Standard Data Format Research Articles

Examining Transit Activity Data from StreetLight Using Ridership Data from Virginia Transit Agencies

Researchers and planners require ridership data to study factors that influence people’s choice to use transit. However, the data can be challenging to obtain directly from transit agencies. Crowdsourced big data platforms such as StreetLight promise easily accessible ridership-related data in standard formats. It is important to assess the reliability of these data, particularly for transit agencies serving small- to medium-sized cities, which are less likely than agencies in large cities to have ridership data in standard formats. In this study, hourly ridership data from 2019 were collected from four bus transit agencies and one rail agency in Virginia and compared with StreetLight data. Comparisons for rail data were made on a station-to-station basis. Bus data comparisons were made at the city-limit level and at an aggregated-route level for each agency. In sum, StreetLight could not provide 2019 bus activity data for more than half of the localities in Virginia. Comparisons between transit agency and StreetLight data showed smaller root mean square errors when longer periods were analyzed (e.g., 4 versus 2 months). Although order of magnitude of ridership may indicate whether StreetLight can provide bus activity data, the former was not found to be correlated with the accuracy of the latter. Using data from StreetLight’s current algorithm might not be appropriate without verification against agency data, especially for agencies in small- to medium-sized cities.

Critical Factors Influencing Data Use and Utilization in Health Systems: A Focus on Data and Interoperability Standards for Health Information Exchange (HIE) in Uganda’s Healthcare System

Abstract Limited use and utilization of health data in Uganda's healthcare system is influenced by various factors including; absence of standardized data formats, lack of data governance policies, unskilled data management human resources, limited data use culture, poor data quality, complacency, limited political will, and lack of interoperability. Worse still, the existing data interoperability standards majorly designed for developed world health systems, may not be suitable for Uganda, due to differences in health information maturity levels. This paper examines the critical factors affecting data use and utilization in Uganda, specifically focusing on interoperability and data standards. A cross-sectional design was employed in this study in selected health facilities with electronic systems in Uganda. Purposive sampling was used to select sites and participants based on predetermined criteria. The study included 28 health center IVs, referral hospitals, government entities, and eHealth stakeholders. The findings highlight key factors including limited data collection and management systems, poor data quality, inadequate data analysis capacity, absence of data exchange standards, limited technology access, inadequate funding, and deficiencies in data sharing and dissemination. These factors if addressed through data and interoperability standards, can play a pivotal role in promoting efficient and effective healthcare delivery and outcomes in Uganda.

Application of Machine Learning in Healthcare and Medicine: A Review

This extensive literature review investigates the integration of Machine Learning (ML) into the healthcare sector, uncovering its potential, challenges, and strategic resolutions. The main objective is to comprehensively explore how ML is incorporated into medical practices, demonstrate its impact, and provide relevant solutions. The research motivation stems from the necessity to comprehend the convergence of ML and healthcare services, given its intricate implications. Through meticulous analysis of existing research, this method elucidates the broad spectrum of ML applications in disease prediction and personalized treatment. The research's precision lies in dissecting methodologies, scrutinizing studies, and extrapolating critical insights. The article establishes that ML has succeeded in various aspects of medical care. In certain studies, ML algorithms, especially Convolutional Neural Networks (CNNs), have achieved high accuracy in diagnosing diseases such as lung cancer, colorectal cancer, brain tumors, and breast tumors. Apart from CNNs, other algorithms like SVM, RF, k-NN, and DT have also proven effective. Evaluations based on accuracy and F1-score indicate satisfactory results, with some studies exceeding 90% accuracy. This principal finding underscores the impressive accuracy of ML algorithms in diagnosing diverse medical conditions. This outcome signifies the transformative potential of ML in reshaping conventional diagnostic techniques. Discussions revolve around challenges like data quality, security risks, potential misinterpretations, and obstacles in integrating ML into clinical realms. To mitigate these, multifaceted solutions are proposed, encompassing standardized data formats, robust encryption, model interpretation, clinician training, and stakeholder collaboration.

Methods of building database to establish flooding map for coastal areas using a combination of artificial intelligence and GIS technology

As a country with a coastline stretching from North to South, in recent years natural disasters, especially floods and inundation, have severely affected people and properties in Vietnam. In order to prevent and control natural disasters and adapt to climate change, there have been many researches to establish the flood-related map in the country. Among the methods of creating flood maps, the application of AI (Artificial Intelligence) combined with GIS (Geography Information System) has outstanding advantages due to its ability to handle a mixture of many types of input data in a geographical space unification. This method is also used widely in the world in general and Vietnam in particular. When applying the aforementioned method, building the input database of machine learning and artificial intelligence models is an essential issue. Based on the Sentinel-1, Landsat 8/9 images, digital elevation model (DEM), and soil maps, the authors have built the input database for modeling by using AI models. This paper introduces the method of building the input database for making flood maps using machine learning, and artificial intelligence combined with GIS. The computation process is divided into two steps: (1) Editing the component data layers from input data and (2) Standardization of data to transfer the component data layers into the same unit with the standard data format of Weka software. The research’s results are 11 data layers including the flood map in the past, elevation, slope, slope direction, curvature, terrain energy, geology, land use, soil, NDVI, NDWI for Quang Nam province.

Using DICONDE for NDT Data Exchange

The processing of multiple data sets from different NDT vendors and systems can be challenging. Accessing the data requires dealing with different and often proprietary data formats. In addition to that the correct spatial alignment requires knowledge and information about the acquisition system and must consider different approaches for markers and reference coordinate systems. The key features of NDE 4.0 and digital twin concepts are storage and interoperability with standard data formats. In this example data from thermography, manual ultrasonic testing and immersion ultrasonic testing are considered. For established devices multiple tools exist for the conversion of data into image-based formats. However, besides the lack of spatial alignment there is a need to access the raw data for further processing. In medical and CT-like applications the DICOM format has become a widely used exchange format. Its derivative DICONDE transfers this into the field of NDT. This study demonstrates the use of this format for data processing from multiple NDT methods and addresses issues related to interoperability and the integration of less structured data from inspections.

Digital Twins in the Construction Industry: A Comprehensive Review of Current Implementations, Enabling Technologies, and Future Directions

This paper presents a comprehensive understanding of current digital twin (DT) implementations in the construction industry, along with providing an overview of technologies enabling the operation of DTs in the industry. To this end, 145 publications were identified using a systematic literature review. The results revealed eight key areas of DT implementation including (i) virtual design, (ii) project planning and management, (iii) asset management and maintenance, (iv) safety management, (v) energy efficiency and sustainability, (vi) quality control and management, (vii) supply chain management and logistics, and (viii) structural health monitoring. The findings demonstrate that DT technology has the capacity to revolutionise the construction industry across these areas, enabling optimised designs, improved collaboration, real-time monitoring, predictive maintenance, enhanced safety practices, energy performance optimisation, quality inspections, efficient supply chain management, and proactive maintenance. This study also identified several challenges that hinder the widespread implementation of DT in construction, including (i) data integration and interoperability, (ii) data accuracy and completeness, (iii) scalability and complexity, (iv) privacy and security, and (v) standards and governance. To address these challenges, this paper recommends prioritising standardised data formats, protocols, and APIs for seamless collaboration, exploring semantic data modelling and ontologies for data integration, implementing validation processes and robust data governance for accuracy and completeness, harnessing high-performance computing and advanced modelling techniques for scalability and complexity, establishing comprehensive data protection and access controls for privacy and security, and developing widely accepted standards and governance frameworks with industry-wide collaboration. By addressing these challenges, the construction industry can unlock the full potential of DT technology, thus enhancing safety, reliability, and efficiency in construction projects.

ADVANCING MEDICAL DEVICE MANUFACTURING: THE CONVERGENCE OF EDGE COMPUTING AND INDUSTRY 5.0

The integration of edge computing and Industry 5.0 has the potential to revolutionize medical device manufacturing, enhancing the effectiveness, functionality, and safety of medical devices. This research article explores the opportunities and challenges associated with this convergence in the medical device sector. The paper highlights how edge computing enables real-time data processing and analysis at the production site, leading to enhanced real-time monitoring, diagnostics, and predictive maintenance for medical devices. Additionally, Industry 5.0 principles promote human-robot collaboration, personalized healthcare, and intelligent decision-making through data analytics and AI. The paper also addresses the challenges of cybersecurity, privacy concerns, scalability, network infrastructure, and interoperability issues in the implementation of edge computing and Industry 5.0. The article emphasizes the importance of standardized communication protocols and data formats to ensure seamless interoperability. Lastly, it outlines potential areas of research and development, such as realtime analytics, cybersecurity, and human-robot collaboration, to further enhance the integration of edge computing and Industry 5.0 in medical device manufacturing. Researchers, manufacturers, and policymakers stand to benefit from a comprehensive understanding of the opportunities and challenges, enabling the development of strategies to drive positive industry-wide outcomes in medical device manufacturing.

An Edge Device Framework in SEMAR IoT Application Server Platform

Nowadays, the Internet of Things (IoT) has become widely used at various places and for various applications. To facilitate this trend, we have developed the IoT application server platform called SEMAR (Smart Environmental Monitoring and Analytical in Real-Time), which offers standard features for collecting, displaying, and analyzing sensor data. An edge device is usually installed to connect sensors with the server, where the interface configuration, the data processing, the communication protocol, and the transmission interval need to be defined by the user. In this paper, we proposed an edge device framework for SEMAR to remotely optimize the edge device utilization with three phases. In the initialization phase, it automatically downloads the configuration file to the device through HTTP communications. In the service phase, it converts data from various sensors into the standard data format and sends it to the server periodically. In the update phase, it remotely updates the configuration through MQTT communications. For evaluations, we applied the proposal to the fingerprint-based indoor localization system (FILS15.4) and the data logging system. The results confirm the effectiveness in utilizing SEMAR to develop IoT application systems.

CDISC Enables Efficient Streamlining of Clinical Trial Safety Evaluation

The use of standardized data formats has increasingly facilitated both the submission of clinical trial data by pharmaceutical companies and its review by regulatory agencies. The Clinical Data Interchange Standards Consortium (CDISC) format has become the de facto worldwide standard for clinical trial data submissions and review. CDISC data are captured and organized in defined datasets, or domains, with predefined variable names and attributes. As a result, researchers and reviewers have a consistent, repeatable, and rapid way to load, review, and analyze data. The benefits provided by this standardization have culminated in the development of software that intuitively utilizes CDISC structures to quickly import and analyze the data as well as summarize the results for scientists and clinicians in a collection of readable and easy-to-understand reports. In this paper, we will show how CDISC datasets with their required domains and respective variables can be utilized by JMP® Clinical to create clinical trial analysis reports easily and quickly with interactive graphs and tables for use in FDA New Drug Applications and Clinical Reviews. These reports are also ready for inclusion in regulatory submissions such as regulatory documents and clinical study reports to improve reviewability, interpretability, and efficiency of regulatory submission and review.

Assessment of the Croatian Open Data Portal Using User-Oriented Metrics

Open data portals are web services that serve as a central access point for all government-published open data and can exist at local, regional, national, and international levels. They are an important element of most open data initiatives that have enabled a large amount of government data to be widely available. However, data quantity and quality are not the only aspects that should be considered when publishing data. To improve the reusability of data and to achieve greater impact and benefits from open data, it is important to consider user-oriented aspects of the portal management, discovery, and use of data (e.g., organizing the portal in a user-centric way, providing accurate metadata, using a standardized and open data format, etc.). In this paper, we adopted the metrics proposed by the European Commission to assess compliance of the Croatian Open Data Portal with 10 user-oriented principles that open data portals should implement in terms of sustainability and added value. While the results show the government’s efforts in publishing data, some aspects such as better collaboration with data providers and other data portals, offering different visualization tools, etc. need to be improved to achieve active use and impact.

Live semantic data from building digital twins for robot navigation: Overview of data transfer methods

Increasing reliance on automation and robotization presents great opportunities to improve the management of construction sites as well as existing buildings. Crucial in the use of robots in a built environment is their capacity to locate themselves and navigate as autonomously as possible. Robots often rely on planar and 3D laser scanners for that purpose, and building information models (BIM) are seldom used, for a number of reasons, namely their unreliability, unavailability, and mismatch with localization algorithms used in robots. However, while BIM models are becoming increasingly reliable and more commonly available in more standard data formats (JSON, XML, RDF), they become more promising and reliable resources for localization and indoor navigation, in particular in the more static types of existing infrastructure (existing buildings). In this article, we specifically investigate to what extent and how such building data can be used for such robot navigation. Data flows are built from BIM model to local repository and further to the robot, making use of graph data models (RDF) and JSON data formats. The local repository can hereby be considered to be a digital twin of the real-world building. Navigation on the basis of a BIM model is tested in a real world environment (university building) using a standard robot navigation technology stack. We conclude that it is possible to rely on BIM data and we outline different data flows from BIM model to digital twin and to robot. Future work can focus on (1) making building data models more reliable and standard (modelling guidelines and robot world model), (2) improving the ways in which building features in the digital building model can be recognized in 3D point clouds observed by the robots, and (3) investigating possibilities to update the BIM model based on robot feedback.

A Standardized Clinical Data Harmonization Pipeline for Scalable AI Application Deployment (FHIR-DHP): Validation and Usability Study

Increasing digitalization in the medical domain gives rise to large amounts of health care data, which has the potential to expand clinical knowledge and transform patient care if leveraged through artificial intelligence (AI). Yet, big data and AI oftentimes cannot unlock their full potential at scale, owing to nonstandardized data formats, lack of technical and semantic data interoperability, and limited cooperation between stakeholders in the health care system. Despite the existence of standardized data formats for the medical domain, such as Fast Healthcare Interoperability Resources (FHIR), their prevalence and usability for AI remain limited. In this paper, we developed a data harmonization pipeline (DHP) for clinical data sets relying on the common FHIR data standard. We validated the performance and usability of our FHIR-DHP with data from the Medical Information Mart for Intensive Care IV database. We present the FHIR-DHP workflow in respect of the transformation of "raw" hospital records into a harmonized, AI-friendly data representation. The pipeline consists of the following 5 key preprocessing steps: querying of data from hospital database, FHIR mapping, syntactic validation, transfer of harmonized data into the patient-model database, and export of data in an AI-friendly format for further medical applications. A detailed example of FHIR-DHP execution was presented for clinical diagnoses records. Our approach enables the scalable and needs-driven data modeling of large and heterogenous clinical data sets. The FHIR-DHP is a pivotal step toward increasing cooperation, interoperability, and quality of patient care in the clinical routine and for medical research.

A Platform for Data-Centric, Continuous Epidemiological Analyses (EpiGraphHub): Descriptive Analysis.

Guaranteeing durability, provenance, accessibility, and trust in open data sets can be challenging for researchers and organizations that rely on public repositories of data critical for epidemiology and other health analytics. The required data repositories are often difficult to locate and may require conversion to a standard data format. Data-hosting websites may also change or become unavailable without warning. A single change to the rules in one repository can hinder updating a public dashboard reliant on data pulled from external sources. These concerns are particularly challenging at the international level, because policies on systems aimed at harmonizing health and related data are typically dictated by national governments to serve their individual needs. In this paper, we introduce a comprehensive public health data platform, EpiGraphHub, that aims to provide a single interoperable repository for open health and related data. The platform, curated by the international research community, allows secure local integration of sensitive data while facilitating the development of data-driven applications and reports for decision-makers. Its main components include centrally managed databases with fine-grained access control to data, fully automated and documented data collection and transformation, and a powerful web-based data exploration and visualization tool. EpiGraphHub is already being used for hosting a growing collection of open data sets and for automating epidemiological analyses based on them. The project has also released an open-source software library with the analytical methods used in the platform. The platform is fully open source and open to external users. It is in active development with the goal of maximizing its value for large-scale public health studies.

Valuation of Urban Public Bus Electrification with Open Data and Physics-Informed Machine Learning

This research provides a novel framework to estimate the economic, environmental, and social values of electrifying public transit buses, for cities across the world, based on open-source data. Electric buses are a compelling candidate to replace diesel buses for their environmental and social benefits. However, the state-of-art models to evaluate the value of bus electrification require granular and bespoke data on bus operation that can be difficult to procure. This strict requirement on data and modeling can hinder potential collaborators on bus electrification, such as electric vehicle manufacturers or electricity providers, from selecting the candidate public transit agencies in the initial phase of the market investigation. Our valuation tool uses General Transit Feed Specification, a standard data format used by transit agencies worldwide, to provide high-level guidance on developing a prioritization strategy for electrifying a bus fleet. We develop physics-informed machine learning models to evaluate energy consumption, carbon emissions, health impacts, and the total cost of ownership for each transit route. We demonstrate our tool with a case study of the bus lines in the Greater Boston and Milan metropolitan areas. While the capital cost of electric buses and the price of diesel fuel may impact the economic benefit of electrification, we expect significant improvements in health and environmental performances. We estimate about 50% health cost reduction by electrifying only 4 routes out of 15 to 19 routes in each city. For Boston and Milan, some routes are estimated to produce less than 5% of the diesel bus emissions and 14%, respectively, for which investment in electrification can be prioritized.

BIM and Ontology-Based DfMA Framework for Prefabricated Component

The integration of Design for Manufacture and Assembly (DfMA) into the design process of industrialized construction has the potential to reduce errors and changes occurring after the design has been finalized, ultimately improving overall productivity. Based on DfMA, the designers would need to consider whether their designs meet the architectural and performance requirements, as well as the manufacturing and assembly requirements from assembly and manufacturing technicians. However, some limitations present challenges for DfMA-oriented prefabricated design, such as lack of information interoperability, lack of conflict detection and management, and inefficient data processing and requirement checking. Thus, this research presents a novel BIM and ontology-based framework for DfMA of prefabricated and modular components. Various types of algorithms, plugins, and programming are also integrated to support the operation of the framework. The primary functions of this framework include: (1) collection of various stakeholder requirements in a standardized data format; (2) conflict detection and resolution between the design, manufacturing, and assembly requirements; and (3) automated compliance checking of whether the designed BIM models meet DfMA requirements. This research applies the framework on a prefabricated hotel project as a case study to validate the feasibility of the framework. Based on the results of a user experience survey, the developed framework shows promise for improving the DfMA process and stakeholder communication. Although a few limitations were encountered, such as the low computer operating speed and the limited ontology, the framework has been validated and shows great potential in advancing prefabricated component design applications

Game Learning Analytics:

Game learning analytics (GLA) comprise the collection, analysis, and visualization of player interactions with serious games. The information gathered from these analytics can help us improve serious games and better understand player actions and strategies, as well as improve player assessment. However, the application of analytics is a complex and costly process that is not yet generalized in serious games. Using a standard data format to collect player interactions is essential: the standardization allows us to simplify and systematize every step in developing tools and processes compatible with multiple games. In this paper, we explore a combination of 1) an exploratory visualization tool that analyzes player interactions in the game and provides an overview of their actions, and 2) an assessment approach, based on the collection of interaction data for player assessment. We describe some of the different opportunities offered by analytics in game-based learning, the relevance of systematizing the process by using standards and game-independent analyses and visualizations, and the different techniques (visualizations, data mining models) that can be applied to yield meaningful information to better understand learners’ actions and results in serious games.

Voltaiq data format—A standard data format for collection of battery data to enable big data comparisons and analyses across the battery lifecycle

Batteries have enabled modernization of society through portability of electricity. Batteries are also a crucial component to enabling clean technologies of the future such as grid storage and electrified transportation. Because of their ubiquity in modern society, global organizations develop and commercialize batteries for their electrified products. Across the field of battery development, in both commercial and academic settings, there is broad utility in standardization of data formats amongst disparate data sources, labs, equipment, organizations, industries, and lifecycle phases. Due to the way the nascent industry developed, there is a lack of standardization for how performance data is recorded, which is now hindering the industry’s ability to learn from data and accelerate growth. Herein, we describe the different types of data, formats, conventions, and standardization for each phase in the battery lifecycle. Next, we provide a standard data format and conventions for the community to either utilize in their data collection practices or map their existing data into: the Voltaiq Data Format (VDF). This standard data format provides the flexibility needed to capture the variability in data formats and conventions along the battery lifecycle. The utility of this standard format aids in collaboration within and across organizations, accelerating innovation across the industry, and paves the way for the battery community to start utilizing the power of machine learning and data science.

VeHIF: An Accessible Vegetation High-Impedance Fault Data Set Format

High-impedance faults are a challenging problem in power distribution systems. They often do not trigger protection devices and can result in serious hazards such as igniting fires when in contact with vegetation. The current research field dedicated to studying these faults is extensive but suffers from a constraining bottleneck of a lack of real experimental data. Many works set to detect and localize such faults rely on high-impedance fault low-fidelity models, and the lack of public data sets makes it impractical to have objective performance benchmarks. This letter describes and proposes a format for a data set of more than 900 vegetation high-impedance faults funded by the Victorian Government in Australia recorded in high-sampling resolution. The original data set is public, but it was made available through an obscure format that limits its accessibility. The presented format in this letter uses the standard hierarchical data format (HDF5), which makes it easily accessible in many languages such as MATLAB, Python, C++, and more. The data set compiler and visualizer script are also provided in the work repository <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .

XAPI Made Easy: A Learning Analytics Infrastructure for Interdisciplinary Projects

Learning Analytics provides plenty of pedagogical uses. However, the integration of learning analytics must be accompanied by different perspectives: technical, organizational, and pedagogical. At this point, there are still gaps, e.g., the need to connect the various stakeholders and support the systematic, structured, and sustainable process. This paper presents different approaches to making the learning data standard xAPI for interdisciplinary projects easier by working on other starting points. Starting with a basic infrastructure to support the interdisciplinary collection of definitions for the standardized data format, it continues with a graphical user interface supporting different stakeholders. A modular tool for quickly connecting programming IDEs with the vocabulary is also presented. Last, a connector is shown for easier multi-modal data management using virtual reality as an example.

MIBiG 3.0: a community-driven effort to annotate experimentally validated biosynthetic gene clusters.

With an ever-increasing amount of (meta)genomic data being deposited in sequence databases, (meta)genome mining for natural product biosynthetic pathways occupies a critical role in the discovery of novel pharmaceutical drugs, crop protection agents and biomaterials. The genes that encode these pathways are often organised into biosynthetic gene clusters (BGCs). In 2015, we defined the Minimum Information about a Biosynthetic Gene cluster (MIBiG): a standardised data format that describes the minimally required information to uniquely characterise a BGC. We simultaneously constructed an accompanying online database of BGCs, which has since been widely used by the community as a reference dataset for BGCs and was expanded to 2021 entries in 2019 (MIBiG 2.0). Here, we describe MIBiG 3.0, a database update comprising large-scale validation and re-annotation of existing entries and 661 new entries. Particular attention was paid to the annotation of compound structures and biological activities, as well as protein domain selectivities. Together, these new features keep the database up-to-date, and will provide new opportunities for the scientific community to use its freely available data, e.g. for the training of new machine learning models to predict sequence-structure-function relationships for diverse natural products. MIBiG 3.0 is accessible online at https://mibig.secondarymetabolites.org/.