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http://www.nihpromis.org/

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   

•   Source: SciCrunch Registry

http://www.gensat.org/

Gene expression data and maps of mouse central nervous system. Gene expression atlas of developing adult central nervous system in mouse, using in situ hybridization and transgenic mouse techniques. Collection of pictorial gene expression maps of brain and spinal cord of mouse. Provides tools to catalog, map, and electrophysiologically record individual cells. Application of Cre recombinase technologies allows for cell-specific gene manipulation. Transgenic mice created by this project are available to scientific community.

Proper citation: Gene Expression Nervous System Atlas (RRID:SCR_002721) Copy   

•   Source: SciCrunch Registry

http://rsb.info.nih.gov/nih-image/index.html

Public image processing and analysis program for Macintosh.

Proper citation: NIH Image (RRID:SCR_003073) Copy   

•   Source: SciCrunch Registry

http://trans.nih.gov/bmap/resources/resources.htm

As part of BMAP gene discovery efforts, mouse brain cDNA libraries and Expressed Sequence Tags (ESTs) have been generated. Through this project a BMAP mouse brain UniGene set consisting of over 24,000 non-redundant members of unique clusters has been developed from EST sequencing of more than 50,000 cDNA clones from 10 regions of adult mouse brain, spinal cord, and retina (http://brainEST.eng.uiowa.edu/). In 2001, NIMH along with NICHD, NIDDK, and NIDA, awarded a contract to the University of Iowa ( M.B. Soares, PI) to isolate full-length cDNA clones corresponding to genes expressed in the developing mouse nervous system and determine their full-coding sequences. The BMAP mouse brain EST sequences can be accessed at NCBI's dbEST database (http://www.ncbi.nlm.nih.gov/dbEST/). Arrayed sets of BMAP mouse brain UniGenes and cDNA libraries, and individual BMAP cDNA clones can be purchased from Open Biosystems, Huntsville, AL (http://www.openbiosystems.com

Proper citation: BMAP cDNA Resources (RRID:SCR_002973) Copy   

•   Source: SciCrunch Registry

https://neurobiobank.nih.gov/

National resource for investigators utilizing human post-mortem brain tissue and related biospecimens for their research to understand conditions of the nervous system. Federated network of brain and tissue repositories in the United States that collects, evaluates, stores, and makes available to researchers, brain and other tissues in a way that is consistent with the highest ethical and research standards. The NeuroBioBank ensures protection of the privacy and wishes of donors. Provides information to the public about the need for tissue donation and how to register as a donor.

Proper citation: NIH NeuroBioBank (RRID:SCR_003131) Copy   

•   Source: SciCrunch Registry

http://datasharing.net

The U.S. National Institutes of Health Final NIH Statement on Sharing Research Data (NIH-OD-03-032) is now in effect. It specifies that all high-direct-cost NIH grant applications include plans for sharing of research data. To support and encourage collegial, enabling, and rewarding data sharing for neuroscience and beyond, the Laboratory of Neuroinformatics at Weill Medical College of Cornell University has established this site. A source of, and portal to, tools and proposals supporting the informed exchange of neuroscience data.

Proper citation: Datasharing.net (RRID:SCR_003312) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/

Software repository for comparing structural (MRI) and functional neuroimaging (fMRI, PET, EEG, MEG) software tools and resources. NITRC collects and points to standardized information about structural or functional neuroimaging tool or resource.

Proper citation: NeuroImaging Tools and Resources Collaboratory (NITRC) (RRID:SCR_003430) Copy   

•   Source: SciCrunch Registry

http://www.pediatricmri.nih.gov/

Data sets of clinical / behavioral and image data are available for download by qualified researchers from a seven year, multi-site, longitudinal study using magnetic resonance technologies to study brain maturation in healthy, typically-developing infants, children, and adolescents and to correlate brain development with cognitive and behavioral development. The information obtained in this study is expected to provide essential data for understanding the course of normal brain development as a basis for understanding atypical brain development associated with a variety of developmental, neurological, and neuropsychiatric disorders affecting children and adults. This study enrolled over 500 children, ranging from infancy to young adulthood. The goal was to study each participant at least three times over the course of the project at one of six Pediatric Centers across the United States. Brain MR and clinical/behavioral data have been compiled and analyzed at a Data Coordinating Center and Clinical Coordinating Center. Additionally, MR spectroscopy and DTI data are being analyzed. The study was organized around two objectives corresponding to two age ranges at the time of enrollment, each with its own protocols. * Objective 1 enrolled children ages 4 years, 6 months through 18 years (total N = 433). This sample was recruited across the six Pediatric Study Centers using community based sampling to reflect the demographics of the United States in terms of income, race, and ethnicity. The subjects were studied with both imaging and clinical/behavioral measures at two year intervals for three time points. * Objective 2 enrolled newborns, infants, toddlers, and preschoolers from birth through 4 years, 5 months, who were studied three or more times at two Pediatric Study Centers at intervals ranging from three months for the youngest subjects to one year as the children approach the Objective 1 age range. Both imaging and clinical/behavioral measures were collected at each time point. Participant recruitment used community based sampling that included hospital venues (e.g., maternity wards and nurseries, satellite physician offices, and well-child clinics), community organizations (e.g., day-care centers, schools, and churches), and siblings of children participating in other research at the Pediatric Study Centers. At timepoint 1, of those enrolled, 114 children had T1 scans that passed quality control checks. Staged data release plan: The first data release included structural MR images and clinical/behavioral data from the first assessments, Visit 1, for Objective 1. A second data release included structural MRI and clinical/behavioral data from the second visit for Objective 1. A third data release included structural MRI data for both Objective 1 and 2 and all time points, as well as preliminary spectroscopy data. A fourth data release added cortical thickness, gyrification and cortical surface data. Yet to be released are longitudinally registered anatomic MRI data and diffusion tensor data. A collaborative effort among the participating centers and NIH resulted in age-appropriate MR protocols and clinical/behavioral batteries of instruments. A summary of this protocol is available as a Protocol release document. Details of the project, such as study design, rationale, recruitment, instrument battery, MRI acquisition details, and quality controls can be found in the study protocol. Also available are the MRI procedure manual and Clinical/Behavioral procedure manuals for Objective 1 and Objective 2.

Proper citation: NIH MRI Study of Normal Brain Development (RRID:SCR_003394) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/ir/

Data repository for neuroimaging data in DlCOM and NIFTI formats. It allows users to search for and freely download publicly available data sets relating to normal subjects and those with diagnoses such as: schizophrenia, ADHD, autism, and Parkinson's disease.XNAT-based image registry that supports both NIfTI and DICOM images to promote re-use and integration of NIH funded data.

Proper citation: NITRC-IR (RRID:SCR_004162) Copy   

•   Source: SciCrunch Registry

https://sea-ad.shinyapps.io/ACEapp/

Web application for comparing cell type assignments and other cell-based annotations (e.g., donor demographics, anatomic locations, batch variables, and quality control metrics). Used for connecting brain cell types across studies of health and Alzheimer's Disease.

Proper citation: Annotation Comparison Explorer (RRID:SCR_026496) Copy   

•   Source: SciCrunch Registry

https://github.com/dattalab/keypoint-moseq

Software application as machine learning-based platform for identifying behavioral modules from keypoint data without human supervision. Package provides tools for fitting MoSeq model to keypoint tracking data. Used to infer pose dynamics with keypoint data in addition to behavioral syllables.

Proper citation: Keypoint MoSeq (RRID:SCR_025032) Copy   

•   Source: SciCrunch Registry

https://github.com/TonnesenLab/Diffusion-Model/

Software code for simulating diffusion in brain extracellular space images.

Proper citation: Diffusion-Model (RRID:SCR_027942) Copy   

•   Source: SciCrunch Registry

https://github.com/sccn/labstreaminglayer

System for unified collection of measurement time series in research experiments that handles networking, time synchronization, near real time access as well as optionally centralized collection, viewing and disk recording of data. System for synchronizing streaming data for live analysis or recording.

Proper citation: Lab Streaming Layer (RRID:SCR_017631) Copy   

•   Source: SciCrunch Registry

http://pklab.med.harvard.edu/scde/pagoda.links.html

Software tool for analyzing transcriptional heterogeneity to detect statistically significant ways in which measured cells can be classified. Used to resolve multiple, potentially overlapping aspects of transcriptional heterogeneity by testing gene sets for coordinated variability among measured cells.

Proper citation: PAGODA (RRID:SCR_017099) Copy   

•   Source: SciCrunch Registry

https://github.com/FeeLab/seqNMF

Software tool for unsupervised discovery of sequential structure. Used to detect sequences in neural data generated by internal behaviors, such as animal thinking or sleeping. Used for unsupervised discovery of temporal sequences in high dimensional datasets in neuroscience without reference to external markers.

Proper citation: seqNMF (RRID:SCR_017068) Copy   

•   Source: SciCrunch Registry

http://kesm.cs.tamu.edu

A web-based, light-weight 3D volume viewer that serves large volumes (typically the whole brain) of high-resolution mouse brain images (~1.5 TB per brain, ~1 um resolution) from the Knife-Edge Scanning Microscope (KESM), invented by Bruce H. McCormick. Currently, KESMBA serves the following data sets: * Mouse: Whole-brain-scale Golgi (acquired 2008 spring): neuronal morphology: Choe et al. (2009) * Mouse: Whole-brain India Ink (acquired 2008 spring): vascular network: Choe et al. (2009); Mayerich et al. (2011); * Mouse: Whole-brain Golgi (acquired 2011 summer): neuronal morphology: Choe et al. (2011); Chung et al. (2011); * Mouse: Whole-brain Nissl (acquired 2009-2010 winter): somata (Choe et al. 2010) (Coming soon) They will ship you the full data set on a hard drive if you provide them with the hard drive and shipping cost.

Proper citation: KESM brain atlas (RRID:SCR_001559) Copy   

•   Source: SciCrunch Registry

http://www.pd-doc.org/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on December 02, 2011. Notice: This domain name expired on 10/29/11 and is pending renewal or deletion PD-DOC is a portal and a database resource, hosting a database and linking to other databases and data sets of clinical and translational data. PD-DOC functions to organize and facilitate clinical and translational research in Parkinson's disease. The PD-DOC Database contains standardized data collected by user institutions on large numbers of patients with Parkinsons disease and other parkinsonian disorders. In some cases, data is obtained at a single point in time, while in others data is collected repeatedly over time. The PD-DOC Database is composed of the Core Data Set (CDS) which consists of those variables required to be gathered for each subject whose data is entered into the PD-DOC database. In 2005, working groups of Udall Center and invited experts deliberated to establish the components of each CDS section (e.g. General Clinical, Cognitive/Behavioral, Postmortem Brain Neuropathological Findings). The PD-DOC CDS was established and designed to optimize data analyses and data mining for large numbers of subjects participating in a variety of research studies. In most cases corresponding DNA samples are available form the NINDS Human Genetic Repository (at Coriell). Much of the website is publicly available for viewing. To request access to sections of the website dealing with downloading or requesting data, requesting a consultation, or submitting data or other information you will need to register. Before registering, you should read the PD-DOC Policies. Note that PD-DOC data can be used for research purposes only. Once your registration is successfully completed you will be automatically logged into the website.

Proper citation: PD-DOC (RRID:SCR_001596) Copy   

•   Source: SciCrunch Registry

http://tbi.ci.uchicago.edu/

Project to define a roadmap for diffusion MR imaging of traumatic brain imaging and design an infrastructure to implement the recommendations and tested to ensure feasibility, disseminate results, and facilitate deployment and adoption. The research roadmap and infrastructure development will concentrate on three areas: 1) standardization of diffusion imaging methodology, 2) trial design and patient selection for acute or chronic therapy, and 3) development of multi-center collaborations and repositories for evaluating whether advanced diffusion imaging does improve decision making and TBI patients' outcomes. # DTI MRI reproducability: One of the major areas of investigation in this project is to study the reproducibility of data acquisition and image analysis algorithms. Understanding reproducibility defines a base level of deviation from which scans can be analyzed with statistical significance. As part of this work they are also developing site qualification criteria with the intention of setting limits on the MR system minimal performance for acceptable use in TBI evaluation. # Infrastructure for image storage, analysis and visualization: There is a continuing need to refine and extend software methods for diffusion MRI data analysis and visualization. Not only to translate tools into clinical practice, but also to encourage continuation of the innovation and development of new tools and techniques. To deliver upon these goals they are designing and implementing a storage and computational infrastructure to provide access to shared datasets and intuitive interfaces for analysis and visualization through a variety of tools. A strong emphasis has been placed on providing secure data sharing and the ability to add community defined common data elements. The infrastructure is built upon a Software-as-a-Service model, in which tools are hosted and managed remotely allowing users access through well-defined interfaces. The final service will also facilitate composition or orchestration of workflows composed of different analysis and processing tasks (for example using LONI or XNAT pipelines) with the ultimate goal of providing automated no-click evaluations of diffusion MRI data. # Tool development: The final aspect of this project aims to facilitate and encourage tool development and contribution. By providing access to open datasets, they will create a platform on which tool developers can compare and improve and their tools. When tools are sufficiently mature they can be exposed in the infrastructure mentioned above and used by researchers and other developers.

Proper citation: Diffusion MRI of Traumatic Brain Injury (RRID:SCR_001637) Copy   

•   Source: SciCrunch Registry

http://neuromorpho.org/index.jsp

Centrally curated inventory of digitally reconstructed neurons associated with peer-reviewed publications that contains some of the most complete axonal arborizations digitally available in the community. Each neuron is represented by a unique identifier, general information (metadata), the original and standardized ASCII files of the digital morphological reconstruction, and a set of morphometric features. It contains contributions from over 100 laboratories worldwide and is continuously updated as new morphological reconstructions are collected, published, and shared. Users may browse by species, brain region, cell type or lab name. Users can also download morphological reconstructions for research and analysis. Deposition and distribution of reconstruction files ultimately prevents data loss. Centralized curation and annotation aims at minimizing the effort required by data owners while ensuring a unified format. It also provides a one-stop entry point for all available reconstructions, thus maximizing data visibility and impact.

Proper citation: NeuroMorpho.Org (RRID:SCR_002145) Copy   

•   Source: SciCrunch Registry

https://www.phenxtoolkit.org/

Set of measures intended for use in large-scale genomic studies. Facilitate replication and validation across studies. Includes links to standards and resources in effort to facilitate data harmonization to legacy data. Measurement protocols that address wide range of research domains. Information about each protocol to ensure consistent data collection.Collections of protocols that add depth to Toolkit in specific areas.Tools to help investigators implement measurement protocols.

Proper citation: Phenotypes and eXposures Toolkit (RRID:SCR_006532) Copy   

•   Source: SciCrunch Registry