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https://github.com/kaizhang/SnapATAC2
Software Python/Rust package for single-cell epigenomics analysis.
Proper citation: SnapATAC2 (RRID:SCR_026622) Copy
https://github.com/Yonghao-Holden/TEProf3
Software pipeline to detect Transposable Elements transcripts. Used to identify TE-derived promoters and transcripts using transcriptomic data from multiple sources, including short-read RNA-seq data, long-read RNA-seq data and single cell RNA-seq data.
Proper citation: TEProf3 (RRID:SCR_027288) Copy
https://github.com/smorabit/hdWGCNA
Software R package for performing weighted gene co-expression network analysis in high dimensional transcriptomics data such as single-cell RNA-seq or spatial transcriptomics.
Proper citation: hdWGCNA (RRID:SCR_027496) Copy
https://github.com/atakanekiz/CIPR-Package
Software R package for annotating cell clusters in scRNAseq data.
Proper citation: CIPR-Package (RRID:SCR_027697) Copy
Repository of person centered measures that evaluates and monitors physical, mental, and social health in adults and children.
Proper citation: Patient-Reported Outcomes Measurement Information System (RRID:SCR_004718) Copy
http://ki.se/en/meb/satsa-the-swedish-adoptiontwin-study-of-aging
Longitudinal twin study to understand individual differences in aging with corresponding data and biological samples. The twin design and the inclusion of twins reared apart makes it possible to study the importance of genetic and environmental factors that may underlie differing aging outcomes. Further, the broad spectrum of biological, psychological, and social domains assessed across the life span makes it possible to study patterns of change within and across domains and how these predict health and diseases of aging. The study is comprised of several longitudinal components including, a comprehensive questionnaire that was sent to all twins in the Swedish Twin Registry who were separated at an early age and reared apart and a control sample of twins reared together. The questionnaires include items concerning rearing, family, adult, and working environment, health status, health related behaviors (e.g. alcohol, tobacco, and dietary habits) as well as relationships, and personality measures. The questionnaires were sent again at 3 year intervals in 1987, 1990, 1993 and after a break again in 2004, 2007, and 2010. Thus far more than 2,000 twins have responded to at least one of the seven questionnaire assessments conducted between 1984 and 2010. Additionally there is information about midlife life style factors from the Swedish Twin Registry that were collected about twenty years before SATSA started. In the second component a subsample of 861 individuals have participated in at least one wave of in-person testing (IPT). The first IPT started in 1986 and since then eight IPTs have been collected and the last wave will be collected during 2012-2013. The IPT includes a health examination, structured interviews, tests of functional capacity, and memory and thinking abilities. To date, over 76% of the sample has participated in 3 or more measurement waves. At IPT9 a third component was added to SATSA, a measure of day-to-day fluctuations in memory and thinking abilities, and emotions. Information about social interactions is also collected. After the visit by the research nurses the twins fill out the day-to-day booklet during the next five days. This procedure will be repeated in IPT10. This will add information about small and short-term changes and more changes are supposed to indicate the beginning of poor health. Data from SATSA can be used to study various aspects of aging. For example, the relative importance of genetic and environmental factors for individual differences in aging especially in cognitive and physical domains has been studied. A further main focus is to study changes within and across domains and which genetic and life style factors predict these changes. Given the wide spectrum of data from measured genes to social relationships collected over more than two decades they dare to say that SATSA is a unique study, with the possibility to answer many questions within gerontology and geriatrics. Types of samples * Serum * DNA Number of sample donors: 674 (June 2010)
Proper citation: KI Biobank - SATSA (RRID:SCR_005966) Copy
http://picsl.upenn.edu/software/histolozee/
Software tool that integrates histology reconstruction, MRI co-registration, and manual segmentation tools in easy-to-use and intuitive interface. Permits real-time interaction with complex and large histology datasets during co-registration steps of histology reconstruction. Software tool for interactively mapping 2D and 3D molecular and anatomical histology into Common Coordinate Frameworks. Has simple, interactive registration workflows that connect user images with CCFs.
Proper citation: HistoloZee (RRID:SCR_019263) Copy
http://www.nltcs.aas.duke.edu/index.htm
A data set of a longitudinal survey designed to study changes in the health and functional status of older Americans (aged 65+). It also tracks health expenditures, Medicare service use, and the availability of personal, family, and community resources for caregiving. The survey began in 1982, and follow-up surveys were conducted in 1984, 1989, 1994, 1999, and 2004. The surveys are of the entire Medicare-enrolled aged population with a particular emphasis on the functionally impaired. As sample persons are followed through the Medicare record system, virtually 100% of cases can be longitudinally tracked so that declines, as well as increases, in disability may be identified as well as exact dates of death. NLTCS sample persons are followed until death and are permanently and continuously linked to the Medicare record system from which they are drawn. Linkage to the Medicare Part A and B service use records extends from 1982 to 2004, so that detailed Medicare expenditures and types of service use may be studied. Through the careful application of methods to reduce non-sampling error, the surveys provide nationally representative data on: * The prevalence and patterns of functional limitations, both physical and cognitive; * Longitudinal and cohort patterns of change in functional limitation and mortality over 22 years; * Medical conditions and recent medical problems; * Health care services used; * The kind and amount of formal and informal services received by impaired individuals and how it is paid for; * Demographic and economic characteristics like age, race, sex, marital status, education, and income and assets; * Out-of-pocket expenditures for health care services and other sources of payment; * Housing and neighborhood characteristics. In each of the six surveys, large samples (N~20,000) of the oldest-old population (i.e., those 85 and over) are obtained. The survey data (i.e., detailed community and institutional interviews. The linkage to Medicare enrollment files between 1982 and 2004 was 100%, i.e., there was complete follow-up of all cases (including survey non-respondents) for Medicare eligibility (and for most years, detailed Part A and B use), mortality, and date of death. Medicare mortality records (and dates of death) are available for 1982 to 2005. The number of deaths (i.e., about 32,000 from 1982 to 2005) is large enough that detailed mortality analyses can be done. Over the 22 years spanned by the six surveys, a total of 49,242 distinct individuals were followed from and linked to Medicare records. Data Availability: The data are available through ICPSR as Study No. 9681. The data are available only on CD-ROM and only upon completion of a signed Data Use Agreement. Continuously linked Medicare data (1982 through 2004) for the National Long Term Care Surveys are only available from CMS. * Dates of Study: 1982-2004 * Study Features: Longitudinal, Anthropometric Measures * Sample Size: ** 1982: 20,485 ** 1984: 25,401 ** 1989: 17,565 ** 1994: 19,171 ** 1999: 19,907 ** 2004: 20,474 Link: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/09681
Proper citation: National Long Term Care Survey (RRID:SCR_008943) Copy
A center that works with the Oregon Alzheimer's Disease Center's Data Core, and collects and stores tissue samples, family history and genotype data of various populations. These include samples and data from subjects from the following sources: OADC clinical studies, the Oregon Brain Aging Study, the Community Brain Donor Program, the Preventing Cognitive Decline with Alternative Therapies program (informally called the Dementia Prevention Study or DPS), the African American Dementia and Aging Project, and the Klamath Exceptional Aging Project. The collected data samples include genomic DNA, lymphoblast cell lines, genome-wide and candidate region SNP marker data, APOE, AD candidate gene markers.
Proper citation: Layton Center Biomarkers and Genetics (RRID:SCR_008824) Copy
http://www.icpsr.umich.edu/icpsrweb/NACDA/studies/09915/version/3
A data set and sister study to the Established Populations for Epidemiologic Study of the Elderly (EPESE). It complements the findings of the three other EPESE sites (East Boston, MA; New Haven, CT; and north-central North Carolina) and has common items and methods in many domains. The target population was all persons 65 years and older in two rural counties in east central Iowa: Iowa and Washington counties. In 1981 a census of older persons in the target area was conducted by the investigators, creating an ascertainment list having 99% of the persons identified in the previous year by the US Decennial Census. The baseline survey was conducted between December 1991 and August 1992. Overall, 3,673 persons, or 80% of the target population were interviewed: 65-69 (N = 986), 70-74 (N = 988), 75-79 (N = 815), 80-84 (N = 523), and 85+ (N = 361). The population is virtually entirely Caucasian. Subsequently, personal follow-up surveys were conducted 3, 6, and 10 years after the baseline survey. Telephone surveys were conducted 1, 2, 4, 5, and 7 years after the baseline survey. Data collected from respondents included information about demographics, major health conditions, health care utilization, hearing and vision, weight and height, elements of nutrition, sleep problems, depressive and anxiety symptoms, alcohol and tobacco use, cognitive performance and dementia screening, incontinence measures, life satisfaction index, social networks and support, worries, medication use, activities of daily living, dental problems, satisfaction with medical care, life events, brief economic status, automobile driving habits, multiple measures of physical and disability status, and blood pressure. At follow-up #6, there were a series of physical function performance tests, the so-called NIA-MacArthur Battery, and blood was drawn for biochemical tests and potentially other determinations. In addition, some datasets were linked to the EPESE dataset under appropriate restrictions, including Iowa state driving records and clinical diagnoses and medical care utilization from the Centers for Medicare and Medicaid Services. Data Availability: The dataset has been shared with several investigative teams under special arrangement with the Principal Investigator. Early surveys are available from ICPSR. A small storage of blood is available for exploratory analyses. * Dates of Study: 1991-2001 * Study Features: Longitudinal, Anthropometric Measures, Biomarkers * Sample Size: 1991-2: 3,673 (baseline) Link: EPESE 1981-93 ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/09915
Proper citation: Iowa 65+ Rural Health Study (RRID:SCR_008937) Copy
http://www.norc.org/Research/Projects/Pages/national-social-life-health-and-aging-project.aspx
A longitudinal, population-based study of health and social factors, aiming to understand the well-being of older, community-dwelling Americans by examining the interactions among physical health and illness, medication use, cognitive function, emotional health, sensory function, health behaviors, social connectedness, sexuality, and relationship quality. NSHAP provides policy makers, health providers, and individuals with useful information and insights into these factors, particularly on social and intimate relationships. The study contributes to finding new ways to improve health as people age. In 2005 and 2006, NORC and Principal Investigators at the University of Chicago conducted the first wave of NSHAP, completing more than 3,000 interviews with a nationally representative sample of adults aged 57 to 85. In 2010 and 2011, nearly 3,400 interviews were completed for Wave 2 with these Wave 1 Respondents, Wave 1 Non-Interviewed Respondents, and their spouses or cohabiting romantic partners. The second wave of NSHAP is essential to understanding how social and biological characteristics change. NSHAP, by eliciting a variety of information from respondents over time, provides data that will allow researchers in a number of fields to examine how specific factors may or may not affect each other across the life course. For both waves, data collection included three measurements: in-home interviews, biomeasures, and leave-behind respondent-administered questionnaires. The face-to-face interviews and biomeasure collection took place in respondents'''' homes. NSHAP uses a national area probability sample of community residing adults born between 1920 and 1947 (aged 57 to 85 at the time of the Wave 1 interview), which includes an oversampling of African-Americans and Hispanics. The NSHAP sample is built on the foundation of the national household screening carried out by the Health and Retirement Study (HRS) in 2004. Through a collaborative agreement, HRS identified households for the NSHAP eligible population. A sample of 4,400 people was selected from the screened households. NSHAP made one selection per household. Ninety-two percent of the persons selected for the NSHAP interview were eligible. For Wave 2 in 2010 and 2011, NSHAP returned to Wave 1 Respondents and eligible non-interviewed respondents from Wave 1 (Wave 1 Non-Interviewed Respondents). NSHAP also extended the Wave 2 sample to include the cohabiting spouses and romantic partners of Wave 1 Respondents and Wave 1 Non-Interviewed Respondents. Partners were considered to be eligible to participate in NSHAP if they resided in the household with the Wave 1 Respondent/Wave 1 Non-Interviewed Respondent at the time of the Wave 2 interview and were at least 18 years of age. Wave I biomeasures: height; weight; waist circumference; blood pressure; smell; taste; vision; touch; respondent-administered vaginal swabs; oral mucosal transudate (OMT) for HIV-1 antibody screening; saliva; ����??get up and go����??; and blood spots. Technological advances in biomeasure collection methods have decreased respondent burden and increased ease of collection, storage, and yield of various biomeasures for the second wave of NSHAP. Wave II biomeasures: anthropometrics, including height, hip and waist circumference, and weight; cardiovascular function, including blood pressure, heart rate variability, and pulse; 2 of the 3 components of the short physical performance battery (SPPB) including chair stands and a timed walk; sensory function including smell; and actigraphy. In addition, we collect dried blood spots, microtainer blood, passive drool and salivettes, urine, and respondent-administered vaginal swabs, each of which are analyzed using multiple assays for a variety of measures and rationales. Furthermore, we assess respondents����?? cognition using the Montreal Cognitive Assessment (MoCA). Data Availability: NSHAP data made available to the public does not contain any identifiable respondent information and uses code numbers instead of names for all data. De-identified data from the 2005 and 2006 interviews are available to researchers through the National Archive of Computerized Data on Aging, located within Inter-University Consortium for Political and Social Research (ICPSR). Data from the Wave 2 interviews in 2010 and 2011 will be available in the summer of 2012. * Dates of Study: 2005-2006, 2010-2011 * Study Features: Biospecimens, Anthropometric Measures * Sample Size: ** Wave 1: 3,005 ** Wave 2: 3,377 Links: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/20541
Proper citation: National Social Life Health and Aging Project (NSHAP) (RRID:SCR_008950) Copy
A research program of the NIA which focuses on neuroscience, aging biology, and translational gerontology. The central focus of the program's research is understanding age-related changes in physiology and the ability to adapt to environmental stress, and using that understanding to develop insight about the pathophysiology of age-related diseases. The IRP webpage provides access to other NIH resources such as the Biological Biochemical Image Database, the Bioinformatics Portal, and the Baltimore Longitudinal Study of Aging., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.
Proper citation: Intramural Research Program (RRID:SCR_012734) Copy
http://www.nia.nih.gov/research/dab/nia-mutant-mouse-aging-colony-handbook
THIS RESOURCE IS NO LONGER IN SERVICE, documented on September 09, 2013. Supply aged mutant and transgenic mice for NIH-supported research directly related to the biology of aging. The mice are raised by the NIA's contractor, Taconic Farms, in Specific Pathogen-Free (SPF) barrier facilities. The strains in the mutant mouse aging colony have been donated by the investigators who developed the models, and those investigators are still the legally recognized owners of the intellectual property. A Material Transfer Agreement (MTA) is required to purchase the mice (a one-time requirement per strain). There are restrictions to the use of this colony as described in the MTA. These restrictions include a prohibition against breeding the mice purchased from the NIA Mutant Mouse Aging Colony, agreement that the mice will not be used for commercial purposes, and agreement that the mice and all derivatives will not be transferred to third parties. The restrictions are further spelled out in the MTA. Animals are sold by age, not weight, and ages are stated in 1 month intervals only; all animals born within a calendar month are considered to be the same age, so date of birth (DOB) is given as month/year. All mice are virgins. The mutant mouse aging colony is slated to end in September 2013. Old mice will be available until September 2013 but the availability of young mice will end earlier. Entries of different strains into the mutant mouse aging colony will end at different times, dependent on the lifespan and pattern of use of the strain. Mouse models include: * Snell Dwarf (3623) ??????????????? last entry will be the November 2011 DOB (date of birth) * Ames Dwarf (324) ??????????????? last entry will be the October 2012 DOB * A53T ???????????????????????-synuclein Transgenic (322) ??????????????? last entry will be the December 2012 DOB * GFP Transgenic (317) ??????????????? last entry will be the January 2013 DOB
Proper citation: NIA Mutant Mouse Aging Colony Handbook (RRID:SCR_007328) Copy
Trans-NIH project to assess the state of longitudinal and epidemiological research on demographic, social and biologic determinants of cognitive and emotional health in aging adults and the pathways by which cognitive and emotional health may reciprocally influence each other. A database of large scale longitudinal study relevant to healthy aging in 4 domains was created based on responses of investigators conducting these studies and is available for query. The four domains are: * Cognitive Health * Emotional Health * Demographic and Social Factors * Biomedical and Physiologic Factors
Proper citation: Cognitive and Emotional Health Project: The Healthy Brain (RRID:SCR_007390) Copy
http://senselab.med.yale.edu/ordb/
Database of vertebrate olfactory receptors genes and proteins. It supports sequencing and analysis of these receptors by providing a comprehensive archive with search tools for this expanding family. The database also incorporates a broad range of chemosensory genes and proteins, including the taste papilla receptors (TPRs), vomeronasal organ receptors (VNRs), insect olfaction receptors (IORs), Caenorhabditis elegans chemosensory receptors (CeCRs), and fungal pheromone receptors (FPRs). ORDB currently houses chemosensory receptors for more than 50 organisms. ORDB contains public and private sections which provide tools for investigators to analyze the functions of these very large gene families of G protein-coupled receptors. It also provides links to a local cluster of databases of related information in SenseLab, and to other relevant databases worldwide. The database aims to house all of the known olfactory receptor and chemoreceptor sequences in both nucleotide and amino acid form and serves four main purposes: * It is a repository of olfactory receptor sequences. * It provides tools for sequence analysis. * It supports similarity searches (screens) which reduces duplicate work. * It provides links to other types of receptor information, e.g. 3D models. The database is accessible to two classes of users: * General public www users have full access to all the public sequences, models and resources in the database. * Source laboratories are the laboratories that clone olfactory receptors and submit sequences in the private or public database. They can search any sequence they deposited to the database against any private or public sequence in the database. This user level is suited for laboratories that are actively cloning olfactory receptors.
Proper citation: Olfactory Receptor DataBase (RRID:SCR_007830) Copy
A research center associated with the University of Pittsburgh that specializes in the diagnosis of Alzheimer's disease and related disorders. The overall objective of the ADRC is to study the pathophysiology of Alzheimer's disease, with the aim of improving the reliability of diagnosis of Alzheimer's and developing effective treatment strategies. Current research foci emphasize neuropsychiatry and neuropsychology, molecular genetics and epidemiology, basic neuroscience, and structural and functional imaging that aid in the diagnosis and treatment of Alzheimer's disease. Specific services at the ADRC include: comprehensive diagnostic evaluation of patients with suspected Alzheimer's disease and other forms of dementia; evaluation of memory, language, judgment, and other cognitive abilities; and education and counseling for patients and families.
Proper citation: University of Pittsburgh Alzheimer Disease Research Center (RRID:SCR_008084) Copy
http://www.nia.nih.gov/research/scientific-resources
A resource that provides information on the vast number of resources available from the National Institute of Aging. NIA maintains approximately 150 primates (Macaca mulatta) at four regional primate centers where aging-related research is conducted. NIA also maintains colonies of aged rats and mice that are used for age-related disease research. This resource supports a multi-institutional study, the Interventions Testing Program (ITP), that investigates diets and dietary supplements that extend lifespan, delay disease and avoid dysfunction. NIA is also in charge of a microarray facility which provides filter arrays of 17,000 mouse cDNA clone sets that were developed at the NIA Intramural Research Program Laboratory of Genetics. NIA supports studies that provide biospecimens that can be shared for later research. This resource also helps the C. elegans Genetic Center at the University of Minnesota, which contains 1,000 strains of C. elegans that can be used for aging studies. This resource also provides a searchable database for epidemiological research on aging. There is access to social and behavioral research materials, including books on aging and health, from the research was conducted and supported by NIA. There are links to federal web sites that are further resources for aging research that were supported by NIA.
Proper citation: NIA Scientific Resources (RRID:SCR_008269) Copy
http://www.loni.usc.edu/Software/LOVE
A versatile 1D, 2D and 3D data viewer geared for cross-platform visualization of stereotactic brain data. It is a 3-D viewer that allows volumetric data display and manipulation of axial, sagittal and coronal views. It reads Analyze, Raw-binary and NetCDF volumetric data, as well as, Multi-Contour Files (MCF), LWO/LWS surfaces, atlas hierarchical brain-region labelings ( Brain Trees). It is a portable Java-based software, which only requires a Java interpreter and a 64 MB of RAM memory to run on any computer architecture. LONI_Viz allows the user to interactively overlay and browse through several data volumes, zoom in and out in the axial, sagittal and coronal views, and reports the intensities and the stereo-tactic voxel and world coordinates of the data. Expert users can use LONI_Viz to delineate structures of interest, e.g., sulcal curves, on the 3 cardinal projections of the data. These curves then may be use to reconstruct surfaces representing the topological boundaries of cortical and sub-cortical regions of interest. The 3D features of the package include a SurfaceViewer and a full real-time VolumeRenderer. These allow the user to view the relative positions of different anatomical or functional regions which are not co-planar in any of the axial, sagittal or coronal 2D projection planes. The interactive part of LONI_Viz features a region drawing module used for manual delineation of regions of interest. A series of 2D contours describing the boundary of a region in projection planes (axial, sagittal or coronal) could be used to reconstruct the surface-representation of the 3D outer shell of the region. The latter could then be resliced in directions complementary to the drawing-direction and these complementary contours could be loaded in all tree cardinal views. In addition the surface object could be displayed using the SurfaceViewer. A pre-loading data crop and sub-sampling module allows the user to load and view practically data of any size. This is especially important when viewing cryotome, histological or stained data-sets which may reach 1GB (109 bytes) in size. The user could overlay several pre-registered volumes, change intensity colors and ranges and the inter-volume opacities to visually inspect similarities and differences between the different subjects/modalities. Several image-processing aids provide histogram plotting, image-smoothing, etc. Specific Features: * Region description DataBase * Moleculo-genetic database * Brain anatomical data viewer * BrainMapper tool * Surface (LightWave objects/scenes) and Volume rendering tools * Interactive Contour Drawing tool Implementation Issues: * Applet vs. Application - the software is available as both an applet and a standalone application. The former could be used to browse data from within the LONI database, however, it imposes restrictions on file-size, Internet connection and network-bandwidth and client/server file access. The later requires a local install and configuration of the LONI_Viz software * Extendable object-oriented code (Java), computer architecture independent * Complete online software documentation is available at http://www.loni.ucla.edu/LONI_Viz and a Java-Class documentation is available at http://www.loni.ucla.edu/~dinov/LONI_Vis.dir/doc/LONI_Viz_Java_Docs.html
Proper citation: LONI Visualization Tool (RRID:SCR_000765) Copy
https://github.com/automaticanalysis/automaticanalysis
Integration framework for major open source packages in neuroimaging including SPM, FSL, FreeSurfer, EEGLAB, and Fieldtrip. Efficient neuroimaging workflows and parallel processing using Matlab and XML. Addresses challenges of processing multimodal datasets, like combining anatomy, functional MRI, diffusion, and EEG, to yield integrated views of brain. Allows to design, execute, and share pipelines utilizing multiple open source packages. Supports parallelized execution to address challenges of large cohort studies and provides quality control offering group statistics and reporting facilities to help identify outlier subjects and erroneous processing steps.
Proper citation: Automatic Analysis (RRID:SCR_003560) Copy
Consortium to conduct genome-wide association studies (GWAS) to identify genes associated with an increased risk of developing late-onset Alzheimer''''s disease (LOAD). The goal of the ADGC is to identify genetic variants associated with risk for AD. It plans to do this through the following collaborative goals: # Identify genes responsible for AD susceptibility # Identify AD sub-phenotype genes rate-of-progression plaque / tangle load / distribution biomarker variability # Generate a genetic data resource for the AD research community Data generated by ADGC is available at the following website: https://www.niagads.org/content/alzheimers-disease-genetics-consortium-adgc-collection
Proper citation: Alzheimers Disease Genetics Consortium (RRID:SCR_004004) Copy
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