CernVM Users Workshop

This talk will cover how big data and big computation is being handled in the Cloud. It gives practical examples using GCP including tools that apply like Google BigQuery, DataFlow, Container Engine/Kubernetes, and ways to evaluate their performance using Google's open source benchmarking tool PerfKitBenchmarker. About the author. **Anthony F. Voellm** is currently leading the Google Cloud Performance Team and has a wide range of experience from kernel and database engines to graphics and automated image and map extraction from satellite images. Anthony is an avid inventor with 7 technology patents issued. In his current role at Google Anthony is focused on delivering Price Performance to existing products like Google Compute Engine and Google Cloud Storage while also innovating new offerings. Anthony holds a Master of Science from George Washington University, BA in Physics and a BS in Computer Science and Mathematics from the University of Vermont

Replication Data for: 2018 and 2019 NBA and WNBA Three Point Contest

The 2017-18 and 2018-19 NBA and WNBA Three Point Contest data. This data was collected by students in the Berlin Sport Analytics Academy at Syracuse University during August of 2019

Algorithmic surveillance of ICU patients with acute respiratory distress syndrome (ASIC): protocol for a multicentre stepped-wedge cluster randomised quality improvement strategy

Introduction The acute respiratory distress syndrome (ARDS) is a highly relevant entity in critical care with mortality rates of 40%. Despite extensive scientific efforts, outcome-relevant therapeutic measures are still insufficiently practised at the bedside. Thus, there is a clear need to adhere to early diagnosis and sufficient therapy in ARDS, assuring lower mortality and multiple organ failure.Methods and analysis In this quality improvement strategy (QIS), a decision support system as a mobile application (ASIC app), which uses available clinical real-time data, is implemented to support physicians in timely diagnosis and improvement of adherence to established guidelines in the treatment of ARDS. ASIC is conducted on 31 intensive care units (ICUs) at 8 German university hospitals. It is designed as a multicentre stepped-wedge cluster randomised QIS. ICUs are combined into 12 clusters which are randomised in 12 steps. After preparation (18 months) and a control phase of 8 months for all clusters, the first cluster enters a roll-in phase (3 months) that is followed by the actual QIS phase. The remaining clusters follow in month wise steps. The coprimary key performance indicators (KPIs) consist of the ARDS diagnostic rate and guideline adherence regarding lung-protective ventilation. Secondary KPIs include the prevalence of organ dysfunction within 28 days after diagnosis or ICU discharge, the treatment duration on ICU and the hospital mortality. Furthermore, the user acceptance and usability of new technologies in medicine are examined. To show improvements in healthcare of patients with ARDS, differences in primary and secondary KPIs between control phase and QIS will be tested.Ethics and dissemination Ethical approval was obtained from the independent Ethics Committee (EC) at the RWTH Aachen Faculty of Medicine (local EC reference number: EK 102/19) and the respective data protection officer in March 2019. The results of the ASIC QIS will be presented at conferences and published in peer-reviewed journals.Trial registration number DRKS00014330