Academic literature on the topic 'Common locale data repository'
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Journal articles on the topic "Common locale data repository"
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Ronaldson, Amy, Evangelos Chandakas, Qiongwen Kang, Katie Brennan, Aminat Akande, Irene Ebyarimpa, Eleanor Wyllie, et al. "Cohort profile: he East London Health and Care Partnership Data Repository: using novel integrated data to support commissioning and research." BMJ Open 10, no. 9 (September 2020): e037183. http://dx.doi.org/10.1136/bmjopen-2020-037183.
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PurposeThe East London Health and Care Partnership (ELHCP) Data Repository was established to support commissioning decisions in London. This dataset comprises routine clinical data for the general practitioner (GP)-registered populations of two London boroughs, Tower Hamlets and City and Hackney, and provides a rich source of demographic, clinical and health service use data of relevance to clinicians, commissioners, researchers and policy makers. This paper describes the dataset in its current form, its representativeness and data completeness.ParticipantsThere were 351 749 and 344 511 members of the GP-registered population in the two boroughs, respectively, for the financial year 2017/2018. Demographic information and prevalence data were available for 9 mental health and 15 physical health conditions. Prevalence rates from the cohort were compared with local and national data. In order to illustrate the health service use data available in the dataset, emergency department use across mental health conditions was described. Information about data completeness was provided.Findings to dateThe ELHCP Data Repository provides a rich source of information about a relatively young, urban, ethnically diverse, population within areas of socioeconomic deprivation. Prevalence data were in line with local and national statistics with some exceptions. Physical health conditions were more common in those with mental health conditions, reflecting that comorbidities are the norm rather than the exception. This has implications for integrated care. Data completeness for risk factors (eg, blood pressure, cholesterol) was high in patients with long-term conditions.Future plansThe data are being further cleaned and evaluated using imputation, Bayesian and economic methods, principally focusing on specific cohorts, including type II diabetes, depression and personality disorder. Data continue to be collected for the foreseeable future to support commissioning decisions, which will also enable more long-term prospective analysis as data become available at the end of each financial year.
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Kapsner, Lorenz A., Jonathan M. Mang, Sebastian Mate, Susanne A. Seuchter, Abishaa Vengadeswaran, Franziska Bathelt, Noemi Deppenwiese, Dennis Kadioglu, Detlef Kraska, and Hans-Ulrich Prokosch. "Linking a Consortium-Wide Data Quality Assessment Tool with the MIRACUM Metadata Repository." Applied Clinical Informatics 12, no. 04 (August 2021): 826–35. http://dx.doi.org/10.1055/s-0041-1733847.
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Abstract Background Many research initiatives aim at using data from electronic health records (EHRs) in observational studies. Participating sites of the German Medical Informatics Initiative (MII) established data integration centers to integrate EHR data within research data repositories to support local and federated analyses. To address concerns regarding possible data quality (DQ) issues of hospital routine data compared with data specifically collected for scientific purposes, we have previously presented a data quality assessment (DQA) tool providing a standardized approach to assess DQ of the research data repositories at the MIRACUM consortium's partner sites. Objectives Major limitations of the former approach included manual interpretation of the results and hard coding of analyses, making their expansion to new data elements and databases time-consuming and error prone. We here present an enhanced version of the DQA tool by linking it to common data element definitions stored in a metadata repository (MDR), adopting the harmonized DQA framework from Kahn et al and its application within the MIRACUM consortium. Methods Data quality checks were consequently aligned to a harmonized DQA terminology. Database-specific information were systematically identified and represented in an MDR. Furthermore, a structured representation of logical relations between data elements was developed to model plausibility-statements in the MDR. Results The MIRACUM DQA tool was linked to data element definitions stored in a consortium-wide MDR. Additional databases used within MIRACUM were linked to the DQ checks by extending the respective data elements in the MDR with the required information. The evaluation of DQ checks was automated. An adaptable software implementation is provided with the R package DQAstats. Conclusion The enhancements of the DQA tool facilitate the future integration of new data elements and make the tool scalable to other databases and data models. It has been provided to all ten MIRACUM partners and was successfully deployed and integrated into their respective data integration center infrastructure.
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Juárez, D., E. E. Schmidt, S. Stahl-Toyota, F. Ückert, and M. Lablans. "A Generic Method and Implementation to Evaluate and Improve Data Quality in Distributed Research Networks." Methods of Information in Medicine 58, no. 02/03 (September 2019): 086–93. http://dx.doi.org/10.1055/s-0039-1693685.
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Abstract Background With the increasing personalization of clinical therapies, translational research is evermore dependent on multisite research cooperations to obtain sufficient data and biomaterial. Distributed research networks rely on the availability of high-quality data stored in local databases operated by their member institutions. However, reusing data documented by independent health providers for the purpose of care, rather than research (“secondary use”), reveal a high variability in terms of data formats, as well as poor data quality, across network sites. Objectives The aim of this work is the provision of a process for the assessment of data quality with regard to completeness and syntactic accuracy across independently operated data warehouses using common definitions stored in a central (network-wide) metadata repository (MDR). Methods For assessment of data quality across multiple sites, we employ a framework of so-called bridgeheads. These are federated data warehouses, which allow the sites to participate in a research network. A central MDR is used to store the definitions of the commonly agreed data elements and their permissible values. Results We present the design for a generator of quality reports within a bridgehead, allowing the validation of data in the local data warehouse against a research network's central MDR. A standardized quality report can be produced at each network site, providing a means to compare data quality across sites, as well as to channel feedback to the local data source systems, and local documentation personnel. A reference implementation for this concept has been successfully utilized at 10 sites across the German Cancer Consortium. Conclusions We have shown that comparable data quality assessment across different partners of a distributed research network is feasible when a central metadata repository is combined with locally installed assessment processes. To achieve this, we designed a quality report and the process for generating such a report. The final step was the implementation in a German research network.
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Do Viet Duc, Ngo Thanh Long, Ha Trung Hai, Chu Van Hai, and Nghiem Van Tam. "A possibilistic Fuzzy c-means algorithm based on improved Cuckoo search for data clustering." Journal of Military Science and Technology, CSCE6 (December 30, 2022): 3–15. http://dx.doi.org/10.54939/1859-1043.j.mst.csce6.2022.3-15.
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Possibilistic Fuzzy c-means (PFCM) algorithm is a powerful clustering algorithm. It is a combination of two algorithms Fuzzy c-means (FCM) and Possibilistic c-means (PCM). PFCM algorithm deals with the weaknesses of FCM in handling noise sensitivity and the weaknesses of PCM in the case of coincidence clusters. However, PFCM still has a common weakness of clustering algorithms that is easy to fall into local optimization. Cuckoo search (CS) is a novel evolutionary algorithm, which has been tested on some optimization problems and proved to be stable and high-efficiency. In this study, we propose a hybrid method encompassing PFCM and improved Cuckoo search to form the proposed PFCM-ICS. The proposed method has been evaluated on 4 data sets issued from the UCI Machine Learning Repository and compared with recent clustering algorithms such as FCM, PFCM, PFCM based on particle swarm optimization (PSO), PFCM based on CS. Experimental results show that the proposed method gives better clustering quality and higher accuracy than other algorithms.
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Mate, Sebastian, Marvin Kampf, Wolfgang Rödle, Stefan Kraus, Rumyana Proynova, Kaisa Silander, Lars Ebert, et al. "Pan-European Data Harmonization for Biobanks in ADOPT BBMRI-ERIC." Applied Clinical Informatics 10, no. 04 (August 2019): 679–92. http://dx.doi.org/10.1055/s-0039-1695793.
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Abstract Background High-quality clinical data and biological specimens are key for medical research and personalized medicine. The Biobanking and Biomolecular Resources Research Infrastructure-European Research Infrastructure Consortium (BBMRI-ERIC) aims to facilitate access to such biological resources. The accompanying ADOPT BBMRI-ERIC project kick-started BBMRI-ERIC by collecting colorectal cancer data from European biobanks. Objectives To transform these data into a common representation, a uniform approach for data integration and harmonization had to be developed. This article describes the design and the implementation of a toolset for this task. Methods Based on the semantics of a metadata repository, we developed a lexical bag-of-words matcher, capable of semiautomatically mapping local biobank terms to the central ADOPT BBMRI-ERIC terminology. Its algorithm supports fuzzy matching, utilization of synonyms, and sentiment tagging. To process the anonymized instance data based on these mappings, we also developed a data transformation application. Results The implementation was used to process the data from 10 European biobanks. The lexical matcher automatically and correctly mapped 78.48% of the 1,492 local biobank terms, and human experts were able to complete the remaining mappings. We used the expert-curated mappings to successfully process 147,608 data records from 3,415 patients. Conclusion A generic harmonization approach was created and successfully used for cross-institutional data harmonization across 10 European biobanks. The software tools were made available as open source.
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Paris, Nicolas, Antoine Lamer, and Adrien Parrot. "Transformation and Evaluation of the MIMIC Database in the OMOP Common Data Model: Development and Usability Study." JMIR Medical Informatics 9, no. 12 (December 14, 2021): e30970. http://dx.doi.org/10.2196/30970.
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Background In the era of big data, the intensive care unit (ICU) is likely to benefit from real-time computer analysis and modeling based on close patient monitoring and electronic health record data. The Medical Information Mart for Intensive Care (MIMIC) is the first open access database in the ICU domain. Many studies have shown that common data models (CDMs) improve database searching by allowing code, tools, and experience to be shared. The Observational Medical Outcomes Partnership (OMOP) CDM is spreading all over the world. Objective The objective was to transform MIMIC into an OMOP database and to evaluate the benefits of this transformation for analysts. Methods We transformed MIMIC (version 1.4.21) into OMOP format (version 5.3.3.1) through semantic and structural mapping. The structural mapping aimed at moving the MIMIC data into the right place in OMOP, with some data transformations. The mapping was divided into 3 phases: conception, implementation, and evaluation. The conceptual mapping aimed at aligning the MIMIC local terminologies to OMOP's standard ones. It consisted of 3 phases: integration, alignment, and evaluation. A documented, tested, versioned, exemplified, and open repository was set up to support the transformation and improvement of the MIMIC community's source code. The resulting data set was evaluated over a 48-hour datathon. Results With an investment of 2 people for 500 hours, 64% of the data items of the 26 MIMIC tables were standardized into the OMOP CDM and 78% of the source concepts mapped to reference terminologies. The model proved its ability to support community contributions and was well received during the datathon, with 160 participants and 15,000 requests executed with a maximum duration of 1 minute. Conclusions The resulting MIMIC-OMOP data set is the first MIMIC-OMOP data set available free of charge with real disidentified data ready for replicable intensive care research. This approach can be generalized to any medical field.
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Paris, Nicolas, Antoine Lamer, and Adrien Parrot. "Transformation and Evaluation of the MIMIC Database in the OMOP Common Data Model: Development and Usability Study." JMIR Medical Informatics 9, no. 12 (December 14, 2021): e30970. http://dx.doi.org/10.2196/30970.
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Background In the era of big data, the intensive care unit (ICU) is likely to benefit from real-time computer analysis and modeling based on close patient monitoring and electronic health record data. The Medical Information Mart for Intensive Care (MIMIC) is the first open access database in the ICU domain. Many studies have shown that common data models (CDMs) improve database searching by allowing code, tools, and experience to be shared. The Observational Medical Outcomes Partnership (OMOP) CDM is spreading all over the world. Objective The objective was to transform MIMIC into an OMOP database and to evaluate the benefits of this transformation for analysts. Methods We transformed MIMIC (version 1.4.21) into OMOP format (version 5.3.3.1) through semantic and structural mapping. The structural mapping aimed at moving the MIMIC data into the right place in OMOP, with some data transformations. The mapping was divided into 3 phases: conception, implementation, and evaluation. The conceptual mapping aimed at aligning the MIMIC local terminologies to OMOP's standard ones. It consisted of 3 phases: integration, alignment, and evaluation. A documented, tested, versioned, exemplified, and open repository was set up to support the transformation and improvement of the MIMIC community's source code. The resulting data set was evaluated over a 48-hour datathon. Results With an investment of 2 people for 500 hours, 64% of the data items of the 26 MIMIC tables were standardized into the OMOP CDM and 78% of the source concepts mapped to reference terminologies. The model proved its ability to support community contributions and was well received during the datathon, with 160 participants and 15,000 requests executed with a maximum duration of 1 minute. Conclusions The resulting MIMIC-OMOP data set is the first MIMIC-OMOP data set available free of charge with real disidentified data ready for replicable intensive care research. This approach can be generalized to any medical field.
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Beecham, Roger, Jason Dykes, Layik Hama, and Nik Lomax. "On the Use of ‘Glyphmaps’ for Analysing the Scale and Temporal Spread of COVID-19 Reported Cases." ISPRS International Journal of Geo-Information 10, no. 4 (April 1, 2021): 213. http://dx.doi.org/10.3390/ijgi10040213.
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Recent analysis of area-level COVID-19 cases data attempts to grapple with a challenge familiar to geovisualization: how to capture the development of the virus, whilst supporting analysis across geographic areas? We present several glyphmap designs for addressing this challenge applied to local authority data in England whereby charts displaying multiple aspects related to the pandemic are given a geographic arrangement. These graphics are visually complex, with clutter, occlusion and salience bias an inevitable consequence. We develop a framework for describing and validating the graphics against data and design requirements. Together with an observational data analysis, this framework is used to evaluate our designs, relating them to particular data analysis needs based on the usefulness of the structure they expose. Our designs, documented in an accompanying code repository, attend to common difficulties in geovisualization design and could transfer to contexts outside of the UK and to phenomena beyond the pandemic.
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Nkosi, Vusumuzi, Angela Mathee, Suzana Blesic, Thandi Kapwata, Zamantimande Kunene, David Jean du Preez, Rebecca Garland, and Caradee Yael Wright. "Exploring Meteorological Conditions and Human Health Impacts during Two Dust Storm Events in Northern Cape Province, South Africa: Findings and Lessons Learnt." Atmosphere 13, no. 3 (March 5, 2022): 424. http://dx.doi.org/10.3390/atmos13030424.
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Dust storms are meteorological hazards associated with several adverse health impacts including eye irritations, respiratory and cardiovascular disorders, and vehicular road accidents due to poor visibility. This study investigated relations between admissions from a large, public hospital that serves people living in Northern Cape and Free State provinces, South Africa during 2011 to 2017, and meteorological variables (temperature and air quality) during two dust storms, one in October 2014 (spring) and the second in January 2016 (summer), identified from the media as no repository of such events exists for South Africa. Distributed nonlinear lag analysis and wavelet transform analysis were applied to explore the relationships between hospital admissions for respiratory and cardiovascular diseases, eye irritation, and motor vehicle accidents; maximum temperature, and two air quality ‘proxy measures,’ aerosol optical depth and Ångström exponent, were used as ground-based air quality data were unavailable. Eye irritation was the most common dust-related hospital admission after both dust storm events. No statistically significant changes in admissions of interest occurred at the time of the two dust storm events, using either of the statistical methods. Several lessons were learnt. For this type of study, ground-based air quality and local wind data are required; alternative statistical methods of analysis should be considered; and a central dust storm repository would help analyze more than two events. Future studies in South Africa are needed to develop a baseline for comparison of future dust storm events and their impacts on human health.
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Sampurno, Fanny, Ashwini Kannan, Mark Lucas, John Liman, Sarah E. Connor, Emily Pearman, Jeremy L. Millar, et al. "Development of Technologic Solutions to Address Complex Local Requirements of an International Prostate Cancer Clinical Quality Registry." JCO Clinical Cancer Informatics, no. 3 (December 2019): 1–11. http://dx.doi.org/10.1200/cci.18.00114.
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PURPOSE To detail the process for importing a defined data set into a centralized global registry via a secure file transfer platform and to understand the barriers to the establishment of a centralized global registry. RESULTS A bespoke solution was developed to allow transmission of data from international local data centers to a centralized repository. Data elements included in the import template were drawn from existing International Consortium for Health Outcome Measurement variables and refined to ensure accurate benchmarking as well as feasibility in data completeness. The data set was organized in accordance with the prostate cancer care trajectory. Key considerations in developing the data transfer platform included import file format, process of input validation, and technical provisions. Given the diversity in the legislation and ethical requirements with respect to consent, data handling, and cross-border data transfer across geographic locations, we encouraged each local data center to consult with its legal advisors and research ethics committee early on in the process. DISCUSSION A global collaboration, although highly valuable, posed many challenges because of inconsistent methods of data collection. User acceptance of a system is paramount to the success of establishing a metaregistry. Local information technology support and regular regression testing ensures quality and maintenance of the database. CONCLUSION We developed a Web-based system to facilitate the collection and secure storage of common data, which is scalable and secure. It is anticipated that through systematic recording of data, global standards of clinical practice and outcomes of care will see vast improvements.
Dissertations / Theses on the topic "Common locale data repository"
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"Materialized Views over Heterogeneous Structured Data Sources in a Distributed Event Stream Processing Environment." Doctoral diss., 2011. http://hdl.handle.net/2286/R.I.8991.
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abstract: Data-driven applications are becoming increasingly complex with support for processing events and data streams in a loosely-coupled distributed environment, providing integrated access to heterogeneous data sources such as relational databases and XML documents. This dissertation explores the use of materialized views over structured heterogeneous data sources to support multiple query optimization in a distributed event stream processing framework that supports such applications involving various query expressions for detecting events, monitoring conditions, handling data streams, and querying data. Materialized views store the results of the computed view so that subsequent access to the view retrieves the materialized results, avoiding the cost of recomputing the entire view from base data sources. Using a service-based metadata repository that provides metadata level access to the various language components in the system, a heuristics-based algorithm detects the common subexpressions from the queries represented in a mixed multigraph model over relational and structured XML data sources. These common subexpressions can be relational, XML or a hybrid join over the heterogeneous data sources. This research examines the challenges in the definition and materialization of views when the heterogeneous data sources are retained in their native format, instead of converting the data to a common model. LINQ serves as the materialized view definition language for creating the view definitions. An algorithm is introduced that uses LINQ to create a data structure for the persistence of these hybrid views. Any changes to base data sources used to materialize views are captured and mapped to a delta structure. The deltas are then streamed within the framework for use in the incremental update of the materialized view. Algorithms are presented that use the magic sets query optimization approach to both efficiently materialize the views and to propagate the relevant changes to the views for incremental maintenance. Using representative scenarios over structured heterogeneous data sources, an evaluation of the framework demonstrates an improvement in performance. Thus, defining the LINQ-based materialized views over heterogeneous structured data sources using the detected common subexpressions and incrementally maintaining the views by using magic sets enhances the efficiency of the distributed event stream processing environment.Dissertation/Thesis
Ph.D. Computer Science 2011
Book chapters on the topic "Common locale data repository"
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Bianchin, Alvise, and George Smyrnakis. "Recycling and Upcycling: FENIX Validation on Three Use Cases." In New Business Models for the Reuse of Secondary Resources from WEEEs, 123–53. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74886-9_10.
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AbstractWithin FENIX a set of three use cases has been developed in order to test in practice the selected business models. After the description of a common data repository virtually connecting all the use cases and describing the common step of PCB disassembly, this chapter presents each use case into detail, by evidencing the main findings.
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Bernasconi, Anna. "Model, Integrate, Search... Repeat: A Sound Approach to Building Integrated Repositories of Genomic Data." In Special Topics in Information Technology, 89–99. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-85918-3_8.
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AbstractA wealth of public data repositories is available to drive genomics and clinical research. However, there is no agreement among the various data formats and models; in the common practice, data sources are accessed one by one, learning their specific descriptions with tedious efforts. In this context, the integration of genomic data and of their describing metadata becomes—at the same time—an important, difficult, and well-recognized challenge. In this chapter, after overviewing the most important human genomic data players, we propose a conceptual model of metadata and an extended architecture for integrating datasets, retrieved from a variety of data sources, based upon a structured transformation process; we then describe a user-friendly search system providing access to the resulting consolidated repository, enriched by a multi-ontology knowledge base. Inspired by our work on genomic data integration, during the COVID-19 pandemic outbreak we successfully re-applied the previously proposed model-build-search paradigm, building on the analogies among the human and viral genomics domains. The availability of conceptual models, related databases, and search systems for both humans and viruses will provide important opportunities for research, especially if virus data will be connected to its host, provider of genomic and phenotype information.
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Pavan, Alberto, Vittorio Caffi, Alessandro Valra, Davide Madeddu, Diego Farina, Jacopo Chiappetti, and Claudio Mirarchi. "Development of BIM Management System." In Innovative Tools and Methods Using BIM for an Efficient Renovation in Buildings, 29–49. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04670-4_3.
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AbstractWith BS 1192:2007 and even more so with BS PAS 1199-2:2013 and 1192-3:2014, the concept of Common Data Environment (CDE) of the order (project, construction or management that is). Originating from a standard dedicated to design (BS 1192:2007) and although its concept has been extended to information management in general: Capex (strategy, project, construction: PAS 1192-2) and Opex (exercise: PAS 1192-3), the CDE, as it is understood today in common practice, is still very much affected by the original link with the design and the design phase (and in particular the design in the new building). All this according to an information flow that is still very linear and sequential: client, designer, builder, manager, user; more than circular, as the so-called BIM methodology would like. The risk, therefore, is that the commercial software market is affected by this CDE approach, which is also useful for the very rich real estate market of the emerging economies, neglecting the construction market of the more consolidated economies (Europe for before), very built up, and aimed more at housing quality, sustainability, reuse, and renewal of the existing heritage rather than the new one. It is consequently necessary to define new information flows and a new type of information management environment (CDE) for the phases of use, conservation, and renovation of buildings for the European market. The need arises for a specific BIM Management System (BIMMS, overcoming the classic CDE) for asset management and their enhancement that collects information from the buildings themselves and its users (Digital Twins, IoT, sensors, etc.). A new CDE / BIMMS that uses semantics and open dialogue, via API, with multiple Tools rather than acting as a repository of files and models. BIMMS is a new concept of CDE created for the operation/renovation phases in mature real estate markets (such as the European one).
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Sundara Kumar, M. R., S. Sankar, Vinay Kumar Nassa, Digvijay Pandey, Binay Kumar Pandey, and Wegayehu Enbeyle. "Innovation and Creativity for Data Mining Using Computational Statistics." In Methodologies and Applications of Computational Statistics for Machine Intelligence, 223–40. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-7701-1.ch012.
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In this digital world, a set of information about the real-world entities is collected and stored in a common place for extraction. When the information generated has no meaning, it will convert into meaningful information with a set of rules. Those data have to be converted from one form to another form based on the attributes where it was generated. Storing these data with huge volume in one place and retrieving from the repository reveals complications. To overcome the problem of extraction, a set of rules and algorithms was framed by the standards and researchers. Mining the data from the repository by certain principles is called data mining. It has a lot of algorithms and rules for extraction from the data warehouses. But when the data is stored under a common structure on the repository, the values derived from that huge volume are complicated. Computing statistical data using data mining provides the exact information about the real-world applications like population, weather report, and probability of occurrences.
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Martinez-Costa, Catalina, and Francisco Abad-Navarro. "Towards a Semantic Data Harmonization Federated Infrastructure." In Studies in Health Technology and Informatics. IOS Press, 2021. http://dx.doi.org/10.3233/shti210116.
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Data integration is an increasing need in medical informatics projects like the EU Precise4Q project, in which multidisciplinary semantically and syntactically heterogeneous data across several institutions needs to be integrated. Besides, data sharing agreements often allow a virtual data integration only, because data cannot leave the source repository. We propose a data harmonization infrastructure in which data is virtually integrated by sharing a semantically rich common data representation that allows their homogeneous querying. This common data model integrates content from well-known biomedical ontologies like SNOMED CT by using the BTL2 upper level ontology, and is imported into a graph database. We successfully integrated three datasets and made some test queries showing the feasibility of the approach.
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Majeed, Raphael W., Mark R. Stöhr, and Andreas Günther. "HIStream-Import: A Generic ETL Framework for Processing Arbitrary Patient Data Collections or Hospital Information Systems into HL7 FHIR Bundles." In German Medical Data Sciences: Bringing Data to Life. IOS Press, 2021. http://dx.doi.org/10.3233/shti210053.
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Data integration is a necessary and important step to perform translational research and improve the sample size beyond single data collections. For health information, the most recent established communication standards is HL7 FHIR. To bridge the concepts of “minimal invasive” data integration and open standards, we propose a generic ETL framework to process arbitrary patient related data collections into HL7 FHIR – which in turn can then be used for loading into target data warehouses. The proposed algorithm is able to read any relational delimited text exports and produce a standard HL7 FHIR bundle collection. We evaluated an implementation of the algorithm using different lung research registries and used the resulting FHIR resources to fill our i2b2 based data warehouse as well an OMOP common data model repository.
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Schaaf, Jannik, James Chalmers, Heymut Omran, Petra Pennekamp, Olivier Sitbon, Thomas O. F. Wagner, Abilio Reis, Dennis Kadioglu, and Holger Storf. "The Registry Data Warehouse in the European Reference Network for Rare Respiratory Diseases – Background, Conception and Implementation." In German Medical Data Sciences: Bringing Data to Life. IOS Press, 2021. http://dx.doi.org/10.3233/shti210049.
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Rare lung diseases affect 1.5–3 million people in Europe while causing bad prognosis or early deaths for patients. The European Reference Network for Respiratory Diseases (ERN-Lung) is a patient centric network, funded by the European Union (EU). The aims of ERN-LUNG is to increase healthcare and research regarding rare respiratory diseases. An initial need for cross-border healthcare and research is the use of registries and databases. A typical problem in registries for RDs is the data exchange, since the registries use different kind of data with different types or descriptions. Therefore, ERN-Lung decided to create a new Registry Data-Warehouse (RDW) where different existing registries are connected to enable cross-border healthcare within ERN-Lung. This work facilitates the aims, conception and implementation for the RDW, while considering a semantic interoperability approach. We created a common dataset (CDS) to have a common descriptions of respiratory diseases patients within the ERN registries. We further developed the RDW based on Open Source Registry System for Rare Diseases (OSSE), which includes a Metadata Repository with the Samply.MDR to unique describe data for the minimal dataset. Within the RDW, data from existing registries is not stored in a central database. The RDW uses the approach of the “Decentral Search” and can send requests to the connected registries, whereas only aggregated data is returned about how many patients with specific characteristics are available. However, further work is needed to connect the different existing registries to the RDW and to perform first studies.
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Suganya, R., Rajaram S., and Kameswari M. "A Literature Review on Thyroid Hormonal Problems in Women Using Data Science and Analytics." In Advances in Data Mining and Database Management, 416–28. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-3053-5.ch021.
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Currently, thyroid disorders are more common and widespread among women worldwide. In India, seven out of ten women are suffering from thyroid problems. Various research literature studies predict that about 35% of Indian women are examined with prevalent goiter. It is very necessary to take preventive measures at its early stages, otherwise it causes infertility problem among women. The recent review discusses various analytics models that are used to handle different types of thyroid problems in women. This chapter is planned to analyze and compare different classification models, both machine learning algorithms and deep leaning algorithms, to classify different thyroid problems. Literature from both machine learning and deep learning algorithms is considered. This literature review on thyroid problems will help to analyze the reason and characteristics of thyroid disorder. The dataset used to build and to validate the algorithms was provided by UCI machine learning repository.
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B., Shiva Shanta Mani, and Manikandan V. M. "Heart Disease Prediction Using Machine Learning." In Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning, 373–81. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-2742-9.ch018.
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Heart disease is one of the most common and serious health issues in all the age groups. The food habits, mental stress, smoking, etc. are a few reasons for heart diseases. Diagnosing heart issues at an early stage is very much important to take proper treatment. The treatment of heart disease at the later stage is very expensive and risky. In this chapter, the authors discuss machine learning approaches to predict heart disease from a set of health parameters collected from a person. The heart disease dataset from the UCI machine learning repository is used for the study. This chapter discusses the heart disease prediction capability of four well-known machine learning approaches: naive Bayes classifier, KNN classifier, decision tree classifier, random forest classifier.
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Ferrarini, Francesca. "Anforette a fondo piatto con anse rimontanti da Altino: una possibile produzione locale?" In Antichistica. Venice: Edizioni Ca' Foscari, 2020. http://dx.doi.org/10.30687/978-88-6969-380-9/010.
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In the magazines of the National Archaeological Museum of Altino (VE) a type of small amphora is preserved, which is not described in the most common typologies. Numerous fragments characterised by a rim with double step and remounting ribbon handles have been found; the inner surface of rim and neck shows a thick layer of pitch. It is highly possible that this represents a wine amphora, probably of local production, which, based on the analysis of some fragments with stamp, would date back to the first half of the first century BC. First preliminary data on this container are here discussed.
Conference papers on the topic "Common locale data repository"
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L. Mitchell, Jennie, and Nicholas Farha. "Syntax and Semantics of Learning Object Meta-data." In InSITE 2005: Informing Science + IT Education Conference. Informing Science Institute, 2005. http://dx.doi.org/10.28945/2905.
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Initially, many repositories developed their own tools to facilitate the indexing of learning objects, adopting the standard that best fit their users at the time. Although metadata should be the common language of learning objects, without a common standard compliance system, one repository may not interact well with another. With the increased emphasis on sharing learning objects, metadata schemas are being asked to do more than just index a learning object at a local level. This chapter reviews some of the leading object repository software, the push for RSS services, federated search capability, a clear interpretation of the fields, and the need to count usage as well as the impact on learning.
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Dupuis, Bruce R. "The Canadian Energy Pipeline Association Stress Corrosion Cracking Database." In 1998 2nd International Pipeline Conference. American Society of Mechanical Engineers, 1998. http://dx.doi.org/10.1115/ipc1998-2067.
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The SCC (stress corrosion cracking) database was initiated by the CEPA (Canadian Energy Pipeline Association) SCC Working. The current generation of the database has a broad scope, containing detailed data for each and every colony and its associated environmental conditions. The database also includes corrosion and dents amongst other integrity concerns to identify any correlation with SCC and provide a common industry data format to investigate these other integrity issues. The intent of the current version of the database is to provide for the most detailed data entry that one could typically capture at an investigative dig. With the wide acceptance of the current version the CEPA database it is evolving into the industry standard for investigative excavation data. The initial trending results are based on the dataset generated by CEPA member companies, which represents over a thousand investigative excavations. The trend results should only be interpreted broadly at this time, although they do generally support industry’s understanding of SCC. The development and implementation of the CEPA SCC database is premised on the belief, developed through extensive field investigations and laboratory research, that SCC is not a random development, but it initiates and grows at specific locations susceptible to SCC. It is further premised on the belief that such susceptible sites can be generally located by appropriate prioritization techniques. Thus, the objective of the database is to explore correlation among the various operational and environmental variables to improve the current understanding of how to locate SCC, and in particular ‘significant’ SCC, in order that measures can be taken to prevent operational failures and enhance the safety of Canadian pipelines. The need for an industry database regarding SCC was identified by the CEPA SCC working group shortly after its formation 1994. It was apparent that the various companies were collecting the field data from investigative excavations in significantly different formats, only some of which were electronic. The need for a common data structure and data repository to facilitate trending was reinforced numerous times at the Banff Conferences and by the NEB during its inquiry into SCC in 1995/96.
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Kojo, Matti, and Phil Richardson. "Stakeholder Opinions on the Use of the Added Value Approach in Siting Radioactive Waste Management Facilities." In ASME 2013 15th International Conference on Environmental Remediation and Radioactive Waste Management. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/icem2013-96068.
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In some countries nuclear waste facility siting programs include social and economic benefits, compensation, local empowerment and motivation measures and other incentives for the potential host community. This can generally be referred to as an ‘added value approach’. Demonstration of the safety of a repository is seen as a precondition of an added value approach. Recently much focus has been placed on studying and developing public participation approaches but less on the use of such incentive and community benefit packages, although they are becoming a more common element in many site selection strategies for nuclear waste management facilities. The primary objective of this paper is to report on an ongoing study of stakeholders’ opinions of the use of an added value approach in siting a radioactive waste facility in the Czech Republic, Poland and Slovenia. The paper argues that an added value approach should adapt to the interests and needs of stakeholders during different stages of a siting process. The main question posed in the study is as follows: What are the measures which should be included in ‘added value approach’ according to the stakeholders? The research data consists of stakeholders’ responses to a survey focusing on the use of added value (community benefits) and incentives in siting nuclear waste management facilities. The survey involved use of a questionnaire developed as part of the EU-funded IPPA* project in three countries: the Czech Republic, Poland and Slovenia. (* Implementing Public Participation Approaches in Radioactive Waste Disposal, FP7 Contract Number: 269849). The target audiences for the questionnaires were the stakeholders represented in the national stakeholder groups established to discuss site selection for a nuclear waste repository in their country. A total of 105 questionnaires were sent to the stakeholders between November 2011 and January 2012. 44 questionnaires were returned, resulting in a total response rate of 41% (10/29 in the Czech Republic, 11/14 in Poland and in 23/64 in Slovenia).
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Meakins, Michael E., Nicole C. Okamoto, and Cullen E. Bash. "An Energy and Exergy Analysis of Economizer-Based Data Centers." In ASME 2009 3rd International Conference on Energy Sustainability collocated with the Heat Transfer and InterPACK09 Conferences. ASMEDC, 2009. http://dx.doi.org/10.1115/es2009-90067.
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Electrical consumption for data centers is on the rise as more and more of them are being built. Data center owners and operators are looking for methods to reduce energy consumption and electrical costs. One method of reducing facility costs for a chilled water plant is by adding an economizer. Most studies concerning economizer systems are largely conducted by looking at energy alone since the primary focus has reducing electrical costs. However, little research has been published that examines exergy destruction for these types of systems based on varying outdoor air conditions by locale. The purpose of this study has been to develop energy and exergy-based models of each of the most common economizer systems. In this paper, a normal chiller plant without an economizer and a chiller plant with an indirect wet-side economizer (the most common type of economizer system) are compared. A method for simulating a mechanical cooling system is provided and results show outdoor conditions influence facility energy consumption and exergy destruction.
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Perisic, Ana, Marko Lazic, and Branko Perisic. "The foundation for cooperative services in engineering buildings — The common ontology for architectural and urban data repository modeling." In 2015 IEEE 10th Jubilee International Symposium on Applied Computational Intelligence and Informatics (SACI). IEEE, 2015. http://dx.doi.org/10.1109/saci.2015.7208241.
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Fledderjohn, Matthew, Steven B. Shooter, and Robert B. Stone. "Augmenting a Design Repository to Facilitate Product Family Planning." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-81846.
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A Design Repository has been created in an effort to archive existing products and the components in each product. With this function-based archiving system, designers can retrieve design information on existing products to assist in a new design project. The use of product families has emerged as an approach to exploit commonality for more efficient product development. However, the Design Repository does not contain explicit design information on platforms and modules. This paper describes information for the design of a platform and proposes a new data structure that organizes the information for augmenting the Design Repository. An information flow model for the development of a single product is modified to describe the flow of information needed for product platform design. The information flow model and associated data structure has been shown to be effective in representing three common product families: the Black & Decker Firestorm tool set, Kodak single-use cameras, and the IceDozer family of ice scrapers. With this data structure implemented into the existing repository, designers can find useful information on how to create different products based on the a common platform.
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Qian, Yiyue, Yiming Zhang, Yanfang Ye, and Chuxu Zhang. "Adapting Meta Knowledge with Heterogeneous Information Network for COVID-19 Themed Malicious Repository Detection." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/507.
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As cyberattacks caused by malware have proliferated during the pandemic, building an automatic system to detect COVID-19 themed malware in social coding platforms is in urgent need. The existing methods mainly rely on file content analysis while ignoring structured information among entities in social coding platforms. Additionally, they usually require sufficient data for model training, impairing their performances over cases with limited data which is common in reality. To address these challenges, we develop Meta-AHIN, a novel model for COVID-19 themed malicious repository detection in GitHub. In Meta-AHIN, we first construct an attributed heterogeneous information network (AHIN) to model the code content and social coding properties in GitHub; and then we exploit attention-based graph convolutional neural network (AGCN) to learn repository embeddings and present a meta-learning framework for model optimization. To utilize unlabeled information in AHIN and to consider task influence of different types of repositories, we further incorporate node attribute-based self-supervised module and task-aware attention weight into AGCN and meta-learning respectively. Extensive experiments on the collected data from GitHub demonstrate that Meta-AHIN outperforms state-of-the-art methods.
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Dupuis, Bruce, and Jason Humber. "The Evolution of Data Management to Empower Integrity Management Decisions: A Case Study of an Enterprise Implementation." In 2002 4th International Pipeline Conference. ASMEDC, 2002. http://dx.doi.org/10.1115/ipc2002-27415.
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BP’s Natural Gas Liquids business unit (NGLBU) has conducted integrity investigation and mitigation activities on its pipelines and has been following this best practice for numerous years. In recent times, NGLBU’s data management initiatives focused on establishing an enterprise Geographic Information System (GIS) coupled tightly with a derivative of the Pipeline Open Data Standard (PODS) data model. During successful implementation of the GIS, an analysis identified gaps in existing data management processes for pipeline integrity information. Consequently, the business unit adopted Baseline Technology’s Pipeline Information Control System (PICS) and its modules to support the pipeline integrity decision-making process on its 9000km of pipeline. The PICS implementation leverages the existing GIS implementation while addressing a number of unresolved data management and integration issues, including: • Integration of inline inspection with excavation results; • Migration of above ground surveys to a common repository; • Integration of multiple inline inspections; • Facilitation of corrosion growth modeling; • Structured process for prioritization of remediation; • Structured process for integration of inline inspections with risk parameters; • Defined data collection, storage, and integration standards. Data management solutions based solely on a GIS require pipeline surveys without explicit positional information to be converted into a common linear reference system (typically chainage or stationing) such that disparate data sets may be overlaid and compared. This conversion, or spatial normalization, process is where much of the data management effort is spent and is often prone to error introduction. Even when small errors are introduced, the normalization process is often performed such that it is not auditable. If the underlying spatial errors are not reported, addressed, and understood, the value of the data integration and any subsequent analysis of the combined data set is questionable.
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Omar, Nizam, Pollyana Notargiacomo Mustaro, Ismar Frango Silveira, and Daniel Arndt Alves. "Multiplatform Distributed Architecture of Learning Content Management System." In InSITE 2005: Informing Science + IT Education Conference. Informing Science Institute, 2005. http://dx.doi.org/10.28945/2911.
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Learning objects are constructed and used by a community during an undefined period of time and formatted as digital entities on diverse document types to be used, reused or referenced during a technology-mediated learning process. A Learning Content Management System (LCMS) is needed to their storage and retrieval. Electronic document management, data warehouse and data mining techniques will be presented. Effective management of a really big repository of Learning Objects by a community on a large network needs a system that facilitates the right access to the right document by its content and not only by title, author or other usual indexing fields. Learning Objects must be found by their full content and indexed and customized according to each user or user groups’ necessities. The main indexing and retrieving techniques will be discussed and a solution will be presented. Different learning objects can be stored on a common repository and duplication must be avoided. To fulfill this requirement the system needs to implement smart strategies that can be constructed based on AI techniques. Considering the diversity of users, machines and operating systems, the LCMS must be platform independent and manage portable resources, thus giving access to any user from any machine. LCMS must be scalable enough in order to avoid abrupt changes from small applications to big ones, without losing performance. A multiplatform distributed LCMS architecture is presented, and it is composed by: Interface server, data server, parser server, index server and repository server. These servers can run on a single machine or on a cluster of machines according to the needs of the application.
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Presnyakov, Sergey, Grigory Boyarshinov, Tatyana Borovskaya, and Alena Rybkina. "Graphic File Formats for Web Virtual Globe." In 31th International Conference on Computer Graphics and Vision. Keldysh Institute of Applied Mathematics, 2021. http://dx.doi.org/10.20948/graphicon-2021-3027-580-588.
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In this paper various graphic formats were reviewed as resources for web virtual globe. Interactive spherical visualization (ISV) platform called “Orbus” was considered as an example of a graphic resources repository for web virtual globe. Various raster and vector static and dynamic cartographic layers can be set as resources of applications of this type. Such data should fulfill requirements for high informativeness with a minimum disk volume. In this paper traditional formats used for geodata storing, as well as becoming popular formats that can be adapted to work with geodata were explored. As a result of the analysis of the characteristics of common raster formats, the optimized raster format WebP (in the lossless specification), as well as the associated WebM media container, were selected as the most efficient. As a result of analysis of the characteristics of common vector formats, conclusions about their limitations in visual design of the virtual globe were made.
Reports on the topic "Common locale data repository"
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Yu, Weixiang, and Gordon T. Richards. LSSTC AGN Data Challenge 2021. GitHub, July 2021. http://dx.doi.org/10.17918/agn_datachallenge.
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We provide a code and data repository that can be used to facilitate planning for AGN science with the upcoming Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST). For this purpose, we have produced a common exploratory dataset that can be used to develop tools for parameterization of AGN light curves, AGN selection, and AGN photometric redshifts
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Paglialonga, Lisa, and Carsten Schirnick. Ready to use implementation of project’s data portal with catalogue for findability, common services (SFTP,OPeNDAP) and persistent identifier for accessibility and interoperability. OceanNETs, 2020. http://dx.doi.org/10.3289/oceannets_d8.2.
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