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Found 1060 result(s)
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The World Data Centre section provides software and data catalogue information and data produced by IPS Radio and Space Services over the past few past decades. You can download data files, plot graphs from data files, check data availability, retrieve data sets and station information.
dbEST is a division of GenBank that contains sequence data and other information on "single-pass" cDNA sequences, or "Expressed Sequence Tags", from a number of organisms. Expressed Sequence Tags (ESTs) are short (usually about 300-500 bp), single-pass sequence reads from mRNA (cDNA). Typically they are produced in large batches. They represent a snapshot of genes expressed in a given tissue and/or at a given developmental stage. They are tags (some coding, others not) of expression for a given cDNA library. Most EST projects develop large numbers of sequences. These are commonly submitted to GenBank and dbEST as batches of dozens to thousands of entries, with a great deal of redundancy in the citation, submitter and library information. To improve the efficiency of the submission process for this type of data, we have designed a special streamlined submission process and data format. dbEST also includes sequences that are longer than the traditional ESTs, or are produced as single sequences or in small batches. Among these sequences are products of differential display experiments and RACE experiments. The thing that these sequences have in common with traditional ESTs, regardless of length, quality, or quantity, is that there is little information that can be annotated in the record. If a sequence is later characterized and annotated with biological features such as a coding region, 5'UTR, or 3'UTR, it should be submitted through the regular GenBank submissions procedure (via BankIt or Sequin), even if part of the sequence is already in dbEST. dbEST is reserved for single-pass reads. Assembled sequences should not be submitted to dbEST. GenBank will accept assembled EST submissions for the forthcoming TSA (Transcriptome Shotgun Assembly) division. The individual reads which make up the assembly should be submitted to dbEST, the Trace archive or the Short Read Archive (SRA) prior to the submission of the assemblies.
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.
The EUROLAS Data Center (EDC) is one of the two data centers of the International Laser Ranging Service (ILRS). It collects, archives and distributes tracking data, predictions and other tracking relevant information from the global SLR network. Additionally EDC holds a mirror of the official Web-Pages of the ILRS at Goddard Space Flight Center (GSFC). And as result of the activities of the Analysis Working Group (AWG) of the ILRS, DGFI has been selected as analysis centers (AC) and as backup combination center (CC). This task includes weekly processing of SLR observations to LAGEOS-1/2 and ETALON-1/2 to compute station coordinates and earth orientation parameters. Additionally the combination of SLR solutions from the various analysis centres to a combinerd ILRS SLR solution.
The USDA Economics, Statistics and Market Information System contains reports and datasets of multiple agencies within the United States Department of Agriculture, including the Agricultural Marketing Service, the Economic Research Service, the Foreign Agricultural Service, the National Agricultural Statistics Service, and the World Agricultural Outlook Board. Historical and current reports and datasets are included.
This Animal Quantitative Trait Loci (QTL) database (Animal QTLdb) is designed to house all publicly available QTL and trait mapping data (i.e. trait and genome location association data; collectively called "QTL data" on this site) on livestock animal species for easily locating and making comparisons within and between species. New database tools are continuely added to align the QTL and association data to other types of genome information, such as annotated genes, RH / SNP markers, and human genome maps. Besides the QTL data from species listed below, the QTLdb is open to house QTL/association date from other animal species where feasible. Note that the JAS along with other journals, now require that new QTL/association data be entered into a QTL database as part of their publication requirements.
The Plant Metabolic Network (PMN) provides a broad network of plant metabolic pathway databases that contain curated information from the literature and computational analyses about the genes, enzymes, compounds, reactions, and pathways involved in primary and secondary metabolism in plants. The PMN currently houses one multi-species reference database called PlantCyc and 22 species/taxon-specific databases.
The Scholarly Database (SDB) at Indiana University aims to serve researchers and practitioners interested in the analysis, modeling, and visualization of large-scale scholarly datasets. The online interface provides access to six datasets: MEDLINE papers, registered Clinical Trials, U.S. Patent and Trademark Office patents (USPTO), National Science Foundation (NSF) funding, National Institutes of Health (NIH) funding, and National Endowment for the Humanities funding – over 26 million records in total.
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The India Water Portal is a web-based platform for sharing water management knowledge in India amongst practitioners and the general public. It includes approximately 200 datasets that can be browsed by data type, location, time, and other metadata. Data include rainfall, watersheds, groundwater, water quality, and irrigation.
4TU.ResearchData, previously known as 3TU.Datacentrum, is an archive for research data. It offers the knowledge, experience and the tools to share and safely store scientific research data in a standardized, secure and well-documented manner. 4TU.Centre for Research Data provides the research community with: Advice and support on data management; A long-term archive for scientific research data; Support for current research projects; Tools for reusing research data.
---<<< This repository is no longer available. This record is out-dated >>>--- The ONS challenge contains open solubility data, experiments with raw data from different scientists and institutions. It is part of the The Open Notebook Science wiki community, ideally suited for community-wide collaborative research projects involving mathematical modeling and computer simulation work, as it allows researchers to document model development in a step-by-step fashion, then link model prediction to experiments that test the model, and in turn, use feeback from experiments to evolve the model. By making our laboratory notebooks public, the evolutionary process of a model can be followed in its totality by the interested reader. Researchers from laboratories around the world can now follow the progress of our research day-to-day, borrow models at various stages of development, comment or advice on model developments, discuss experiments, ask questions, provide feedback, or otherwise contribute to the progress of science in any manner possible.
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The project brings together national key players providing environmentally related biological data and services to develop the ‘German Federation for Biological Data' (GFBio). The overall goal is to provide a sustainable, service oriented, national data infrastructure facilitating data sharing and stimulating data intensive science in the fields of biological and environmental research.
Stanford Network Analysis Platform (SNAP) is a general purpose network analysis and graph mining library. It is written in C++ and easily scales to massive networks with hundreds of millions of nodes, and billions of edges. It efficiently manipulates large graphs, calculates structural properties, generates regular and random graphs, and supports attributes on nodes and edges. SNAP is also available through the NodeXL which is a graphical front-end that integrates network analysis into Microsoft Office and Excel. The SNAP library is being actively developed since 2004 and is organically growing as a result of our research pursuits in analysis of large social and information networks. Largest network we analyzed so far using the library was the Microsoft Instant Messenger network from 2006 with 240 million nodes and 1.3 billion edges. The datasets available on the website were mostly collected (scraped) for the purposes of our research. The website was launched in July 2009.
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CBS offers Comprehensive public databases of DNA- and protein sequences, macromolecular structure, g ene and protein expression levels, pathway organization and cell signalling, have been established to optimise scientific exploitation of the explosion of data within biology. Unlike many other groups in the field of biomolecular informatics, Center for Biological Sequence Analysis directs its research primarily towards topics related to the elucidation of the functional aspects of complex biological mechanisms. Among contemporary bioinformatics concerns are reliable computational interpretation of a wide range of experimental data, and the detailed understanding of the molecular apparatus behind cellular mechanisms of sequence information. By exploiting available experimental data and evidence in the design of algorithms, sequence correlations and other features of biological significance can be inferred. In addition to the computational research the center also has experimental efforts in gene expression analysis using DNA chips and data generation in relation to the physical and structural properties of DNA. In the last decade, the Center for Biological Sequence Analysis has produced a large number of computational methods, which are offered to others via WWW servers.
The Objectively Analyzed air-sea Fluxes (OAFlux) project is a research and development project focusing on global air-sea heat, moisture, and momentum fluxes. The project is committed to produce high-quality, long-term, global ocean surface forcing datasets from the late 1950s to the present to serve the needs of the ocean and climate communities on the characterization, attribution, modeling, and understanding of variability and long-term change in the atmosphere and the oceans. - Links überprüft 14.6.2017 Re
BSRN is a project of the Radiation Panel (now the Data and Assessment Panel) from the Global Energy and Water Cycle Experiment (GEWEX) under the umbrella of the World Climate Research Programme (WCRP). It is the global baseline network for surface radiation for the Global limate Observing System (GCOS), contributing to the Global Atmospheric Watch (GAW), and forming a ooperative network with the Network for the Detection of Atmospheric Composition Change NDACC).
The objective of this Research Coordination Network project is to develop an international network of researchers who use genetic methodologies to study the ecology and evolution of marine organisms in the Indo-Pacific to share data, ideas and methods. DIPnet was created to advance genetic diversity research in the Indo-Pacific by aggregating population genetic metadata into a searchable database (GeOME).
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.
The NCAA Student-Athlete Experiences Data Archive provides access to data about student athletes and will grow to include a handful of user-friendly data collections related to graduation rates; team-level Academic Progress Rates in Division I; and individual-level data on the experiences of current and former student-athletes from the NCAA's Growth, Opportunities, Aspirations and Learning of Students in college study (GOALS), and the Study of College Outcomes and Recent Experiences (SCORE). In the long run, the NCAA expects to follow this initial release with the publication of as much data as possible from its archives. The data is used by college presidents, athletic personnel, faculty, student-athlete groups, media members, and researchers in looking at issues related to intercollegiate athletics and higher education.
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heidICON is provided by Heidelberg University Library and is the "Virtual Slide Collection" in progress of organization of Heidelberg University. In addition to record graphic material on current interest for research and teaching, the University departments and institutes can digitize and transfer their already existing slide collections.
The DOE Data Explorer (DDE) is an information tool to help you locate DOE's collections of data and non-text information and, at the same time, retrieve individual datasets within some of those collections. It includes collection citations prepared by the Office of Scientific and Technical Information, as well as citations for individual datasets submitted from DOE Data Centers and other organizations.
ScholarSphere is a secure repository service enabling the Penn State community to share its research and scholarly work with a worldwide audience. Faculty, staff, and students can use ScholarSphere to collect their work in one location and create a durable and citeable record of their papers, presentations, publications, data sets, or other scholarly creations. Through this service, Penn State researchers can also comply with grant-funding-agency requirements for sharing and managing research data.