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Pooling and comparing data from the existing global network of burn registers represents a powerful, yet untapped, opportunity to improve burn prevention and care. There have been no studies investigating whether registers are sufficiently similar to allow data comparisons. It is also not known what differences exist that could bias analyses. Understanding this information is essential prior to any future data sharing. The aim of this project was to compare the variables collected in countrywide and intercountry burn registers to understand their similarities and differences. Register custodians were invited to participate and share their data dictionaries. Inclusion and exclusion criteria were compared to understand each register population. Descriptive statistics were calculated for the number of unique variables. Variables were classified into themes. Definition, method, timing of measurement, and response options were compared for a sample of register concepts. 13 burn registries participated in the study. Inclusion criteria varied between registers. Median number of variables per register was 94 (range 28 - 890), of which 24% (range 4.8 – 100%) were required to be collected. Six themes (patient information, admission details, injury, inpatient, outpatient, other) and 41 subthemes were identified. Register concepts of age and timing of injury show similarities in data collection. Intent, mechanism, inhalational injury, infection, and patient death show greater variation in measurement. We found some commonalities between registers and some differences. Commonalities would assist in any future efforts to pool and compare data between registers. Differences between registers could introduce selection and measurement bias, which needs to be addressed in any strategy aiming to facilitate burn register data sharing. We recommend the development of common data elements used in an international minimum data set for burn injuries, including standard definitions and methods of measurement, as the next step in achieving burn register data sharing. • We investigated similarities and differences of 13 international burn registers. • The 2759 variables collected can be categorised into 6 themes and 41 subthemes. • Inclusion criteria differences may lead to selection bias in data comparisons. • Mapping examples are provided to show how variable harmonisation could be achieved. • Creation of common data elements would reduce measurement bias in data comparisons. [ABSTRACT FROM AUTHOR]

IntroductionThe conect4children (c4c) project aims to facilitate efficient planning and delivery of paediatric clinical trials. One objective of c4c is data standardization and reuse. Interoperability and reusability of paediatric clinical trial data is challenging due to a lack of standardization. The Clinical Data Interchange Standards Consortium (CDISC) standards that are required or recommended for regulatory submissions in several countries lack paediatric specificity with limited awareness within academic institutions. To address this, c4c and CDISC collaborated to develop the Pediatrics User Guide (PUG) consisting of cross-cutting data items that are routinely collected in paediatric clinical trials, factoring in all paediatric age ranges.Methods and ResultsThe development of the PUG consisted of six stages. During the scoping phase, subtopics (each containing several clinically relevant concepts) were suggested and debated for inclusion in the PUG. Ninety concepts were selected for the modelling phase. Concept maps describing the Research Topic and representation procedure were developed for the 19 concepts that had no (or partial) previous modelling in CDISC. Next, metadata and implementation examples were developed for concepts. This was followed by a CDISC internal review and a public review. For both these review stages, the feedback comments were either implemented or rejected based on budget, timelines, expert review, and scope. The PUG was published on the CDISC website on February 23, 2023.DiscussionThe PUG is a first step in bridging the lack of child specific CDISC standards, particularly within academia. Several academic and industrial partners were involved in the development of the PUG, and c4c has undertaken multiple steps to publicize the PUG within its academic partner organizations – in particular, the European Reference Networks (ERNs) that are developing registries and dictionaries in 24 disease areas. In the long term, continued use of the PUG in paediatric clinical trials will enable the pooling of data from multiple trials, which is particularly important for medical domains with small populations.

Background Generation Scotland (GS) is large family-based cohort study established as a resource for research into the genetic and environmental determinants of physical and mental health. Participants completed a baseline questionnaire and clinic visit in 2006-2011. 24,084 Participants 5,501 Families • NHS data linkage 22,402 (93%) • Genotype array data 20,026 (83%) • DNA methylation data 18,876 (78%) In 2019 funding was obtained to expand the existing GS cohort. By 2025 NextGenScot aims to: • Recruit 20,000 new participants online • Expand eligibility to 12-17 year olds • Collect saliva samples by post for genotyping • Linkage to NHS and other administrative data Details on how to join at www.gen.scot Participants consented to linkage to NHS Scotland medical records which offer longitudinal phenotype information. Linkage is conducted using the Community Health Index (CHI). Introduction GS receives 30-40 requests for access to data and/or samples every year, and has been included in over 380 publications. Research proposals are subject to review by the GS Access Committee. Details of the application process terms and conditions for access are available on the GS website, and now on the new research access website. The previous GS access process was based on an application form in Word, downloaded from the main GS website. We are replacing this with a new online research access system, adhering to transparency standards1, helped by HDR Alliance Transparency funding. After researchers publish their results, a link to the publication and a lay summary is currently posted on the GS website, with a spreadsheet listing papers. We will now make this information available and searchable for researchers, participants and the public on the new research access website. Methods We are developing a website, using in-house staff, with HDR Alliance Transparency contributing to their salaries. The system coding uses Django (a Python tool) linked to a MySQL database, a platform widely used for such projects, enabling faster development in a secure environment. Transparent access to GS data for registered users • View summary of data available • Query the summary data to see if it fits research requirements • Select data requested from data dictionaries • Get a detailed quote for access charges • Complete application form showing the project complies with the Five Safes2 • Upload supporting documentation (e.g. protocol, DPIA, ethics, etc) • Track progress of their application review, approval and delivery • Researchers will be required to submit a lay summary of their application GS participants, researchers and the public can view: • List of approved applications with lay summaries • Summaries of ongoing projects which will link to eventual publications • Searchable list of publications Result The new system is undergoing internal testing, using data imported from previous and ongoing access requests (May 2024). We will then pilot it with selected external researchers and GS participants (June 2024), with a plan to launch it for public access in July/August 2024. The GS Access Committee and admin team will be able to streamline the review and approval process, giving an improved turnaround time. Conclusions We were already planning to develop a new online application system, but the Transparency grant funding has enabled us to extend the functionality beyond a simple form-filling system. The new access website will give researchers more information about the application process, the data and samples available, see what other research has been approved in their area of interest, and track the progress of their project from enquiry through to publication. By adding a participant/public access area, with lay summaries of projects and publications, GS participants will be able to see how their data is being used for public benefit, as can potential new volunteers or other interested parties. Notes The URL for the new website is www.gsaccess.org The main GS website is at www.generationscotland.org References • Transparency Standards https://zenodo.org/record/8262453 • Five Safes https://ukdataservice.ac.uk/help/secure-lab/what-is-the-five-safes-framework/

Objective: Based on software engineering principles, we designed and implemented a medical device data dictionary system that aligns with data integration and industry standards to fulfill the data management needs of medical institutions and healthcare professionals. Materials and Methods: Utilizing the web-based framework alongside open-source technologies such as Spring Boot, Spring Security, Apache MyBatis, Vue, and Element UI, along with database technologies such as MySql, MongoDB, and Redis, we analyze the basic elements of medical device data dictionary system and incorporate layered architecture technology, design the system architecture, functional workflows and database model required for the dictionary system, and conduct professional testing and trial evaluation of the system. Results and Analysis: In this study, we constructed a web-based open medical device data dictionary system that supports front-end and back-end operations. Using a ventilator as a sample for non-integrity assessment, the results of the system utility test showed that the system is functionally complete, satisfies the compatibility requirements of mainstream browsers and poses no significant risks or vulnerabilities to system operation. Conclusion: A medical device data dictionary that characterizes life cycle usage attributes based on their structural and functional properties was constructed. It provides a unified and standardized base for the management and analysis of medical device data, and helps realize the interaction and exchange of medical device data between disparate medical information systems.

Model-driven software development has become a hot research topic and discovery trend in the field of software engineering. Its core idea is to treat analysis and design models as equivalent to code. Better integration of models and code can greatly increase the chances of effective improvement and achieve automated software development through abstract models. In this paper, we first constructed a data model-driven architecture system based on the meta-modeling hierarchy, using a data dictionary for data storage. The mapping relation loader transforms the data extracted from the dictionary. Using the differential evolution algorithm, the model is defined as a metamodel that actually exists. At the same time, the MapReduce framework is combined to parallelize the computation of the DE algorithm based on the island model in order to solve the problem of poor optimization of the differential algorithm. Apply the model to actual software development and realize data visualization and display using Flex technology. Simulation experiments are set up to test the performance of the model and the platform. After CSI uncertainty estimation, the MSE index of the model is analyzed. This paper’s data model-driven method, with an MSE value of only 0.01084, stands out among the five methods in Case 4. Under the condition of 300 users concurrently, the user’s access response time is tested, and it can be seen that the user’s response time is within 1~2.9s, which passes the performance test.

This letter announces that PDBx/mmCIF format files will become mandatory for crystallographic depositions to the Protein Data Bank (PDB).

Background: Trauma registry staff are tasked with high-quality data collection to support program requirements. Hospital-specific data dictionaries are increasingly used to ensure accurate data collection, yet it is unknown how such a resource impacts a trauma registry team's competency with data collection. Objective: This study sought to explore whether having a hospital-specific data dictionary affected trauma service team members' self-reported competency level with abstracting required and nonrequired data elements. Methods: This study used an anonymous, cross-sectional survey distributed (July 2020 to September 2020) by the Society of Trauma Nurses, the American College of Surgeons, and the Trauma System News outlets to trauma registrars, trauma nurse coordinators, clinical quality specialists, program managers, program directors, and trauma research personnel. A 26-question survey was designed using a visual sliding scale from 0 to 100 to measure self-reported competence and associated variables. Results: A total of 881 respondents completed the survey from at least 495 centers. Six hundred ninety-six (79.0%) respondents were from Level I or Level II programs. Several factors were associated with team members feeling highly competent in collecting data for various reporting requirements, including the level of trauma center verification, tenure working in trauma services, and the presence of a hospital-specific data dictionary. Conclusion: Trauma centers should consider establishing a hospital-specific data dictionary as they are associated with higher registry staff competence working with trauma registry data. [ABSTRACT FROM AUTHOR]

In order to optimize workflow and improve efficiency, modern enterprises usually entrust software providers to build the Enterprise Information System (EIS). However, the software providers generally do not provide the data dictionary for the EIS relational database, which brings great difficulties for enterprises to use the data stored in the EIS database. This paper proposes a method named database translation mechanism for generating the data dictionary of the EIS relational database which only utilizes the data collected from the interfaces of EIS. Inspired by the great success that Graph Neural Networks (GNNs) have achieved, the study builds a graph structure for the EIS relational database by mining the relationships between columns and then train a GNN-based classifier to predict to which column the value belongs. In addition, a table-based sampling method is designed to construct graph datasets for minibatch training on the large-scale graph structure. Furthermore, a uniform encoding method and a hybrid aggregator function are proposed to improve the performance of the GNN-based classifier. The trained GNN-based classifier can be used to predict the matching relationships between the columns of the EIS database and the tables extracted from the EIS interfaces given the fact that the data in the database is entered at the interfaces. In this way, the detailed information at the interface to translate the database can be used. Experimental results on a realworld ERP relational database demonstrate the superior performance of the proposed method, which efficiently exploits and utilizes the graph structure information in the relational database. [ABSTRACT FROM AUTHOR]

BACKGROUND: Trauma programs are required to collect a uniform set of trauma variables and submit data to regional, state, and or national registries. Programs may also collect unique data elements to support hospital-specific initiatives. OBJECTIVE: This study explored what additional data elements are being collected by U.S. trauma programs and the impact of having a hospital-specific data dictionary. METHODS: An anonymous, cross-sectional survey exploring what additional data are being collected, and the impact of having a hospital-specific data dictionary, was distributed by the Society of Trauma Nurses, Trauma System News, and the American College of Surgeons. The survey was open from July 2020 to September, 2020. RESULTS: There were 693 respondents from approximately 368 Level I/II trauma programs. The estimated trauma center response rate was 59.4% (n = 368/620). Level I programs had a higher response rate than Level II programs (66.9% and 53.4%, respectively). In our sample, 85.5% of responding centers collect additional data. The most common additional data collected at Level I/II programs concerned quality improvement initiatives (70.3% and 66.1%, respectively). Other commonly collected data pertained to deaths (60.6%) and complications (50.3%). Only 43% of responding centers (n = 161/368) have a hospital-specific data dictionary. Hospitals that collect additional data were more likely to have such a resource compared with those that do not (n = 147/315, 46.7% vs. n = 14/53, 26.4%, p =.01). CONCLUSION: Most trauma programs collect data outside required fields. Fewer than half define these data in a data dictionary. Centers should consider establishing a data dictionary to define data collected. [ABSTRACT FROM AUTHOR]

Featured Application: To demonstrate the advantages of using a modelling tool to define and structure the clinical data items represented in the conect4children (c4c) Cross Cutting Paediatric Data Dictionary (CCPDD). By facilitating the unambiguous definition of data items, we show how a modelling tool can improve the interoperability of data collected in paediatric clinical trials. We demonstrate how the clinical application of a modelling approach to semantic interoperability can be used to represent the c4c CCPDD. We highlight how modelling via tooling of terminology is a better means of data dictionary representation compared to an implicit model captured in Microsoft Excel. We report on how the Direcht tool facilitates the export of data models into electronic capture (EDC) systems, such as REDCap and Castor. Finally, we illustrate how the Direcht tool formalises the modelling of data items to improve the content of the CCPDD and makes it semantically and contextually complete. Data dictionaries for clinical trials are often created manually, with data structures and controlled vocabularies specific for a trial or family of trials within a sponsor's portfolio. Microsoft Excel is commonly used to capture the representation of data dictionary items but has limited functionality for this purpose. The conect4children (c4c) network is piloting the Direcht clinical data modelling tool to model their Cross Cutting Paediatric Data Dictionary (CCPDD) in a more formalised way. The first pilot had the key objective of testing whether a clinical data modelling tool could be used to represent data items from the CCPDD. The key objective of the second pilot is to establish whether a small team with little or no experience of clinical data modelling can use Direcht to expand the CCPDD. Clinical modelling is the process of structuring clinical data so it can be understood by computer systems and humans. The model contains all of the elements that are needed to define the data item. Results from the pilots show that Direcht creates a structured environment to build data items into models that fit into the larger CCPDD. Models can be represented as an HTML document, mind map, or exported in various formats for import into a computer system. Challenges identified over the course of both pilots are being addressed with c4c partners and external stakeholders. [ABSTRACT FROM AUTHOR]

Abstract Background The Kentucky Cancer Registry (KCR) is a central cancer registry for the state of Kentucky that receives data about incident cancer cases from all healthcare facilities in the state within 6 months of diagnosis. Similar to all other U.S. and Canadian cancer registries, KCR uses a data dictionary provided by the North American Association of Central Cancer Registries (NAACCR) for standardized data entry. The NAACCR data dictionary is not an ontological system. Mapping between the NAACCR data dictionary and the National Cancer Institute (NCI) Thesaurus (NCIt) will facilitate the enrichment, dissemination and utilization of cancer registry data. We introduce a web-based system, called Interactive Mapping Interface (IMI), for creating mappings from data dictionaries to ontologies, in particular from NAACCR to NCIt. Method IMI has been designed as a general approach with three components: (1) ontology library; (2) mapping interface; and (3) recommendation engine. The ontology library provides a list of ontologies as targets for building mappings. The mapping interface consists of six modules: project management, mapping dashboard, access control, logs and comments, hierarchical visualization, and result review and export. The built-in recommendation engine automatically identifies a list of candidate concepts to facilitate the mapping process. Results We report the architecture design and interface features of IMI. To validate our approach, we implemented an IMI prototype and pilot-tested features using the IMI interface to map a sample set of NAACCR data elements to NCIt concepts. 47 out of 301 NAACCR data elements have been mapped to NCIt concepts. Five branches of hierarchical tree have been identified from these mapped concepts for visual inspection. Conclusions IMI provides an interactive, web-based interface for building mappings from data dictionaries to ontologies. Although our pilot-testing scope is limited, our results demonstrate feasibility using IMI for semantic enrichment of cancer registry data by mapping NAACCR data elements to NCIt concepts.

Introduction: The conect4children (c4c) project aims to facilitate efficient planning and delivery of paediatric clinical trials. One objective of c4c is data standardization and reuse. Interoperability and reusability of paediatric clinical trial data is challenging due to a lack of standardization. The Clinical Data Interchange Standards Consortium (CDISC) standards that are required or recommended for regulatory submissions in several countries lack paediatric specificity with limited awareness within academic institutions. To address this, c4c and CDISC collaborated to develop the Pediatrics User Guide (PUG) consisting of cross-cutting data items that are routinely collected in paediatric clinical trials, factoring in all paediatric age ranges., Methods and Results: The development of the PUG consisted of six stages. During the scoping phase, subtopics (each containing several clinically relevant concepts) were suggested and debated for inclusion in the PUG. Ninety concepts were selected for the modelling phase. Concept maps describing the Research Topic and representation procedure were developed for the 19 concepts that had no (or partial) previous modelling in CDISC. Next, metadata and implementation examples were developed for concepts. This was followed by a CDISC internal review and a public review. For both these review stages, the feedback comments were either implemented or rejected based on budget, timelines, expert review, and scope. The PUG was published on the CDISC website on February 23, 2023., Discussion: The PUG is a first step in bridging the lack of child specific CDISC standards, particularly within academia. Several academic and industrial partners were involved in the development of the PUG, and c4c has undertaken multiple steps to publicize the PUG within its academic partner organizations - in particular, the European Reference Networks (ERNs) that are developing registries and dictionaries in 24 disease areas. In the long term, continued use of the PUG in paediatric clinical trials will enable the pooling of data from multiple trials, which is particularly important for medical domains with small populations., Competing Interests: The following authors declare employment with respective commercial entities: BA with Drug Safety Consulting S.A.S.U., JT with Institut de Recherches Internationales Servier, SN with Novartis Pharmaceuticals, and RM with Accurate Systems Limited. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Owen, Sen, Aurich, Engel, Cavallaro, Degraeuwe, Kalra, Cornet, Walsh, Berkery, Palmeri, Mahler, Malik, Persijn, Amadi, Thuet, Woodworth, Nally, Leary, Marshall and Straub.)

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The development of colorectal cancer outcome registries internationally has been organic, with differing datasets, data definitions and infrastructure across registries which has limited data pooling and international comparison. Currently there is no comprehensive data dictionary identified as a standard. This study is part of an international collaboration that aims to identify areas of data capture and usage which may be optimised to improve understanding of colorectal cancer outcomes. This study aimed to compare and identify commonalities and areas of difference across major colorectal cancer registries. We sought to establish datasets comprising of mutually collected common fields, and a combined comprehensive dataset of all collected fields across major registries to aid in establishing a future colorectal cancer registry database standard. This mixed qualitative and quantitative study compared data dictionaries from three major colorectal cancer outcome registries: Bowel Cancer Outcomes Registry (BCOR) (Australia and New Zealand), National Bowel Cancer Audit (NBOCA) (United Kingdom) and Dutch ColoRectal Audit (DCRA) (Netherlands). Registries were compared and analysed thematically, and a common dataset and combined comprehensive dataset were developed. These generated datasets were compared to data dictionaries from Sweden (SCRCR), Denmark (DCCG), Argentina (BNCCR-A) and the USA (NAACCR and ACS NSQIP). Fields were assessed against prominent quality indicator metrics from the literature and current case-use. We developed a combined comprehensive dataset of 225 fields under seven domains: demographic, pre-operative, operative, post-operative, pathology, neoadjuvant therapy, adjuvant therapy, and follow up/recurrence. A common dataset was developed comprising 38 overlapping fields, showing a low degree of mutually collected data, especially in preoperative, post operative and adjuvant therapy domains. The BNCCR-A, SCRCR and DCCG databases all contained a high percentage of common dataset fields. Fields were poorly comparable when viewed form current quality indicator metrics. This study mapped data dictionaries of prominent colorectal cancer registries and highlighted areas of commonality and difference The developed common field dataset provides a foundation for registries to benchmark themselves and work towards harmonisation of data dictionaries. This has the potential to enable meaningful large-scale international outcomes research. [ABSTRACT FROM AUTHOR]

Clinical research studies generate data that need to be shared and statistically analyzed by their participating institutions. The distributed nature of research and the different domains involved present major challenges to data sharing, exploration, and visualization. The Data Portal infrastructure was developed to support ongoing research in the areas of neurocognition, imaging, and genetics. Researchers benefit from the integration of data sources across domains, the explicit representation of knowledge from domain experts, and user interfaces providing convenient access to project specific data resources and algorithms. The system provides an interactive approach to statistical analysis, data mining, and hypothesis testing over the lifetime of a study and fulfills a mandate of public sharing by integrating data sharing into a system built for active data exploration. The web-based platform removes barriers for research and supports the ongoing exploration of data.

Data dictionaries for clinical trials are often created manually, with data structures and controlled vocabularies specific for a trial or family of trials within a sponsor’s portfolio. Microsoft Excel is commonly used to capture the representation of data dictionary items but has limited functionality for this purpose. The conect4children (c4c) network is piloting the Direcht clinical data modelling tool to model their Cross Cutting Paediatric Data Dictionary (CCPDD) in a more formalised way. The first pilot had the key objective of testing whether a clinical data modelling tool could be used to represent data items from the CCPDD. The key objective of the second pilot is to establish whether a small team with little or no experience of clinical data modelling can use Direcht to expand the CCPDD. Clinical modelling is the process of structuring clinical data so it can be understood by computer systems and humans. The model contains all of the elements that are needed to define the data item. Results from the pilots show that Direcht creates a structured environment to build data items into models that fit into the larger CCPDD. Models can be represented as an HTML document, mind map, or exported in various formats for import into a computer system. Challenges identified over the course of both pilots are being addressed with c4c partners and external stakeholders.

Background: The Kentucky Cancer Registry (KCR) is a central cancer registry for the state of Kentucky that receives data about incident cancer cases from all healthcare facilities in the state within 6 months of diagnosis. Similar to all other U.S. and Canadian cancer registries, KCR uses a data dictionary provided by the North American Association of Central Cancer Registries (NAACCR) for standardized data entry. The NAACCR data dictionary is not an ontological system. Mapping between the NAACCR data dictionary and the National Cancer Institute (NCI) Thesaurus (NCIt) will facilitate the enrichment, dissemination and utilization of cancer registry data. We introduce a web-based system, called Interactive Mapping Interface (IMI), for creating mappings from data dictionaries to ontologies, in particular from NAACCR to NCIt.Method: IMI has been designed as a general approach with three components: (1) ontology library; (2) mapping interface; and (3) recommendation engine. The ontology library provides a list of ontologies as targets for building mappings. The mapping interface consists of six modules: project management, mapping dashboard, access control, logs and comments, hierarchical visualization, and result review and export. The built-in recommendation engine automatically identifies a list of candidate concepts to facilitate the mapping process.Results: We report the architecture design and interface features of IMI. To validate our approach, we implemented an IMI prototype and pilot-tested features using the IMI interface to map a sample set of NAACCR data elements to NCIt concepts. 47 out of 301 NAACCR data elements have been mapped to NCIt concepts. Five branches of hierarchical tree have been identified from these mapped concepts for visual inspection.Conclusions: IMI provides an interactive, web-based interface for building mappings from data dictionaries to ontologies. Although our pilot-testing scope is limited, our results demonstrate feasibility using IMI for semantic enrichment of cancer registry data by mapping NAACCR data elements to NCIt concepts. [ABSTRACT FROM AUTHOR]

Abstract Background In most research projects budget, staff and IT infrastructures are limiting resources. Especially for small-scale registries and cohort studies professional IT support and commercial electronic data capture systems are too expensive. Consequently, these projects use simple local approaches (e.g. Excel) for data capture instead of a central data management including web-based data capture and proper research databases. This leads to manual processes to merge, analyze and, if possible, pseudonymize research data of different study sites. Results To support multi-site data capture, storage and analyses in small-scall research projects, corresponding requirements were analyzed within the MOSAIC project. Based on the identified requirements, the Toolbox for Research was developed as a flexible software solution for various research scenarios. Additionally, the Toolbox facilitates data integration of research data as well as metadata by performing necessary procedures automatically. Also, Toolbox modules allow the integration of device data. Moreover, separation of personally identifiable information and medical data by using only pseudonyms for storing medical data ensures the compliance to data protection regulations. This pseudonymized data can then be exported in SPSS format in order to enable scientists to prepare reports and analyses. Conclusions The Toolbox for Research was successfully piloted in the German Burn Registry in 2016 facilitating the documentation of 4350 burn cases at 54 study sites. The Toolbox for Research can be downloaded free of charge from the project website and automatically installed due to the use of Docker technology.

Copyright of Ciência da Informação is the property of Instituto Brasileiro de Informacao em Ciencia e Tecnologia (IBICT) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Express Logistics produced big express tracking data in E-commerce Environment, however, express tracking has a significant weakness due to the lack of an industry-wide standard for archiving and exchanging the tracking information of the basic express item. It was difficult for express company to share and exchange express data with each other. In this paper, we introduced XML to describe data and analyzed data exchange mode of tracking information of express logistics, the data dictionary was draw up first, and then put forward a data describe language based on XML, which called express logistics tracking information markup language (ELTIML), ELTIML was intended to be a common “language” for describing express tracking data, and proposed a schema of ELTIML. At last, with regard to tracking information heterogeneous in four express logistics information management system, we designed an information tracking application framework using ELTIML. The results indicates that data exchanging model based on ELTIML shields heterogeneity of data resources and it is a efficient method of data exchange. [ABSTRACT FROM AUTHOR]

Background: There is a recognized need to improve how scholarly data are managed and accessed. The scientific community has proposed the findable, accessible, interoperable, and reusable (FAIR) data principles to address this issue.Objective: The objective of this case study was to develop a system for improving the FAIRness of Healthcare Cost and Utilization Project's State Emergency Department Databases (HCUP's SEDD) within the context of data catalog availability.Methods: A search tool, EDCat (Emergency Department Catalog), was designed to improve the "FAIRness" of electronic health databases and tested on datasets from HCUP-SEDD. ElasticSearch was used as a database for EDCat's search engine. Datasets were curated and defined. Searchable data dictionary-related elements and unified medical language system (UMLS) concepts were included in the curated metadata. Functionality to standardize search terms using UMLS concepts was added to the user interface.Results: The EDCat system improved the overall FAIRness of HCUP-SEDD by improving the findability of individual datasets and increasing the efficacy of searches for specific data elements and data types.Discussion: The databases considered for this case study were limited in number as few data distributors make the data dictionaries of datasets available. The publication of data dictionaries should be encouraged through the FAIR principles, and further efforts should be made to improve the specificity and measurability of the FAIR principles.Conclusion: In this case study, the distribution of datasets from HCUP-SEDD was made more FAIR through the development of a search tool, EDCat. EDCat will be evaluated and developed further to include datasets from other sources. [ABSTRACT FROM AUTHOR]