Data Management Plans for the Photon and Neutron Communities

This paper presents the interdisciplinary work undertaken by six photon and neutron research institutes to develop a common approach and implementation to data management plans in the context of the Photon and Neutron Open Science Cloud project. The paper introduces the context of the project and then describes the approach based on the concept of active data management plans and a common template of questions. The comparison of and choice of a common tool, the Data Stewardship Wizard, is then described. Finally, the deployment of the tool and its integration in the different facilities’ data workflows is described in more detail. The paper concludes with a summary of the achievements, the lessons learned, and which issues still need to be addressed

Des outils de support à l'investigation scientifique : intégration dans les plates-formes SCY-Lab et LabBook

DémonstrationEIAH 2011 : Environnements Informatiques d'Apprentissage Humain. Mons, Belgique, 25-27 mai 2011International audienceno abstrac

Small Animal Shanoir (SAS) A Cloud-Based Solution for Managing Preclinical MR Brain Imaging Studies

International audienc

Outcomes in Patients with Acute Myocardial Infarction and Known Sleep Apnea: A Nationwide Analysis

Background. Sleep apnea (SA) is a common breathing disorder characterized by repetitive upper airway narrowing and closure. Although SA has been demonstrated to be an independent risk factor for all-cause mortality, the direct contribution of SA to worse cardiovascular prognosis may be difficult to evaluate, and its independent association with the different types of cardiovascular outcomes may be debated, particularly in the context of patients with acute myocardial infarction (AMI). The aim of this study was to assess the impact of known SA on the outcomes of hospitalized patients who have had an AMI by analyzing 10-year data collected from a national registry. Methods. This longitudinal cohort study was based on the national hospitalization database that covers hospital care for the entire French population, including all patients admitted with AMI from January 2010 to June 2019. The clinical outcomes for the analysis were as follows: all-cause death, cardiovascular death, ischemic stroke, new-onset atrial fibrillation (FA), and re-hospitalization for heart failure (HF). Results. Among the 797,212 patients who presented with an AMI (528,351 men and 268,861 women), 37,075 (4.7%) had documented SA. During follow-up (mean [SD] 1.8 [2.4] years, median [interquartile range] 0.7 [0.1–3.1] years), 163,845 deaths (of which 85,649 were cardiovascular deaths), 20,168 ischemic strokes, 58,498 new-onset AF, and 92,381 rehospitalizations due to HF were recorded. Patients with known SA had a worse prognosis in the short and medium term, but after adjusting for all covariables, SA was only independently associated with a higher risk of rehospitalization for HF and new-onset AF in men and women. Conclusion. Data from our large nationwide analysis confirm that known SA is associated with poor cardiovascular outcomes in patients who have had an AMI. However, this impact is tem-pered when the model is adjusted for age, cardiovascular risk, or other covariables. Further studies need to be conducted to assess the independent impact of SA on the prognosis of patients with AMI

MASSIF-1: a beamline dedicated to the fully automatic characterization and data collection from crystals of biological macromolecules

International audienceMASSIF-1 (ID30A-1) is an ESRF undulator beamline operating at a fixed wavelength of 0.969 angstrom (12.8 keV) that is dedicated to the completely automatic characterization of and data collection from crystals of biological macromolecules. The first of the ESRF Upgrade MASSIF beamlines to be commissioned, it has been open since September 2014, providing a unique automated data collection service to academic and industrial users. Here, the beamline characteristics and details of the new service are outline

Cohort Creation and Visualization Using Graph Model in the PREDIMED Health Data Warehouse

International audienceGrenoble Alpes University Hospital (CHUGA) is currently deploying a health data warehouse called PREDIMED [1], a platform designed to integrate and analyze for research, education and institutional management the data of patients treated at CHUGA. PREDIMED contains healthcare data, administrative data and, potentially, data from external databases. PREDIMED is hosted by the CHUGA Information Systems Department and benefits from its strict security rules. CHUGA’s institutional project PREDIMED aims to collaborate with similar projects in France and worldwide. In this paper, we present how the data model defined to implement PREDIMED at CHUGA is useful for medical experts to interactively build a cohort of patients and to visualize this cohort

Learning by creating and exchanging objects: the SCY experience

International audienceno abstrac