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http://www.aquatichabitats.com

Aquatic Habitats (AHAB) is the worlds largest manufacturer of housing systems for aquatic research animals. We are biologists first and engineers second, so we understand the complexity of aquatic life and how to sustain it. Our turnkey systems are secure, efficient and as fail-safe as possible.

Proper citation: Aquatic Habitats (RRID:SCR_008597) Copy   

•   Source: SciCrunch Registry

http://www.ebtox.org

This website is an invitation. an invitation to join scientists and stakeholders in an effort to review the scientific basis of traditional toxicological risk assessment, to provide the toxicological community with the tools it needs to efficiently and transparently judge risks of a diversifying nature and to make toxicology thus fit to meet the challenges of the 21st century. an invitation to participate in the inception and continuous implementation of a new movement in toxicology that aims at adopting an evidence-based approach. an invitation to help bridging the gap between modern toxicological science and risk assessment in order to exploit the wealth of information from new technologies in modern life sciences & toxicological research and to arrive at informed, transparent, judicious and conscientious decisions made on the basis of all evidence available. an invitation to develop a framework that allows combining precious expert insight grown over years of practical experience with structured approaches in basic science, in method assessment and in decision-making. Such evidence-based toxicology might help to make the best possible use of all sources of evidence in an efficient, productive, reliable, acceptable and transparent manner. Why do we need evidence-based toxicology (EBT)? Toxicology and the delivery of effective safety assessment critically relies on concepts and understanding generated by basic scientific research and must therefore adapt constantly to advances in knowledge. However, particularly from the perspective of regulatory toxicology, some of the assessment paradigms and methodologies were established decades ago and have changed little in response to scientific progress. At the same time, changes in our understanding of human disease, changes in the types of product now requiring safety assessment, and changes in the legislative landscape and public expectations pose significant challenges for industry, academia and regulators alike. It is necessary to challenge the status quo and ensure that as a matter of course best scientific practice and technical sophistication is reflected in safety assessment practices such that current and future challenges can be met. It is important therefore to ensure that structures are available that will encourage, facilitate and support a process of critical appraisal and renewal of the toxicological repertoire available for safety assessment. Part of this process is to embrace evidence-based toxicology such that the best possible scientific evidence is applied to judge product safety and likely risks to human health.

Proper citation: Evidence Based Toxicology (RRID:SCR_008507) Copy   

•   Source: SciCrunch Registry

http://www.biodas.org

The Distributed Annotation System (DAS) defines a communication protocol used to exchange annotations on genomic or protein sequences. It is motivated by the idea that such annotations should not be provided by single centralized databases, but should instead be spread over multiple sites. Data distribution, performed by DAS servers, is separated from visualization, which is done by DAS clients. The advantages of this system are that control over the data is retained by data providers, data is freed from the constraints of specific organisations and the normal issues of release cycles, API updates and data duplication are avoided. DAS is a client-server system in which a single client integrates information from multiple servers. It allows a single machine to gather up sequence annotation information from multiple distant web sites, collate the information, and display it to the user in a single view. Little coordination is needed among the various information providers. DAS is heavily used in the genome bioinformatics community. Over the last years we have also seen growing acceptance in the protein sequence and structure communities. A DAS-enabled website or application can aggregate complex and high-volume data from external providers in an efficient manner. For the biologist, this means the ability to plug in the latest data, possibly including a user''s own data. For the application developer, this means protection from data format changes and the ability to add new data with minimal development cost. Here are some examples of DAS-enabled applications or websites for end users: :- Dalliance Experimental Web/Javascript based Genome Viewer :- IGV Integrative Genome Viewer java based browser for many genomes :- Ensembl uses DAS to pull in genomic, gene and protein annotations. It also provides data via DAS. :- Gbrowse is a generic genome browser, and is both a consumer and provider of DAS. :- IGB is a desktop application for viewing genomic data. :- SPICE is an application for projecting protein annotations onto 3D structures. :- Dasty2 is a web-based viewer for protein annotations :- Jalview is a multiple alignment editor. :- PeppeR is a graphical viewer for 3D electron microscopy data. :- DASMI is an integration portal for protein interaction data. :- DASher is a Java-based viewer for protein annotations. :- EpiC presents structure-function summaries for antibody design. :- STRAP is a STRucture-based sequence Alignment Program. Hundreds of DAS servers are currently running worldwide, including those provided by the European Bioinformatics Institute, Ensembl, the Sanger Institute, UCSC, WormBase, FlyBase, TIGR, and UniProt. For a listing of all available DAS sources please visit the DasRegistry. Sponsors: The initial ideas for DAS were developed in conversations with LaDeana Hillier of the Washington University Genome Sequencing Center.

Proper citation: Distributed Annotation System (RRID:SCR_008427) Copy   

•   Source: SciCrunch Registry

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

DelPhi provides numerical solutions to the Poisson-Boltzmann equation (both linear and nonlinear form) for molecules of arbitrary shape and charge distribution. The current version is fast, accurate, and can handle extremely high lattice dimensions. It also includes flexible features for assigning different dielectric constants to different regions of space and treating systems containing mixed salt solutions. DelPhi takes as input a coordinate file format of a molecule or equivalent data for geometrical objects and/or charge distributions and calculates the electrostatic potential in and around the system, using a finite difference solution to the Poisson-Boltzmann equation. DelPhi is a versatile electrostatics simulation program that can be used to investigate electrostatic fields in a variety of molecular systems. Features of DelPhi include solutions to mixtures of salts of different valence; solutions to different dielectric constants to different regions of space; and estimation of the best relaxation parameter at run time.

Proper citation: DelPhi (RRID:SCR_008669) Copy   

•   Source: SciCrunch Registry

http://www.liden.cc/Visionary/

It is a dictionary for terminology used in the study of human and animal vision. It includes terms from the areas of biological and machine vision, visual psychophysics, visual neuroscience and other related fields. Sponsors: Visionary is sponsored by Educational Software for Autism.

Proper citation: Visionary: A Dictionary for the Study of Vision (RRID:SCR_008307) Copy   

•   Source: SciCrunch Registry

http://biosig.sourceforge.net/

Software library for processing of electroencephalogram (EEG) and other biomedical signals like electroencephalogram (EEG), electrocorticogram (ECoG), electrocardiogram (ECG), electrooculogram (EOG), electromyogram (EMG), respiration, and so on. Biosig contains tools for quality control, artifact processing, time series analysis, feature extraction, classification and machine learning, and tools for statistical analysis. Many tools are able to handle data with missing values (statistics, time series analysis, machine learning). Another feature is that more then 40 different data formats are supported, and a number of converters for EEG,, ECG and polysomnography are provided. Biosig has been widely used for scientific research on EEG-based BraiN-Computer Interfaces (BCI), sleep research, and ECG and HRV analysis. It provides software interfaces several programming languages (C, C++, Matlab/Octave, Python), and it provides also an interactive viewing and scoring software for adding, and editing of annotations, markers and events.

Proper citation: BioSig: An Imaging Bioinformatics System for Phenotypic Analysis (RRID:SCR_008428) Copy   

•   Source: SciCrunch Registry

http://biq-analyzer.bioinf.mpi-sb.mpg.de

BiQ Analyzer is a software tool for easy visualization and quality control of DNA methylation data from bisulfite sequencing. Highlights: - End-to-end support of the analysis process: from raw sequence files to a comprehensive documentation and visualization. - Automatically generate publication-quality lollipop diagrams (show example.) - Integrated 1-click multiple sequence alignment. - Automated CpG highlighting- never spend your time highlighting CpGs by hand anymore. - Open electropherogram files to check for sequencing problems (requires an electropherogram viewer such as Chromas LITE.) - Generate MethDB-compatible DNA methylation files for database submission. - Factor 5 speedup of sequence analysis while at the same time achieving better data quality. Intended users: - Anyone who works with DNA methylation data from bisulfite sequencing. - Occasional users as well as experts (the former will benefit from the help that the program gives in order to achieve a good quality management whereas the latter will save hours and days of tedious work.) Sponsors: This resource is supported by the Max Planck Institute. Keywords: Software, Visualization, DNA, Methylation, Data, Bisulfite, Sequencing, Electropherogram, Analysis,

Proper citation: BiQ Analyzer: A Software Tool for DNA Methylation Analysis (RRID:SCR_008423) Copy   

•   Source: SciCrunch Registry

http://www.sanger.ac.uk/PostGenomics/S_pombe/

The laboratory studies global gene expression programs in fission yeast (S. pombe). They apply a wide range of integrated approaches to analyse regulatory networks during cell proliferation, differentiation and quiescence including genetic and environmental perturbations. They are also interested in genetic diversity, genome evolution, and the complex interactions between genotypes, phenotypes, and the environment. The relative simplicity of the yeast cell promises a deeply satisfying, systems-level understanding of its inner workings within our life time Sponsors: This research is mainly funded by Cancer Research UK and the EC FP7 PhenOxiGEn project. Keywords: Gene, Expression, S.pombe, Yeast, Cell, Proliferation, Differentiation, Environmental, Genetic, Diversity, Genome, Evolution, Genotype, Phenotype, Environment,

Proper citation: Bahler Laboratory: Genome Regulation (RRID:SCR_008422) Copy   

•   Source: SciCrunch Registry

http://www.amber.org.au/

THIS RESOURCE IS NO LONGER IN SERVICE, documented September 6, 2016. AMBeR's aim is to bring together Australia's unique resources for genetic epidemiology and genomics with high level expertise in bioinformatics and statistical science, conduct advanced methodological research, develop new research capacity and competitiveness in cutting-edge techniques, bring them to bear on important medical research problems, train young Australians in bioinformatics and advanced biostatistics, and transfer this expertise to the medical research community.

Proper citation: Australian Medical Bioinformatics Resource (RRID:SCR_008385) Copy   

•   Source: SciCrunch Registry

http://qneuro.rutgers.edu

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. The brain is made of billions of neurons, which together form the world''s most powerful information-processing machine. Despite decades of research, the fundamental principle by which these cells work together is still unknown. Many theories for brain function have been proposed over the last century. But only in the last few years has it become possible to record simultaneously from large enough numbers of neurons to put these theories to the test experimentally. This is an unprecedented opportunity, but it opens up a new question: how do we go from the gigabytes of experimental data that we now have, to concise conclusions about the function of the brain? The data processing methods traditionally used in neuroscience are not sophisticated enough to exploit this new flood of information. Fortunately, modern statistics and machine learning theory is making great strides in precisely the type of techniques needed to process these large multivariate databases. By applying these methods to neuronal data, we can now test long-standing hypotheses about brain function. The Cell Assembly The main focus of our research is an experimental search for cell assemblies. Before describing what a cell assembly is, it will be useful to describe what it is not. The brain is often thought of as a feed-forward system. In this scheme, sensory information is processed by successive levels of cortical analyzers, each of which transforms the results of previous levels, until sensory information is in a suitable form to guide the animals behavior. In support of this idea, the pattern of connections in the cortex does appear to respect a hierarchical organization, with the output of low-level areas corresponding to a single sensory modality being integrated into high-level multi-modal areas. Responses in higher-level sensory areas appear to have more complex responses to sensory stimuli, in agreement with increased abstraction as the hierarchy is traversed. However, there are several levels at which this feed-forward picture is incomplete. At the circuit diagram level, there more connections projecting across and down the hierarchy, than there are feed-forward projections. What''s more, if information were processed in a strictly feed-forward manner, one would expect a neuron to respond identically to repeated presentations of the same sensory stimulus. Although this is a fairly good approximation in primary sensory areas of cortex, in high-level structures responses are often more variable than expected from strict sensory control. Finally, although feed-forward processing can describe how an animal could perform simple stimulus-response behaviors, it cannot explain more complex top-down behaviors such as memory or thought. An alternative point of view, put forward over 50 years ago by Canadian psychologist Donald Hebb, holds that recurrent and feedback connections play an essential role in brain function. The principal actor in this view is the cell assembly, an anatomically distributed subset of neurons, amongst which mutually excitatory connections have been strengthened by repeated co-activation, allowing the assembly to later maintain its activity through reverberation without direct sensory stimulation. This theory allows for sensory-response behavior, and also behavior resulting purely from internally generated cognitive activity, by the sequential activation of a series of assemblies, leading in turn to the production of motion. In our research, we search for signatures of assembly activity in simultaneous recordings from multiple neurons, and aim to characterize the properties of assembly activity in ways not possible from theory alone. Software for Automatic Clustering KlustaKwik is a program developed in the lab for automatic cluster analysis, specifically designed to run fast on large data sets. In order facilitate open-source development, it is now located at klustakwik.sourceforge.net. This study was supported by NIH grants MH073245 and DC009947; NSF grant SBE-0542013 to the Temporal Dynamics of Learning Center, an NSF Science of Learning Center; a National Institute on Deafness and Other Communication Disorders, NIH, grant DC-005787-01A1; and a Spanish grant FIS 2006-09294. K.D.H. is an Alfred P. Sloan fellow. We would like to dedicate this work to the memory of D. J. Amit.

Proper citation: Rutgers University Quantitative Neuroscience Laboratory (RRID:SCR_008541) Copy   

•   Source: SciCrunch Registry

http://www.broad.mit.edu/mpg/grail/

A tool to examine relationships between genes in different disease associated loci. Given several genomic regions or SNPs associated with a particular phenotype or disease, GRAIL looks for similarities in the published scientific text among the associated genes. As input, users can upload either (1) SNPs that have emerged from a genome-wide association study or (2) genomic regions that have emerged from a linkage scan or are associated common or rare copy number variants. SNPs should be listed according to their rs#''s and must be listed in HapMap. Genomic Regions are specified by a user-defined identifier, the chromosome that it is located on, and the start and end base-pair positions for the region. Grail can take two sets of inputs - Query regions and Seed regions. Seed regions are definitely associated SNPs or genomic regions, and Query regions are those regions that the user is attempting to evaluate agains them. In many applications the two sets are identical. Based on textual relationships between genes, GRAIL assigns a p-value to each region suggesting its degree of functional connectivity, and picks the best candidate gene. GRAIL is developed by Soumya Raychaudhuri in the labs of David Altshuler and Mark Daly at the Center for Human Genetic Research of Massachusetts General Hospital and Harvard Medical School, and the Broad Institute. GRAIL is described in manuscript, currently in preparation.

Proper citation: Gene Relationships Across Implicated Loci (RRID:SCR_008537) Copy   

•   Source: SciCrunch Registry

http://bioinf.uni-greifswald.de/augustus/

Software for gene prediction in eukaryotic genomic sequences. Serves as a basis for further steps in the analysis of sequenced and assembled eukaryotic genomes.

Proper citation: Augustus (RRID:SCR_008417) Copy   

•   Source: SciCrunch Registry

http://herpnet.org/

HerpNET is a collaborative effort by natural history museums to establish a global network of herpetological collections data. Sixty-four institutions are participating in the HerpNET community, with an open ended invitation to institutions who would like to join. Currently 56 institutions are available on the specimen searching portal, with data from over 5.5 million specimens available for searching. VertNet: The Future HerpNET, ORNIS, MaNIS and FishNet2 are taxon-based web portals that now serve georeferenced data on vertebrates from over 90 global institutions. Together these comprise VertNET, a cooperative project working to maintain and expand these distributed database projects. Future plans include biodiversity informatics workshops, enhancement of the portal design, better searching capabilities, and a dynamic cache to expand performance and analytic features. Sponsors: This resource is supported by the National Science Foundation (NSF No. 0132303) and by a GBIF DIGIT .

Proper citation: HerpNET (RRID:SCR_008374) Copy   

•   Source: SciCrunch Registry

http://www.invitrogen.com/site/us/en/home/Products-and-Services/Applications/Cell-Analysis/Antibodies-and-Secondary-Detection.html

Supports research in cellular analysis, genomics, proteomics, and drug discovery. It has merged with Thermo Fisher Scientific. One of several brands under Thermo Fisher Scientific corporation.

Proper citation: Invitrogen Antibodies (RRID:SCR_008410) Copy   

•   Source: SciCrunch Registry

http://www.sciencebuddies.org

Many science fairs have a group of people called a Scientific Review Committee (or SRC) that reviews each project application to ensure that all safety and legal requirements will be met and that the appropriate forms have been completed. The committee also reviews the completed project displays. Typically, a Scientific Review Committee is composed of at least three people: a biomedical scientist, a physical scientist, and a science teacher. Some fairs also have an Institutional Review Board (IRB), which reviews all proposed projects that in any way involve human beings. An IRB''s purpose is to ensure that the project will not present undue risk to the subjects.

Proper citation: Scientific Review Committee (RRID:SCR_008652) Copy   

•   Source: SciCrunch Registry

http://www.oege.org/software/hwe-mr-calc.shtml

This portal leads to the Chi-sq Hardy-Weinberg equilibrium test calculator for biallelic markers (SNPs, indels etc), including analysis for ascertainment bias for dominant/recessive models (due to biological or technical causes.) The purpose of this web program is for estimating possible missingness and an approach to evaluating missingness under different genetic models. Mendelian randomization (MR) permits causal inference between exposures and a disease. It can be compared with randomized controlled trials. Whereas in a randomized controlled trial the randomization occurs at entry into the trial, in MR the randomization occurs during gamete formation and conception. Several factors, including time since conception and sampling variation, are relevant to the interpretation of an MR test. Particularly important is consideration of the missingness of genotypes that can be originated by chance, genotyping errors, or clinical ascertainment. Testing for Hardy-Weinberg equilibrium (HWE) is a genetic approach that permits evaluation of missingness. Through this tool, the authors demonstrate evidence of nonconformity with HWE in real data. They also perform simulations to characterize the sensitivity of HWE tests to missingness. Unresolved missingness could lead to a false rejection of causality in an MR investigation of trait-disease association. These results indicate that large-scale studies, very high quality genotyping data, and detailed knowledge of the life-course genetics of the alleles/genotypes studied will largely mitigate this risk. Sponsors: This resource is supported by an Intermediate Fellowship (grant FS/05/065/19497) from the British Heart Foundation.

Proper citation: Hardy-Weinberg Equilibrium Calculator (RRID:SCR_008371) Copy   

•   Source: SciCrunch Registry

http://ompc.juricap.com

OMPC aims to enable reuse of the huge open and free code base of MATLAB on a free and faster growing Python platform. Running Python and MATLAB in a single interpreter avoids issues with running two separate applications. Python adds general purpose programming libraries to the convenient syntax of the language of technical computing. OMPC is not an interpreter, it lets Python to do the work. This means that if Python gets faster OMPC gets faster too. OMPC translates the m-files preserving the structure of the original programs as much as possible. Although OMPC comes with a library that emulates the features of numerical array of MATLAB there is nothing that will stop you from running the translated code the way you like it. This means that you could run the OMPC generated code on IronPython, Jython, PyPy or whatever else if you write your own numerical class. Sponsors: This resource is supported by RIKEN Brain Science Institute.

Proper citation: An Open-Source MATLAB-to-Python Compiler (RRID:SCR_008409) Copy   

•   Source: SciCrunch Registry

http://faculty.washington.edu/chudler/ehc.html

This web site focuses on neuroscience, the study of the nervous system. Links on this page are limited to those Dr. Chundler finds to be the most interesting and useful.

Proper citation: Eric H. Chundlers Links (RRID:SCR_008328) Copy   

•   Source: SciCrunch Registry

http://www.wheaton.com

Wheaton Industries Inc. is a leading marketer, manufacturer and re-packager of containers, laboratory ware, instrumentation and associated products and services sold principally to customers in the general laboratory, life sciences, and diagnostics and reagent / chemicals packaging markets. Our products and services are marketed and sold globally through two divisions. The laboratory research products are sold through Wheaton Science Products, and the packaging products are sold through Wheaton Science Packaging.

Proper citation: Wheaton Industry Inc (RRID:SCR_008565) Copy   

•   Source: SciCrunch Registry

http://www.randomizer.org

This site is designed for researchers and students who want a quick way to generate random numbers or assign participants to experimental conditions. Research Randomizer can be used in a wide variety of situations, including psychology experiments, medical trials, and survey research. The program uses a JavaScript random number generator to produce customized sets of random numbers. Since its release in 1997, Research Randomizer has been used to generate number sets over 10.7 million times. This service is part of Social Psychology Network and is fast, free, and runs with any recent web browser as long as JavaScript isn''t disabled. Research Randomizer is a free service offered to students and researchers interested in conducting random assignment and random sampling. By using this service, you agree to abide by the SPN User Policy and to hold Research Randomizer and its staff harmless in the event that you experience a problem with the program or its results. Although every effort has been made to develop a useful means of generating random numbers, Research Randomizer and its staff do not guarantee the quality or randomness of numbers generated. Any use to which these numbers are put remains the sole responsibility of the user who generated them. What are the system requirements needed to run Research Randomizer? This program works best with Firefox and other recent web browsers. If you''re using a browser that came with America Online, or older browsers made prior to 2003, you may experience some difficulties with Research Randomizer. You may also not be able to use Research Randomizer with some limited-function browsers that do not fully support JavaScript, such as the Opera broswer used on certain game consoles. We would suggest that you update to a fairly recent, fully- functional stand-alone browser. How do I know what browser I am using? The easiest way to find this out is to click Help on the pulldown menu at the top of the screen. One of the options should be About Mozilla Firefox, About Internet Explorer, About Netscape, or something similar. Selecting this option will open a window that displays the name, version number, and copyright date of your browser. How does Research Randomizer generate its numbers? Research Randomizer uses the Math.random method within the JavaScript programming language to generate its random numbers for all modern web browsers. If you are using an older version of Microsoft Internet Explorer or Netscape Navigator (that is prior to version 4.0 of either), Research Randomizer uses an adaptation of the Central Randomizer by Paul Houle. Note that Research Randomizer no longer supports much-older browsers by other vendors (e.g., Mosaic). Who designed Research Randomizer? The original idea and programming for Research Randomizer came from Geoffrey C. Urbaniak in 1997. Research Randomizer was then jointly developed with Scott Plous, webmaster of Social Psychology Network, and online tutorials were added to the main program. In 1999 the site was redesigned with the assistance of Mike Lestik, in 2003 Mike Lestik added the download function, and in 2007 Mike Lestik and Scott Plous redesigned the site and added new content.

Proper citation: Research Randomizer (RRID:SCR_008563) Copy   

•   Source: SciCrunch Registry