Fill Data Gaps Research Articles

Developing survey instruments for low back and neck pain to improve disease severity estimates for burden of disease assessment

Abstract Two people living with the same disease can have very different experiences; one has little consequence, whilst the other struggles with much more debilitating consequences that impacts how they live their lives day-to-day. A growing body of research highlights the need for better estimates of disease severity to facilitate better estimation of the non-fatal burden of disease (BOD). Routine data does not always offer solutions because it’s often not coded at a granular enough level to map to individual health states. Data offering solutions are scarce and therefore not available across time or space. Most commentators have quantified the problem of assuming the same proportional severity splits to fill data gaps; a problem common to the Global Burden of Disease (GBD) study and independent BOD studies. However, whilst the need for highlighting the problems associated with this assumption remains, there are other positive steps that can be taken to overcome it. We propose how reproducible approaches can be developed, and present how they can be used to develop disease severity estimates. This involves developing a survey instrument to estimate the overall and health state prevalence of back pain and neck pain, basing health state definitions from the GBD non-fatal disease models, where the classification of cases by severity is based on the combination of respondent’s answers. Furthermore, we discuss how self-assessed severity estimates allow for internal validity checks, but also more widely offer opportunities to appraise the appropriateness of GBD non-fatal disease models. The development, and publication, of survey instruments can assist both independent studies and the GBD study to improve estimates of disease severity, as well as retaining important opportunities for cross-country comparability. Understanding the spectrum of severity is important for understanding how disease impacts populations and can inform healthcare provision and expenditure strategies.

Involving citizens in monitoring the Kunming–Montreal Global Biodiversity Framework

AbstractThe Kunming–Montreal Global Biodiversity Framework (GBF) and its monitoring framework aims to reverse the decline of nature. The GBF tasks governments to report progress towards 23 targets and four goals but also “invites Parties and relevant organizations to support community-based monitoring and information systems and citizen science” to improve information for decision-making and build support for conservation efforts throughout society. We assessed how Indigenous Peoples, local communities and citizen scientists and professional scientists can help monitor the GBF. Of the 365 indicators of the GBF monitoring framework, 110 (30%) can involve Indigenous Peoples, local communities and citizen scientists in community-based monitoring programmes, 185 (51%) could benefit from citizen involvement in data collection and 180 (49%) require scientists and governmental statistical organizations. A smaller proportion of indicators for GBF targets are amenable to citizen monitoring than for the previous Aichi targets or other multilateral environment agreements—largely because 196 GBF indicators are analytically complex (54%) and 175 require legislative overview (48%). Greater involvement of citizens in the GBF would increase societal engagement in international agreements, harness knowledge from those living close to nature to fill data gaps and enhance local to national decision-making based on improved information, leading to better conservation actions.

Computational Strategies for Assessing Adverse Outcome Pathways: Hepatic Steatosis as a Case Study.

The evolving landscape of chemical risk assessment is increasingly focused on developing tiered, mechanistically driven approaches that avoid the use of animal experiments. In this context, adverse outcome pathways have gained importance for evaluating various types of chemical-induced toxicity. Using hepatic steatosis as a case study, this review explores the use of diverse computational techniques, such as structure-activity relationship models, quantitative structure-activity relationship models, read-across methods, omics data analysis, and structure-based approaches to fill data gaps within adverse outcome pathway networks. Emphasizing the regulatory acceptance of each technique, we examine how these methodologies can be integrated to provide a comprehensive understanding of chemical toxicity. This review highlights the transformative impact of in silico techniques in toxicology, proposing guidelines for their application in evidence gathering for developing and filling data gaps in adverse outcome pathway networks. These guidelines can be applied to other cases, advancing the field of toxicological risk assessment.

Integrating material flow analysis and supply chain resilience analysis to study silicon carbide

AbstractSilicon carbide (SiC) is a niche nonmetallic material that is essential in many industrial processes. Here, we integrate material flow analysis and supply chain resilience analysis to understand global SiC stocks and flows and to assess its supply chain. We use industry interviews to fill data gaps and collect information on the SiC system to overcome data scarcity. We find that globally around 1000 kt of SiC is produced each year. The biggest use of SiC is the abrasives industry (40%), followed by metallurgy (28%), refractories (20%), technical ceramics (0.7%), other uses (0.7%), and semiconductors (0.01%). As an energy‐intensive material, the SiC supply chain is under pressure, increasing the relevance of resilience considerations. Besides typical supply chain risks such as low diversity of supply and geopolitical trade restrictions, SiC particularly faces risks due to its energy‐intensive production process and associated emissions. In the SiC semiconductor supply chain, losses of nearly 75% are a particular issue. Due to high demand in the SiC market, stockpiles are negligible, and substitution is difficult in most sectors. We find that in the case of SiC, sustainability measures such as use reduction, recycling, or decreasing energy use or emissions would also positively contribute to supply chain resilience. This article met the requirements for a gold‐gold JIE data openness badge described at http://jie.click/badges.

NOAAGlobalTemp version 6: An AI-based global surface temperature dataset

Abstract The National Oceanic and Atmospheric Administration (NOAA) Global Surface Temperature (NOAAGlobalTemp) dataset is widely used for scientific research, operational monitoring, and climate assessment activities. Aligning with NOAA’s mission values, NOAAGlobalTemp has been updated to version 6 (i.e., NGTv6), which includes two enhancements over its predecessor (NGTv5). The first enhancement is the expansion of the spatial coverage to encompass the entire globe and the extension of temporal coverage back to 1850 (an interim version of NOAAGlobalTemp with these features was released in February 2023). The expansion of spatial coverage is accomplished by utilizing surface air temperatures over the Arctic Ocean and by eliminating the data reconstruction mask used in NGTv5 that had suppressed interpolation in data-sparse regions. This change has important implications for global temperature trends since the Arctic region has been warming at a much faster pace, more than four times the global average, in the twenty-first century to date. The second enhancement is the implementation of a methodology based on artificial intelligence (AI) for reconstructing surface air temperature over the global land surface and the Arctic Ocean. The AI model employs an artificial neural network to fill data gaps and is demonstrated to be more robust, stable, and accurate than the previous gap-filling method, particularly in observation-sparse areas such as the polar regions. The model outperforms the previous approach across all evaluated statistical metrics, and the output reaches a stable state more quickly as observations are received, which facilitates climate monitoring.

Explainable Ensemble Learning Approaches for Predicting the Compression Index of Clays

Accurate prediction of the compression index (cc) is essential for geotechnical infrastructure design, especially in clay-rich coastal regions. Traditional methods for determining cc are often time-consuming and inconsistent due to regional variability. This study presents an explainable ensemble learning framework for predicting the cc of clays. Using a comprehensive dataset of 1080 global samples, four key geotechnical input variables—liquid limit (LL), plasticity index (PI), initial void ratio (e0), and natural water content w—were leveraged for accurate cc prediction. Missing data were addressed with K-Nearest Neighbors (KNN) imputation, effectively filling data gaps while preserving the dataset’s distribution characteristics. Ensemble learning techniques, including Random Forest (RF), Gradient Boosting Decision Trees (GBDT), Extreme Gradient Boosting (XGBoost), and a Stacking model, were applied. Among these, the Stacking model demonstrated the highest predictive performance with a Root Mean Squared Error (RMSE) of 0.061, a Mean Absolute Error (MAE) of 0.043, and a Coefficient of Determination (R2) value of 0.848 on the test set. Model interpretability was ensured through SHapley Additive exPlanations (SHAP), with e0 identified as the most influential predictor. The proposed framework significantly improves both prediction accuracy and interpretability, offering a valuable tool to enhance geotechnical design efficiency in coastal and clay-rich environments.

Higher education of sustainable food systems: a literature review

PurposeThis study aims to fill data gaps concerning solutions and practices used in sustainable food systems (SFS) in higher education (HE). The development of SFS is a vital global challenge in which HE may play a significant role.Design/methodology/approachLiterature search and content analysis of found papers were performed. Additionally, the quantitative time trend of the emergence of research connected to SFS HE and the connectivity of content within the research papers about research questions were determined by regression analysis and data visualization, respectively.FindingsIt is evident that SFS education in universities (higher education institutes) is emerging, and the number of research papers is rapidly increasing. In the reviewed papers, universities recognized their significant role in managing wicked problems. The motivation for developing SFS education was high, with strong ambitions. SFS in HE includes developing education in-house through trans- and multidisciplinary solutions, developing education with stakeholders and supporting student growth to become responsible professionals and citizens.Practical implicationsWhen developing SFS education in HE both practical and theoretical research is needed. Ethical dimensions should be included in both research orientations because of the moral complexity that exists in SFS issues.Social implicationsThis study shows that competence in trans- and multidisciplinary working is needed. Concomitantly, the ability of cooperation between HE, business and society is vital when solving global food challenges. Also, the local tradition of food cultivation should be respected and maintained.Originality/valueTo the best of the authors’ knowledge, this is the first review on the development of SFS education in HEIs. A qualitative content analysis and data visualization were used to enrich the review.

Providing data analytic services for national knowledge users in a federated system: learnings from two Canadian use cases

Objective and ApproachCanada’s federated health data system along with access pathways geared toward academic research, pose challenges for knowledge Users (KUs) requiring timely, pan-Canadian evidence to inform decisions. To understand challenges and explore solutions, we conducted two use cases for a pan-Canadian health technology assessment organization and a federal agency, using health administrative data at one federal and six provincial data centres. The population-based cohort studies described socio-demographics, comorbidities, treatment patterns, service utilization and costs. One focused on spinal muscular atrophy, a rare disease and the other on dementia, a complex chronic disease. ResultsAdministrative challenges included aligning varied ethical review and data access policies/procedures including requiring local and/or academic principal investigators and differing definitions of “research” vs. planning, evaluation, and monitoring. Data-related challenges included differences in structure, timeliness, and completeness across regions resulting in difficulty aligning constructs such as incident cases and episodes of care. Privacy requirements prohibited pooling jurisdictional estimates resulting in “small cells” that couldn’t be shared with KUs. ConclusionsWe provided analytic outputs from multiple regions, albeit with some differences and gaps, increasing knowledge around both diseases while developing capacity for combined analyses and gaining insight into national possibilities for data access. ImplicationsAs decision makers must rely on best available information, some data is better than none. However, to improve data-analytic services for Pan-Canadian KUs, next steps will include improving data harmonization, expanding data assets and filling data gaps, implementing common data models, and exploring options for federated and/or pooled analyses.

Herbarium collections remain essential in the age of community science

The past decade has yielded more biodiversity observations from community science than the past century of traditional scientific collection. This rapid influx of data is promising for overcoming critical biodiversity data shortfalls, but we also have vast untapped resources held in undigitized natural history collections. Yet, the ability of these undigitized collections to fill data gaps, especially compared against the constant accumulation of community science data, remains unclear. Here, we compare how well community science (iNaturalist) observations and digitized herbarium specimens represent the diversity, distributions, and modeling needs of vascular plants in Canada. We find that, despite having only a third as many records, herbarium specimens capture more taxonomic, phylogenetic, and functional diversity and more efficiently capture species’ environmental niches. As such, the digitization of Canada’s 7.3M remaining specimens has the potential to more than quintuple our ability to model biodiversity. In contrast, it would require over 27M more iNaturalist observations to produce similar benefits. Our findings indicate that digitizing Earth’s remaining herbarium specimens is likely an efficient, feasible, and potentially critical investment when it comes to improving our ability to predict and protect biodiversity into the future.

The maternal and newborn health eCohort to track longitudinal care quality: study protocol and survey development.

The MNH eCohort was developed to fill gaps in maternal and newborn health (MNH) care quality measurement. In this paper, we describe the survey development process, recruitment strategy, data collection procedures, survey content and plans for analysis of the data generated by the study. We also compare the survey content to that of existing multi-country tools on MNH care quality. The eCohort is a longitudinal mixed-mode (in-person and phone) survey that will recruit women in health facilities at their first antenatal care (ANC) visit. Women will be followed via phone survey until 10-12 weeks postpartum. User-reported information will be complemented with data from physical health assessments at baseline and endline, extraction from MNH cards, and a brief facility survey. The final MNH eCohort instrument is centered around six key domains of high-quality health systems including competent care (content of ANC, delivery, and postnatal care for the mother and newborn), competent systems (prevention and detection, timely care, continuity, integration), user experience, health outcomes, confidence in the health system, and economic outcomes. The eCohort combines the maternal and newborn experience and, due to its longitudinal nature, will allow for quality assessment according to specific risks that evolve throughout the pregnancy and postpartum period. Detailed information on medical and obstetric history and current health status of respondents and newborns will allow us to determine whether women and newborns at risk are receiving needed care. The MNH eCohort will answer novel questions to guide health system improvements and to fill data gaps in implementing countries.

Mapping safe drinking water use in low- and middle-income countries.

Safe drinking water access is a human right, but data on safely managed drinking water services (SMDWS) is lacking for more than half of the global population. We estimate SMDWS use in 135 low- and middle-income countries (LMICs) at subnational levels with a geospatial modeling approach, combining existing household survey data with available global geospatial datasets. We estimate that only one in three people used SMDWS in LMICs in 2020 and identified fecal contamination as the primary limiting factor affecting almost half of the population of LMICs. Our results are relevant for raising awareness about the challenges and limitations of current global monitoring approaches and demonstrating how globally available geospatial data can be leveraged to fill data gaps and identify priority areas in LMICs.

Analyses of groundwater level in a data-scarce region based on assessed precipitation products and machine learning

Groundwater, a vital natural resource globally, faces challenges related to data scarcity, particularly in data-limited regions. To address this issue in the region of Bahira in Morocco, we developed an innovative approach for estimating groundwater levels by utilizing precipitation products and employed a random forest (RF) machine learning (ML) model to fill data gaps. Our study involved a comprehensive assessment of ten precipitation products, comprising six Reanalysis Precipitation Products (RPPs) and four Satellite-based Precipitation Products (SPPs). We evaluated their performance across various time scales, including daily, monthly, and seasonal, across different topographical classes. The outcomes highlighted the consistency of ERA5-based datasets, boasting daily correlation to values higher than 0.6, whereas monthly and seasonal correlations exceed 0.8, except during summer. GPM-IMERG, MERRA2, and CPC-UPP also demonstrated commendable accuracy, particularly in plain and mountainous areas. Nonetheless, CFSR, CHIRPS, PERSIANN CDR, and TRMM datasets exhibited limitations, particularly in high mountain areas. To address data gaps, we initially explored correlations between RPPs, SPPs, and groundwater data. However, these correlations failed to meet the accuracy standards required for precise predictions. Notably, the strongest correlations were observed in monitoring stations located in mountainous regions, indicating significant aquifer recharge activities in these areas. In the subsequent phase, the Multiple Imputation by Chained Equations (MICE) machine learning-based imputation method served as a valuable tool for estimating groundwater levels in regions where ground observations were insufficient. Our trend analysis yielded significant insights, with approximately 95% of groundwater points displaying negative trends, with a maximum rate of −0.91 m. In contrast, 69% of precipitation stations exhibited negative trends, with a maximum rate of −0.06 mm. Our approach offers a promising potential to address the challenges associated with the scarcity of groundwater and precipitation data, making it a valuable tool for the assessment, monitoring, and management of groundwater resources.

Limitations of recent cellulose studies: How overzealous dispersion technique, fluorescence labeling and interpretation limit the findings of recent safety assessments for cellulose materials

Microfibrillated, nanofibrillated, and nanocrystalline cellulose materials are next-generation biobased cellulose materials with applications in the food industry as food contact materials and functional ingredients. Manufacturers have adopted a proactive approach to demonstrate the safety of these cellulose materials as they contribute toward a more sustainable economy. Animal studies and alternative testing approaches are needed to fill data gaps in the literature for the physicochemical and toxicological characterization of cellulose materials. Recently, the European Food Safety Authority (EFSA) funded a project that used New Approach Methodologies (NAMs) to assess the hazards of nanofibers, including methods to evaluate simulated gastrointestinal digestion, cellular uptake, local effects via simulated oral exposure, and gut-on-a-chip toxicity models. Upon review, we identified methodological and analytical limitations in these studies, which may affect the study conclusions that oral exposure to cellulose materials is associated with adverse effects. The aim of this Short Communication is to discuss the limitations of the reported findings in the NAM study and provide a balanced perspective of the study and of the safety of cellulose materials. This Short Communication highlights a major concern that the NAM study was published in the public domain without any peer review from cellulose experts. We identify and discuss several major limitations, including an unrealistic dispersion method, lack of controls in fluorescent labeling, lack of proper control groups in the experimental design, incomplete reporting of statistical analyses, bias toward reporting adverse effects, and inconsistent explanations of discordant results within the greater scientific literature.

Depredation: An old conflict with the sea

AbstractDepredation (the partial or complete removal of a hooked species by a non‐target species) is a human–wildlife conflict as old as humans and the sea. In some ways, depredation is no different today than it was a century ago. But in many ways, this conflict has become more complicated. Following three decades of successful management, some US shark populations have begun to rebuild. However, many anglers attribute perceived increases in shark depredation to management measures, claiming they have led to ‘overpopulation’ of sharks and/or learned behaviour by sharks. We investigated whether these factors could explain the reported increases in depredation. Based on fishery‐independent surveys, neither shark population increases nor learned behaviour by sharks is evident. However, increases in angler effort provide an alternative explanation that is not often considered. While far from a smoking gun, at least four themes emerge from this thought exercise. First, it is important to understand historical predator baselines. Second, it is important to acknowledge lifting baselines, that is, instances where previously depleted populations are recovering. Third, it is important to remember that there are many instances when stakeholder observations were initially misaligned with traditional scientific observations but were ultimately recognized as pivotal for filling data gaps. Finally, and perhaps most important, is the acknowledgement that perceived conflict is as potent as real conflict. Arguably, it may not matter if depredation has increased or decreased; the overwhelming perception from stakeholders is an increase in depredation, and this is the perceived (or real) conflict that must be addressed.

Filling data gaps in long-term solar UV monitoring by statistical imputation methods

Knowledge of long-term time trends of solar ultraviolet (UV) radiation on ground level is of high scientific interest. For this purpose, precise measurements over a long time are necessary. One of the challenges solar UV monitoring faces is the permanent and gap-free data collection over several decades. Data gaps hamper the formation and comparison of monthly or annual means, and, in the worst case, lead to incorrect conclusions in further data evaluation and trend analysis of UV data. For estimating data to fill gaps in long-term UV data series (daily radiant exposure and highest daily irradiance), we developed three statistical imputation methods: a model-based imputation, considering actual local solar radiation conditions using predictors correlated to the local UV values in an empirical model; an average-based imputation based on a statistical approach of averaging available local UV measurement data without predictors; and a mixture of these two imputation methods. A detailed validation demonstrates the superiority of the model-based imputation method. The combined method can be considered the best one in practice. Furthermore, it has been shown that the model-based imputation method can be used as an useful tool to identify systematic errors at and between calibration steps in long-term erythemal UV data series.

A probabilistic extension of existing site-specific liquefaction triggering and liquefaction manifestation methods for regional scale evaluations

Liquefaction hazard analyses are needed for regional-scale applications, such as risk analysis and upgrades of electrical transmission, water conveyance, and highway systems. In this paper, we expand well-established site-specific liquefaction triggering and manifestation analysis methods for regional scale application by explicitly addressing the uncertainty in groundwater conditions, soil behavior classification, and cyclic resistance. The uncertainty inherent to regional-scale geotechnical properties is quantified by considering the inputs into conventional liquefaction triggering and manifestation analyses as random variables. With this flexible and extensional formulation, the geostatistical properties needed for regional scale analyses may be estimated by either averaging borehole information or spatially interpolating geotechnical properties across broad geologic deposits. We demonstrate the utility of the method by analyzing the liquefaction hazard throughout the south San Francisco Bay area, California, U.S.A. The resulting estimates of the probability of liquefaction manifestation tend to be lower than previously published estimates, largely owing to the geologic structure of the area and the uncertainty in liquefaction surface manifestation once liquefaction is triggered at depth. Uncertainties in groundwater conditions, soil behavior classification, penetration resistance, and liquefaction manifestation are quantified by the regional scale analyses, which provide a complete and systematic evaluation of the liquefaction hazards, localize the uncertainties in subsurface conditions, and facilitate supplemental investigations at areas of high uncertainty to fill data gaps.

Perfluoroalkyl substances (PFAS) occurrence, concentrations and spatial distribution along the French Mediterranean coast and lagoons, based on active biomonitoring

Tracking PFAS in ecosystems is challenging. In this context, monitoring programs are crucial to fill data gaps, especially in marine environments, which are the ultimate outlets for these forever chemicals. The 2021 chemical contamination monitoring campaign along the French Mediterranean coast established a baseline for PFAS concentrations in mussels, with 90 % of measurements below quantification limits. When detected, long-chain PFCA's were predominant. Spatial distribution patterns suggested continuous PFAS inputs and complex dynamics, shaped by the influence of large watersheds and rivers (Rhône, Aude, Huveaune). Lapeyrade shallow lagoon stood out as the most contaminated site. Similar PFAS profiles in connected sites implied shared sources but raised questions about accumulation processes in mussels. While certain sites had evident sources (e.g., military airbase for Palo lagoon), others remained uncertain (e.g., Toulon bay). Coastal stations (Banyuls, Cap Agde, Brégançon, Pampelonne) showed PFAS contamination without clear onshore sources, possibly due to insufficient transportation process understanding.

Prediction of physicochemical and pharmacokinetic properties of botanical constituents by computational models.

Botanicals contain complex mixtures of chemicals most of which lack pharmacokinetic data in humans. Since physicochemical and pharmacokinetic properties dictate the in vivo exposure of botanical constituents, these parameters greatly impact the pharmacological and toxicological effects of botanicals in consumer products. This study sought to use computational (i.e., in silico) models, including quantitative structure-activity relationships (QSAR) and physiologically based pharmacokinetic (PBPK) modeling, to predict properties of botanical constituents. One hundred and three major constituents (e.g., withanolides, mitragynine, and yohimbine) in 13 botanicals (e.g., ashwagandha, kratom, and yohimbe) were investigated. The predicted properties included biopharmaceutical classification system (BCS) classes based on aqueous solubility and permeability, oral absorption, liver microsomal clearance, oral bioavailability, and others. Over half of these constituents fell into BCS classes I and II at dose levels no greater than 100 mg per day, indicating high permeability and absorption (%Fa > 75%) in the gastrointestinal tract. However, some constituents such as glycosides in ashwagandha and Asian ginseng showed low bioavailability after oral administration due to poor absorption (BCS classes III and IV, %Fa < 40%). These in silico results fill data gaps for botanical constituents and could guide future safety studies. For example, the predicted human plasma concentrations may help select concentrations for in vitro toxicity testing. Additionally, the in silico data could be used in tiered or batteries of assays to assess the safety of botanical products. For example, highly absorbed botanical constituents indicate potential high exposure in the body, which could lead to toxic effects.

Data Drought in the Humid Tropics: How to Overcome the Cloud Barrier in Greenhouse Gas Remote Sensing

AbstractDiagnosing land‐atmosphere fluxes of carbon‐dioxide (CO2) and methane (CH4) is essential for evaluating carbon‐climate feedbacks. Greenhouse gas satellite missions aim to fill data gaps in regions like the humid tropics but obtain very few valid measurements due to cloud contamination. We examined data yields from the Orbiting Carbon Observatory alongside Sentinel‐2 cloud statistics. We find that the main contribution to low data yields are frequent shallow cumulus clouds. In the Amazon, the success rate in obtaining valid measurements vary from 0.1% to 1.0%. By far the lowest yields occur in the wet season, consistent with Sentinel‐2 cloud patterns. We find that increasing the spatial resolution of observations to ∼200 m would increase yields by 2–3 orders of magnitude and allow regular measurements in the wet season. Thus, the key to effective tropical greenhouse gas observations lies in regularly acquiring high‐spatial resolution data.

Monsoon-Based Linear Regression Analysis for Filling Data Gaps in Gravity Recovery and Climate Experiment Satellite Observations

Over the past two decades, the Gravity Recovery and Climate Experiment (GRACE) satellite mission and its successor, GRACE-follow on (GRACE-FO), have played a vital role in climate research. However, the absence of certain observations during and between these missions has presented a persistent challenge. Despite numerous studies attempting to address this issue with mathematical and statistical methods, no definitive optimal approach has been established. This study introduces a practical solution using Linear Regression Analysis (LRA) to overcome data gaps in both GRACE data types—mascon and spherical harmonic coefficients (SHCs). The proposed methodology is tailored to monsoon patterns and demonstrates efficacy in filling data gaps. To validate the approach, a global analysis was conducted across eight basins, monitoring changes in total water storage (TWS) using the technique. The results were compared with various geodetic products, including data from the Swarm mission, Institute of Geodesy and Geoinformation (IGG), Quantum Frontiers (QF), and Singular Spectrum Analysis (SSA) coefficients. Artificial data gaps were introduced within GRACE observations for further validation. This research highlights the effectiveness of the monsoon method in comparison to other gap-filling approaches, showing a strong similarity between gap-filling results and GRACE’s SHCs, with an absolute relative error approaching zero. In the mascon approach, the coefficient of determination (R2) exceeded 91% for all months. This study offers a readily usable gap-filling product—SHCs and smoothed gridded observations—with accurate error estimates. These resources are now accessible for a wide range of applications, providing a valuable tool for the scientific community.