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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.

Chronological uncertainty is a hallmark of the paleoenvironmental sciences and geosciences. While many tools have been made available to researchers to quantify age uncertainties suitable for various settings and assumptions, disparate tools and output formats often discourage integrative approaches. In addition, associated tasks like propagating age-model uncertainties to subsequent analyses, and visualizing the results, have received comparatively little attention in the literature and available software. Here, we describe geoChronR, an open-source R package to facilitate these tasks. geoChronR is built around an emerging data standard (Linked PaleoData, or LiPD) and offers access to four popular age-modeling techniques (Bacon, BChron, OxCal, BAM). The output of these models is used to conduct ensemble data analysis, quantifying the impact of chronological uncertainties on common analyses like correlation, regression, principal component, and spectral analyses by repeating the analysis across a large collection of plausible age models. We present five real-world use cases to illustrate how geoChronR may be used to facilitate these tasks, visualize the results in intuitive ways, and store the results for further analysis, promoting transparency and reusability.

A digital twin is a digital asset that simulates the behaviours of a physical counterpart. Digital twin ship literature identifies that the concept is already being applied to specialised problems, but no clear guide exists for creating broader interdisciplinary digital twins. Relevant dimensions of product data modelling and previous attempts at standardizing ship data elucidate the requirements for effective data modelling in a digital twin context. Such requirements are placed in a broader perspective for digital twin implementation that encompasses challenges and directions for future development of services, networks, and software. Finally, an open standardization for digital twin data is proposed based on lessons extracted from this panorama, proposing its application to a research vessel.

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.

Passive ultra-high frequency (UHF) radio frequency identification (RFID) technology has been widely adopted by retail and other industries for serialized item-level identification and data sharing. This article introduces the standards that support and define all procedures in various passive UHF RFID applications. Electronic Product Code (EPC) Radio-Frequency Identity Protocols Generation-2 UHF RFID Standard, or C1G2, is a foundational standard that defines the format, encoding, and procedures within the air interfaces of RFID systems. Low Level Reader Protocol provides a standard and portable interface between different applications and RFID readers from different vendors. The format and encoding of the EPC information are defined by the EPC Tag Data Standard, which enables each tag to be uniquely identified (e.g., item-level identity of many goods in a warehouse). Additional protocols, such as Discovery, Configuration and Initialization and Reader Management, specify and clarify more processes (e.g., the management of many readers) that can be deployed in various business applications. We provide a general introduction of passive UHF RFID technology standards and review each protocol's features and procedures.

An international system should be established to support personal protective equipment (PPE) inventory monitoring, particularly within the healthcare industry. In this article, the authors discuss the development and 15-week deployment of a proof-of-concept prototype that included the use of a Healthcare Trust Data Platform to secure and transmit PPE-related data. Seventy-eight hospitals participated, including 66 large hospital systems, 11 medium-sized hospital systems, and a single hospital. Hospitals reported near-daily inventory information for N95 respirators, surgical masks, and face shields, ultimately providing 159 different PPE model numbers. Researchers cross-checked the data to ensure the PPE could be accurately identified. In cases where the model number was inaccurately reported, researchers corrected the numbers whenever possible. Of the PPE model numbers reported, 74.2% were verified-60.5% of N95 respirators, 40.0% of face shields, and 84.0% of surgical masks. The authors discuss the need to standardize how PPE is reported, possible aspects of a PPE data standard, and standards groups who may assist with this effort. Having such PPE data standards would enable better communication across hospital systems and assist in emergency preparedness efforts during pandemics or natural disasters.

The utopian vision is of a future where a digital representation of each object in our collections is accessible through the internet and sustainably linked to other digital resources. This is a long term goal however, and in the meantime there is an urgent need to share data about our collections at a higher level with a range of stakeholders (Woodburn et al. 2020). To sustainably achieve this, and to aggregate this information across all natural science collections, the data need to be standardised (Johnston and Robinson 2002). To this end, the Biodiversity Information Standards (TDWG) Collection Descriptions (CD) Interest Group has developed a data standard for describing collections, which is approaching formal review for ratification as a new TDWG standard. It proposes 20 classes (Suppl. material 1) and over 100 properties that can be used to describe, categorise, quantify, link and track digital representations of natural science collections, from high-level approximations to detailed breakdowns depending on the purpose of a particular implementation. The wide range of use cases identified for representing collection description data means that a flexible approach to the standard and the underlying modelling concepts is essential. These are centered around the ‘ObjectGroup’ (Fig. 1), a class that may represent any group (of any size) of physical collection objects, which have one or more common characteristics. This generic definition of the ‘collection’ in ‘collection descriptions’ is an important factor in making the standard flexible enough to support the breadth of use cases. For any use case or implementation, only a subset of classes and properties within the standard are likely to be relevant. In some cases, this subset may have little overlap with those selected for other use cases. This additional need for flexibility means that very few classes and properties, representing the core concepts, are proposed to be mandatory. Metrics, facts and narratives are represented in a normalised structure using an extended MeasurementOrFact class, so that these can be user-defined rather than constrained to a set identified by the standard. Finally, rather than a rigid underlying data model as part of the normative standard, documentation will be developed to provide guidance on how the classes in the standard may be related and quantified according to relational, dimensional and graph-like models. So, in summary, the standard has, by design, been made flexible enough to be used in a number of different ways. The corresponding risk is that it could be used in ways that may not deliver what is needed in terms of outputs, manageability and interoperability with other resources of collection-level or object-level data. To mitigate this, it is key for any new implementer of the standard to establish how it should be used in that particular instance, and define any necessary constraints within the wider scope of the standard and model. This is the concept of the ‘collection description scheme,’ a profile that defines elements such as: which classes and properties should be included, which should be mandatory, and which should be repeatable; which controlled vocabularies and hierarchies should be used to make the data interoperable; how the collections should be broken down into individual ObjectGroups and interlinked, and how the various classes should be related to each other. which classes and properties should be included, which should be mandatory, and which should be repeatable; which controlled vocabularies and hierarchies should be used to make the data interoperable; how the collections should be broken down into individual ObjectGroups and interlinked, and how the various classes should be related to each other. Various factors might influence these decisions, including the types of information that are relevant to the use case, whether quantitative metrics need to be captured and aggregated across collection descriptions, and how many resources can be dedicated to amassing and maintaining the data. This process has particular relevance to the Distributed System of Scientific Collections (DiSSCo) consortium, the design of which incorporates use cases for storing, interlinking and reporting on the collections of its member institutions. These include helping users of the European Loans and Visits System (ELViS) (Islam 2020) to discover specimens for physical and digital loans by providing descriptions and breakdowns of the collections of holding institutions, and monitoring digitisation progress across European collections through a dynamic Collections Digitisation Dashboard. In addition, DiSSCo will be part of a global collections data ecosystem requiring interoperation with other infrastructures such as the GBIF (Global Biodiversity Information Facility) Registry of Scientific Collections, the CETAF (Consortium of European Taxonomic Facilities) Registry of Collections and Index Herbariorum. In this presentation, we will introduce the draft standard and discuss the process of defining new collection description schemes using the standard and data model, and focus on DiSSCo requirements as examples of real-world collection descriptions use cases.

This paper collects a large number of cases and makes a comparative analysis of the typical application of Chinese and American open government data for public governance. Through comparison, this paper finds the gap between China's open government data and the United States, and then analyzes the reasons. On this basis, through the investigation of advanced experience, this paper puts forward the suggestions of open government data to innovate public governance, including data catalogue compilation, data standard formulating, data quality assessment and open government data sharing cooperation, in order to improve Chinese open government data to innovate the public governance level.

Advances in multi-omics technologies have led to an explosion of multimodal datasets to address questions ranging from basic biology to translation. While these rich data provide major opportunities for discovery, they also come with data management and analysis challenges, thus motivating the development of tailored computational solutions to deal with multi-omics data.Here, we present a data standard and an analysis framework for multi-omics — MUON — designed to organise, analyse, visualise, and exchange multimodal data. MUON stores multimodal data in an efficient yet flexible data structure, supporting an arbitrary number of omics layers. The MUON data structure is interoperable with existing community standards for single omics, and it provides easy access to both data from individual omics as well as multimodal dataviews. Building on this data infrastructure, MUON enables a versatile range of analyses, from data preprocessing, the construction of multi-omics containers to flexible multi-omics alignment.

The global movement of people and goods has increased the risk of biosecurity threats and their potential to induce large economic, social, and environmental harm. Integration of biosafety monitoring networks has become a top priority for addressing biosafety issues. In order to resolve the data standards and integration problems in the field of biosafety in China, the Biosafety Surveillance Conceptual Data Model (BSCDM), which is an object-oriented, hierarchically designed, flexible and scalable biosafety surveillance concept data model, is proposed in this article. This model is based on the integration of business process management and data resources of disease surveillance, animal disease surveillance and potential invasive biological monitoring. In reference to the Public Health Conceptual Data Model (PHCDM) and Federal Enterprise Architecture (FEA), BSCDM conducts a thorough analysis of biosafety monitoring activities, records basic information requirements of biosafety monitoring and provides data set standards for biosafety-related activities. It is developed with the Unified Modeling Language (UML) and could be applied as an open standard for accelerating data analysis and promoting collaboration. This study attempts to integrate biosafety monitoring data and the presented model has been tested in the detection of dengue fever in China and will be applied to other biosafety fields.

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.

Purpose Government e-Procurement systems are generating tender-level process event data which are not being analyzed much. The purpose of this paper is to present a data visualization tool to benchmark the government tendering process. This tool collects, collates and presents e-Procurement process data in a meaningful way that enables comparisons and benchmarking leading to insights for process improvements and identification of the best practices. This tool is accessible on the website of South Asia’s first public procurement observatory (www.procurementobservatoryup.com) founded by the authors. Design/methodology/approach The authors started with an initial set of key performance indicators (KPIs) based on extant literature and existing practices. The authors got them verified by various relevant stakeholders through a series of formal and informal discussions. Some of them were dropped due to observed variations or their inability to offer insights to arrive at the final set of KPIs. In this paper, the authors use actual process-level data. Government of India’s National Informatics Center has implemented e-Procurement portals in various states and public enterprises (PEs) in India which provide tender-wise detailed e-tendering process data. The authors designed a web crawler that collects these data in a tabular format, which allows an easy analysis and comparison to measure and compare government tendering process performance for the last five years for the two large PEs. The authors also engaged in discussions with the procurement executives of the two PSEs to derive meaningful managerial insights from the results obtained. Findings Using the public procurement data visualization tool, the authors compare the procurement process of two of the largest Indian PEs, Coal India Limited and Indian Oil Corporation on the developed KPIs and draw insights. The results show significant difference in their procurement process performance due to different practices followed by them. Through interaction with the procurement managers of these two organizations, the authors identify few good procurement practices that can be applied to improve public procurement process. Research limitations/implications This paper uses actual process-level data which can be used not just to identify improvement opportunities but also to gauge the impact of any process-level change. It presents a data visualization tool to generate insights from data generated by a procurement/tendering system. Such visualization and benchmarking can uncover better procurement practices and provide an impetus toward data-driven policy formulation. Apart from the two PEs as reported in this paper, this tool has also been applied on the public procurement data of eight Indian states. Practical implications The KPIs presented in this paper are aligned with the various dimensions of public procurement’s objectives. The visualization tool presented in this paper is based on the Open Contract Data Standard and has a universal application. Social implications The use of technology and open data sharing as the enablers of benchmarking and process improvements help in establishing a dynamic competitive environment leading to financial savings, better services to citizens and proper use of taxpayers’ money. Originality/value This paper presents an original work carried out under the aegis of South Asia’s first procurement observatory at IIM Lucknow. The benchmarking tool presented in this paper uses open contract data standard and can be applied in most of the public procurement processes. This paper takes the discussions on e-Procurement to the next level, where the concern is no longer restricted to only adoption and assimilation issues, but also on how to make use of the data that these e-Procurement systems generate.

Background Atrial fibrillation (AF) affects an estimated 33 million people worldwide, leading to increased mortality and an increased risk of heart failure and stroke. Many AF patient registries exist, but the ability to link and compare data across registries is hindered by differences in the outcome measures collected by each registry and a lack of harmonization. Objectives The purpose of this project was to develop a minimum set of standardized outcome measures that could be collected in AF patient registries and clinical practice. Methods AF patient registries were identified through multiple sources and invited to join the workgroup and submit outcome measures. Additional measures were identified through literature searches and reviews of consensus statements. Outcome measures were categorized using the Agency for Healthcare Research and Quality's supported Outcome Measures Framework (OMF). A minimum set of broadly relevant measures was identified. Measure definitions were harmonized through in-person and virtual meetings. Results One hundred twelve outcome measures, including those from thirteen registries, were curated according to the OMF and then harmonized into a minimum set of measures in the OMF categories of survival (3 measures), clinical response (3 measures), events of interest (9 measures), patient-reported outcomes (2 measures), and resource utilization (3 measures). The harmonized definitions build on existing consensus statements. Conclusions The harmonized measures represent a minimum set of outcomes that are relevant in AF research and clinical practice. Routine and consistent collection of these measures in registries and in other systems would support creation of a research infrastructure to efficiently address new questions and improve patient outcomes.

Background Medical research and machine learning for health care depend on high-quality data. Electronic data capture (EDC) systems have been widely adopted for metadata-driven digital data collection. However, many systems use proprietary and incompatible formats that inhibit clinical data exchange and metadata reuse. In addition, the configuration and financial requirements of typical EDC systems frequently prevent small-scale studies from benefiting from their inherent advantages. Objective The aim of this study is to develop and publish an open-source EDC system that addresses these issues. We aim to plan a system that is applicable to a wide range of research projects. Methods We conducted a literature-based requirements analysis to identify the academic and regulatory demands for digital data collection. After designing and implementing OpenEDC, we performed a usability evaluation to obtain feedback from users. Results We identified 20 frequently stated requirements for EDC. According to the International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) 25010 norm, we categorized the requirements into functional suitability, availability, compatibility, usability, and security. We developed OpenEDC based on the regulatory-compliant Clinical Data Interchange Standards Consortium Operational Data Model (CDISC ODM) standard. Mobile device support enables the collection of patient-reported outcomes. OpenEDC is publicly available and released under the MIT open-source license. Conclusions Adopting an established standard without modifications supports metadata reuse and clinical data exchange, but it limits item layouts. OpenEDC is a stand-alone web app that can be used without a setup or configuration. This should foster compatibility between medical research and open science. OpenEDC is targeted at observational and translational research studies by clinicians.

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.

This study aims to create a classifiable and comparable carbon footprint map of Turkey on the basis of cities with the provincial data inventory and data standard. Therefore, the basic parameters of carbon footprint change were determined by conducting the literature review. The carbon footprint change of each province was modeled using artificial neural networks for 2015 due to data availability. The performance metrics of the model created are (R2 0.97622, MSE 3.19809, RMSE 1.78832). The carbon footprint values of each province were predicted with the lowest error for the years 2014, 2015, 2016, and 2017, for which access data were available with the help of modeling. The spatial distributions of the prediction values were mapped using the geographic information system (GIS). An increase of 7 Mt on average was detected in the carbon footprint change of Turkey in 4 years. Furthermore, it was determined which parameter had a weighted effect in determining the carbon footprint values using the RReliefF algorithm, the feature selection method based on the established model’s prediction values. Accordingly, the parameters affecting the carbon footprint change on Turkey’s model were determined according to their weight as follows (income level 0.057, housing 0.029, the number of motor vehicles 0.028, sectoral industry size 0.025, population density 0.024, energy consumption 0.017, infrastructure 0.005, agricultural area 0.004, and agricultural production value 0.0008), respectively. This study draws a way for local governments to model their climate action plans with a standard data inventory, considering the parameters with weighted effects on carbon footprint change.

The amount of functional MRI (fMRI) data processed in research is growing, yet no practice or protocol to store them in a lossy format exists. Many researchers are struggling with limited storage space, and speed of common processing tools are often bound by storage speed. In this work, we present a lossy compression framework for fMRI data with user adjustable trade-off between compression ratio and root mean squared error (RMSE). Our goal is to demonstrate the usability of on-the-fly lossy compression for fMRI data. On one hand, the storage footprint and processing speeds both benefit from higher data compression rates achieved with lossy compression. On the other hand, data quality for functional analysis remains effectively the same. With this short demonstration we encourage the research community to develop a lossy data standard for fMRI data.

In order to overcome the problems existing in the logistics construction of the armed police force, such as the data standard is not unified and the real-time monitoring is not available, this paper proposes a novel application strategy of the armed police force logistics construction Based on data system engineering. The application strategy starts from three dimensions: logistics command decision, logistics fine management and logistics precise support. In addition, the application strategy is introduced into data system engineering, which makes full use of the advantages of data system engineering, and analyzes the feasibility of data acquisition, data use and data processing. The research results show that the application strategy can improve the efficiency and quality of the armed police force logistics work scientifically and efficiently.

Flexible, scalable services on massive Earth data receive much attention today, in particular spatio-temporal data homogenized into datacubes. OGC has taken a lead in defining interoperable spatio-temporal datacubes through its coverage paradigm, together with tailored services, the Web Coverage Service (WCS) standards suite. This OGC Big Geo Data standard has attracted a who's who of 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, in particular the ongoing work on the OpenAPI based OAPI-Coverages specification.

The prescription and administration of drugs are the most common process that takes place in hospitals. Although a relatively simple process, it is considered the riskiest process in hospitals because mistakes during drug administration are among the most common ones. The aim is to introduce technological and process changes that will contribute to maximally increase the safety of the medication process and the efficiency of drug management. To support the automation of the medication process, it is desirable to use the international standard Health Level 7 (HL7). However, the Czech healthcare system currently supports the local healthcare standard&mdash, DASTA. For that reason, the paper introduces some of the options how to transfer data from DASTA to HL7 and deals with the development of a software (SW) interface that converts data necessary for robotic preparation of patient medication from the Czech DASTA data standard to the HL7 international standard used by selected robotics. Based on the performed analyses, a combination of robotics for the preparation of single-dose packages of drugs with one of the automated warehouses is recommended.

Synthetic biology aims to develop novel biological systems and increase their reproducibility using engineering principles such as standardization and modularization. It is important that these systems can be represented and shared in a standard way to ensure they can be easily understood, reproduced, and utilized by other researchers. The Synthetic Biology Open Language (SBOL) is a data standard for sharing biological designs and information about their implementation and characterization. Previously, this standard has only been used to represent designs in systems where the same design is implemented in every cell; however, there is also much interest in multicellular systems, in which designs involve a mixture of different types of cells with differing genotype and phenotype. Here, we show how the SBOL standard can be used to represent multicellular systems, and, hence, how researchers can better share designs with the community and reliably document intended system functionality.

The American College of Surgeons requires that trauma centers collect and enter data into the National Trauma Data Registry in compliance with the National Trauma Data Standard. ProMedica supports employment of 4 trauma data analysts who are responsible for entering information in a timely manner, validating the data, and analyzing data to evaluate established benchmarks and support the performance improvement and patient safety process. Historically, these analysts were located on-site at ProMedica Toledo Hospital. In 2017, a proposal was developed including modifications to data collection to streamline processes, move toward paperless documentation, and allow for the analysts to telecommute. To measure the effect of these changes, the timeliness of data entry, rate of data validation, productivity, and staff satisfaction were measured. After the transition to electronic data management and home-based workstations, registry data were being entered within 30 days and 100% of cases were being validated, without sacrificing effective and efficient communication between in-hospital and home-based staff. The institution also benefitted from reduced expense for physical space, employee turnover, and decreased employee absenteeism. The analysts appreciated benefits related to time, travel, environment, and job satisfaction.It is feasible to transition trauma data analysts to a work-from-home situation. An all-electronic system of data management and communication makes such an arrangement possible and sustainable. This quality improvement project solved a workspace issue and was beneficial to the trauma program overall, with the timeliness and validation of data entry vastly improved.

This paper discusses the lack of reliability and reproducibility validation in digital forensics for a criminal trial. It is argued that this challenge can be addressed with standard data-representation for digital evidence. The representation must include reproducibility documentation on processing operations including automation, human interaction, and investigation steps. Analyzed are two blueprint articles - the CASE specification language for cyber-investigations [1] and the WANDA data standard for the documenting semi-automated hand-writing examination [2]. These two generic frameworks are studied for their granularity to support reproducibility testing by representing: (i) artefact characteristics, forensic - tool parameters and input - output logic; (ii) human and tool data interpretation; and (iii) parallel-running forensic tasks or chains of processes. Proposed is the integration of WANDA-based schema as CASE expression. The utility of such integration is demonstrated as a new module in CASE designed to meet the high standard of proof and scientific validation typically required in criminal investigations and trials. The expression ensures compliance without overburdening digital forensic practitioners.

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.

Metro protection is the task of evaluating, detecting, and controlling the potential threats presented by external projects to metro structures. To solve the problems encountered in metro protection work, including the isolation of models, difficulties in information extraction, and lack of data processing methods, this study introduced a building information model/modeling (BIM) technology and proposed a metro protection information model (the MPIM) based on the Industry Foundation Classes (IFC) international data standard and its expansion mechanism. According to the information exchange requirements for metro protection, the MPIM defines new customized IFC entities, property sets, and enumeration types to express the models in the metro protection domain. Then the conversion methods of the MPIM and other model formats and the information extraction and application technologies are studied. A metro protection system was developed to implement the MPIM-based model integration and provide assist in preliminary evaluation of external projects via information-rich the MPIM. A trial of the system from Guangzhou Metro’s Modiesha Station to Xingangdong Station indicated that the utilization of the MPIM in metro protection workflow could improve working efficiency and save time obviously; management staff claimed that the application of the system saved more than 40% of the time for project evaluation.

The Flow Cytometry Standard (FCS) format is a widely accepted norm for storing Flow Cytometry (FCM) data. Its goal as a standard is to allow FCM data sharing and re-analysis. Over more than three decades of its existence FCS has evolved into a well-defined, flexible file format reflecting technical changes in the FCM field. Its flexibility as well as rising numbers of instrument vendors leads to suboptimal implementations of FCS in some cases. Such situations compromise the primary goal of the standard and hinder the ability to reproduce FCM analyses. It is further underlined by rapid rise of advanced FCM analyses, often carried out outside traditional software tools and heavily relying on standard data storage and presentation. We have developed flowIO, an R package which tests FCS file conformance with the standard as defined by International Society for Advancement of Cytometry (ISAC) normative. Along with the package we provide a web based application (also at http://bioinformin.cesnet.cz/flowIO/) allowing user friendly access to the conformance testing as well as FCS file editing and export for further analysis.

The aim of this paper is to set out the principles of an ideal data system. Good data is crucial to effective policy and practice development in all social policy spheres and this is a particular challenge in the context of homelessness policy. Policy makers, practitioners and researchers have been highly critical of the current state of homelessness data across the globe, with concerns largely focused on the incompleteness of the data. Most research has narrowly focused on the strengths and weaknesses of different data collection techniques, such as Point-In-Time counts. However, good data does not only derive from the data collection method - consideration must also be given to the wider data system, including how data are generated, reported, analysed, and crucially, how they are made accessible and to who. The evidence base for the paper is a desk-based review of 49 data collection systems from 8 countries, including systems in health and social care settings—where data are being increasingly used to drive more effective care. The different systems are synthesised to generate 8 areas of design, being: data architecture, governance, data quality, ethical and legal, privacy/security, data access, and importantly, purpose. Drawing these elements together, the paper concludes that data collection should adopt a common data standard shared across the sector, enabling inter-organisational information sharing and improving collaboration; reporting to local and central government must not be one-sided, instead data providers should receive some tangible benefit for their engagement; the focus of analysis needs to shift from statistics toward evaluation into the effectiveness of interventions; and access must be available to a range of sector actors, including service providers and academia. Importantly, the paper also concludes that in delivering the ideal system, care must be taken not to interrupt the delivery of effective homelessness interventions.

Museums and their collections have specially customized databases in order to optimally gather and record their contents and associated metadata associated with their specimens. To share, exchange, and publish data, an appropriate data standard is essential. ABCD (Access to Biological Collection Data) is a standard for biological collection units, including living and preserved specimen, together with field observation data. Its extension, EFG (Extension for Geoscience), enables sharing and publishing data related to paleontological, mineralogical, and petrological objects. The standard is very granular and allows detailed descriptions, including information about the collection event itself, the holding institution, stratigraphy, chemical analysis, and host rock. The standard extension was developed in 2006 and has been used since then by different initiatives and applied for the publication of collection-related data in domain-specific and interdisciplinary portals.

Open government data (OGD) is a valuable resource for both policy transparency and government accountability. All levels of the United States government are working hard to promote open data and its portals. However, there is still a lack of stud-ies on local-level OGD portals in the United States, particularly on the quality of metadata adopted by these portals. By ex-amining 200 US cities, a list of 112 local-level portals is sampled and we investigate the current usages of open data plat-forms for building local-level OGD portals. This study further investigates and discusses the adoption and potential issues of metadata on those OGD portals. Our result findings discuss the platform distributions among US local-level OGD portals, and also highlight several critical issues associated with metadata on the portals. We anticipate the results will inspire further studies on identifying solutions to improve the metadata and enhance the usability of open government data portals

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.

Small cells are deployed in the long-term evolution—advanced (LTE-A) data standard to satisfy rapidly increasing data rates at hotspots and enhance coverage in buildings. Small cells are low-cost, ...

This research reviews recent advances in the domain of Automated Rule Checking (ARC) and argues that current systems are predominantly designed to validate models in post-design stages, useful for applications such as e-permitting. However, such a design-check-separated paradigm imposes a burden on designers as they need to iteratively fix the fail-to-pass issues. Accordingly, the study reviews the best-practices of IFC-based ARC systems and proposes a framework for ARC system development, aiming to achieve proactive bottom-up solutions building upon the requirements and resources of end-users. To present and evaluate its capabilities, the framework is implemented in a real-life case study. The case study presents all the necessary steps that should be taken for the development of an ARC solution from rule selection and analysis, to implementation and feedback. It is explained how a rule checking problem can be broken down into separate modules implemented in an iterative approach. Results show that the proposed framework is feasible for successful implementation of ARC systems and highlight that a stable data standard and modeling guideline is needed to achieve proactive ARC solutions. The study also discusses that there are some critical limitations in using IFC which need to be addressed in future studies.

Objectives As yet, there is no European data standard for naming and describing oncology regimens. To enable real-world cancer treatment data comparisons, the Oncology Data Network created a unified reference database for systemic anti-cancer regimens used in practice across Europe. Data are extracted from clinical systems and mapped to a single standard called the “Core Regimen Reference Library (CRRL)”. An automated matching algorithm has been designed based on: drug combinations; administration schedule; and dosing and route of administration. Incomplete matches are flagged for expert review. The aim of this pilot study is to have an expert pharmacist panel test the algorithm’s feasibility by comparing computerised and manual matching of regimens that are currently in use in different European countries. Methods The combined team pooled a diverse sample of 47 reference regimens used in Europe for solid and haematological cancers. These were then codified to the developed common data standard and the algorithm was used to match them to the CRRL. The expert pharmacist panel from the European Society of Oncology Pharmacy (ESOP) selected 12 regimens from the sample set, ranging from simple to complex, and performed a single-blind test of the algorithm, by systematically matching each original regimen to the CRRL. Results ESOP validated the algorithm’s feasibility based on full concurrence between manual and computer matches thereby validating the algorithm rules and logic with regard to what defines the core characteristics of a regimen and how to compare similarities and differences. Conclusions ESOP’s validation of the matching algorithm and approach to curating a master library provides confidence in their utility for reliable comparison of real-world regimen usage across Europe.

What are the opportunities and challenges for upscaling the energy retrofit of heritage buildings? Heritage buildings comprise approximately 20% of the UK building stock and are challenging to retrofit sensitively because of their heritage values and traditional construction. These buildings may therefore be unconducive to standard retrofitting approaches. Twelve case studies in the UK are examined. Three key findings are presented together with their implications for upscaling retrofit. First, heritage residents are found to engage in positive energy behaviours, which differ from standard assumptions and have a significant impact on energy demand. Second, standard energy models are shown to considerably overestimate the energy use within heritage buildings, failing to accurately portray both traditional construction and residents’ behaviours. Third, residents consider many common retrofits, such as replacement windows and wall insulation, to be unacceptable to their heritage values. A number of more acceptable and less invasive ‘soft retrofits’ were modelled and shown to have significant potential for reducing energy and carbon. Therefore, a more holistic approach to heritage building retrofitting needs to be taken, treating the complex interrelationship of buildings and their users as a system, and expanding notions of retrofitting to include soft retrofits and user behaviour.\ud \ud Policy relevance\ud This research identified the importance of appropriately retrofitting heritage buildings, which include around 20% of the UK building stock. Standard solutions such as wall insulation and window replacement are unlikely to be enacted by most heritage residents because they are not acceptable to their heritage values, suggesting the need to prioritise other measures. Standard energy models such as Reduced data Standard Assessment Procedure (RdSAP) were found to be inaccurate for heritage buildings, overestimating energy use by both buildings and occupants, and should not be used to inform retrofit decisions for these buildings. Notions of retrofit should be expanded beyond fabric alterations to include behavioural changes and non-technical measures, including thermal curtains or shutters, which are more acceptable to residents and therefore more likely to be actioned. The potential exists to upscale retrofitting in heritage buildings, but approaches must consider specific user behaviour and view buildings and their occupants as interconnected systems.

Field observations of the soil-to-atmosphere CO2 flux—soil respiration, RS—are a prime example of ‘long tail’ data that historically have had neither centralized databases nor an agreed-upon reporting format. This has hindered scientific transparency, analytical reproducibility, and syntheses with respect to this globally-important component of the carbon cycle. Here we propose a new data and metadata reporting format for RS data, based on engagement with a wide range of researchers in the earth and ecological sciences as well as expert advisory panels. Our goal was a reporting format that would be relevant and useful for synthesis activities, optimizing data discoverability and usability while not placing an undue burden on data contributors. We describe previous RS data collection efforts, lessons learned from related databases and data-oriented networks (e.g., FLUXNET) in earth and ecological sciences, and the process of community consultation. The proposed reporting format focuses on chamber-level data and metadata, specifying measurement conditions and, for a given measurement period defined by beginning and ending timestamps, a mean RS flux (or CO2 concentration) and associated ancillary measurements. With input from the research community, we have also developed research data and metadata templates to support data collection adhering to the reporting format. Fundamentally, this format aims to enable findable, accessible, interoperable, and reusable data, while providing ‘future-proofing’ capabilities to support reanalyses using as yet unknown algorithms or approaches. This proposed RS reporting format is openly available online and is intended to be a dynamic document, subject to further community feedback and/or change.

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

Fueled by the explosion of (meta)genomic data, genome mining of specialized metabolites has become a major technology for drug discovery and studying microbiome ecology. In these efforts, computational tools like antiSMASH have played a central role through the analysis of Biosynthetic Gene Clusters (BGCs). Thousands of candidate BGCs from microbial genomes have been identified and stored in public databases. Interpreting the function and novelty of these predicted BGCs requires comparison with a well-documented set of BGCs of known function. The MIBiG (Minimum Information about a Biosynthetic Gene Cluster) Data Standard and Repository was established in 2015 to enable curation and storage of known BGCs. Here, we present MIBiG 2.0, which encompasses major updates to the schema, the data, and the online repository itself. Over the past five years, 851 new BGCs have been added. Additionally, we performed extensive manual data curation of all entries to improve the annotation quality of our repository. We also redesigned the data schema to ensure the compliance of future annotations. Finally, we improved the user experience by adding new features such as query searches and a statistics page, and enabled direct link-outs to chemical structure databases. The repository is accessible online at https://mibig.secondarymetabolites.org/.

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.

With the gradually wide application of Internet of things on green building, Internet of Building Energy System (iBES) has gained more and more attention and application in the green building. Based on conception, technology and standard of the Internet of things, it acquires building energy consumption data through a series of sensors in the Intelligent Gateway (IG) and unifies a data standard. After data aggregation and software process, an effective building consumption data report can be provided in time, further adjusting building energy consumption in order to attain the goal of energy saving and consumption reducing. Application results show that, using the Internet of things technology for building power adjustment, reduce energy consumption, reduce carbon dioxide emissions, are a valuable technique.

Open education data is one of the important measures to ensure education equity and promote lifelong learning. The UK government's practice of open education data is at the forefront of the world, which can be used as reference for China's implementation of the “Education Informatization 2.0 Action Plan.”. This paper introduces the UK government's open education data by means of network survey, literature analysis and comparative analysis, including concept definition, type distribution and typical application. The development of open education data in the UK has been quite in-depth and extensive, and has achieved practical results. China has a big gap in terms of open scope, data quality, data standard, development and application, and should refer to and learn from international advanced experience and practices.

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.

Digital Imaging and Communications in Medicine (DICOM) is an international data standard used worldwide to store and transmit various medical images. In addition to the image pixel data, DICOM files also consist of private information and other important information for diagnosis. However, they are easily modified, and alterations are not detectable with current DICOM file format. Consequently, the security of this kind of data over the Internet arises more challenges. This paper presents an efficient solution to secure embedded information in DICOM images by exploiting different techniques of data encoding, encryption and watermarking. Firstly, the proposed method encodes personal information, encrypts it and then embeds it into the DICOM images by two secret keys to increase the security. After that, personal information is removed from the DICOM images. As a result, medical images are still handled in both normal mode and secure mode without leakage of personal information. Finally, the experimental results on different medical images demonstrate the imperceptibility, capacity and efficiency of the proposed design to discuss its use in telemedicine.

Building design review is the procedure of checking a design against codes and standard provisions to satisfy the accuracy of the design and identify non-compliances before construction begins. The current approaches for conducting the design review process in an automatic or semi-automatic manner are either based on proprietary, domain-specific or hard-coded rule-based mechanisms. These methods may be effective in their specific applications, but they have the downsides of being costly to maintain, inflexible to modify, and lack a generalized framework of rules and regulations modeling that can adapt to various engineering design realms, and thus don’t support a neutral data standard. They are often referred to as ‘Black Box’ or ‘Gray Box’ approaches. This research offers a new comprehensive framework that reduces the limitations of the cited methods. Building regulations, for instance, are legal documents transcribed and approved by professionals to be interpreted and applied by people. They are hardly as precise as formal logic. Engineers, architects, and contractors can read those technical documents and transform them into scientific notations and software applications. They can extract any data they need, reason about it, and apply it at various phases of the project. How these extraction and use are carried out is a critical component of automating the design review process. The chief goal is to address this issue by developing a Generalized Adaptive Framework (GAF) for a neutral data standard (Industry Foundation Classes (IFC)) that enables automating the code compliance checking processes to achieve design efficiency and cost-effectiveness. The objectives of this study comprise i) to develop a theoretical background to an adaptive framework that supports a neutral data standard for transforming the written code regulations and rules into a computable model, and ii) to define the various modules required for computerizing of the code compliance verification process.

The data management landscape associated with the Global Ocean Observing System is distributed, complex, and only loosely coordinated. Yet interoperability across this distributed landscape is essential to enable data to be reused, preserved, and integrated and to minimize costs in the process. A building block for a distributed system in which component systems can exchange and understand information is standardization of data formats, distribution protocols, and metadata. By reviewing several data management use cases we attempt to characterize the current state of ocean data interoperability and make suggestions for continued evolution of the interoperability standards underpinning the data system. We reaffirm the technical data standard recommendations from previous OceanObs conferences and suggest incremental improvements to them that can help the GOOS data system address the significant challenges that remain in order to develop a truly multidisciplinary data system. Fil: Snowden, Derrick. No especifíca; Fil: Tsontos, Vardis M.. NASA Jet Propulsion Laboratory; Estados Unidos Fil: Handegard, Nils Olav. Norwegian Institute of Marine Research; Noruega Fil: Zárate, Marcos Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Centro para el Estudio de Sistemas Marinos; Argentina Fil: O' Brien, Kevin M.. University of Washington; Estados Unidos Fil: Casey, Kenneth S. National Centers for Environmental Information; Estados Unidos Fil: Smith, Neville. No especifíca; Fil: Sagen, Helge. Norwegian Institute of Marine Research; Noruega Fil: Bailey, Kathleen. No especifíca; Fil: Lewis, Mirtha Noemi. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Centro para el Estudio de Sistemas Marinos; Argentina Fil: Arms, Sean. University Corporation for Atmospheric Research; Estados Unidos

With the wide application of BIM, the technology of Operation and Maintenance (O&M) management is developing. By combining BIM with facility management, it can visually manage the model and highly integrate information of the model. As a common data standard of BIM, IFC (Industry Foundation Classes) standard can avoid the problems caused by the mismatch of information between different models. This paper proposes a method of BIM-based facility management and describes the key technical issues in the development of O&M systems. The O&M system leverages BIMserver and its public plug-in interface as a system framework to analyze IFC data. Through the study in this paper, a new method can be developed for future underground space O&M management.