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Found 102 result(s)
The tree of life links all biodiversity through a shared evolutionary history. This project will produce the first online, comprehensive first-draft tree of all 1.8 million named species, accessible to both the public and scientific communities. Assembly of the tree will incorporate previously-published results, with strong collaborations between computational and empirical biologists to develop, test and improve methods of data synthesis. This initial tree of life will not be static; instead, we will develop tools for scientists to update and revise the tree as new data come in. Early release of the tree and tools will motivate data sharing and facilitate ongoing synthesis of knowledge.
The nationally recognized National Cancer Database (NCDB)—jointly sponsored by the American College of Surgeons and the American Cancer Society—is a clinical oncology database sourced from hospital registry data that are collected in more than 1,500 Commission on Cancer (CoC)-accredited facilities. NCDB data are used to analyze and track patients with malignant neoplastic diseases, their treatments, and outcomes. Data represent more than 70 percent of newly diagnosed cancer cases nationwide and more than 34 million historical records.
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Silkworm Pathogen Database (SilkPathDB) is a comprehensive resource for studying on pathogens of silkworm, including microsporidia, fungi, bacteria and virus. SilkPathDB provides access to not only genomic data including functional annotation of genes and gene products, but also extensive biological information for gene expression data and corresponding researches. SilkPathDB will be help with researches on pathogens of silkworm as well as other Lepidoptera insects.
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Veterinar – Electronic Repository of Research and Scientific Papers is the institutional digital repository of the University of Belgrade - Faculty of Veterinary Medicine. It provides open access to publications and other research outputs resulting from the projects implemented by the Faculty of Veterinary Medicine. The software platform of the repository is adapted to the modern standards applied in the dissemination of scientific publications and is compatible with international infrastructure in this field.
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KU Leuven RDR (pronounced "RaDaR") is KU Leuven's Research Data Repository, built on Dataverse.org - open source repository software built by Harvard University. RDR gives KU Leuven researchers a one-stop platform to upload, describe, and share their research data, conveniently and with support from university staff.
Academic Commons provides open, persistent access to the scholarship produced by researchers at Columbia University, Barnard College, Jewish Theological Seminary, Teachers College, and Union Theological Seminary. Academic Commons is a program of the Columbia University Libraries. Academic Commons accepts articles, dissertations, research data, presentations, working papers, videos, and more.
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bonndata is the institutional, FAIR-aligned and curated, cross-disciplinary research data repository for the publication of research data for all researchers at the University of Bonn. The repository is fully embedded into the University IT and Data Center and curated by the Research Data Service Center (https://www.forschungsdaten.uni-bonn.de/en). The software that bonndata is based on is the open source software Dataverse (https://dataverse.org)
The Research Collection is ETH Zurich's publication platform. It unites the functions of a university bibliography, an open access repository and a research data repository within one platform. Researchers who are affiliated with ETH Zurich, the Swiss Federal Institute of Technology, may deposit research data from all domains. They can publish data as a standalone publication, publish it as supplementary material for an article, dissertation or another text, share it with colleagues or a research group, or deposit it for archiving purposes. Research-data-specific features include flexible access rights settings, DOI registration and a DOI preview workflow, content previews for zip- and tar-containers, as well as download statistics and altmetrics for published data. All data uploaded to the Research Collection are also transferred to the ETH Data Archive, ETH Zurich’s long-term archive.
The Gene database provides detailed information for known and predicted genes defined by nucleotide sequence or map position. Gene supplies gene-specific connections in the nexus of map, sequence, expression, structure, function, citation, and homology data. Unique identifiers are assigned to genes with defining sequences, genes with known map positions, and genes inferred from phenotypic information. These gene identifiers are used throughout NCBI's databases and tracked through updates of annotation. Gene includes genomes represented by NCBI Reference Sequences (or RefSeqs) and is integrated for indexing and query and retrieval from NCBI's Entrez and E-Utilities systems.
OrthoMCL is a genome-scale algorithm for grouping orthologous protein sequences. It provides not only groups shared by two or more species/genomes, but also groups representing species-specific gene expansion families. So it serves as an important utility for automated eukaryotic genome annotation. OrthoMCL starts with reciprocal best hits within each genome as potential in-paralog/recent paralog pairs and reciprocal best hits across any two genomes as potential ortholog pairs. Related proteins are interlinked in a similarity graph. Then MCL (Markov Clustering algorithm,Van Dongen 2000; www.micans.org/mcl) is invoked to split mega-clusters. This process is analogous to the manual review in COG construction. MCL clustering is based on weights between each pair of proteins, so to correct for differences in evolutionary distance the weights are normalized before running MCL.
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Exposures in the period from conception to early childhood - including fetal growth, cell division, and organ functioning - may have long-lasting impact on health and disease susceptibility. To investigate these issues the Danish National Birth Cohort (Better health in generations) was established. A large cohort of pregnant women with long-term follow-up of the offspring was the obvious choice because many of the exposures of interest cannot be reconstructed with suffcient validity back in time. The study needed to be large, and the aim was to recruit 100,000 women early in pregnancy, and to continue follow-up for decades. Exposure information was collected by computer-assisted telephone interviews with the women twice during pregnancy and when their children were six and 18 months old. Participants were also asked to fill in a self-administered food frequency questionnaire in mid-pregnancy. Furthermore, a biological bank has been set up with blood taken from the mother twice during pregnancy and blood from theumbilical cord taken shortly after birth.
SUNScholarData is an institutional research data repository which can be used for the registration, archival storage, sharing and dissemination of research data produced or collected in relation to research conducted under the auspices of Stellenbosch University. The repository has a public interface which can be used for finding content. It also has private user accounts which can be used by Stellenbosch University users in order to upload, share or publish their research data. In addition to this Stellenbosch University researchers can also use SUNScholarData in order to collaborate with researchers from other institutions whilst working on their research projects. The repository creates a medium through which Stellenbosch University’s research data can be made findable and accessible. It also facilitates the interoperability and re-usability of the university’s research data.
The CancerData site is an effort of the Medical Informatics and Knowledge Engineering team (MIKE for short) of Maastro Clinic, Maastricht, The Netherlands. Our activities in the field of medical image analysis and data modelling are visible in a number of projects we are running. CancerData is offering several datasets. They are grouped in collections and can be public or private. You can search for public datasets in the NBIA (National Biomedical Imaging Archive) image archives without logging in.
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DataverseNO (https://dataverse.no) is a curated, FAIR-aligned national generic repository for open research data from all academic disciplines. DataverseNO commits to facilitate that published data remain accessible and (re)usable in a long-term perspective. The repository is owned and operated by UiT The Arctic University of Norway. DataverseNO accepts submissions from researchers primarily from Norwegian research institutions. Datasets in DataverseNO are grouped into institutional collections as well as special collections. The technical infrastructure of the repository is based on the open source application Dataverse (https://dataverse.org), which is developed by an international developer and user community led by Harvard University.
The Cancer Genome Atlas (TCGA) Data Portal provides a platform for researchers to search, download, and analyze data sets generated by TCGA. It contains clinical information, genomic characterization data, and high level sequence analysis of the tumor genomes. The Data Coordinating Center (DCC) is the central provider of TCGA data. The DCC standardizes data formats and validates submitted data.
This database serves forest tree scientists by providing online access to hardwood tree genomic and genetic data, including assembled reference genomes, transcriptomes, and genetic mapping information. The web site also provides access to tools for mining and visualization of these data sets, including BLAST for comparing sequences, Jbrowse for browsing genomes, Apollo for community annotation and Expression Analysis to build gene expression heatmaps.
Our research focuses mainly on the past and present bio- and geodiversity and the evolution of animals and plants. The Information Technology Center of the Staatliche Naturwissenschaftliche Sammlungen Bayerns is the institutional repository for scientific data of the SNSB. Its major tasks focus on the management of bio- and geodiversity data using different kinds of information technological structures. The facility guarantees a sustainable curation, storage, archiving and provision of such data.
The WashU Research Data repository accepts any publishable research data set, including textual, tabular, geospatial, imagery, computer code, or 3D data files, from researchers affiliated with Washington University in St. Louis. Datasets include metadata and are curated and assigned a DOI to align with FAIR data principles.
This interface provides access to several types of data related to the Chesapeake Bay. Bay Program databases can be queried based upon user-defined inputs such as geographic region and date range. Each query results in a downloadable, tab- or comma-delimited text file that can be imported to any program (e.g., SAS, Excel, Access) for further analysis. Comments regarding the interface are encouraged. Questions in reference to the data should be addressed to the contact provided on subsequent pages.
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University of Warsaw Research Data Repository aims to collect, archive, preserve and make available all types of research data. Storing and making data available is possible for users affiliated with the University of Warsaw, Poland, or those involved in projects carried out in partnership with the University of Warsaw. Browsing and downloading publicly available research data is open to all interested.