Suchergebnisse

SAIN4 is a project funded by the Valencian Institute for Business Competitiveness (IVACE) and the European Union through the European Regional Development Fund (FEDER). This document aims to specify the design of the global architecture of the Big Data Analytics and Data Capture infrastructure. The problem is divided into three levels: (i) conceptual architecture, which details the organization of the modules, components and elements interfaces, (ii) the software architecture in which the different services are described and technologies that make up the infrastructure, and (iii) the hardware architecture that describes the physical systems needed for the proposed architectural deployment. ; SAIN4. Project funded by the Valencian Institute of Business Competitiveness (IVACE) and European Union through the European Regional Development Fund (ERDF), within the public grant program adressed to Technological Institutes of the Valencian Community for 2016 with 67.395,60€. File number: IIMDEEA/2017/73

SAIN4 is a project funded by the Valencian Institute for Business Competitiveness (IVACE) and the European Union through the European Regional Development Fund (FEDER). The purpose of this document is to collect the results of the construction of the Big Data Analytics and Data Capture infrastructure, which will allow the digitization of the production processes and serve the Advanced Management System (AMS) of the data necessary for its operation. ; SAIN4. Project funded by the Valencian Institute of Business Competitiveness (IVACE) and European Union through the European Regional Development Fund (ERDF), within the public grant program adressed to Technological Institutes of the Valencian Community for 2016 with 67.395,60€. File number: IIMDEEA/2017/73

A letter report issued by the General Accounting Office with an abstract that begins "Pursuant to a congressional request, GAO provided information on the Bureau of the Census' Data Capture System (DCS) 2000 for the tabulation of 2000 census data, focusing on the status and quality of DCS 2000, as well as the risks Census faces in successfully completing the system."

Endangered Data Week emerged in the early months of 2017 as an effort to encourage conversations about government-produced, open data and the many factors that can limit its access. The event offers an internationally-coordinated series of events that includes publicizing the availability of datasets, increasing critical engagement with them, encouraging open data policies at all levels of government, and the fostering of data skills through workshops on curation, documentation and discovery, improved access, and preservation. The reflection provides an outline of the curriculum development happening through Endangered Data Week and encourages others to contribute.

Endangered Data Week emerged in the early months of 2017 as an effort to encourage conversations about government-produced, open data and the many factors that can limit its access. The event offers an internationally-coordinated series of events that includes publicizing the availability of datasets, increasing critical engagement with them, encouraging open data policies at all levels of government, and the fostering of data skills through workshops on curation, documentation and discovery, improved access, and preservation. The reflection provides an outline of the curriculum development happening through Endangered Data Week and encourages others to contribute.

Die ethnischen Minoritäten und speziell die Arbeitsmigranten in der Bundesrepublik Deutschland stellen eine Population dar, die nach Meinung des Autors in der allgemeinen Umfrageforschung wenig berücksichtigt werden. Dies hängt unter anderem mit folgenden Problemstellungen zusammen: (1) die Antreffbarkeit von gewünschten Zielpersonen, (2) die mangelnden Sprachkenntnisse sowohl auf Seiten der Zielpersonen als auch auf Seiten der Forscher, (3) die Verständigung auf eine gleiche Begrifflichkeit und (4) die Praxis der Umfrageinstitute. Der Autor weist in seinem Beitrag auf die Erhebungsprobleme hin, die sich bei der Sonderpopulation der Arbeitsmigranten ergeben. Seine Ausführungen beziehen sich auf den Weg von der Fragestellung zur Datenanalyse, auf die Auswahl der Methode, auf die Festlegung einer bestimmten Kommunikationsform und damit einer Kommunikationskette sowie auf die Erhebungsproblematik als sprachliche Problematik. (ICI)

In an effort to combat growing demands on players, athlete monitoring has become a central component of professional sport. Despite the introduction of new technologies for athlete monitoring, little is understood about the practices employed in professional rugby clubs. A questionnaire was circulated amongst conditioning staff across the 12 Premiership rugby clubs to capture the methods used, relative importance, perceived effectiveness and barriers to the use of multiple different athlete monitoring measurements. Previous injury, Global Positioning System (GPS) metrics, collision counts and age were deemed the most important risk factors for managing future injury risk. A wide range of GPS metrics are collected across clubs with high-speed running (12/12 clubs), distance in speed zones (12/12 clubs) and total distance (11/12 clubs) the most commonly used. Of the metrics collected, high-speed running was deemed the most important for managing future injury risk (5/12 clubs); however, there was considerable variation between clubs as to the exact definition of high-speed running, with both absolute and relative measures utilised. While the use of such monitoring tools is undertaken to improve athlete welfare by minimising injury risk, this study demonstrates the significant heterogeneity of systems and methods used by clubs for GPS capture. This study therefore questions whether more needs to be done to align practices within the sport to improve athlete welfare.

Background: Electronic medical record notes have been determined to be lacking in quality, accessibility and content. Structured note templates could provide a way to improve these aspects, particularly with regard to data availability for research and quality improvement. Objective: To determine whether the implementation of a standardised template for hernia documentation can improve data completeness and timeliness. Method: Retrospective review of clinic notes of 30 patients, 15 prior to implementation of a standardised note template and 15 after implementation of the template. The number of the 21 Americas Hernia Society Quality Collaborative (AHSQC) variables which were present in the notes was recorded, as was the time that the consultation ended and the time that the note was submitted. Results: Mean number of variables collected prior to implementation of the template was 5.9 ± 1.6 vs. 20 ± 0.4 after implementation ( p < 0.001). In the pre-implementation group, 20% of the notes were completed after the day of the visit, while all of the notes in the post-implementation group were completed on the same day as the visit ( p = 0.367). Conclusion: Implementation of a structured note template resulted in significantly improved capture of specific database variables within clinical notes. Structured note templates are an effective tool to improve data capture from the clinical setting for research and quality improvement.

Databases covering all individuals of a population are increasingly used for research and decision-making. The massive size of such databases is often mistaken as a guarantee for valid inferences. However, population data have characteristics that make them challenging to use. Various assumptions on population coverage and data quality are commonly made, including how such data were captured and what types of processing have been applied to them. Furthermore, the full potential of population data can often only be unlocked when such data are linked to other databases. Record linkage often implies subtle technical problems, which are easily missed. We discuss a diverse range of myths and misconceptions relevant for anybody capturing, processing, linking, or analysing population data. Remarkably, many of these myths and misconceptions are due to the social nature of data collections and are therefore missed by purely technical accounts of data processing. Many are also not well documented in scientific publications. We conclude with a set of recommendations for using population data.