Support Data Integration Research Articles

Development of a Enhanced System Review for Public Agency Multi-Sectoral Integrated Data Systems

While traditional security assessments (such as mandated external security reviews which include a penetration test tied to a security framework) are a critical component to securing your data infrastructure, the complexities and governance structures around integrated data systems that involve multiple public sector agencies require a more comprehensive framework to address comprehensive risk, regulatory, and governance considerations that are critical to the confidentiality of the data within these systems. In January of 2024, the Data Integration Support Center (DISC) created and piloted an enhanced system review that includes components that are often overlooked or oversimplified when working with today's technologies such as cloud-based secure enclaves. By following up on a mandated security review, an enhanced system review can help a maturing Integrated Data System (IDS) address both security and privacy risks as it moves forward. An enhanced system review looks primarily, though not necessarily exclusively, at the following aspects of an IDS, principally in the interest of ensuring privacy: IDS alignment with its legal framework and other structuring documents (e.g., policies, procedures, interagency agreements) Training to support privacy and security Use of technology to prevent or mitigate human error The results of the review are provided directly to the IDS leadership and generally include a list of recommendations in the areas of improved legal agreements, leveraging privacy enhancing technologies to mitigate or reduce errors, procedural/policy gap identification and/or architectural recommendations to improve the security.

Lab Inventory Management using QR Code

Laboratory inventory management is vital for the efficient operation of scientific research institutions. Conventional inventory tracking methods often involve manual data entry, which can be error-prone and time-consuming, leading to operational inefficiencies and potential research disruptions. To address these challenges, this project introduces a Lab Inventory Management System (LIMS) that leverages QR code technology. The LIMS assigns unique QR codes to each laboratory item, encoding critical information such as item name, quantity, and expiration date. This technology enables mobile devices equipped with QR code scanners to facilitate real-time updates, eliminating manual data entry errors and ensuring the accuracy of inventory information. Furthermore, a centralized database supports data integrity and enables seamless integration with existing laboratory systems. The project's advantages include enhanced accuracy, significant time and cost savings, and streamlined workflow efficiency. However, the successful implementation of the LIMS necessitates careful planning and comprehensive user training to ensure a smooth transition to this innovative inventory management solution. Key Words: Automated, Scientific Research, Data Integrity, Data Visualization, Barcode Scanning

The Using Of Information Systems To Calculate Z-Score And To Determination Of Stunting Categories In Toddlers

Information technology is an inseparable part of human work today. It is growing very rapidly along with the human need for the speed at which information is processed until it is displayed to the user. Information systems are now widely used in all sectors, ranging from: economy, agriculture, health and so on. In the health sector, many health facilities have used information systems to improve services, one of which is Posyandu. Posyandu is the front line in providing family health services at the village level. Currently, Posyandu focus is preventing malnutrition or stunting. Standardization of stunt calculation data often changes. In addition, the calculation and documentation of toddler examination data is also still done manually. This has an impact on the efficiency of the performance of staff in determining toddlers who are affected by stunting. Data integration with the Ministry of Health is also a problem when recorded manually. Therefore, it is proposed that an information system that functions to perform Z-Score calculations and classify them into stunting or non-stunting groups. The existence of this information system can also support the government to encourage data integration between local governments and the center regarding stunting data. It is hoped that this information system can help the work efficiency of health cadres to record toddlers who fall into the Stunting category. In addition, it is also possible for the government to access data stored in the system to support data integration.

A workflow towards a strongly typed AutomationML API

Abstract Over the lifecycle of a production plant digitalization leads to a large data footprint along different software (SW) components. A digital data exchange is the fundament to support data integrity, prevent data loss and avoid duplicate work. AutomationML (AML) is a commonly accepted tool-independent format to exchange data of different domains along the lifecycle of a production plant. This contribution provides an easy-to-use workflow that empowers SW components to transform AML into a strongly typed class hierarchy which is the basis for an efficient and maintainable SW solution.

The Effect of Information Technology on Employee Performance at the Makassar City Communication and Information Service

The role of information technology in the bureaucracy can facilitate the implementation of tasks in information management, make work effective and efficient, and shorten service time so that performance becomes optimal. Information technology is currently based on the Internet of Thing (IoT) to support the acceleration of information, and in the government sector it is based on E-Government which supports data integration and can be accessed in real time within the Central Government and collaborating agencies. This study aims to describe information technology, employee performance and the influence of information technology on employee performance at the Makassar City Communication and Information Service. This research is an associative quantitative research which shows a causal relationship. Data collection techniques used are observation, questionnaires and documentation. The data analysis technique used is descriptive statistical analysis and inferential statistics. The test results show that the variables of information technology and employee performance are in a very good category, based on the product moment correlation test, the relationship between variables is very strong. Based on the results of simple linear regression analysis it was concluded that there is a positive and significant influence of information technology on employee performance at the Makassar City Communication and Information Service.

Improving Data Integrity and Quality From Online Health Surveys of Women With Infant Children.

Online surveys have proven to be an efficient method to gather health information in studies of various populations, but these are accompanied by threats to data integrity and quality. We draw on our experience with a nefarious intrusion into an online survey and our efforts to protect data integrity and quality in a subsequent online survey. We aim to share lessons learned regarding detecting and preventing threats to online survey data integrity and quality. We examined data from two online surveys we conducted, as well as findings of others reported in the literature, to delineate threats to and prevention strategies for online health surveys. Our first survey was launched inadvertently without available security features engaged in Qualtrics, resulting in a number of threats to data integrity and quality. These threats included multiple submissions, often within seconds of each other, from the same internet protocol (IP) address; use of proxy servers or virtual private networks, often with suspicious or abusive IP address ratings and geolocations outside the United States; and incoherent text data or otherwise suspicious responses. After excluding fraudulent, suspicious, or ineligible cases, as well as cases that terminated before submitting data, 102 of 224 (45.5%) eligible survey respondents remained with partial or complete data. In a second online survey with security features in Qualtrics engaged, no IP addresses were associated with any duplicate submissions. To further protect data integrity and quality, we added items to detect inattentive or fraudulent respondents and applied a risk scoring system in which 23 survey respondents were high risk, 16 were moderate risk, and 289 of 464 (62.3%) were low or no risk and therefore considered eligible respondents. Technological safeguards, such as blocking repeat IP addresses and study design features to detect inattentive or fraudulent respondents, are strategies to support data integrity and quality in online survey research. For online data collection to make meaningful contributions to nursing research, it is important for nursing scientists to implement technological, study design, and methodological safeguards to protect data integrity and quality and for future research to focus on advancing data protection methodologies.

DbGaPCheckup: pre-submission checks of dbGaP-formatted subject phenotype files

BackgroundData archiving and distribution are essential to scientific rigor and reproducibility of research. The National Center for Biotechnology Information’s Database of Genotypes and Phenotypes (dbGaP) is a public repository for scientific data sharing. To support curation of thousands of complex data sets, dbGaP has detailed submission instructions that investigators must follow when archiving their data.ResultsWe developed dbGaPCheckup, an R package which implements a series of check, awareness, reporting, and utility functions to support data integrity and proper formatting of the subject phenotype data set and data dictionary prior to dbGaP submission. For example, as a tool, dbGaPCheckup ensures that the data dictionary contains all fields required by dbGaP, and additional fields required by dbGaPCheckup; the number and names of variables match between the data set and data dictionary; there are no duplicated variable names or descriptions; observed data values are not more extreme than the logical minimum and maximum values stated in the data dictionary; and more. The package also includes functions that implement a series of minor/scalable fixes when errors are detected (e.g., a function to reorder the variables in the data dictionary to match the order listed in the data set). Finally, we also include reporting functions that produce graphical and textual descriptives of the data to further reduce the likelihood of data integrity issues. The dbGaPCheckup R package is available on CRAN (https://CRAN.R-project.org/package=dbGaPCheckup) and developed on GitHub (https://github.com/lwheinsberg/dbGaPCheckup).ConclusiondbGaPCheckup is an innovative assistive and timesaving tool that fills an important gap for researchers by making dbGaP submission of large and complex data sets less error prone.

Demonstrating efficiency through data connectivity between asset management systems and IM

The CoDEC (Connected Data for Effective Collaboration) project has developed standardised tools – an Asset Data Dictionary and associated Ontology – to support data integration between Building Information Modelling (BIM) and Asset Management Systems (AMS). CoDEC has also developed an OpenAPI (Application Protocol Interface) to enable automatic data connection between the two systems. Having developed the standardised process, the potential benefits offered for connecting and linking data have been shown in three pilot projects to demonstrate (1) how tunnel monitoring data can enrich BIM models of tunnels (2) how data from bridge sensors can be linked and visualised and (3) how highway data from a BIM model can be linked to GIS-based AMS for the operational phase. This paper describes the project outcomes.

Review of Best Practices for an Environmental Laboratory

Key TakeawaysUtilities with environmental laboratories can monitor water and wastewater treatment plant processes and water quality from source, through distribution, and to the consumer's tap.“Best laboratory practices” are criteria intended to ensure that environmental laboratories produce reliable and defensible water quality data that comply with regulatory requirements.Accreditation and robust quality control and quality assurance, ethics, safety, and other laboratory program criteria support data integrity and protection of public health.

Plinian Core: The long and winding road

Plinian Core (PliC) is a set of vocabulary terms that can be used to describe different aspects of biological species information. Under "biological species information" all kinds of properties or traits related to taxa—biological and non-biological—are included. Thus, for instance, terms pertaining to descriptions, legal aspects, conservation, management, demographics, nomenclature, or related resources are incorporated. Plinian Core started as a collaborative project between Instituto Nacional de Biodiversidad (INBio, Costa Rica) and the Global Biodiversity Information Facility Spain (GBIF Spain) in 2005. In successive years, the National Commission for Knowledge and Use of Biodiversity (CONABIO, Mexico), the National Network of Open Biodiversity Data (SiB Colombia)/the Humboldt Institute (Colombia), the University of Granada (Spain) and the University of São Paulo (USP, Brazil) were also involved. In 2012. PliC aligned with the Biodiversity Information Standards (TDWG) and an umbrella Interest Group was created (Species Information IG, Pando 2017b) with developments carried out by its corresponding TDWG Task Group (Plinian Core Task Group 2020b). PliC design requirements include: ease of use, being self-contained, able to support data integration from multiple databases, and ability to handle different levels of granularity. Since its early stages, PliC was formalized as an XML Schema. Its terms can be grouped in its current version as follows: Metadata Base elements Record metadata Nomenclature and classification Taxonomic description Natural history Invasive species Habitat and distribution Demography and threats Uses, management and conservation Miscellaneous: associated party, measurement or fact, references and ancillary data Metadata Base elements Record metadata Nomenclature and classification Taxonomic description Natural history Invasive species Habitat and distribution Demography and threats Uses, management and conservation Miscellaneous: associated party, measurement or fact, references and ancillary data During its years of existence, PliC has been implemented in several real-life contexts (e.g., Spanish Ministry for the Ecological Transition and the Demographic Challenge 2020, CRBIO 2020, SIB Colombia 2020), and a set of extensions for using PliC in GBIF Integrated Publishing Toolkit (IPT) was developed (Plinian Core Task Group 2020a). PliC has been featured in some TDWG conferences, either to explore and explain how it relates to other TDWG standards (Pando 2018), how PliC handles specific information aspects (Pando 2017a), or how it may be used in biodiversity portals (Vargas et al. 2018, Vargas et al. 2019). Recent work has focused on how to represent terms and codify structure as an XML Schema (XSD) under the current TDWG Documentation Standard (SDS, Vocabulary Maintenance Specification Task Group 2017). Challenges identified in the process comprise: how to handle terms used in different contexts, how to preserve the semantic context of terms reflected in the XSD hierarchy in the SDS flat layout, and how to refer to terms and definitions defined in other data specifications. These are the areas to focus our contribution.

Data Integration Capabilities and Performance of Commercial Banks in Kenya

The purpose of this paper is to present an empirical evaluation of the effect of data integration capabilities on the performance of commercial banks in Kenya. An explanatory non-experimental design was applied to conduct a census of the 43 commercial banks in Kenya. A semi-structured questionnaire supported in collection of primary data. Secondary data from commercial banks annual financial results for period 2013 to 2019 was compiled using data collection sheet. SPSS tool was applied for analysis of data. The regression results disclosed that data integration capabilities positively and significantly affect performance of commercial banks. It was recommended that the managers of commercial banks and other stakeholders should continue investing and adopting data integration capabilities as it is an effective performance enhancement strategy. Further, the policymakers and regulators should employ policies that support data integration capabilities adoption to augment performance.

Animal Husbandry and Faunal Material: Integrating Data from Finland (AD 1200–1800)

ABSTRACT This paper is a synthesis of zooarchaeological evidence from 27 medieval and post-medieval sites from Finland. These faunal samples derive from rural, town, manor, castle and ecclesiastic sites, and their production and consumption patterns were examined by studying the role of the major domesticates (cattle, sheep, goats and pigs). As taxonomic abundance in faunal material may be altered by taphonomic processes, such as burning, these factors were assessed before comparison. The animal husbandry system in Finland was shaped by environmental constraints that limited the number of animals that could be kept over winter. However, some specialisations were observed within the frame of the basic pattern. This paper demonstrates the usefulness of zooarchaeological data in the study of animal husbandry and consumption and production patterns. These findings support data integration as a useful tool for understanding general large-scale processes, such as urbanisation, development, environmental adaptation and the specialisation of animal production.

GMQN: A Reference-Based Method for Correcting Batch Effects and Probe Bias in HumanMethylation BeadChip.

The Illumina HumanMethylation BeadChip is one of the most cost-effective methods to quantify DNA methylation levels at single-base resolution across the human genome, which makes it a routine platform for epigenome-wide association studies. It has accumulated tens of thousands of DNA methylation array samples in public databases, providing great support for data integration and further analysis. However, the majority of public DNA methylation data are deposited as processed data without background probes which are widely used in data normalization. Here, we present Gaussian mixture quantile normalization (GMQN), a reference based method for correcting batch effects as well as probe bias in the HumanMethylation BeadChip. Availability and implementation: https://github.com/MengweiLi-project/gmqn.

BASIN-3D: A brokering framework to integrate diverse environmental data

Diverse observational and simulation datasets are needed to understand and predict complex ecosystem behavior over seasonal to decadal and century time-scales. Integration of these datasets poses a major barrier towards advancing environmental science, particularly due to differences in the structure and formats of data provided by various sources. Here, we describe BASIN-3D (Broker for Assimilation, Synthesis and Integration of eNvironmental Diverse, Distributed Datasets), a data integration framework designed to dynamically retrieve and transform heterogeneous data from different sources into a common format to provide an integrated view. BASIN-3D enables users to adopt a standardized approach for data retrieval and avoid customizations for the data type or source. We demonstrate the value of BASIN-3D with two use cases that require integration of data from regional to watershed spatial scales. The first application uses the BASIN-3D Python library to integrate time-series hydrological and meteorological data to provide standardized inputs to analytical and machine learning codes in order to predict the impacts of hydrological disturbances on large river corridors of the United States. The second application uses the BASIN-3D Django framework to integrate diverse time-series data in a mountainous watershed in East River, Colorado, United States to enable scientific researchers to explore and download data through an interactive web portal. Thus, BASIN-3D can be used to support data integration for both web-based tools, as well as data analytics using Python scripting and extensions like Jupyter notebooks. The framework is expected to be transferable to and useful for many other field and modeling studies.

Next Generation of Central Cancer Registries.

For central cancer registries to become a more significant public health resource, they must evolve to capture more timely, accurate, and extensive data. Key stakeholders have called for a faster time to deliver work products, data extensions such as social determinants of health, and more relevant information for cancer control programs at the local level. The proposed model consists of near real-time reporting stages to replace the current time and labor-intensive efforts to populate a complete cancer case abstract on the basis of the 12- and 24-month data submission timelines. The first stage collects a cancer diagnosis minimum data set sufficient to describe population incidence and prevalence, which is then followed by a second stage capturing subsequent case updates and treatment data. A third stage procures targeted information in response to identified research projects' needs. The model also provides for further supplemental reports as may be defined to gather additional data. All stages leverage electronic health records' widespread development and the many emerging standards for data content, including national policies related to healthcare and technical standards for interoperability, such as the Fast Healthcare Interoperability Resources specifications to automate and accelerate reporting to central cancer registries. The emergence of application programming interfaces that allow for more interoperability among systems would be leveraged, leading to more efficient information sharing. Adopting this model will expedite cancer data availability to improve cancer control while supporting data integrity and flexibility in data items. It presents a long-term and feasible solution that addresses the extensive burden and unsustainable manual data collection requirements placed on Certified Tumor Registrars at disease reporting entities nationally.

Hash Algorithm In Verification Of Certificate Data Integrity And Security

The hash function is the most important cryptographic primitive function and is an integral part of the blockchain data structure. Hashes are often used in cryptographic protocols, information security applications such as Digital Signatures and message authentication codes (MACs). In the current development of certificate data security, there are 2 (two) types of hashes that are widely applied, namely, MD and SHA. However, when it comes to efficiency, in this study the hash type SHA-256 is used because it can be calculated faster with a better level of security. In the hypothesis, the Merkle-Damgård construction method is also proposed to support data integrity verification. Moreover, a cryptographic hash function is a one-way function that converts input data of arbitrary length and produces output of a fixed length so that it can be used to securely authenticate users without storing passwords locally. Since basically, cryptographic hash functions have many different uses in various situations, this research resulted in the use of hash algorithms in verifying the integrity and authenticity of certificate information.

GRSciColl: Registry of Scientific Collections maintained by the community for the community

GRSciColl: Registry of Scientific Collections maintained by the community for the community

Scalable analysis of multi-modal biomedical data.

BackgroundTargeted diagnosis and treatment options are dependent on insights drawn from multi-modal analysis of large-scale biomedical datasets. Advances in genomics sequencing, image processing, and medical data management have supported data collection and management within medical institutions. These efforts have produced large-scale datasets and have enabled integrative analyses that provide a more thorough look of the impact of a disease on the underlying system. The integration of large-scale biomedical data commonly involves several complex data transformation steps, such as combining datasets to build feature vectors for learning analysis. Thus, scalable data integration solutions play a key role in the future of targeted medicine. Though large-scale data processing frameworks have shown promising performance for many domains, they fail to support scalable processing of complex datatypes.SolutionTo address these issues and achieve scalable processing of multi-modal biomedical data, we present TraNCE, a framework that automates the difficulties of designing distributed analyses with complex biomedical data types.PerformanceWe outline research and clinical applications for the platform, including data integration support for building feature sets for classification. We show that the system is capable of outperforming the common alternative, based on “flattening” complex data structures, and runs efficiently when alternative approaches are unable to perform at all.

DeepBlockShield: Blockchain Agent-Based Secured Clinical Data Management Model from the Deep Web Environment

With the growth of artificial intelligence in healthcare and biomedical research, many researchers are interested in large amounts of data in hospitals and medical research centers. Then the need for remote medicine services and clinical data utilization are expanding. However, since the misuse and abuse of clinical data causes serious problems, the scope of its use is bound to have a limited range physically and logically. Then a security-enhanced data distribution system for medical deep web environments. Therefore, in this paper, we propose a blockchain-based clinical data management model named DeepBlockshield to prevent information leakage between the deep web and the surface web. Blockchain supports data integrity and user validation to support data sharing in closed networks. Meanwhile, the agent performs integrity verification between the blockchain and the deep web and strengthens the security between the surface web and the deep web. DeepBlockShield verifies the user’s validity through the records of the deep web and blockchain. Furthermore, we wrap the results analyzed by the valid request into a web interface and provide information to the requester asynchronously. In the experiment, the block generation cycle and size on the delay time was analyzed for verifying the stability of the blockchain network. As a result, it showed that the proposed approach guarantees the integrity and availability of clinical data in the deep web environment.

RDFAdaptor: Efficient ETL Plugins for RDF Data Process

Abstract Purpose The interdisciplinary nature and rapid development of the Semantic Web led to the mass publication of RDF data in a large number of widely accepted serialization formats, thus developing out the necessity for RDF data processing with specific purposes. The paper reports on an assessment of chief RDF data endpoint challenges and introduces the RDF Adaptor, a set of plugins for RDF data processing which covers the whole life-cycle with high efficiency. Design/methodology/approach The RDFAdaptor is designed based on the prominent ETL tool—Pentaho Data Integration—which provides a user-friendly and intuitive interface and allows connect to various data sources and formats, and reuses the Java framework RDF4J as middleware that realizes access to data repositories, SPARQL endpoints and all leading RDF database solutions with SPARQL 1.1 support. It can support effortless services with various configuration templates in multi-scenario applications, and help extend data process tasks in other services or tools to complement missing functions. Findings The proposed comprehensive RDF ETL solution—RDFAdaptor—provides an easy-to-use and intuitive interface, supports data integration and federation over multi-source heterogeneous repositories or endpoints, as well as manage linked data in hybrid storage mode. Research limitations The plugin set can support several application scenarios of RDF data process, but error detection/check and interaction with other graph repositories remain to be improved. Practical implications The plugin set can provide user interface and configuration templates which enable its usability in various applications of RDF data generation, multi-format data conversion, remote RDF data migration, and RDF graph update in semantic query process. Originality/value This is the first attempt to develop components instead of systems that can include extract, consolidate, and store RDF data on the basis of an ecologically mature data warehousing environment.