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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.

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.

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.

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 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.

To be computationally reproducible and efficient, integration of disparate data depends on shared entities whose matching meaning (semantics) can be computationally assessed. For biodiversity data one of the most prevalent shared entities for linking data records is the associated taxon concept. Unlike Linnaean taxon names, the traditional way in which taxon concepts are provided, phylogenetic definitions are native to phylogenetic trees and offer well-defined semantics that can be transformed into formal, computationally evaluable logic expressions. These attributes make them highly suitable for phylogeny-driven comparative biology by allowing computationally verifiable and reproducible integration of taxon-linked data against Tree of Life-scale phylogenies. To achieve this, the first step is transforming phylogenetic definitions from the natural language text in which they are published to a structured interoperable data format that maintains strong ties to semantics and lends itself well to sharing, reuse, and long-term archival. To this end, we developed the Phyloreference Exchange Format (Phyx), a JSON-LD-based text format encompassing rich metadata for all elements of a phylogenetic definition, and we created a supporting software library, phyx.js, to streamline computational management of such files. Together they form a foundation layer for digitizing and computing with phylogenetic definitions of clades.

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.

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.

Vertebrate fossil tracks have been studied through 3D modelling techniques during the last two decades to improve tracking procedures. Different laser scanners and software have been used during field investigation and this differentiation caused incompatibility between many analysis programs. Post processing procedures are not standardized among scientists and file precision give different results in additionally constrain comparison. The starting point to find a solution to the problem is to point out the main technologies used to collect data in the field. The resulting protocol does not oblige to uniform hardware but it does suggest combining different laser scanners with a minimum post processing to optimize the result. Important inkling would be to use 2D relief as a reference to organize 3D tracking procedures. Saving formats during post processing are improved by setting fixed landmarks integrated in the 3D model in a multitask file. The D.I.C.O.M. medical standard (*.cdm format) and the 3D printing international standard (*.stl format) when combined, may be a good solution for uniform the format. This approach might be the first step for the standardization of source and derived files and the creation of a worldwide 3D ichnological catalogue. This will allow creating a scientific improvement in terms of reproducibility and comparison of the experience., Rivista italiana di Paleontologia e Stratigrafia, Vol 117, No 3

Leaf-level gas exchange data support the mechanistic understanding of plant fluxes of carbon and water. These fluxes inform our understanding of ecosystem function, are an important constraint on parameterization of terrestrial biosphere models, are necessary to understand the response of plants to global environmental change, and are integral to efforts to improve crop production. Collection of these data using gas analyzers can be both technically challenging and time consuming, and individual studies generally focus on a small range of species, restricted time periods, or limited geographic regions. The high value of these data is exemplified by the many publications that reuse and synthesize gas exchange data, however the lack of metadata and data reporting conventions make full and efficient use of these data difficult. Here we propose a reporting format for leaf-level gas exchange data and metadata to provide guidance to data contributors on how to store data in repositories to maximize their discoverability, facilitate their efficient reuse, and add value to individual datasets. For data users, the reporting format will better allow data repositories to optimize data search and extraction, and more readily integrate similar data into harmonized synthesis products. The reporting format specifies data table variable naming and unit conventions, as well as metadata characterizing experimental conditions and protocols. For common data types that were the focus of this initial version of the reporting format, i.e., survey measurements, dark respiration, carbon dioxide and light response curves, and parameters derived from those measurements, we took a further step of defining required additional data and metadata that would maximize the potential reuse of those data types. To aid data contributors and the development of data ingest tools by data repositories we provided a translation table comparing the outputs of common gas exchange instruments. Extensive consultation with data collectors, data users, instrument manufacturers, and data scientists was undertaken in order to ensure that the reporting format met community needs. The reporting format presented here is intended to form a foundation for future development that will incorporate additional data types and variables as gas exchange systems and measurement approaches advance in the future. The reporting format is published in the U.S. Department of Energy's ESS-DIVE data repository, with documentation and future development efforts being maintained in a version control system. publishedVersion

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 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.

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.

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.

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.

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 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.

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.

INTRODUCTION: The 2019 coronavirus disease (COVID-19) is a major global health concern. Joint efforts for effective surveillance of COVID-19 require immediate transmission of reliable data. In this regard, a standardized and interoperable reporting framework is essential in a consistent and timely manner. Thus, this research aimed at to determine data requirements towards interoperability. MATERIALS AND METHODS: In this cross-sectional and descriptive study, a combination of literature study and expert consensus approach was used to design COVID-19 Minimum Data Set (MDS). A MDS checklist was extracted and validated. The definitive data elements of the MDS were determined by applying the Delphi technique. Then, the existing messaging and data standard templates (Health Level Seven-Clinical Document Architecture [HL7-CDA] and SNOMED-CT) were used to design the surveillance interoperable framework. RESULTS: The proposed MDS was divided into administrative and clinical sections with three and eight data classes and 29 and 40 data fields, respectively. Then, for each data field, structured data values along with SNOMED-CT codes were defined and structured according HL7-CDA standard. DISCUSSION AND CONCLUSION: The absence of effective and integrated system for COVID-19 surveillance can delay critical public health measures, leading to increased disease prevalence and mortality. The heterogeneity of reporting templates and lack of uniform data sets hamper the optimal information exchange among multiple systems. Thus, developing a unified and interoperable reporting framework is more effective to prompt reaction to the COVID-19 outbreak.

To accelerate the clinical translation of photoacoustic (PA) imaging, IPASC aims to provide open and publicly available reference datasets for testing of data reconstruction and spectral processing algorithms in a widely accepted data format. The International Photoacoustic Standardisation Consortium (IPASC) has identified and agreed on a list of essential metadata parameters to describe raw time series PA data and used it to develop an initial prototype of a standardized PA data format. We aim to apply the proposed format in an open database that provides reference datasets for testing of processing algorithms, thereby facilitating and advancing PA research and translation.

This resource defines the Brain Imaging Data Structure (BIDS) specification, including the core specification as well as many modality-specific extensions. To get started, check out the introduction. For an overview of the BIDS ecosystem, visit the BIDS homepage. The entire specification can also be browsed in an HTML version. See Appendix I for a list of the BIDS contributors who jointly created this specification.

The detection of anomalies in public procurements can enable the improvement of the quality of purchases, and consequently enable a better quality of life in the country through the correct use of public funds. In this paper, we use as a case study the public procurement data from Paraguay, available in the open data format of the Open Contracting Data Standard, to train an unsupervised learning model for anomaly detection based on the Isolation Forest algorithm. The Open Contracting Data Standard allows the developed technique to be replicated in other countries that implement the same data standard, thus achieving an interoperable solution. The resulting classification enables the scoring of contracts and procurement processes which can be used to identify anomalies and make it possible to obtain an intelligent sampling of the data. This can be utilized as a support in the task of the government in its role to regulate and control the public procurements. The effectiveness of the model is validated with local known anomalous procurement processes, which are: a)processes protested by entities involved in the contracting process, which were determined in favor of the protestant, and b)complaints about the contracting process from external entities with the possibility of anonymity. The results show an accuracy of over 90 % in detecting these known anomalies as early as in the tender stage and during the contracting stage. Thus indicating a feasible approach for anomaly detection in public procurements.

The study is intended to create a unified modeling and framework that addresses the issue of transparency in the procurement process and utilized an algorithm that will help the University in checking uploaded documents of the Bids and Awards Committee (BAC) tendered items compliant to RA 9184 Procurement Act of the Philippines with open contracting data standard. The research design utilized in the study was descriptive method and prototyping methodology. In order to support the unified modeling and procurement process, the paper presents the architectural view of the system through use case diagram and class diagram to show the interconnections of the entities on the activities in the procurement process, and to identify the methods, functions and relationships of the classes. Furthermore, the researchers designed a system framework to provide a clear perspective of the entire system process.

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.

Most users will foresee the use of genetic sequences in the context of molecular ecology or phylogenetic research, however, a sequence with coordinates and a timestamp is a valuable biodiversity occurrence that is useful in a much broader context than its original purpose. To uncover this potential, sequence-derived data need to become findable, accessible, interoperable, and reusable through generalist biodiversity data platforms. Stimulated by the Biodiversity_Next discussions in 2019, we have worked for about 10 months to put together practical data mapping and data publishing experiences in Norway, Australia, Sweden, and Denmark, as well as in the UNITE and the GBIF (Global Biodiversity Information Facility) networks. The resulting guide was put together to provide practical instruction for mapping sequence-derived data. Biodiversity data communities remain dominated by the macroscopic, easily detectable, morphologically identifiable species. This is not only true for citizen science and other forms of biodiversity popularization, but is also visible in the university and museum department structures, financial resource allocations, biodiversity legislation, and policy design. Recent decades of molecular advances have increased the power of genetic methods for detecting, describing, and documenting global biodiversity. We have yet to see the wide shift of data generating efforts from the traditional taxonomic foci of biodiversity assesments to the more balanced and inclusive systems focusing on all functionally important taxa and environments. These include soil, limnic and marine environments, decomposing plants and deadwood, and all life therein. Environmental DNA data enable recording of present and past presence of micro- and macroscopic organisms with minimal effort and by non-invasive methods. The apparent ease of these methods requires a cautious approach to the resulting data and their interpretation. It remains important to define and agree on the organism recording and reporting routines for genetic data. DNA data represent a major addition to the many ways in which GBIF and other biodiversity data platforms index the living world. Our guide is resting on the shoulders of those who have been developing and improving MIxS (Minimum Information about any (x) Sequence), GGBN (Global Genome Biodiversity Network) and other data standards. The added value of publishing sequence-derived data through non-genetic biodiversity discovery platforms relates to spatio-temporal occurrences and sequence-based names. Reporting sequence-derived occurrences in an open and reproducible way has a wide range of benefits: notably, it increases citability, highlights the taxa concerned in the context of biological conservation, and contributes to taxonomic and ecological knowledge.

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.

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.

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.

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