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https://painseq.shinyapps.io/harmonized_painseq_v1/

Harmonized cell atlases using sc/snRNA-seq data obtained from dorsal root ganglia and trigeminal ganglio mammalian datasets.

Proper citation: Harmonized DRG and TG Reference Atlas (RRID:SCR_025720) Copy   

•   Source: SciCrunch Registry

https://gseapy.readthedocs.io/en/latest/

Software Python package for performing gene set enrichment analysis. Used for characterizing gene expression changes by analysis of large single-cell datasets.

Proper citation: GSEApy (RRID:SCR_025803) Copy   

•   Source: SciCrunch Registry

https://www.ohsu.edu/custom/library/digital-collections/projectionmap

Data set of thalamo-centric mesoscopic projection maps to the cortex and striatum. The maps are established through two-color, viral (rAAV)-based tracing images and high throughout imaging.

Proper citation: Mouse Thalamic Projectome Dataset (RRID:SCR_015702) Copy   

•   Source: SciCrunch Registry

https://abcdstudy.org

Long-term study of brain development and child health in the United States. The study tracks subjects' biological and behavioral development through adolescence into young adulthood to determine how childhood experiences (such as sports, videogames, social media, unhealthy sleep patterns, and smoking) interact with each other and with a child’s changing biology to affect brain development and social, behavioral, academic, health, and other outcomes.

Proper citation: ABCD Study (RRID:SCR_015769) Copy   

•   Source: SciCrunch Registry

http://lucene1.neuinfo.org/nif_resource/monthly_results/current/

An automatic pipeline based on an algorithm that identifies new resources in publications every month to assist the efficiency of NIF curators. The pipeline is also able to find the last time the resource's webpage was updated and whether the URL is still valid. This can assist the curator in knowing which resources need attention. Additionally, the pipeline identifies publications that reference existing NIF Registry resources as this is also of interest. These mentions are available through the Data Federation version of the NIF Registry, http://neuinfo.org/nif/nifgwt.html?query=nlx_144509 The RDF is based on an algorithm on how related it is to neuroscience. (hits of neuroscience related terms). Each potential resource gets assigned a score (based on how related it is to neuroscience) and the resources are then ranked and a list is generated.

Proper citation: NIF Registry Automated Crawl Data (RRID:SCR_012862) Copy   

•   Source: SciCrunch Registry

https://github.com/SciCrunch/Antibody-Watch

Text mining antibody specificity from literature. Helps researchers identify potential problems with antibody specificity. By mining the scientific literature and linking findings to Research Resource Identifiers (RRIDs), it provides alerts on antibodies that may yield unreliable results, supporting reproducibility in biomedical research.

Proper citation: Antibody Watch (RRID:SCR_027424) Copy   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

https://doi.org/10.17605/OSF.IO/WDR78

Open source resource of manually curated and expert reviewed infant brain segmentations hosted on OpenNeuro.org. and OSF.io. Anatomical MRI data was segmented from 71 infant imaging visits across 51 participants, using both T1w and T2w images per visit. Images showed dramatic differences in myelination and intensities across 1–9 months, emphasizing the need for densely sampled gold-standard segmentations across early life. This dataset provides a benchmark for evaluating and improving pipelines dependent upon segmentations in the youngest populations. As such, this dataset provides a vitally needed foundation for early-life large-scale studies such as HBCD.

Proper citation: Baby Open Brains (RRID:SCR_027836) Copy   

•   Source: SciCrunch Registry

http://fcon_1000.projects.nitrc.org/indi/CoRR/html/

Consortium that has aggregated resting state fMRI (R-fMRI) and diffusion imaging data from laboratories around the world, creating an open science resource for the imaging community, that facilitates the assessment of test-retest reliability and reproducibility for functional and structural connectomics. Given that this was a retrospective data collection, they have focused on basic phenotypic measures that are relatively standard in the neuroimaging field, as well as fundamental for analyses and sample characterization. Their phenotypic key is organized to reflect three classifications of variables: 1) core (i.e., minimal variables required to characterize any dataset), 2) preferred (i.e., variables that were strongly suggested for inclusion due to their relative import and/or likelihood of being collected by most sites), and 3) optional (variables that are data-set specific or only shared by a few sites). CoRR includes 33 datasets consisting of: * 1629 Subjects * 3357 Anatomical Scans * 5093 Resting Functional Scans * 1302 Diffusion Scans * 300 CBF and ASL Scans

Proper citation: Consortium for Reliability and Reproducibility (RRID:SCR_003774) Copy   

•   Source: SciCrunch Registry

https://datashare.nida.nih.gov

Website which allows data from completed clinical trials to be distributed to investigators and public. Researchers can download de-identified data from completed NIDA clinical trial studies to conduct analyses that improve quality of drug abuse treatment. Incorporates data from Division of Therapeutics and Medical Consequences and Center for Clinical Trials Network.

Proper citation: NIDA Data Share (RRID:SCR_002002) Copy   

•   Source: SciCrunch Registry

http://www.drugabuse.gov/about/organization/CEWG/

A network composed of researchers from major metropolitan areas of the United States and selected foreign countries which meet semiannually to discuss the current epidemiology of drug abuse. The primary mission of the Work Group is to provide ongoing community-level surveillance of drug abuse through analysis of quantitative and qualitative research data. Through this program the CEWG provides current descriptive and analytical information regarding the nature and patterns of drug abuse, emerging trends, characteristics of vulnerable populations and social and health consequences. Reports Reports are available from the biannual meetings at which the network members discuss current and emerging problems of substance abuse. At the meetings, CEWG members present data on drug abuse from a variety of city, State, Federal, and other sources. These data are enhanced with information gathered through ethnographic research, focus groups, interviews, and other qualitative methods. This integration of quantitative with qualitative data provides invaluable insight into emerging drug use trends. Book In 1998, the National Institute on Drug Abuse (NIDA) published the first edition of Assessing Drug Abuse Within and Across Communities: Community Epidemiology Surveillance Networks on Drug Abuse to share information on establishing drug abuse epidemiology networks at community and State levels. Its purpose is to provide guidelines for establishing epidemiology networks to monitor and assess drug abuse patterns and trends and emerging drug problems at community and State levels to provide a foundation of information for public health response. The second edition differs from the first in format. For each data source, there is a description of the source and database, followed by guidelines on how to access the data (including Web sites) and what to request, and examples of how the data have been used by epidemiology work groups or Federal agencies. NIDA hopes that this revised guide is helpful to agencies, organizations, and researchers that are involved in or wish to establish epidemiology networks in their communities or States.

Proper citation: Community Epidemiology Work Group (RRID:SCR_002751) Copy   

•   Source: SciCrunch Registry

http://neuinfo.org

Framework for identifying, locating, relating, accessing, integrating, and analyzing information from neuroscience research. Users can search for and add neuroscience-related resources at NIF portal and receive and RRID to track and cite resources within scientific manuscripts.

Proper citation: Neuroscience Information Framework (RRID:SCR_002894) Copy   

•   Source: SciCrunch Registry

http://www.drugabuseresearchtraining.org/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on November 07, 2012. Decemeber 15, 2011 - Thank you for your interest in DrugAbuseResearchTraining.org. The site, courses, and resources are no longer available. Please send an email to inquiry (at) md-inc.com if you would like to be notified if the site or courses become available again. Introduction to Clinical Drug and Substance Abuse Research Methods is an online training program intended to introduce clinicians and substance abuse professionals to basic clinical research methods. The program is divided into four modules. Each module covers an entire topic and includes self-assessment questions, references, and online resources: * The Neurobiology of Drug Addiction * Biostatistics for Drug and Substance Abuse Research * Evaluating Drug and Substance Abuse Programs * Designing and Managing Drug and Substance Abuse Clinical Trials The learning objectives of this program are to help you: * Evaluate the benefits of alternative investigative approaches for answering important questions in drug abuse evaluation and treatment. * Define the proper levels of measurement and appropriate statistical methods for a clinical study. * Address common problems in data collection and analysis. * Anticipate key human subjects and ethical issues that arise in drug abuse studies. * Interpret findings from the drug abuse research literature and prepare a clinical research proposal. * Prepare research findings for internal distribution or publication in the peer reviewed literature. * Recognize drug addiction as a cyclical, chronic disease. * Understand and describe the brain circuits that are affected by addicting drugs, and explain to others the effects of major classes of addicting drugs on brain neurotransmitters. * Utilize new pharmacologic treatments to manage persons with drug addiction. Physicians can earn AMA PRA Category 1 Credit and purchase a high resolution printable electronic CME certificate(view sample); non-physicians can purchase high resolution printable electronic certificate of course participation that references AMA PRA Category 1 credit (view sample). This program does not offer printed certificates.

Proper citation: Online Education for the International Research Community: AboutIntroduction to Clinical Drug and Substance Abuse Research Methods (RRID:SCR_000802) Copy   

•   Source: SciCrunch Registry

http://www.emouseatlas.org/emap/home.html

Detailed multidimensional digital multimodal atlas of the C57BL/6J mouse nervous system with a high-throughput data and informatics pipeline that can automatically register, annotate, and visualize large scale neuroanatomical and connectivity data produced in commonly used methodologies including histology, neuronal tract tracing, MR imaging, and genetic labeling. Using multimodal imaging methods on adult mouse brains, this high-resolution atlas forms the basis of a useful interchange reference system for the mouse. MAP2.0 interoperates with commonly used publicly available databases to bring together brain architecture, gene expression, and imaging information into a single, simple interface. The project enables scientists to leverage the results of brain atlasing efforts in their investigations of gene function, connectomics, and detailed mouse neuroanatomy. The MAP atlas is comprised of a magnetic resonance microscopy (MRM) and an anatomic delineation volume, and a set of software tools for visualization, navigation, and registration of image volumes. The software tools bundled with the atlas volumes are SHIVA (visualization and analysis), BrainGraph (navigation and connectivity), and GEMporter (registration and importation). A database of ancillary volumes is available online, a supplement to the Mouse Atlas Project core volumes. They represent different developmental time points and methods of preparation. To facilitate the process of selecting and analyzing image data from this collection, we have developed a relational database application to manage and catalog the images and related data. Attributes of the subject such as age, strain, weight and anatomical structure are recorded, as are image volume properties such as modality, resolution and anatomical structure. The ongoing partnership with the Brain Architecture Center has led to the development of a robust and flexible nomenclature and anatomical ontology for MAP2.0. This collaboration extends both Mouse Atlas Project and the Brain Architecture Management System.

Proper citation: Mouse Atlas Project (RRID:SCR_007281) Copy   

•   Source: SciCrunch Registry

http://bellsouthpwp.net/c/a/capowski//NTSPublic.html

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. A hardware and software package with which a scientist could trace the structure of neurons and other neuroscientific features directly from tissue sections or from a stack of their images into a computer. Then it also could edit, merge, filter, display in 3D, and make realistic plots of the structures. The NTS also includes a substantial statistical package that provided many, now standardized, mathematical and statistical summaries that described each neuron and compared one population to another. Additionally, NTS also provided an embryonic electrotonic modeler that simulates and displayes the electrical functioning of a cell. The NTS uses a special purpose graphics display processor called the VDP3 whose output is presented on a very high resolution CRT. During tracing, the VDP3 presents a variable-diameter cursor and other information directly in the microscope and enables tracing at a high spatial resolution and with measurement of process diameters limited only by the microscope''s optics. Control of tracing is done with a 3D joystick that allows easy control of five input variables: X,Y,Z position, cursor diameter, and a numeric tag. Finally, superb 3D interactive displays of completed cells are provided on the VDP3.

Proper citation: Eutectic NTS (RRID:SCR_008062) Copy   

•   Source: SciCrunch Registry

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

The Brain Molecular Anatomy Project is a trans-NIH project aimed at understanding gene expression and function in the nervous system. BMAP has two major scientific goals: # Gene discovery: to catalog of all the genes expressed in the nervous system, under both normal and abnormal conditions. # Gene expression analysis: to monitor gene expression patterns in the nervous system as a function of cell type, anatomical location, developmental stage, and physiological state, and thus gain insight into gene function. In pursuit of these goals, BMAP has launched several initiatives to provide resources and funding opportunities for the scientific community. These include several Requests for Applications and Requests for Proposals, descriptions of which can be found in this Web site. BMAP is also in the process of establishing physical and electronic resources for the community, including repositories of cDNA clones for nervous system genes, and databases of gene expression information for the nervous system. Most of the BMAP initiatives so far have focused on the mouse as a model species because of the ease of experimental and genetic manipulation of this organism, and because many models of human disease are available in the mouse. However, research in humans, other mammalian species, non-mammalian vertebrates, and invertebrates is also being funded through BMAP. For the convenience of interested investigators, we have established this Web site as a central information resource, focusing on major NIH-sponsored funding opportunities, initiatives, genomic resources available to the research community, courses and scientific meetings related to BMAP initiatives, and selected reports and publications. When appropriate, we will also post initiatives not directly sponsored by BMAP, but which are deemed relevant to its goals. Posting decisions are made by the Trans-NIH BMAP Committee

Proper citation: BMAP - Brain Molecular Anatomy Project (RRID:SCR_008852) Copy   

•   Source: SciCrunch Registry

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

A fast, scalable tool developed at the Johns Hopkins University to automatically segment the major anatomical fiber tracts within the human brain from clinical quality diffusion tensor MR imaging. With an atlas-based Markov Random Field representation, DOTS directly estimates the tract probabilities, bypassing tractography and associated issues. Overlapping and crossing fibers are modeled and DOTS can also handle white matter lesions. DOTS is released as a plug-in for the MIPAV software package and as a module for the JIST pipeline environment. They are therefore cross-platform and compatible with a wide variety of file formats.

Proper citation: DOTS WM tract segmentation (RRID:SCR_009459) Copy   

•   Source: SciCrunch Registry

http://antibodyregistry.org/

Public registry of antibodies with unique identifiers for commercial and non-commercial antibody reagents to give researchers a way to universally identify antibodies used in publications. The registry contains antibody product information organized according to genes, species, reagent types (antibodies, recombinant proteins, ELISA, siRNA, cDNA clones). Data is provided in many formats so that authors of biological papers, text mining tools and funding agencies can quickly and accurately identify the antibody reagents they and their colleagues used. The Antibody Registry allows any user to submit a new antibody or set of antibodies to the registry via a web form, or via a spreadsheet upload.

Proper citation: Antibody Registry (RRID:SCR_006397) 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

http://cancercontrol.cancer.gov/tcrb/tturc/

A transdisciplinary approach to the full spectrum of basic and applied research on tobacco use to reduce the disease burden of tobacco use, including: * Etiology of tobacco use and addiction * Impact of advertising and marketing * Prevention of tobacco use * Treatment of tobacco use and addiction * Identification of biomarkers of tobacco exposure * Identification of genes related to addiction and susceptibility to harm from tobacco Goals * Increase the number of investigators from relevant disciplines who focus on the study of tobacco use as part of transdisciplinary teams. * Generate basic research evidence to improve understanding of the etiology and natural history of tobacco use. * Produce evidence-based tobacco use interventions that can translate to the community and specific understudied or underserved populations. * Increase the number of evidence-based interventions that are novel, including the development, testing and dissemination of innovative behavioral treatments and prevention strategies based upon findings from basic research. * Train transdisciplinary investigators capable of conducting cutting-edge tobacco use research. * Increase the number of peer-reviewed publications in the areas of tobacco use, nicotine addiction, and treatment.

Proper citation: Transdisciplinary Tobacco Use Research Centers (RRID:SCR_006858) Copy   

•   Source: SciCrunch Registry