and biodiversity loss affect women and men in different ways, but the limited availability of related data still prevents a full understanding of the multiplicity of ways in which these differences manifest. In recent years, numerous methodological advancements have taken place, which are enabling the production of gender and environment statistics to better understand these connections. From dedicated surveys to uses of non-conventional data sources, empirical evidence on this topic is increasingly available. Still, substantial efforts are needed to fill existing data gaps and gain a thorough understanding of the connections. This paper showcases available evidence on various aspects of the gender-environment nexus, and highlights possible solutions to fill remaining data gaps.

Bridging Knowledge and Data Gaps in Odonata Rarity: A South Korean Case Study Using Multispecies Occupancy Models and the Rabinowitz Framework

Accurate assessment of species rarity and conservation status requires an approach that integrates data-driven models with established ecological knowledge. In this study, we applied multispecies occupancy (MSO) and latent factor multispecies occupancy (LFMSO) models to estimate the occurrence of 133 Odonata species in South Korea. Using the model outputs, we implemented the Rabinowitz rarity framework to conduct data-based rarity assessments, which were then compared with known ecological information, including geographic ranges, habitat preferences, regional Red List statuses, and citizen science observations. Our findings reveal both alignments and discrepancies between these data-driven rarity assessments and traditional ecological knowledge. For example, species classified as near threatened (NT) or vulnerable (VU) on the regional Red List generally corresponded with high-rarity classifications based on the Rabinowitz framework. However, significant inconsistencies were identified, particularly for certain lentic Odonata species traditionally considered common. These results suggest that spatial biases in field surveys, combined with limited access to data on legally protected species, can impede accurate rarity assessments. These findings underscore the need for standardized survey protocols and improved data-sharing policies for sensitive species to reduce biases and enhance the reliability of rarity assessments. This is essential for effective conservation planning and biodiversity management in freshwater ecosystems.

Association between different GLP-1 receptor agonists and acute pancreatitis: case series and real-world pharmacovigilance analysis

ObjectiveGlucagon-like peptide-1 receptor agonists (GLP-1 RAs) have shown notable advancements in managing blood sugar control. Nevertheless, there remains a gap in real-world data regarding the variation in acute pancreatitis (AP) risk among different GLP-1 RAs. Our study aimed to characterize and evaluate AP associated with different GLP-1 RAs (exenatide, lixisenatide, liraglutide, albiglutide, semaglutide, dulaglutide and tirzepatide) in a public adverse events database and to review the relevant case reports.MethodsWe described a case series of patients experiencing AP while on GLP-1 RAs. Additionally, we utilized various algorithms including reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian confidence propagation neural network (BCPNN), and multi-item gamma Poisson shrinker (MGPS) to analyze data from the Food and Drug Administration’s Adverse Event Reporting System (FAERS) regarding suspected adverse events of AP linked to GLP-1 RAs from January 2005 to September 2023.ResultsOur case series comprised thirty-nine patients who experienced AP events while on GLP-1 RAs. Within the FAERS database, we retrieved a total of 6,751 individual case safety reports (ICSRs) involving various GLP-1 RAs. The median age of the patients included in our study was 57 years (range: 14–99), with 98.3% of cases classified as serious. Signals indicating AP were observed across all GLP-1 RAs, with particular emphasis on exenatide and liraglutide.ConclusionThere is a notable reporting signal of AP associated with all GLP-1 RAs. Healthcare providers must remain vigilant and closely monitor this potentially life-threatening adverse event.

Oral Health Data Gaps and Needs in North Carolina

Effective oral health-related policymaking and program implementation rely on comprehensive, accurate, and timely data. This commentary will address the lack of data available and the need for a robust data dashboard, in addition to the importance of community water fluoridation.

Mountains and Waves: Fontan Circulation in Different Environmental Conditions.

As surgical options and medical care for patients with univentricular heart physiology continue to improve, leading to increased life expectancy and quality of life, a new population of Fontan patients is growing up with the desire to participate in leisure activities, including aquatic activities, high-altitude stays, and air travel. Due to significant data gaps and insufficient experience, current guidelines do not provide clear recommendations, leading to uncertainty and sometimes restrictive patient management. This review summarizes new insights and the current state of research on this subject and provides an overview of the long overdue change in policies toward less restrictive counseling for Fontan patients regarding swimming, diving, high-altitude stays, and air travel. The current review summarizes the physiologic impact of aquatic and high-altitude activities on the cardiovascular system and presents currently available data on this topic in Fontan patients. Patients with Fontan circulation in good clinical shape can tolerate activities in the water and in the mountains as well as air traveling without critical events. In order to be able to make general recommendations, further studies with larger numbers of cases must be carried out.

Shedding light on trawl fishing activity in the Mediterranean Sea with remote sensing data

Abstract This study uses Synthetic Aperture Radar (SAR) vessel detections and Automatic Identification System (AIS) to predict trawl fishing intensity and distribution of fishing activity in areas where public AIS data are not available. By processing SAR data, considering spatial and temporal autocorrelation, and building a General Additive Model, a statistical relationship between SAR vessel detections and AIS fishing activity was established. The study provides spatially explicit estimates of trawler fishing activity, compared with official fleet records published by the General Fisheries Commission of the Mediterranean, revealing the distribution and intensity of trawl fishing activity not previously publicly tracked. Fishing grounds in the Strait of Sicily along the coast of Tunisia and North of Egypt showed an intensity of trawl fishing activity similar to the Adriatic Sea. This area is historically known to be subject to the highest trawling pressure in the Mediterranean, and also as one of the most heavily trawled regions in the world. The study shows that the integration of remote sensing data, such as SAR, offers a promising avenue to overcome data gaps and improve fisheries management in the Mediterranean where only a portion of the fishing fleet is publicly tracked.

The Role of Simulation Science in Public Health at the Agency for Toxic Substances and Disease Registry: An Overview and Analysis of the Last Decade

Environmental exposures are ubiquitous and play a significant, and sometimes understated, role in public health as they can lead to the development of various chronic and infectious diseases. In an ideal world, there would be sufficient experimental data to determine the health effects of exposure to priority environmental contaminants. However, this is not the case, as emerging chemicals are continuously added to this list, furthering the data gaps. Recently, simulation science has evolved and can provide appropriate solutions using a multitude of computational methods and tools. In its quest to protect communities across the country from environmental health threats, ATSDR employs a variety of simulation science tools such as Physiologically Based Pharmacokinetic (PBPK) modeling, Quantitative Structure–Activity Relationship (QSAR) modeling, and benchmark dose (BMD) modeling, among others. ATSDR’s use of such tools has enabled the agency to evaluate exposures in a timely, efficient, and effective manner. ATSDR’s work in simulation science has also had a notable impact beyond the agency, as evidenced by external researchers’ widespread appraisal and adaptation of the agency’s methodology. ATSDR continues to advance simulation science tools and their applications by collaborating with researchers within and outside the agency, including other federal/state agencies, NGOs, the private sector, and academia.

Gaps and Fiscal Adjustment for Debt Stability in Climate-Vulnerable Developing Countries: How Large and by How Much?

Abstract We explore the extent of the fiscal resource gap described by climate-vulnerable developing countries which have called for debt flexibility. We compute indices of fiscal sustainability, defined as the difference between an economy’s current tax rate and the “sustainable” tax rate, or the rate which, if constant, would enable the debt-to-GDP ratio at time n to be equal to the debt-to-GDP ratio at time 0, given current policy rules. We estimate short- and medium-term gap indices for 2010 to 2020, and a “climate change gap” (CCG) which incorporates countries’ reported climate change needs for the period 2021–2030. Estimated short- and medium-term gaps capture the 2020 disruption to fiscal sustainability, while CCGs indicate a highly diverse picture: one-third of the sample have CCGs ranging from 10 to 99 % of GDP, another third have CCGs from −8.6 to −0.3 %. Negative CCGs, which imply that immediate adjustments in taxes or spending are not required for fiscal policy sustainability, are surprising but come with a crucial caveat: CCGs are likely to be significantly understated because of data gaps, especially regarding adaptation requirements. This highlights the urgent need for finance and technical assistance dedicated to building national capacities for determining and costing climate change needs.

The Great Lakes Winter Grab: Limnological data from a multi‐institutional winter sampling campaign on the Laurentian Great Lakes

AbstractInterest in winter limnology is growing rapidly, but progress is hindered by a shortage of standardized multivariate datasets on winter conditions. Addressing the winter data gap will enhance our understanding of winter ecosystem function and of lake response to environmental change. Here, we describe a dataset generated by a multi‐institutional winter sampling campaign across all five Laurentian Great Lakes and some of their connecting waters (the Great Lakes Winter Grab). The objective of Winter Grab was to characterize mid‐winter limnological conditions in the Great Lakes using standard sample collection and analysis methods. Nineteen research groups sampled 49 locations varying widely in depth and trophic status, collecting a range of limnological data. This dataset includes physical, chemical, and biological measurements. These data can be used to examine diverse aspects of Great Lakes ecosystems or integrated with winter observations from other lakes to improve understanding of winter limnology across different aquatic systems.

Crowd-sourced trait data can be used to delimit global biomes

Abstract. Terrestrial biomes and their biogeographic patterns have been derived from a large variety of variables including species distributions and bioclimate or remote sensing products. However, classifying the biosphere into biomes from a functional perspective using biophysical traits has rarely been tested. Such a trait-based biome classification has been limited by data availability. Here, we aimed to exploit crowd-sourced plant observations and trait databases to systematically assess which traits are most suitable for biome classification. We derived global patterns of 33 biophysical traits covering around 50 % of the land surface by combining crowd-sourced species distribution data from the Global Biodiversity Information Facility (GBIF) and trait observations from the TRY database. Using these trait maps as predictors for supervised cluster analyses, we tested to what extent we can reconstruct 31 published biome maps. A sensitivity analysis with randomly sampled combinations of traits was performed to identify the traits that are most appropriate for biome classification. Performance was quantified by comparing modeled biome maps and the respective observation-based biome maps. Finally, spatial gaps in the resulting biome maps were filled using species distribution models to obtain continuous global biome maps. We showed that traits can be used for biome classification and that the most appropriate traits are conduit density; rooting depth; height; and different leaf traits, including specific leaf area and leaf nitrogen content. The best performance of the biome classification was obtained for biome maps based on biogeographic zonation and species distributions, in contrast to biome maps derived from optical reflectance. The availability of crowd-sourced plant observations is heterogeneous, and, despite its exponential growth, large data gaps are prevalent. Nonetheless, it was possible to derive biome classification schemes from these data to predict global biome patterns with good agreement. Therefore, our analysis is a valuable approach towards understanding biome patterns based on biophysical traits and associated ecological strategies.

A database on pharmaceuticals in the environment: What do stakeholders need?

Pharmaceutical pollution has raised concerns about how these contaminants affect ecosystems. Data to assess the environmental risk of pharmaceuticals exist but are dispersed and not always publicly accessible. To address this issue, the European Commission recently proposed to develop a data source for human medicinal products, involving relevant stakeholders, including healthcare and industry representatives. The aim of our study was to define the user requirements for such a database on pharmaceuticals in the environment (PiE). We reached out to over 100 professionals that work on PiE, asking what data should be incorporated in the database and what features it should have. The results show that most stakeholders are affected by data gaps, mainly related to ecotoxicity, monitoring, transformation products, metabolites and removal rates in wastewater treatment plants. Interest in the mechanism of action of active pharmaceutical ingredients was specifically expressed by the pharma sector, including stakeholders related to the authorisation, production and use of medicines. Researchers and stakeholders dealing with environmental and water quality have greater interest for mass spectrum data, modelled environmental concentrations and data on transformation products and metabolites. While showing that the key actors working on PiE strongly endorse the development of an accessible and transparent database, we provide recommendations for creating such a data repository on pharmaceuticals.

Conceptual advances in petrophysical inversion techniques: The synergy of machine learning and traditional inversion models

Petrophysical inversion techniques are essential for estimating reservoir properties such as porosity, permeability, and fluid saturation, which are critical for optimizing hydrocarbon recovery and reservoir management. Traditionally, deterministic and stochastic inversion methods have relied on well log data, core samples, and seismic information to generate models of subsurface properties. However, these techniques face limitations in handling large datasets, nonlinear relationships, and uncertainties in complex reservoir environments. Recent advances in machine learning (ML) offer an opportunity to enhance these traditional methods by providing a data-driven approach capable of learning patterns and refining inversion models. This review explores the potential synergy between traditional petrophysical inversion techniques and machine learning algorithms to create a new paradigm for reservoir parameter estimation and analysis. Machine learning’s ability to process large volumes of diverse data and capture nonlinear relationships complements the physics-based constraints of traditional inversion methods. By combining these two approaches, hybrid models can provide improved accuracy in predicting reservoir properties and handling data gaps or uncertainties. The review discusses specific case studies where machine learning-assisted inversion models have been successfully applied, such as the estimation of porosity and permeability, seismic inversion for lithology identification, and fluid saturation modeling. These hybrid models demonstrate enhanced predictive accuracy, faster computational efficiency, and real-time adaptability, transforming reservoir characterization and management. The integration of machine learning with traditional inversion techniques represents a major advancement in petrophysical analysis. This synergy holds the potential to revolutionize reservoir parameter estimation, making it more accurate, robust, and responsive to real-time data, ultimately improving decision-making in reservoir engineering and maximizing hydrocarbon recovery. Keywords: Petrophysical Inversion, Machine Learning, Traditional Inversion, Models.

Gravity Predictions in Data-Missing Areas Using Machine Learning Methods

Gravity data, comprising a key foundational dataset, are crucial for various research, including land subsidence monitoring, geological exploration, and navigational positioning. However, the collection of gravity data in specific regions is difficult because of environmental, technical, and economic constraints, resulting in a non-uniform distribution of the observational data. Traditionally, interpolation methods such as Kriging have been widely used to deal with data gaps; however, their predictive accuracy in regions with sparse data still needs improvement. In recent years, the rapid development of artificial intelligence has opened up a new opportunity for data prediction. In this study, utilizing the EGM2008 satellite gravity model, we conducted a comprehensive analysis of three machine learning algorithms—random forest, support vector machine, and recurrent neural network—and compared their performances against the traditional Kriging interpolation method. The results indicate that machine learning methods exhibit a marked advantage in gravity data prediction, significantly enhancing the predictive accuracy.

Knowledge, Attitudes, and Practices Regarding Influenza Vaccination Among Healthcare Workers in Saudi Arabia: A Cross-sectional Study.

Influenza vaccination is important for healthcare workers in order to prevent both the illness itself and transmission to patients. Previous studies in Saudi Arabia have revealed low influenza vaccine coverage among healthcare workers due to misconceptions. This study aimed to assess knowledge, attitudes, and practices regarding influenza vaccination among healthcare workers during 2021, addressing the current data gap. A cross-sectional study was conducted, including 1,273 healthcare workers from the Ministry of Health in Saudi Arabia. A self-administered questionnaire was distributed to participants via email. Statistical analysis was conducted using Jamovi software (version 2.3). Ethical approval was obtained from the Ministry of Health Ethics Committee. Most participants had an appropriate extent of knowledge, with 37.1% having a high level and 26.6% having a moderate level. Positive attitudes toward the influenza vaccine were observed in 41.2% of participants, and 80.2% demonstrated good vaccine practices. However, the vaccine coverage was 51% in the past 12 months. Factors associated with vaccine uptake included previous vaccination, workplace availability, awareness of guidelines, engagement in training programs, type of workplace settings, and having positive attitudes toward the vaccine. The most common reason for not getting vaccinated was the perception of being at low risk, making vaccination unnecessary. Participants exhibited positive knowledge, attitudes, and practices regarding influenza vaccination. However, the observed vaccine uptake rate fell below the recommended coverage rate, indicating the presence of a knowledge-behavior gap. Targeted interventions are recommended to improve vaccination uptake among healthcare workers in Saudi Arabia.

Role of Tick Commensal Bacteria in the Propagation of Emerging Infectious Diseases: Data Gaps and One Health Implications

Ticks are obligate hematophagous ectoparasites notorious for their role as vectors of pathogens that affect humans and animals, particularly relative to the propagation of emerging infectious diseases (EIDs). Two important factors facilitating the role of ticks in the propagation of EIDs are their potential for the development of resistance to acaricides and the expansion of nonindigenous tick species into new geographic regions. The acquisition of acaricide resistance enables tick populations to be less susceptible to vector control programs. Expansion of the geographic distribution of tick populations increases the likelihood of access to new host species as well as new pathogens. Understanding of the microbiome of ticks continues to evolve, providing critical insights into tick biology. The tick microbiome largely comprises endosymbionts, pathogenic organisms, and commensal bacteria. Endosymbionts are highly preserved and vertically transmitted in ticks from mother to offspring, and their role in the survival of ticks is well recognized. Similarly, the role of ticks as vectors of pathogens is well established. However, commensal bacteria in ticks are acquired from the environment and while ingesting a blood meal. Because many tick species spend most of their lifetime off the hosts, it can be assumed that the richness and diversity of commensal bacteria are highly variable and dependent on the ecosystem in which the tick exists. This mini-review identifies some of the critical data gaps relevant to the role of and influence of commensal bacteria on the vectorial capacity of ticks. As ticks move into new habitats, are locally acquired commensal bacteria playing a role in adaptation to the new habitat? Apart from the conventionally understood mechanisms of acaricide resistance in ticks, are the commensal bacteria influencing the development of acaricide resistance at the population level?