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Found 6 result(s)

CottonGen

Cotton Database Resources

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CottonGen is a new cotton community genomics, genetics and breeding database being developed to enable basic, translational and applied research in cotton. It is being built using the open-source Tripal database infrastructure. CottonGen consolidates and expands the data from CottonDB and the Cotton Marker Database, providing enhanced tools for easy querying, visualizing and downloading research data.

China National GeneBank DataBase

国家基因库生命大数据平台

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The China National GeneBank database (CNGBdb) is a unified platform for biological big data sharing and application services. CNGBdb has now integrated a large amount of internal and external biological data from resources such as CNGB, NCBI, and the EBI. There are several sub-databases in CNGBdb, including literature, variation, gene, genome, protein, sequence, organism, project, sample, experiment, run, and assembly. Based on underlying big data and cloud computing technologies, it provides various data services, including archive, analysis, knowledge search, and management authorization of biological data. CNGBdb adopts data structures and standards of international omics, health, and medicine, such as The International Nucleotide Sequence Database Collaboration (INSDC), The Global Alliance for Genomics and Health GA4GH (GA4GH), Global Genome Biodiversity Network (GGBN), American College of Medical Genetics and Genomics (ACMG), and constructs standardized data and structures with wide compatibility. All public data and services provided by CNGBdb are freely available to all users worldwide. CNGB Sequence Archive (CNSA) is the bionomics data repository of CNGBdb. CNGB Sequence Archive (CNSA) is a convenient and efficient archiving system of multi-omics data in life science, which provides archiving services for raw sequencing reads and further analyzed results. CNSA follows the international data standards for omics data, and supports online and batch submission of multiple data types such as Project, Sample, Experiment/Run, Assembly, Variation, Metabolism, Single cell, and Sequence. Moreover, CNSA has achieved the correlation of sample entities, sample information, and analyzed data on some projects. Its data submission service can be used as a supplement to the literature publishing process to support early data sharing.CNGB Sequence Archive (CNSA) is a convenient and efficient archiving system of multi-omics data in the life science of CNGBdb, which provides archiving services for raw sequencing reads and further analyzed results. CNSA follows the international data standards for omics data, and supports online and batch submission of multiple data types such as Project, Sample, Experiment/Run, Assembly, Variation, Metabolism, Single cell, Sequence. Its data submission service can be used as a supplement to the literature publishing process to support early data sharing.

TreeSource

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Database of forestry research installations in Canada with a focus on tree-level datasets (dendrometry, physical properties, dendrochronology, phenology, etc.).

LIVIVO

ZB MED Search Portal for Life Sciences

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LIVIVO is an interdisciplinary search engine for literature and information in the field of life sciences. It is run by ZB MED – Information Centre for Life Sciences. LIVIVO automatically searches for the terms you enter in a central index of all the databases. The ZB MED Searchportal already provides a large amount of research data from DataCite data centres (e.g. Beijing Genomics Institute, Natural Environment Research Council) in the field of life sciences. These can be searched directly using the "Documenttype=research data" filter. A further integration of data from life science data repositories is planned.

National Center for Forestry and Grassland Genetic Resources

国家林业和草原种质资源库

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The National Center for Forestry and Grassland Genetic Resources (Forestry and Grassland Repository) consists of a series of in situ and ex situ repositories and ex situ repositories, including 15 in situ repositories, 137 ex situ repositories and 3 facility repositories (attached), all of which are recognized by the Seedling Department of the State Forestry and Grassland Administration or the National Forestry Germplasm Resource Platform to collect and preserve forest, grass, flower, bamboo and rattan germplasm resources, and to establish a big data system through standardization, digitization. The purpose of the Forestry and Grassland Resource Bank is to strengthen the germplasm resources of forests, grasses, flowers, bamboos and rattan. The purpose of the Forestry and Grass Resource Bank is to strengthen the collection and preservation of forestry germplasm resources and open sharing, and to promote sustainable use; the objective is to use ultra-low temperature freezing, genomics, artificial intelligence and other high technology to carry out long-term preservation, accurate identification and in-depth exploration of germplasm resources, and to achieve safe preservation and efficient use of germplasm resources. The Forestry and Grassland Resource Bank undertakes the rendezvous of scientific and technological projects in the forestry germplasm resource category. By building an integrated sharing service platform for germplasm resource production, academia and research, it improves the innovation and exploitation capacity of forestry germplasm resources, supports major national needs in scientific research, ecological construction and economic development, promotes the docking of resources and needs, and facilitates the use of resources and the transformation of results. It realizes information and physical sharing, so that forest germplasm resources can be safely preserved and scientifically utilized.

CIRAD Dataverse

Données de recherche du CIRAD

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The range of CIRAD's research has given rise to numerous datasets and databases associating various types of data: primary (collected), secondary (analysed, aggregated, used for scientific articles, etc), qualitative and quantitative. These "collections" of research data are used for comparisons, to study processes and analyse change. They include: genetics and genomics data, data generated by trials and measurements (using laboratory instruments), data generated by modelling (interpolations, predictive models), long-term observation data (remote sensing, observatories, etc), data from surveys, cohorts, interviews with players.