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http://www.nia.nih.gov/research/nonhuman-primate-tissue-bank-handbook

A repository of tissue collected from nonhuman primate (NHP) species under contractual arrangement with Wisconsin National Primate Research Center (WI NPRC). NIA''''s Nonhuman Primate Tissue Bank collects and archives tissue from necropsies performed at primate centers nationwide. The goal is to collect various tissues from aged monkeys with smaller amounts of the same tissues from young and middle-aged monkeys. Tissue will be provided as: (1) fresh frozen, stored at ����?��������??80 degrees Celsius; (2) formalin fixed; or (3) fresh frozen tissue in OCT medium.Most frozen tissues are provided in approximately 1 gram of tissue per vial. Fixed tissue is available as slides (sections) from paraffin-embedded blocks. Slides can be stained if requested. Tissue from NIA''''s Nonhuman Primate Tissue Bank is available to investigators at academic and nonprofit research institutions who are engaged in funded research on aging. The project name and funding source must accompany all orders. The NIA will not be able to ship non-human primate tissue outside of the United States or US territories. Investigators at for-profit entities are not eligible to purchase tissue from NIA''''s Nonhuman Primate Tissue Bank unless it is for a Small Business Innovation Research grant from NIA. NIA provides the health information as given by the donor site and cannot guarantee other aspects of the health status not explicitly stated in the Vital Statistics Information Sheet. Concerns about the specific health status of donor animals should be indicated on the order form.

Proper citation: NIA Nonhuman Primate Tissue Bank (RRID:SCR_007324) Copy   

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http://senselab.med.yale.edu/odordb

OdorDb is a database of odorant molecules, which can be searched in a few different ways. One can see odorant molecules in the OdorDB, and the olfactory receptors in ORDB that they experimentally shown to bind. You can search for odorant molecules based on their attributes or identities: Molecular Formula, Chemical Abstracts Service (CAS) Number and Chemical Class. Functional studies of olfactory receptors involve their interactions with odor molecules. OdorDB contains a list of odors that have been identified as binding to olfactory receptors.

Proper citation: Odor Molecules DataBase (RRID:SCR_007286) Copy   

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http://senselab.med.yale.edu/odormapdb

OdorMapDB is designed to be a database to support the experimental analysis of the molecular and functional organization of the olfactory bulb and its basis for the perception of smell. It is primarily concerned with archiving, searching and analyzing maps of the olfactory bulb generated by different methods. The first aim is to facilitate comparison of activity patterns elicited by odor stimulation in the glomerular layer obtained by different methods in different species. It is further aimed at facilitating comparison of these maps with molecular maps of the projections of olfactory receptor neuron subsets to different glomeruli, especially for gene targeted animals and for antibody staining. The main maps archived here are based on original studies using 2-deoxyglucose and on current studies using high resolution fMRI in mouse and rat. Links are also provided to sites containing maps by other laboratories. OdorMapDB thus serves as a nodal point in a multilaboratory effort to construct consensus maps integrating data from different methodological approaches. OdorMapDB is integrated with two other databases in SenseLab: ORDB, a database of olfactory receptor genes and proteins, and OdorDB, a database of odor molecules that serve as ligands for the olfactory receptor proteins. The combined use of the three integrated databases allows the user to identify odor ligands that activate olfactory receptors that project to specific glomeruli that are involved in generating the odor activity maps.

Proper citation: Olfactory Bulb Odor Map DataBase (OdorMapDB) (RRID:SCR_007287) Copy   

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http://senselab.med.yale.edu

The SenseLab Project is a long-term effort to build integrated, multidisciplinary models of neurons and neural systems. It was founded in 1993 as part of the original Human Brain Project, which began the development of neuroinformatics tools in support of neuroscience research. It is now part of the Neuroscience Information Framework (NIF) and the International Neuroinformatics Coordinating Facility (INCF). The SenseLab project involves novel informatics approaches to constructing databases and database tools for collecting and analyzing neuroscience information, using the olfactory system as a model, with extension to other brain systems. SenseLab contains seven related databases that support experimental and theoretical research on the membrane properties: CellPropDB, NeuronDB, ModelDB, ORDB, OdorDB, OdorMapDB, BrainPharmA pilot Web portal that successfully integrates multidisciplinary neurocience data.

Proper citation: SenseLab (RRID:SCR_007276) Copy   

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http://www.mayo.edu/research/centers-programs/alzheimers-disease-research-center

A clinical research department that specializes in the study of Alzheimer's disease. The Mayo Clinic Alzheimer's Disease Research Center conducts many types of research studies related to dementia, as well as normal or successful aging. The purpose of the center is to provide care for dementia patients and promote research and education on Alzheimer's Disease and related dementias.

Proper citation: Mayo Alzheimer's Disease Research Center (RRID:SCR_008727) Copy   

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http://www.omicsdi.org/

Portal for dataset discovery across a heterogeneous, distributed group of transcriptomics, genomics, proteomics and metabolomics data resources. These resources span eight repositories in three continents and six organisations, including both open and controlled access data resources.

Proper citation: Omics Discovery Index (RRID:SCR_010494) Copy   

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http://surfer.nmr.mgh.harvard.edu/fswiki/Tracula

Software tool developed for automatically reconstructing a set of major white matter pathways in the brain from diffusion weighted images using probabilistic tractography. This method utilizes prior information on the anatomy of the pathways from a set of training subjects. By incorporating this prior knowledge in the reconstruction procedure, our method obviates the need for manual intervention with the tract solutions at a later stage and thus facilitates the application of tractography to large studies. The trac-all script is used to preprocess raw diffusion data (correcting for eddy current distortion and B0 field inhomogenities), register them to common spaces, model and reconstruct major white matter pathways (included in the atlas) without any manual intervention. trac-all may be used to execute all the above steps or parts of it depending on the dataset and user''''s preference for analyzing diffusion data. Alternatively, scripts exist to execute chunks of each processing pipeline, and individual commands may be run to execute a single processing step. To explore all the options in running trac-all please refer to the trac-all wiki. In order to use this script to reconstruct tracts in Diffusion images, all the subjects in the dataset must have Freesurfer Recons.

Proper citation: TRACULA (RRID:SCR_013152) Copy   

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

A software application developed to support computational anatomy and shape analysis. The capabilities of CAWorks include: interactive landmark placement to create segmentation (mask) of desired region of interest; specialized landmark placement plugins for subcortical structures such as hippocampus and amygdala; support for multiple Medical Imaging data formats, such as Nifti, Analyze, Freesurfer, DICOM and landmark data; Quadra Planar view visualization; and shape analysis plugin modules, such as Large Deformation Diffeomorphic Metric Mapping (LDDMM). Specific plugins are available for landmark placement of the hippocampus, amygdala and entorhinal cortex regions, as well as a browser plugin module for the Extensible Neuroimaging Archive Toolkit.

Proper citation: CAWorks (RRID:SCR_014185) Copy   

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https://github.com/kstreet13/slingshot

Software R package for identifying and characterizing continuous developmental trajectories in single cell data. Cell lineage and pseudotime inference for single-cell transcriptomics.

Proper citation: Slingshot (RRID:SCR_017012) Copy   

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http://www.nitrc.org/projects/sri24/

An MRI-based atlas of normal adult human brain anatomy, generated by template-free nonrigid registration from images of 24 normal control subjects. The atlas comprises T1, T2, and PD weighted structural MRI, tissue probability maps (GM, WM, CSF), maximum-likelihood tissue segmentation, DTI-based measures (FA, MD, longitudinal and transversal diffusivity), and two labels maps of cortical regions and subcortical structures. The atlas is provided at 1mm isotropic image resolution in Analyze, NIFTI, and Nrrd format. We are also providing an experimental packaging for use with SPM8.

Proper citation: SRI24 Atlas: Normal Adult Brain Anatomy (RRID:SCR_002551) Copy   

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http://www.dian-info.org/default.htm

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. An international research partnership of leading scientists determined to understand a rare form of Alzheimers disease that is caused by a gene mutation and to establish a research database and tissue repository to support research on Alzheimers disease by other investigators around the world. One goal of DIAN is to study possible brain changes that occur before Alzheimers disease is expressed in people who carry an Alzheimers disease mutation. Other family members without a mutation will serve as a comparison group. People in families in which a mutation has been identified will be tracked in order to detect physical or mental changes that might distinguish people who inherited the mutation from those who did not. DIAN currently involves eleven outstanding research institutions in the United States, United Kingdom, and Australia. John C. Morris, M.D., Friedman Distinguished Professor of Neurology at Washington University School of Medicine in St. Louis, is the principal investigator of the project., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: DIAN - Dominantly Inherited Alzheimer Network (RRID:SCR_000812) Copy   

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http://www.uniprot.org/

Collection of data of protein sequence and functional information. Resource for protein sequence and annotation data. Consortium for preservation of the UniProt databases: UniProt Knowledgebase (UniProtKB), UniProt Reference Clusters (UniRef), and UniProt Archive (UniParc), UniProt Proteomes. Collaboration between European Bioinformatics Institute (EMBL-EBI), SIB Swiss Institute of Bioinformatics and Protein Information Resource. Swiss-Prot is a curated subset of UniProtKB.

Proper citation: UniProt (RRID:SCR_002380) Copy   

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http://www.mitomap.org/

Database of polymorphisms and mutations of the human mitochondrial DNA. It reports published and unpublished data on human mitochondrial DNA variation. All data is curated by hand. If you would like to submit published articles to be included in mitomap, please send them the citation and a pdf.

Proper citation: MITOMAP - A human mitochondrial genome database (RRID:SCR_002996) Copy   

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https://cloudreg.neurodata.io/

Software automated, terascale, cloud based image analysis pipeline for preprocessing and cross modal, nonlinear registration between volumetric datasets with artifacts. Automatic terabyte scale cross modal brain volume registration.

Proper citation: CloudReg (RRID:SCR_022795) Copy   

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http://wwwcf.nlm.nih.gov/hsrr_search/view_hsrr_record_table.cfm?TITLE_ID=479&PROGRAM_CAME=toc_with_source2.cfm

Data set of annual questionnaires of a long-term prospective study of 1,337 former Johns Hopkins University medical students to identify precursors of premature cardiovascular disease and hypertension. The purpose of the study has broadened, however, as the cohort has aged. The study has been funded for 15 years. Participants were an average of 22 years of age at entry and have been followed to an average age of 69 years. Data are collected through annual questionnaires, supplemented with phone calls and substudies. Self-reports of diseases and risk factors have been validated. Every year from 1988 to 2003, anywhere from 2 to 6 questionnaires have been administered, in categories such as the following, which repeat periodically: Morbidity, Supplemental Illness, Health Behavior, Family and Career, Retirement, Job Satisfaction, Blood Pressure and Weight, Medications, Work Environment, Social Network, Diabetes, Osteoarthritis, Health Locus of Control, Preventive Health Services, General Health, Functional Limitations, Memory Functioning, Smoking, Religious Beliefs and Practices, Links with Administrative Data, National Death Index searches for all nonrespondents * Dates of Study: 1946-2003 * Study Features: Longitudinal * Sample Size: 1,337 (1946)

Proper citation: Precursors of Premature Disease and Death (RRID:SCR_010483) Copy   

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http://www.flinders.edu.au/sabs/fcas/alsa/alsa_home.cfm

The general purpose of ALSA is to examine how social, biomedical, psychological, economic, and environmental factors are associated with age-related changes in the health and wellbeing of persons aged 70 years and older. The aim is to analyze the complex relationships between individual and social factors and changes in health status, health care needs and service utilization dimensions, with emphasis given to the effects of social and economic factors on morbidity, disability, acute and long-term care service use, and mortality. The study was designed to have common instrumentation with US studies. ALSA collected data from a random, stratified sample of all persons (both community and institution-dwelling) aged 70 years and older living in the metropolitan area of Adelaide, South Australia, using the State Electoral Database as the sampling frame. Spouses aged 65 and older and other household members aged 70 years and older also were invited to participate. The initial baseline data collection for ALSA began in September 1992 and was completed in March 1993. In the first wave, personal interviews were carried out for 2,087 participants, including 566 couples (that is, persons 70 years of age and over and their spouse, if 65 and over). Clinical assessments were obtained for 1,620 of the participants. Respondents were recontacted by telephone a year after initial interview (wave 2). The third wave of the study began in September 1994 and involved a complete reassessment, with a total of 1,679 interviews and 1,423 clinical assessments. To date, eleven waves of data have been collected, with the latest collection in May 2010, from 168 participants. Six of these waves were conducted via face-to-face interviews and clinical assessments, and five were telephone interviews. Future waves are planned, however are dependent on grant funding. Ancillary data collection has been ongoing since the initiation of the study, e.g., from secondary providers. Lists of ALSA participants are compared biannually with the agencies'' lists to determine the prevalence and incidence of receipt of services from these organizations. Another source of information has been the collection of data from the participants'' general practitioners about the respondent''s health status, history of services received, medication use, referrals to specialists, and current services provided. Baseline Sample Size: 2087 Dates of Study: 1992����������2010 (potentially ongoing) Study Features: * Longitudinal * International * Anthropometric Measures * Biospecimens Waves 1-5 (ICPSR), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06707 Wave 6 (ICPSR), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/03679

Proper citation: ALSA - The Australian Longitudinal Study of Ageing (RRID:SCR_013146) Copy   

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http://www.demogr.mpg.de/databases/ktdb/

A database that includes data on death counts and population counts classified by sex, age, year of birth, and calendar year for more than 30 countries. This database was established for estimating the death rates at the highest ages (above age 80). The core set of data in the database was assembled, tested for quality, and converted into cohort mortality histories by V��in�� Kannisto, the former United Nations advisor on demographic and social statistics. Comparable materials on England and Wales, was made available by A. Roger Thatcher, the former Director of the Office of Population Censuses and Surveys and Registrar-General of England and Wales (Kannisto, 1994). The Kannisto-Thatcher database was computerized under the supervision of James W. Vaupel at the Aging Research Unit of the Centre for Health and Social Policy at Odense University Medical School in 1993. Currently, the database is maintained by the Max Planck Institute for Demographic Research, Germany.

Proper citation: Kannisto-Thatcher Database on Old Age Mortality (RRID:SCR_008936) Copy   

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http://www.icpsr.umich.edu/icpsrweb/NACDA/Pledge/all.jsp

A data set of cross-nationally comparable microdata samples for 15 Economic Commission for Europe (ECE) countries (Bulgaria, Canada, Czech Republic, Estonia, Finland, Hungary, Italy, Latvia, Lithuania, Romania, Russia, Switzerland, Turkey, UK, USA) based on the 1990 national population and housing censuses in countries of Europe and North America to study the social and economic conditions of older persons. These samples have been designed to allow research on a wide range of issues related to aging, as well as on other social phenomena. A common set of nomenclatures and classifications, derived on the basis of a study of census data comparability in Europe and North America, was adopted as a standard for recoding. This series was formerly called Dynamics of Population Aging in ECE Countries. The recommendations regarding the design and size of the samples drawn from the 1990 round of censuses envisaged: (1) drawing individual-based samples of about one million persons; (2) progressive oversampling with age in order to ensure sufficient representation of various categories of older people; and (3) retaining information on all persons co-residing in the sampled individual''''s dwelling unit. Estonia, Latvia and Lithuania provided the entire population over age 50, while Finland sampled it with progressive over-sampling. Canada, Italy, Russia, Turkey, UK, and the US provided samples that had not been drawn specially for this project, and cover the entire population without over-sampling. Given its wide user base, the US 1990 PUMS was not recoded. Instead, PAU offers mapping modules, which recode the PUMS variables into the project''''s classifications, nomenclatures, and coding schemes. Because of the high sampling density, these data cover various small groups of older people; contain as much geographic detail as possible under each country''''s confidentiality requirements; include more extensive information on housing conditions than many other data sources; and provide information for a number of countries whose data were not accessible until recently. Data Availability: Eight of the fifteen participating countries have signed the standard data release agreement making their data available through NACDA/ICPSR (see links below). Hungary and Switzerland require a clearance to be obtained from their national statistical offices for the use of microdata, however the documents signed between the PAU and these countries include clauses stipulating that, in general, all scholars interested in social research will be granted access. Russia requested that certain provisions for archiving the microdata samples be removed from its data release arrangement. The PAU has an agreement with several British scholars to facilitate access to the 1991 UK data through collaborative arrangements. Statistics Canada and the Italian Institute of statistics (ISTAT) provide access to data from Canada and Italy, respectively. * Dates of Study: 1989-1992 * Study Features: International, Minority Oversamples * Sample Size: Approx. 1 million/country Links: * Bulgaria (1992), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/02200 * Czech Republic (1991), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06857 * Estonia (1989), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06780 * Finland (1990), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06797 * Romania (1992), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06900 * Latvia (1989), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/02572 * Lithuania (1989), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/03952 * Turkey (1990), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/03292 * U.S. (1990), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06219

Proper citation: Census Microdata Samples Project (RRID:SCR_008902) Copy   

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http://lehd.did.census.gov/led/

A dataset that combines federal and state administrative data on employers and employees with core Census Bureau censuses and surveys, while protecting the confidentiality of people and firms that provide the data. This data infrastructure facilitates longitudinal research applications in both the household / individual and firm / establishment dimensions. The specific research is targeted at filling an important gap in the available data on older workers by providing information on the demand side of the labor market. These datasets comprise Title 13 protected data from the Current Population Surveys, Surveys of Income and Program Participation, Surveys of Program Dynamics, American Community Surveys, the Business Register, and Economic Censuses and Surveys. With few exceptions, states have partnered with the Census Bureau to share data. As of December 2008, Connecticut, Massachusetts, New Hampshire and Puerto Rico have not signed a partnership agreement, while a partnership with the Virgin Islands is pending. LEHD's second method of developing employer-employee data relations through the use of federal tax data has been completed. LEHD has produced summary tables on accessions, separation, job creation, destruction and earnings by age and sex of worker by industry and geographic area. The data files consist of longitudinal datasets on all firms in each participating state (quarterly data, 1991- 2003), with information on age, sex, turnover, and skill level of the workforce as well as standard information on employment, payroll, sales and location. These data can be accessed for all available states from the Project Website. Data Availability: Research conducted on the LEHD data and other products developed under this proposal at the Census Bureau takes place under a set of rules and limitations that are considerably more constraining than those prevailing in typical research environments. If state data are requested, the successful peer-reviewed proposals must also be approved by the participating state. If federal tax data are requested, the successful peer-reviewed proposals must also be approved by the Internal Revenue Service. Researchers using the LEHD data will be required to obtain Special Sworn Status from the Census Bureau and be subject to the same legal penalties as regular Census Bureau employees for disclosure of confidential information. Basic instructions on how to download the data files and restrictions can be found on the Project Website. * Dates of Study: 1991-present * Study Features: Longitudinal * Sample Size: 48 States or U.S. territories

Proper citation: Longitudinal Employer-Household Dynamics (RRID:SCR_000817) Copy   

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http://crag.uab.edu/crag/active.asp

Data set from a randomized controlled trial of cognitive interventions designed to maintain functional independence in elders by improving basic mental abilities. Several features made ACTIVE unique in the field of cognitive interventions: (a) use of a multi-site, randomized, controlled, single-blind design; (b) intervention on a large, diverse sample; (c) use of common multi-site intervention protocols, (d) primary outcomes focused on long-term, cognitively demanding functioning as measured by performance-based tests of daily activities; and (e) an intent-to-treat analytical approach. The clinical trial ended with the second annual post-test in January 2002. A third annual post-test was completed in December 2003. The area population and recruitment strategies at the six field sites provided a study sample varying in racial, ethnic, gender, socioeconomic, and cognitive characteristics. At baseline, data were collected by telephone for eligibility screening, followed by three in-person assessment sessions, including two individual sessions and one group session, and a self-administered questionnaire. At post-tests, data were collected in-person in one individual session and one group session as well as by self-administered questionnaire. There were four major categories of measures: proximal outcomes (measures of cognitive abilities that were direct targets of training), primary outcomes (measures of everyday functioning, both self-report and performance), secondary outcomes (measures of health, mobility, quality of life, and service utilization), and covariates (chronic disease, physical characteristics, depressive symptoms, cognitive impairment, psychosocial variables, and demographics). Phase I of ACTIVE was a randomized controlled, single-blind trial utilizing a four-group design, including three treatment arms and a no-contact control group. Each treatment arm consisted of a 10-session intervention for one of three cognitive abilities memory, reasoning, and speed of processing. Testers were blind to participant treatment assignment. The design allowed for testing of both social contact effects (via the contact control group) and retest effects (via the no-contact control group) on outcomes. Booster training was provided in each treatment arm to a 60% random subsample prior to first annual post-test. Phase II of ACTIVE started in July, 2003 as a follow-up study focused on measuring the long-term impact of training effects on cognitive function and cognitively demanding everyday activities. The follow-up consisted of one assessment to include the Phase I post-test battery. This was completed in late 2004.

Proper citation: Advanced Cognitive Training for Independent and Vital Elderly (ACTIVE) (RRID:SCR_000813) Copy   

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