The Experts below are selected from a list of 21207 Experts worldwide ranked by ideXlab platform
Kyle Chard - One of the best experts on this subject based on the ideXlab platform.
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the modern research Data portal a design pattern for networked Data intensive science
PeerJ, 2018Co-Authors: Kyle Chard, Eli Dart, Ian Foster, David Shifflett, Steven Tuecke, Jason B WilliamsAbstract:Author(s): Chard, K; Dart, E; Foster, I; Shifflett, D; Tuecke, S; Williams, J | Abstract: © 2018 Chard et al. We describe best practices for providing convenient, high-speed, secure access to large Data via research Data portals. We capture these best practices in a new design pattern, the Modern Research Data Portal, that disaggregates the traditional monolithic web-based Data portal to achieve orders-of-magnitude increases in Data transfer performance, support new deployment architectures that decouple control logic from Data storage, and reduce development and operations costs. We introduce the design pattern; explain how it leverages high-performance Data enclaves and cloud-based Data management services; review representative examples at research laboratories and universities, including both experimental facilities and supercomputer sites; describe how to leverage Python APIs for authentication, Authorization, Data transfer, and Data sharing; and use coding examples to demonstrate how these APIs can be used to implement a range of research Data portal capabilities. Sample code at a companion web site, https://docs.globus.org/mrdp, provides application skeletons that readers can adapt to realize their own research Data portals.
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The Modern Research Data Portal: A design pattern for networked, Data-intensive science
2017Co-Authors: Kyle Chard, Eli Dart, Ian Foster, David Shifflett, Steven Tuecke, Jason WilliamsAbstract:We describe best practices for providing convenient, high-speed, secure access to large Data via research Data portals. We capture these best practices in a new design pattern, the Modern Research Data Portal, that disaggregates the traditional monolithic web-based Data portal to achieve orders-of-magnitude increases in Data transfer performance, support new deployment architectures that decouple control logic from Data storage, and reduce development and operations costs. We introduce the design pattern; explain how it leverages high-performance Science DMZs and cloud-based Data management services; review representative examples at research laboratories and universities, including both experimental facilities and supercomputer sites; describe how to leverage Python APIs for authentication, Authorization, Data transfer, and Data sharing; and use coding examples to demonstrate how these APIs can be used to implement a range of research Data portal capabilities. Sample code at a companion web site, https://docs.globus.org/mrdp, provides application skeletons that readers can adapt to realize their own research Data portals.
Jim Slattery - One of the best experts on this subject based on the ideXlab platform.
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Validation of Statistical Signal Detection Procedures in EudraVigilance Post-Authorization Data
Drug Safety, 2010Co-Authors: Yolanda Alvarez, Ana Hidalgo, Francois Maignen, Jim SlatteryAbstract:Background: Screening large Databases of spontaneous case reports of possible adverse drug reactions (ADRs) is an established method of identifying hitherto unknown adverse effects of medicinal products; however, there is a lack of consensus concerning the value of formal statistical screening procedures in guiding such a process. This study was performed to clarify the nature of any added benefits and additional effort required when established pharmacovigilance techniques are supplemented with statistical screening. Objective: To evaluate whether statistical signal detection in spontaneous reporting Data can lead to earlier detection of drug safety problems and to assess the additional regulatory work entailed. Methods: Using the EudraVigilance post-Authorization module (EVPM), a screening procedure based on the proportional reporting ratio (PRR) was applied retrospectively to examine if regulatory investigations concerning ADRs in a predefined set of products could have been initiated earlier than occurred in practice. During the same time period, between September 2003 and March 2007, the number of PRR-based signals of disproportionate reporting (SDR) that arose in the same set of products was calculated and evaluated to determine the number requiring investigation. The outcome is expressed as the ratio of the number of SDRs requiring investigation compared with the number of signals pre-empted by the statistical screening approach. In those cases where the signal was discovered earlier, the delay was calculated between identification by the PRR method and by the method that originally identified the signal. Results: In 191 chemically different products, 532 adverse reactions were added to the summary of product characteristics during the study period. Of these, 405 were designated as important medical events (IMEs) based on a comprehensive predefined list. Of the IMEs, 217 (53.6%) were identified earlier by the statistical screening technique, 79 (19.6%) were detected after the date at which they were raised by standard pharmacovigilance methods and 109 (26.9%) were not signalled during the study period. 1561 SDRs requiring further evaluation were detected during the study period, giving a ratio of 7.2 assessments for each signal pre-empted. The mean delay between the discovery of signals using the statistical methods in the EVPM and established methods in the 217 cases detected earlier was 2.45 years. A review resulted in clear explanation for why the statistical method had not preempted detection in all but 77 of 188 cases. Conclusions: The form of statistical signal detection tested in this study can provide significant early warning in a large proportion of drug safety problems; however, it cannot detect all safety issues more quickly than other pharmacovigilance processes and hence it should be used in addition to, rather than as an alternative to, established methods.
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Validation of statistical signal detection procedures in eudravigilance post-Authorization Data: a retrospective evaluation of the potential for earlier signalling.
Drug safety, 2010Co-Authors: Yolanda Alvarez, Ana Hidalgo, Francois Maignen, Jim SlatteryAbstract:Background: Screening large Databases of spontaneous case reports of possible adverse drug reactions (ADRs) is an established method of identifying hitherto unknown adverse effects of medicinal products; however, there is a lack of consensus concerning the value of formal statistical screening procedures in guiding such a process. This study was performed to clarify the nature of any added benefits and additional effort required when established pharmacovigilance techniques are supplemented with statistical screening.
David Shifflett - One of the best experts on this subject based on the ideXlab platform.
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the modern research Data portal a design pattern for networked Data intensive science
PeerJ, 2018Co-Authors: Kyle Chard, Eli Dart, Ian Foster, David Shifflett, Steven Tuecke, Jason B WilliamsAbstract:Author(s): Chard, K; Dart, E; Foster, I; Shifflett, D; Tuecke, S; Williams, J | Abstract: © 2018 Chard et al. We describe best practices for providing convenient, high-speed, secure access to large Data via research Data portals. We capture these best practices in a new design pattern, the Modern Research Data Portal, that disaggregates the traditional monolithic web-based Data portal to achieve orders-of-magnitude increases in Data transfer performance, support new deployment architectures that decouple control logic from Data storage, and reduce development and operations costs. We introduce the design pattern; explain how it leverages high-performance Data enclaves and cloud-based Data management services; review representative examples at research laboratories and universities, including both experimental facilities and supercomputer sites; describe how to leverage Python APIs for authentication, Authorization, Data transfer, and Data sharing; and use coding examples to demonstrate how these APIs can be used to implement a range of research Data portal capabilities. Sample code at a companion web site, https://docs.globus.org/mrdp, provides application skeletons that readers can adapt to realize their own research Data portals.
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The Modern Research Data Portal: A design pattern for networked, Data-intensive science
2017Co-Authors: Kyle Chard, Eli Dart, Ian Foster, David Shifflett, Steven Tuecke, Jason WilliamsAbstract:We describe best practices for providing convenient, high-speed, secure access to large Data via research Data portals. We capture these best practices in a new design pattern, the Modern Research Data Portal, that disaggregates the traditional monolithic web-based Data portal to achieve orders-of-magnitude increases in Data transfer performance, support new deployment architectures that decouple control logic from Data storage, and reduce development and operations costs. We introduce the design pattern; explain how it leverages high-performance Science DMZs and cloud-based Data management services; review representative examples at research laboratories and universities, including both experimental facilities and supercomputer sites; describe how to leverage Python APIs for authentication, Authorization, Data transfer, and Data sharing; and use coding examples to demonstrate how these APIs can be used to implement a range of research Data portal capabilities. Sample code at a companion web site, https://docs.globus.org/mrdp, provides application skeletons that readers can adapt to realize their own research Data portals.
Steven Tuecke - One of the best experts on this subject based on the ideXlab platform.
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the modern research Data portal a design pattern for networked Data intensive science
PeerJ, 2018Co-Authors: Kyle Chard, Eli Dart, Ian Foster, David Shifflett, Steven Tuecke, Jason B WilliamsAbstract:Author(s): Chard, K; Dart, E; Foster, I; Shifflett, D; Tuecke, S; Williams, J | Abstract: © 2018 Chard et al. We describe best practices for providing convenient, high-speed, secure access to large Data via research Data portals. We capture these best practices in a new design pattern, the Modern Research Data Portal, that disaggregates the traditional monolithic web-based Data portal to achieve orders-of-magnitude increases in Data transfer performance, support new deployment architectures that decouple control logic from Data storage, and reduce development and operations costs. We introduce the design pattern; explain how it leverages high-performance Data enclaves and cloud-based Data management services; review representative examples at research laboratories and universities, including both experimental facilities and supercomputer sites; describe how to leverage Python APIs for authentication, Authorization, Data transfer, and Data sharing; and use coding examples to demonstrate how these APIs can be used to implement a range of research Data portal capabilities. Sample code at a companion web site, https://docs.globus.org/mrdp, provides application skeletons that readers can adapt to realize their own research Data portals.
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The Modern Research Data Portal: A design pattern for networked, Data-intensive science
2017Co-Authors: Kyle Chard, Eli Dart, Ian Foster, David Shifflett, Steven Tuecke, Jason WilliamsAbstract:We describe best practices for providing convenient, high-speed, secure access to large Data via research Data portals. We capture these best practices in a new design pattern, the Modern Research Data Portal, that disaggregates the traditional monolithic web-based Data portal to achieve orders-of-magnitude increases in Data transfer performance, support new deployment architectures that decouple control logic from Data storage, and reduce development and operations costs. We introduce the design pattern; explain how it leverages high-performance Science DMZs and cloud-based Data management services; review representative examples at research laboratories and universities, including both experimental facilities and supercomputer sites; describe how to leverage Python APIs for authentication, Authorization, Data transfer, and Data sharing; and use coding examples to demonstrate how these APIs can be used to implement a range of research Data portal capabilities. Sample code at a companion web site, https://docs.globus.org/mrdp, provides application skeletons that readers can adapt to realize their own research Data portals.
Ian Foster - One of the best experts on this subject based on the ideXlab platform.
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the modern research Data portal a design pattern for networked Data intensive science
PeerJ, 2018Co-Authors: Kyle Chard, Eli Dart, Ian Foster, David Shifflett, Steven Tuecke, Jason B WilliamsAbstract:Author(s): Chard, K; Dart, E; Foster, I; Shifflett, D; Tuecke, S; Williams, J | Abstract: © 2018 Chard et al. We describe best practices for providing convenient, high-speed, secure access to large Data via research Data portals. We capture these best practices in a new design pattern, the Modern Research Data Portal, that disaggregates the traditional monolithic web-based Data portal to achieve orders-of-magnitude increases in Data transfer performance, support new deployment architectures that decouple control logic from Data storage, and reduce development and operations costs. We introduce the design pattern; explain how it leverages high-performance Data enclaves and cloud-based Data management services; review representative examples at research laboratories and universities, including both experimental facilities and supercomputer sites; describe how to leverage Python APIs for authentication, Authorization, Data transfer, and Data sharing; and use coding examples to demonstrate how these APIs can be used to implement a range of research Data portal capabilities. Sample code at a companion web site, https://docs.globus.org/mrdp, provides application skeletons that readers can adapt to realize their own research Data portals.
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The Modern Research Data Portal: A design pattern for networked, Data-intensive science
2017Co-Authors: Kyle Chard, Eli Dart, Ian Foster, David Shifflett, Steven Tuecke, Jason WilliamsAbstract:We describe best practices for providing convenient, high-speed, secure access to large Data via research Data portals. We capture these best practices in a new design pattern, the Modern Research Data Portal, that disaggregates the traditional monolithic web-based Data portal to achieve orders-of-magnitude increases in Data transfer performance, support new deployment architectures that decouple control logic from Data storage, and reduce development and operations costs. We introduce the design pattern; explain how it leverages high-performance Science DMZs and cloud-based Data management services; review representative examples at research laboratories and universities, including both experimental facilities and supercomputer sites; describe how to leverage Python APIs for authentication, Authorization, Data transfer, and Data sharing; and use coding examples to demonstrate how these APIs can be used to implement a range of research Data portal capabilities. Sample code at a companion web site, https://docs.globus.org/mrdp, provides application skeletons that readers can adapt to realize their own research Data portals.
