Searching the RRID Resource Information Network
Are you sure you want to leave this community? Leaving the community will revoke any permissions you have been granted in this community.
SciCrunch Registry is a curated repository of scientific resources, with a focus on biomedical resources, including tools, databases, and core facilities - visit SciCrunch to register your resource.
https://bams1.org/ontology/viewer.php
Ontology designed for neuroscience. Includes complete set of concepts that describe parts of rat nervous system, growing set of concepts that describe neuron populations identified in different brain regions, and relationships between concepts.
Proper citation: BAMS Neuroanatomical Ontology (RRID:SCR_004616) Copy
Open platform for analyzing and sharing neuroimaging data from human brain imaging research studies. Brain Imaging Data Structure ( BIDS) compliant database. Formerly known as OpenfMRI. Data archives to hold magnetic resonance imaging data. Platform for sharing MRI, MEG, EEG, iEEG, and ECoG data.
Proper citation: OpenNeuro (RRID:SCR_005031) Copy
This resource provides a list of federal program officials in the neurosciences. An informal compendium of names and contact information for nearly 300 research grant and scientific review administrators in 21 organizational units.
Proper citation: NRF Contacts (RRID:SCR_001473) Copy
http://hopkinsneuro.org/research/jhu_nimh/
The Johns Hopkins NIMH Center is comprised of an interdisciplinary research team who has pooled their talents to study the nature of HIV-associated neurocognitive disorders (HAND). Their aim is to translate discoveries of the pathophysiological mechanisms into novel therapeutics for HAND. Objectives * To integrate aspects of ongoing research in HAND and SIV encephalitis * Develop high-throughput and screening assays for identifying novel therapeutic compounds * Use proteomics and lipidomics approaches to indentifying surrogate markers of disease activity * Disseminate information and education about HAND through existing and new educational systems, including the JHU AIDS Education Training Center and the JHU Center for Global Clinical Education * Facilitate the entry of new investigators into Neuro-AIDS research, and to catalyze new areas of research, particularly where relevant for drug discovery or the development of validated surrogate markers
Proper citation: Johns Hopkins NIMH Research Center Novel Therapeutics of HIV-associated Cognitive Disorders (RRID:SCR_001891) Copy
THIS RESOURCE IS NO LONGER IN SERVICE, documented on April 24, 2017. Database of images depicting the spatial distribution of 2-deoxyglucose uptake evoked in the glomerular layer of the rat olfactory bulb in response to a wide range of defined odorant stimuli. A number of different display and comparison tools are provided allowing patterns to be viewed from different perspectives, and descriptions of the methods and interpretations of these data are provided. Some of the more advanced tools require you to download software.
Proper citation: Glomerular Activity Response Archive (RRID:SCR_002089) Copy
http://www.nitrc.org/projects/voxbo
Software package for brain image manipulation and analysis, focusing on fMRI and lesion analysis. VoxBo can be used independently or in conjunction with other packages. It provides GLM-based statistical tools, an architecture for interoperability with other tools (they encourage users to incorporate SPM and FSL into their processing pipelines), an automation system, a system for parallel distributed computing, numerous stand-alone tools, decent wiki-based documentation, and lots more.
Proper citation: VoxBo (RRID:SCR_002166) Copy
http://www.nitrc.org/projects/penncnv
A free software tool for Copy Number Variation (CNV) detection from SNP genotyping arrays. Currently it can handle signal intensity data from Illumina and Affymetrix arrays. With appropriate preparation of file format, it can also handle other types of SNP arrays and oligonucleotide arrays. PennCNV implements a hidden Markov model (HMM) that integrates multiple sources of information to infer CNV calls for individual genotyped samples. It differs form segmentation-based algorithm in that it considered SNP allelic ratio distribution as well as other factors, in addition to signal intensity alone. In addition, PennCNV can optionally utilize family information to generate family-based CNV calls by several different algorithms. Furthermore, PennCNV can generate CNV calls given a specific set of candidate CNV regions, through a validation-calling algorithm.
Proper citation: PennCNV (RRID:SCR_002518) Copy
https://trialweb.dcri.duke.edu/tads/index.html
THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 16,2023. Multi-site clinical research study examining the short- and long-term effectiveness of an antidepressant medication and psychotherapy alone and in combination for treating depression in adolescents ages 12 to 17. For teens treated in TADS, the trial is designed to provide best-practice practical care for depression.
Proper citation: TADS - Treatment for Adolescents with Depression Study (RRID:SCR_000037) Copy
https://github.com/davidaknowles/leafcutter/
Software tool for identifying and quantifying RNA splicing variation. Used to study sample and population variation in intron splicing. Identifies variable intron splicing events from short read RNA-seq data and finds alternative splicing events of high complexity. Used for detecting differential splicing between sample groups, and for mapping splicing quantitative trait loci (sQTLs).
Proper citation: LeafCutter (RRID:SCR_017639) Copy
https://github.com/zburkett/VoICE
Software that groups vocal elements of birdsong by creating a high dimensionality dataset through scoring spectral similarity between vocalizations.
Proper citation: Vocal Inventory Clustering Engine (VoICE) (RRID:SCR_016004) Copy
https://github.com/broadinstitute/Drop-seq
Software Java tools for analyzing Drop-seq data. Used to analyze gene expression from thousands of individual cells simultaneously. Analyzes mRNA transcripts while remembering origin cell transcript.
Proper citation: Drop-seq tools (RRID:SCR_018142) Copy
https://github.com/satijalab/seurat
Software R package designed for QC, analysis, and exploration of single cell RNA-seq data. Enable users to identify and interpret sources of heterogeneity from single cell transcriptomic measurements, and to integrate diverse types of single cell data. Used for quality control, analysis, and exploration of single-cell RNA sequencing (scRNA-seq) data.
Proper citation: Seurat (RRID:SCR_016341) Copy
Web based tool to visualize gene expression and metadata annotation distribution throughout single cell dataset or multiple datasets. Interactive viewer for single cell expression. You can click on and hover over cells to get meta information, search for genes to color on and click clusters to show cluster specific marker genes.
Proper citation: UCSC Cell Browser (RRID:SCR_023293) Copy
Realistic simulated MEG datasets ranging from basic sensory to oscillatory sets that mimic functional connectivity; as well as basic visual, auditory, and somatosensory empirical sets. The simulated sets were created for the purpose of testing analysis algorithms across the different MEG systems when the truth is known. MEG baseline recordings were obtained from 5 healthy participants, using three MEG systems: VSM/CTF Omega, Elekta Neuromag Vectorview, 4-D Magnes 3600. Simulated signals were embedded within the CTF and Neuromag 306 baseline recordings (4-D to be added). Participant MRIs are available. Averaged simulation files are available as netcdf files. Neuromag 306 averaged simulations are also available in fif format. Also available: single trials of data where the simulated signal is jittered about a mean value, continuous fif files where the simulated signal is marked by a trigger, and simulations with oscillations added to mimic functional connectivity.
Proper citation: MEGSIM (RRID:SCR_002420) Copy
http://humanconnectome.org/connectome/connectomeDB.html
Data management platform that houses all data generated by the Human Connectome Project - image data, clinical evaluations, behavioral data and more. ConnectomeDB stores raw image data, as well as results of analysis and processing pipelines. Using the ConnectomeDB infrastructure, research centers will be also able to manage Connectome-like projects, including data upload and entry, quality control, processing pipelines, and data distribution. ConnectomeDB is designed to be a data-mining tool, that allows users to generate and test hypotheses based on groups of subjects. Using the ConnectomeDB interface, users can easily search, browse and filter large amounts of subject data, and download necessary files for many kinds of analysis. ConnectomeDB is designed to work seamlessly with Connectome Workbench, an interactive, multidimensional visualization platform designed specifically for handling connectivity data. De-identified data within ConnectomeDB is publicly accessible. Access to additional data may be available to qualified research investigators. ConnectomeDB is being hosted on a BlueArc storage platform housed at Washington University through the year 2020. This data platform is based on XNAT, an open-source image informatics software toolkit developed by the NRG at Washington University. ConnectomeDB itself is fully open source.
Proper citation: ConnectomeDB (RRID:SCR_004830) Copy
Study of mental health risk and resilience factors ever conducted among military personnel. The purpose of Army STARRS is to identify as quickly as possible factors that protect or pose risks to Soldiers'' emotional well-being and overall mental health so that the Army may apply the knowledge to its ongoing health promotion, risk reduction, and suicide prevention efforts. Army STARRS investigators will use four separate study components the Historical Data Study, New Soldier Study, All Army Study, and Soldier Health Outcomes Study to identify factors that help protect a Soldier''s mental health and factors that put a Soldier''s mental health at risk. Army STARRS is a five-year study that will run through 2014. Findings will be reported as they become available, so that the Army may apply them to its ongoing health promotion, risk reduction, and suicide prevention efforts. Given its length and scope, Army STARRS will generate a vast amount of information and will allow investigators to focus on periods in a military career that are known to be high risk for psychological problems. The information gathered from volunteer participants throughout the study will help researchers identify not only potentially relevant risk factors, but potential protective factors as well. Because promoting mental health and reducing suicide risk are important for all Americans, the findings from Army STARRS will benefit not only servicemembers but the nation as a whole. NIMH has assembled a group of renowned experts to carry out this research including teams from the Uniformed Services University of the Health Sciences (USUHS), the University of California, San Diego, University of Michigan, Harvard Medical School, and NIMH. Additional Army and NIMH program staff will contribute to the oversight and implementation of the study. This research team brings together international leaders in military health, health and behavior surveys, epidemiology, suicide, and genetic and neurobiological factors involved in psychological health.
Proper citation: Army STARRS (RRID:SCR_006708) Copy
http://www.nimh.nih.gov/funding/clinical-trials-for-researchers/practical/step-bd/index.shtml
A long-term outpatient study designed to find out which treatments, or combinations of treatments, are most effective for treating episodes of depression and mania and for preventing recurrent episodes in people with bipolar disorder. This study has been completed. (2005) STEP-BD is evaluating all the best-practice treatment options used for bipolar disorder: mood-stabilizing medications, antidepressants, atypical antipsychotics, and psychosocial interventions - or talk therapies - including Cognitive Behavioral Therapy, Family-focused Therapy, Interpersonal and Social Rhythm Therapy, and Collaborative Care (psychoeducation). There are two kinds of treatment pathways in STEP-BD, and participants may have the opportunity to take part in both. The medications and psychosocial interventions provided in these pathways are considered among the best choices of treatment for bipolar disorder in everyday clinical practice. In the Best Practice Pathway, participants are followed by a STEP-BD certified doctor and all treatment choices are individualized. Everyone enrolled in STEP-BD may participate in this pathway. Participants and their doctors work together to decide on the best treatment plans and to change these plans if needed. Also, anyone who wishes to stay on his or her current treatment upon entering STEP-BD may do so in this pathway. Adolescents and adults age 15 years and older may participate in the Best Practice Pathway. For adults age 18 and older, another way to participate is in the STEP-BD Randomized Care Pathways. Depending on their symptoms, participants may be offered treatment in one or more of these pathways during the course of the study. The participants remain on mood-stabilizing medication. However, because doctors are uncertain which of several treatment strategies work best for bipolar disorder, another medication and/or talk therapy may be added. Each Randomized Care Pathway involves a different set of these additional treatments. Unlike in the Best Practice Pathway, the participants in the Randomized Care Pathways are randomly assigned to treatments. Also, in some cases, neither the participant nor the doctor will be told which of the different medications is being added. This is called a double-blind study and is done so that the medication effects can be evaluated objectively, without any unintended bias that may come from knowing what has been assigned. Participants will not be assigned medications that they have had bad reactions to in the past, that they are strongly opposed to, or that the doctor feels are unsuitable for them. The medication(s) participants may be randomly assigned to in the Randomized Care Pathways are free of charge. There are other treatment options for participants if they do not respond well to the treatment assigned to them. Also, participants may return to the Best Practice Pathway at any time. About 1,500 individuals will be enrolled in at least one Randomized Care Pathway during their period of participation in STEP-BD. It is important to note that STEP-BD provides continuity of care. For example, if a participant starts out in the Best Practice Pathway and later chooses to enter one of the Randomized Care Pathways, he or she continues with the same STEP-BD doctor and treatment team. Then, after completing the Randomized Care Pathway, the participant may return to the Best Practice Pathway for ongoing, individually-tailored treatment. Follow the link to view study info at Clinicaltrials.gov, http://www.clinicaltrials.gov/ct/show/NCT00012558?order=1
Proper citation: Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD) (RRID:SCR_008844) Copy
http://research.mssm.edu/cnic/
Center to advance research and training in mathematical, computational and modern imaging approaches to understanding the brain and its functions. Software tools and associated reconstruction data produced in the center are available. Researchers study the relationships between neural function and structure at levels ranging from the molecular and cellular, through network organization of the brain. This involves the development of new computational and analytic tools for imaging and visualization of 3-D neural morphology, from the gross topologic characteristics of the dendritic arbor to the fine structure of spines and their synapses. Numerical simulations of neural mechanisms based on these structural data are compared with in-vivo and in-vitro electrophysiological recordings. The group also develops new theoretical and analytic approaches to exploring the function of neural models of working memory. The goal of this analytic work is to combine biophysically realistic models and simulations with reduced mathematical models that capture essential dynamical behaviors while reproducing the functionally important features of experimental data. Research areas include: Imaging Studies, Volume Integration, Visualization Techniques, Medial Axis Extraction, Spine Detection and Classification, Applications of Rayburst, Analysis of Spatially Complex Structures, Computational Modeling, Mathematical and Analytic Studies
Proper citation: Computational Neurobiology and Imaging Center (RRID:SCR_013317) Copy
http://interactome.baderlab.org/
Project portal for the Human Reference Protein Interactome Project, which aims generate a first reference map of the human protein-protein interactome network by identifying binary protein-protein interactions (PPIs). It achieves this by systematically interrogating all pairwise combinations of predicted human protein-coding genes using proteome-scale technologies.
Proper citation: Human Reference Protein Interactome Project (RRID:SCR_015670) Copy
Project aimed at making neuroimaging data sets of brain freely available to scientific community. By compiling and freely distributing neuroimaging data sets, future discoveries in basic and clinical neuroscience are facilitated.
Proper citation: Open Access Series of Imaging Studies (RRID:SCR_007385) Copy
Can't find your Tool?
We recommend that you click next to the search bar to check some helpful tips on searches and refine your search firstly. Alternatively, please register your tool with the SciCrunch Registry by adding a little information to a web form, logging in will enable users to create a provisional RRID, but it not required to submit.
Welcome to the RRID Resources search. From here you can search through a compilation of resources used by RRID and see how data is organized within our community.
You are currently on the Community Resources tab looking through categories and sources that RRID has compiled. You can navigate through those categories from here or change to a different tab to execute your search through. Each tab gives a different perspective on data.
If you have an account on RRID then you can log in from here to get additional features in RRID such as Collections, Saved Searches, and managing Resources.
Here is the search term that is being executed, you can type in anything you want to search for. Some tips to help searching:
You can save any searches you perform for quick access to later from here.
We recognized your search term and included synonyms and inferred terms along side your term to help get the data you are looking for.
If you are logged into RRID you can add data records to your collections to create custom spreadsheets across multiple sources of data.
Here are the sources that were queried against in your search that you can investigate further.
Here are the categories present within RRID that you can filter your data on
Here are the subcategories present within this category that you can filter your data on
If you have any further questions please check out our FAQs Page to ask questions and see our tutorials. Click this button to view this tutorial again.
