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THIN is a medical data collection scheme that collects anonymised patient data from its members through the healthcare software Vision. The UK Primary Care database contains longitudinal patient records for approximately 6% of the UK Population. The anonymised data collection, which goes back to 1994, is nationally representative of the UK population.
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The National Microbial Resource Center (NMRC) is an important part of the national science and technology resources sharing service platform, responsible for the research, conservation, management and sharing of national microbial strain resources, ensuring the strategic security and sustainable use of microbial strain resources, and providing support for scientific and technological innovation, industrial development and social progress. The main tasks of the NMRC are: to collect, organize and preserve microbial strain resources around national needs and scientific research; to undertake the task of remitting, organizing and preserving strain resources resulting from the implementation of science and technology projects; to be responsible for the development and improvement of microbial strain resource standards, and to standardize and guide the development of microbial strain resources in various fields. The company is responsible for the development and improvement of microbial strain resource standards, standardizing and guiding the protection and utilization of microbial strain resources in various fields; building and maintaining the national strain resource online service system, and carrying out social sharing of physical and information resources of strains; developing key common technologies, creating new resources, and carrying out customized services according to innovative needs; carrying out scientific popularization for the society; carrying out international exchange and cooperation on strain resources, participating in relevant international academic organizations, and safeguarding national interests and Security
The International Food Policy Research Institute (IFPRI) seeks sustainable solutions for ending hunger and poverty. In collaboration with institutions throughout the world, IFPRI is often involved in the collection of primary data and the compilation and processing of secondary data. The resulting datasets provide a wealth of information at the local (household and community), national, and global levels. IFPRI freely distributes as many of these datasets as possible and encourages their use in research and policy analysis. IFPRI Dataverse contains following dataverses: Agricultural Science and Knowledge Indicators - ASTI, HarvestChoice, Statistics on Public Expenditures for Economic Development - SPEED, International Model for Policy Analysis of Agricultural Commodities and Trade - IMPACT, Africa RISING Dataverse and Food Security Portal Dataverse.