Data Infrastructure Research Articles

Pemanfaatan Data Science dalam Meningkatkan Kualitas Pembinaan SDM Militer

In today's digital era, data has become a very valuable asset in decision making in the military field. Where, data science can be used for various purposes, such as risk prediction and analysis, policy development, human resource development, operational efficiency improvement, and predicting someone's length of service in the military. However, effective implementation of data science also requires adequate data infrastructure, strict data security, and skilled and trained human resources in data science. The purpose of this study is to discuss the effect of using data science in developing the quality of human resources, where from this point of view data science is considered to be able to make a significant contribution in increasing the effectiveness and efficiency of military human resources development in Indonesia. This research method is a survey method with the aim of obtaining data on the use and benefits of data science in developing military human resources with a sample of 50 people. As for data processing using SPSS software analysis tools. Utilization of data science is the independent variable in this study, while the quality of military human resource development in Indonesia is the dependent variable, with the control variable being the level of education and experience of military human resources. The results of multiple linear regression tests show that the utilization of data science, education level, and experience of military human resources simultaneously has a significant effect on the quality of military human resource development in Indonesia.

CIMLA: A Modular and Modifiable Data Preparation, Organization, and Fusion Infrastructure to Partially Support the Development of Context-aware MMLA Solutions

Multimodal Learning Analytics (MMLA) solutions aim to provide a more holistic picture of a learning situation by processing multimodal educational data. Considering contextual information of a learning situation is known to help in providing more relevant outputs to educational stakeholders. However, most of the MMLA solutions are still in prototyping phase and dealing with different dimensions of an authentic MMLA situation that involve multiple cross-disciplinary stakeholders like teachers, researchers, and developers. One of the reasons behind still being in prototyping phase of the development lifecycle is related to the challenges that software developers face at different levels in developing context-aware MMLA solutions. In this paper, we identify the requirements and propose a data infrastructure called CIMLA. It includes different data processing components following a standard data processing pipeline and considers contextual information following a data structure. It has been evaluated in three authentic MMLA scenarios involving different cross-disciplinary stakeholders following the Software Architecture Analysis Method. Its fitness was analyzed in each of the three scenarios and developers were interviewed to assess whether it meets functional and non-functional requirements. Results showed that CIMLA supports modularity in developing context-aware MMLA solutions and each of its modules can be reused with required modifications in the development of other solutions. In the future, the current involvement of a developer in customizing the configuration file to consider contextual information can be investigated.

Health Systems Need to Transform Data Collection to Advance Health Equity.

Health Systems Need to Transform Data Collection to Advance Health Equity.

Geotargeting spatial and temporal data of Italian freshwater high-altitude macroinvertebrates

A data set including information on macroinvertebrates identified to genus/species group/species level was created within the monitoring activities of several European and national projects. The data set includes 2111 macroinvertebrate records on temporal fragmentary data from lakes Paione (upper, middle, and lower lakes Paione), and 530 records on spatial data relative to eight other high-altitude lakes from the Ossola Valley (North-western Italy, Piedmont, Central Alps). The study area is included within the Lake Maggiore watershed. All records are georeferenced because, since the beginning of the studies, temporal data were taken in the same sampling sites over years. The temporal data span over the period 1989-2020, the spatial data refer to the 2019-2020 sampling activity. The dataset is available for download as csv format at the Global Biodiversity Information Facility (GBIF) data infrastructure.

Reconstructing Three-dimensional geological structures by the Multiple-point statistics method coupled with a deep neural network: A case study of a metro station in Guangzhou, China

A credible large-scale three-dimensional (3D) geological model constructed from a small amount of local high-density data provides a vital data infrastructure for the design and construction of metro lines. The multiple-point statistics (MPS) method has successfully generated 3D geological models in many fields. However, the global cognition of the spatial relations between geological objects is difficult to reconstruct using a local optimization strategy with moving templates in the MPS simulation. This study presented an MPS simulation approach for constructing 3D geological structures with two-dimensional (2D) cross-sections, where the MPS process was coupled with a fully connected deep neural network (FCDNN) simulation. The core idea of the proposed method was that the global features of each geological object were generated from the trained FCDNN, the input parameter of which was the subsurface depth. Then, the entire model was constructed on the initial model using the following iterative MPS process: the semantic relationship of geological objects was extracted from 2D cross-sections and used as a constraint in the modeling process, which ensured the order of the stratigraphic sequence and rationality of the geological structure in the final model. The joint objective function of the proposed method was composed of the loss function of the FCDNN and conventional MPS objective function constrained by the spatial semantics of geological objects. The concrete example in the Jiangtai Road Metro Station of Guangzhou Line 11, China illustrated that the details of the complex geological structures could be obtained accurately by the presented method. The global features from the trained FCDNN provided the basic geometry for the final model, which overcame the limitations of the conventional MPS method.

Evaluation of the "TeleCOVID Hesse" project after 1 year in operation

The SARS-CoV‑2 pandemic posed unexpected challenges for hospitals worldwide and in addition to the supply emergency, simultaneously caused ahigh pressure to innovate. Due to the high number of cases of COVID-19patients requiring intensive care, structured networking of hospitals gained particular importance. The tele-ICU communication platform TeleCOVID was developed to improve the quality of intensive care both by enabling teleconsultations and by supporting patient transfers. The present study aimed to survey user experiences with TeleCOVID. The study investigated the extent to which the app is used, the user experiences of the participating hospitals, and the resulting implications for the further development of the telemedicine application. Auser survey was conducted in May 2022 using an online questionnaire. The survey contained both closed and open questions with afree text field. It was sent via the Hessian Ministry of Social Affairs and Integration (HMSI). All 135 hospitals in Hesse were contacted by e‑mail and invited to participate in the study. The results of the closed questions were analyzed using descriptive statistics, and the results of the open questions were clustered and thematically summarized using qualitative content analysis. The study showed that TeleCOVID was used primarily for transfer requests, followed by the need for atreatment consultation without atransfer request. Most often, ECMO treatment or treatment in ahospital of ahigher care level was required. The content analysis showed that users particularly rated the possibility of adata protection-compliant and structured transfer of patient data as advantageous. It is also worth mentioning that in almost 25% of the cases atransfer of patients could be prevented by TeleCOVID. Disadvantages frequently mentioned by respondents were the lack of connection to the electronic hospital information system, the increased time required for the registration process, and the poor primary accessibility of contact persons. In afurther development of the application the connection to the electronic hospital information system should be considered particularly urgent. In addition, the time expenditure should be reduced by asimplified login process. Due to interface barriers, an alternative data infrastructure would also be conceivable to create interoperability. The introduction of aweb client could also increase usability. The main beneficiaries of hospital networking are physicians and patients in acontext associated with ahigh workload and specific medical issues. Continuation and expansion of the app to intensive care medicine and beyond are therefore recommended. In further studies on the project, personal interviews with decision makers could be useful to conduct amore targeted needs analysis.

The introduction of a data-driven population health management approach in the Netherlands since 2019: The Extramural LUMC Academic Network data infrastructure

Improving population health and reducing inequalities through better integrated health and social care services is high up on the agenda of policymakers internationally. In recent years, regional cross-domain partnerships have emerged in several countries, which aim to achieve better population health, quality of care and a reduction in the per capita costs. These cross-domain partnerships aim to have a strong data foundation and are committed to continuous learning in which data plays an essential role. This paper describes our approach towards the development of the regional integrative population-based data infrastructure Extramural LUMC (Leiden University Medical Center) Academic Network (ELAN), in which we linked routinely collected medical, social and public health data at the patient level from the greater The Hague and Leiden area. Furthermore, we discuss the methodological issues of routine care data and the lessons learned about privacy, legislation and reciprocities. The initiative presented in this paper is relevant for international researchers and policy-makers because a unique data infrastructure has been set up that contains data across different domains, providing insights into societal issues and scientific questions that are important for data driven population health management approaches.

Enhancing the nation's public health information infrastructure: a report from the ACMI symposium.

The COVID-19 pandemic exposed multiple weaknesses in the nation's public health system. Therefore, the American College of Medical Informatics selected "Rebuilding the Nation's Public Health Informatics Infrastructure" as the theme for its annual symposium. Experts in biomedical informatics and public health discussed strategies to strengthen the US public health information infrastructure through policy, education, research, and development. This article summarizes policy recommendations for the biomedical informatics community postpandemic. First, the nation must perceive the health data infrastructure to be a matter of national security. The nation must further invest significantly more in its health data infrastructure. Investments should include the education and training of the public health workforce as informaticians in this domain are currently limited. Finally, investments should strengthen and expand health data utilities that increasingly play a critical role in exchanging information across public health and healthcare organizations.

Opening up New Geographical Ontologies around Adapting to Climate Change.

Opening up regional ontologies for climate action is a necessary and underexplored dimension of climate change policymaking. This commentary explores how a regional lens might be integrated into the complex mosaic of climate governance, particularly in the context of resilient regions. I argue regional ontologies for climate policymaking could have greater analytical power if integrated into a theoretical framing of action that goes beyond the nation-state, beyond formal policy processes and beyond a strict binary between science and policy. Applying this lens to resilient regions, I argue there are particular opportunities at the regional scale for highlighting diverse perspectives or adaptation issues obscured through a national ontology, using existing transnational data infrastructure and community-led data systems to support the regional ontology and reframing the scale of collective future visions for a climate-adapted world.

Increasing Efficiency of Nautical Chart Production and Accessibility to Marine Environment Data through an Open-Science Compilation Workflow

Electronic Navigational Chart (ENC) data are essential for safe maritime navigation and have multiple other uses in a wide range of enterprises. Charts are relied upon to be as accurate and as up-to-date as possible by the vessels moving vast amounts of products to global ports each year. However, cartographic generalization processes for updating and creating ENCs are complex and time-consuming. Increasing the efficiency of the chart production workflow has been long sought by the nautical charting community. Toward this effort, approaches must consider intended scale, data quality, various chart features, and perform consistently in different scenarios. Additionally, supporting open-science initiatives through standardized open-source workflows will increase marine data accessibility for other disciplines. Therefore, this paper reviews, improves, and integrates available open-source software, and develops new custom generalization tools, for the semi-automated processing of land and hydrographic features per nautical charting specifications. The robustness of this approach is demonstrated in two areas of very different geographic configurations and the effectiveness for use in nautical charting was confirmed by winning the first prize in an international competition. The presented rapid data processing combined with the ENC portrayal of results as a web-service provides new opportunities for applications such as the development of base-maps for marine spatial data infrastructures.

Investigating the Secondary Use of Clinical Research Data: Protocol for a Mixed Methods Study.

The increasing emphasis to share patient data from clinical research has resulted in substantial investments in data repositories and infrastructure. However, it is unclear how shared data are used and whether anticipated benefits are being realized. The purpose of our study is to examine the current utilization of shared clinical research data sets and assess the effects on both scientific research and public health outcomes. Additionally, the study seeks to identify the factors that hinder or facilitate the ethical and efficient use of existing data based on the perspectives of data users. The study will utilize a mixed methods design, incorporating a cross-sectional survey and in-depth interviews. The survey will involve at least 400 clinical researchers, while the in-depth interviews will include 20 to 40 participants who have utilized data from repositories or institutional data access committees. The survey will target a global sample, while the in-depth interviews will focus on individuals who have used data collected from low- and middle-income countries. Quantitative data will be summarized by using descriptive statistics, while multivariable analyses will be used to assess the relationships between variables. Qualitative data will be analyzed through thematic analysis, and the findings will be reported in accordance with the COREQ (Consolidated Criteria for Reporting Qualitative Research) guidelines. The study received ethical approval from the Oxford Tropical Research Ethics Committee in 2020 (reference number: 568-20). The results of the analysis, including both quantitative data and qualitative data, will be available in 2023. The outcomes of our study will offer crucial understanding into the current status of data reuse in clinical research, serving as a basis for guiding future endeavors to enhance the utilization of shared data for the betterment of public health outcomes and for scientific progress. Thai Clinical Trials Registry TCTR20210301006; https://tinyurl.com/2p9atzhr. DERR1-10.2196/44875.

The actual state of the geodetic and cartographic resources and legislation in Poland

Abstract Official data, including national geodetic and cartographic resources, are integrated in a spatial reference system. This study outlines national resources in Poland as an important part of the spatial information infrastructure in the European Community. They also provide reference data, including: geodetic control networks, base map, and topographic maps, for other resources of spatial data infrastructure. The study presents changes in the methods of sharing geo-data and technical standards for their collecting in the last few years, and additionally also presents some aspects of progressing geodetic and cartographic resources and legislation in Poland. It was noted that modern national geoportal or regional and district resources and progress, especially in the form of their standardization of presentation, enable to promote the use of spatial data in Poland and their popularization among the international community.

Rural Public Health Data Challenges During the COVID-19 Pandemic: The Case for Building Better Systems Ahead of a Public Health Crisis.

Rural public health personnel serve communities that have been particularly susceptible to COVID-19 and yet faced the pandemic with far less well-resourced capacity than their urban counterparts. A critical aspect of addressing local health inequities is access to high-quality population data and the capacity to effectively use data to support decision making. However, much of the data required to investigate inequities are not readily available to rural local health departments and the tools and training to analyze data are often lacking. The purpose of our effort was to explore rural data challenges related to COVID-19 and provide recommendations for improving rural data access and capacity ahead of future crises. We gathered qualitative data in 2 phases, more than 8 months apart, from rural public health practice personnel. Initial data were gathered in October-November 2020 regarding rural public health data needs during the COVID-19 pandemic and then to later identify whether the same findings held true in July 2021 or whether access to and capacity to use data to address the pandemic and related inequities improved as the pandemic progressed. In our 4-state exploration focused on access and use of data among rural public health systems to promote health equity in the Northwest United States, we found tremendous and ongoing unmet data needs, challenges with communicating data, and a lack of capacity to meet this public health crisis. Recommendations for addressing these challenges include increasing dedicated resources specifically to rural public health systems, improving data access and infrastructure, and providing dedicated data-related workforce development.

The future of crime data

AbstractCriminology lacks sufficient data for many types of crime that are of great concern to society. This lack of data poses significant problems for determining whether resources are adequate for responding to these crimes or whether programmatic, legislative, or target‐hardening efforts to prevent or reduce their occurrence are effective. Inadequate data about crime also produces a selective and incomplete narrative about crime that makes it easier for political and vested interests to exploit public concerns about crime for their own ends. In this address, I discuss what is needed to resolve these gaps and the ways in which criminologists can support a significant expansion of the crime data infrastructure. Such work is necessary to help ensure the future relevance of criminological research.

A comprehensive methodology report for maternity and perinatal mortality research in the New Zealand Integrated Data Infrastructure.

The highest quality perinatal data in New Zealand is collected and collated by the Perinatal and Maternal Mortality Review Committee (PMMRC) and is made available to a limited number of researchers. Therefore, maternity, and perinatal mortality studies are generally performed on Government-held data. This report offers an alternative approach with in-depth justification for the methodology, while simultaneously improving the understanding of the data sources. A standardised method for creating a comprehensive maternity dataset within the Statistics New Zealand Integrated Data Infrastructure (IDI) was developed and a validation dataset was created to include all births between 2008 and 2017. A close approximation to the PMMRC annual report data was found, with 4.0% over-reporting of perinatal deaths and 0.05% over-reporting of live births in the IDI dataset. Several variables, including important pregnancy risk factors, were validated for use. Limitations to the datasets were explored and additional tables in the IDI were proposed, to include variables on pregnancy complications, ethnicity and country of birth, and socio-economic data. This methodological report describes an opportunity for standardised, high-quality maternity research in New Zealand using the IDI, including a variety of national data sources. Recommendations for further enhancement of these resources have been offered.

Data sustainability: Data governance in data infrastructures across technological and human generations

Data sustainability: Data governance in data infrastructures across technological and human generations

Scientific ethics in the context of digital transformation

This study analyzes the definitions associated with the term digital transformation. Through this study, we aim to contribute to academic research and public discussion of the impact of digital transformation on scientific research. We focus on data infrastructures and their implications for research within and across disciplinary and national boundaries. The study aims to reflect and share our experiences with digital tools in the digital humanities, using approaches from research in science and technology, the history and philosophy of science. In addition, the ambivalent effects of digital transformation on the development of science are considered. The characteristics of digital transformation are analyzed. Issues between digital transformation and scientific ethics were considered. Key words: digitalization, digital transformation, scientific ethics, technology, democratic openness, access, open use, digital infrastructure, plagiarism, Internet space, scientific space.

HBM4EU feasibility studies: Lessons learned in combining health and human biomonitoring studies

The European Human Biomonitoring Initiative (HBM4EU) is a joint program evaluating humans' exposure to several environmental substances and their potential health effects. One of the main objectives of HBM4EU is to make use of human biomonitoring (HBM) to assess human exposure to chemicals in Europe to better understand the associated health impacts and to improve chemical risk assessment. In parallel to HBM studies, health examination surveys (HESs), nutrition/dietary surveys, and disease specific health surveys are conducted in many European countries. In HESs, information collected by questionnaire(s) is supplemented with physical examinations and analysis of clinical and biological biomarkers in biological samples. HBM and health examination survey (HES) use similar data collection methods and infrastructures hence the feasibility of combining these two is explored in this paper. Within HBM4EU, three feasibility studies (in Finland, Germany, and UK/England) were conducted to evaluate opportunities and obstacles of combining HBM and health studies. In this paper we report lessons learned from these feasibility studies. The Finnish feasibility study called KouBio-KUOPIO study was a new initiative without links to existing studies. The German feasibility study added a HBM module to the first follow-up examination of the LIFE-Adult-Study, a population-based cohort study. The UK feasibility integrates a sustainable HBM module into the Health Survey for England (HSfE), an annual health examination survey. Benefits of combining HBM and HESs include the use of shared infrastructures. Furthermore, participants can receive additional health information from HES, and participation rates tend to be higher due to the potential to obtain personal health information. Preparatory phases including obtaining ethical approval can be time-consuming and complicated. Recruitment of participants and low participation rates are common concerns in survey research and therefore designing user-friendly questionnaires with low participant burden is important. Unexpected events such as the COVID-19 pandemic can cause substantial challenges and delays for such studies. Furthermore, experiences from several countries demonstrated that long-term funding for combined studies can be difficult to obtain. In the future, incorporating HBM modules into existing HESs can provide a feasible and cost-effective method to conduct HBM studies and obtain a wide range of relevant data to support public health policies and research.

Identifying the important outcomes to measure for pharmacy-led, clinical services within primary care: A nominal group technique approach.

The introduction of clinical pharmacy services is part of a multi-disciplinary approach to reduce pressure on primary care. Ascertaining the impact of clinical pharmacists in general practice is vital to ensure intended benefits are achieved. However, this is complicated by poor quality evidence, multiple interventions, and a lack of agreement regarding outcome measures. To develop an outcomes framework for clinical pharmacy services delivered in Scottish general practice using a consensus methodology. A modified nominal group technique (NGT) was conducted using Microsoft Teams and Qualtrics. This involved a pre-NGT questionnaire, silent generation of ideas, round robin, discussions, ranking, and a final consensus exercise. A selective sampling strategy recruited experienced pharmacists from Scottish health regions. NGT ranking results were used to signify relative importance of the outcome areas. NGT discussions underwent inductive thematic analysis to explore key areas considered. Overall, 13 (median: 24 years of experience) pharmacists participated, representing 11 of 14 Scottish regions. In total, 21 outcome areas, derived from the literature and a pre-NGT questionnaire, were considered during the NGT ranking exercise. Consensus identified five important outcome areas: Patient Experience, Medication Related Adverse Events, Cost-Effectiveness, Medication Optimisation, and Health Related Quality of Life. Thematic analysis highlighted the importance of the outcome framework's target audience, factors influencing the interpretation of outcomes, and the feasibility of the associated outcome measures. The five key outcome areas will facilitate evidence-based decisions regarding service delivery. Future work should develop a measurement plan, involving routinely collected sources of outcomes data. The feasibility of collecting outcomes in the real-world context should be considered, identifying measures which are easy to collect within existing data infrastructures. This paper describes a replicable method to gain consensus for a national approach to data collection from a strong theoretical basis using an online methodology.

Recommendation of precision medicine application in Indonesia from multiple perspective: a review

Recently, precision medicine has gained much attention. Precision medicine helps reduce trial-and-error prescribing and minimize adverse drug reactions while improving the drug's effectiveness. Precision medicine is a medical method that seeks to maximize healthcare quality by tailoring the healthcare process to each patient's unique characteristics. In the era of rapid technological growth and data explosion, precision medicine's development has become very promising. Development of techniques for obtaining medical data; significant reductions in the cost of bio sequencing; massive development of computing tools, storage systems, and wireless communication systems; as well as the development of data analysis techniques based on artificial intelligence have all become significant driving forces in the advancement of precision medicine. In this manuscript, we summarized previous studies on precision medicine development. In addition, we discussed the opportunities and potential challenges of precision medicine development, particularly in Indonesia. Despite its promising potential, many challenges remain to be overcome to develop precision medicine. Good regulations for patient data ownership and clinical data-sharing, followed by sufficient data infrastructure and human resource capabilities, are the most critical factors in ensuring success in precision medicine. Moreover, guaranteeing data safety, patient privacy, and genome fairness are other issues that must be addressed.