Improve Data Interoperability Research Articles

Exploring The Role of Archaeological Information Systems in Improving Data Management and Interoperability

Exploring The Role of Archaeological Information Systems in Improving Data Management and Interoperability

Healthcare 5.0: A secure and distributed network for system informatics in medical surgery

IntroductionHealth records serve not only as a database of a patient's health history and treatment process but also as a crucial tool for doctors to diagnose and treat patients. However, the storage and sharing of these records are sensitive issues as they involve maintaining patient privacy and ensuring data transparency, security, and interoperability between different parties. Challenges to achieving these goals in the current surgical process can impact the allocation of medical resources and surgical outcomes. MethodsThis article proposes a healthcare 5.0 framework for medical surgery that deploys a secure and distributed network using Blockchain to demonstrate transactions between different parties in the orthopedic surgery process. The proposed network uses the Hyperledger Composer platform for deployment, and a patient-doctor-supplier orthopedic surgery network is designed and implemented to enable the safe sharing of medical records. ResultsA benchmarking tool was implemented for analyzing different scenarios of applying blockchain technology to orthopedic surgery. The application of blockchain technology to orthopedic surgery presents a promising solution for data sharing and supply chain management in the field. The integration of blockchain with cloud storage and hybrid encryption ensures secure and efficient storage of Electronic Health Record (EHR) and Personal Health Record (PHR) data. By leveraging the tamper-proof nature of blockchain and addressing concerns regarding centralized data storage, this scenario demonstrates enhanced security, improved access efficiency, and privacy protection in medical data sharing. ConclusionsThe article demonstrates the feasibility of using an IoT-based blockchain network in orthopedic surgery, which can reduce medical errors and improve data interoperability among different parties. This unique application of blockchain enables secure sharing of medical records, ensuring transparency, security, and interoperability. The network design may also be applicable to other surgeries and medical applications in the future.

Feasibility of implementing a rapid-learning methodology to inform radiotherapy treatments: key professional stakeholders’ views

ObjectivePragmatic methodologies, often termed rapid-learning, are being pursued that can match the pace of innovation in radiotherapy and generate evidence from the real-world treatment setting. It is important to understand...

A novel approach to assess and improve syntactic interoperability in data integration

Data integration is essential to enrich a database with external information. One effective approach is to match shared identifiers across diverse databases. However, a lack of syntactic interoperability, which refers to the ability to match data based on their syntax, can pose challenges. In this paper, we present a novel method to evaluate and enhance syntactic interoperability, considering associated costs. First, we introduce the linking index and completeness index as generic measures of fine-grained syntactic interoperability. Second, we analyze the data consistency level of the identifiers using a rule-based framework for data quality assessment. Third, we propose a data integration strategy that strikes a balance between fixing data inconsistencies and the resulting benefits, as measured by the linking and completeness indices. The approach is illustrated through two use cases: bibliographic databases and clinical trial registries. The results demonstrate that standardizing identifiers’ representations can significantly improve syntactic interoperability in certain scenarios while in others, the standardization process does not yield improvements, discouraging, hence integration decisions. By conducting a cost–benefit analysis of improving data interoperability, this analysis enables data integrators to make informed decisions regarding the feasibility and advantages of proceeding with data integration.

ARE WE THERE OR DO WE HAVE MORE TO DO? METAVERSE IN FACILITY MANAGEMENT AND FUTURE PROSPECTS

Businesses are eager to adopt new technologies such as augmented reality, blockchain, extended reality, and artificial intelligence, among others, as they see beneficial applications for strategic empowerment. As one of the technological disruptions, metaverse has caught the attention of the larger business community and facility management (FM) sector. In a world where various virtual realities converge, the hyper-connected digital universe known as the metaverse has the potential to fundamentally alter how FM and business interactions are conducted in the future. More people are spending time conducting business online, increasing the metaverse’s potential. Businesses are also making significant investments in the development of metaverse techniques. This study reviews the current literature to comprehend how metaverse techniques have shaped the building blocks of FM by looking at 206 papers. There is currently a dearth of functional metaverse research. This article aims to provide a thorough analysis of the integration of metaverse techniques for facility management. The review reveals several opportunities for further research into the FM-metaverse ecosystem, including improving data interoperability, enhancing the precision of point cloud data for as-built models of existing facilities, and producing efficient virtual property database integration.

Tidyproteomics: an open-source R package and data object for quantitative proteomics post analysis and visualization

BackgroundThe analysis of mass spectrometry-based quantitative proteomics data can be challenging given the variety of established analysis platforms, the differences in reporting formats, and a general lack of approachable standardized post-processing analyses such as sample group statistics, quantitative variation and even data filtering. We developed tidyproteomics to facilitate basic analysis, improve data interoperability and potentially ease the integration of new processing algorithms, mainly through the use of a simplified data-object.ResultsThe R package tidyproteomics was developed as both a framework for standardizing quantitative proteomics data and a platform for analysis workflows, containing discrete functions that can be connected end-to-end, thus making it easier to define complex analyses by breaking them into small stepwise units. Additionally, as with any analysis workflow, choices made during analysis can have large impacts on the results and as such, tidyproteomics allows researchers to string each function together in any order, select from a variety of options and in some cases develop and incorporate custom algorithms.ConclusionsTidyproteomics aims to simplify data exploration from multiple platforms, provide control over individual functions and analysis order, and serve as a tool to assemble complex repeatable processing workflows in a logical flow. Datasets in tidyproteomics are easy to work with, have a structure that allows for biological annotations to be added, and come with a framework for developing additional analysis tools. The consistent data structure and accessible analysis and plotting tools also offers a way for researchers to save time on mundane data manipulation tasks.

A survey of epidemic management data models.

The COVID-19 epidemic has demonstrated the important role that data plays in the response to and management of public health emergencies. It has also heightened awareness of the role that ontologies play in the design of semantically precise data models that improve data interoperability among stakeholders. This paper surveys vocabularies and ontologies relevant to the task of achieving epidemic-related data interoperability. The paper first reviews 16 vocabularies and ontologies with respect to the use cases. Next it identifies patterns of knowledge that are common across multiple vocabularies and ontologies, followed by an analysis of patterns that are missing, based on the use cases. Conclusions show that existing vocabularies and ontologies provide significant coverage of the concepts underlying epidemic use cases, but there remain gaps in the coverage. More work is required to cover missing but significant concepts.

Real-world performance of SARS-Cov-2 serology tests in the United States, 2020.

Real-world performance of COVID-19 diagnostic tests under Emergency Use Authorization (EUA) must be assessed. We describe overall trends in the performance of serology tests in the context of real-world implementation. Six health systems estimated the odds of seropositivity and positive percent agreement (PPA) of serology test among people with confirmed SARS-CoV-2 infection by molecular test. In each dataset, we present the odds ratio and PPA, overall and by key clinical, demographic, and practice parameters. A total of 15,615 people were observed to have at least one serology test 14-90 days after a positive molecular test for SARS-CoV-2. We observed higher PPA in Hispanic (PPA range: 79-96%) compared to non-Hispanic (60-89%) patients; in those presenting with at least one COVID-19 related symptom (69-93%) as compared to no such symptoms (63-91%); and in inpatient (70-97%) and emergency department (93-99%) compared to outpatient (63-92%) settings across datasets. PPA was highest in those with diabetes (75-94%) and kidney disease (83-95%); and lowest in those with auto-immune conditions or who are immunocompromised (56-93%). The odds ratios (OR) for seropositivity were higher in Hispanics compared to non-Hispanics (OR range: 2.59-3.86), patients with diabetes (1.49-1.56), and obesity (1.63-2.23); and lower in those with immunocompromised or autoimmune conditions (0.25-0.70), as compared to those without those comorbidities. In a subset of three datasets with robust information on serology test name, seven tests were used, two of which were used in multiple settings and met the EUA requirement of PPA ≥87%. Tests performed similarly across datasets. Although the EUA requirement was not consistently met, more investigation is needed to understand how serology and molecular tests are used, including indication and protocol fidelity. Improved data interoperability of test and clinical/demographic data are needed to enable rapid assessment of the real-world performance of in vitro diagnostic tests.

A Framework for Automatic Clustering of EHR Messages Using a Spatial Clustering Approach.

Although Health Level Seven (HL 7) message standards (v2, v3, Clinical Document Architecture (CDA)) have been commonly adopted, there are still issues associated with them, especially the semantic interoperability issues and lack of support for smart devices (e.g., smartphones, fitness trackers, and smartwatches), etc. In addition, healthcare organizations in many countries are still using proprietary electronic health record (EHR) message formats, making it challenging to convert to other data formats-particularly the latest HL7 Fast Health Interoperability Resources (FHIR) data standard. The FHIR is based on modern web technologies such as HTTP, XML, and JSON and would be capable of overcoming the shortcomings of the previous standards and supporting modern smart devices. Therefore, the FHIR standard could help the healthcare industry to avail the latest technologies benefits and improve data interoperability. The data representation and mapping from the legacy data standards (i.e., HL7 v2 and EHR) to the FHIR is necessary for the healthcare sector. However, direct data mapping or conversion from the traditional data standards to the FHIR data standard is challenging because of the nature and formats of the data. Therefore, in this article, we propose a framework that aims to convert proprietary EHR messages into the HL7 v2 format and apply an unsupervised clustering approach using the DBSCAN (density-based spatial clustering of applications with noise) algorithm to automatically group a variety of these HL7 v2 messages regardless of their semantic origins. The proposed framework's implementation lays the groundwork to provide a generic mapping model with multi-point and multi-format data conversion input into the FHIR. Our experimental results show the proposed framework's ability to automatically cluster various HL7 v2 message formats and provide analytic insight behind them.

LightAMR format standard and lossless compression algorithms for adaptive mesh refinement grids: RAMSES use case

The evolution of parallel I/O library as well as new concepts such as ‘in transit’ and ‘in situ’ visualization and analysis have been identified as key technologies to circumvent I/O bottleneck in pre-exascale applications. Nevertheless, data structure and data format can also be improved for both reducing I/O volume and improving data interoperability between data producer and data consumer. In this paper, we propose a very lightweight and purpose-specific post-processing data model for AMR meshes, called lightAMR. Based on this data model, we introduce a tree pruning algorithm that removes data redundancy from a fully threaded AMR octree. In addition, we present two lossless compression algorithms, one for the AMR grid structure description and one for AMR double/single precision physical quantity scalar fields. Then we present performance benchmarks on RAMSES simulation datasets of this new lightAMR data model and the pruning and compression algorithms. We show that our pruning algorithm can reduce the total number of cells from RAMSES AMR datasets by 10-40% without loss of information. Finally, we show that the RAMSES AMR grid structure can be compacted by ∼3 orders of magnitude and the float scalar fields can be compressed by a factor ∼1.2 for double precision and ∼1.3−1.5 in single precision with a compression speed of ∼1 GB/s.

Boosting Climate Analysis With Semantically Uplifted Knowledge Graphs

Nowadays, the fast expansion of heterogeneous climate data resources accessible on the Internet has led to substantial data fragmentation on the web. For example, station-based sensor data from different sources are likely to be interrelated but may be stored in disparate formats, such as <monospace>CSV</monospace>, <monospace>JSON</monospace>, and <monospace>XML</monospace>. To address the data isolation problem, several semantically uplifted knowledge graphs are proposed for climate data exchange. While these knowledge graphs improve data interoperability, the advancement in multisource data interchange is limited to data included inside knowledge graphs. As a result, the exclusive interoperability of current climatic knowledge graphs hampers the flow of data into typical climate analysis workflows in contexts, where analytical models often need data in nonknowledge graph formats. This article addresses this issue by focusing on enhancing climate analysis workflows within the context of the Python machine learning environment, with a preference for tabular data. We propose an analysis workflow able to automatically integrate remote climate knowledge graph data with local tabular data so as to enhance the data usability with respect to certain climate analysis tasks. To underscore the importance of our study, we illustrate how the workflow streamlines the access to multisource climatic variables in the Python environment from a semantic perspective. The additional knowledge graph data have the potential to augment local datasets in the climate domain, as evidenced by an improvement in accuracy of up to 10&#x0025; for machine learning geared on rainfall detection.

Formalization of gene regulation knowledge using ontologies and gene ontology causal activity models

Gene regulation computational research requires handling and integrating large amounts of heterogeneous data. The Gene Ontology has demonstrated that ontologies play a fundamental role in biological data interoperability and integration. Ontologies help to express data and knowledge in a machine processable way, which enables complex querying and advanced exploitation of distributed data. Contributing to improve data interoperability in gene regulation is a major objective of the GREEKC Consortium, which aims to develop a standardized gene regulation knowledge commons. GREEKC proposes the use of ontologies and semantic tools for developing interoperable gene regulation knowledge models, which should support data annotation. In this work, we study how such knowledge models can be generated from cartoons of gene regulation scenarios. The proposed method consists of generating descriptions in natural language of the cartoons; extracting the entities from the texts; finding those entities in existing ontologies to reuse as much content as possible, especially from well known and maintained ontologies such as the Gene Ontology, the Sequence Ontology, the Relations Ontology and ChEBI; and implementation of the knowledge models. The models have been implemented using Protégé, a general ontology editor, and Noctua, the tool developed by the Gene Ontology Consortium for the development of causal activity models to capture more comprehensive annotations of genes and link their activities in a causal framework for Gene Ontology Annotations. We applied the method to two gene regulation scenarios and illustrate how to apply the models generated to support the annotation of data from research articles.

A streamlined workflow for conversion, peer review, and publication of genomics metadata as omics data papers.

BackgroundData papers have emerged as a powerful instrument for open data publishing, obtaining credit, and establishing priority for datasets generated in scientific experiments. Academic publishing improves data and metadata quality through peer review and increases the impact of datasets by enhancing their visibility, accessibility, and reusability.ObjectiveWe aimed to establish a new type of article structure and template for omics studies: the omics data paper. To improve data interoperability and further incentivize researchers to publish well-described datasets, we created a prototype workflow for streamlined import of genomics metadata from the European Nucleotide Archive directly into a data paper manuscript.MethodsAn omics data paper template was designed by defining key article sections that encourage the description of omics datasets and methodologies. A metadata import workflow, based on REpresentational State Transfer services and Xpath, was prototyped to extract information from the European Nucleotide Archive, ArrayExpress, and BioSamples databases.FindingsThe template and workflow for automatic import of standard-compliant metadata into an omics data paper manuscript provide a mechanism for enhancing existing metadata through publishing.ConclusionThe omics data paper structure and workflow for import of genomics metadata will help to bring genomic and other omics datasets into the spotlight. Promoting enhanced metadata descriptions and enforcing manuscript peer review and data auditing of the underlying datasets brings additional quality to datasets. We hope that streamlined metadata reuse for scholarly publishing encourages authors to create enhanced metadata descriptions in the form of data papers to improve both the quality of their metadata and its findability and accessibility.

A scalable ledger-assisted architecture for secure query processing over distributed IoT data

Massive amount of IoT data poses unique challenges in centralized data management systems. Specifically, IoT data can originate from heterogeneous and distributed sources, and commonly regulations forbid data from different IoT stakeholders to be managed via central governance. To serve for IoT applications, recent proposals leverage distributed ledgers (e.g., blockchains) to function on top of distributed data storage with improved data interoperability. Unfortunately, most of them do not consider data security and privacy in the first place. Along with this transformative paradigm, in this paper, we propose a ledger-assisted architecture for secure distributed IoT data management. This architecture adapts searchable encryption to decentralized storage network to enable encrypted query processing. Meanwhile, it is designed to leverage the distributed ledger to harden both data and query integrity. To deal with continuously generated IoT data, we further devise an efficient secure data insertion protocol, and employ a recent variant of blockchain for users to validate updated query results in a scalable manner. Evaluations on Azure blockchain service confirm the practicality of our proposed architecture.

Opioid Epidemic or Pain Crisis? Using the Virginia All Payer Claims Database to Describe Opioid Medication Prescribing Patterns and Potential Harms for Patients With Cancer.

A key challenge regarding the current opioid epidemic is understanding how concerns regarding opioid-related harms affect access to pain management, an essential element of cancer care. In certain regions of the United States where disproportionately high cancer mortality and opioid fatality rates coexist (such as southwest Virginia in central Appalachia), this dilemma is particularly pronounced. This longitudinal, exploratory, secondary analysis used the Commonwealth of Virginia All Payer Claims Database to describe prescription opioid medication (POM) prescribing patterns and potential harms for adult patients with cancer living in rural southwest Virginia between 2011 and 2015. Descriptive and inferential statistical analyses were conducted at the patient, prescriber, and prescription levels to identify patterns and predictors of POM prescribing and potential harms. To explore geographic patterns, choropleth and heat maps were created. Of the total sample of patients with cancer (n = 4,324), less than 25% were prescribed a Controlled Substance Schedule II POM at least three times in any study year. More than 60% of patients never received a Controlled Substance Schedule II POM prescription. Six hundred fifty-two patients (15.1%) experienced 1,599 hospitalizations for any reason; 10 or fewer patients were admitted for 11 opioid use disorder-related hospitalizations. The main findings suggest potential undertreatment of cancer-related pain; no difference in risk for opioid-related hospitalization on the basis of frequency of POM prescriptions; and geographic disparities where opioid overdoses are occurring versus where POM prescription use is highest. These findings have significant opioid policy and practice implications related to the need for cancer-specific prescribing guidelines, how to optimally allocate health delivery services, and the urgent need to improve data interoperability and access related to POMs.

Model consent clauses for rare disease research

BackgroundRare Disease research has seen tremendous advancements over the last decades, with the development of new technologies, various global collaborative efforts and improved data sharing. To maximize the impact of and to further build on these developments, there is a need for model consent clauses for rare diseases research, in order to improve data interoperability, to meet the informational needs of participants, and to ensure proper ethical and legal use of data sources and participants’ overall protection.MethodsA global Task Force was set up to develop model consent clauses specific to rare diseases research, that are comprehensive, harmonized, readily accessible, and internationally applicable, facilitating the recruitment and consent of rare disease research participants around the world. Existing consent forms and notices of consent were analyzed and classified under different consent themes, which were used as background to develop the model consent clauses.ResultsThe IRDiRC-GA4GH MCC Task Force met in September 2018, to discuss and design model consent clauses. Based on analyzed consent forms, they listed generic core elements and designed the following rare disease research specific core elements; Rare Disease Research Introductory Clause, Familial Participation, Audio/Visual Imaging, Collecting, storing, sharing of rare disease data, Recontact for matching, Data Linkage, Return of Results to Family Members, Incapacity/Death, and Benefits.ConclusionThe model consent clauses presented in this article have been drafted to highlight consent elements that bear in mind the trends in rare disease research, while providing a tool to help foster harmonization and collaborative efforts.

Managing evidence in food safety and nutrition.

Evidence (‘data’) is at the heart of EFSA's 2020 Strategy and is addressed in three of its operational objectives: (1) adopt an open data approach, (2) improve data interoperability to facilitate data exchange, and (3) migrate towards structured scientific data. As the generation and availability of data have increased exponentially in the last decade, potentially providing a much larger evidence base for risk assessments, it is envisaged that the acquisition and management of evidence to support future food safety risk assessments will be a dominant feature of EFSA's future strategy. During the breakout session on ‘Managing evidence’ of EFSA's third Scientific Conference ‘Science, Food, Society’, current challenges and future developments were discussed in evidence management applied to food safety risk assessment, accounting for the increased volume of evidence available as well as the increased IT capabilities to access and analyse it. This paper reports on presentations given and discussions held during the session, which were centred around the following three main topics: (1) (big) data availability and (big) data connection, (2) problem formulation and (3) evidence integration.

Role of Academic Biobanks in Public-Private Partnerships in the European Biobanking and BioMolecular Resources Research Infrastructure Community.

Public-private partnerships (PPP) are an efficient means to advance scientific discoveries and boost the medical innovations needed to improve precision medicine. The increasing number and novel nature of such collaborations is keeping the biomedical field in constant flux. Here we provide an update on PPP development involving academic biobanks in the BBMRI community (the European Biobanking and BioMolecular Resources Research Infrastructure) and report the views on PPP of 20 key players from this field. The interviewed academic representants broadly show interest for their institution to establish PPP and initiate or partner with BBMRI expert centers. The results indicate that PPP has gained foothold in this area of biomedical research, with great promise to facilitate access to samples and data and to improve data interoperability and reproducibility.

Observations and Recommendations for the Calibration of Landsat 8 OLI and Sentinel 2 MSI for Improved Data Interoperability

Combining data from multiple sensors into a single seamless time series, also known as data interoperability, has the potential for unlocking new understanding of how the Earth functions as a system. However, our ability to produce these advanced data sets is hampered by the differences in design and function of the various optical remote-sensing satellite systems. A key factor is the impact that calibration of these instruments has on data interoperability. To address this issue, a workshop with a panel of experts was convened in conjunction with the Pecora 20 conference to focus on data interoperability between Landsat and the Sentinel 2 sensors. Four major areas of recommendation were the outcome of the workshop. The first was to improve communications between satellite agencies and the remote-sensing community. The second was to adopt a collections-based approach to processing the data. As expected, a third recommendation was to improve calibration methodologies in several specific areas. Lastly, and the most ambitious of the four, was to develop a comprehensive process for validating surface reflectance products produced from the data sets. Collectively, these recommendations have significant potential for improving satellite sensor calibration in a focused manner that can directly catalyze efforts to develop data that are closer to being seamlessly interoperable.

FCA based ontology development for data integration

Data is a valuable asset to our society. Effective use of data can enhance productivity of business and create economic benefit to customers. However with data growing at unprecedented rates, organisations are struggling to take full advantage of available data. One main reason for this is that data is usually originated from disparate sources. This can result in data heterogeneity, and prevent data from being digested easily. Among other techniques developed, ontology based approaches is one promising method for overcoming heterogeneity and improving data interoperability. This paper contributes a formal and semi-automated approach for ontology development based on Formal Concept Analysis (FCA), with the aim to integrate data that exhibits implicit and ambiguous information. A case study has been carried out on several non-trivial industrial datasets, and our experimental results demonstrate that proposed method offers an effective mechanism that enables organisations to interrogate and curate heterogeneous data, and to create the knowledge that meets the need of business.