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Recent developments in collection, computation and communication have expanded the way of data reporting in intelligent Internet of Things (IoT). However, diversity and complexity of data sources also impose new trust challenge in data collection process since untrust reporters tend to report false or even malicious data, which highlights the need to develop a novel methodology to solve such challenge. Thus, based on this domain, inspired by deterrence theory, this paper proposes a blockchain-based trust system with assistant of drones to deter malicious data reporting in intelligent IoT. Specifically, to deter malicious data reporting, based on blockchain technology, the data sensed by fully-trusted drones is public published on blockchain showing participants the data standards, named as malicious deterrence scheme. This scheme provides a barrier for malicious reporters to arbitrarily publish false data to blockchain, since the false data can be easily detected while they cannot deny. Secondly, to further reduce malicious data reporting, a strict penalty mechanism is proposed to punish malicious reporters who have reported false data to blockchain to reduce the mali-cious data reporting in the following task through punishment. Thirdly, note that the sensing of data standard generates addi-tional costs, therefore, a drone flight route scheme based on a simper Deep Reinforcement Learning with Multi-head Attention mechanism (MA-DRL) is designed to reduce the flight distance for drones. Finally, extensive experiments demonstrate efficiency of our proposed system in terms of reducing malicious data reporting in advance as well as reducing drone flight distance.

The article reveals with the logic, mechanism and main parameters for using passenger data (API/PNR). The interrelation of the concepts of “Advance Passenger Information” and “Passenger Name Record” has been revealed. It has been determined that the systems of simplification of formalities also have a positive effect on security. For example, the purpose of data collection in the EU is to effectively combat illegal migration and strengthen border control, as well as to prevent, detect, investigate and prosecute terrorist and serious crimes. This applies to both the Passenger Information System (API) and additional passenger data, such as Passenger Name Record (PNR) data. There is no doubt that the successful application of API and PNR data exchange depends on a unified approach of all participants in information relations (both border agencies and airlines in different countries) regarding the question of data standard and standard of their transmission. Ensuring such a unified approach is a problem even when there is unity of legal regulation on data format and data exchange procedures. There are no special regulations in the national legislation that would regulate the relationship regarding the circulation of passenger registration data, nor is there a specific entity that processes this type of information. Thus, there is a need for special legal regulation of the circulation of information about airline passengers crossing the state border of Ukraine. In particular, the legislation must regulate the procedure, grounds, purpose of obtaining, processing, transmission, storage and destruction of information about passengers (its content), protection of the right to confidential information of persons operating international flights, establish a responsible controller of such information.

Application of Building Information Modeling (BIM) in construction industry has been applied for many years back. This because BIM can provide a better advantage in construction industry in term of controlling and managing construction project during their life cycle. The advantages that can be provide by BIM is focusing on the indoor planning tasks. But, when the construction project involves, besides indoor planning, outdoor planning also is important part that need to be look up. To cover the outdoor planning in construction project, Geographic Information System (GIS) need to be applied. GIS can overcome this problem because GIS mainly for outdoor planning by using their spatial analysis. GIS can offer a high degree of geospatial information and can provide the detailed geometrical and semantic information of building to assisted across improve automation. Towards produce the improved preparation in construction project, BIM and GIS should be integrated. To integrate both domains, the data interoperability between them need to be investigate because they used different data standard. This study focusses on solving the data interoperability through the data integration between BIM and GIS to solve the problem of data mismatch and data missing during data translation process. Industry Foundation Classes (IFC) was used as a data standard to perform the data integration between BIM and GIS. The outcomes from this study show that when the data interoperability applied between BIM and GIS, the problem above can be solved, and the data dimension and their coordinate system also can be control.

PURPOSES : The purpose of this study is to contribute to the utilization of standards while considering the possible upgrade of a local system as a subject of the application. Therefore, this study aims to explore the possible application of LandInfra for a local road management (maintenance) system in the context of enabling the basis of 3D geospatial road information management in Korea. METHODS : Based on a review of related literature and international standards, an analysis of the current system is performed. After reviewing the LandInfra standard, an examination of corresponding classes between each data model (HMS and LandInfra) is performed for the mapping process. After the mapping process, a data model of the LandInfra-based HMS pavement data model is proposed. RESULTS : To apply the LandInfa to the HMS pavement part, an examination of each data model is performed. After this procedure, a LandInfra-based HMS pavement database schema is proposed in the context of enabling 3D geospatial road information management and maintenance, particularly for pavement management information. CONCLUSIONS : This paper presents how the LandInfra international open geospatial standard can be applied to the local road management system (HMS pavement part). As a result of this study, the LandInfra standard could be applied to the HMS; however, an encoding of the standard is required for conformance. Thus, further studies would be the encoding of the proposed data model for conformance with InfaGML encoding standards. In addition, a system prototype may be needed for complete application.

Objective In this study, we aimed to evaluate the capability of the Unified Medical Language System (UMLS) as one data standard to support data normalization and harmonization of datasets that have been developed for rare diseases. Through analysis of data mappings between multiple rare disease resources and the UMLS, we propose suggested extensions of the UMLS that will enable its adoption as a global standard in rare disease. Methods We analyzed data mappings between the UMLS and existing datasets on over 7,000 rare diseases that were retrieved from four publicly accessible resources: Genetic And Rare Diseases Information Center (GARD), Orphanet, Online Mendelian Inheritance in Men (OMIM), and the Monarch Disease Ontology (MONDO). Two types of disease mappings were assessed, (1) curated mappings extracted from those four resources; and (2) established mappings generated by querying the rare disease-based integrative knowledge graph developed in the previous study. Results We found that 100% of OMIM concepts, and over 50% of concepts from GARD, MONDO, and Orphanet were normalized by the UMLS and accurately categorized into the appropriate UMLS semantic groups. We analyzed 58,636 UMLS mappings, which resulted in 3,876 UMLS concepts across these resources. Manual evaluation of a random set of 500 UMLS mappings demonstrated a high level of accuracy (99%) of developing those mappings, which consisted of 414 mappings of synonyms (82.8%), 76 are subtypes (15.2%), and five are siblings (1%). Conclusion The mapping results illustrated in this study that the UMLS was able to accurately represent rare disease concepts, and their associated information, such as genes and phenotypes, and can effectively be used to support data harmonization across existing resources developed on collecting rare disease data. We recommend the adoption of the UMLS as a data standard for rare disease to enable the existing rare disease datasets to support future applications in a clinical and community settings.

During the initial construction of the Puguang Gas Field, information infrastructure was built. Due to the absence of unified planning and deployment, however, many “isolated information islands” were formed in data systems, and the data resources cannot meet the construction requirements of intelligent gas field. In this paper, the status quo and problems of data resources in the Puguang Gas Field were analyzed, and a data resource sharing center was constructed according to the overall architecture design of the Puguang Intelligent Gas Field. Based on the architecture design of data resource sharing center, the overall construction conception of data resource sharing center was put forward and the business data model was designed. Finally, the integrated data collection, storage, calculation and utilization was realized by establishing data standard, combing data sources and designing data services, and then it was applied on site. The following research results were obtained. First, the data resource sharing center is an important foundation for the construction of this project, and its overall architecture is divided into three layers from bottom to top, i.e., data specification and standard, data collection, storage, calculation and utilization, and data control. Second, the data resource sharing center achieves the one-time collection, centralized storage, shared use and unified management of exploration & development, gathering & purification, production & operation and safety & environmental protection data, and provides an important data base for the construction of business system of the intelligent gas field and a comprehensive, reliable and effective data support for the intelligent and mobile application in the Puguang Gas Field. Keywords: Intelligent gas field, Puguang gas field, Standard and specification, Post data, Real time data, Video data, Distributed storage, Data sharing service

Objective Common data elements (CDEs) promote data sharing, standardization, and uniform data collection, which facilitate meta-analyses and comparisons of studies. Currently, there is no set of CDEs for all trauma populations, but their creation would allow researchers to leverage existing databases to maximize research on trauma outcomes. The purpose of this study is to assess the extent of common data collection among 5 trauma databases. Design The data dictionaries of 5 trauma databases were examined to determine the extent of common data collection. Databases included 2 acute care databases (American Burn Association's National Burn Data Standard and American College of Surgeons' National Trauma Data Standard) and 3 longitudinal trauma databases (Burn, Traumatic Brain Injury, Spinal Cord Injury Model System National Databases). Data elements and data values were compared across the databases. Quantitative and qualitative variations in the data were identified to highlight meaningful differences between datasets. Setting N/A. Participants N/A. Interventions N/A. Main Outcome Measures N/A. Results Of the 30 data elements examined, 14 (47%) were present in all 5 databases. Another 9 (30%) elements were present in 4 of the 5 databases. The number of elements present in each database ranged from 23 (77%) to 26 (86%). There were inconsistencies in the data values across the databases. Twelve of the 14 data elements present in all 5 databases exhibited differences in data values. Conclusions This study demonstrates inconsistencies in the documentation of data elements in 5 common trauma databases. These discrepancies are a barrier to database harmonization and to maximizing the use of these databases through linking, pooling, and comparing data. A collaborative effort is required to develop a standardized set of elements for trauma research.

A multi-source data standard must be established to reconcile all available geological data such as borehole data, geological mapping data, and rock property data for building a reliable 3D geological model. In addition, a methodology that considers various available data must be developed to provide accurate results that are easy to interpret and convenient for post-modeling analysis. This paper presents a collaborative analysis approach for geological body modeling using multi-source geological data and interpolation theories in different stages and at different places. This approach aims to provide a detailed and comprehensive description and analysis of data, with emphasis on processing flow. The proposed approach contains three major components: establishment of geological databases based on the multi-source data standard to incorporate borehole, geological mapping and rock property data; Combined Kriging interpolation method for data processing; geological algorithms to build, visualize, and post-analyze the 3D geological model. The model integrates multi-source information and is a representative of the geological context. The proposed method is validated by applying it to the Ma-luan mountain tunnel project in Guangdong province, China.

Background Ensuring a sufficient supply and distribution of health care professionals is essential to meeting public health needs. Regulatory agencies protect their communities by ensuring that new health professionals have the required qualifications to practice safely and by tracking the volume and distribution of those professionals on an ongoing basis. The speed and accuracy of sharing these data could be greatly improved through the adoption of a data standard for information about health professionals. To date, however, no internationally accepted standard has emerged for this purpose. Purpose This study examines three existing XML standards designed for the representation of individual worker data to determine if, and to what degree, each could be used for the tracking of health professionals. Methods The data elements of the Europass schema, the HR Open Standard Recruiting specification, and the MedBiquitous Healthcare Professional Profile standard were fully examined and matching elements were mapped to the 200+ elements identified from a prior content analysis as required by a sample of 20 international regulatory agencies. Results None of the schemas examined addressed more than half of the information elements required by regulators. All three schemas are found lacking in some key areas of interest, especially vital information that could disqualify ineligible applicant practitioners. Conclusions The three standards could all be improved by including new elements essential to regulatory agencies. Regulatory agencies should be consulted in the development of new standards for representing potentially disqualifying information about candidates for professional practice.

High-throughput biological data analysis commonly involves the identification of "interesting" features (e.g., genes, genomic regions, and proteins), whose values differ between two conditions, from numerous features measured simultaneously. To ensure the reliability of such analysis, the most widely-used criterion is the false discovery rate (FDR), the expected proportion of uninteresting features among the identified ones. Existing bioinformatics tools primarily control the FDR based on p-values. However, obtaining valid p-values relies on either reasonable assumptions of data distribution or large numbers of replicates under both conditions, two requirements that are often unmet in biological studies. To address this issue, we propose Clipper, a general statistical framework for FDR control without relying on p-values or specific data distributions. Clipper is applicable to identifying both enriched and differential features from high-throughput biological data of diverse types. In comprehensive simulation and real-data benchmarking, Clipper outperforms existing generic FDR control methods and specific bioinformatics tools designed for various tasks, including peak calling from ChIP-seq data, differentially expressed gene identification from RNA-seq data, differentially interacting chromatin region identification from Hi-C data, and peptide identification from mass spectrometry data. Notably, our benchmarking results for peptide identification are based on the first mass spectrometry data standard that has a realistic dynamic range. Our results demonstrate Clipper9s flexibility and reliability for FDR control, as well as its broad applications in high-throughput data analysis.

Micro-scale models are important to assess processes in complex domains, for example cities. The most common data standard for atmospheric model output data are the CF-conventions, a data standard ...

The PALM model system 6.0 is designed to simulate micro- and mesoscale flow dynamics in realistic urban environments. The simulation results can be very valuable for various urban applications, for example to develop and improve mitigation strategies related to heat stress or air pollution. For the accurate modelling of urban environments, realistic boundary conditions need to be considered for the atmosphere, the local environment, and the soil. The local environment with its geospatial components is described in the static driver of the model and follows a standardized, hereafter called PALM input data standard. The main input parameters describe surface type, buildings and vegetation. Depending on the desired simulation scenario and the available data, the local environment can be described at different levels of detail. To compile a complete static driver describing a whole city, various data sources are used, including remote sensing, municipal data collections and open data such as OpenStreetMap. This manuscript shows how input data sets for three German cities can be derived. Based on these data sets, the static driver for PALM can be generated. As the collection and preparation of input data sets is tedious, prospective research aims at the development of a semi-automated processing chain to support users in formatting their geospatial data.

Data standards allow us to aggregate, compare, compute and communicate data from a wide variety of origins. However, for historical reasons, data are most likely to be stored in many different formats and conform to different models. Every data set might contain a huge amount of information, but it becomes tremendously difficult to compare them without a common way to represent the data. That is when standards development jumps in. Developing a standard is a formidable process, often involving many stakeholders. Typically the initial blueprint of a standard is created by a limited number of people who have a clear view of their use cases. However, as development continues, additional stakeholders participate in the process. As a result, conflicting opinions and interests will influence the development of the standard. Compromises need to be made and the standard might look very different from the initial concept. In order to address the needs of the community, a high level of engagement in the development process is encouraged. However, this does not necessarily increase the usability of the standard. To mitigate this, there is a need to test the standard during the early stages of development. In order to facilitate this, we explored the use of Wikibase to create an initial implementation of the standard. Wikibase is the underlying technology that drives Wikidata. The software is open-source and can be customized for creating collaborative knowledge bases. In addition to containing an RDF (Resource Description Framework) triple store under the hood, it provides users with an easy-to-use graphical user interface (see Fig. 1). This facilitates the use of an implementation of a standard by non-technical users. The Wikibase remains fully flexible in the way data are represented and no data model is enforced. This allows users to map their data onto the standard without any restrictions. Retrieving information from RDF data can be done through the SPARQL query language (W3C 2020). The software package has also a built-in SPARQL endpoint, allowing users to extract the relevant information: Does the standard cover all use cases envisioned? Are parts of the standard underdeveloped? Are the controlled vocabularies sufficient to describe the data? Does the standard cover all use cases envisioned? Are parts of the standard underdeveloped? Are the controlled vocabularies sufficient to describe the data? This strategy was applied during the development of the TDWG Collection Description standard. After completing a rough version of the standard, the different terms that were defined in the first version were transferred to a Wikibase instance running on WBStack (Addshore 2020). Initially, collection data were entered manually, which revealed several issues. The Wikibase allowed us to easily define controlled vocabularies and expand them as needed. The feedback reported from users then flowed back to the further development of the standard. Currently we envisage creating automated scripts that will import data en masse from collections. Using the SPARQL query interface, it will then be straightforward to ensure that data can be extracted from the Wikibase to support the envisaged use cases.

The Synthetic Biology Open Language (SBOL) is a community-developed data standard that allows knowledge about biological designs to be captured using a machine-tractable, ontology-backed representation that is built using Semantic Web technologies. While early versions of SBOL focused only on the description of DNA-based components and their sub-components, SBOL can now be used to represent knowledge across multiple scales and throughout the entire synthetic biology workflow, from the specification of a single molecule or DNA fragment through to multicellular systems containing multiple interacting genetic circuits. The third major iteration of the SBOL standard, SBOL3, is an effort to streamline and simplify the underlying data model with a focus on real-world applications, based on experience from the deployment of SBOL in a variety of scientific and industrial settings. Here, we introduce the SBOL3 specification both in comparison to previous versions of SBOL and through practical examples of its use.

Motivation Standardization and semantic alignment have been considered one of the major challenges for data integration in clinical research. The inclusion of the CDISC SDTM clinical data standard into the tranSMART i2b2 via a guiding master ontology tree positively impacts and supports the efficacy of data sharing, visualization and exploration across datasets. Results We present here a schema for the organization of SDTM variables into the tranSMART i2b2 tree along with a script and test dataset to exemplify the mapping strategy. The eTRIKS master tree concept is demonstrated by making use of fictitious data generated for four patients, including 16 SDTM clinical domains. We describe how the usage of correct visit names and data labels can help to integrate multiple readouts per patient and avoid ETL crashes when running a tranSMART loading routine. Availability and implementation The eTRIKS Master Tree package and test datasets are publicly available at https://doi.org/10.5281/zenodo.1009098 and a functional demo installation at https://public.etriks.org/transmart/datasetExplorer/ under eTRIKS—Master Tree branch, where the discussed examples can be visualized.

A ubiquitous problem in aggregating data across different experimental and observational data sources is a lack of software infrastructure that enables flexible and extensible standardization of data and metadata. To address this challenge, we developed HDMF, a hierarchical data modeling framework for modern science data standards. With HDMF, we separate the process of data standardization into three main components: (1) data modeling and specification, (2) data I/O and storage, and (3) data interaction and data APIs. To enable standards to support the complex requirements and varying use cases throughout the data life cycle, HDMF provides object mapping infrastructure to insulate and integrate these various components. This approach supports the flexible development of data standards and extensions, optimized storage backends, and data APIs, while allowing the other components of the data standards ecosystem to remain stable. To meet the demands of modern, large-scale science data, HDMF provides advanced data I/O functionality for iterative data write, lazy data load, and parallel I/O. It also supports optimization of data storage via support for chunking, compression, linking, and modular data storage. We demonstrate the application of HDMF in practice to design NWB 2.0 [13], a modern data standard for collaborative science across the neurophysiology community.

A aproximação de áreas de negócio com o uso de novas tecnologias, economia em tempo real, transações com vários países e em vários continentes com diferentes garantias legais são necessárias. Essas garantias podem ser adquiridas por meio da Auditoria Contínua (CA). No entanto, para poder realizar a análise, os auditores precisam ter acesso e extrair os dados. Pesquisadores anteriores enfatizam apenas os benefícios da aplicação dos métodos de AC, mas não explicam como recuperar e organizar os dados. Desta forma, propomos o desenvolvimento de um framework para integrar diferentes sistemas de auditoria contínua. O artigo visa contribuir para a literatura com o aprofundamento das formas de acessar, estruturar e coletar dados críticos e necessários para a AC. Com o aprofundamento do Audit Data Standard e do eXtensible Business Reporting Language (XBRL), além de criar uma base para pesquisas futuras com a integração de algoritmos de extração, análise e detecção de exceção que são utilizados pela CA.

UMR BFP - Equipe Métabolisme; International audience; NMR is a widely used analytical technique with a growing number of repositories available. As a result, demands for a vendor-agnostic, open data format for long-term archiving of NMR data have emerged with the aim to ease and encourage sharing, comparison and reuse of NMR data. Here we present nmrML, an open XML-based exchange and storage format for NMR spectral data. The nmrML format is intended to be fully compatible with existing NMR data for chemical, biochemical and metabolomics experiments. nmrML can capture raw NMR data, spectral data acquisition parameters and, where available, spectral metadata such as chemical structures associated with spectral assignments. The nmrML format is compatible with pure-compound NMR data for reference spectral libraries as well as NMR data from complex bio-mixtures i.e. metabolomics experiments. To facilitate format conversions, we provide nmrML converters for Bruker and Agilent/Varian vendor formats. In addition, easy-to-use web-based spectral viewing, processing and spectral assignment tools that read and write nmrML have been developed. Software libraries and web services for data validation are available for tool developers and end-users. The nmrML format has already been adopted for capturing and disseminating NMR data for small molecules by several open source data processing tools and metabolomics reference spectral libraries, e.g. serving as storage format for the MetaboLights data repository. The nmrML open access data standard has been endorsed by the Metabolomics Standards Initiative (MSI) and we here encourage user participation and feedback to increase usability and make it a successful standard.

Objective.To develop a standardized format for exchanging clinical and physiologic data generated in the intensive care unit. Our goal was to develop a format that would accommodate the data collection pipelines of various sites but would not require dataset-specific schemas or ad-hoc tools for decoding and analysis.Approach.A number of centers had independently developed solutions for storing clinical and physiologic data using Hierarchical Data Format-Version 5 (HDF5), a well-supported standard already in use in multiple other fields. These individual solutions involved design choices that made the data difficult to share despite the underlying common framework. A collaborative process was used to form the basis of a proposed standard that would allow for interoperability and data sharing with common analysis tools.Main Results.We developed the HDF5-based critical care data exchange format which stores multiparametric data in an efficient, self-describing, hierarchical structure and supports real-time streaming and compression. In addition to cardiorespiratory and laboratory data, the format can, in future, accommodate other large datasets such as imaging and genomics. We demonstated the feasibility of a standardized format by converting data from three sites as well as the MIMIC III dataset.Significance.Individual approaches to the storage of multiparametric clinical data are proliferating, representing both a duplication of effort and a missed opportunity for collaboration. Adoption of a standardized format for clinical data exchange will enable the development of a digital biobank, facilitate the external validation of machine learning models and be a powerful tool for sharing multiparametric, high frequency patient level data in multisite clinical trials. Our proposed solution focuses on supporting standardized ontologies such as LOINC allowing easy reading of data regardless of the source and in so doing provides a useful method to integrate large amounts of existing data.

Objective The purpose and objective of the research presented is to provide a package for easy importing of Real-Time PCR data markup language (RDML) data to Mathematica. Results Real-Time qPCR is the most widely used experimental method for the accurate quantification of gene expression. To enable the straightforward archiving and sharing of qPCR data and its associated experimental information, an XML-based data standard was developed—the Real-Time PCR data markup language (RDML)—devised by the RDML consortium. Here, we present rdml, a package to parse and import RDML data into Mathematica, allowing the quick loading and extraction of relevant data, thus promoting the re-analysis, meta-analysis or experimental re-validation of gene expression data deposited in RDML format. info:eu-repo/semantics/publishedVersion

The Electronic Laboratory Information and Management Utensil for Molecular Diagnostics (ELIMU-MDx) is a user-friendly platform designed and built to accelerate the turnaround time of diagnostic qPCR assays. ELIMU-MDx is compliant with Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines and has extensive data-import capabilities for all major qPCR instruments by using the RDML data standard. This platform was designed as an open-source software tool and can be accessed through the web browser on all major operating systems.

This paper provides a case study and lessons learned through the roll-out of the U.S. National Institute of Standards and Technology and U.S. Department of Energy Green Button electricity, natural gas, and water data-access initiative: to make readily available energy and water consumption data for consumers and third-party companies assisting mutual customers of utilities while protecting the security and privacy of the data. Energy and water usage data are important for smart cities in addition to individual consumers. Smart-city solutions rely heavily on the availability of such data to provide situational awareness as well as to inform control, actuation, and decision-making processes. However, the data need to be protected both for security and integrity. This paper presents a case study using the Green Button standard and the steps taken to ensure data security and privacy while enabling access to those consumption data by the consumer and third parties. Data security and privacy were achieved through use of the Green Button standard and subsequent implementation by the Green Button Alliance of a compliance-testing program. Considerations and solutions were needed for data in transit, data at rest, and the authorization mechanisms for allowing unregulated third-party companies to interface directly to utilities on behalf of the consumer while ensuring the consumer maintains complete control of what is to be shared and the ability to revoke that sharing at any time. The lessons learned from this approach could be applicable to other smart-city data.

Standard representation of data is key for the reproducibility of designs in synthetic biology. The Synthetic Biology Open Language (SBOL) has already emerged as a data standard to represent information about genetic circuits, and it is based on capturing data using graphs. The language provides the syntax using a free text document that is accessible to humans only. This paper describes SBOL-OWL, an ontology for a machine understandable definition of SBOL. This ontology acts as a semantic layer for genetic circuit designs. As a result, computational tools can understand the meaning of design entities in addition to parsing structured SBOL data. SBOL-OWL not only describes how genetic circuits can be constructed computationally, it also facilitates the use of several existing Semantic Web tools for synthetic biology. This paper demonstrates some of these features, for example, to validate designs and check for inconsistencies. Through the use of SBOL-OWL, queries can be simplified and become more intuitive. Moreover, existing reasoners can be used to infer information about genetic circuit designs that cannot be directly retrieved using existing querying mechanisms. This ontological representation of the SBOL standard provides a new perspective to the verification, representation, and querying of information about genetic circuits and is important to incorporate complex design information via the integration of biological ontologies.

Geospatial data capture and handling of indoor spaces is increasing over the years and has had a varied history of data sources ranging from architectural and building drawings to indoor data acquisition approaches. While these have been more data format and information driven primarily for the physical representation of spaces, it is important to note that many applications look for the semantic information to be made available. This paper proposes a space classification model leading to an ontology for indoor spaces that accounts for both the semantic and geometric characteristics of the spaces. Further, a Space semantic model is defined, based on this ontology, which can then be used appropriately in multiple applications. To demonstrate the utility of the model, we also present an extension to the IndoorGML data standard with a set of proposed classes that can help capture both the syntactic and semantic components of the model. It is expected that these proposed classes can be appropriately harnessed for use in diverse applications ranging from indoor data visualization to more user customised building evacuation path planning with a semantic overtone.

School informatization Construction has gone through 10 years, Multiple systems operate independently. Meanwhile, the role of unstructured data resources about security monitoring, smart card system, online course resources has become more and more important in the construction of intelligent campus. According to the present condition of the school, this paper give a data model for the information construction of colleges and universities. Solving the System Fusion put to use Hadoop distributed system Architecture between structured data and unstructured data, Providing basis for data analysis and decision-making. Big data will become the evolutive direction of Intelligent Campus in the next few years, It will promote the construction of school informatization about Construction and implementation.

Trait-based approaches are widespread throughout ecological research as they offer great potential to achieve a general understanding of a wide range of ecological and evolutionary mechanisms. Accordingly, a wealth of trait data is available for many organism groups, but this data is underexploited due to a lack of standardization and heterogeneity in data formats and definitions. We review current initiatives and structures developed for standardizing trait data and discuss the importance of standardization for trait data hosted in distributed open-access repositories. In order to facilitate the standardization and harmonization of distributed trait datasets by data providers and data users, we propose a standardized vocabulary that can be used for storing and sharing ecological trait data. We discuss potential incentives and challenges for the wide adoption of such a standard by data providers. The use of a standard vocabulary allows for trait datasets from heterogeneous sources to be aggregated more easily into compilations and facilitates the creation of interfaces between software tools for trait-data handling and analysis. By aiding decentralized trait-data standardization, our vocabulary may ease data integration and use of trait data for a broader ecological research community and enable global syntheses across a wide range of taxa and ecosystems.

Author(s): Rubel, Oliver; Tritt, Andrew; Dichter, Benjamin; Braun, Thomas; Cain, Nicholas; Clack, Nathan; Davidson, Thomas; Dougherty, Max; Fillion-Robin, Jean-Christophe; Graddis, Nile; Grauer, Michael; Kiggins, Justin; Niu, Lawrence; Ozturk, Doruk; Schroeder, William; Soltesz, Ivan; Sommer, Friedrich; Svoboda, Karel; Lydia, Ng; Frank, Loren; Bouchard, Kristofer | Abstract: ABSTRACT Neurodata Without Borders: Neurophysiology (NWB:N) is a data standard for neurophysiology, providing neuroscientists with a common standard to share, archive, use, and build common analysis tools for neurophysiology data. With NWB:N version 2.0 (NWB:N 2.0) we made significant advances towards creating a usable standard, software ecosystem, and vibrant community for standardizing neurophysiology data. In this manuscript we focus in particular on the NWB:N data standard schema and present advances towards creating an accessible data standard for neurophysiology.

The explosive growth of geospatial data has stimulated the development of many standards aimed at decreasing data heterogeneity and enhancing data use. Well-established design methods for geospatial data standards typically involve the creation of two schemas for data structure, designated here as logical and physical, but this can lead to conceptual inconsistencies and modelling inefficiencies. In this paper we describe a design method that overcomes these issues by incorporating an additional schema – the conceptual schema – and demonstrate its application to the design of GroundWaterML2 (GWML2), a new international standard for groundwater data. Results include not only a new data standard, robustly constructed and tested, but also an enhanced method for geospatial data standard design.

Improving data consistency and facilitating opportunities for cross-domain urban and rural research is important in order to understand patterns of development and for modelling urban and rural growth. Two key sectors are water and energy, and researchers and policy analysts have previously found it difficult to compare and contrast data in these industries. Variability in formats, spatio-temporal granularity, access methods and differing semantic definitions hinder attempts to access, analyse, interpret, compare and combine datasets. Through the AURIN Lens 6/2 project, we have developed a GML-based standard for exchanging Water and Energy Supply and Consumption (WESC) data, called the WESC Markup Language (WESCML-SF1), as an extension to the OGC/ISO Observation and Measurements (O&M) standard. Limitations of the consuming client applications required creating a simplified version that didn’t incorporate O&M, the WESCML-SF0 information model. WESCML-SF0 and SF1 formalize definitions for the specific domain model features, along with related vocabulary definitions. This provides encoding of fine-scale WESC data for research and policy analysis in a consistent and machine consumable format that can be more readily discovered, accessed and used, rather than having users deal with multiple bespoke formats that impede data use and interoperability. WESCML-SF0 is using the Spatial Information Services Stack (SISS) for the necessary software components that support the provision of data in a federated information architecture via standard OGC web services. This allows greater ease of access to WESC data for a wider community of urban researchers, policy makers and managers.

Background Despite the Health Insurance Portability and Accountability Act (HIPAA) efforts, protected health information (PHI) and patient’s privacy and safety are still under risk. With exposure of greater than 200 million patients’ records for the period 2009–2019, the U.S. government is wondering if enough is being done to keep data safe. Blockchain is becoming a gold standard in health care data management because of its trusted, autonomous, immutable, and secured distributed ledger properties. Objective The goals of this retrospective, randomized, double-blind study were to assess (1) the effectiveness of Blockchain patient data conversion, (2) backup storage requirements, and (3) HIPAA compliance, compared with the current data storing and sharing methods and the feasibility of the use of Blockchain in Oral and Maxillofacial Radiology (OMR). Materials and Methods Head and neck computed tomography (CT) scans (n = 92,903 files) from the Cancer Imaging Archive (Blockchain folder n = 46,465 and Monitoring folder n = 46,438) and deidentified PHI were randomly assigned by 1 operator. Data were then converted into cryptographic Blockchain hashes via syncing the CBCT scans database folder with the DDSBlockchain (Charlotte, NC, 28,277) folder into the Hyperledger private Blockchain platform by a second operator. Data conversion percentage was assessed, and an F-test Two-Samples for Variances was conducted to quantify data upload speed (P Results One hundred percent of data uploaded were converted into Blockchain. Blockchain conversion had an average speed of 0.617 files per second. No statistical difference (P = .562) was found when comparing the 2 folders upload speed (average 26 hours, 11 minutes, and 5 seconds). The Blockchain data report storage size was 1.22 MB, whereas the original data folder storage size was 5.36 GB. No HIPAA breach was found during the data upload, conversion, sharing, and storage processes. Discussion The Blockchain private platform promises to become a gold standard in OMR PHI data backup because of its data conversion effectiveness, low storage requirements, and trusted, autonomous, immutable, and secured distributed ledger capabilities for keeping data private and safe. Preliminary study results indicated the feasibility of adopting Blockchain in OMR as a new data backup management method.

A real-time information system is very crucial in the area that is vulnerable to various disasters like Indonesia. This article discusses an information system that has been built and tested in Aceh area where lately had been hit by many disasters such as earthquake, flood, and tsunami. A robust application is difficult to be implemented in this area, especially during the disaster. Thus, the application implemented here is applying GPS technology and GPRS/EDGE/CDMA for data communication transmission that enables Android smartphone sending information to the user via web application. The application is being tested and evaluated to figure out the performance based on the online multimedia data standard. The testing result shows that the average transmission rate of textual message is 2.3 s, while the average transmission time of the image data is 8.65 s.

Background Standards-based clinical data normalization has become a key component of effective data integration and accurate phenotyping for secondary use of electronic healthcare records (EHR) data. HL7 Fast Healthcare Interoperability Resources (FHIR) is an emerging clinical data standard for exchanging electronic healthcare data and has been used in modeling and integrating both structured and unstructured EHR data for a variety of clinical research applications. The overall objective of this study is to develop and evaluate a FHIR-based EHR phenotyping framework for identification of patients with obesity and its multiple comorbidities from semi-structured discharge summaries leveraging a FHIR-based clinical data normalization pipeline (known as NLP2FHIR). Methods We implemented a multi-class and multi-label classification system based on the i2b2 Obesity Challenge task to evaluate the FHIR-based EHR phenotyping framework. Two core parts of the framework are: (a) the conversion of discharge summaries into corresponding FHIR resources – Composition, Condition, MedicationStatement, Procedure and FamilyMemberHistory using the NLP2FHIR pipeline, and (b) the implementation of four machine learning algorithms (logistic regression, support vector machine, decision tree, and random forest) to train classifiers to predict disease state of obesity and 15 comorbidities using features extracted from standard FHIR resources and terminology expansions. We used the macro- and micro-averaged precision (P), recall (R), and F1 score (F1) measures to evaluate the classifier performance. We validated the framework using a second obesity dataset extracted from the MIMIC-III database. Results Using the NLP2FHIR pipeline, 1237 clinical discharge summaries from the 2008 i2b2 obesity challenge dataset were represented as the instances of the FHIR Composition resource consisting of 5677 records with 16 unique section types. After the NLP processing and FHIR modeling, a set of 244,438 FHIR clinical resource instances were generated. As the results of the four machine learning classifiers, the random forest algorithm performed the best with F1-micro(0.9466)/F1-macro(0.7887) and F1-micro(0.9536)/F1-macro(0.6524) for intuitive classification (reflecting medical professionals’ judgments) and textual classification (reflecting the judgments based on explicitly reported information of diseases), respectively. The MIMIC-III obesity dataset was successfully integrated for prediction with minimal configuration of the NLP2FHIR pipeline and machine learning models. Conclusions The study demonstrated that the FHIR-based EHR phenotyping approach could effectively identify the state of obesity and multiple comorbidities using semi-structured discharge summaries. Our FHIR-based phenotyping approach is a first concrete step towards improving the data aspect of phenotyping portability across EHR systems and enhancing interpretability of the machine learning-based phenotyping algorithms.

Flexible, scalable services on massive geo data receive much attention today. OGC has taken a lead in defining interoperable spatio-temporal datacubes (the coverage paradigm) together with flexible scalable Web services (the Web Coverage Service / WCS standards suite). This OGC Big Geo Data standard has attracted all major implementers of both open-source and proprietary servers and clients. We present status, uptake, and recent progress of the coverage datacube standards in OGC, ISO, and INSPIRE.

A fundamental impediment to the adoption of digital standards in physical anthropology is the vast diversity of this area of research. Even within osteology, many investigations require some modification of existing standards to suit their specific study designs. This might be a reason for researchers not to use database software based exclusively on one particular standard. It also makes it difficult to keep track of research data compatibility and to process data from different investigations in one database system. Up to now, comprehensive and monolithic data standards have failed to address these issues. We propose a different approach, concentrating on the exact definition of individual data items. These are the building blocks researchers can use to describe the various aspects of their research, like skeletal inventories, research methods and work flows, resulting data and their processing employing mathematical transformations or textual conclusions. Because the building blocks of these descriptions are defined beforehand, the degree of compatibility between different investigations becomes evident. Our data standard, RDFBones, is an RDF (Resource Description Framework) ontology, containing a number of classes and properties for describing anthropological research and materials. Individual researchers can use these elements to define their methodology. That way, RDFBones helps to build standards, instead of prescribing them. Once a standard is formulated, however, it can be published and shared otherwise, supporting uniform methodology. RDFBones also creates a perfect means for sustained long-term data storage.

The location check-in data, developing along with social network, are considered as user-generated crowd-sourcing geospatial data. With massive data volume, abundance in contained information, and high up-to-date status, the check-in data provide a new data source for geographic information service represented by location-based service. However, there is a significant quality issue regarding to crowd-sourcing data, which has a direct influence to data availability. In this paper, a data quality analysis approach is designed for the location check-in data and a check-in data uncertainty model is proposed. First of all, the quality issue of location check-in data is discussed. Then, according to the characteristics of check-in data, a location check-in data quality analysis and data processing approach is proposed, using certain standard dataset as reference to conduct an affine transformation for the check-in dataset, during which the RANSAC algorithm is adopted for outlier elimination. Subsequently, combining GIS data uncertainty theory, an uncertainty model of processed check-in data is set up. At last, using location check-in data obtained from jiepang.com as experimental data and selected navigation data as data standard, multiple location check-in data quality analysis and uncertainty modeling experiments are conducted. By comprehensive analysis of experimental results, the feasibility of proposed location checkin data quality analysis and process approach and the availability of proposed uncertainty model are verified. The novel approach is proved to have a certain practical significance to the study of the quality issue of crowd-sourcing geographic data.

Objective: Technology in neurointensive care units can collect and store vast amounts of complex patient data. The CHART-ADAPT project is aimed at developing technology that will allow for the collection, analysis and use of these big data at the patient’s bedside in neurointensive care units. A requirement of this project is to automatically extract and transfer high-frequency waveform data (e.g. ICP) from monitoring equipment to high performance computing infrastructure for analysis. Currently, no agreed data standard exists in neurointensive care for the description of this type of data. In this pilot study, we investigated the use of Medical Waveform Format Encoding Rules (MFER—www.mfer.org-ISO 11073-92001) as a possible data standard for neurointensive care waveform data. Materials and methods: Several waveform formats were explored (e.g. XML, DICOM waveform) and evaluated for suitability given existing computing infrastructure constraints, e.g. NHS network capacity and the processing capabilities of existing integration software. Key requirements of the format included a compact data size and the use of a recognised standard. The MFER waveform format (ISO/TS 11073-92001) met both requirements. To evaluate the practicality of the MFER waveform format, seven waveform signals (ICP, ECG, ART, CVP, EtCO2, Pleth, Resp) collected over a period of 8 h from a patient at the Institute of Neurological Sciences in Glasgow were converted into MFER waveform format. Results: The MFER waveform format has two main components: sampling information and frame information. Sampling information describes the frequency of the data sampling and the resolution of the data. Frame information describes the data itself; it consists of three elements: data block (the actual data), channel (each type of waveform data occupies a channel) and sequence (the repetition of the data). All seven waveform signals were automatically and successfully converted into the MFER waveform format. One MFER file was created for each minute of data (total of 479 files, 181 KB each). Conclusions: The MFER waveform format has potential as a lightweight standard for representing high-frequency neurointensive care waveform data. Further work will include a comparison with other waveform data formats and a live trial of using the MFER waveform format to stream patient data over a longer period.

Maps are important vehicles for storytelling. Although the ubiquity of current web mapping services has brought great conveniences to creating map-based content, they also bring restrictions in map representation. Conventional analog maps are still being created and widely used. Some of the well-designed ones are highly contextual, and are more suitable for storytelling with related backgrounds. To utilize analog maps in location-aware mobile environments for storytelling, local and dynamic georeferencing is introduced to attach geospatial information to the analog map images without stretching them and destroying the artistic designs. A data format extended from a popular GIS data standard is designed, for creating and sharing storytelling content easily by attach multimedia resources to georeferenced map images. In cooperation with local communities, prototype developments realized tools for map-based storytelling content creation and smartphone applications for browsing the content in walking tours.

Water Resource Agency (WRA) has integrated cloud computing technologies based on cloud service framework for setting up easy-to-use data exchanging portal. Due to climate change and its effects on global society, the sustainability of water resource became a significant and highly attentional issue. In Taiwan, the demands of water resource information have increased either government agency, private institution, non-profit organization or domestic. Therefore, WRA followed the trend of open data, expanded nodes of current cloud environment and upgraded the version of a management tool for keeping the high performance, large capacity and utility of the cloud environment. Cloud computing is designed to provide the platform for data storage and user-friendly application procedures and interfaces in WRA. Data security of personnel is seen as well in this paper. In addition, for proceeding with the correction and stability of Taiwan water exchanging data standard, several procedures and systems are development as counterparts that help the affairs of data examination.

Water observation data is a key element of a water resources information system as it is commonly used in national reports, environmental impact assessments and other analysis or modeling applications. A data standard is vital to support replication, synchronization and delivery for these applications. An international standard encoding for transfer of water information (WaterML 2.0) was previously developed to support data transfer requirements at all phases of the ingestion, data fusion, and delivery process, and suitable for adoption internationally by the water data community. Here we report on the design and implementation of a WaterML 2.0 validation service for executing specific schematic and semantic rules of the WaterML 2.0 standard. A WaterML 2.0 validation service allows for WaterML 2.0 encoded data to be tested in a context-sensitive manner. The OGC standard for Modular Specifications has been used for the WaterML 2.0 specification and includes a set of requirements and conformance classes. We report on how the set of requirements and conformance classes relate directly to the validation of WaterML 2.0 XML encodings. The result of a validation run allows users to determine the level of conformance of the respective WaterML 2.0 encoded data against the WaterML 2.0 specification. We also highlight the use of vocabulary services with the WaterML 2.0 validation service to perform code-list content and vocabulary checking. Design and implementation of a WaterML 2.0 validation service is presented.WaterML 2.0 validation service provides online testing of WaterML 2.0 data.WaterML 2.0 validation service features OGC vocabulary and code-list checking.Conformance levels can be tested against the WaterML 2.0 specification.

Background and objective The importance of data standards when integrating clinical research data has been recognized. The common data element (CDE) is a consensus-based data element for data harmonization and sharing between clinical researchers, it can support data standards adoption and mapping. However, the lack of a suitable methodology has become a barrier to data standard adoption. Our aim was to demonstrate an approach that allowed clinical researchers to design electronic case report forms (eCRFs) that complied with the data standard. Methods We used a multi-technique approach, including information retrieval, natural language processing and an ontology-based knowledgebase to facilitate data standard adoption using the eCRF design. The approach took research questions as query texts with the aim of retrieving and associating relevant CDEs with the research questions. Results The approach was implemented using a CDE-based eCRF builder, which was evaluated using CDE- related questions from CRFs used in the Parkinson Disease Biomarker Program, as well as CDE-unrelated questions from a technique support website. Our approach had a precision of 0.84, a recall of 0.80, a F-measure of 0.82 and an error of 0.31. Using the 303 testing CDE-related questions, our approach responded and provided suggested CDEs for 88.8% (269/303) of the study questions with a 90.3% accuracy (243/269). The reason for any missed and failed responses was also analyzed. Conclusion This study demonstrates an approach that helps to cross the barrier that inhibits data standard adoption in eCRF building and our evaluation reveals the approach has satisfactory performance. Our CDE-based form builder provides an alternative perspective regarding data standard compliant eCRF design.

In the near future, the connected car system is expected to grow steadily and form a big mainstream. In this situation, some automakers are developing standard interfaces that can be used to access vehicle information in a vendor-independent way to build larger connected-car ecosystems. In this paper, we introduce the vehicle data standard and the vehicle data access interface standard currently being developed as an industry standard. In addition, we implemented the vehicle data server based on these standards to verify the feasibility of the standard and summarize the issues that need to be resolved in the future.

The Proteomics Standards Initiative (PSI) of the Human Proteome Organization (HUPO) has now been developing and promoting open community standards and software tools in the field of proteomics for 15 years. Under the guidance of the chair, co-chairs, and other leadership positions, the PSI working groups are tasked with the development and maintenance of community standards via special workshops and ongoing work. Among the existing, ratified standards, the PSI working groups continue to update PSI-MI XML, MITAB, mzML, mzIdentML, mzQuantML, mzTab, and the MIAPE (Minimum Information About a Proteomics Experiment) guidelines with the advance of new technologies and techniques. Further, new standards are currently either in the final stages of completion (proBed and proBAM for proteogenomics results, as well as PEFF) or in early stages of design (a spectral library standard format, a universal spectrum identifier, the qcML quality control format, and the Protein Expression Interface (PROXI) web services Application Programming Interface). In this work we review the current status of all these aspects of the PSI, describe synergies with other efforts such as the ProteomeXchange Consortium, the Human Proteome Project, and the metabolomics community, and provide a look at future directions of the PSI.

EXtensible Business Reporting Language (XBRL) is expected to develop into the global data standard for business financial reporting with the potential to change the way that accounting data collaborate processing. The improvement of interactivity of the Internet financial report relies on its data presentation standard. This paper attempts to take advantage of cloud computing technology to conduct comprehensive integration of the enterprise information systems according to XBRL classification criterion, and generate XBRL standard instance documents that will be stored in the cloud computing-based enterprise information data cloud. By employing XBRL processing technology, it is able to realize cross-platform XBRL system processing and offer references for the listed companies to realize XBRL cross-platform collaborated data management.

The ProteomeXchange (PX) consortium has been established to standardize and facilitate submission and dissemination of MS-based proteomics data in the public domain. In the consortium, the PRIDE database at the European Bioinformatics Institute, acts as the initial submission point of MS/MS data sets. In this manuscript, we explain step by step the submission process of MS/MS data sets to PX via PRIDE. We describe in detail the two available workflows: 'complete' and 'partial' submissions, together with the available tools to streamline the process. Throughout the manuscript, we will use one example data set containing identification and quantification data, which has been deposited in PRIDE/ProteomeXchange with the accession number PXD000764 (http://proteomecentral.proteomexchange.org/dataset/PXD000764).

mzTab is the most recent standard format developed by the Proteomics Standards Initiative. mzTab is a flexible tab-delimited file that can capture identification and quantification results coming from MS-based proteomics and metabolomics approaches. We here present an open-source Java application programming interface for mzTab called jmzTab. The software allows the efficient processing of mzTab files, providing read and write capabilities, and is designed to be embedded in other software packages. The second key feature of the jmzTab model is that it provides a flexible framework to maintain the logical integrity between the metadata and the table-based sections in the mzTab files. In this article, as two example implementations, we also describe two stand-alone tools that can be used to validate mzTab files and to convert PRIDE XML files to mzTab. The library is freely available at http://mztab.googlecode.com.

EXtensible Business Reporting Language (XBRL) is expected to develop into the global data standard for business financial reporting with the potential to change the way that decisions are made. Thus, understanding its value realization in the proper business context is a vitally important issue. This study examines the impact of initial mandatory adoption of XBRL on organizations' cost of capital and transaction costs in PR China. As transaction cost theory predicts, the uncertainty related to the unproven technology increases transaction costs and the cost of capital during the early adoption period. Implications have immediate benefits for regulators, filing organizations, information consumers, the accounting profession, and other stakeholders.

The mzQuantML standard from the HUPO Proteomics Standards Initiative has recently been released, capturing quantitative data about peptides and proteins, following analysis of MS data. We present a Java application programming interface (API) for mzQuantML called jmzQuantML. The API provides robust bridges between Java classes and elements in mzQuantML files and allows random access to any part of the file. The API provides read and write capabilities, and is designed to be embedded in other software packages, enabling mzQuantML support to be added to proteomics software tools (http://code.google.com/p/jmzquantml/). The mzQuantML standard is designed around a multilevel validation system to ensure that files are structurally and semantically correct for different proteomics quantitative techniques. In this article, we also describe a Java software tool (http://code.google.com/p/mzquantml-validator/) for validating mzQuantML files, which is a formal part of the data standard.

Exchanging and sharing information are the basic request for the Digital Campus. To deal with the current problems of information sharing and integration, the content and framework of the universal data interchange platform are introduced in terms of the categories of processes and the layers of information exchange. And a data interchange model was developed to elaborate the data exchange between different departments on campus. Four key technologies, such as XML data model, XML data hybrid storage, data standard construction and data import & export module are presented so as to define the implementation and exchange paths. The current practice of implement the exchange standards and future study are also discussed.

The existence and adoption of data standards in computational proteomics, as in any other field, is generally perceived to be crucial for the further development of the discipline. We here give an up-to-date overview of the open standard data formats that have been developed under the umbrella of the Proteomics Standards Initiative (PSI). We will focus in those formats related to mass spectrometry (MS). Most of them are based in XML (Extensible Markup Language) schemas: mzML (for primary MS data, the output of mass spectrometers), mzIdentML (for peptide and protein identification data), mzQuantML (for peptide and protein quantification data) and TraML (for reporting transition lists for selected reaction monitoring approaches). In addition, mzTab was developed as a simpler tab-delimited file to support peptide, protein and small molecule identification and quantification data in the same file. In all cases, we will explain the main characteristics of each format, describe the main existing software implementations and give an update of the ongoing work to extend the formats to support new use cases. Additionally, we will discuss other data formats that have been inspired by the PSI formats. Finally, other PSI data standard formats (not MS related) will be also outlined in brief.