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Through the generous support of The Polonsky Foundation, this project made 1.5 million digitized pages freely available. Portions of the Biblioteca Apostolica Vaticana (Vatican Library) and the Bodleian Libraries’ collections of Hebrew manuscripts, Greek manuscripts, and incunabula were selected for digitization by a team of scholars and curators from around the world. The selection process was informed by a balance of scholarly and practical concerns; conservation staff at the Bodleian and Vatican Libraries worked with curators to assess not only the significance of the content, but the physical condition of the items, prioritizing items that are robust enough to withstand being transported to the imaging studio and handled by the photographers. In order to preserve the integrity and completeness of the manuscript collections, the libraries also agreed to digitize whole collections where appropriate. While the Vatican and the Bodleian had been creating digital images from our collections for a number of years, this project provided an opportunity for both libraries to increase the scale of their digitization services. In both cases, this meant significant investments in the equipment, infrastructure and people that make digitization possible. Over the course of this project, both libraries also revealed information about their digitization techniques and methods.

OKCAM (Ontology-based Knowledgebase for Cell Adhesion Molecules) is an online resource for human genes known or predicted to be related to the processes of cell adhesion. These genes include members of the cadherin, immunoglobulin/FibronectinIII (IgFn), integrin, neurexin, neuroligin, and catenin families.

LIPID Metabolites And Pathways Strategy (LIPID MAPS®) is a multi-institutional supported website and database that provides access to a large number of globally used lipidomics resources. LIPID MAPS® has internationally led the field of lipid curation, classification, and nomenclature since 2003. We strive to produce new open-access databases, informatics tools and lipidomics-focused training activities will be generated and made publicly available for researchers studying lipids in health and disease. LIPID MAPS® is currently funded by a multi-institutional grant from Wellcome, held jointly by Cardiff University, University of California San Diego, the Babraham Institute Cambridge, and Swansea University, as well as an Innovation Study funded by ELIXIR. This current phase will see that LIPID MAPS® is maintained and importantly, further developed in line with the global demand and development of lipidomics. LIPID MAPS® has an internationally recognized classification system and the largest curated lipid structure database in the world.

BuG@Sbase is a microbial gene expression and comparative genomic database containing microarray datasets.

This site provides access to the output of the institution. Users may set up RSS feeds to be alerted to new content. The interface is in English.

NODE (The National Omics Data Encyclopedia) provides an integrated, compatible, comparable, and scalable multi-omics resource platform that supports flexible data management and effective data release. NODE uses a hierarchical data architecture to support storage of muti-omics data including sequencing data, MS based proteomics data, MS or NMR based metabolomics data, and fluorescence imaging data. Launched in early 2017, NODE has collected and published over 900 terabytes of omics data for researchers from China and all over the world in last three years, 22% of which contains multiple omics data. NODE provides functions around the whole life cycle of omics data, from data archive, data requests/responses to data sharing, data analysis, data review and publish.

mirDNMR is a database for the collection of gene-centered background DNMRs obtained from different methods and population variation data. The database has the following functions: (i) browse and search the background DNMRs of each gene predicted by four different methods, including GC content (DNMR-GC), sequence context (DNMR-SC), multiple factors (DNMR-MF) and local DNA methylation level (DNMR-DM); (ii) search variant frequencies in publicly available databases, including ExAC, ESP6500, UK10K, 1000G and dbSNP and (iii) investigate the DNM burden to prioritize candidate genes based on the four background DNMRs using three statistical methods (TADA, Binomial and Poisson test). In conclusion, mirDNMR can be widely used to identify the genetic basis of sporadic genetic diseases.