Data Infrastructure Research Articles

Chronic disease outcome metadata from German observational studies – public availability and FAIR principles

Metadata from epidemiological studies, including chronic disease outcome metadata (CDOM), are important to be findable to allow interpretability and reusability. We propose a comprehensive metadata schema and used it to assess public availability and findability of CDOM from German population-based observational studies participating in the consortium National Research Data Infrastructure for Personal Health Data (NFDI4Health). Additionally, principal investigators from the included studies completed a checklist evaluating consistency with FAIR principles (Findability, Accessibility, Interoperability, Reusability) within their studies. Overall, six of sixteen studies had complete publicly available CDOM. The most frequent CDOM source was scientific publications and the most frequently missing metadata were availability of codes of the International Classification of Diseases, Tenth Revision (ICD-10). Principal investigators’ main perceived barriers for consistency with FAIR principles were limited human and financial resources. Our results reveal that CDOM from German population-based studies have incomplete availability and limited findability. There is a need to make CDOM publicly available in searchable platforms or metadata catalogues to improve their FAIRness, which requires human and financial resources.

The Global Infrastructuring of Research Data and its Risks for (Particularly Qualitative and Critical) Education Scholarship

This essay problematizes the so-far absence of broader critical debate on the governing dynamics around research data infrastructuring (not only) in education scholarship. More specifically, it refers to the growing role of large-scale data infrastructures as well as complexes of Research Data Management (RDM) that seek to foster ‘open’, reusable, and increasingly machine-readable (education) science. While research data infrastructuring in general, and RDM in particular, have so far been predominantly discussed from solutionist-positivist perspectives, this essay discusses the underestimated risks that lie in the governance dynamics of research data infrastructuring, including: (1) the gradual narrowing of what kind of education, but also what kind of education research become visible in and through data infrastructures (namely those that build on already dominant hierarchies in the field); (2) a gradual shift from doing research towards ‘doing data’, including a shift of political attention and research funding, as well as (3) effects of normalization, also triggered through extensive dataveillance, shareveillance, and ‘ethical monitoring’ of researchers. This particularly endangers genuinely qualitative and critical research in education.

Promising Practices in US Sponsoring Institutions to Advance Diversity, Equity, and Inclusion in Graduate Medical Education.

Background Best practices to improve diversity, equity, and inclusion (DEI) in the biomedical workforce remain poorly understood. The Accreditation Council for Graduate Medical Education launched the Barbara Ross-Lee, DO, Diversity, Equity, and Inclusion award for sponsoring institutions to celebrate efforts to improve DEI in graduate medical education (GME). Objective To identify themes in practices used by award applicants to improve DEI efforts at their institutions, using a qualitative design. Methods This qualitative study employed an exploratory, inductive approach and constant comparative method to analyze award applications from 2 submission cycles (2020, 2021). Data analysis involved the use of a preliminary codebook of 29 program applications used in a previous study, which was modified and expanded, to perform a subsequent analysis of 12 sponsoring institution applications. Seven adjudication sessions were conducted to ensure coding consistency and resolve disagreements, resulting in the identification of final themes. Results Institutions' approaches to advancing DEI resulted from work within 5 themes and 10 subthemes. The themes encompassed organizational commitment (policies that reflect DEI mission), data infrastructure (tracking recruitment, retention, and inclusion efforts), community connection (service-learning opportunities), diverse team engagement (coproduction with residents), and systematic strategies for DEI support throughout the educational continuum. Consistent across themes was the importance of collaboration, avoiding silos, and the need for a comprehensive longitudinal approach to DEI to achieve a diverse GME workforce. Conclusions This qualitative study identified 5 themes that can inform and guide sponsoring institutions in promoting DEI.

Easing the data sharing burden: perspectives and principles to successfully leverage AD data repositories, infrastructures and functionality

AbstractCritical Path Institute (C‐Path) is a nonprofit organization that brings together patient groups, academic institutions, the pharmaceutical industry and regulatory agencies from around the world to improve public health. C‐Path provides the legal, scientific, and regulatory infrastructure to generate a unique neutral environment for medical product development, supported by more than a decade of experience in data standards development, platform development and hosting, patient‐level data privacy stewardship, data platform security, and controlled access methodology. The Critical Path for Alzheimer’s Disease (CPAD) is a pre‐competitive consortium that aims to promote, support, and manage a diverse source and format of data and facilitate knowledge sharing from the Alzheimer disease (AD) drug development community. The CPAD database currently contains 65 clinical trials and observational studies, with over 93,000 individual anonymized patient‐level records, with global access to many datasets through the C‐Path Online Data Repository (CODR). The unparalleled breadth, depth and wealth of data will enable us to discover yet‐to‐be identified unmet needs and unanswered questions. Further, CODR is interoperable with the Global Alzheimer’s Association Interactive Network (GAAIN), Vivli and other data platforms. The secure and neutral data sharing framework of CPAD has resulted in the development of several regulatory‐grade tools that help optimize drug development and accelerate bringing new therapies to patients in need. Sufficient case examples of successful data sharing are now available and should inform and encourage future data sharing initiatives.

What factors facilitate partnerships between higher education and local mental health services for students? A case study collective

BackgroundHigher education institutions face challenges in providing effective mental health services for diverse student needs. In the UK, discrepancies between healthcare and education service provision create barriers for students and...

Researching and applying open-source WebGIS technology to build national-standard vector geographic database management software

Geographical database management is a crucial component of spatial data infrastructures (SDIs) and is used by government organizations in provinces, regions, and countries. It plays an important role, especially during the 4.0 technology revolution and digital transformation to build e-government and digital government. A complete and scientific management of geographical databases is the most crucial core foundation of a system. The article applies open-source WebGIS technologies such as Geoserver map server, PostGIS data management software, Python programming language, Vue, and Openlayers map library to build spatial data management software. Spatial data is mainly in vector form, built according to national geographic database standards (QCVN 42: 2020/BTNMT). The system has main functional groups such as user account authorization and user groups; and geographic database management, including seven groups: borders, geodesy, population, topography, traffic, surface cover, and hydrology. In addition, the administration of some thematic classes such as drafting plans, managing equipment and units, etc., are performed. The software system is built in a service-oriented manner, serving as the core foundation to facilitate the development of many geo-portal applications.

A collection of multiregistry data on patients at high risk of lung cancer-a Danish retrospective cohort study of nearly 40,000 patients.

Lung cancer (LC) is the leading cause of cancer related deaths, and several countries are implementing screening programs. Risk models have been introduced to refine the LC screening criteria, but the use of real-world data for this task demands a robust data infrastructure and quality. In this retrospective cohort study, we aim to address the different relevant risk factors in terms of data sources, descriptive statistics, completeness and quality. Data on comorbidity, prescription medication, smoking history, consultations, symptoms, familial predispositions, exposures, laboratory data among others were collected for all patients examined on a risk of LC over a 10-year period in the Region of Southern Denmark. Data were delivered from the regional data warehouse as well as the Danish Lung Cancer Registry. Associations between LC and non-LC groups were examined through Chi-squared test (categorical variables) and Wilcoxon signed-rank test (continuous variables that were non-parametric). These associations were investigated on both the original datasets and the subset of patients with complete data. The number of examined individuals increased over the study period and more patients were diagnosed with LC in stage I-II, from 18% in 2009 to 31% in 2018. LC patients were more likely to be older, smoker, with a registered prescription of the included medication. They also exhibited differences in laboratory analysis indicating inflammation and hyponatremia. Weight loss, fatigue and pain were more prevalent in the LC group, while hemoptysis and fever were more common among the non-LC patients. Advanced-stage LC patients experienced a higher rate of symptoms compared to those in the low stages. Within the sub-cohort with complete dataset results, most observed trends persisted, although data on comorbidities were susceptibility to change. This study provides key insights into LC risk assessment using a robust dataset of patients examined for suspected LC. A consistent positive trend in early-stage LC diagnosis was observed throughout the study period. LC patients exhibited distinct smoking behaviors, medication patterns, variations in lab results, and specific symptoms. These discoveries have the potential to enhance discrimination in machine learning-based prediction models, particularly those capable of handling complex distributions. Serving as a detailed account of real-world data collection and processing, the study establishes a foundation for future development of prediction models aimed at facilitating the early referral of LC patients.

Worldwide Status of Geoportal and Geospatial Data Completeness at National Geoportals

Geoportal is a specific web portal used to share geospatial datasets. It is part of a National Spatial Data Infrastructure (NSDI) which is used to disseminate geospatial data. In the NSDI framework, a geoportal is a platform where citizens can search, discover, and access geospatial datasets. Therefore, the availability of geoportal is an essential element in an NSDI. Further, the availability of geospatial datasets, particularly fundamental datasets, is indispensable. This paper aims to portray the worldwide status of geoportal and the completeness of fundamental datasets in those geoportals. We surveyed the availability of geoportal in the 193 countries in the world. We identify and list geoportal addresses worldwide. For every geoportal, we check the types of fundamental datasets available. Fourteen fundamental datasets have been identified and used as a benchmark. The availability of geoportal and fundamental datasets were then calculated and analysed. We found 105 countries in the world own geoportal. Of these geoportals, there were, on average, 63% fundamental datasets available. Only nine countries provide all fourteen fundamental datasets. Twenty-nine countries provide five or fewer fundamental datasets on their geoportal. We also classify geoportal and fundamental datasets availability based on four regions Asia-Pacific, Europe, Africa, and America. European countries have the highest percentage of countries with geoportal ownership as well as the highest available fundamental datasets. The finding complements a worldwide study on geoportal availability which has not been re-evaluated in recent years.

Preface

The Geomatics International Conference (GeoICON) is an annual scientific meeting organized by Department of Geomatics Engineering, Institut Teknologi Sepuluh Nopember Surabaya, Indonesia, since 2016. The conference carried out a theme, “Geospatial Technology for Mapping the Future: Integrated Mapping for Sustainable Development”. This theme is chosen because we believe that harnessing geospatial technology for comprehensive mapping is instrumental in supporting sustainable development across diverse sectors. Geospatial technology provides a powerful means to collect, analyze, and visualize critical spatial data, enabling informed decision-making for environmental conservation, urban planning, disaster management, resource allocation, and many other aspects crucial to sustainable development. By exploring innovative applications and interdisciplinary approaches within geospatial technology, this conference aims to foster collaboration and knowledge exchange, ultimately contributing to a more sustainable and resilient future.The 8th Geomatics International Conference (GeoICON) 2023 presents a diverse and comprehensive agenda with eight focal topics, each addressing crucial aspects of geospatial technology and its applications. These topics such as: (A) Geodesy and Geodynamics; (B) Geology and Geophysics; (C) Spatial Data Infrastructure; (D) Geography and Urban Environment; (E) Agriculture and Forestry; (F) Mining and Energy; (G) Cadastral Mapping; and (H) Marine Environment. The 8th GeoICON 2023 has the rich and multifaceted backgrounds of our esteemed speakers, drawn from various sectors, including government agencies, industries, and esteemed academics. This convergence of expertise ensures a dynamic exchange of knowledge, perspectives, and innovations, fostering a vibrant platform for the exploration and advancement of geospatial technology across diverse domains.List of Keynote Speakers, The 8th GeoICON 2023 Committee are available in this Pdf.

The ABOARD Cohort Panel: public involvement in a Dutch Cohort study on a personalized medicine approach for Alzheimer’s disease

AbstractBackgroundABOARD is a Dutch public‐private partnership aiming for a personalized medicine approach for Alzheimer’s disease. Within this project, the ABOARD Cohort is initiated: a national prospective data infrastructure with the objective to study the AD disease trajectory using PROMS and linked clinical data. The ABOARD Cohort Panel has been installed to provide advice on the design, execution, and direction of the study. By incorporating the perspective of potential participants the meaningfulness and accessibility of the ABOARD Cohort is ensured.MethodRecruitment for the panel has been initiated by the Dutch Brain Foundation via social media and newsletters, and approximately 300 people responded with potential interest. To further inform them, we hosted a webinar explaining the ABOARD Cohort Panel. Hereafter, respondents received an official Panel invitation and initial survey (including principles for collaboration), resulting in 59 people signing up as panellists.ResultPanel members are on average 62 years old, 78% is female, 96% has no migration background, and 52% participated in a panel before. Panellists vary in education level and geographic distribution. The majority has a personal interest in dementia (64%) and/or friends/family with dementia (62%). When asked how they would like to be involved, they indicated wanting to be involved in online questionnaires (100%), online focus groups (56%), and in‐person focus groups (36%). The majority preferred monthly questionnaires (50%) with a duration of 10‐20 minutes (52%). In the first months, panellists provided feedback in online surveys on the ABOARD Cohort information letter and informed consent form. They considered these lengthy, too difficult, and raising questions. An in‐depth live session was held with panellists to test subsequent adjustments, resulting in higher agreement rates on the informed consent procedure of the ABOARD Cohort. Panellists also advised on the communication strategy of the national ABOARD Cohort campaign, which is currently being implemented. In the coming months, panel members will test the usability of the ABOARD Cohort portal and an online cognitive test and advice on dissemination and variables that are being collected.ConclusionThe ABOARD Cohort Panel incorporates the perspective of potential participants and aims to ensure the meaningfulness and accessibility of the ABOARD Cohort.

Educational Monitoring at the Local Level in Tension Between Science and Politics

Dealing with the tensions between scientific claims and those of political-administrative actors marks a central task of municipal educational monitoring. However, empirical research has not systematically examined the relationship between these significant references. Our contribution focuses on how the tensions are addressed and balanced in practice. The results reveal different relations between the two perspectives, producing various practical forms of local educational monitoring. In this way, establishing data infrastructures in municipalities becomes visible as a complex and situational process. The results reflect educational monitoring's theoretical and practical development needs and implications for education research.

Intelligent Urban Transportation Systems (Iuts): A Survey of AI-Driven Innovations and Future Directions

Urban transportation systems are undergoing a transformative evolution driven by advancements in Artificial Intelligence (AI) technologies. This survey paper explores the landscape of Intelligent Urban Transportation Systems (IUTS) by analyzing AI-driven innovations and forecasting future directions. The study reviews key applications of AI in optimizing traffic flow, enhancing public transit efficiency, and improving overall urban mobility. It examines prominent AI techniques such as machine learning, computer vision, and reinforcement learning, highlighting their roles in data-driven decision-making, predictive modeling, and autonomous vehicle deployment. Moreover, the paper investigates challenges such as data privacy, ethical considerations, and infrastructure requirements that accompany the integration of AI in urban transportation. By synthesizing current research and identifying emerging trends, this survey contributes a comprehensive understanding of the opportunities and challenges in shaping the future of intelligent urban transportation systems.

The prototype of a frailty learning health system: The HARMONY Model.

Rapid translation of research findings into clinical practice through innovation is critical to improve health systems and patient outcomes. Access to efficient systems of learning underpinned with real-time data are the future of healthcare. This type of health system will decrease unwarranted clinical variation, accelerate rapid evidence translation, and improve overall healthcare quality. This paper aims to describe The HARMONY model (acHieving dAta-dRiven quality iMprovement to enhance frailty Outcomes using a learNing health sYstem), a new frailty learning health system model of implementation science and practice improvement. The HARMONY model provides a prototype for clinical quality registry infrastructure and partnership within health care. The HARMONY model was applied to the Western Sydney Clinical Frailty Registry as the prototype exemplar. The model networks longitudinal frailty data into an accessible and useable format for learning. Creating local capability that networks current data infrastructures to translate and improve quality of care in real-time. This prototype provides a model of registry data feedback and quality improvement processes in an inpatient aged care and rehabilitation hospital setting to help reduce clinical variation, enhance research translation capacity, and improve care quality.

An Australian housing conditions data infrastructure

For the past two decades, researchers and policy makers have known very little about conditions within Australia’s housing stock due to a lack of systematic and reliable data. In 2022, a collaboration of Australian universities and researchers commissioned a large survey of 22,550 private rental, social rental and homeowner households to build a data infrastructure on the household and demographic characteristics, housing quality and conditions in the Australian housing stock. This is the third and largest instalment in a national series of housing conditions data infrastructures.

Use of Tokens to Unlock Greater Data Sharing in Health Care

This Viewpoint discusses the use of privacy-preserving record linkage, a token-based record linkage system, as a promising avenue for building a data infrastructure system that bridges isolated data.

Advanced Test Technologies for Heterogeneous & 2.5D/3D Packaging

Automotive, AI/ML and 5G markets are demanding increasing levels of integration with respect to memory and compute density. To meet the demand for high-performance products in mission-critical, high-bandwidth applications, the semiconductor industry must look for innovative technologies to continue scaling beyond the silicon manufacturing processes. This is often referred to as “More than Moore”, and one of the most promising methods is heterogenous integration of multiple dies inside the same package (System in Package, or SiP). These dies are typically referred to as chiplets and their interfaces are optimized to operate within a multi-die package for both power, drive strength, interface mechanics and ESD protection. Heterogeneous packages at manufacturing test are faced with quality and reliability challenges due to the accessibility and visibility of traditional test approaches. Dies are assembled over the silicon interposer using very small micro-bumps which may suffer from latent defects such as voids or cracks, potentially posing a reliability risk to the assembled product. On organic substrates, rare resistive shorts, known as bridging shorts, can cause signal integrity and performance degradation. These types of latent defects, although rare, could cause a system failure over time if not screened out during final test or detected during in-field operation. To address these challenges, proteanTecs and Advantest have deployed a joint solution at manufacturing test. proteanTecs provides a monitoring solution for heterogeneous packaging based on chip telemetry, combining deep data with machine learning algorithms. proteanTecs’ patent-protected solution is comprised of small-footprint, digital-only agents for monitoring the performance of the parallel interface. These agents (monitoring IP) are designed for analytics. The fact that they have a small footprint, allows them to be placed next to each pin inside the parallel die-to-die (D2D) PHY, to achieve 100% coverage and with no impact on the signal behavior, quality, or timing. proteanTecs’ data analytics platform then trains models that are deployed at test or in the field. To achieve the highest levels of performance, proteanTecs has partnered with Advantest on the ACS Edge™ for real-time decision-making. ACS Edge™ is a high-performance, highly secure edge compute and analytics solution that enables ultra-fast algorithmic AI decision-making with millisecond latencies during test execution. It connects to the user’s test equipment via a private, high-speed encrypted link and uses the advanced container hub to run the user’s protected applications while protecting and keeping secure the user’s data and analytics. Another key piece of the ACS ecosystem is the ACS Solution Store which provides ease of access to ACS real-time data infrastructure solutions. In this paper, we will provide details for the integrated solution and silicon results. We will highlight how the solution provides new levels of visibility for real-time adaptive testing.

Developing and Evaluating Data Infrastructure and Implementation Tools to Support Cardiometabolic Disease Indicator Data Collection.

Assessment of aerobic exercise (AE) and lipid profiles among individuals with spinal cord injury or disease (SCI/D) is critical for cardiometabolic disease (CMD) risk estimation. To utilize an artificial intelligence (AI) tool for extracting indicator data and education tools to enable routine CMD indicator data collection in inpatient/outpatient settings, and to describe and evaluate the recall of AE levels and lipid profile assessment completion rates across care settings among adults with subacute and chronic SCI/D. A cross-sectional convenience sample of patients affiliated with University Health Network's SCI/D rehabilitation program and outpatients affiliated with SCI Ontario participated. The SCI-HIGH CMD intermediary outcome (IO) and final outcome (FO) indicator surveys were administered, using an AI tool to extract responses. Practice gaps were prospectively identified, and implementation tools were created to address gaps. Univariate and bivariate descriptive analyses were used. The AI tool had <2% error rate for data extraction. Adults with SCI/D (n = 251; 124 IO, mean age 61; 127 FO, mean age 55; p = .004) completed the surveys. Fourteen percent of inpatients versus 48% of outpatients reported being taught AE. Fifteen percent of inpatients and 51% of outpatients recalled a lipid assessment (p < .01). Algorithms and education tools were developed to address identified knowledge gaps in patient AE and lipid assessments. Compelling CMD health service gaps warrant immediate attention to achieve AE and lipid assessment guideline adherence. AI indicator extraction paired with implementation tools may facilitate indicator deployment and modify CMD risk.

Force Health Surveillance inNATO Does Not Meet the Needs ofIts Users: A Structured Evaluation ofEpiNATO-2.

Disease and non-battle injuries (DNBIs) cause substantial losses among military personnel. NATO has monitored DNBIs among its personnel since 1996 using multiple versions of a tool now called EpiNATO-2, but the surveillance system has never been systematically evaluated. Following a request from NATO to the CDC, the objective of this study was to assess surveillance system attributes of EpiNATO-2 using CDC's updated guidelines for evaluating public health surveillance systems. Between June and October 2022, a literature review and key informant interviews were conducted to assess the following attributes: usefulness, simplicity, flexibility, data quality, acceptability, sensitivity, positive predictive value, representativeness, timeliness, stability, informatics system quality, informatics service quality, and informatics interoperability. Key informant interviews were conducted in Kosovo, Germany, and remotely with EpiNATO-2 users spanning three levels: clinical and data entry personnel (tactical level); regional medical and public health officers (operational level); and senior commanders and other governmental entities (strategic level). Fourteen EpiNATO-2 users participated in interviews, representing 3 of the 5 major NATO missions, 3 partner entities, and 7 nationalities. All users (100%) reported that the system did not meet their needs, with most users noting the following challenges: lack of clearly defined system objectives; poor data quality due to ambiguous case definitions and frequently unsubmitted reports (37% missing during January to June 2022); long delay between the occurrence of health events and the availability of corresponding data (≥2 weeks); and an antiquated and inflexible data management system. Overall, performance was deemed unsatisfactory on 11 of the 13 attributes. This multinational sample of EpiNATO-2 users at all military levels reported that the system is currently not useful with respect to its stated objectives. Opportunities exist to improve the performance and usefulness of EpiNATO-2: improve case definitions, modernize data infrastructure, and regularly evaluate the surveillance system.

The Digital Transformation of the Banking Industry in the Era of Big Data

Big data analytics has become an instrumental tool for digital transformation in the banking industry. By leveraging massive volumes of data, banks can gain valuable insights to optimize operations, reduce risks, and improve customer experience. For risk management, big data techniques enable banks to detect fraud and money laundering activities more accurately. In customer relationship management, big data analytics allows banks to better understand customer needs, provide personalized services, and enhance loyalty. Furthermore, big data can inform investment decisions by identifying new opportunities and mitigating risks. While big data brings significant opportunities, it also presents challenges in data governance, privacy, and security that require prudent management. Technical and business model innovations must align to fully realize the potential of big data. Banks should invest in big data infrastructure, promote a data-driven culture, and pursue strategic partnerships. Regulators and policymakers should establish flexible guidelines to encourage big data adoption while protecting consumer privacy. By harnessing big data responsibly, banks can transform into truly customer-centric digital organizations. Overall, big data is redefining the banking industry, and continuous digital transformation is imperative for banks to thrive in the new data-driven era.

Enhancing Cancer Economic Data Resources

Background: Cancer diagnosis and treatment can substantially affect health and financial outcomes for patients and families. Research in health care delivery across the cancer control continuum includes diverse activities led by multiple government and private sector organizations. Assessing the economic drivers and influencing factors associated with costs across this continuum is challenging as organizations leading research efforts often do not have forums to share data, develop linkages, and explore collaborative opportunities. Objective: To describe the objectives, activities, and goals of the Interagency Consortium to Promote Health Economics Research on Cancer (HEROiC) to strengthen data resources and capacity for collaborative patient-focused cancer health economics research. Main Argument: HEROiC’s goals include assessing the economic burden of cancer; examining the effects of policies, health care setting/system factors, and health service delivery approaches across the cancer control continuum; and enhancing collaborations among researchers and organizations. Conclusions: Data resources to study economic outcomes associated with cancer control are highly fragmented; HEROIC provides a forum to collaboratively develop, enhance, and utilize data resources and infrastructure for patient-centered cancer health economics research. This includes sharing data resources, developing linkages, identifying new data collection venues, and creating and supporting the dissemination of evidence-based information to diverse stakeholders. These efforts provide critical information to address the economic burden of cancer. Relevance to the Special Issue: Cancer diagnosis and treatment affect patient health and financial outcomes. This commentary describes how HEROiC will enhance research data infrastructure and collaborations to support patient-centered research with the goal of reducing the economic burden of cancer.