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http://www.depressionalliance.org/

DA works to relieve and to prevent depression by providing information and support services to those who are affected by it via their publications, supporter services and network of self-help groups for people affected by depression. Depression Alliances services help people to understand, work with and recover from symptoms associated with depression. Depression Alliance believes that the stigma and lack of accurate information surrounding depression continues to prevent people from seeking and finding appropriate and vital help when it is required. Early intervention and information are crucial in enabling those affected by depression to recover quickly and critically in preventing further episodes. Informed by the experiences of people with depression and by research, DA works extensively with government agencies and healthcare professionals to improve the service provision for those affected by depression. DA also campaigns to raise awareness amongst the general public about the realities of this severe and enduring illness by organizing a variety of events and initiatives.

Proper citation: Depression Alliance (RRID:SCR_001709) Copy   

•   Source: SciCrunch Registry

http://www.oege.org/

Portal for researchers to locate information relevant to interpretation and follow-up of human genetic epidemiological discoveries, including: a range of population and case and family genetic epidemiological studies, relevant gene and sequence databases, genetic variation databases, trait measurement, resource labs, journals, software, general information, disease genes and genetic diversity.

Proper citation: Online Encyclopedia for Genetic Epidemiology studies (RRID:SCR_001825) Copy   

•   Source: SciCrunch Registry

http://videolectures.net/

Award-winning free and open access educational video lectures repository. The lectures are given by distinguished scholars and scientists at the most important and prominent events like conferences, summer schools, workshops and science promotional events from many fields of Science. The portal is aimed at promoting science, exchanging ideas and fostering knowledge sharing by providing high quality didactic contents not only to the scientific community but also to the general public. All lectures, accompanying documents, information and links are systematically selected and classified through the editorial process taking into account also users' comments.

Proper citation: VideoLectures.NET (RRID:SCR_001972) Copy   

•   Source: SciCrunch Registry

http://cinteny.cchmc.org/

Online database for finding and analyzing syntenic regions across multiple genomes and measuring the extent of genome rearrangement using reversal distance as a measure.

Proper citation: Cinteny (RRID:SCR_002147) Copy   

•   Source: SciCrunch Registry

http://www.le.ac.uk/genetics/genie/vgec/index.html

Hub of evaluated genetics-related teaching resources for teachers and learners in schools and higher education, health professionals and the general public. Suggest or submit a learning resource to the VGEC. Resources include: * simple experiments suitable for all ages * tutorial material * videos on useful techniques * current and relevant links to other evaluated resources The Virtual Genetics Education Centre (VGEC) * Provides information and genetics education resources for higher education, colleges, schools, health professionals and the general public. * Encourages collaboration in the development, evaluation and sharing of genetics education resources * provides links to, and evaluates, sources of information and educational material about genetics. * Explores innovative approaches to teaching and learning in genetics, such as the SWIFT project for example where Second Life is being used to teach some aspects of genetics in a virtual laboratory.

Proper citation: Virtual Genetics Education Centre (RRID:SCR_001958) Copy   

•   Source: SciCrunch Registry

http://www.comp-bio.physics.leeds.ac.uk

The University of Leeds Computational Biology Group is an interdisciplinary research group based in the Faculty of Biological Sciences, and is part of the Centre for Nonlinear Studies. Research interests are at the interfaces of nonlinear dynamics, computational science and general physiology of excitable tissue (nervous system, cardiac and uterine muscle). A central theme is the reconstruction of tissue and organ physiology and pathologies from networks of intracellular, membrane and cellular models. Sponsors:

Proper citation: University of Leeds - Computational Biology Group (RRID:SCR_001956) Copy   

•   Source: SciCrunch Registry

http://microbes.ucsc.edu/cgi-bin/hgGateway?db=neisMeni_MC58_1

Portal contains detailed information for Neisseria meningitidis MC58. Information include DNA molecule summary, primary annotation summary, and taxonomy. It is a tool that allows the researcher to access all of the bacterial genome sequences completed to date. Users may access information on all of the bacterial genomes or any subset of them. Information in the website about its DNA molecule includes: total number of DNA molecules, total size of all DNA molecules, number of primary annotation coding bases, and number of G + C bases. Its primary annotation summary include: total genes, protein coding genes, tRNA genes, and rRNA genes. Sponsors: The CMR was previously funded by two grants, one from the U.S. Department of Energy (DOE) and one from the National Science Foundation (NSF). It is currently partially funded by a Microbial Sequence Center (MSC) grant from the National Institute of Allergy and Infectious Diseases (NIAID)

Proper citation: Neisseria meningitidis MC58 Genome Page (RRID:SCR_002200) Copy   

•   Source: SciCrunch Registry

http://mpss.danforthcenter.org/

Informational portal that aggregates information about databases for next gen sequencing.

Proper citation: NextGen Sequence Databases (RRID:SCR_002152) Copy   

•   Source: SciCrunch Registry

http://www.humanbrainmapping.org/i4a/pages/index.cfm?pageid=1

International society dedicated to advancing understanding of anatomical and functional organization of human brain using neuroimaging. Primary function of society is to provide educational forums for exchange of up-to-the-minute and groundbreaking research across neuroimaging methods and applications. OHBM achieves this through its member led committees and Annual Meeting that is held in different locations throughout the world.

Proper citation: Organization for Human Brain Mapping (RRID:SCR_001978) Copy   

•   Source: SciCrunch Registry

http://anatomy.uams.edu/anatomyhtml/neuro_atlas.html

Online educational resource for human brain and spinal cord anatomy through images. Each image is annotated with major structures and coarse dissections.

Proper citation: Neuroscience Course - Atlas Images (RRID:SCR_002381) Copy   

•   Source: SciCrunch Registry

http://www.statmethods.net/index.html

Training material created for both current R users, and experienced users of other statistical packages (e.g., SAS, SPSS, Stata) who would like to transition to R to help you quickly access this language in your work. The book inspired by this site takes the material here and significantly expands upon it.

Proper citation: Quick-R (RRID:SCR_002417) Copy   

•   Source: SciCrunch Registry

http://www.r-tutor.com/

Couple of introductory tutorials on basic R concepts that provides an introduction to the R programming language, and illustrates its use by solving elementary statistics textbook exercises. Beyond the basics, they also cover topics of GPU computing in R. An R Tutorial eBook is also available.

Proper citation: R Tutorial - An R Introduction to Statistics (RRID:SCR_002394) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/msseg

Training material for the MS lesion segmentation challenge 2008 to compare different algorithms to segment the MS lesions from brain MRI scans. Data used for the workshop is composed of 54 brain MRI images and represents a range of patients and pathology which was acquired from Children's Hospital Boston and University of North Carolian. Data has initially been randomized into three groups: 20 training MRI images, 24 testing images for the qualifying and 8 for the onsite contest at the 2008 workshop. The downloadable online database consists now of the training images (including reference segmentations) and all the 32 combined testing images (without segmentations). The naming has not been changed in comparison to the workshop compeition in order to allow easy comparison between the workshop papers and the online database papers. One dataset has been removed (UNC_test1_Case02) due to considerable motion present only in its T2 image (without motion artifacts in T1 and FLAIR). Such a dataset unfairly penalizes methods that use T2 images versus methods that don't use the T2 image. Currently all cases have been segmented by expert raters at each institution. They have significant intersite variablility in segmentation. MS lesion MRI image data for this competition was acquired seperately by Children's Hospital Boston and University of North Carolina. UNC cases were acquired on Siemens 3T Allegra MRI scanner with slice thickness of 1mm and in-plane resolution of 0.5mm. To ease the segmentation process all data has been rigidly registered to a common reference frame and resliced to isotrophic voxel spacing using b-spline based interpolation. Pre-processed data is stored in NRRD format containing an ASCII readable header and a separate uncompressed raw image data file. This format is ITK compatible. If you want to join the competition, you can download data set from links here, and submit your segmentation results at http://www.ia.unc.edu/MSseg after registering your team. They require team name, password, and email address for future contact. Once experiment is completed, you can submit the segmentation data in a zip file format. Please refer submission page for uploading data format.

Proper citation: MS lesion segmentation challenge 2008 (RRID:SCR_002425) Copy   

•   Source: SciCrunch Registry

http://phil.cdc.gov/phil/home.asp

Database of CDC's pictures organized into hierarchical categories of people, places, and science, presented as single images, image sets, and multimedia files. Much of the information critical to the communication of public health messages is pictorial rather than text-based. Created by a Working Group at the Centers for Disease Control and Prevention (CDC), the PHIL offers an organized, universal electronic gateway to CDC's pictures. Public health professionals, the media, laboratory scientists, educators, students, and the worldwide public are welcome to use this material for reference, teaching, presentation, and public health messages.

Proper citation: Public Health Image Library (RRID:SCR_002463) Copy   

•   Source: SciCrunch Registry

https://arvados.org/

Bioinformatics platform for storing, organizing, processing, and sharing genomic and other biomedical big data. Designed to make it easier for bioinformaticians to develop analyses, developers to create genomic web applications and IT administers to manage large-scale compute and storage genomic resources. Designed to run on top of cloud operating systems such as Amazon Web Services and OpenStack. Currently, there are implementations that work on AWS and Xen+Debian/Ubuntu. Functionally, Arvados has two major sets of capabilities: (a) data management and (b) compute management.

Proper citation: Arvados (RRID:SCR_002223) Copy   

•   Source: SciCrunch Registry

http://bioafrica.mrc.ac.za/index.html

The BioAfrica HIV-1 Proteomics Resource is a website that contains detailed information about the HIV-1 proteome and protease cleavage sites, as well as data-mining tools that can be used to manipulate and query protein sequence data, a BLAST tool for initiating structural analyses of HIV-1 proteins, and a proteomics tools directory. HIV Proteomics Resource contains information about each HIV-1 gene product in regard to expression, post-transcriptional / post-translational modifications, localization, functional activities, and potential interactions with viral and host macromolecules. The Proteome section contains extensive data on each of 19 HIV-1 proteins, including their functional properties, a sample analysis of HIV-1HXB2, structural models and links to other online resources. The HIV-1 Protease Cleavage Sites section provides information on the position, subtype variation and genetic evolution of Gag, Gag-Pol and Nef cleavage sites.

Proper citation: BioAfrica HIV Informatics in Africa (RRID:SCR_002295) Copy   

•   Source: SciCrunch Registry

http://hardinmd.lib.uiowa.edu/index.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 2, 2025. A medical database with lists, or directories, of information in health and medicine and images of medical conditions. Users may search Hardin MD, browse through the Medical picture gallery, and sort search results by disease or alphabetical letter.

Proper citation: Hardin MD (RRID:SCR_002364) Copy   

•   Source: SciCrunch Registry

http://www.bic.mni.mcgill.ca/software/N3/

The perl script nu_correct implements a novel approach to correcting for intensity non-uniformity in MR data that achieves high performance without requiring supervision. By making relatively few assumptions about the data, the method can be applied at an early stage in an automated data analysis, before a tissue intensity or geometric model is available. Described as Non-parametric Non-uniform intensity Normalization (N3), the method is independent of pulse sequence and insensitive to pathological data that might otherwise violate model assumptions. To eliminate the dependence of the field estimate on anatomy, an iterative approach is employed to estimate both the multiplicative bias field and the distribution of the true tissue intensities. Preprocessing of MR data using N3 has been shown to substantially improve the accuracy of anatomical analysis techniques such as tissue classification and cortical surface extraction.

Proper citation: MNI N3 (RRID:SCR_002484) Copy   

•   Source: SciCrunch Registry

http://millette.med.sc.edu/Lab%209%2610/histology_of_nervous_tissue.htm

A website for a neuroscience lab class from the University of South Carolina that contains images of different parts of the nervous system and allows students to identify each part and answer questions about it. You should be able to (a) recognize nervous tissue in routine histological sections; (b) distinguish peripheral nerves from dense CT and smooth muscle; (c) recognize the morphological differences between myelinated and unmyelinated nerves at both the light microscopic and electron microscopic levels; (d) recognize nerve cell bodies and their component parts; (e) identify and differentiate dendrites and axons; (f) understand and identify various types of neuroglia, including Schwann cells; (g) understand and identify the structural relationship of the Schwann cell cytoplasm and plasma membrane enveloping axons; (h) understand the general features of nerve synapses. You should be able to draw nerves, cell bodies, Nodes of Ranvier, synapses etc. as they would appear under both the electron and light microscopes.

Proper citation: Histology of Nervous Tissue Laboratory Course (RRID:SCR_002367) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/phycaa_plus/

Software algorithm that automatically estimates and removes physiological noise in BOLD fMRI data, including the effects of heartbeat and respiration. This algorithm (1) masks out high-variance CSF and vascular tracts that may otherwise confound analyses, and (2) regresses out noise timeseries in grey matter tissue, using an adaptive multivariate component decomposition (Canonical Autocorrelations Analysis). PHYCAA+ is an efficient, automated procedure that does NOT require external measures of physiology, nor does it require the user to manually identify noise components. Based on the peer-reviewed article: Churchill & Strother (2013). PHYCAA+: An Optimized, Adaptive Procedure for Measuring and Controlling Physiological Noise in BOLD fMRI. NeuroImage 82: 306-325

Proper citation: PHYCAA+: adaptive physiological noise correction for BOLD fMRI (RRID:SCR_002514) Copy   

•   Source: SciCrunch Registry