Data Structure Research Articles

Exploring the significance and priority of digital product passports implementation in building renovation projects targeting circular economy

PurposeThis study explores the significance and implementation priorities for Digital Product Passports (DPP) in the context of building renovation projects. It aims to reveal bottlenecks and how a data-driven workflow bridges the DPP understanding/implementation gap, facilitating the transition towards practices aligned with the EU Green Deal goals.Design/methodology/approachA mixed-methods embedded design was employed for a real-case study exploration. Desk research and field observations ground the two-level analysis combining project documentation, namely the Bill of Quantities (BoQ), with different criteria in digitalisation and sustainability, such as economic ratio, 3D modelling, waste management, hazards, energy performance and facility management. All results were interpreted from the DPP lens.FindingsThe analysis revealed a system for identifying building products representing a significant part of the renovation budget. About 11 priority DPPs were found. Some are crucial for both the deconstruction and construction phases, highlighting the need for an incremental and strategic approach to DPP implementation.Research limitations/implicationsThe study is limited to a single case study. Constraints are minimised given the sample's archetype representativeness. The outcomes introduce the need for strategic thinking for incremental DPP implementation. Future research will explore additional criteria and cases.Originality/valueThe research has resulted in a classification framework for DPPs' significance and priority, which is provided with case results. The outcome of the framework provides views on concept alignment to make the implementation in construction more straightforward. Its practical use can be replicated in other projects, emphasizing the importance of data structure and management for the circular economy.

The role of fault pathways for modern and deep recharge to a semi-confined aquifer: revised analysis of modern water leakage pathways in the Davis wellfield, Memphis, Tennessee, USA

Hydrologic tracer, borehole log and geophysical data provide new insight into recharge sources and pathways to the semi-confined Memphis aquifer in the Davis wellfield, Memphis, Tennessee, U.S.A. New data and recent studies highlight the importance of faults as potential pathways for the migration of modern water into the Memphis aquifer, an important regional public, industrial, and agricultural water supply. Geochemical and environmental tracer data collected over the past 28 years, combined with hydrologic, stratigraphic, and structural data, were used to evaluate changes in water quality and different recharge pathways to the Memphis aquifer. Assessment of stratigraphic and structural relationships argue for the wellfield being bound by a fault along the bluff line (west) and another fault or faults along the eastern and southeastern margins. Modern water recharge (< 60 years old) occurs through the fault along the western boundary of the wellfield, whereas modern water and, seasonally, deep-seated (helium-bearing) fluids recharge along the fault on the southeastern margin. Each recharge source and pathway present different vulnerabilities, suggesting that the role of faults in wellfields within active tectonic regions needs to be assessed.

Training of Teachers for Cadet Classes: Approaches and Successful Practices

The article offers an analysis of approaches and successful practices of teacher training for cadet classes. Various groups of methods were used in the course of the study, including the method of comparative analysis of normative and legal documents; analysis, systematization and generalization of the data obtained; complex analysis, classification and structuring of information and statistical data; modeling method; generalization of research results. As a result, system solutions for the training of teachers of cadet classes have been developed and theoretically substantiated. It should be noted that one of the conceptual ideas of teacher training for cadet classes is the idea of continuity and individualization of professional development of teachers in the field of education and socialization of students. Continuous professional development of teachers, including teachers of cadet classes, is carried out through the integration of formal and non-formal education, as well as through self-education and self-education. Teachers of cadet classes should have the competencies necessary for the full implementation of professional functions in the organization of education and socialization of students, since the teacher of the cadet class works not only with students, but also with cadet families, focusing on issues of social protection, education, formation of healthy public opinion and mood. The tasks of the educator of the training course include studying the mood, requests and needs of child-parent relations in cadet families and systematically informing the management and specialists of various services about the urgent tasks of working with cadet parents. The training of teachers of cadet classes is focused on the formation of a number of skills among educators: to analyze the academic performance and involvement of students in the educational process, to build effective communication, to organize activities to prevent negative manifestations and offenses.

Efficient Data Exchange between WebAssembly Modules

In the past two decades, there has been a noticeable decoupling of machines and operating systems. In this context, WebAssembly has emerged as a promising alternative to traditional virtual machines. Originally designed for execution in web browsers, it has expanded to executing code in restricted and secure environments, and it stands out for its rapid startup, small footprint, and portability. However, WebAssembly presents significant challenges in data transfer and the management of interactions with the module. Its specification requires each module to have its own memory, resulting in a “share-nothing” architecture. This restriction, combined with the limitations of importing and exporting functions that only handle numerical values, and the absence of an application binary interface (ABI) for sharing more complex data structures, leads to efficiency problems; these are exacerbated by the variety of programming languages that can be compiled and executed in the same environment. To address this inefficiency, the Karmem was designed and developed. It includes a new interface description language (IDL) aimed at facilitating the definition of data, functions, and relationships between modules. Additionally, a proprietary protocol—an optimized ABI for efficient data reading and minimal decoding cost—was created. A code generator capable of producing code for various programming languages was also conceived, ensuring harmonious interaction with the ABI and the foreign function interface. Finally, the compact runtime of Karmem, built atop a WebAssembly runtime, enables communication between modules and shared memory. Results of the experiments conducted show that Karmem represents an innovation in data communication for WASM in multiple environments and demonstrates the ability to overcome challenges of efficiency and interoperability.

Real-world data to support post-market safety and performance of embolization coils: evidence generation from a medical device manufacturer and data institute partnership

BackgroundRecognizing the limitations of pre-market clinical data, regulatory authorities have embraced total product lifecycle management with post-market surveillance (PMS) data to assess medical device safety and performance. One method of proactive PMS involves the analysis of real-world data (RWD) through retrospective review of electronic health records (EHR). Because EHRs are patient-centered and focused on providing tools that clinicians use to determine care rather than collecting information on individual medical products, the process of transforming RWD into real-world evidence (RWE) can be laborious, particularly for medical devices with broad clinical use and extended clinical follow-up. This study describes a method to extract RWD from EHR to generate RWE on the safety and performance of embolization coils.MethodsThrough a partnership between a non-profit data institute and a medical device manufacturer, information on implantable embolization coils’ use was extracted, linked, and analyzed from clinical data housed in an electronic data warehouse from the state of Indiana’s largest health system. To evaluate the performance and safety of the embolization coils, technical success and safety were defined as per the Society of Interventional Radiology guidelines. A multi-prong strategy including electronic and manual review of unstructured (clinical chart notes) and structured data (International Classification of Disease codes), was developed to identify patients with relevant devices and extract data related to the endpoints.ResultsA total of 323 patients were identified as treated using Cook Medical Tornado, Nester, or MReye embolization coils between 1 January 2014 and 31 December 2018. Available clinical follow-up for these patients was 1127 ± 719 days. Indications for use, adverse events, and procedural success rates were identified via automated extraction of structured data along with review of available unstructured data. The overall technical success rate was 96.7%, and the safety events rate was 5.3% with 18 major adverse events in 17 patients. The calculated technical success and safety rates met pre-established performance goals (≥ 85% for technical success and ≤ 12% for safety), highlighting the relevance of this surveillance method.ConclusionsGenerating RWE from RWD requires careful planning and execution. The process described herein provided valuable longitudinal data for PMS of real-world device safety and performance. This cost-effective approach can be translated to other medical devices and similar RWD database systems.

A modal momentum method for the identification of structural weak links

In order to meet the demands of weak-link identification of mechanical structures in high-precision equipment, this paper introduces the concepts and equations of modal momentum and modal momentum difference ratio (MMDR), as well as an identification method of weak links based on modal momentum. In this method, the modal data of the mechanical structure is acquired first through experimental modal analysis or finite element analysis. The modal momentum of each measured point is calculated based on the modal data and transformed to the global coordinate system. Then, the MMDRs between the measured point with the largest magnitude of modal momentum and its adjacent measured points are calculated. Weak links are judged if the MMDR exceeds a predefined threshold. Twelve different multi-degrees-of-freedom systems incorporating weak links were designed and then identified using the proposed method. The accuracy of identification is up to 100% for these systems. The method was applied to a double pendulum angle milling head of a five-axis numerically controlled machine tool, and the result reveals that the side plate lacks bending stiffness.

A Knowledge Graph-Based Consistency Detection Method for Network Security Policies

Network security policy is regarded as a guideline for the use and management of the network environment, which usually formulates various requirements in the form of natural language. It can help network managers conduct standardized network attack detection and situation awareness analysis in the overall time and space environment of network security. However, in most cases, due to configuration updates or policy conflicts, there are often differences between the real network environment and network security policies. In this case, the consistency detection of network security policies is necessary. The previous consistency detection methods of security policies have some problems. Firstly, the detection direction is single, only focusing on formal reasoning methods to achieve logical consistency detection and solve problems. Secondly, the detection policy field is not comprehensive, focusing only on a certain type of problem in a certain field. Thirdly, there are numerous forms of data structures used for consistency detection, and it is difficult to unify the structured processing and analysis of rule library carriers and target information carriers. With the development of intelligent graph and data mining technology, the above problems have the possibility of optimization. This article proposes a new consistency detection approach for network security policy, which uses an intelligent graph database as a visual information carrier, which can widely connect detection information and achieve comprehensive detection across knowledge domains, physical devices, and detection methods. At the same time, it can also help users grasp the security associations with the real network environment based on the graph algorithm of the knowledge graph and intelligent reasoning. Furthermore, these actual network situations and knowledge bases can help managers improve policies more tailored to local conditions. This article also introduces the consistency detection process of typical cases of network security policies, demonstrating the practical details and effectiveness of this method.

Geographical Entity Management Model Based on Multi-Classification

Scientific and logical classification is crucial for efficient information storage, management, and sharing. However, there are numerous existing classification systems for geographical entities, and the categories to which the same geographical entity belongs are often different in the business databases constructed according to different classification systems, which brings great obstacles to the management and sharing of geographical information. This study analyzes the complexities of multiple classifications of geographical entities and proposes a multi-classification model for geographical entities based on directed hypergraph theory. This model integrates and transforms different classification systems for the same geographical entity, creating a unified method for expressing multiple classifications. We also designed a data structure to support this unified expression. By implementing this model, the study enables the effective management of geographical entity data, facilitating improved sharing and the exchange of geographical information across different industries and applications. In practical, the multi-classification model proposed in this paper allows geographical entities from different classification systems to be stored and managed within a single geographical database. Data views are then used to provide tailored services to various industry sectors and business applications. This approach effectively reduces data duplication and enhances the efficiency of managing and sharing geographical information. Using land use classification as an example, this study constructs a unified expression of three different land use classification systems based on the multi-classification model. An experiment managing land use data for a specific city was conducted using this model in PostgreSQL. The results indicate that the proposed method not only reduces data redundancy but also improves the query efficiency by over 10% on average compared to the mainstream relational database management mode. This confirms the effectiveness and practical value of the proposed method.

P-Aryl-Substituted Iminopyridine Chromium Complexes as Precatalysts for Ethylene Polymerization: The Question of the Physical Oxidation State Examined by Experimental and Density Functional Study in the Absence of Structural Data

<i>p</i>-Aryl-Substituted Iminopyridine Chromium Complexes as Precatalysts for Ethylene Polymerization: The Question of the Physical Oxidation State Examined by Experimental and Density Functional Study in the Absence of Structural Data

A 3D Seismotectonic Model and the Spatiotemporal Relationship of Two Historical Large Earthquakes in the Linfen Basin, North China

The Shanxi Graben is a transitional zone between the Ordos Block and North China Plain with complex structures and frequent earthquakes. Six earthquakes with M ≥ 7.0 have been recorded in the area, including the 1303 Hongtong M 8 and 1695 Linfen M 7.8 earthquakes in the Linfen Basin. Research on these two large earthquakes, closely related in time and space, is lacking. Our objective was to use deep seismic reflection profiles and 3D velocity structure data from previous research, along with seismological observation results, to interpret the geological structure near the source of the two earthquakes. A 3D geometric model of the seismogenic fault was constructed, and the relationships among the deep and shallow structures, deep seismogenic environment, and two large earthquakes were explored. Differences in seismogenic environment between the southern and northern Linfen Basin were identified. The distribution of small earthquakes in the southern Linfen Basin was scattered, and the overall distribution was at depths &lt;25 km. The small earthquakes in the northern part of the basin were dense and concentrated at depths of 25–35 km. Low-velocity layers at an approximate depth of 15–20 km in the southern basin led to differences in seismogenesis between the two regions. Based on the area of the 3D geometric model of the Huoshan Fault, the maximum magnitude of an earthquake caused by fault rupture is Mw 7.7, so the magnitude of the 1303 Hongtong earthquake might be overestimated. Numerical simulation results of Coulomb stress showed that the 1303 Hongtong earthquake had a stress-loading effect on the 1695 Linfen earthquake. The change in Coulomb rupture stress was 1.008–2.543 bar, which is higher than the generally considered earthquake trigger threshold (0.1 bar). We created a new 3D source model of large earthquakes in the Linfen Basin, Shanxi Province, providing a reference and typical cases for risk assessment of large earthquakes in different regions of the Shanxi Graben.

Leveraging gene correlations in single cell transcriptomic data

BackgroundMany approaches have been developed to overcome technical noise in single cell RNA-sequencing (scRNAseq). As researchers dig deeper into data—looking for rare cell types, subtleties of cell states, and details of gene regulatory networks—there is a growing need for algorithms with controllable accuracy and fewer ad hoc parameters and thresholds. Impeding this goal is the fact that an appropriate null distribution for scRNAseq cannot simply be extracted from data in which ground truth about biological variation is unknown (i.e., usually).ResultsWe approach this problem analytically, assuming that scRNAseq data reflect only cell heterogeneity (what we seek to characterize), transcriptional noise (temporal fluctuations randomly distributed across cells), and sampling error (i.e., Poisson noise). We analyze scRNAseq data without normalization—a step that skews distributions, particularly for sparse data—and calculate p values associated with key statistics. We develop an improved method for selecting features for cell clustering and identifying gene–gene correlations, both positive and negative. Using simulated data, we show that this method, which we call BigSur (Basic Informatics and Gene Statistics from Unnormalized Reads), captures even weak yet significant correlation structures in scRNAseq data. Applying BigSur to data from a clonal human melanoma cell line, we identify thousands of correlations that, when clustered without supervision into gene communities, align with known cellular components and biological processes, and highlight potentially novel cell biological relationships.ConclusionsNew insights into functionally relevant gene regulatory networks can be obtained using a statistically grounded approach to the identification of gene–gene correlations.

Functional network modules overlap and are linked to interindividual connectome differences during human brain development.

The modular structure of functional connectomes in the human brain undergoes substantial reorganization during development. However, previous studies have implicitly assumed that each region participates in one single module, ignoring the potential spatial overlap between modules. How the overlapping functional modules develop and whether this development is related to gray and white matter features remain unknown. Using longitudinal multimodal structural, functional, and diffusion MRI data from 305 children (aged 6 to 14 years), we investigated the maturation of overlapping modules of functional networks and further revealed their structural associations. An edge-centric network model was used to identify the overlapping modules, and the nodal overlap in module affiliations was quantified using the entropy measure. We showed a regionally heterogeneous spatial topography of the overlapping extent of brain nodes in module affiliations in children, with higher entropy (i.e., more module involvement) in the ventral attention, somatomotor, and subcortical regions and lower entropy (i.e., less module involvement) in the visual and default-mode regions. The overlapping modules developed in a linear, spatially dissociable manner, with decreased entropy (i.e., decreased module involvement) in the dorsomedial prefrontal cortex, ventral prefrontal cortex, and putamen and increased entropy (i.e., increased module involvement) in the parietal lobules and lateral prefrontal cortex. The overlapping modular patterns captured individual brain maturity as characterized by chronological age and were predicted by integrating gray matter morphology and white matter microstructural properties. Our findings highlight the maturation of overlapping functional modules and their structural substrates, thereby advancing our understanding of the principles of connectome development.

Structure‐Based Design and in‐Flow Synthesis of Aromatic Endoperoxides Acting as Oxygen Releasing Agents

AbstractHypoxia occurs in different pathological settings as a consequence of poor vascularization, and it results in reduced efficacy of some current nosocomial treatments i. e. chemotherapy and photodynamic therapy. In order to overcome these boundaries, aromatic endoperoxides (EPOs) have been studied and proposed as oxygen‐releasing agents (ORAs) due to their ability to reversibly bind molecular oxygen (O2), liberating it upon suitable triggering. DFT calculation of the dissociation energy (Ediss) of the intramolecular O−O bridge and structural crystallographic data of synthesized and studied EPOs drove the design of an array of 9,10‐disubstituted anthracenes among which three candidates were carefully selected. Once optimized the synthesis of the aromatic substrates, for the first time the corresponding EPOs have been produced under continuous flow irradiation in the presence of a sub‐stoichiometric amount of a photosensitizer in organic solvents. The release of O2 could be obtained straightforwardly at 37.5 °C by thermolysis. In accordance with the calculated Ediss=3.2 kcal mol−1 and an experimental t1/2=40 minutes, 3,3′‐(anthracene‐9,10‐diyl)bis(prop‐2‐yn‐1‐ol) resulted as the best candidate for the sustained release of O2 under physiologically relevant conditions. Its exploitation as ORA to relieve hypoxia will be evaluated and described in due course.

Influence of Preprocessing Methods of Automated Milking Systems Data on Prediction of Mastitis with Machine Learning Models

Missing data and class imbalance hinder the accurate prediction of rare events such as dairy mastitis. Resampling and imputation are employed to handle these problems. These methods are often used arbitrarily, despite their profound impact on prediction due to changes caused to the data structure. We hypothesize that their use affects the performance of ML models fitted to automated milking systems (AMSs) data for mastitis prediction. We compare three imputations—simple imputer (SI), multiple imputer (MICE) and linear interpolation (LI)—and three resampling techniques: Synthetic Minority Oversampling Technique (SMOTE), Support Vector Machine SMOTE (SVMSMOTE) and SMOTE with Edited Nearest Neighbors (SMOTEEN). The classifiers were logistic regression (LR), multilayer perceptron (MLP), decision tree (DT) and random forest (RF). We evaluated them with various metrics and compared models with the kappa score. A complete case analysis fitted the RF (0.78) better than other models, for which SI performed best. The DT, RF, and MLP performed better with SVMSMOTE. The RF, DT and MLP had the overall best performance, contributed by imputation or resampling (SMOTE and SVMSMOTE). We recommend carefully selecting resampling and imputation techniques and comparing them with complete cases before deciding on the preprocessing approach used to test AMS data with ML models.

Using multi-level regression to determine associations and estimate causes and effects in clinical anesthesia due to patient, practitioner and hospital or health system practice variability.

In this research methods tutorial of clinical anesthesia, we will explore techniques to estimate the influence of a myriad of factors on patient outcomes. Big data that contain information on patients, treated by individual anesthesiologists and surgical teams, at different hospitals, have an inherent multi-level data structure (Fig.1). While researchers often attempt to determine the association between patient factors and outcomes, that does not provide clinicians with the whole story. Patient care is clustered together according to clinicians and hospitals where they receive treatment. Therefore, multi-level regression models are needed to validly estimate the influence of each factor at each level. In addition, we will explore how to estimate the influence that variability-for example, one anesthesiologist deciding to do one thing, while another takes a different approach-has on outcomes for patients, using the intra-class correlation coefficient for continuous outcomes and the median odds ratio for binary outcomes. From this tutorial, you should acquire a clearer understanding of how to perform and interpret multi-level regression modeling and estimate the influence of variable clinical practices on patient outcomes in order to answer common but complex clinical questions. Fig.1 Infographics.

Conducting power analysis for meta-analysis with dependent effect sizes: Common guidelines and an introduction to the POMADE R package.

Sample size and statistical power are important factors to consider when planning a research synthesis. Power analysis methods have been developed for fixed effect or random effects models, but until recently these methods were limited to simple data structures with a single, independent effect per study. Recent work has provided power approximation formulas for meta-analyses involving studies with multiple, dependent effect size estimates, which are common in syntheses of social science research. Prior work focused on developing and validating the approximations but did not address the practice challenges encountered in applying them for purposes of planning a synthesis involving dependent effect sizes. We aim to facilitate the application of these recent developments by providing practical guidance on how to conduct power analysis for planning a meta-analysis of dependent effect sizes and by introducing a new R package, POMADE, designed for this purpose. We present a comprehensive overview of resources for finding information about the study design features and model parameters needed to conduct power analysis, along with detailed worked examples using the POMADE package. For presenting power analysis findings, we emphasize graphical tools that can depict power under a range of plausible assumptions and introduce a novel plot, the traffic light power plot, for conveying the degree of certainty in one's assumptions.

Laboratory Tests of Metrological Characteristics of a Non-Repetitive Low-Cost Mobile Handheld Laser Scanner

The popularity of mobile laser scanning systems as a surveying tool is growing among construction contractors, architects, land surveyors, and urban planners. The user-friendliness and rapid capture of precise and complete data on places and objects make them serious competitors for traditional surveying approaches. Considering the low cost and constantly improving availability of Mobile Laser Scanning (MLS), mainly handheld surveying tools, the measurement possibilities seem unlimited. We conducted a comprehensive investigation into the quality and accuracy of a point cloud generated by a recently marketed low-cost mobile surveying system, the MandEye MLS. The purpose of the study is to conduct exhaustive laboratory tests to determine the actual metrological characteristics of the device. The test facility was the surveying laboratory of the University of Agriculture in Kraków. The results of the MLS measurements (dynamic and static) were juxtaposed with a reference base, a geometric system of reference points in the laboratory, and in relation to a reference point cloud from a higher-class laser scanner: Leica ScanStation P40 TLS. The Authors verified the geometry of the point cloud, technical parameters, and data structure, as well as whether it can be used for surveying and mapping objects by assessing the point cloud density, noise and measurement errors, and detectability of objects in the cloud.

Implicit Is Not Enough: Explicitly Enforcing Anatomical Priors inside Landmark Localization Models

The task of localizing distinct anatomical structures in medical image data is an essential prerequisite for several medical applications, such as treatment planning in orthodontics, bone-age estimation, or initialization of segmentation methods in automated image analysis tools. Currently, Anatomical Landmark Localization (ALL) is mainly solved by deep-learning methods, which cannot guarantee robust ALL predictions; there may always be outlier predictions that are far from their ground truth locations due to out-of-distribution inputs. However, these localization outliers are detrimental to the performance of subsequent medical applications that rely on ALL results. The current ALL literature relies heavily on implicit anatomical constraints built into the loss function and network architecture to reduce the risk of anatomically infeasible predictions. However, we argue that in medical imaging, where images are generally acquired in a controlled environment, we should use stronger explicit anatomical constraints to reduce the number of outliers as much as possible. Therefore, we propose the end-to-end trainable Global Anatomical Feasibility Filter and Analysis (GAFFA) method, which uses prior anatomical knowledge estimated from data to explicitly enforce anatomical constraints. GAFFA refines the initial localization results of a U-Net by approximately solving a Markov Random Field (MRF) with a single iteration of the sum-product algorithm in a differentiable manner. Our experiments demonstrate that GAFFA outperforms all other landmark refinement methods investigated in our framework. Moreover, we show that GAFFA is more robust to large outliers than state-of-the-art methods on the studied X-ray hand dataset. We further motivate this claim by visualizing the anatomical constraints used in GAFFA as spatial energy heatmaps, which allowed us to find an annotation error in the hand dataset not previously discussed in the literature.

Molecular Classification with Graph ConvolutionalNetworks: Exploring the MUTAG Dataset for Mutagenicity Prediction

This paper presents the implementation of a Graph Convolutional Network (GCN) for the classification of chemical compounds using the MUTAG dataset, which consists of 188 ni- troaromatic compounds labeled according to their mutagenicity. The GCN model leverages the inherent graph structure of molec-ular data to capture and learn from the relationships between atoms and bonds, represented as nodes and edges, respectively. By utilizing three graph convolutional layers followed by a global mean pooling layer, the model effectively aggregates node features to generate meaningful graph-level representations. The model was trained using the Adam optimizer with a learning rate of 0.01, and cross-entropy loss was employed to supervise the classification task. The results demonstrate the efficacy of GCNs in graph classification tasks, with the model achieving a training accuracy of 79.33% and a test accuracy of 76.32%. This study highlights the potential of GCNs in cheminformatics and other domains where graph-structured data is prevalent, paving the way for further exploration and application of advanced graph neural networks in similar tasks.

Biogeochemical cycling of trace elements and nutrients in ferruginous waters: Constraints from a deep oligotrophic ancient lake

AbstractIron‐rich, ferruginous waters were the dominant geochemical regime for most of Earth's history. Modern ferruginous waters are found in stratified, sulfur‐poor lakes, and serve as crucial analogs for biogeochemical cycling throughout Earth's past. Here we present the first depth‐resolved data of physical structure, nutrients and trace elements from Lake Poso (Indonesia), a deep oligotrophic ancient lake. Lake Poso is ferruginous, with anoxia below ~ 90 m depth, placing it among the world's largest ferruginous lakes. Physical stratification is weaker than other tropical anoxic lakes, indicating sensitivity for paleoclimate reconstructions. Trace elements and nutrients are predominantly shaped by the oxic–anoxic transition. Manganese– and Fe oxyhydroxide–driven biogeochemical cycling occurs at distinct depth horizons, with Co and Ni controlled by Mn and showing shallow release in anoxic waters, while V, Cr, P, and As are controlled by Fe, with release in surface sediments and diffusive transport. Chromium is nonquantitatively removed in anoxic waters, in contrast to widespread assumptions in Cr‐based paleoreconstructions. Oxycline U and Se removal corresponds to a local N minimum, suggesting biological reduction and/or uptake. These first ferruginous water Se data also show removal in sediments, indicating sediment signals reflect multiple removal processes and informing Se‐based paleoreconstructions, while the absence of sediment U removal contrasts other anoxic basins. A comparison with other ferruginous lakes demonstrates how local influences drive deviations from expectations in other systems, and highlight common, generalizable ferruginous basin features. Therefore, these data will guide research in ferruginous settings across space and time, and improve paleoreconstructions from ferruginous sediment records.