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Health Level Seven International (HL7®) and the Observational Health Data Sciences and Informatics (OHDSI) network announced a collaboration to address the sharing and tracking of data in the healthcare and research industries by creating a single common data model. The organizations will integrate HL7 Fast Healthcare Interoperability Resources (FHIR®) and OHDSI s Observational Medical Outcomes Partnership (OMOP) common data model to achieve this goal.
HL7 International CEO Dr. Charles Jaffe, M.D., Ph.D., underscored the significance of this partnership. The Covid-19 pandemic has emphasized the need to share global health and research data. He continued, Collaboration with OHDSI is critical to solving this challenge and will help our mutual vision of a world in which everyone can securely access and use the right data when and where they need it.
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A huge volume of digital data has been harvested, stored and shared in the last few years - from sources such as social media, geolocation systems and aerial images from drones and satellites - giving researchers many new ways to study information and decrypt our world. In Switzerland, the Federal Statistical Office (FSO) has taken an interest in the big data revolution and the possibilities it offers to generate predictive statistics for the benefit of society.
Conventional methods such as censuses and surveys remain the benchmark for generating socio-economic indicators at the municipal, cantonal and national levels. But these methods can now be supplemented with secondary, mostly pre-existing data, from sources such as cell-phone subscriptions and credit cards. According to the FSO s 2017 Data Innovation Strategy, The goal of data innovation is to enhance the quality, scope and cost-efficiency of statistical products and to reduce the response burden on households and
In Subverting Privacy-Preserving GANs: Hiding Secrets in Sanitized Images, researchers at the NYU Tandon School of Engineering led by Siddharth Garg, professor of electrical and computer engineering, explored whether private data could still be recovered from images that had been sanitized by such deep-learning discriminators as privacy protecting GANs (PP-GANs).
Researchers in the U.S. and Germany decided to explore which pathways transport debris to the middle of the oceans, causing garbage patches, as well as the relative strengths of different subtropical gyres and how they influence long-term accumulation of debris. In Chaos, they report creating a model of the oceans surface dynamics from historical trajectories of surface buoys. Their model describes the probability of plastic debris being transported from one region to another.