Data Portal Research Articles

Dados abertos governamentais: Modelo de diagnóstico de maturidade voltado a qualidade de dados para publicação na web

Abstract Increased interest in open government data initiatives stems from long debates about state modernization. This study investigates the challenges imposed on public administration regarding data publishing from an open data perspective to propose a maturity diagnosis model for open data portals, aiming at the provision of in-depth, consistent, efficient, and transparent government information. The research comprised three phases. Firstly, we conducted a pre-test on open data portals from the United States, the United Kingdom, Brazil and Ireland, motivated by studies on the history of the open data movement as well as the current panorama of initiatives, evolution stages and challenges, which defined the fundamental analytical dimensions for the proposed maturity model. Secondly, we developed a maturity diagnosis model for open government data portals. Finally, the model was applied and validated on the pre-tested portals. Results from the diagnosis instrument can guide public administration in realizing a more efficient and responsible data governance, benefiting the government, the open data movement and civil society.

An innovative technological infrastructure for managing SARS-CoV-2 data across different cohorts in compliance with General Data Protection Regulation.

The ORCHESTRA project, funded by the European Commission, aims to create a pan-European cohort built on existing and new large-scale population cohorts to help rapidly advance the knowledge related to the prevention of the SARS-CoV-2 infection and the management of COVID-19 and its long-term sequelae. The integration and analysis of the very heterogeneous health data pose the challenge of building an innovative technological infrastructure as the foundation of a dedicated framework for data management that should address the regulatory requirements such as the General Data Protection Regulation (GDPR). The three participating Supercomputing European Centres (CINECA - Italy, CINES - France and HLRS - Germany) designed and deployed a dedicated infrastructure to fulfil the functional requirements for data management to ensure sensitive biomedical data confidentiality/privacy, integrity, and security. Besides the technological issues, many methodological aspects have been considered: Berlin Institute of Health (BIH), Charité provided its expertise both for data protection, information security, and data harmonisation/standardisation. The resulting infrastructure is based on a multi-layer approach that integrates several security measures to ensure data protection. A centralised Data Collection Platform has been established in the Italian National Hub while, for the use cases in which data sharing is not possible due to privacy restrictions, a distributed approach for Federated Analysis has been considered. A Data Portal is available as a centralised point of access for non-sensitive data and results, according to findability, accessibility, interoperability, and reusability (FAIR) data principles. This technological infrastructure has been used to support significative data exchange between population cohorts and to publish important scientific results related to SARS-CoV-2. Considering the increasing demand for data usage in accordance with the requirements of the GDPR regulations, the experience gained in the project and the infrastructure released for the ORCHESTRA project can act as a model to manage future public health threats. Other projects could benefit from the results achieved by ORCHESTRA by building upon the available standardisation of variables, design of the architecture, and process used for GDPR compliance.

A trawl collected dataset of Johnius (Actinopterygii, Sciaenidae) species in central-western Taiwanese waters.

Sciaenidae is one of the most important coastal fisheries in Taiwan, both in production and economic value. It is also significant as the main targetted diet of Chinese white dolphins, Sousachinensis, especially for the genus Johnius, such as J.taiwanensis, J.belangerii and J.distinctus, which is primarily found in central-western Taiwan coastal waters. Despite an abundance of Johnius species occurrences reported in the Global Biodiversity Information Facility (GBIF) and the Taiwan Biodiversity Information Facility (TaiBIF) data portals (Mozambique, Australia, Taiwan, Korea, India, Indonesia, South Africa, Pakistan, Vietnam and China), there are no specific datasets that properly document the regional distribution of this genus, especially in Taiwanese waters. Thus, this paper describes a dataset of genus Johnius occurrences in waters on the central-western coast of Taiwan. The data collection for the present study was conducted from 2009 until 2020 and comprised 62 sampling events and 133 occurrence records. All fish specimens were collected by trawling in Miaoli, Changhwa and Yunlin Counties, Taiwan and brought back to the lab for identification, individual number count and body weight measurement. These processing data have been integrated and established in the Taiwan Fish Database and published in GBIF. This dataset contains six Johnius species and 2,566 specimens, making it comprehensive Johnius fish fauna and spatial distributional data on the coastal habitat in central-western Taiwanese waters. This dataset contains 133 occurrence records of Johnius species (Sciaenidae) with 2,566 specimens, making it the most extensive public dataset of Johnius distribution records in Taiwan. The publication of this dataset through the TaiBIF and GBIF dataset platforms demonstrated that the number of Johnius spatial and temporal records in Taiwan waters is influenced by the topographical structure of the Changyun Rise (CYR) in combination with the cold current of the China Coastal currents and bound with the warm currents of the Kuroshio and the South China Sea on the central-western coast of Taiwan. The data serve as the foundation for understanding the biogeography and Johnius species ecology in Taiwan's coastal waters, which present a 2°C water temperature difference split at the CYR.

Nerve growth factor promote VCAM-1-dependent monocyte adhesion and M2 polarization in osteosarcoma microenvironment: Implications for larotrectinib therapy.

Osteosarcoma is the most prevalent form of primary malignant bone tumor, primarily affecting children and adolescents. The nerve growth factors (NGF) referred to as neurotrophins have been associated with cancer-induced bone pain; however, the role of NGF in osteosarcoma has yet to be elucidated. In osteosarcoma samples from the Genomic Data Commons data portal, we detected higher levels of NGF and M2 macrophage markers, but not M1 macrophage markers. In cellular experiments, NGF-stimulated osteosarcoma conditional medium was shown to facilitate macrophage polarization from the M0 to the M2 phenotype. NGF also enhanced VCAM-1-dependent monocyte adhesion within the osteosarcoma microenvironment by down-regulating miR-513c-5p levels through the FAK and c-Src cascades. In in vivo xenograft models, the overexpression of NGF was shown to enhance tumor growth, while the oral administration of the TrK inhibitor larotrectinib markedly antagonized NGF-promoted M2 macrophage expression and tumor progression. These results suggest that larotrectinib could potentially be used as a therapeutic agent aimed at mitigating NGF-mediated osteosarcoma progression.

Clinical Significance of Disulfidptosis-related Genes and Functional Analysis in Gastric Cancer.

Background: Worldwide, gastric cancer (GC) remains intractable due to its poor prognosis and high morbidity and mortality. Disulfidptosis is a novel kind of cell death mediated by abnormal accumulation of intracellular disulphides. The correlation between disulfidptosis and GC is still unknown. Therefore, it is necessary to elucidate the pathogenesis and mechanism of disulfidptosis and GC for clinical diagnosis and intervention. Methods: RNA-sequencing data from several public data portals and clinical samples were collected. We compared the expression levels of four key genes of disulfidptosis, including SLC7A11, SLC3A2, RPN1, and NCKAP1, in GC and selected prognostic genes to build a novel GC prognosis-related nomogram model. The biological functions and immune landscape of the identified prognostic genes were explored. Results: Overexpressed NCKAP1 and SLC7A11 were prognostic disulfidptosis-related genes in GC. We combined these genes and several clinicopathological factors to build a prognostic nomogram model for GC. Meanwhile, the ROC curves showed that NCKAP1 and SLC7A11 were promising biomarkers for GC screening. The biological and cellular functions were focused on actin activities, GTPase and immunoreaction. The tumour immune microenvironment and immune therapy targets were identified. Competing endogenous RNA network was built to explore the downstream regulatory mechanisms. Finally, the elevated NCKAP1 and SLC7A11 expression in GC was validated via qRT-PCR in a cell line and tissue line. Conclusion: In conclusion, NCKAP1 and SLC7A11 are promising prognostic and diagnostic biomarkers for GC that correlate with the activities of actin, energy metabolism of GTPase, immune infiltration and immunotherapy.

National income accounting attributes and economic welfare. Evidence from Pakistan.

Economic welfare is essential in the modern economy since it directly reflects the standard of living, distribution of resources, and general social satisfaction, which influences individual and social well-being. This study aims to explore the relationship between national income accounting different attributes and the economic welfare in Pakistan. However, this study used data from 1950 to 2022, and data was downloaded from the World Bank data portal. Regression analysis is used to investigate the relationship between them and is very effective in measuring the relationship between endogenous and exogenous variables. Moreover, generalized methods of movement (GMM) are used as the robustness of the regression. Our results show that foreign direct investment outflow, Gross domestic product growth rate, GDP per capita, higher Interest, market capitalization, and population growth have a significant negative on the unemployment rate, indicating the rise in these factors leads to a decrease in the employment rate in Pakistan. Trade and savings have a significant positive impact on the unemployment rate, indicating the rise in these factors leads to an increase in the unemployment rate for various reasons. Moreover, all the factors of national income accounting have a significant positive relationship with life expectancy, indicating that an increase in these factors leads to an increase in economic welfare and life expectancy due to better health facilities, many resources, and correct economic policies. However, foreign direct investment, inflation rate, lending interest rate, and population growth have significant positive effects on age dependency, indicating these factors increase the age dependency. Moreover, GDP growth and GDP per capita negatively impact age dependency. Similarly, all the national income accounting factors have a significant negative relationship with legal rights that leads to decreased legal rights. Moreover, due to better health facilities and health planning, there is a negative significant relationship between national income accounting attributes and motility rate among children. Our study advocated the implications for the policymakers and the government to make policies for the welfare and increase the social factors.

Scientific Data Spaces - Experiences from the EGI-ACE project.

This paper presents the approach adopted by the EGI-ACE project for the setup and delivery of Data Spaces for various scientific domains. The work was implemented by members of the EGI e-infrastructure and of several European Research Infrastructures in the context of the European Open Science Cloud programme. Our results are several Data Space services that enable the reuse and exploitation of open, scientific big data for compute intensive use cases. The paper illustrates the EGI-ACE approach through two examples: (1) EMSO ERIC Data Portal for seafloor and water column research and (2) ENES Data Space for climate research.

Analysis of sea currents, sea temperature, and sea salinity variations in the malacca strait during january and july 2022 using vertical sections

This study analyzes the sea currents, temperature, and salinity in the Malacca Strait during the northeast (NE) and southwest (SW) monsoons of 2022, represented by monthly average data from the Copernicus Marine Service (CMEMS) data portal for January and July, respectively. Vertical sections are created to visualize the layer structure and variations of sea parameters within the water column. The findings reveal differences in sea currents, temperature, and salinity between the NE and SW monsoons. July (SW monsoon) records consistently warmer temperatures than January (NE monsoon), both at the sea surface and seabed. Surface salinity is higher in July than in January, while seabed salinity is greater in January than in July. In January, the salinity-influenced mixed layer depth (MLD) appears to be thicker than the temperature-influenced MLD. There are no significant differences in the thickness of the temperature- and salinity-influenced MLDs during July. These variations in sea currents, temperature, and salinity at different depths provide a comprehensive understanding of the marine environmental structure and dynamics of the Malacca Strait.

Optimizing Skin Cancer Survival Prediction with Ensemble Techniques.

The advancement in cancer research using high throughput technology and artificial intelligence (AI) is gaining momentum to improve disease diagnosis and targeted therapy. However, the complex and imbalanced data with high dimensionality pose significant challenges for computational approaches and multi-omics data analysis. This study focuses on predicting skin cancer and analyzing overall survival probability. We employ the Kaplan-Meier estimator and Cox proportional hazards regression model, utilizing high-throughput machine learning (ML)-based ensemble methods. Our proposed ML-based ensemble techniques are applied to a publicly available dataset from the ICGC Data Portal, specifically targeting skin cutaneous melanoma cancers (SKCM). We used eight baseline classifiers, namely, random forest (RF), decision tree (DT), gradient boosting (GB), AdaBoost, Gaussian naïve Bayes (GNB), extra tree (ET), logistic regression (LR), and light gradient boosting machine (Light GBM or LGBM). The study evaluated the performance of the proposed ensemble methods and survival analysis on SKCM. The proposed methods demonstrated promising results, outperforming other algorithms and models in terms of accuracy compared to traditional methods. Specifically, the RF classifier exhibited outstanding precision results. Additionally, four different ensemble methods (stacking, bagging, boosting, and voting) were created and trained to achieve optimal results. The performance was evaluated and interpreted using accuracy, precision, recall, F1 score, confusion matrix, and ROC curves, where the voting method achieved a promising accuracy of 99%. On the other hand, the RF classifier achieved an outstanding accuracy of 99%, which exhibits the best performance. We compared our proposed study with the existing state-of-the-art techniques and found significant improvements in several key aspects. Our approach not only demonstrated superior performance in terms of accuracy but also showcased remarkable efficiency. Thus, this research work contributes to diagnosing SKCM with high accuracy.

Biodiversity of Drosophila species at a new university campus in Kunshan, China

The project aimed to explore the biodiversity of Drosophila species in Kunshan using genomic approaches. DNA sequences, specifically the Cytochrome Oxidase I (COI) gene, were utilized as genetic "barcodes" to identify the organisms. Three wild-caught fruit flies and a D. melanogaster control were selected, and their DNA was isolated through PCR amplification of the COI gene. The PCR products were sequenced, and the results were used to create phylogenetic trees and uploaded to the Barcode of Life Database Student Data Portal (BOLD-SDP) alongside species photos. The results identified 10 Drosophila species, with 6 species remaining unchanged over two years and 3 new species discovered this year. One sample was identified as D. neohypocausta, a Chinese endemic species, which had appeared only in the last two years in Kunshan. However, its sample size was small, suggesting it may not be native to the area. Further research revealed significant genetic differentiation between different clades of D. immigrans, which likely explained the limited number of D. neohypocausta samples, as most D. immigrans species are confined to specific regions. To gain a comprehensive understanding of biogeographic, ecological, and evolutionary patterns across the Drosophila genus, expanded sampling and taxonomic revision are necessary, particularly for the large immigrans species groups that have been inferred as polyphyletic in some studies. This study underscores the importance of genomic approaches in taxon diagnosis and highlights the need for further exploration to uncover the intricate relationships within the Drosophila genus.

"We are still tired": staff and administrators’ experiences during the COVID-19 pandemic within California residential care facilities for older adults

BackgroundExtensive research has been conducted on the impacts of the COVID-19 pandemic on long-term care workers in specialized care facilities. However, little is known about the impacts faced by facilities that provide generalized long-term support and care, such as residential care facilities for older adults (RCFs). This study describes the challenges experienced by staff and administrators of RCFs during the COVID-19 pandemic.MethodsAn electronic questionnaire collecting data using both closed- and open-ended questions on staff experiences was sent to 5,721 unique RCF administrator emails within the state of California between June-December 2021. Email addresses were obtained from the public database of RCFs available through the California Health and Human Services Open Data Portal. Descriptive statistics were calculated on quantitative data regarding staff preparedness training, access to resources, and administrators’ confidence in meeting recommended guidelines during the pandemic. Inductive thematic analysis was conducted on qualitative data regarding the confidence levels in meeting pandemic guidelines and challenges faced related to staff stress and morale.ResultsA total of 150 RCF administrators across California (response rate of 2.6%) completed the survey. Over three-fourths of respondents indicated their facilities had a designated staff member to train other staff members on emergency preparedness plans and the most frequently used resources during the COVID-19 pandemic were the Department of Social Services Community Care Licensing Division (88.7%), the county health department (86.7%), and the Centers for Disease Control and Prevention (80.7%). Administrators felt least confident in their facilities’ ability to maintain adequate staffing (52.0%), communication with nearby hospitals (62.1%) and communication with state and local public health officials (69.8%) during the pandemic. Three central themes emerged from the thematic analysis on staff stress and morale: (1) physical safety, mental and emotional impact of the COVID-19 pandemic; (2) staffing issues; and (3) challenges with guidelines in managing the ongoing pandemic.ConclusionsFindings from this research study can be used to actively target training resources for facility administrators and staff that have been identified as most frequently used and relevant for emergency preparedness in these understudied facilities. Additionally, developing a better understanding of the staffing stress and morale difficulties in RCFs can provide insight on how policymakers can assist these critical facilities in better preparing for future crises.

An integrated individual-level trait-based phytoplankton dataset from transitional waters

Functional trait-based approaches have undergone an extraordinary expansion in phytoplankton ecology. Morpho-functional traits have been shown to vary both within and between populations and species, potentially affecting individual fitness and the network of inter-individual relationships. Here we integrate six fully harmonized phytoplankton morpho-functional trait datasets, characterized by a fine data grain, reporting individual-level data over a large biogeographical area. Datasets refer to transitional water ecosystems, from five biogeographical areas: Northern Atlantic Ocean (Scotland), South-Western Atlantic Ocean (Brazil), South-Western Pacific Ocean (Australia), Indo Pacific Ocean (Maldives) and Mediterranean Sea (Greece and Turkey). The integrated dataset includes 127311 individual phytoplankton records with sampling locations, taxonomic and morphometric information according to Darwin Core standards and semantic annotations. The six FAIR datasets are openly available in the LifeWatch Italy data portal. The datasets have already been used for morpho-functional analyses and hypothesis testing on phytoplankton guilds at different levels of data aggregation and scale, from local to global.

MAPPING STATISTICAL DATA: POSSIBILITIES OF EUROSTAT-MAP.JS LIBRARY

Abstract. Access, availability, and general interest in open data have increased significantly over the past decade. Open data can be published by various organizations and individuals, with public organizations being the largest producers of open data. Despite governments' efforts to publish a large number of high value datasets on open data portals, data reuse is still not satisfactory because users often lack the technical skills required to reuse data. Research has shown that providing open-source software and tools can help solve this problem. Since statistical datasets are among the high-value datasets and their visualization on a map can help in decision-making, this research explores the possibilities of using the Eurostat-map.js library, a novel JavaScript tool, to enable non-specialized users (e.g., researchers, journalists, and citizens) to use Eurostat statistical data to create thematic maps. By analysing use cases that utilize the Eurostat-map.js library and exploring cartographic principles and techniques available for mapping official statistical data, this research addresses its capabilities, limitations, and opportunities for future development. Although the eurostat-map.js library aims to provide a user-friendly approach, it is important to note that non-specialist users may still face challenges in using the library due to their limited technical and cartographic expertise. Creating maps and effectively visualizing Eurostat data requires a certain level of technical skills and cartographic knowledge. Non-specialist users, such as researchers, statisticians, journalists, and government agency personnel, who do not have extensive experience in programming or cartography, may find it more difficult to use the library than specialists in these fields.

Solo: Data Discovery Using Natural Language Questions Via A Self-Supervised Approach

Most deployed data discovery systems, such as Google Datasets, and open data portals only support keyword search. Keyword search is geared towards general audiences but limits the types of queries the systems can answer. We propose a new system that lets users write natural language questions directly. A major barrier to using this learned data discovery system is it needs expensive-to-collect training data, thus limiting its utility. In this paper, we introduce a self-supervised approach to assemble training datasets and train learned discovery systems without human intervention. It requires addressing several challenges, including the design of self-supervised strategies for data discovery, table representation strategies to feed to the models, and relevance models that work well with the synthetically generated questions. We combine all the above contributions into a system, Solo, that solves the problem end to end. The evaluation results demonstrate the new techniques outperform state-of-the-art approaches on well-known benchmarks. All in all, the technique is a stepping stone towards building learned discovery systems.

Multi-dimension metabolic prognostic model for gastric cancer.

Metabolic reprogramming is involved in different stages of tumorigenesis. There are six widely recognized tumor-associated metabolic pathways, including cholesterol catabolism process, fatty acid metabolism, glutamine metabolic process, glycolysis, one carbon metabolic process, and pentose phosphate process. This study aimed to classify gastric cancer patients into different metabolic bio-similar clusters. We analyzed six tumor-associated metabolic pathways and calculated the metabolic pathway score through RNA-seq data using single sample gene set enrichment analysis. The consensus clustering analysis was performed to classify patients into different bio-similar clusters by multi-dimensional scaling. Kaplan-Meier curves were presented between different metabolic bio-similar groups for OS analysis. A training set of 370 patients from the Cancer Genome Atlas database with primary gastric cancer was chosen. Patients were classified into four metabolic bio-similar clusters, which were identified as metabolic non-specificity, metabolic-active, cholesterol-silence, and metabolic-silence clusters. Survival analysis showed that patients in metabolic-active cluster and metabolic-silence cluster have significantly poor prognosis than other patients (p=0.031). Patients in metabolic-active cluster and metabolic-silence cluster had significantly higher intra-tumor heterogeneity than other patients (p=0.032). Further analysis was performed in metabolic-active cluster and cholesterol-silence cluster. Three cell-cycle-related pathways, including G2M checkpoints, E2F targets, and MYC targets, were significantly upregulated in metabolic-active cluster than in cholesterol-silence cluster. A validation set of 192 gastric cancer patients from the Gene Expression Omnibus data portal verified that metabolic bio-similar cluster can predict prognosis in gastric cancer. Our study established a multi-dimension metabolic prognostic model in gastric cancer, which may be feasible for predicting clinical outcome.

The Predictability of the 30 October 2020 İzmir-Samos Tsunami Hydrodynamics and Enhancement of Its Early Warning Time by LSTM Deep Learning Network

Although tsunamis occur less frequently compared to some other natural disasters, they can be extremely devastating in the nearshore environment if they occur. An earthquake of magnitude 6.9 Mw occurred on 30 October 2020 at 12:51 p.m. UTC (2:51 p.m. GMT+03:00) and its epicenter was approximately 23 km south of İzmir province of Turkey, off the Greek island of Samos. The tsunami event triggered by this earthquake is known as the 30 October 2020 İzmir-Samos (Aegean) tsunami, and in this paper, we study the hydrodynamics of this tsunami using some of these artificial intelligence (AI) techniques applied to observational data. More specifically, we use the tsunami time series acquired from the UNESCO data portal at different stations of Bodrum, Syros, Kos, and Kos Marina. Then, we investigate the usage and shortcomings of the Long Short Term Memory (LSTM) DL technique for the prediction of the tsunami time series and its Fourier spectra. More specifically we study the predictability of the offshore water surface elevation dynamics, their spectral frequency and amplitude features, possible prediction success and enhancement of the accurate early prediction time scales. The uses and applicability of our findings and possible research directions are also discussed.

The influence of cultural factors on the reidentification of Ukrainian refugees

Refugees experience double cultural trauma: both because of war and moving abroad. The process of moving abroad is accompanied by disruption of habitual social connections and causes restoration of those connections. Identity needs the proof of Others, forcing refugees to engage in processes of interaction. The theoretical concept of ethnomethodology allows the authors to trace the process of restoring these ties: how refugees present themselves and define themselves as Ukrainians with the help of cultural factors embedded in daily practices. We observe the rise of Ukrainian identity with the start of war in 2014 and then escalation in 2022. Research of the national identity of Ukrainian refugees in Germany has been based on unique survey data gathered in June 2022 using the Google Forms online survey tool (Germany, June 2022, n = 423). Survey questions were used to measure the components of national identity: constructive patriotism, civic citizenship, ethnic citizenship. We rely also on representative statistical data gathered by Ukrainian research centres and the UNHCR data portal. In conclusion it is seen that cultural factors help the process of reidentification of Ukrainian refugees.

Assessing the readability of open data portals: a case study of Open Data Pakistan

Open data portals have been implemented in numerous countries to provide accessible, usable, and reproducible data. This study aims to assess the readability of datasets from Open Data Pakistan. Using the URL (https://opendata.com.pk/) in an online readability checking tool, six well-known readability formulas were employed, including Flesch Kincaid Reading Ease, Flesch Kincaid Grade Level, Gunning Fog Score, Smog Index, Coleman Liau Index, and Automated Readability Index. The average score of each formula was calculated to analyze the standard readability of the open data portal. Additionally, the website was explored to evaluate the overall status of open datasets, showcases, and features like "Connect" on Open Data Pakistan. The findings indicated that the Open Data Pakistan website has a reading difficulty, scoring an average of 47.4 out of 100, making it challenging for the general public to read. However, the overall analysis suggested that the readability status of open data in Pakistan is satisfactory. These results offer insights into improving the readability of open data for enhanced citizen participation and informed decision-making. Moreover, the study has implications for policymakers and stakeholders regarding the readability of Open Data Pakistan, emphasizing the importance of considering readability in open data publication policies and guidelines, with a recommendation to decrease the percentage of open data portals categorized as hard to read.

One Data Indonesia Policy Adoption for Telkom University Data Warehouse Framework

The Indonesian government has implemented a data warehouse named One Data (Satu Data) Indonesia (ODI) to support its operations since 2019. However, the implementation of this concept in universities has been limited, with only a few universities adopting it. Telkom University is one of the few universities in Indonesia that has already taken steps to implement ODI at the university level. The adoption of ODI at Telkom University is known as the One Data Telkom University (ODTU) project. This project aims to create a platform for universities to share data and collaborate more effectively. This paper thoroughly examines the implementation of the ODI policy and data warehouse framework at Telkom University, focusing on the ODTU data warehouse design and architecture. This paper discusses the implementation of ODTU into several applications, including the One Data Portal, One Data Dashboard, and One Data Market. Moreover, it identifies the challenges encountered during the implementation process, such as data integration, data privacy and security, standardized data models, and the promotion of a shared vision among stakeholders with varying levels of data literacy. Our analysis results demonstrate the effectiveness of the ODTU framework in improving data management practices at Telkom University. The customer satisfaction index (CSI) shows that across key reliability, assurance, and responsiveness measures, Telkom University experienced average score improvements of 3-6% after implementing ODTU. This study contributes to the existing literature on ODI policy adoption in the context of higher education institutions, providing insights for institutions seeking to improve their data management practices.

Systemic lupus erythematosus Association between Osteomyelitis: A two-sample Mendelian randomization study in European population

BackgroundSystemic lupus erythematosus (SLE) has been correlated with osteomyelitis (OM), yet the underlying causal relationship remains poorly understood. This study aims to investigate the causal association between SLE and OM using Mendelian randomization (MR) analysis. MethodsGenetic instrumental variables (IVs) correlated with SLE were extracted from a comprehensive genome-wide association study (GWAS) summary database (5201 cases and 9066 controls). OM was considered a SLE phenotype, and summary data from the fast GWA data portal were utilized for the analysis. Eligible IVs were extracted following rigorous quality control measures (P < 5 × 10-8, LD r2>0.001, distance 1 Mb, and F > 10). MR analysis was conducted using the Inverse Variance Weighted (IVW), MR-Egger, and Weighted Median (WM) methods after excluding potential confounders. Cochran's Q was applied for heterogeneity test. Pleiotropy was evaluated through MR-Egger intercept, MR-Pleiotropy Residual Sum and Outlier (MR-PRESSO) method, and Leave-one-SNP-out analysis. ResultA total of 40 eligible IVs were included for MR analysis. IVW results demonstrated a positive causal association between SLE and OM (P = 0.049, OR = 1.167). Heterogeneity analysis reveal no significant heterogeneity in the IVW analysis (P = 0.5503). Pleiotropy tests, including MR-PRESSO global test and MR-Egger intercept, indicated no evidence of pleiotropy in our findings (P > 0.05). Additionally, the Leave-one-SNP-out analysis showed no substantial deviations when removing individual SNPs, thus supporting the robustness of our results. ConclusionThis study establishes a genetic causal relationship between SLE and OM, indicating an increased risk of developing OM in individuals with SLE. Therefore, proactive management of SLE is advised to mitigate the risk of developing OM.