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http://www.unavco.org/

A non-profit university-governed consortium that facilitates geoscience research and education using geodesy. It rovides access to and submission of Geodetic GPS / GNSS Data, Geodetic Imaging Data, Strain and Seismic Borehole Data, and Meteorological Data. Data access web services/API provides the ability to use a command line interface to query metadata and obtain URLs to data and products. UNAVCO also provides a variety of software, including web applications, and desktop utilities for scientists, instructors, students, and others. Web-based data visualization and mapping tools provide users with the ability to view postprocessed data while web-based geodetic utilities provide ancillary information. Downloadable stand-alone software utilities include applications for configuring instruments, managing data collection, download and transfer, and performing computations on the raw data, e.g., data pre-processing or processing. The UNAVCO Facility in Boulder, Colorado is the primary operational activity of UNAVCO and exists to support university and other research investigators in their use of geophysical sensor technology for Earth sciences research. The Facility performs this task in part by archiving GNSS/GPS data and data products for current and future applications. Other data types that scientists use for Earth deformation studies are also held in the UNAVCO Archive collections. UNAVCO operates a community Archive, which provides long-term secure storage and easy retrieval of GNSS data, strain data, various derived products and related metadata. The Archive primarily stores high-precision geodetic data used for research purposes, collected under National Science Foundation and NASA sponsored projects. UNAVCO provides many learning opportunities including: Short Courses and Workshops, Educational Resources, RESESS Research Student Internships, and Technical Training.

Proper citation: UNAVCO (RRID:SCR_006706) Copy   

•   Source: SciCrunch Registry

http://media.marine-geo.org/

A collection of high-quality images and videos for education and outreach from the Integrated Earth Data Applications Facility. Albums include: Ridge2000, MARGINS, GeoMapApp, GeoPRISMS, Antarctic and Southern Ocean, Global Multi-Resolution Topography. To contribute your media to Media Bank, you are asked to supply metadata with each image/video supplied.

Proper citation: Marine Geosciences Data System MediaBank (RRID:SCR_006875) Copy   

•   Source: SciCrunch Registry

http://bio.math.berkeley.edu/eXpress/index.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented January 29, 2018.
From website: "Note that the eXpress software is also no longer being developed. We recommend you use kallisto instead." Kallisto can be found at http://pachterlab.github.io/kallisto/.

Software for streaming quantification for high-throughput DNA/RNA sequencing.
Can be used in any application where abundances of target sequences need to be estimated from short reads sequenced from them.

Proper citation: eXpress (RRID:SCR_006873) Copy   

•   Source: SciCrunch Registry

http://rankprop.gs.washington.edu/svm-fold/

This web server makes predictions of family, superfamily and fold level classifications of proteins based on the Structural Classification of Proteins (SCOP) hierarchy using the Support Vector Machine (SVM) learning algorithm. SVM-FOLD detects subtle protein sequence similarities by learning from all available annotated proteins, as well as utilizing potential hits as identified by PSI-BLAST. Predictions of classes of proteins that do not have any known example with a significant pairwise PSI-BLAST E-value can still be found using SVMs.

Proper citation: SVM-fold: Protein Fold Prediction (RRID:SCR_006834) Copy   

•   Source: SciCrunch Registry

http://www.compneurosci.com/CoSMo2012/

This unique summer school focuses on computational techniques integrating the multi-disciplinary nature of sensory-motor neuroscience through combined empirical-theoretical teaching modules and a focus on the use of databases of movement data (NSF CRCNS). Major breakthroughs in brain research have been achieved through computational models. The goal of the Summer School in Computational Sensory-Motor Neuroscience is to provide cross-disciplinary training in mathematical modelling techniques relevant to understanding brain function, dysfunction and treatment. In a unique approach bridging experimental research, clinical pathology and computer simulations, students will learn how to translate ideas and empirical findings into mathematical models. Students will gain a profound understanding of the brain''s working principles and diseases using advanced modelling techniques in hands-on simulations of models during tutored sessions by making use of data / model sharing. This summer school aims at propelling promising students into world-class researchers. Dates: August 5-19, 2012 Location: Northwestern University Chicago (Evanston campus), Illinois, USA Deadlines: * April 22, 2012: Application due, including letters of reference (extended!!!) * May 1, 2012: Notification of acceptance * May 20, 2012: Attendance confirmation of applicants and registration payment This summer school is directed at graduate students and post-doctoral fellows from multi-disciplinary backgrounds, including Life Sciences, Psychology, Computer Science, Mathematics and Engineering. We will also accept highly motivated outstanding under-graduate students. There are no formal prerequisites, but basic knowledge in calculus, linear algebra, neuroscience and the Matlab simulation environment is expected. Enrollment will be limited to 40 participants.

Proper citation: Summer School in Computational Sensory-Motor Neuroscience (RRID:SCR_006894) Copy   

•   Source: SciCrunch Registry

http://www.softpedia.com/get/Science-CAD/DynGO.shtml

DynGO is a client-server application that provides several advanced functionalities in addition to the standard browsing capability. DynGO allows users to conduct batch retrieval of GO annotations for a list of genes and gene products, and semantic retrieval of genes and gene products sharing similar GO annotations (which requires more disk and memory to handle the semantic retrieval). The result are shown in an association tree organized according to GO hierarchies and supported with many dynamic display options such as sorting tree nodes or changing orientation of the tree. For GO curators and frequent GO users, DynGO provides fast and convenient access to GO annotation data. DynGO is generally applicable to any data set where the records are annotated with GO terms, as illustrated by two examples. Requirements: Java Platform: Windows compatible, Linux compatible, Unix compatible

Proper citation: DynGO (RRID:SCR_007009) Copy   

•   Source: SciCrunch Registry

http://wiki.c2b2.columbia.edu/honiglab_public/index.php/Software:Jackal

Jackal is a collection of programs designed for the modeling and analysis of protein structures. Its core program is a versatile homology modeling package. It contains twelve individual programs, each with their own function.

Proper citation: Jackal (RRID:SCR_008665) Copy   

•   Source: SciCrunch Registry

http://pslid.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented August 23, 2017.

Annotated database of fluorescence microscope images depicting subcellular location proteins with two interfaces: a text and image content search interface, and a graphical interface for exploring location patterns grouped into Subcellular Location Trees. The annotations in PSLID provide a description of sample preparation and fluorescence microscope imaging.

Proper citation: Protein Subcellular Location Image Database (RRID:SCR_008663) Copy   

•   Source: SciCrunch Registry

http://iubio.bio.indiana.edu:8089/

Provides summary of gene and genomic information from eukaryotic organism databases. This includes gene symbol and full name, chromosome, genetic and molecular map information, Gene Ontology (Function/Location/Process) and gene homology, product information, links to extended gene information.

Proper citation: Eukaryote Genes (RRID:SCR_008617) Copy   

•   Source: SciCrunch Registry

http://www.nescent.org/

The National Evolutionary Synthesis Center (NESCent) is a nonprofit science center dedicated to cross-disciplinary research in evolution. NESCent promotes the synthesis of information, concepts and knowledge to address significant, emerging, or novel questions in evolutionary science and its applications. NESCent achieves this by supporting research and education across disciplinary, institutional, geographic, and demographic boundaries. Synthetic research in evolutionary science takes many forms but includes integrating novel data sets and models to address important problems within a discipline, developing new analytical approaches and tools, and combining methods and perspectives from multiple disciplines to answer and even create new fundamental scientific questions. NESCent facilitates such synthetic research by providing an environment for fertile interactions among scientists. Our Science and Synthesis program sponsors postdoctoral fellows and sabbatical scholars as resident scientists, and two kinds of meetings, working groups and catalysis meetings. Catalysis meetings provide a novel mechanism for bringing together diverse research communities and cultures to identify common interests, while working groups provide an opportunity for scientists to work together intensively on fundamental synthetic questions over a several-year period. These activities are community driven through our application process and evaluated by an external advisory board. Our Informatics program provides state of the art informatics tools to visiting and in-house scientists and aims to take the lead in assembling novel databases and developing new analytical tools for evolutionary biology. Finally it is sponsoring a major initiative to provide a digital data repository for work in evolutionary biology. NESCent''s Education and Outreach group communicates the results of evolutionary biology research to the general public and scientific community, provides outreach to groups who are underrepresented in evolutionary biology and works to improve evolution education.

Proper citation: NESCent - National Evolutionary Synthesis Center (RRID:SCR_005911) Copy   

•   Source: SciCrunch Registry

https://datadryad.org

International, curated, digital repository that makes the data underlying scientific publications discoverable, freely reusable, and citable. Particularly data for which no specialized repository exists. Provides the infrastructure for, and promotes the re-use of, data underlying the scholarly literature. Governed by a nonprofit membership organization. Membership is open to any stakeholder organization, including but not limited to journals, scientific societies, publishers, research institutions, libraries, and funding organizations. Most data are associated with peer-reviewed articles, although data associated with non-peer reviewed publications from reputable academic sources, such as dissertations, are also accepted. Used to validate published findings, explore new analysis methodologies, repurpose data for research questions unanticipated by the original authors, and perform synthetic studies.UC system is member organization of Dryad general subject data repository.

Proper citation: Dryad Digital Repository (RRID:SCR_005910) Copy   

•   Source: SciCrunch Registry

http://martinos.org/mne/

Software suite for processing magnetoencephalography and electroencephalography data. Open source Python software for exploring, visualizing, and analyzing human neurophysiological data including MEG, EEG, sEEG, ECoG . Implements all functionality of MNE Matlab tools in Python and extends capabilities of MNE Matlab tools to, e.g., frequency-domain and time-frequency analyses and non-parametric statistics.

Proper citation: MNE software (RRID:SCR_005972) Copy   

•   Source: SciCrunch Registry

http://songbirdtranscriptome.net/

Database containing cDNA clone information of the brains of songbirds. These clones are annotated with behavioral information, as well as links to information of homologous genes of other species. The database includes over 91,000 zebra finch brain cDNAs (2009) sequenced by Duke, ESTIMA, and Rockefeller research groups. The project is a collaborative effort of the Jarvis Laboratory of Duke University, Duke Bioinformatics, and The Genomics group of RIKEN, with Erich D. Jarvis as P.I. and Kazuhiro Wada as Co-P.I. Microarrays with the cDNAs in this database are available at Duke http://mgm.duke.edu/genome/dna_micro/core/spotted.htm and through the NIH Neurosciences Microarray Consortium http://arrayconsortium.tgen.org/np2/public/overview.jsp

Proper citation: Songbird Brain Transcriptome Database (RRID:SCR_006182) Copy   

•   Source: SciCrunch Registry

http://galaxyproject.org/

Open, web-based platform providing bioinformatics tools and services for data intensive genomic research. Platform may be used as a service or installed locally to perform, reproduce, and share complete analyses. Galaxy automatically tracks and manages data provenance and provides support for capturing the context and intent of computational methods. Galaxy Community has created Galaxy instances in many different forms and for many different applications including Galaxy servers, cloud services that support Galaxy instances, and virtual machines and containers that can be easily deployed for your own server.The Galaxy team is a part of BX at Penn State, and the Biology and Mathematics and Computer Science departments at Emory University.Training Infrastructure as a Service (TIaaS) is a service offered by some UseGalaxy servers to specifically support training use cases.

Proper citation: Galaxy (RRID:SCR_006281) Copy   

•   Source: SciCrunch Registry

http://rankprop.gs.washington.edu/

THIS RESOURCE IS NO LONGER IN SERVICE, documented May 10, 2017. A pilot effort that has developed a centralized, web-based biospecimen locator that presents biospecimens collected and stored at participating Arizona hospitals and biospecimen banks, which are available for acquisition and use by researchers. Researchers may use this site to browse, search and request biospecimens to use in qualified studies. The development of the ABL was guided by the Arizona Biospecimen Consortium (ABC), a consortium of hospitals and medical centers in the Phoenix area, and is now being piloted by this Consortium under the direction of ABRC. You may browse by type (cells, fluid, molecular, tissue) or disease. Common data elements decided by the ABC Standards Committee, based on data elements on the National Cancer Institute''s (NCI''s) Common Biorepository Model (CBM), are displayed. These describe the minimum set of data elements that the NCI determined were most important for a researcher to see about a biospecimen. The ABL currently does not display information on whether or not clinical data is available to accompany the biospecimens. However, a requester has the ability to solicit clinical data in the request. Once a request is approved, the biospecimen provider will contact the requester to discuss the request (and the requester''s questions) before finalizing the invoice and shipment. The ABL is available to the public to browse. In order to request biospecimens from the ABL, the researcher will be required to submit the requested required information. Upon submission of the information, shipment of the requested biospecimen(s) will be dependent on the scientific and institutional review approval. Account required. Registration is open to everyone.. Documented on May,18,2020. Ranking algorithm that exploits global network structure of similarity relationships among proteins in database by performing diffusion operation on protein similarity network with weighted edges. Source code and web server for searching non-redundant protein database. Web server ranks proteins found in NRDB40 (from PairsDB) against query sequence of amino acids using Rankprop algorithm.

Proper citation: Rankprop - Protein Ranking by Network Propagation (RRID:SCR_007159) Copy   

•   Source: SciCrunch Registry

http://krasnow1.gmu.edu/cn3/index3.html

Multidisciplinary research team devoted to the study of basic neuroscience with a specific interest in the description and generation of dendritic morphology, and in its effect on neuronal electrophysiology. In the long term, they seek to create large-scale, anatomically plausible neural networks to model entire portions of a mammalian brain (such as a hippocampal slice, or a cortical column). Achievements by the CNG include the development of software for the quantitative analysis of dendritic morphology, the implementation of computational models to simulate neuronal structure, and the synthesis of anatomically accurate, large scale neuronal assemblies in virtual reality. Based on biologically plausible rules and biophysical determinants, they have designed stochastic models that can generate realistic virtual neurons. Quantitative morphological analysis indicates that virtual neurons are statistically compatible with the real data that the model parameters are measured from. Virtual neurons can be generated within an appropriate anatomical context if a system level description of the surrounding tissue is included in the model. In order to simulate anatomically realistic neural networks, axons must be grown as well as dendrites. They have developed a navigation strategy for virtual axons in a voxel substrate.

Proper citation: Computational Neuroanatomy Group (RRID:SCR_007150) Copy   

•   Source: SciCrunch Registry

http://organelledb.lsi.umich.edu/

Database of organelle proteins, and subcellular structures / complexes from compiled protein localization data from organisms spanning the eukaryotic kingdom. All data may be downloaded as a tab-delimited text file and new localization data (and localization images, etc) for any organism relevant to the data sets currently contained in Organelle DB is welcomed. The data sets in Organelle DB encompass 138 organisms with emphasis on the major model systems: S. cerevisiae, A. thaliana, D. melanogaster, C. elegans, M. musculus, and human proteins as well. In particular, Organelle DB is a central repository of yeast protein localization data, incorporating results from both previous and current (ongoing) large-scale studies of protein localization in Saccharomyces cerevisiae. In addition, we have manually curated several recent subcellular proteomic studies for incorporation in Organelle DB. In total, Organelle DB is a singular resource consolidating our knowledge of the protein composition of eukaryotic organelles and subcellular structures. When available, we have included terms from the Gene Ontologies: the cellular component, molecular function, and biological process fields are discussed more fully in GO. Additionally, when available, we have included fluorescent micrographs (principally of yeast cells) visualizing the described protein localization. Organelle View is a visualization tool for yeast protein localization. It is a visually engaging way for high school and undergraduate students to learn about genetics or for visually-inclined researchers to explore Organelle DB. By revealing the data through a colorful, dimensional model, we believe that different kinds of information will come to light.

Proper citation: Organelle DB (RRID:SCR_007837) Copy   

•   Source: SciCrunch Registry

http://www.fgsc.net/

The Fungal Genetics Stock Center is a resource available to the Fungal Genetics research community and to educational and research organizations in general. While some fungi can cause disease in humans, most people have innate immunity against fungi. Some people with diseases of the immune system are at increased risk of infection by fungi. Drugs have been developed in the last 5 years that help with this. Fungal Genetics is the study of genes and genetic traits in fungi. In the past this has been important in the elucidation of what a gene is, what the genetic material is, how genes relate to enzymes, how enzymes relate to traits and how important traits change or evolve. In the present, Fungal Genetics is important to understanding how fungi are pathogens of plants and animals, how fungi can be used in industry for the production of enzymes, chemicals, food, and drugs. Fungi are also essential to processing bio-mass in the attempt to use ethanol as a fuel source. The FGSC is funded largely by a grant from the National Science Foundation (Award Number 0235887) of the United States of America. Sponsors: Supported by a grant from the National Science Foundation.

Proper citation: Fungal Genetics Stock Center (RRID:SCR_008143) Copy   

•   Source: SciCrunch Registry

http://www.poissonboltzmann.org/apbs/

APBS is a software package for modeling biomolecular solvation through solution of the Poisson-Boltzmann equation (PBE), one of the most popular continuum models for describing electrostatic interactions between molecular solutes in salty, aqueous media. APBS was designed to efficiently evaluate electrostatic properties for such simulations for a wide range of length scales to enable the investigation of molecules with tens to millions of atoms. It also provides implicit solvent models of nonpolar solvation which accurately account for both repulsive and attractive solute-solvent interactions. APBS uses FEtk (the Finite Element ToolKit) to solve the Poisson-Boltzmann equation numerically. FEtk is a portable collection of finite element modeling class libraries written in an object-oriented version of C. It is designed to solve general coupled systems of nonlinear partial differential equations using adaptive finite element methods, inexact Newton methods, and algebraic multilevel methods.

Proper citation: Adaptive Poisson-Boltzmann Solver (RRID:SCR_008387) Copy   

•   Source: SciCrunch Registry

http://divvy.ucsd.edu/

Software application for performing unsupervised machine learning and visualization with a focus on the clustering (separating data into groups) and dimensionality reduction (finding low dimensional structure in high dimensional data) subfields of machine learning. For visualization we provide support for both the whole dataset (e.g. a scatter plot) and points (e.g. transforming a particular point into an image). * Endlessly extensible. Every clusterer, reducer, point visualizer and dataset visualizer in Divvy is a plugin. We''ve provided a few big ones (K-means, PCA, scatter plot, &c.) and we''re hoping the community will use our plugin protocol to build many more. Each plugin defines its own UI, so your algorithm can look and behave the way that you want it to without top-down constraints. * Have lots of cores? Divvy is both task and data parallel. No longer will you be waiting for one algorithm to complete before you start another. Start as many as you want and keep using the UI. Only started one? With data parallelism we''ll still push your new MacBook Pro to 800% CPU utilization. * Part of your workflow: Export your clusterings and reductions to .csv and your visualizations to .png. Use your Matlab or R data with our Matlab/R to Divvy export tools available at http://github.com/jmlewis/divvy.

Proper citation: Divvy (RRID:SCR_006336) Copy   

•   Source: SciCrunch Registry