Six-month adherence to Statin use and subsequent risk of major adverse cardiovascular events (MACE) in patients discharged with acute coronary syndromes

Acknowledgements: The authors thank all participants who contributed to the study. Funding: CPACS-1 was funded by unrestricted educational grants from Guidant and Sanofi-Aventis, and grants from The Royal Australasian College of Physicians. AP is supported by an Australian National Heart Foundation Career Development Award. CPACS-2 was funded by an unrestricted grant from Sanofi-Aventis China. The George Institute for Global Health at Peking University Health Science Center sponsored the study and owns the data. Data analyses and reports were supported by Beijing Science and Technology Key Research Plan (D151100002215001). However, the authors are solely responsible for the design, analyses, the drafting and editing of the manuscript, and its final contents.Peer reviewedPublisher PD

Detection of Gamma-Ray Emission from the Vela Pulsar Wind Nebula with AGILE

Pulsars are known to power winds of relativistic particles that can produce bright nebulae by interacting with the surrounding medium. These pulsar wind nebulae (PWNe) are observed in the radio, optical, x-rays and, in some cases, also at TeV energies, but the lack of information in the gamma-ray band prevents from drawing a comprehensive multiwavelength picture of their phenomenology and emission mechanisms. Using data from the AGILE satellite, we detected the Vela pulsar wind nebula in the energy range from 100 MeV to 3 GeV. This result constrains the particle population responsible for the GeV emission, probing multivavelength PWN models, and establishes a class of gamma-ray emitters that could account for a fraction of the unidentified Galactic gamma-ray sources.Comment: Accepted by Science; first published online on December 31, 2009 in Science Express. Science article and Supporting Online Material are available at http://www.sciencemag.or

Responsible data science: impartiality, accuracy, confidentiality and transparency of data

Introdução: no contexto Big Data, surge, como necessidade urgente, a aplicação de direitos individuais e empresariais e de normas regulatórias que resguardem a privacidade, a imparcialidade, a precisão e a transparência. Nesse cenário, a Responsible Data Science desponta como uma iniciativa que tem como base as diretrizes FACT, que correspondem à adoção de quatro princípios: imparcialidade, precisão, confidencialidade e transparência. Objetivo: abordar alternativas que podem assegurar a aplicação das diretrizes FACT. Metodologia: foi desenvolvida investigação exploratória e descritiva com abordagem qualitativa. Foram realizadas pesquisas nas bases de dados bibliográficas Web of Science, Scopus e pelo motor de busca Scholar Google com a utilização dos termos “Responsible Data Science”, “Fairness, Accuracy, Confidentiality, Transparency + Data Science”, FACT e FAT relacionados com Data Science. Resultados: a Responsible Data Science desponta como uma iniciativa que tem como base as diretrizes FACT, que correspondem à adoção dos princípios: imparcialidade, precisão, confidencialidade e transparência. Para a implementação dessas diretrizes, deve-se considerar o uso de técnicas e abordagens que estão sendo desenvolvidas pela Green Data Science. Conclusões: concluiu-se que a Green Data Science e as diretrizes FACT contribuem significativamente para a salvaguarda dos direitos individuais, não sendo necessário recorrer a medidas que impeçam o acesso e a reutilização de dados. Os desafios para implementar as diretrizes FACT requerem estudos, condição sine qua non para que as ferramentas para análise e disseminação dos dados sejam desenvolvidas ainda na fase de concepção de metodologias.Introduction: In the Big Data context, as an urgent need arises the application of individual and corporate rights and regulatory standards that safeguard privacy, impartiality, accuracy and transparency. In this scenario, Responsible Data Science emerges as an initiative based on the FACT guidelines, which correspond to the adoption of four principles: impartiality, accuracy, confidentiality and transparency. Objective: To address alternatives that can ensure the application of the FACT guidelines. Methodology: An exploratory and descriptive research with a qualitative approach was developed. Searches were performed on the Web of Science, Scopus, and Scholar Google search engines using Responsible Data Science, Fairness, Accuracy, Confidentiality, Transparency Data Science, FACT, and FAT related to Data Science. Results: Responsible Data Science emerges as an initiative based on the FACT guidelines, which correspond to the adoption of the principles: impartiality, accuracy, confidentiality and transparency. In implementing these guidelines, consideration should be given to the use of techniques and approaches being developed by Green Data Science. Conclusions: It is concluded that Green Data Science and the FACT guidelines contribute significantly to safeguarding individual rights and that no measures need to be taken to prevent access and reuse of data. Challenges for implementing the FACT guidelines require studies, sine qua non conditions for tools for data analysis and dissemination to be developed at the design stage of methodologies.Introducción: en el contexto de Big Data, como una necesidad urgente surge la aplicación de los derechos individuales y corporativos y las normas reguladoras que salvaguardan la privacidad, imparcialidad, precisión y transparencia. En este escenario, Responsible Data Science surge como una iniciativa basada en las pautas de FACT, que corresponden a la adopción de cuatro principios: imparcialidad, precisión, confidencialidad y transparencia. Objetivo: abordar alternativas que puedan garantizar la aplicación de las pautas de FACT. Metodología: se desarrolló una investigación exploratoria y descriptiva con un enfoque cualitativo. Las búsquedas se realizaron en los motores de búsqueda de Web of Science, Scopus y Scholar Google utilizando los términos "Ciencia de datos responsable", "Justicia, precisión, confidencialidad, transparencia + ciencia de datos", FACT y FAT relacionados con ciência de los datos. Resultados: Responsible Data Science surge como una iniciativa basada en los lineamientos de FACT, que corresponden a la adopción de los principios: imparcialidad, precisión, confidencialidad y transparencia. Al implementar estas pautas, se debe considerar el uso de técnicas y enfoques desarrollados por Green Data Science. Conclusiones: Se concluye que Green Data Science y las pautas FACT contribuyen significativamente a salvaguardar los derechos individuales y que no es necesario tomar medidas para evitar el acceso y la reutilización de datos. Los desafíos para implementar las pautas FACT requieren estudios, condiciones sine qua non para desarrollar herramientas para el análisis y la difusión de datos en la etapa de diseño de las metodologias

Introduction to the special issue on social mining and big data ecosystem for open, responsible data science

n/

Data sciences and teaching methods—learning

Data Science (DS) is an interdisciplinary field responsible for extracting knowledge from the data. This discipline is particularly complex in the face of Big Data: large volumes of data make it difficult to store, process and analyze with standard computer science technologies. The new revolution in Data Science is already changing the way we do business, healthcare, politics, education and innovation. This article describes three different teaching and learning models for Data Science, inspired by the experiential learning paradigm

Guidelines for submitting data to the National Space Science Data Center

The mission of the National Space Science Data Center (NSSDC) is to disseminate space science data for further analysis beyond that provided by the principal investigators (PIs) or team leaders (TLs) and their coworkers. Consequently, the NSSDC is responsible for the acquisition, organization, storage, retrieval, announcement, and distribution of scientific data obtained mainly from satellites and spacecraft. Any scientist may acquired data from the NSSDC and use them in further studies, either alone or in conjunction with data from ground-based or spacecraft experiments. With the responsibility for archiving data is the concomitant responsibility for distributing the documentation necessary to make those data usable. Since the group most knowledgeable about a particular experiment and its data is the PI or TL and his coworkers, and since the NSSDC cannot possibly supply the qualified personnel needed to write this documentation comprehensively, it is the responsibility of the PI or TL to provide the essential documentation. The NSSDC will support this effort by defining what is needed, by reviewing what is provided, and by reproducing and distributing the resulting documentation with the data. For a high-use data set, the NSSDC may publish the documentation as a Data Users Note; for a low-use data set, the NSSDC may distribute a Xerox, microfilm, or microfiche copy of the documentation