Data Definition Research Articles

Research on BIM sub-model data of construction engineering based on MVD

BIM technology has been widely applied to various stages of construction projects in China. The data required at each stage and scenario of construction projects varies, necessitating the definition of target BIM data according to data needs. When BIM software generates IFC models, different MVD templates can be configured to output BIM sub-models. To study the characteristics of data configuration for different MVDs, this article takes construction projects as an example and designs an analysis experiment for BIM sub-model output based on MVD. It analyzes the differences and characteristics of IFC model data from both the overall project level and the professional component level according to key indicators, with the aim of providing references for construction project personnel to select matching MVDs, ensuring the effectiveness of BIM sub-models, and meeting the needs of business scenario applications.

National early warning systems for emerging AMR in high-income countries: a systematic review

Abstract Background An urgent need of implementing national surveillance systems for timely detection and reporting of emerging antimicrobial resistance (AMR) was recently advocated by the World Health Organization (WHO). However, public information on existing national early warning systems (EWSs) is often incomplete. Furthermore, when findings are available, understanding these systems is challenging due to different approaches used for data collection, reporting and definitions, with a comprehensive overview on this topic currently lacking. The aim of this study was to map existing EWSs for emerging AMR, focusing on high-income countries, and describe their main characteristics. Methods A systematic review was performed on bibliographic databases, and a targeted search was conducted on national websites. Any article, report or webpage describing national EWSs in high-income countries was eligible for inclusion. EWSs were identified considering the emerging AMR reporting WHO framework. Results We identified seven national EWSs in 72 high-income countries: two (Australia, Japan) in the East Asia and Pacific Region, three (France, Sweden, United Kingdom) in Europe and Central Asia, and two (United States, Canada) in North America. The systems were established quite recently; in most cases they covered both community and hospital settings, but their main characteristics varied widely across countries in terms of organization and microorganisms under surveillance, with also different definitions of emerging AMR and alert functioning. A formal system assessment was available only in Australia. Conclusions A broader implementation and investment of national surveillance systems that allow early detection of emerging AMR is still needed to establish EWSs in countries and regions lacking such capabilities. A more standardized data collection and reporting is also advisable to improve cooperation on a global scale. Key messages • This study provides a synthesis of publicly available information on national EWSs for emerging AMR in high-income countries, highlighting the urgent need for a broader implementation of such systems. • Main characteristics of EWSs have been outlined, varying widely across countries. Findings could help stakeholders in strengthening current standard national AMR surveillance systems.

Standardised and hierarchically classified heart failure and complementary disease monitoring outcome measures: european Unified Registries for heart Care evaluation and randomised trials (EuroHeart).

The lack of standardised definitions for heart failure outcome measures limits the ability to reliably assess effectiveness of heart failure therapies. The European Unified Registries for Heart Care Evaluation and Randomised Trials (EuroHeart) aimed to produce a catalogue of internationally endorsed data definitions for heart failure outcome measures. Following the EuroHeart methods for the development of cardiovascular data standards, a working group was formed of representatives from the European Society of Cardiology Heart Failure Association and other leading heart failure experts. A systematic review of observational and randomised clinical trials identified current outcome measures, which was supplemented by clinical practice guidelines and existing registries for contemporary definitions. A modified Delphi process was employed to gain consensus for variable inclusion and whether collection should be mandatory (Level 1) or optional (Level 2) within EuroHeart. In addition, a set of complementary outcome measures were identified by the Working Group as of scientific and clinical importance for longitudinal monitoring for people with heart failure. Five Level 1 and two Level 2 outcome measures were selected and defined, alongside five complementary monitoring outcomes for patients with heart failure. We present a structured, hierarchical catalogue of internationally endorsed heart failure outcome measures. This will facilitate quality improvement, high quality observational research, registry-based trials, and post market surveillance of medical devices.

Expert consensus on core data elements and definitions for adult invasive mechanical ventilation (2024)

Invasive mechanical ventilation stands as a principal therapeutic intervention in the intensive care unit (ICU), yet there exists a lack of standardized data management protocols. To address this, the Chinese Society of Critical Care Medicine organized a working group from disciplines including critical care medicine, respiratory and critical care medicine, respiratory therapy, and health informatics. This group formulated the Expert consensus on core data elements and definitions for adult invasive mechanical ventilation (2024). This consensus established a standardized framework comprising 246 key data elements through a comprehensive literature review. The elements cover patient demographic information, clinical data, ICU admission details, parameters of invasive mechanical ventilation therapy, vital signs, and blood gas analysis, aiming to standardize data management in both clinical practice and research. Moreover, the consensus accentuates the necessity for continuous updates to the data element standards in line with advancements in mechanical ventilation technology, ensuring that they reflect the latest clinical practices and technological progress.

Efficient Data Exchange between WebAssembly Modules

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

When we talk about Big Data, What do we really mean? Toward a more precise definition of Big Data.

Despite the lack of consensus on an official definition of Big Data, research and studies have continued to progress based on this "no consensus" stance over the years. However, the lack of a clear definition and scope for Big Data results in scientific research and communication lacking a common ground. Even with the popular "V" characteristics, Big Data remains elusive. The term is broad and is used differently in research, often referring to entirely different concepts, which is rarely stated explicitly in papers. While many studies and reviews attempt to draw a comprehensive understanding of Big Data, there has been little systematic research on the position and practical implications of the term Big Data in research environments. To address this gap, this paper presents a Systematic Literature Review (SLR) on secondary studies to provide a comprehensive overview of how Big Data is used and understood across different scientific domains. Our objective was to monitor the application of the Big Data concept in science, identify which technologies are prevalent in which fields, and investigate the discrepancies between the theoretical understanding and practical usage of the term. Our study found that various Big Data technologies are being used in different scientific fields, including machine learning algorithms, distributed computing frameworks, and other tools. These manifestations of Big Data can be classified into four major categories: abstract concepts, large datasets, machine learning techniques, and the Big Data ecosystem. This study revealed that despite the general agreement on the "V" characteristics, researchers in different scientific fields have varied implicit understandings of Big Data. These implicit understandings significantly influence the content and discussions of studies involving Big Data, although they are often not explicitly stated. We call for a clearer articulation of the meaning of Big Data in research to facilitate smoother scientific communication.

Creating a Data Cleaning and Pre-Processing Module for Generalisable Data Linkage

ObjectiveAcute renal replacement therapy (RRT) is an important life saving technology used in critical care. However, variation in clinical practice for initiation and maintenance of RRT can increase healthcare costs and worsen patient outcomes. In Alberta, Canada, a province-wide electronic health record provides a unique opportunity to measure and evaluate clinical practice. The present study aims to improve the quality of RRT delivery in Alberta critical care units by providing feedback on key performance indicators (KPIs) for RRT based on current evidence. ApproachKPIs for RRT included an initiation pathway based on threshold lab values, and measures of RRT quality such as the time from order to treatment initiation, the average life of dialysis filters, and the actual vs. prescribed fluid removal. Clinical KPI definitions were mapped to data available in the electronic health record to evaluate current practice and track changes in KPIs over time. ResultsMapping the clinical guidelines to the electronic health record took significant time and effort in a large team with expertise on both the data and clinical sides. Each KPI data definition went through several cycles of development, validation and refinement before being included in the dashboards and reports given to participating sites. ConclusionsProviding accurate data on RRT ordering and delivery practices is an important step in aligning acute RRT delivery with current best practice, ultimately improving the quality of care and reducing unnecessary costs. ImplicationsElectronic health records provide a powerful tool for evaluating clinical decisions and implementing best practice guidelines.

Statistics Evolution and Revolution to Meet Data Science Challenges

AbstractThe advent of the Big Data era has necessitated a transformational shift in statistical research, responding to the novel demands of data science. Despite extensive discourse within statistical communities on confronting these emerging challenges, we offer our unique perspectives, underscoring the extended responsibilities of statisticians in pre-analysis and post-analysis tasks. Moreover, we propose a new definition and classification of Big Data based on data sources: Type I Big Data, which is the result of aggregating a large number of small datasets via data sharing and curation, and Type II Big Data, which is the Real-World Data (RWD) amassed from business operations and practices. Each category necessitates distinct data preprocessing and preparation (DPP) methods, and the objectives of analysis as well as the interpretation of results can significantly diverge between these two types of Big Data. We further suggest that the statistical communities should consider adopting and rapidly incorporating new paradigms and cultures by learning from other disciplines. Particularly, beyond Breiman’s (Stat Sci 16(3):199–231, 2021) two modeling cultures, statisticians may need to pay more attention to a newly emerging third culture: the integration of algorithmic modeling with multi-scale dynamic modeling based on fundamental physics laws or mechanisms that generate the data. We draw from our experience in numerous related research projects to elucidate these novel concepts and perspectives.

A Review on Testosterone: Estradiol Ratio-Does It Matter, How Do You Measure It, and Can You Optimize It?

There is a natural balance between the major sex steroids, testosterone and estradiol, controlled by gonadal secretion and peripheral conversion by aromatase. This balance is impacted by a variety of inborn and acquired conditions, and, more recently, by a growing use of exogenous testosterone therapy and off-label aromatase use under the guise of "men's health." We summarize reported testosterone:estradiol ratios, both naturally occurring and with pharmacologic manipulation and consider the ramifications of significant changes in these ratios. However, significant limitations exist in terms of steroid separation and measurement techniques, timing of samples, and lack of consistency from one assay to another, as well as definition of normative data. Limited data on the testosterone:estradiol ratio in men exists, particularly due to the scan data on concurrent estradiol values in men receiving testosterone therapy or aromatase inhibitors. Nonetheless, there seems to be a range of apparently beneficial values of the testosterone: estradiol radio at between 10 and 30, calculated as: testosterone in ng/dL/estradiol in pg/mL. Higher values appear to be associated with improved spermatogenesis and reduced bone density while lower values are associated with thyroid dysfunction. While there is growing awareness of the significance of the testosterone:estradiol ratio, and a sense of a desired range, the optimal value has not yet been determined. Further work is needed to clarify the measurement strategies and clearly-defined outcome measures related to the testosterone:estradiol ratio.

Implementasi Perlindungan Data Pribadi Berdasarkan Undang-Undang Nomor 11 Tahun 2020

The protection of personal data is becoming increasingly important in today's digital era, where information and communication technology is rapidly advancing. This raises the risk of data breaches that can have negative impacts, ranging from identity theft to harassment. The personal data protection law has become crucial for regulating the management and protection of such data, with the aim of providing a clear legal framework. Personal data protection includes the definition and scope of personal data as well as fundamental principles such as transparency, limitation of use, and data security. The regulation of personal data protection in Indonesia has evolved over time, with Law Number 11 of 2020 concerning Information and Electronic Transactions serving as a more comprehensive legal foundation. This research employs a descriptive approach, document analysis, and previous studies. Data analysis is conducted qualitatively to describe the implementation of the Personal Data Protection Law in Indonesia as well as the factors influencing it. The implementation of this law faces challenges, such as the gap between policy formulation and its execution, a lack of public awareness, and inadequate infrastructure. However, supporting factors like public campaigns and infrastructure improvements can strengthen the implementation of the law. Despite the challenges, the implementation of the Personal Data Protection Law in Indonesia can be improved through increased public awareness, investment in infrastructure, and collaboration among stakeholders. This recommendation is expected to effectively strengthen personal data protection in Indonesia.

Synergies and complementarities between ecosystem accounting and the Red List of Ecosystems.

Safeguarding biodiversity and human well-being depends on sustaining ecosystems. Two global standards for quantifying ecosystem change, the International Union for Conservation of Nature Red List of Ecosystems (RLE) and the United Nations System of Environmental-Economic Accounting Ecosystem Accounting (EA), underpin headline indicators for the Kunming-Montreal Global Biodiversity Framework. We analyse similarities and differences between the standards to understand their complementary roles in environmental policy and decision-making. The standards share key concepts, definitions of ecosystems and spatial data needs, meaning that similar data can be used in both. Their complementarities stem from their differing purposes and thus how data are analysed and interpreted. Although both record changes in ecosystem extent and condition, the RLE analyses the magnitude of change in terms of risk of ecosystem collapse and biodiversity loss, whereas EA links ecosystem change with the ecosystem's contributions to people and the economy. We recommend that the RLE and EA should not be treated as unrelated nor undertaken in isolation. Developing them in concert can exploit their complementarities while ensuring consistency in foundational data, in particular ecosystem classifications, maps and condition variables. Finding pathways for co-investment in foundational data, and for knowledge-sharing between people and organizations who undertake RLE assessments and accounting, will improve both processes and outcomes for biodiversity, ecosystems and people.

Personal data protection: Between human rights protection and national security

This study aimed to ascertain the equilibrium between safeguarding citizensʼ personal data and maintaining national security in a digital world. The research analysed the regulatory frameworks and judicial practices of the European Union (EU), Ukraine, and the USA through several methodologies. EU regulation offers the most stringent personal data protection, with substantial penalties for infractions. Ukrainian legislation is progressively aligning with European standards; however, procedures for protection and liability require enhancement. The research indicated an increasing tendency in the utilization of artificial intelligence and big data technologies within national security, presenting new issues for safeguarding personal information from disclosure. The research investigated the ethical implications of utilizing such technologies and their potential effects on citizen privacy. The study examined global regulatory procedures, focusing on the European Court of Human Rightsʼ approach to balancing the objectives of safeguarding personal information and national security. The research identified the necessity to broaden the definition of personal data to include communal dimensions and indirect ramifications of data processing in the context of big data and the Internet of Things. This studyʼs findings underscore the importance of an interdisciplinary approach to personal data security, encompassing legal, technological, ethical, and social dimensions. The analysis presented a conceptual model for harmonizing the regulatory framework for the protection of privileged information, including contemporary technical problems and national security requirements. The research holds practical importance for enhancing regulations regarding personal data protection and can assist the formulation of information security plans

2024 ACC/AHA Key Data Elements and Definitions for Social Determinants of Health in Cardiology: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Data Standards.

2024 ACC/AHA Key Data Elements and Definitions for Social Determinants of Health in Cardiology: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Data Standards.

Problem-solving? No, problem-opening! A method to reframe and teach data ethics as a transdisciplinary endeavour

Starting from the recognition of the limits of today's common essentialist and axiological understandings of data and ethics, in this article we make the case for an ecosystemic understanding of data ethics (for the city) that accounts for the inherent value-laden entanglements and unintended (both positive and negative) consequences of the development, implementation, and use of data-driven technologies in real-life contexts. To operationalize our view, we conceived and taught a master course titled ‘Ethics for the data-driven city’ delivered within the Department of Urbanism at the Delft University of Technology. By endorsing a definition of data as a sociotechnical process, of ethics as a collective practice, and of the city as a complex system, the course enacts a transdisciplinary approach and problem-opening method that compel students to recognize and tackle the unavoidable multifacetedness of all ethical stances, as well as the intrinsic open-endedness of all tech solutions, thus seeking a fair balance for the whole data-driven urban environment. The article discusses the results of the teaching experience, which took the form of a research-and-design workshop, alongside the students’ feedback and further pedagogical developments.

Securing Privacy: Safeguarding Against Cyber Threats in the UAE and Morocco

This research evaluates the effectiveness of the current legal frameworks in the United Arab Emirates (UAE) and Morocco in organizing cybersecurity risks and safeguarding the privacy of individuals in the digital world. The research focuses on assessing the response of the legislation of these countries to the constantly evolving cybersecurity threats and determining its adequacy in ensuring the protection of the privacy of individuals while complying with European Union (EU) standards. The study explores different legal perspectives on protecting privacy rights by comparing the legislation from Morocco and the UAE with recent EU regulations. It recommended that effective control over electronic services be sought to mitigate illegal forms of privacy and personal data abuse. The legislative body of the UAE is urged to develop legislation on the right to information, taking cues from the legislative actions of Morocco. Furthermore, the Moroccan legislature emphasizes expanding the definition of personal data beyond the confines of sound and image.

Automated Versus Semi-Automated Lab Value Extraction for the VA Cardiac Surgical Quality Improvement Program

IntroductionThe Veterans Affairs Surgical Quality Improvement Program (VASQIP) trains surgical quality nurses (SQNs) at each Veterans Affairs (VA) hospital to extract or verify 187 variables from the medical record for all cardiac surgical cases. For ten preoperative laboratory values, VASQIP has a semiautomated (SA) system in which local lab values are automatically extracted, verified by SQNs, and lab values recorded at other VA facilities are manually extracted. The objective of this study was to develop and validate a method to automate the extraction of these ten preoperative laboratory values and compare results with the current SA method. Materials and methodsWe developed methods to extract ten preoperative laboratory values and measurement dates from the VA Corporate Data Warehouse using Logical Observation Identifiers Names and Codes. Automated (A) versus SA information extraction was compared in terms of agreement, conformance to data definitions, proximity to surgery, and missingness. ResultsFor surgeries with both A and SA lab values, the intraclass correlation coefficients for the ten variables ranged from 0.90 to 0.98. For several variables, the A method resulted in much lower rates of missing data (e.g., 2.4% versus 22.5% missing data for high-density lipoprotein) and eliminated out-of-date-range entries. ConclusionsAlthough SQN-extracted data are widely considered the gold standard within National Surgical Quality Improvement Programs, there may be advantages to fully automating extraction of lab values, including high congruence with SA SQN-extracted or verified values and lower rates of missingness and out-of-date-range data.

How are nature-based interventions defined in mild cognitive impairment and dementia studies? A conceptual systematic review and novel taxonomy.

To systematically review research testing nature-based interventions for people living with mild cognitive impairment or dementia, and to report how authors have defined their interventions by presenting a taxonomy of the nature-based interventions. A conceptual systematic review of research published between 2008 and 2024 investigating nature-based interventions for people living with mild cognitive impairment or dementia was conducted. Three reviewers contributed independently. Exclusion criteria: not specifying if participants had mild cognitive impairment or dementia, only recruiting caregivers, no primary data, study protocols, abstracts, reviews, not peer-reviewed journal articles and any other grey literature. Intervention descriptions within the papers were thematically analysed. Fifty-two articles reporting fifty-one studies were included. The most common interventions were nature virtual reality (VR technology) and gardening. From the definition data, we produced a taxonomy with overarching domains of: (a) Other terms used; (b) Characteristics; (c) Activities. Subdomains included: development or approach, modes of action, location, physical features, and activities. Some interventions could be grouped. Structure and standardisation of the interventions varied, with a lack of clear reporting. This taxonomy provides conceptualisations of nature-based interventions that can be used by future researchers to guide the development, evaluation and reporting of robust interventions in this area.

Characteristics of surveillance systems for suicide and self-harm: A scoping review.

Suicide is a complex public health issue. Surveillance systems play a vital role in identifying trends and epidemiologic needs, informing public health strategies, and tailoring effective context-based suicide prevention interventions. To identify and summarise the characteristics of specific surveillance systems and general health behaviour that include data onsuicide and self-harm. A scoping review following the JBI recommendations and PRISMA-ScR guidelines identified 29 relevant studies on suicide and self-harm surveillance systems. A systematic search was performed on Cinahl, Embase, Lilacs-Latin American and Caribbean Health Sciences Literature, PubMed-US National Library of Medicine, Scopus, and Google Scholar. The eligibility criteria include papers that use qualitative, quantitative or mixed methods with no restrictions on time or language. The following papers were excluded regarding euthanasia and assisted suicide, as well as papers that did not explicitly describe suicide, self-harm, and surveillance systems. Two researchers independently screened the materials for eligibility and extracted data from the included studies. Data analysis was conducted using content analysis. Twenty-nine references were included, and 30 surveillance systems were identified and classified into general health behaviour surveillance (n = 15) and specific systems for suicide and self-harm (n = 15). General health behaviour systems often operate at national data collection level, collecting non-fatal data in healthcare settings, mainly emergency departments. The specific systems exhibited greater variability in terms of context, involved actors, data collection level, data collection procedures, and case classification. Limitations found by the studies pointed mostly to case definitions and data quality. Co-production, intersectoral collaboration, clear case definition criteria and data standardisation are essential to improve surveillance systems for suicide and self-harm. This review identified the characteristics of surveillance systems for suicide and self-harm. Monitoring and evaluation are crucial for ongoing relevance and impact on prevention efforts.

The challenges and opportunities of continuous data quality improvement for healthcare administration data.

Various data quality issues have prevented healthcare administration data from being fully utilized when dealing with problems ranging from COVID-19 contact tracing to controlling healthcare costs. (i) Describe the currently adopted approaches and practices for understanding and improving the quality of healthcare administration data. (ii) Explore the challenges and opportunities to achieve continuous quality improvement for such data. We used a qualitative approach to obtain rich contextual data through semi-structured interviews conducted at a state health agency regarding Medicaid claims and reimbursement data. We interviewed all data stewards knowledgeable about the data quality issues experienced at the agency. The qualitative data were analyzed using the Framework method. Sixteen themes emerged from our analysis, collected under 4 categories: (i) Defect characteristics: Data defects showed variability, frequently remained obscure, and led to negative outcomes. Detecting and resolving them was often difficult, and the work required often exceeded the organizational boundaries. (ii) Current process and people issues: The agency adopted primarily ad-hoc, manual approaches to resolving data quality problems leading to work frustration. (iii) Challenges: Communication and lack of knowledge about legacy software systems and the data maintained in them constituted challenges, followed by different standards used by various organizations and vendors, and data verification difficulties. (iv) Opportunities: Training, tool support, and standardization of data definitions emerged as immediate opportunities to improve data quality. Our results can be useful to similar agencies on their journey toward becoming learning health organizations leveraging data assets effectively and efficiently.

It is not just a click: protection of in-game behaviour data as biometric data under the GDPR

This article investigates the potential classification of players’ in-game behaviour data from basic computer peripherals, like keyboards and mouses (behavioural telemetry), as biometric data under the General Data Protection Regulation (GDPR). Modern technologies allow these types of data to reveal a wealth of personal information, including age, gender, emotional states, collaboration abilities, problem-solving skills, economic status, consumption habits, and character traits. The analysis is structured into three sections: understanding the nature of in-game behaviour data, assessing its alignment with the GDPR’s definition of biometric data, and examining the repercussions for gaming companies if such data is deemed biometric. The conclusion asserts that behavioural telemetry and gameplay metrics can indeed be considered biometric under the GDPR, as it plays a crucial role in player identification and exhibits similarities with biometric systems in the context of game analytics processes. This classification implies heightened legal responsibilities for gaming companies, triggering risk-based provisions within the GDPR. The article introduces a novel perspective by suggesting that seemingly innocuous in-game behaviour data may fall under the purview of biometric data.