Systematic Data Analysis Research Articles

Mortality and morbidity patterns in Yaoundé, Cameroon: an ICD-11 classification-based analysis

BackgroundIn Cameroon, like in many other resource-limited countries, data generated by health settings including morbidity and mortality parameters are not always uniform. In the absence of a national guideline necessary for the standardization and harmonization of data, precision of data required for effective decision-making is therefore not guaranteed. The objective of the present study was to assess the reporting style of morbidity and mortality data in healthcare settings.MethodsAn institutional-based cross-sectional study was carried out from May to June 2022 at the Yaoundé Central Hospital. A questionnaire was used to assess the need to set up a standard tool to improve the reporting system. Medical records were used to collect mortality and morbidity data which were then compared to the International Statistical Classification of Diseases and Related Health Problems-11 (ICD-11) codification. Data were analyzed using IBM-SPSS version 26.ResultsOut of 200 patients’ morbidity causes recorded, nearly three-quarter (74.0%) were heterogeneous, and two over five (41.0%) of mortality causes reported were also heterogeneous. Most of respondents stated the need to set up a standard tool for collecting mortality and morbidity data (83.6%). Less than one-fifth (18.2%) of health care providers were able to understand data flow, correctly archived data (36.6%) and used electronic tools for data quality control (40.0%).ConclusionThere were high levels of heterogeneities of morbidity and mortality causes among patients admitted to the Yaoundé Central Hospital in 2021. It is therefore urgent that Cameroon national health authorities implement the ICD-11 to allow the systematic recording, analysis, interpretation and comparison of mortality and morbidity data collected in Yaoundé Central Hospital at different times; and ensure interoperability and reusability of recorded data for medical decision support.

Discovery of novel tepovirus genomes with a nucleic acid-binding protein homolog by systematic analysis of plant transcriptome data

Discovery of novel tepovirus genomes with a nucleic acid-binding protein homolog by systematic analysis of plant transcriptome data

Pharmacology and Mechanism of Action of Suzetrigine, a Potent and Selective NaV1.8 Pain Signal Inhibitor for the Treatment of Moderate to Severe Pain.

There is a high unmet need for safe and effective non-opioid medicines to treat moderate to severe pain without risk of addiction. Voltage-gated sodium channel 1.8 (NaV1.8) is a genetically and pharmacologically validated pain target that is selectively expressed in peripheral pain-sensing neurons and not in the central nervous system (CNS). Suzetrigine (VX-548) is a potent and selective inhibitor of NaV1.8, which has demonstrated clinical efficacy and safety in multiple acute pain studies. Our study was designed to characterize the mechanism of action of suzetrigine and assess both nonclinical and clinical data to test the hypothesis that selective NaV1.8 inhibition translates into clinical efficacy and safety, including lack of addictive potential. Preclinical pharmacology and mechanism of action studies were performed in vitro using electrophysiology and radiolabeled binding methods in cells recombinantly expressing human NaV channels, human proteins, and primary human dorsal root ganglion (DRG) sensory neurons. Safety and addictive potential assessments included in vitro secondary pharmacology studies, nonclinical repeat-dose toxicity and dependence studies in rats and/or monkeys, and a systematic analysis of adverse event data generated from 2447 participants from phase 3 acute pain studies of suzetrigine. Suzetrigine is selective against all other NaV subtypes (≥ 31,000-fold) and 180 other molecular targets. Suzetrigine inhibits NaV1.8 by binding to the protein's second voltage sensing domain (VSD2) to stabilize the closed state of the channel. This novel allosteric mechanism results in tonic inhibition of NaV1.8 and reduces pain signals in primary human DRG sensory neurons. Nonclinical and clinical safety assessments with suzetrigine demonstrate no adverse CNS, cardiovascular or behavioral effects and no evidence of addictive potential or dependence. The comprehensive pharmacology assessment presented here indicates that suzetrigine represents the first in a new class of non-opioid analgesics that are selective NaV1.8 pain signal inhibitors acting in the peripheral nervous system to safely treat pain without addictive potential.

Deciphering glioblastoma pathogenesis: Insights from mitophagy dysregulation and SNX7 as a therapeutic target.

Glioblastoma is a highly aggressive and invasive brain tumor with an extremely poor prognosis. The aims of the present study are to investigate the pathogenesis of glioblastoma and identify potential therapeutic targets. We performed a systematic analysis of gene expression data from multiple datasets, including GEO and TCGA, to identify hub genes and pathways associated with glioblastoma progression. Bioinformatics tools were utilized to analyze differential gene expression, pathway enrichment and survival prognosis. Both in vitro and in vivo functional experiments were conducted to validate biological roles of SNX7. Pathway analysis revealed significant enrichment of the mitophagy pathway in glioblastoma, indicating its critical role in tumor development. We identified 12 hub genes associated with glioblastoma prognosis, with high-risk patients having worse survival outcomes. Among the hub gene set, sorting nexin 7 (SNX7) was found to be the most significant regulator of glioblastoma progression. Our results also demonstrated that SNX7 expression is associated with tumor ferroptosis and genomic variations, representing potential biomarkers for clinical diagnosis and treatment. Furthermore, functional experiments confirmed that SNX7 promotes glioblastoma cell proliferation, invasion and survival by inhibiting protective mitophagy. Our results highlight the importance of mitophagy dysregulation in the pathogenesis of glioblastoma and identify SNX7 as a novel therapeutic target. Further research is needed to elucidate the underlying mechanisms of SNX7 in glioblastoma and validate its clinical significance. These findings may facilitate the development of personalized treatment strategies and improve outcomes for glioblastoma patients.

E-Waste Disposal Behavior: A Mediator for Sustainable Consumption

This review focuses on sustainable consumption behaviors which play a crucial role in reducing electronic waste. E-waste has become a global environmental concern due to the massive generation of discarded electronic products, with improper disposal contributing to pollution, health risks, and resource depletion. Through a systematic analysis of secondary data, this review identifies key factors that can inform strategies for improving e-waste management. The collective findings demonstrate that factors such as attitude towards a safe environment, eco-literacy, subjective norms, perceived behavioral control, volume accumulation, monetary return expectation, and others; play a crucial role in influencing the behavior of urban consumers towards e-waste disposal behavior and sustainable consumption. By reviewing the existing research, this review highlights the need for targeted interventions to promote responsible e-waste disposal, contributing to broader efforts toward a circular economy and sustainable urban living.

Mitochondrial DNA variant detection in over 6,500 rare disease families by the systematic analysis of exome and genome sequencing data resolves undiagnosed cases.

Variants in the mitochondrial genome (mtDNA) cause a diverse collection of mitochondrial diseases and have extensive phenotypic overlap with Mendelian diseases encoded on the nuclear genome. The mtDNA is often not specifically evaluated in patients with suspected Mendelian disease, resulting in overlooked diagnostic variants. Using dedicated pipelines to address the technical challenges posed by the mtDNA - circular genome, variant heteroplasmy, and nuclear misalignment - single nucleotide variants, small indels, and large mtDNA deletions were called from exome and genome sequencing data, in addition to RNA-sequencing when available. A cohort of 6,660 rare disease families were analyzed (5,625 genetically undiagnosed, 84%) from the Genomics Research to Elucidate the Genetics of Rare diseases (GREGoR) Consortium as well as other rare disease cohorts. Diagnostic mtDNA variants were identified in 10 previously genetically undiagnosed families (one large deletion, eight reported pathogenic variants, one novel pathogenic variant). In one additional undiagnosed proband, the detection of >900 heteroplasmic variants provided functional evidence of pathogenicity to a novel de novo variant in the nuclear gene POLG (DNA polymerase gamma), responsible for mtDNA replication and repair. mtDNA variant calling from data generated by exome and genome sequencing for nuclear variant analysis resulted in a genetic diagnosis or detection of a candidate variant for 0.4% of undiagnosed families affected by a broad range of rare diseases.

From Data and Metadata to HBIM-GIS Integration

ABSTRACT The management activities of architectural heritage, including documentation, restoration, and conservation, are complex due to the unique characteristics of historical buildings, their construction systems, cultural value, and scales when integrated into cultural landscapes. Managing a heritage ensemble becomes even more challenging given its scope. To address these challenges, new technologies have emerged, such as digital photogrammetry, 3D scanning, semantic modeling, and the integration of Historic Building Information Modeling (HBIM) and Geographic Information Systems (GIS). The integration of BIM and GIS facilitates the extraction and transformation of geometric and semantic information; however, data loss during software transfers and the need to structure information according to specific standards pose challenges to ensuring effective integration. This study addresses the integration of HBIM and GIS in the management of the cultural landscape of the Pampulha Complex, designed by Oscar Niemeyer, focusing on the Pampulha Art Museum as a case study. We adopt a systematic analysis of data and metadata to construct the HBIM-GIS model, exploring different types of data integration and their advantages and limitations. This analysis contributes to the understanding of this integration, providing a foundation for the dissemination of knowledge about architectural heritage based on HBIM-GIS.

Sales strategies with a probabilistic business model of an insurance company: The role of updating and targeting

The major goal of decisions made by a business organization is to enhance business performance. These days, owners, managers and other stakeholders are seeking for opportunities of modelling and automating decisions by analysing the most recent data with the help of artificial intelligence (AI). This study outlines a simple theoretical model framework using internal and external information on current and potential clients and performing calculations followed by immediate updating of contracting probabilities after each sales attempt. This can help increase sales efficiency, revenues, and profits in an easily programmable way and serve as a basis for focusing on the most promising deals customising personal offers of best-selling products for each potential client. The search for new customers is supported by the continuous and systematic collection and analysis of external and internal statistical data, organising them into a unified database, and using a decision support model based on it. As an illustration, the paper presents a fictitious model setup and simulations for an insurance company considering different regions, age groups and genders of clients when analysing probabilities of contracting, average sales and profits per contract. The elements of the model, however, can be generalised or adjusted to any sector. Results show that dynamic targeting strategies based on model calculations and most current information outperform static or non-targeted actions. The process from data to decision-making to improve business performance and the decision itself can be easily algorithmised. The feedback of the results into the model carries the potential for automated self-learning and self-correction. The proposed framework can serve as a basis for a self-sustaining artificial business intelligence system.

Investigating the therapeutic effects of Shenzhiling oral liquid on Alzheimer's disease: a network pharmacology and experimental approach.

There is currently no effective treatment for Alzheimer's disease (AD). This research explored Shenzhiling Oral Liquid (SZLD) against AD by pinpointing crucial elements and understanding its molecular mechanisms through network pharmacology and in vitro experiment. First, we used network pharmacology to screen the main targets and mechanisms of SZLD to improve AD. Then we conducted experiments with Aβ42-induced SH-SY5Y cells to assess SZLD's impact, focusing particularly on apoptotic pathways, thereby uncovering its mechanism of action in AD. Through our analysis, we discovered a notable link between SZLD's effect on AD and apoptosis processes. Specifically, the critical proteins Casapse3 and BCL-2 showed strong correlations in this context. Through systematic data analysis and experimental verification, we unveiled the healing advantages and the foundational molecular mechanisms of SZLD in AD. These findings underscore the promising and compelling potential of targeting the PI3K/Akt signaling pathway and apoptosis with SZLD as a therapeutic strategy to ameliorate AD.

Statistical Measures of Center: A Focus on Mean, Median, and Mode

In statistics, systematic data collection, organization, analysis, interpretation, and presentation are the main topics of study. Understanding variability, seeing trends, and coming to wise conclusions in a variety of fields all depend on it. In general, statistics can be separated into: Descriptive statistics: These statistics include the description and summarisation of datasets. Inferential statistics: Inferential statistics is the capacity to infer and forecast information about a population from a sample. Through methods like regression analysis, probability, hypothesis testing, and data visualisation, statistics— which are extensively employed in fields including business, healthcare, social sciences, and science—assist in decision-making. It transforms unstructured data into meaningful knowledge that supports problem-solving and strategic planning. Key Words: Mean, Median, Mode, Statistics, discrete maths, maths, Descriptive statistics

Анализ устойчивости механизма бережливого производства при проектировании производственной системы в условиях неопределённости

In conditions of high uncertainty in international markets and complex supply chains, manufacturing companies face the need to integrate resilience into their systems to counter disruptions caused by external and internal factors such as pandemics and climate crisis. The object of the study is manufacturing systems vulnerable to these factors, and the aim is to develop an approach to designing systems that combines the principles of lean manufacturing and resilience. The main tasks include analyzing existing approaches to designing production systems, identifying key principles of lean manufacturing to ensure resilience, and developing recommendations for its integration. The research methodology is based on a systematic literature review and data analysis to identify gaps and directions for further research. Methods used include comparative analysis and data synthesis based on a systems approach to production management. The results show that lean manufacturing, aimed at optimizing processes and eliminating waste has significant potential for integrating resilience. The proposed approach contributes to the creation of flexible and resilient production systems capable of effectively responding to disruptions.

China's A-share Market Forecast and Investment Strategy Study for January 2025 Based on Astrology

This paper tries to apply astrology in the January 2025 sentiment and trend forecast of the Chinese A-share market. Based on the chart for January 1st, 2025, with the zodiac, positions of planets, and their aspects-such as conjunctions, squares, and trines-the research reveals the pattern of the influence of astronomical events like full moons and new moons on the volatility of the market. Therefore, through various observations that involve both systematic analysis and visualization of data, the study finds significant results on the association between aspects on the planet and market sentiments or industry performances. The surges in market sentiment over high-time cycles of full moon reveal extraordinary uptakes within the technology sector, with green energy at the front, while in formations of new moons, market sentiments fall, attracting buyers from sectors that are usually more steady, like infrastructure or finance. Capricorn phase indicates stability and conservativism, while innovative and growth-oriented is Aquarius. It would therefore seem that the symbolic astrological forecasts are highly correlated with the dynamics of market sentiment and could serve investors as a strong reference for optimizing their cyclic strategy: highgrowth sector focus during full moons, shifting to steady investment during new moons in order to reach a dynamic balance between return and risk.

Evaluating current status of network pharmacology for herbal medicine focusing on identifying mechanisms and therapeutic effects.

Network pharmacology has gained significant traction as a tool for identifying the mechanisms and therapeutic effects of herbal medicines. However, despite the usefulness of these approaches, their diversity underscores the critical need for a systematic evaluation to ensure consistency and reliability. We aimed to evaluate the network pharmacological analyses, focusing on identifying the mechanisms and therapeutic effects of herbal medicines. We employed a comprehensive approach involving systematic data retrieval, network construction, and analysis. Herbal compounds and their targets were meticulously extracted from five distinct network pharmacology databases to ensure extensive coverage and high data reliability. Advanced network-based methods were used to identify key herbal targets and predict therapeutic effects, thereby enriching the depth and breadth of the analysis. Experimental validation was performed on prostate cancer models to substantiate the computational predictions. The results of the recapitulating task for known herbal ingredient targets revealed distinct patterns in performance and coverage based on network construction and aggregation methods. We performed the same analysis to identify herbal targets and found that network centrality, path counts, and downweighted path counts had their own pros and cons. By comparing network-based methods, we found that considering the impact on the multiscale interactome yielded the highest accuracy in discriminating known therapeutic effects. Using optimal conditions, we successfully identified new indications for herbal medicines and validated these findings through follow-up in vitro and in vivo experiments. This study presents the first comprehensive and critical evaluation of the current network pharmacology analyses in the field of herbal medicine and provides valuable guidance for continued advances in the elucidation of the mechanisms and therapeutic effects.

VIOLÊNCIA CONTRA A MULHER NO CAMPO DE TRABALHO

Violence against women in the workplace is a persistent issue affecting the health and well-being of victims and organizational dynamics. The rationale for this study lies in the need to understand the magnitude of this problem and the shortcomings of current prevention and support policies. The main objective was to analyze the prevalence and types of violence faced by women at work, assess the impacts on victims’ health, and examine the effectiveness of existing public policies. The methodology involved a systematic literature review and analysis of secondary data from recent research on the subject. Studies and reports from 2020 to 2024 were examined to identify patterns and gaps in policies combating violence against women at work. Results indicated a high prevalence of sexual and moral harassment, with significant psychological and physical impacts on victims. The research revealed that despite existing policies, there is inadequate implementation and low awareness, contributing to the perpetuation of the problem.

Enhancing Environmental and Human Health Management Through the Integration of Advanced Revitalization Technologies Utilizing Artificial Intelligence.

Pollution can be broadly defined as the presence of contaminants or energy sources detrimental to ecosystems and human health. The human organism serves as a valuable indicator of ecosystem contamination. However, understanding physiological disorders and correlating specific contaminants with disease development is a complex and arduous task, necessitating extensive scientific research spanning years or even decades. To facilitate a more rapid and precise understanding of the physiological impairments induced by various contaminants, a comprehensive approach is indispensable. This review proposes a model for such an approach, which involves the systematic collection and analysis of data from ecosystem contamination monitoring, integrated with biomedical data on compromised physiological conditions in humans across different temporal and spatial scales. Given the complexity and sheer volume of data, alongside the imperative for strategic decision-making, this model leverages the capabilities of artificial intelligence (AI) tools. Although this paper exemplifies the model by investigating the effects of contaminants on the human organism, the model is adaptable to all ecosystem components, thereby supporting the conservation of plant and animal species.

Hydrogen bombardment-induced nano blisters in multilayered Mo/Si coatings

Nanometer-thick multilayered Mo/Si coatings, employed as artificial Bragg structures, are essential for reflecting specific wavelengths of light in synchrotrons, space telescopes, and extreme ultraviolet optical systems. However, these coatings are prone to blistering failures when exposed to energetic fluxes, such as hydrogen bombardment and solar wind particles. The blistering mechanism is investigated through systematic analysis of experimental data and the development of a multilayered mechanical model based on pockets of energy concentration theory. Energy release rates for pure mode fracture at blister tips, the evolution of blister morphologies, and interface fracture toughness are assessed through theoretical derivations. The relationship between blister radii and heights is elucidated and quantitatively validated against experimental data. Variations in fracture toughness are correlated to hydrogen characteristics, and the influence of hydrogen species, exposure temperature, dose, energy, and strained layer thickness on blister formation is evaluated using the developed model. These findings provide crucial insights for optimizing exposure parameters to mitigate blistering and enhance the performance and reliability of multilayered Mo/Si coatings.

Optimizing Heparin Quality Assurance Utilizing Electronic Data Abstraction.

Background: Heparin is a high-risk medication with significant variability across patients. Systematic data analysis can help hospitals improve heparin management, ensuring safe and effective anticoagulation. An opportunity exists to create a more efficient data collection process, allowing hospitals to streamline quality assurance programs. Objective: To assess the agreement between manual and electronic data abstraction for heparin quality assurance. Methods: This is a single-center, observational cohort study that evaluated patients who received therapeutic unfractionated heparin from September to November 20, 2023. Patients treated for less than 24 hours were excluded. Data were collected manually from pharmacist monitoring forms and the electronic medical record; electronic data abstraction was queried from an institutional data warehouse. The primary outcome was agreement in percentage of patients achieving a therapeutic aPTT within 24 hours. Secondary outcomes included agreement on time to therapeutic aPTT, agreement on time to therapeutic or supratherapeutic aPTT, and clinical outcomes. Results: The study included 288 patients. Manual data collection indicated 44.1% of patients were therapeutic within 24 hours, whereas electronic data collection showed 46.9% (kappa = 0.86). The kappa value for agreement of therapeutic or supratherapeutic aPTT within 24 hours was substantial (kappa = 0.69), with manual data showing 61.5% of patients therapeutic within 24 hours compared with 73.3% in electronic data. However, poor agreement was found when identifying subsequent heparin boluses (kappa = 0.13) and new venous thromboembolism cases (kappa = -0.01). Conclusions and Relevance: The metrics from the 2 data collection methods varied in reliability, ranging from highly consistent to poorly aligned. A hybrid approach, integrating manual and reliable electronic methods, has been implemented at our institution to improve efficiency. Further studies are needed to assess generalizability, and enhance electronic data capture for clinical outcomes.

Assessing attitudes toward research and plagiarism among medical students: a multi-site study

BackgroundResearch involves the systematic collection and analysis of data to enhance understanding of a particular phenomenon. Participation in medical research is crucial for advancing healthcare practices. However, there has been limited focus on understanding the factors that motivate medical students to engage in research. Additionally, in the era of e-learning, the easy accessibility of online resources has contributed to a widespread ‘copy-paste culture’ among digital-native students, which is recognized in academia as plagiarism. Existing studies suggest that a contributing factor to the increasing prevalence of plagiarism is students’ limited understanding of this act. The purpose of this study was to assess medical students’ attitudes toward research and plagiarism, and to evaluate the psychometric properties of the Attitudes Toward Research (ATR) and Attitudes Toward Plagiarism (ATP) questionnaires.MethodsThis was a multicenter study conducted among medical undergraduate and postgraduate students attending the three medical universities who were involved in research. Students’ attitudes toward research and plagiarism were assessed using the ATR and ATP questionnaires. The research instruments underwent translation and cultural adaptation in accordance with internationally accepted methodology. The psychometric properties of the ATR and ATP, including validity and reliability, were assessed. Confirmatory factor analysis was used to test the model’s fit to the data.ResultsThe ATR and ATP questionnaires were completed by 793 medical students who were involved in research (647 undergraduates and 146 PhD students). Cronbach’s alpha coefficients of 0.917 and 0.822 indicated excellent and good scale reliability for the ATR and ATP questionnaires, respectively. The five-and three- factor structures of ATR and ATP have been validated with maximum likelihood confirmatory analysis, and the results demonstrated an adequate level of model fit (TLI = 0.930, CFI = 0.942 and TLI = 0.924, CFI = 0.943, respectively). Medical students showed a high degree of positive attitudes toward research and favorable scores across all three domains of attitudes toward plagiarism. In multivariate regression models, age was found to be positively associated with favorable attitudes of research usefulness, positive attitudes, relevance to life subscales and total ATR scale (p < 0.001), while PhD study level was related to research anxiety (p < 0.001) and favorable attitudes across all three ATP domains (p < 0.001).ConclusionMedical students who were involved in research showed a high degree of favorable attitudes toward research and plagiarism. Adjusting medical school curricula to include research courses would broaden the students’ interest in scientific research and maximize their impact on the full preservation of research ethics and integrity.

Development of an Integrated Surveillance System to Improve Preparedness for Arbovirus Outbreaks in a Dengue Endemic Setting: Descriptive Study.

Dengue fever, transmitted by Aedes aegypti and Aedes albopictus mosquitoes, poses a significant public health challenge in tropical and subtropical regions. Dengue surveillance involves monitoring the incidence, distribution, and trends of infections through systematic data collection, analysis, interpretation, and dissemination. It supports public health decision-making, guiding interventions like vector control, vaccination campaigns, and public education. Herein, we report the development of a surveillance system already in use to support public health managers against dengue transmission in Foz do Iguaçu, a dengue-endemic Brazilian city located in the Triple Border with Argentina and Paraguay. We present data encompassing the fieldwork organization of more than 100 health agents; epidemiological and entomological data were gathered from November 2022 to April 2024, totalizing 18 months of data collection. By registering health agents, we were able to provide support for those facing issues to fill their daily milestone of inspecting 16 traps per working day. We filtered dengue transmission in the city by patient age, gender, and reporting units, as well as according to dengue virus serotype. The entomological indices presented a strong seasonal pattern, as expected. Several longtime established routines in Foz do Iguaçu have been directly impacted by the adoption of Vigilância Integrada com Tecnologia (VITEC). The implementation of VITEC has enabled more efficient and accurate diagnostics of local transmission risk, leading to a better understanding of operational activity patterns and risks. Lately, local public health managers can easily identify hot spots of dengue transmission and optimize interventions toward those highly sensitive areas.

Unveiling the hidden hands: Analysis of corporate ownership of industrial tuna fishing vessels in the Eastern Pacific Ocean

Distant-water fishing (DWF) refers to fishing operations conducted by companies in waters beyond their national exclusive economic zones (EEZs), often targeting the EEZs of other coastal states or international waters. Research on DWF typically emphasizes the flag states of vessels, rather than the nationalities of the corporations that own them, despite evidence of efforts to obscure ownership. This paper examines the corporate owners of 1648 industrial and semi-industrial tuna fishing vessels in the Eastern Pacific Ocean (EPO) using Orbis data, analyzing ownership at three levels: flag state, direct corporate owners, and ultimate corporate owners. Results show that flag state data alone understate the true fishing capacity of key DWF nations. Shifts in ownership nationality across levels are significant, notably for Taiwanese and Spanish corporations owning vessels through proxies in the Global South. These ownership shifts impact 6 % of vessels and 14 % of gross tonnage in the registered EPO tuna fleet. Furthermore, spatial analysis using Global Fishing Watch data highlights the Galapagos Islands EEZ as a critical fishing zone, frequently accessed by foreign fishing corporations through Ecuadorian intermediaries. This study underscores the need to incorporate ownership data into fisheries governance for greater transparency and accountability. The systematic collection and analysis of ownership data would allow fisheries managers to better monitor capacity, address power concentration, and promote policies that ensure fairer distribution of resources and benefits.