Tools   Select Another Resource Report Type

http://research.mssm.edu/integrative-network-biology/Software.html

Software tool as probabilistic multi omics data matching procedure to curate data, identify and correct data annotation and errors in large databases. Used to check potential labeling errors in profiles where number of cis relationships is small, such as miRNA and RPPA profiles.

Proper citation: proMODMatcher (RRID:SCR_017219) Copy   

•   Source: SciCrunch Registry

https://imputationserver.sph.umich.edu/

Web server to implement whole genotype imputation workflow for efficient parallelization of computationally intensive tasks. Service for imputation that facilitates access to new reference panels and greatly improves user experience and productivity. Used to find haplotype segments and reference panel of sequenced genomes, assign genotypes at untyped markers, improve genome coverage, facilitate comparison and combination of studies that use different marker panels, increase power to detect genetic association, and guide fine mapping.

Proper citation: Michigan Imputation Server (RRID:SCR_017579) Copy   

•   Source: SciCrunch Registry

http://www.uky.edu/coa/adc/investigators-research-resources

An organization which includes a tissue bank, a database, study design consultation, clinical resources, and a community registry database. The UK-ADC shares data with the NIA national database (NACC), as well as with independent, qualified investigators both within and outside the UK-ADC. This resource's associated tissue bank is comprised of anonymized brain tissue, blood, and cerebrospinal fluid samples from patients in the clinic, as well as frozen post-mortem brain tissue samples. This organization also shares research resources with the National Alzheimer's Coordinating Center (NACC), NACC collaborative initiatives, the Alzheimer's Disease Neuroimaging Initiative (ADNI), other Alzheimer Disease Centers (ADCs), and any qualified investigators from either the University of Kentucky or the general scientific community.

Proper citation: University of Kentucky's Alzheimer's Disease Center (RRID:SCR_008766) Copy   

•   Source: SciCrunch Registry

http://www.adcs.org/

An initiative for Alzheimer's disease clinical studies that works to facilitate the discovery, development and testing of new drugs, and is a part of the Alzheimer's Disease Prevention Initiative. This resource has an emphasis on expanding the range of its patients, mainly by enhancing the recruitment of minority groups. There is a further emphasis placed on testing agents that cannot be patented, as well as developing novel compounds that had been developed by individuals, academic institutions and drug discovery units. This resource also helps in the development of Alzheimer's disease centers to carry out studies, as well as establish administrative, data, operations and medical cores in San Diego. This organization is specifically involved in studies demonstrating the lack of benefit associated, previously used treatments such as: the use of estrogen, non-steroidal anti-inflammatory drugs, B vitamins and a statin drug. The Alzheimer's Disease Cooperative Study also develops assessment instruments to be used in clinical trials. The most frequently used of these tools include: the Alzheimer's Disease Assessment Scale-Cognitive sub-scale (ADAS-cog), Activities of Daily Living (ADL), and the Clinical Global Impression of Change Scale (CGIC). There is also an associated tissue bank at UCSD that includes materials from the clinical trials including: human tissue, blood, plasma, DNA, urine and cerebrospinal fluid.

Proper citation: Alzheimer's Disease Cooperative Study (RRID:SCR_008254) Copy   

•   Source: SciCrunch Registry

http://brainmap.wisc.edu/monkey.html

NO LONGER AVAILABLE. Documented on September 17, 2019. A set of multi-subject atlas templates to facilitate functional and structural imaging studies of the rhesus macaque. These atlases enable alignment of individual scans to improve localization and statistical power of the results, and allow comparison of results between studies and institutions. This population-average MRI-based atlas collection can be used with common brain mapping packages such as SPM or FSL.

Proper citation: Rhesus Macaque Atlases for Functional and Structural Imaging Studies (RRID:SCR_008650) Copy   

•   Source: SciCrunch Registry

http://www.lisdatacenter.org/

A cross-national data archive located in Luxembourg that contains two primary databases: the Luxembourg Income Study Database (LIS Database) includes income microdata from a large number of countries at multiple points in time. The newer Luxembourg Wealth Study Database(LWS Database) includes wealth microdata from a smaller selection of countries. Both databases include labor market and demographic data as well. Our mission is to enable, facilitate, promote, and conduct cross-national comparative research on socio-economic outcomes and on the institutional factors that shape those outcomes. Since its beginning in 1983, the LIS has grown into a cooperative research project with a membership that includes countries in Europe, North America, and Australia. The database now contains information for more than 30 countries with datasets that span up to three decades. The LIS databank has a total of over 140 datasets covering the period 1968 to 2005. The primary objectives of the LIS are as follows: * Test the feasibility for creating a database containing social and economic data collected in household surveys from different countries; * Provide a method which allows researchers to use the data under restrictions required by the countries providing the data; * Create a system that allows research requests to be received from and returned to users at remote locations; and * Promote comparative research on the social and economic status of various populations and subgroups in different countries. Data Availability: The dataset is accessed globally via electronic mail networks. Extensive documentation concerning technical aspects of the survey data, variables list, and the social institutions of income provision in member countries are also available to users through the project Website. * Dates of Study: 1968-present * Study Features: International * Sample Size: 30+ Countries Link: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/00150

Proper citation: Luxembourg Income Study (RRID:SCR_008732) Copy   

•   Source: SciCrunch Registry

http://centerforaging.duke.edu/index.php?option=com_content&view=article&id=115&Itemid=152

The project has been collecting detailed panel data about the health, disability, demographic, family, socioeconomic, and behavioral risk-factors for mortality and healthy longevity of the oldest old, with a comparative sub-sample of younger elders, to examine the factors in healthy longevity. The baseline survey was conducted in 1998 and the follow-up surveys with replacement to compensate for deceased elders were conducted in 2000, 2002, 2005, and 2008, For each centenarian, one near-by octogenarian (aged 80-89) and one near-by nonagenarian (aged 90-99) of pre-designated age and sex were interviewed. Near-by is loosely defined it could be in the same village or street if available, or in the same town or in the same county or city. The idea was to have comparable numbers of male and female octogenarians and nonagenarians at each age from 80 to 99. In 2002, the study added a refresher sub-sample of 4,845 interviewees aged 65-79, and a sub-sample of 4,478 adult children (aged 35-65) of the elderly interviewees aged 65-110 in eight provinces Comparative study of intergenerational relationships in the context of rapid aging and healthy longevity between Mainland China and Taiwan is possible. At each wave, the longitudinal survivors were re-interviewed, and the deceased interviewees were replaced by additional participants. Data on mortality and health status before dying for the 12,136 elders aged 65-112 who died between the waves were collected in interviews with a close family member of the deceased. The study also included interviews and follow-ups with 4,478 elderly interviewees'''' children aged 35-65. * Dates of Study: 1998-2005 * Study Features: Longitudinal, International * Sample Size: ** 1998: 8,993 ** 2000: 11,199 ** 2002: 16,064 ** 2005: 14,923 Links * Data Archive, http://www.geri.duke.edu/china_study/CLHLS6.htm * ICPSR, http://www.icpsr.umich.edu/icpsrweb/NACDA/studies/03891

Proper citation: Chinese Longitudinal Healthy Longevity Survey (CLHLS) (RRID:SCR_008904) Copy   

•   Source: SciCrunch Registry

http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/4050?geography=South+Carolina

The Charleston Heart Study (CHS) is a prospective cohort study of 2,283 subjects (1,394 whites, 889 blacks) in which risk factors of coronary disease have been examined for the past 43 years. The CHS began enrolling a random selection of community residents who in 1960 were 35 years of age and older ����?? including men and women, black and white. A unique feature of this cohort is the fact that 102 high socio-economic status (SES) black men were purposefully included. The primary hypothesis of the original study was to investigate racial differences in the manifestation and risk factors for coronary disease. Over the ensuing 40+ years, a variety of outcome measurements were incorporated into the re-examination of the participants, including psychosocial, behavioral, aging and functional measures. Subjects were initially interviewed and examined in 1960 and 1963. Subsequent interviews and examinations took place during the following time periods: 1974-1975, 1984-1985, 1987-1989, and 1990-1991. During the most recent questionnaire (1990-1991), the following topics were examined: general health, smoking, functional disability, physical disability, cardiovascular health, sexual dysfunction, cognitive disability, depression, coffee consumption, medication history, medical history, nutrition, and body image. In addition, serum samples and blood pressure measurements were taken, and a physical exam was performed by a physician. A search of the National Death Index was completed through the year 2000, matching individuals with date and cause of death. Vital status of the CHS study participants through 12-31-2000 is presented below. Dead * White Men 539 (82.5%) * White Women 500 (67.5%) * Black Men 281 (84.4%) * High SES Black Men 59 (57.8%) * Black Women 343 (75.6%) Data Availability: Datasets are stored in the National Archive of Computerized Data on Aging (NACDA) in the ICPSR as Study No. 4050. Data are also available from the Medical University of South Carolina Library; contact a PI, Paul J. Nietert, nieterpj (at) musc.edu for further information. * Dates of Study: 1960-2000 * Study Features: Longitudinal, Minority Oversamples, Anthropometric Measures * Sample Size: 1960: 2,283 (baseline) Link ICPSR, http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/04050

Proper citation: Charleston Heart Study (RRID:SCR_008895) Copy   

•   Source: SciCrunch Registry

http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000674.v1.p1

Human genetics data from an immense (78,000) and ethnically diverse population available for secondary analysis to qualified researchers through the database of Genotypes and Phenotypes (dbGaP). It offers the opportunity to identify potential genetic risks and influences on a broad range of health conditions, particularly those related to aging. The GERA cohort is part of the Research Program on Genes, Environment, and Health (RPGEH), which includes more than 430,000 adult members of the Kaiser Permanente Northern California system. Data from this larger cohort include electronic medical records, behavioral and demographic information from surveys, and saliva samples from 200,000 participants obtained with informed consent for genomic and other analyses. The RPGEH database was made possible largely through early support from the Robert Wood Johnson Foundation to accelerate such health research. The genetic information in the GERA cohort translates into more than 55 billion bits of genetic data. Using newly developed techniques, the researchers conducted genome-wide scans to rapidly identify single nucleotide polymorphisms (SNPs) in the genomes of the people in the GERA cohort. These data will form the basis of genome-wide association studies (GWAS) that can look at hundreds of thousands to millions of SNPs at the same time. The RPGEH then combined the genetic data with information derived from Kaiser Permanente''s comprehensive longitudinal electronic medical records, as well as extensive survey data on participants'' health habits and backgrounds, providing researchers with an unparalleled research resource. As information is added to the Kaiser-UCSF database, the dbGaP database will also be updated.

Proper citation: Resource for Genetic Epidemiology Research on Adult Health and Aging (RRID:SCR_010472) Copy   

•   Source: SciCrunch Registry

http://www.nia.nih.gov/research/dab/aged-rodent-tissue-bank-handbook

A repository of tissue collected from the NIA Aged Rodent Colonies under contractual arrangement with BioReliance. The NIA colonies are barrier maintained and Specific Pathogen Free. Tissues are fresh frozen and stored at -80 degrees Celsius. Tissue from the NIA Aged Rodent Tissue Bank is available to investigators at academic and nonprofit research institutions who are engaged in funded research on aging. The project name and source of funding must accompany all orders. It may not be possible to ship tissue to foreign countries that have restrictions on the import of animal tissues or products. Please Note: Incomplete order forms will be returned. We can only offer following week delivery for those orders for which completed order forms are received by the deadline of Tuesday noon, Eastern time. Starting April 1, 2012, a copy (.pdf) of the purchase order must be emailed along with the order form.

Proper citation: Aged Rodent Tissue Bank (RRID:SCR_010607) Copy   

•   Source: SciCrunch Registry

http://www.nia.nih.gov/research/blog

Blog intended for grantees of the National Institute on Aging (NIA) at the NIH, as well as applicants for funding, those with an application in mind, application reviewers, and students pursuing careers in research on aging and Alzheimer's disease.

Proper citation: Inside NIA: A Blog for Researchers (RRID:SCR_012812) Copy   

•   Source: SciCrunch Registry

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

Atlas of segmented and normalized high-resolution postmortem MRI of the human hippocampus. Additional data (raw images) is available through the SCM link. It requires knowing how to use CVS.

Proper citation: Penn Hippocampus Atlas (RRID:SCR_000421) Copy   

•   Source: SciCrunch Registry

https://imputationserver.sph.umich.edu/index.html#!pages/home

Web based service for imputation that facilitates access to new reference panels and improves user experience and productivity. Server implements whole genotype imputation workflow using MapReduce programming model for efficient parallelization of computationally intensive tasks. Genotype imputation service using Minimac4.

Proper citation: Michigan Imputation Server (RRID:SCR_023554) Copy   

•   Source: SciCrunch Registry

https://www.rdocumentation.org/packages/DGCA/versions/1.0.2

Software R package to perform differential gene correlation analysis. Performs differential correlation analysis on input matrices, with multiple conditions specified by design matrix.

Proper citation: Differential Gene Correlation Analysis (RRID:SCR_020964) Copy   

•   Source: SciCrunch Registry

https://github.com/denisecailab/minian

Software miniscope analysis pipeline that requires low memory and computational demand so it can be run without specialized hardware. Offers interactive visualization that allows users to see how parameters in each step of pipeline affect output.

Proper citation: Minian (RRID:SCR_022601) Copy   

•   Source: SciCrunch Registry

https://www.archrproject.com/

Software R package for processing and analyzing single-cell ATAC-seq data. Used for integrative single cell chromatin accessibility analysis.Provides intuitive, user focused interface for complex single cell analysis, including doublet removal, single cell clustering and cell type identification, unified peak set generation, cellular trajectory identification, DNA element-to-gene linkage, transcription factor footprinting, mRNA expression level prediction from chromatin accessibility and multi-omic integration with single-cell RNA sequencing.

Proper citation: ArchR (RRID:SCR_020982) Copy   

•   Source: SciCrunch Registry

https://alzped.nia.nih.gov

Publicly available, searchable, data resource that aims to increase transparency, reproducibility and translatability of preclinical efficacy studies of candidate therapeutics for Alzheimer’s disease. Knowledge platform for dissemination of data and analysis to scientists, from academic centers, industry, disease focused foundations. Provides quick access and visibility to integrated preclinical efficacy data from published and unpublished studies.

Proper citation: Alzheimer Disease Preclinical Efficacy Database (RRID:SCR_021230) Copy   

•   Source: SciCrunch Registry

http://www.brain.northwestern.edu/research/for-researchers/index.html

Tissue bank for collecting, cataloging and storing postmortem brain tissue samples from subjects with and without neurological disorders. Specimens are available for research on cognitive impairment, Alzheimer's, dementia and other disorders along with clinical data such as demographic information, health and family history and neuropsychological test scores. The bank provides services to distribute postmortem brain tissue and other samples to investigators for use in research that will provide qualitative and quantitative diagnostic information to physicians, families, and researchers.

Proper citation: Northwestern CNADC Tissue Bank / Neuropathology Core (RRID:SCR_013178) Copy   

•   Source: SciCrunch Registry

https://www.synapse.org/ampad

Repository for distribution of various types of molecular data from human, cell-based and animal model biosamples, analytical results and research tools generated through multiple NIA-supported programs. Currently Portal supports AMP-AD Target Discovery and Preclinical Validation and MOVE-AD Consortia and translational center, MODEL-AD.

Proper citation: AMP-AD Knowledge Portal (RRID:SCR_016316) Copy   

•   Source: SciCrunch Registry

http://gero.usc.edu/CBPH/network/index.shtml

A network to improve measurement of biological risk for late life health outcomes in large representative samples of populations. Activities of the network include designing and carrying out a series of focused meetings, interactive activities, workshops, and pilot projects to harmonize and develop measurement of biological risk in populations. This project will improve the methods of measuring health used in populations and improve comparability of results over time and across studies, which is important for monitoring population health. Biological risk represents objective measurement of major dimensions of population health. The level of risk can indicate the health of the population, need for health care treatment in a population, and the effectiveness of that treatment in controlling risk or delaying disease progression, and death. The measurement of biological risk in large populations often requires adoption of methods not used in laboratory settings. The overarching goal of the network is to promote interdisciplinary research that clarifies the biological paths to health outcomes that can be measured or monitored in population surveys. The network will address the following questions: * What array of biological markers can be included reliably and validly in population studies in order to better monitor health and predict health outcomes at the older ages? * What are the best methods of collecting biological risk information under a variety of circumstances? * What are the best methods for processing the biological risk information collected? * What methods of harmonization will allow us to compare biological risk across studies? * What are the best approaches to measurement of cumulative biological risk or dimensions of biological risk for a variety of health outcomes in a variety of settings? * What are the best approaches in including indicators of genetic risk for complex diseases and conditions into data from population-based surveys? * How do we best capture indicators of life-long social, psychological and economic conditions along with lifelong biological risk to explain later life health outcomes? * What particular ethical issues are posed by our linking of biological data to extensive social, psychological, and economic information? A dataset of descriptions of Selected Population Studies with Biomarkers is available.

Proper citation: Biomarker Network (RRID:SCR_008951) Copy   

•   Source: SciCrunch Registry