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>>>!!!<<< SMD has been retired. After approximately fifteen years of microarray-centric research service, the Stanford Microarray Database has been retired. We apologize for any inconvenience; please read below for possible resolutions to your queries. If you are looking for any raw data that was directly linked to SMD from a manuscript, please search one of the public repositories. NCBI Gene Expression Omnibus EBI ArrayExpress All published data were previously communicated to one (or both) of the public repositories. Alternatively, data for publications between 1997 and 2004 were likely migrated to the Princeton University MicroArray Database, and are accessible there. If you are looking for a manuscript supplement (i.e. from a domain other than smd.stanford.edu), perhaps try searching the Internet Archive: Wayback Machine https://archive.org/web/ . >>>!!!<<< The Stanford Microarray Database (SMD) is a DNA microarray research database that provides a large amount of data for public use.
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.
<<<!!!<<< checked 20.03.2017 SumsDB was offline; for more information and archive see http://brainvis.wustl.edu/sumsdb/ >>>!!!>>> SumsDB (the Surface Management System DataBase) is a repository of brain-mapping data (surfaces & volumes; structural & functional data) from many laboratories.
OpenWorm aims to build the first comprehensive computational model of the Caenorhabditis elegans (C. elegans), a microscopic roundworm. With only a thousand cells, it solves basic problems such as feeding, mate-finding and predator avoidance. Despite being extremely well studied in biology, this organism still eludes a deep, principled understanding of its biology. We are using a bottom-up approach, aimed at observing the worm behaviour emerge from a simulation of data derived from scientific experiments carried out over the past decade. To do so we are incorporating the data available in the scientific community into software models. We are engineering Geppetto and Sibernetic, open-source simulation platforms, to be able to run these different models in concert. We are also forging new collaborations with universities and research institutes to collect data that fill in the gaps All the code we produce in the OpenWorm project is Open Source and available on GitHub.
<<<!!!<<< Efforts to obtain renewed funding after 2008 were unfortunately not successful. PANDIT has therefore been frozen since November 2008, and its data are not updated since September 2005 when version 17.0 was released (corresponding to Pfam 17.0). The existing data and website remain available from these pages, and should remain stable and, we hope, useful. >>>!!!>>> PANDIT is a collection of multiple sequence alignments and phylogenetic trees. It contains corresponding amino acid and nucleotide sequence alignments, with trees inferred from each alignment. PANDIT is based on the Pfam database (Protein families database of alignments and HMMs), and includes the seed amino acid alignments of most families in the Pfam-A database. DNA sequences for as many members of each family as possible are extracted from the EMBL Nucleotide Sequence Database and aligned according to the amino acid alignment. PANDIT also contains a further copy of the amino acid alignments, restricted to the sequences for which DNA sequences were found.
The SuiteSparse Matrix Collection is a large and actively growing set of sparse matrices that arise in real applications. The Collection is widely used by the numerical linear algebra community for the development and performance evaluation of sparse matrix algorithms. It allows for robust and repeatable experiments. Its matrices cover a wide spectrum of domains, include those arising from problems with underlying 2D or 3D geometry (as structural engineering, computational fluid dynamics, model reduction, electromagnetics, semiconductor devices, thermodynamics, materials, acoustics, computer graphics/vision, robotics/kinematics, and other discretizations) and those that typically do not have such geometry (optimization, circuit simulation, economic and financial modeling, theoretical and quantum chemistry, chemical process simulation, mathematics and statistics, power networks, and other networks and graphs.
Data.gov increases the ability of the public to easily find, download, and use datasets that are generated and held by the Federal Government. Data.gov provides descriptions of the Federal datasets (metadata), information about how to access the datasets, and tools that leverage government datasets