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https://portal.dbmi.hms.harvard.edu/projects/GRDR/

Data repository of de-identified patient data, aggregated in a standardized manner, to enable analyses across many rare diseases and to facilitate various research projects, clinical studies, and clinical trials. The aim is to facilitate drug and therapeutics development, and to improve the quality of life for the many millions of people who are suffering from rare diseases. The goal of GRDR is to enable analyses of data across many rare diseases and to facilitate clinical trials and other studies. During the two-year pilot program, a web-based template will be developed to allow any patient organization to establish a rare disease patient registry. At the conclusion of the program, guidance will be available to patient groups to establish a registry and to contribute de-identified patient data to the GRDR repository. A Request for Information (RFI) was released on February 10, 2012 requesting information from patient groups about their interest in participating in a GRDR pilot project. ORDR selected 30 patient organizations to participate in this pilot program to test the different functionalities of the GRDR. Fifteen (15) organizations with established registries and 15 organizations that do not have patient registry. The 15 patient groups, each without a registry, were selected to assist in testing the implementation of the ORDR Common Data Elements (CDEs) in the newly developed registry infrastructure. These organizations will participate in the development and promotion of a new patient registry for their rare disease. The GRDR program will fund the development and hosting of the registry during the pilot program. Thereafter, the patient registry is expected to be self-sustaining.The 15 established patient registries were selected to integrate their de-identified data into the GRDR to evaluate the data mapping and data import/export processes. The GRDR team will assist these organizations in mapping their existing registry data to the CDEs. Participating registries must have a means to export their de-identified registry data into a specified data format that will facilitate loading the data into the GRDR repository on a regular basis. The GRDR will also develop the capability to link patients'''' data and medical information to donated biospecimens by using a Voluntary Global Unique Patient Identifier (GUID). The identifier will enable the creation of an interface between the patient registries that are linked to biorepositories and the Rare Disease Human Biospecimens/Biorepositories (RD-HUB) http://biospecimens.ordr.info.nih.gov/.

Proper citation: GRDR (RRID:SCR_008978) Copy   

•   Source: SciCrunch Registry

http://www.jst.go.jp/csc/virtual/find/mindlab/english/base.html

Interactive laboratory of the mind composed of 4 themed sessions housing four short introductory movies and sixteen trials with those you can experience visual phenomena and illusions used for study in psychological experiments.

Proper citation: Mind Lab (RRID:SCR_009025) Copy   

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http://ageing-map.org/

Database of age-related changes covering different biological levels, including molecular, physiological, psychological and pathological age-related data, to create an interactive portal that serves as a centralized collection of human aging changes and pathologies. To facilitate integrative, system-level studies of aging, the DAA provides a centralized source for aging-related data as well as basic tools to query and visualize the data, including anatomical models. Data in the DAA is manually curated from the literature and retrieved from public databases. For more detailed analyses users are able to download the entire database. More information on how to use the DAA is available on the help page. The DAA primarily focuses on human aging, but also includes supplementary mouse data, in particular gene expression data, to enhance and expand the information on human aging. If you would like to contribute to the database yourself, for instance if you have new data on aging, please use the contribute page to submit your data.

Proper citation: Digital Ageing Atlas (RRID:SCR_009020) Copy   

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http://bejerano.stanford.edu/prism/public/html/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 5,2022.Tool that predicts interactions between transcription factors and their regulated genes from binding motifs. Understanding vertebrate development requires unraveling the cis-regulatory architecture of gene regulation. PRISM provides accurate genome-wide computational predictions of transcription factor binding sites for the human and mouse genomes, and integrates the predictions with GREAT to provide functional biological context. Together, accurate computational binding site prediction and GREAT produce for each transcription factor: 1. putative binding sites, 2. putative target genes, 3. putative biological roles of the transcription factor, and 4. putative cis-regulatory elements through which the factor regulates each target in each functional role., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: PRISM (Stanford database) (RRID:SCR_005375) Copy   

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http://fcon_1000.projects.nitrc.org/

Collection of resting state fMRI (R-fMRI) datasets from sites around world. It demonstrates open sharing of R-fMRI data and aims to emphasize aggregation and sharing of well-phenotyped datasets.

Proper citation: 1000 Functional Connectomes Project (RRID:SCR_005361) Copy   

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http://statgenpro.psychiatry.hku.hk/limx/kggseq/

A biological Knowledge-based mining platform for Genomic and Genetic studies using Sequence data. The software platform, constituted of bioinformatics and statistical genetics functions, makes use of valuable biologic resources and knowledge for sequencing-based genetic mapping of variants / genes responsible for human diseases / traits. It facilitates geneticists to fish for the genetic determinants of human diseases / traits in the big sea of DNA sequences. KGGSeq has paid attention to downstream analysis of genetic mapping. The framework was implemented to filter and prioritize genetic variants from whole exome sequencing data.

Proper citation: KGGSeq (RRID:SCR_005311) Copy   

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http://www.med.harvard.edu/AANLIB/

An atlas of normal and abnormal brain images intended as an introduction to basic neuroanatomy, with emphasis on the pathoanatomy of several leading central nervous system diseases that integrates clinical information with magnetic resonance (MR), x-ray computed tomography (CT), and nuclear medicine images. A range of brain abnormalities are presented including examples of certain brain disease presented with various combinations of image type and imaging frequency. Submissions of concise, exemplary, clinically driven examples of neuroimaging are welcome.

Proper citation: Whole Brain Atlas (RRID:SCR_005390) Copy   

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https://scicrunch.org/scicrunch/data/source/nlx_154697-14/search?q=*

A virtual database currently indexing the following scientific Job resources: Naturejobs, Monster, Indeed, Hays, jobs.ac.uk, New Scientist Jobs, Science Careers, Access-ScienceJobs.co.uk, TheScienceJobs.com, ScienceBlogs: Jobs, and It Takes 30.

Proper citation: Integrated Jobs (RRID:SCR_005384) Copy   

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http://services.nbic.nl/copub/portal/

Text mining tool that detects co-occuring biomedical concepts in abstracts from the MedLine literature database. It allows batch input of multiple human, mouse or rat genes and produces lists of keywords from several biomedical thesauri that are significantly correlated with the set of input genes. These lists link to Medline abstracts in which the co-occurring input genes and correlated keywords are highlighted. Furthermore, CoPub can graphically visualize differentially expressed genes and over-represented keywords in a network, providing detailed insight in the relationships between genes and keywords, and revealing the most influential genes as highly connected hubs.

Proper citation: CoPub (RRID:SCR_005327) Copy   

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http://en.wikibooks.org/wiki/MINC/Atlases

A linear average model atlas produced by the International Consortium for Brain Mapping (ICBM) project. A set of full- brain volumetric images from a normative population specifically for the purposes of generating a model were collected by the Montreal Neurological Institute (MNI), UCLA, and University of Texas Health Science Center at San Antonio Research Imaging Center (RIC). 152 new subjects were scanned using T1, T2 and PD sequences using a specific protocol. These images were acquired at a higher resolution than the original average 305 data and exhibit improved contrast due predominately to advances in imaging technology. Each individual was linearly registered to the average 305 and a new model was formed. In total, three models were created at the MNI, the ICBM152_T1, ICBM152_T2 and ICBM152_PD from 152 normal subjects. This resulting model is now known as the ICBM152 (although the model itself has not been published). One advantage of this model is that it exhibits better contrast and better definition of the top of the brain and the bottom of the cerebellum due to the increased coverage during acquisition. The entirely automatic analysis pipeline of this data also included grey/white matter segmentation via spatial priors. The averaged results of these segmentations formed the first MNI parametric maps of grey and white matter. The maps were never made publicly available in isolation but have formed parts of other packages for some time including SPM, FSL AIR and as models of grey matter for EEG source location in VARETTA and BRAINWAVE. Again, as these models are an approximation of Talairach space, there are differences in varying areas, to continue our use of origin shift as an example, the ICBM models are approximately 152: +3.5mm in Z and +-co-ordinate -3.5mm and 2.0mm in Y as compared to the original Talairach origin. In addition to the standard analysis performed on the ICBM data, 64 of the subjects data were segmented using model based segmentation. 64 of the original 305 were manually outlined and a resulting parametric VOI atlas built. The native data from these acquisitions was 256x256 with 1mm slices. The final image resolution of this data was 181x217x181 with 1mm isotropic voxels. Refer to the ICBM152 NonLinear if you are fitting an individual to model and do not care about left/right comparisons. A short history of the various atlases that have been produced at the BIC (McConnell Brain Imaging Center, Montreal Neurological Institute) is provided.

Proper citation: MINC/Atlases (RRID:SCR_005281) Copy   

•   Source: SciCrunch Registry

http://fcon_1000.projects.nitrc.org/indi/adhd200/index.html#

A grassroots initiative dedicated to accelerating the scientific community''''s understanding of the neural basis of ADHD through the implementation of open data-sharing and discovery-based science. They believe that a community-wide effort focused on advancing functional and structural imaging examinations of the developing brain will accelerate the rate at which neuroscience can inform clinical practice. The ADHD-200 Global Competition invited participants to develop diagnostic classification tools for ADHD diagnosis based on functional and structural magnetic resonance imaging (MRI) of the brain. Applying their tools, participants provided diagnostic labels for previously unlabeled datasets. The competition assessed diagnostic accuracy of each submission and invited research papers describing novel, neuroscientific ideas related to ADHD diagnosis. Twenty-one international teams, from a mix of disciplines, including statistics, mathematics, and computer science, submitted diagnostic labels, with some trying their hand at imaging analysis and psychiatric diagnosis for the first time. The data for the competition was provided by the ADHD-200 Consortium. Consortium members from institutions around the world provided de-identified, HIPAA compliant imaging datasets from almost 800 children with and without ADHD. A phenotypic file including all of the test set subjects and their diagnostic codes can be downloaded. Winner is presented. The ADHD-200 consortium included: * Brown University, Providence, RI, USA (Brown) * The Kennedy Krieger Institute, Baltimore, MD, USA (KKI) * The Donders Institute, Nijmegen, The Netherlands (NeuroImage) * New York University Medical Center, New York, NY, USA (NYU) * Oregon Health and Science University, Portland, OR, USA (OHSU) * Peking University, Beijing, P.R.China (Peking 1-3) * The University of Pittsburgh, Pittsburgh, PA, USA (Pittsburgh) * Washington University in St. Louis, St. Louis, MO, USA (WashU)

Proper citation: ADHD-200 Sample (RRID:SCR_005358) Copy   

•   Source: SciCrunch Registry

http://www.ncbi.nlm.nih.gov/gtr/

Central location for voluntary submission of genetic test information by providers including the test''s purpose, methodology, validity, evidence of the test''s usefulness, and laboratory contacts and credentials. GTR aims to advance the public health and research into the genetic basis of health and disease. GTR is accepting registration of clinical tests for Mendelian disorders, complex tests and arrays, and pharmacogenetic tests. These tests may include multiple methods and may include multiple major method categories such as biochemical, cytogenetic, and molecular tests. GTR is not currently accepting registration of tests for somatic disorders, research tests or direct-to-consumer tests.

Proper citation: Genetic Testing Registry (RRID:SCR_005565) Copy   

•   Source: SciCrunch Registry

https://github.com/laserson/vdj

Python package for analysing immune receptor sequences (antibodies and T cell receptors).

Proper citation: VDJ (RRID:SCR_005475) Copy   

•   Source: SciCrunch Registry

http://headit.ucsd.edu

Platform for sharing, download, and re-analysis or meta-analysis of sophisticated, fully annotated, human electrophysiological data sets. It uses EEG Study Schema (ESS) files to provide task, data collection, and subject metadata, including Hierarchical Event Descriptor (HED) tag descriptions of all identified experimental events. Visospatial task data also available from, http://sccn.ucsd.edu/eeglab/data/headit.html: A 238-channel, single-subject EEG data set recorded at the Swartz Center, UCSD, by Arnaud Delorme, Julie Onton, and Scott Makeig is al.

Proper citation: HeadIT (RRID:SCR_005657) Copy   

•   Source: SciCrunch Registry

http://www.gene-regulation.com/pub/databases.html#transpath

Database on eukaryotic transcription factors, their experimentally-proven binding sites, consensus binding sequences (positional weight matrices) and regulated genes. Its broad compilation of binding sites allows the derivation of positional weight matrices. It can either be used as an encyclopedia, for both specific and general information on signal transduction, or can serve as a network analyzer. Cross-references to important sequence and signature databases such as EMBL/GenBank UniProt/Swiss-Prot InterPro or Ensembl EntrezGene RefSeq are provided. The database is equipped with the tools for data visualization and analysis. It has three modules: the first one is the data, which have been manually extracted, mostly from the primary literature; the second is PathwayBuilder, which provides several different types of network visualization and hence facilitates understanding; the third is ArrayAnalyzer, which is particularly suited to gene expression array interpretation, and is able to identify key molecules within signalling networks (potential drug targets). These key molecules could be responsible for the coordinated regulation of downstream events. Manual data extraction focuses on direct reactions between signalling molecules and the experimental evidence for them, including species of genes/proteins used in individual experiments, experimental systems, materials and methods. This combination of materials and methods is used in TRANSPATH to assign a quality value to each experimentally proven reaction, which reflects the probability that this reaction would happen under physiological conditions. Another important feature in TRANSPATH is the inclusion of transcription factor-gene relations, which are transferred from TRANSFAC, a database focused on transcription regulation and transcription factors. Since interactions between molecules are mainly direct, this allows a complete and stepwise pathway reconstruction from ligands to regulated genes.

Proper citation: TRANSPATH (RRID:SCR_005640) Copy   

•   Source: SciCrunch Registry

http://www.nimh.nih.gov/about/advisory-boards-and-groups/namhc/reports/mri-research-safety-ethics.pdf

NIMH recognizes the need to consider safety and ethical issues related to both the administration of MR (magnetic resonance) facilities and the use of these facilities for research. This document summarizes the points to consider discussed by the National Advisory Mental Health Council (NAMHC) Workgroup. Examples of safe and ethical practices are discussed in relation to several issues. These examples are intended to be illustrative and should not be interpreted as an exhaustive or exclusive list. This document was presented to the full NIMH Council on September 15, 2006 and approved unanimously. By making the points to consider document available publicly, NIMH intends to provide a resource for researchers and institutions that use MRI in research. The agenda was organized into six topics, which provide the organization for the points to consider that follow: A. MRI screening B. Training, operating, and emergency procedures C. Physical facilities D. Scanning/participant health variables E. Context- Specific Considerations: University vs. medical settings F. Additional data needs and updating The NIMH believes that investigators, institutions and facilities can use this document as a resource for the development, administration, evaluation, and use of MRI research facilities.

Proper citation: MRI Research Safety and Ethics (RRID:SCR_005642) Copy   

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http://llama.mshri.on.ca/

The Roth Laboratory is designing and interpreting large-scale experiments to understand pathway structure and its relationship to phenotype and human disease. Software for research focused on a specific research goal is available. Current experimental interests: * Exploiting parallel sequencing technology to phenotype all pairwise gene deletion combinations in S. cerevisiae, with initial application to genes involved in transcription. * Generation of S. cerevisiae strains carrying dozens of chosen targeted deletions, with initial application to delete all ABC transporters imparting multidrug resistance. * Targeted insertion of gene sets encoding entire human pathways into S. cerevisiae, with initial application to genes involved in drug metabolism. Current computational interests: * Systematic analysis of genetic interaction to reveal redundant systems and order of action in genetic pathways * Integrating large-scale studies - including phenotype, genetic epistasis, protein-protein and transcription-regulatory interactions and sequence patterns - to quantitatively assign function to genes and guide experimentation and disease association studies. * Alternative splicing and its relationship to protein interaction networks.

Proper citation: Roth Laboratory (RRID:SCR_005711) Copy   

•   Source: SciCrunch Registry

http://genemania.org/

Data analysis service to predict the function of your favorite genes and gene sets. Indexing 1,421 association networks containing 266,984,699 interactions mapped to 155,238 genes from 7 organisms. GeneMANIA interaction networks are available for download in plain text format. GeneMANIA finds other genes that are related to a set of input genes, using a very large set of functional association data. Association data include protein and genetic interactions, pathways, co-expression, co-localization and protein domain similarity. You can use GeneMANIA to find new members of a pathway or complex, find additional genes you may have missed in your screen or find new genes with a specific function, such as protein kinases. Your question is defined by the set of genes you input. If members of your gene list make up a protein complex, GeneMANIA will return more potential members of the protein complex. If you enter a gene list, GeneMANIA will return connections between your genes, within the selected datasets. GeneMANIA suggests annotations for genes based on Gene Ontology term enrichment of highly interacting genes with the gene of interest. GeneMANIA is also a gene recommendation system. GeneMANIA is also accessible via a Cytoscape plugin, designed for power users. Platform: Online tool, Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: GeneMANIA (RRID:SCR_005709) Copy   

•   Source: SciCrunch Registry

http://ki.se/ki/jsp/polopoly.jsp?d=29354&a=31610&l=en

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 29, 2016. KI Biobank - Gallstone aims at investigating genetics of gallstone disease on Swedish Twins. Types of samples * EDTA whole blood * DNA * Plasma Number of sample donors: 82

Proper citation: KI Biobank (RRID:SCR_005664) Copy   

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http://www.bscs.org/science-mental-illness

A set of lessons for students used to gain insight into the biological basis of mental illnesses and how scientific evidence and research can help us understand its causes and lead to treatments and, ultimately, cures. Both the Web version and the free supplement are available. It is a creative, inquiry-based instruction program designed to promote active learning and stimulate student interest in medical topics. This curriculum supplement aims to help students experience the process of scientific inquiry and develop an enhanced understanding of the nature and methods of science.

Proper citation: Science of Mental Illness: Grades 6- 8 (RRID:SCR_005612) Copy   

•   Source: SciCrunch Registry