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Rutgers University Quantitative Neuroscience Laboratory Resource Report Resource Website 1+ mentions |
Rutgers University Quantitative Neuroscience Laboratory (RRID:SCR_008541) | data or information resource, database, laboratory portal, organization portal, portal | 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. | THIS RESOURCE IS NO LONGER IN SERVICE | nif-0000-31389 | SCR_008541 | Rutgers, Quantitative Neuroscience Laboratory | 2026-09-12 12:57:05 | 8 | ||||||||||
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BLAST Similarity Search Resource Report Resource Website 10+ mentions |
BLAST Similarity Search (RRID:SCR_008419) | data or information resource, portal, topical portal | The goals of this sequencing effort are to produce and publicly release a whole-genome assembly and auto-annotation of the Aedes genome representing 8X sequence coverage. In collaboration, these centers have delivered the target 8X draft coverage of the disease vector genome. Assembly of the genome was performed using the Broad''s whole genome assembly package ARACHNE (Batzoglou et al., 2002 and Jaffe et al., 2003). The Aedes genome will be annotated in a collaborative effort involving both MSCs and Vectorbase, which is a bioinformatics resource center at the University of Notre Dame. Sponsor: This resource is supported by the National Institute of Allergy and Infectious Diseases. Keywords: Genome, BLAST, Similarity, Search, Engine, Sequence, Bioinformatics, Resource, | has parent organization: Massachusetts Institute of Technology; Massachusetts; USA; | nif-0000-30152 | SCR_008419 | BLAST | 2026-09-12 12:57:03 | 10 | ||||||||||
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Gene Relationships Across Implicated Loci Resource Report Resource Website 50+ mentions |
Gene Relationships Across Implicated Loci (RRID:SCR_008537) | analysis service resource, data analysis service, production service resource, resource, service resource | 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. | software, text mining, genotype, phenotype, snp |
is listed by: 3DVC has parent organization: Broad Institute |
NIAMS 1K08AR055688-01A1; NIAMS AR007530; NHGRI U01HG004171; NIDDK R01DK083759 |
PMID:19557189 | nif-0000-30627 | SCR_008537 | GRAIL | 2026-09-12 12:57:05 | 72 | |||||||
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QIAGEN Resource Report Resource Website 10000+ mentions |
QIAGEN (RRID:SCR_008539) | commercial organization | A commercial organization which provides assay technologies to isolate DNA, RNA, and proteins from any biological sample. Assay technologies are then used to make specific target biomolecules, such as the DNA of a specific virus, visible for subsequent analysis. | is parent organization of: Ingenuity Pathways Knowledge Base | nif-0000-31384 | SCR_008539 | QIAGEN | 2026-09-12 12:57:05 | 85960 | ||||||||||
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Augustus Resource Report Resource Website 1000+ mentions |
Augustus (RRID:SCR_008417) | data analysis software, data processing software, sequence analysis software, software application, software resource, web application | Software for gene prediction in eukaryotic genomic sequences. Serves as a basis for further steps in the analysis of sequenced and assembled eukaryotic genomes. | software, gene, prediction, eucaryotic, genomic, sequence |
is used by: BRAKER is used by: BRO_annotation is listed by: Debian is listed by: OMICtools is listed by: SoftCite works with: Gsnap2Augustus |
Deutsche Forschungsgemeinschaft (DFG) HO4545/1-1;; STA1009/6-1 ; Institute for Mathematics and Computer Science ; Ernst Moritz Arndt University of Greifswald |
PMID:23700307 DOI:10.1093/bioinformatics/btw494 |
Free, Available for download, Freely available | SCR_015981, OMICS_07777, nif-0000-30133 | https://sources.debian.org/src/autodock-vina/ | SCR_008417 | Augustus: Gene Prediction, WebAUGUSTUS, Augustus, Augustus [gene prediction] | 2026-09-12 12:57:03 | 3886 | |||||
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Q-Value Software Resource Report Resource Website 1+ mentions |
Q-Value Software (RRID:SCR_008538) | software resource | Features: * This software takes a list of p-values resulting from the simultaneous testing of many hypotheses and estimates their q-values. A point-and-click interface is now available! * The q-value of a test measures the proportion of false positives incurred (called the false discovery rate) when that particular test is called significant. * A short tutorial on q-values and false discovery rates is provided with the manual. * Various plots are automatically generated, allowing one to make sensible significance cut-offs. * Several mathematical results have recently been shown on the conservative accuracy of the estimated q-values from this software. * The software can be applied to problems in genomics, brain imaging, astrophysics, and data mining. This research was supported in part by a National Science Foundation graduate research fellowship. | has parent organization: Stanford University; Stanford; California | nif-0000-31382 | SCR_008538 | Q-Value Software | 2026-09-12 12:57:05 | 5 | ||||||||||
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Apache Tomcat Resource Report Resource Website 1+ mentions |
Apache Tomcat (RRID:SCR_008411) | software resource | Apache Tomcat is an open source software implementation of the Java Servlet and JavaServer Pages technologies. The Java Servlet and JavaServer Pages specifications are developed under the Java Community Process. Apache Tomcat is developed in an open and participatory environment and released under the Apache License version 2. Apache Tomcat is intended to be a collaboration of the best-of-breed developers from around the world. We invite you to participate in this open development project. Apache Tomcat powers numerous large-scale, mission-critical web applications across a diverse range of industries and organizations. Some of these users and their stories are listed on the PoweredBy wiki page. | software, technology, application, java | Yahoo ; Microsoft ; Google ; Facebook ; HP ; Basis Technology ; Covalent ; IONA ; AirPlus International ; BlueNog ; Intuit ; Joost |
nif-0000-30114 | SCR_008411 | Apache Tomcat | 2026-09-12 12:57:03 | 7 | |||||||||
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GEDI Resource Report Resource Website 50+ mentions |
GEDI (RRID:SCR_008530) | GEDI | software resource | A program that opens a new perspective to the analysis of microarray data (e.g., gene expression profiling). Unlike traditional gene clustering software, GEDI is primarily sample-oriented rather than gene-oriented. By treating each high-dimensional sample, such as one microarray experiment, as an object, it accentuates the genome-wide response of a tissue or a patient and treats it as an integrated biological entity. Hence, GEDI honors the new spirit of a system-level approach in biology. Yet, it also allows the researcher to quickly zoom-in from global patterns onto individual genes that exhibit interesting expression behavior and retrieve gene-specific information. Therefore, GEDI unites a novel holistic perspective with the traditional gene-centered approach in molecular biology. GEDI allows experimental biologists or clinicians with no bioinformatics background to efficiently and intuitively navigate through a large number of expression profiles, each with a memorizable face, and inspect, group and collect them, like managing a stack of baseball cards. DYNAMIC ANALYSIS: The unique strength of GEDI, for which GEDI was originally developed, is that it can display the results of parallel monitoring of multiple high-dimensional time courses, such as the comparison of expression profile time evolution in response to a series of drugs. GEDI creates animated graphics showing how 10,000s of genes change their expression over time in response to 100s of separately tested drugs. STATIC ANAYLSIS: The signature graphical output of GEDI, the GEDI-mosaics provide a unique, one-glance visual engram that gives each microarray or other high-dimensional dataset a face. A characteristic of GEDI''s analysis is that it does not prejudicate any particular structure in the data (such as clusters or hierarchical organization). Thus, it allows the researcher to use human pattern recognition to perform a global first-level analysis of the data. Sponsor. The project was supported by the Air Force Office of Scientific Research and the National Health Institutes. It is distributed for free academic use by the Childrens Hospital, Boston., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025. | THIS RESOURCE IS NO LONGER IN SERVICE | nif-0000-30604 | SCR_008530 | The Gene Expression Dynamics Inspector, Gene Expression Dynamics Inspector | 2026-09-12 12:57:05 | 73 | |||||||||
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ELDERMET Gut microbiota as an indicator and agent of nutritional health in elderly Irish subjects Resource Report Resource Website 10+ mentions |
ELDERMET Gut microbiota as an indicator and agent of nutritional health in elderly Irish subjects (RRID:SCR_008492) | data or information resource, portal, topical portal | Latest publications: ELDERMET research has recently been published in the Proceedings of the National Academy of Sciences (USA). This work focuses on the composition and stability of the intestinal bacteria in older Irish adults. Read the paper here. Would you like to be part of ELDERMET? We are currently looking for people, aged 65 years or older, living in the community. All we ask is that you live in the Cork area, or are willing to travel to Cork, and have recently (within the last two/three weeks) taken any kind of antibiotic. It doesnt matter if you are still taking the antibiotic, as long as the finishing date isnt more than four weeks before your first visit to ELDERMET. ELDERMET Objectives To assess the composition of the faecal microbiota of elderly volunteers in the Irish population, using state-of-the-art molecular techniques. To correlate diversity, composition, and metabolic potential of the faecal microbial metagenome with health, diet and lifestyle indices that are a) likely to be influenced by the microbiota or b) to influence the microbiota. To develop recommendations for specific dietary ingredients, foodstuffs, functional foods and/or dietary supplements, that will improve the health of elderly consumers. To provide evidence-based recommendations for prospective studies to determine the molecular mechanisms for health improvements promoted by specific food ingredients that modulate components of the microbiota. ELDERMET Rationale The human intestinal microbiota is made up of approximately 1000 genetically unique organisms (phylotypes ) [1]. The bacteria present in the intestine make an important contribution to: metabolism executed in the gut [2] health, in diverse activites from pain perception [3] to cognitive function [4]. There is an increasing body of evidence linking alterations in the human gut microbiota with Inflammatory Bowel Disease [5, 6] and Irritable Bowel Syndrome [7]. The changing pattern of the gut microbiota in elderly subjects [8, 9] may be linked to host changes such as immunosenescence, increased susceptibility to disease and potentially systemic effects. The composition of the intestinal microbiota may be modulated by dietary components including prebiotics [10]. ELDERMET will determine the baseline composition of the gut microbiota of several hundred elderly Irish subjects using a combination of traditional culutre and molecular (culture-independent) methodologies. ELDERMET will explore potential correlations between microbiota composition and a range of health indices; cross-referencing data to dietary intake. Data will be analyzed in the context of the related FHRI projects in Nutrigenomics, Food Consumption, Food Safety, and Diet-Health. ELDERMET will provide recommendations to all stakeholders (including health practitioners and the health service, the food industry and the general public) on how to improve health based on defined modifications to dietary intake. Sponsor. This work is supported by the Goverment of Ireland Department of Agriculture Fisheries and Food/Health Research Board Food for Health Research Initiative award to the ELDERMET project as well as by a Science Foundation Ireland award to the Alimentary Pharmabiotic Centre. M.J.C. is now funded by a fellowship from the Health Research Board of Ireland. | Aging | nif-0000-30485 | SCR_008492 | ELDERMET | 2026-09-12 12:57:04 | 12 | ||||||||||
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CGEMS Resource Report Resource Website 10+ mentions |
CGEMS (RRID:SCR_008445) | CGEMS | data or information resource, portal, topical portal | The project began as a pilot study to identify inherited genetic susceptibility to prostate and breast cancer. CGEMS has developed into a robust research program involving genome-wide association studies (GWASs) for a number of cancers to identify common genetic variants that affect a person''s risk of developing cancer. In collaboration with extramural scientists, NCI''s Division of Cancer Epidemiology and Genetics (DCEG) has carried out genome-wide scans for breast, prostate, pancreatic, and lung cancers, while a GWAS of bladder cancer is currently underway. By making the data available to both intramural and extramural research scientists, as well as those in the private sector through rapid posting, NIH can leverage its resources to ensure that the dramatic advances in genomics are incorporated into rigorous population-based studies. Ultimately, findings from these studies may yield new preventive, diagnostic, and therapeutic interventions for cancer. Sponsors: This resource is supported by the U.S. National Institues Of Health. | cancer, genetic, marker, prostate, breast, research, genome, association, development, scientist, pancreatic, lung, bladder, science, genomics, population, study, theraphy, diagnosis |
has parent organization: National Cancer Institute has parent organization: National Cancer Institute |
nif-0000-30287 | SCR_008445 | The Cancer Genetic Markers of Susceptibility Project, Cancer Genetic Markers of Susceptibility, The Cancer Genetic Markers of Susceptibility | 2026-09-12 12:57:04 | 28 | ||||||||
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Wide Tag Resource Report Resource Website |
Wide Tag (RRID:SCR_008566) | software resource |
Headquartered in Redwood City California, WideTag is a pioneer in architecting computing systems that integrate sensors, positioning devices and memory with social, Web 2.0-style services in applications that revolutionize business and push consumer technology. |
nif-0000-31904 | http://www.openspime.com | SCR_008566 | Wide Tag | 2026-09-12 12:57:06 | 0 | ||||||||||
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Program DynaFit Resource Report Resource Website 100+ mentions |
Program DynaFit (RRID:SCR_008444) | software resource | Program DynaFit Analysis of (bio)chemical kinetics and equilibria Welcome to the DynaFit home page. Purpose Symbolic Notation Bibliographic Reference Numerical Methods Minimum System Requirements Purpose The main purpose of the program DynaFit is to perform nonlinear least-squares regression of chemical kinetic, enzyme kinetic, or ligand-receptor binding data. The experimental data can be either initial reaction velocities in dependence on the concentration of varied species (e.g., inhibitor concentration vs. velocity), or the reaction progress curves (e.g., time vs. absorbance). Symbolic Notation The main advantage in using the program DynaFit is in the ability to characterize the (bio)chemical reacting system in terms of symbolic, or stoichiometric, equations. For example, the ``slow, tight'''' inhibition of a dissociative dimeric enzyme is described by the following text: Monomer Monomer <==> Enzyme : k1 k2 Enzyme Inhibitor <==> Complex : k3 k4 Enzyme Substrate <==> ReactiveX : k5 k6 ReactiveX --> Product Enzyme : k7 k8 The names of chemical species (Monomer, Enzyme, etc.) are entirely arbitrary and can be freely chosen by the investigator. Bibliographic Reference If you publish any results obtained by using DYNAFIT, plase cite the following reference: Kuzmic, P. (1996) Anal. Biochem. 237, 260-273. Program DYNAFIT for the Analysis of Enzyme Kinetic Data: Application to HIV Proteinase ABSTRACT A computer program with the code name DYNAFIT was developed for fitting either the initial velocities, or the time-course of enzyme reactions, to an arbitrary molecular mechanism represented symbolically by a set of chemical equations. Seven numerical tests and five graphical tests are applied to judge the goodness of fit. Experimental data on the inhibition of the dissociative dimeric proteinase from HIV were used in four test examples. A set of initial velocities was analyzed to see if a tight-binding inhibitor could bind to the HIV proteinase monomer. Three different sets of progress curves were analyzed (i) to determine the kinetic properties of an irreversible inhibitor; (ii) to investigate the dissociation and denaturation mechanism for the protease dimer; and (iii) to investigate the inhibition mechanism for a transient inhibitor. See a MEDLINE abstract with related references concerning the kinetics of HIV-1 protease. Numerical Methods The nonlinear regression module uses the Levenberg-Marquardt algorithm [1]. The time-course of (bio)chemical reactions is computed by the numerical integration of simultaous first-order ordinary differential equations, using the Livermore Solver of ODe Systems (LSODE, [2]). The composition of complex mixtures at equilibrium (e.g., in the concentration jump experiment where a complex mixture is incubated prior to the addition of a reagent) is computed by solving simultaneous nonlinear algebraic equations, namely, the mass balance equations for the component species, by using the multidimensional Newton-Raphson method [3]. References G. A. F. Seber and C. J. Wild (1989) Nonlinear Regression, Wiley, New York, p. 624. A. C. Hindmarsh (1983) ODEPACK: a systematized collection of ODE solvers; in Scientific Computing, ed. R. S. Stepleman et al., North Holland, Amsterdam, pp. 55--64. E. Kreyszig (1993) Advanced Engineering Mathematics; 7th ed., John Wiley, New York, p. 929. Minimum System Requirements DynaFit for Windows Intel Pentium III or Celeron class 800 MHz or faster processor Microsoft Windows XP (SP1) or 2000 (SP2) 128 MB RAM 20 MB Hard Disk Space Ethernet Network Interface Card required for license activation(1) CD/DVD-ROM drive required for software installation(2) (1) The Network Interface Card is used to compute a unique Computer ID, tied to a particular DynaFit license. Essentially the Computer ID required for license activation is an encrypted Media Access Control (MAC address) associated with the given Network Card. (2) CD/DVD-ROM is not required if the software is being installed by using the downloadable installer file dynafit-install.zip. Sponsor. This work has been supported by the NIH, grant No. R43 AI52587-02 and the U.S. Department of Defense, U.S. Army Medical Research and Materials Command, Ft. Detrick, MD, administered by the Pacific Telehealth & Technology Hui, Honolulu, HI, contract No. V549P-6073. | nif-0000-30477 | SCR_008444 | DynaFit | 2026-09-12 12:57:04 | 132 | |||||||||||
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Wheaton Industry Inc Resource Report Resource Website 1+ mentions |
Wheaton Industry Inc (RRID:SCR_008565) | instrument manufacture, material service resource, production service resource, service resource | 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. | nif-0000-31902 | SCR_008565 | Wheaton | 2026-09-12 12:57:05 | 9 | |||||||||||
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Public Health Resources Unit Resource Report Resource Website 1+ mentions |
Public Health Resources Unit (RRID:SCR_008564) | software resource | THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. Tools were developed by the Critical Appraisal Skills Programme (CASP) to help with the process of critically appraising articles of the following types of research. These are available and free to download for personal use. | THIS RESOURCE IS NO LONGER IN SERVICE | nif-0000-31450 | http://www.sph.nhs.uk/what-we-do/public-health-workforce/resources/critical-appraisals-skills-programme | http://www.phru.nhs.uk/Pages/PHD/resources.htm | SCR_008564 | PHRU | 2026-09-12 12:57:05 | 1 | ||||||||
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Research Randomizer Resource Report Resource Website 1000+ mentions |
Research Randomizer (RRID:SCR_008563) | data access protocol, software resource, web service | 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. | is listed by: SoftCite | nif-0000-31448 | SCR_008563 | Research Randomizer | 2026-09-12 12:57:05 | 3018 | ||||||||||
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ARIAD Resource Report Resource Website 50+ mentions |
ARIAD (RRID:SCR_008559) | ARIAD | material service resource, production service resource, reagent manufacture, service resource | Cambridge, Massachusetts-based biotechnology company focused on cancer. Focus areas are blood cancers and solid tumors. Compounds: ponatinib, AP26113, ridaforolimus and AP1903., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025. | oncology, drug, therapy, treatment, drug development | uses: FluoroFinder | Cancer | THIS RESOURCE IS NO LONGER IN SERVICE | nif-0000-31436 | http://www.ariad.com/wt/page/regulation_kits | SCR_008559 | ARIAD Pharmaceuticals | 2026-09-12 12:57:05 | 55 | |||||
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LAGAN Resource Report Resource Website 50+ mentions |
LAGAN (RRID:SCR_008558) | software resource | About the LAGAN Toolkit The LAGAN Tookit consists of four components: CHAOS CHAOS is a pairwise local aligner optimized for non-coding, and other poorly conserved regions of the genome. It uses both exact matching and degenerate seeds, and is able to find homology in the presence of gaps. LAGAN LAGAN is our highly parametrizable pairwise global alignment program. It takes local alignments generated by CHAOS as anchors, and limits the search area of the Needleman-Wunsch algorithm around these anchors; Multi-LAGAN Multi-LAGAN is a generalization of the pairwise algorithm to multiple sequence alignment. M-LAGAN performs progressive pairwise alignments, guided by a user-specified phylogenetic tree. Alignments are aligned to other alignments using the sum-of-pairs metric. Shuffle-LAGAN Shuffle-LAGAN is a novel glocal alignment algorithm that is able to find rearrangements (inversions, transpositions and some duplications) in a global alignment framework. It uses CHAOS local alignments to build a map of the rearrangements between the sequences, and LAGAN to align the regions of conserved synteny. The website uses scripts written by Alex Poliakov. The website was designed by Marina Sirota. | has parent organization: Stanford University; Stanford; California | DOI:10.1101/gr.926603 | OMICS_08469, nif-0000-31432 | https://sources.debian.org/src/lagan/ | SCR_008558 | LAGAN | 2026-09-12 12:57:05 | 89 | ||||||||
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LexisNexis Resource Report Resource Website 100+ mentions |
LexisNexis (RRID:SCR_008433) | data or information resource, portal, topical portal | A commercial software provider designed for legal, risk management, corporate, government, law enforcement, accounting, and academic markets. Sponsors: This resource is Reed Elsevier, Inc. Keywords: Workflow, Professional, Legal, Risk, Management, Corporate, Government, Law, Enforcement, Accounting, Academic, Technology, Information, | nif-0000-30229 | SCR_008433 | LexisNexis | 2026-09-12 12:57:04 | 230 | |||||||||||
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SAFC Resource Report Resource Website 100+ mentions |
SAFC (RRID:SCR_008554) | commercial organization | THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. A business division of Sigma-Aldrich Corporation, focusing on providing custom manufactured products and specialized services used in the industrial development and manufacturing, including processes, that bring new drugs and new electronic products to market., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025. | THIS RESOURCE IS NO LONGER IN SERVICE | nif-0000-31481 | SCR_008554 | SAFC | 2026-09-12 12:57:05 | 380 | ||||||||||
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UCSD Center for AIDS Research Molecular Biology Core Resource Report Resource Website |
UCSD Center for AIDS Research Molecular Biology Core (RRID:SCR_008435) | access service resource, core facility, service resource | THIS RESOURCE IS NO LONGER IN SERVICE, documented on January 31, 2022. The UCSD CFAR/VMRF Molecular Biology Core (MBC) is a service core designed to facilitate and support HIV/AIDS research at the University of California San Diego (UCSD), the VA San Diego Healthcare System (VASDHCS), the Veterans Medical Research Foundation (VMRF), the UCSD Antiviral Research Center (AVRC), the Scripps Research Institute, and others in the San Diego HIV/AIDS research community. The MBC provides a variety of services, including DNA sequencing, viral DNA and RNA quantification, cDNA microarray analysis of herpesvirus expression, lentiviral vectors, RNAi design and synthesis, custom vector and plasmid design and construction, plasmids and other reagents of interest to HIV/AIDS research, shared access to computational biology software, and a variety of other services. The core is operated in association with the VMRF, the UCSD AIDS Research Institute (ARI), the VASDHCS, and the VA Research Center for AIDS and HIV Infection (RACHI). The VMRF/CFAR MBC is open to all UCSD, VA, and VMRF investigators as well as those from outside institutions. Keywords: Biology, Research, Medical, Molecular, DNA, Sequencing, Healthcare, RNA, DNA, cDNA, Microarray, Analysis, Herpesvirus, Expression, Lentiviral, | has parent organization: University of California at San Diego; California; USA | THIS RESOURCE IS NO LONGER IN SERVICE | nif-0000-30249 | SCR_008435 | UCSD CFAR MBC | 2026-09-12 12:57:04 | 0 |
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The data sets included in this resource are published in Kravitz et al, 2012. Distinct roles for direct and indirect pathway striatal neurons in reinforcement (Nat. Neurosci).
