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http://rover.bsd.uchicago.edu/lfepr/

Biomedical technology research center that develops instrumentation, analysis techniques, spin probes and spin traps, and methodologies for imaging physiologically relevant aspects of tissue fluids, including high-resolution oxygen maps, with very low frequency electron paramagnetic resonance imaging (EPRI). Novel bridges and high-access, low-field magnet/gradient systems have produced physiologically relevant measurements and accommodate a number of resonant structures. The Center is a consortium between the University of Chicago, the University of Denver, the University of Maryland and Novosibirsk Institute of Organic Chemistry (NIOC), Russia.

Proper citation: Center for EPR Imaging in Vivo Physiology (RRID:SCR_001410) Copy   

•   Source: SciCrunch Registry

http://www.nephromine.org/

THIS RESOURCE IS NO LONGER IN SERVICE; REPLACED BY NEPHROSEQ; A growing database of publicly available renal gene expression profiles, a sophisticated analysis engine, and a powerful web application designed for data mining and visualization of gene expression. It provides unique access to datasets from the Personalized Molecular Nephrology Research Laboratory incorporating clinical data which is often difficult to collect from public sources and mouse data.

Proper citation: Nephromine (RRID:SCR_003813) Copy   

•   Source: SciCrunch Registry

http://fcon_1000.projects.nitrc.org/indi/pro/BeijingShortTR.html

Dataset of resting state fMRI scans obtained using two different TR's in healthy college-aged volunteers. Specifically, for each participant, data is being obtained with a short TR (0.4 seconds) and a long TR (2.0 seconds). In addition this dataset contains a 64-direction DTI scan for every participant. The following data are released for every participant: * 8-minute resting-state fMRI scan (TR = 2 seconds, # repetitions = 240) * 8-minute resting-state fMRI scans (TR = 0.4 seconds, # repetitions = 1200) * MPRAGE anatomical scan, defaced to protect patient confidentiality * 64-direction diffusion tensor imaging scan (2mm isotropic) * Demographic information

Proper citation: Beijing: Short TR Study (RRID:SCR_003502) Copy   

•   Source: SciCrunch Registry

http://www.physionet.org/physiobank/database/sleep-edf/

Sleep EEG dataset from 8 subjects in European Data Format (EDF) including original recordings and their hypnograms as described in B Kemp, AH Zwinderman, B Tuk, HAC Kamphuisen, JJL Obery��. Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG. IEEE-BME 47(9):1185-1194 (2000). The recordings were obtained from Caucasian males and females (21 - 35 years old) without any medication; they contain horizontal EOG, FpzCz and PzOz EEG, each sampled at 100 Hz. The sc* recordings also contain the submental-EMG envelope, oro-nasal airflow, rectal body temperature and an event marker, all sampled at 1 Hz. The st* recordings contain submental EMG sampled at 100 Hz and an event marker sampled at 1 Hz. The 4 sc* recordings were obtained in 1989 from ambulatory healthy volunteers during 24 hours in their normal daily life, using a modified cassette tape recorder. The 4 st* recordings were obtained in 1994 from subjects who had mild difficulty falling asleep but were otherwise healthy, during a night in the hospital, using a miniature telemetry system with very good signal quality.

Proper citation: Sleep-EDF Database (RRID:SCR_006976) Copy   

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http://snyderome.stanford.edu/

Data set generated by personal omics profiling of Dr. Michael Snyder at Stanford University. It combines genomic, transcriptomic, proteomic, metabolomic, and autoantibody profiles from a single individual over a 14 month period. The analysis revealed various medical risks, including type II diabetes. It also uncovered extensive, dynamic changes in diverse molecular components and biological pathways across healthy and diseased conditions.

Proper citation: iPOP (RRID:SCR_008991) Copy   

•   Source: SciCrunch Registry

http://fcon_1000.projects.nitrc.org/indi/pro/VirginiaTech.html

Dataset including a T1 weighted anatomical image as well as two 10-minute resting state scans acquired during the same session from 25 psychiatrically screened healthy adults (community sample) ranging in age from 18 to 65 years old, with age, sex, education level, and ethnicity provided. Some subjects also returned several weeks after the first scan for a second scanning session. The number of days between scan sessions, for subjects that had two sessions, is indicated in the demographics spreadsheet. The study scanning protocol included: # 13 sec localizer # 4 minute 38 second T1 weighted anatomical # Subject given instructions for resting state scan #1 # 10 minute 4 second resting state scan #1 # Subject given instructions for resting state scan #2 # 10 minute 4 second resting state scan #2 Scanning was performed on one of three different 3T Siemens TIM TRIOs at the Human Neuroimaging Lab at Baylor College of Medicine in Houston, Texas. All scans were acquired using the standard Siemen''s TIM 12-channel head matrix. The resting state scans were acquired with a custom sequence that is a slight modification to the standard Siemen''s EPI sequence that supports real-time fMRI. Images were acquired slightly oblique to minimize dephasing in the orbito-frontal cortex. Detailed scanning parameters are included in separate .pdf files.

Proper citation: Virginia Tech Carilion Research Institute Sample (RRID:SCR_010459) Copy   

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http://www.stsiweb.org/SWGR/

Whole genome sequencing data for 454 unrelated Scripps Wellderly Study participants with European ancestry from a project that is studying the genetic architecture of exceptional healthspan from a cohort comprised of more than 1300 healthy individuals over the age of 80 years. SWGR_v1.0 includes chromosome-specific VCF4.1 bgzipped and tabix indexed files. Annotations for each variant can be found at Scripps Genome ADVISER (SG-ADVISER, http://genomics.scripps.edu/) Additional data releases are expected.

Proper citation: Scripps Wellderly Genome Reference (RRID:SCR_010250) Copy   

•   Source: SciCrunch Registry

http://www.physionet.org/physiobank/database/gaitdb/

A mini-collection of human gait data that was constructed as a teaching resource for an intensive course (The Modern Science of Human Aging, conducted at MIT) that includes walking stride interval time series from 15 subjects: 5 healthy young adults (23 - 29 years old), 5 healthy old adults (71 - 77 years old), and 5 older adults (60 - 77 years old) with Parkinson's disease. For each subject, two columns of data are included. The first column is time (in seconds) and the second is the stride interval (variously known as stride time, gait cycle duration, and time between successive heel strikes of the same foot). The same data are also available as standard PhysioBank-format annotation (.str) and header (.hea) files, for viewing or analysis using PhysioToolkit software from this site. Subjects walked continuously on level ground around an obstacle-free path. The stride interval was measured using ultra-thin, force sensitive resistors placed inside the shoe. The analog force signal was sampled at 300 Hz with a 12 bit A/D converter, using an ambulatory, ankle-worn microcomputer that also recorded the data. Subsequently, the time between foot-strikes was automatically computed. The method for determining the stride interval is a modification of a previously validated method that has been shown to agree with force-platform measures, a gold standard. Data were collected from the healthy subjects as they walked in a roughly circular path for 15 minutes, and from the subjects with Parkinson's disease as they walked for 6 minutes up and down a long hallway.

Proper citation: Gait in Aging and Disease Database (RRID:SCR_006886) 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.cafevariome.org/

Clearinghouse and exchange portal for gene variant (mutation) data produced by diagnostics laboratories, offering users a portal through which to announce, discover and acquire a comprehensive listing of observed neutral and disease-causing gene variants in patients and unaffected individuals. Cafe Variome is not a ''''database'''' for the hosting/display/release of data, but a shop window for finding data. As such, it holds only core info for each record, and uses this merely to enable holistic searching across resources. Diagnostics laboratories routinely assess DNA samples from patients with various inherited disorders, and so produce a great wealth of data on the genetic basis of disease. Unfortunately, those data are not usually shared with others. To address this gross deficiency, a novel system has been developed that aims to facilitate the automated transfer of diagnostic laboratory data to the wider community, via an internet based Cafe for routinely exchanging genetic variation data. The flow of research data concerning the genetic basis of health and disease is critical to understanding and developing treatments for a range of genetic diseases. Overall, the project aims to lower the barriers and provide incentives for a willing community to share data, and thereby facilitate the broader exploitation of diagnostic laboratory data. Cafe Variome aims to address the above data flow problems by: # Minimizing the effort required to publish variant data # Ensuring attribution for data creators working in diagnostic laboratories Key elements of the project strategy are: * Data publication will be automated by endowing standard analysis tools used by laboratories with an online data submission function. Submissions will be received by a central Internet depot, which will serve as a place where published datasets are advertised, and subsequently discovered by diverse 3rd parties. * Each dataset will be unambiguously linked with the data submitter''''s identity, and systems devised to facilitate citation of published variant datasets so they can be cited in the literature. Data creators will thus be credited for their contributions. Data submitters can use Cafe Variome to simply announce or publicize their data to the world. To enable this, only core, non-identifiable data is submitted to the central repository, enabling users to search and discover records of interest in the source repository. The data are not automatically handed on to the user (unless intended by the submitters). Hence, the concept is used to deal with the challenge of maximally sharing data whilst fully respecting ethico-legal considerations.

Proper citation: cafe variome (RRID:SCR_006162) Copy   

•   Source: SciCrunch Registry

http://www.grc.nia.nih.gov/branches/blsa/blsanew.htm

America''s longest-running scientific study of human aging, begun in 1958. BLSA scientists are learning what happens as people age and how to sort out changes due to aging from those due to disease or other causes. More than 1,400 men and women are study volunteers. They range in age from their 20s to their 90s. This study is currently recruiting healthy seniors over 70.

Proper citation: Baltimore Longitudinal Study of Aging (BLSA) (RRID:SCR_013148) Copy   

•   Source: SciCrunch Registry

http://brainmap.org/

A community database of published functional and structural neuroimaging experiments with both metadata descriptions of experimental design and activation locations in the form of stereotactic coordinates (x,y,z) in Talairach or MNI space. BrainMap provides not only data for meta-analyses and data mining, but also distributes software and concepts for quantitative integration of neuroimaging data. The goal of BrainMap is to develop software and tools to share neuroimaging results and enable meta-analysis of studies of human brain function and structure in healthy and diseased subjects. It is a tool to rapidly retrieve and understand studies in specific research domains, such as language, memory, attention, reasoning, emotion, and perception, and to perform meta-analyses of like studies. Brainmap contains the following software: # Sleuth: database searches and Talairach coordinate plotting (this application requires a username and password) # GingerALE: performs meta-analyses via the activation likelihood estimation (ALE) method; also converts coordinates between MNI and Talairach spaces using icbm2tal # Scribe: database entry of published functional neuroimaging papers with coordinate results

Proper citation: brainmap.org (RRID:SCR_003069) Copy   

•   Source: SciCrunch Registry

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

Scan-rescan imaging sessions on 21 healthy volunteers (no history of neurological disease) intended to be a resource for statisticians and imaging scientists to be able to quantify the reproducibility of their imaging methods using data available from a generic 1 hour session at 3T. Imaging modalities include MPRAGE, FLAIR, DTI, resting state fMRI, B0 and B1 field maps, ASL, VASO, quantitative T1 mapping, quantitative T2 mapping, and magnetization transfer imaging. All data have been converted to NIFTI format. Please cite: Bennett. A. Landman, Alan J. Huang, Aliya Gifford, Deepti S. Vikram, Issel Anne L. Lim, Jonathan A.D. Farrell, John A. Bogovic, Jun Hua, Min Chen, Samson Jarso, Seth A. Smith, Suresh Joel, Susumu Mori, James J. Pekar, Peter B. Barker, Jerry L. Prince, and Peter C.M. van Zijl. ?Multi-Parametric Neuroimaging Reproducibility: A 3T Resource Study?, NeuroImage. (2010) NIHMS/PMC:252138 doi:10.1016/j.neuroimage.2010.11.047

Proper citation: Multi-Modal MRI Reproducibility Resource (RRID:SCR_002442) Copy   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

http://healthybrain.umn.edu/

Research forum portal to address brain status by acquiring comprehensive, multimodal data from healthy humans across the lifespan to characterize brain status, assess its change over time, and associate composite descriptors of brain status. Specifically, the measurements are acquired noninvasively by existing neuroimaging technologies (structural MRI, functional MRI, magnetic resonance spectroscopy, diffusion MRI, and magnetoencephalography); in addition, genetic, cognitive, language, and lifestyle data are acquired. Goals: * Derive the Brain Health Index- An integrative assessment of brain status derived from multimodal measurements of brain structure, function, and chemistry. * Continue acquiring data to construct the first-ever databank on brain, cognitive, language and genetic measurements for healthy people across the lifespan. * Provide a novel and unique dataset by which to: characterize brain status, assess its change over time, and associate it with genetic makeup, cognitive function, and language abilities. * Forecast future brain health and disease based on current measurements and guide physicians towards new interventions and evaluate interventions as they develop. * Extend to siblings and other family members to further assess the genetic influences and inheritability.

Proper citation: HBP: Healthy Brain Project (RRID:SCR_013137) Copy   

•   Source: SciCrunch Registry

http://ccr.coriell.org/Sections/Collections/NIGMS/?SsId=8

Highly characterized cell lines and high quality DNA for cell and genetic research representing a variety of disease states, chromosomal abnormalities, apparently healthy individuals and many distinct human populations. The NIGMS Repository contains more than 10,600 cell lines, primarily fibroblasts and transformed lymphoblasts, and over 5,500 DNA samples. The NIGMS Repository has a major emphasis on heritable diseases and chromosomally aberrant cell lines. In addition, it contains a large collection dedicated to understanding human variation that includes samples from populations around the world, the CEPH collection, the Polymorphism Discovery Resource, and many apparently healthy controls. Human induced pluripotent stem cell lines, many of which were derived from NIGMS Repository fibroblasts, have recently become available through the NIGMS Repository. Sample donation facilitates all areas of research by making available well-characterized materials to any qualified researcher who might have otherwise been unable to invest the time and resources to collect needed samples independently. Donations to the Repository have created a resource of unparalleled scope. Samples from the collection have been used in more than 5,500 publications and are distributed to scientists in more than 50 countries. This resource is continuously expanding to support new directions in human genetics.

Proper citation: NIGMS Human Genetic Cell Repository (RRID:SCR_004517) Copy   

•   Source: SciCrunch Registry

http://www.lionseyeinstitute.org/about-us/

A nonprofit ocular research center with the largest eye bank in the world that provides eye tissue for research and transplantation to ophthalmic specialists and surgeons nationwide. They empower researchers to conduct real-time studies of healthy and diseased ocular tissue leading to a greater understanding of the events that lead to blindness. Their mission is to improve visual outcomes and quality of life for those who are blind or visually impaired through innovative ocular endeavors.

Proper citation: Lions Eye Institute for Transplant and Research (RRID:SCR_004008) Copy   

•   Source: SciCrunch Registry

http://www.hipsci.org/

A UK national induced pluripotent stem (iPS) cell resource that will create and characterize more than 1000 human iPSCs from healthy and diseased tissue for use in cellular genetic studies. Between 2013 and 2016 they aim to generate iPS cells from over 500 healthy individuals and 500 individuals with genetic disease. They will then use these cells to discover how genomic variation impacts on cellular phenotype and identify new disease mechanisms. Strong links with NHS investigators will ensure that studies on the disease-associated cell lines will be linked to extensive clinical information. Further key features of the project are an open access model of data sharing; engagement of the wider clinical genetics community in selecting patient samples; and provision of dedicated laboratory space for collaborative cell phenotyping and differentiation.

Proper citation: HipSci (RRID:SCR_003909) Copy   

•   Source: SciCrunch Registry

http://www.alsconsortium.org/neals_samples.php

Repository of serum, plasma, cerebrospinal fluid (CSF), whole blood, extracted DNA, and urine samples from NEALS and Massachusetts General Hospital Neurology Clinical Trials Unit (NCTU) research studies of amyotrophic lateral sclerosis (ALS). Samples from this repository are available to researchers for the purpose of furthering the understanding of ALS or developing disease biomarkers. Applications will be accepted at any time, but the committee meets bi-monthly to review applications. The application requires a brief description and scientific justification for the use of the samples. Priority will be given to members of NEALS and investigators from sites that participated in the collection of samples. Investigators must provide IRB approval from their institution. Applications may be submitted to: mghneuroclinicaltrialsunit (at) partners.org (please cc: tlincoln (at) partners.org) NEALS collects an administrative fee of $1,000 at the time of application submission to offset processing costs. If an application for samples is denied, 80% of the administrative fee will be returned. The administrative fee is waived for NEALS members. Checks may be made payable to: The Northeast ALS Consortium.

Proper citation: NEALS Sample Repository (RRID:SCR_004271) Copy   

•   Source: SciCrunch Registry

http://bbmri-eric.eu

BBMRI is a pan-European and internationally broadly accessible research infrastructure and a network of existing and de novo biobanks and biomolecular resources. The infrastructure will include samples from patients and healthy persons, representing different European populations (with links to epidemiological and health care information), molecular genomic resources and biocomputational tools to optimally exploit this resource for global biomedical research. During the past 3 years BBMRI has grown into a 53-member consortium with over 280 associated organizations (largely biobanks) from over 30 countries, making it the largest research infrastructure project in Europe. During the preparatory phase the concept of a functional pan-European biobank was formulated and has now been presented to Member States of the European Union and for associated states for approval and funding. BBMRI will form an interface between specimens and data (from patients and European populations) and top-level biological and medical research. This can only be achieved through a distributed research infrastructure with operational units in all participating Member States. BBMRI will be implemented under the ERIC (European Research Infrastructure Consortium) legal entity. BBMRI-ERIC foresees headquarters (central coordination) in Graz, Austria, responsible for coordination of the activities of National Nodes established in participating countries. BBMRI is in the process of submitting its application to the European Commission for a legal status under the ERIC regulation, with an expected start date at the end of 2011. Major synergism, gain of statistical power and economy of scale will be achieved by interlinking, standardizing and harmonizing - sometimes even just cross-referencing - a large variety of well-qualified, up-to date, existing and de novo national resources. The network should cover (1) major European biobanks with blood, serum, tissue or other biological samples, (2) molecular methods resource centers for human and model organisms of biomedical relevance, (3) and biocomputing centers to ensure that databases of samples in the repositories are dynamically linked to existing databases and to scientific literature as well as to statistical expertise. Catalog of European Biobanks www.bbmriportal.eu Username: guest / Password: catalogue The catalogue is intended to be used as a reference for scientists seeking information about biological samples and data suitable for their research. The BBMRI catalogue of European Biobanks provides a high-level description of Europe''s biobanks characteristics using a portal solution managing metadata and aggregate data of biobanks. The catalogue can be queried by country, by biobank, by ICD-groups, by specimen types, by specific strengths, by funding and more. A search function is available for all data.

Proper citation: Biobanking and Biomolecular Resources Research Infrastructure (BBMRI) (RRID:SCR_004226) Copy   

•   Source: SciCrunch Registry