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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
http://courses.jax.org/2012/addiction.html
THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. This course emphasizes genetic applications and approaches to drug addiction research through methodological instruction based on literature, data sets and informatics resources drawn from studies of addiction related phenotypes. The course includes plenary sessions on major progress in addiction genetics, and discussion sessions in which students present their work for discussion on applications of genetic methods. Students will leave the course able to design and interpret genetic and genomic studies of addiction as they relate to their specific research question, and will be able to make use of current bioinformatics resources to identify research resources and make use of public data sources in their own research.
Proper citation: Short Course on the Genetics of Addiction (RRID:SCR_005560) Copy
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
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
Project portal dedicated to understand animal and machine intelligence and repository of data and tools. Suite of tools to analyze and graph imaging data. Image and data repository for large, publicly available neuro-specific data files and images. Contains tools for analytics, databases, cloud computing, and Web-services applied to both big neuroimages and big neurographs.
Proper citation: neurodata (RRID:SCR_014264) Copy
http://www.wakeforestinnovations.com/technology-for-license/demon-voltammetry-and-analysis-software/
A software for performing fast scan cyclic voltammetry recordings in brain tissue for detection of neurotransmitters. It was written in the LabView programming language and can be used to provide command voltage to equipment and record the resulting waveforms. The analysis portion of the software can view and export data, apply noise filters, perform chemometric and waveform kinetic analysis, and create figures.
Proper citation: Demon Voltammetry and Analysis Software (RRID:SCR_014468) Copy
A community encyclopaedia that links brain research concepts with data, models and literature from around the world. It is an open project where users can participate and contribute to the global research community.
Proper citation: KnowledgeSpace (RRID:SCR_014539) Copy
Web based gene set analysis toolkit designed for functional genomic, proteomic, and large-scale genetic studies from which large number of gene lists (e.g. differentially expressed gene sets, co-expressed gene sets etc) are continuously generated. WebGestalt incorporates information from different public resources and provides a way for biologists to make sense out of gene lists. This version of WebGestalt supports eight organisms, including human, mouse, rat, worm, fly, yeast, dog, and zebrafish.
Proper citation: WebGestalt: WEB-based GEne SeT AnaLysis Toolkit (RRID:SCR_006786) Copy
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
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
Brain connectivity atlas to create systematic, digital repository for data on connections between different cortical areas, in primate species. Data repository for connections between different cortical areas in marmoset monkeys. Allows access to data set and enables other interpretations of data, in light of future evolution of knowledge about marmoset cortex.
Proper citation: Marmoset Brain Connectivity Atlas (RRID:SCR_015964) Copy
https://github.com/ABCD-STUDY/auto-scoring
Visualization software that calculates derived scores for the electronic record system REDCap (Research Electronic Data Capture) to build and manage online surveys and databases. Used in the ABCD-STUDY (Adolescent Brain Cognitive Development - STUDY) report framework.
Proper citation: auto-scoring (RRID:SCR_016015) Copy
https://github.com/ABCD-STUDY/little-man-task
Software tool to manage data and derived results. It is used for import of derived measures into REDCap (Research Electronic Data Capture).
Proper citation: little-man-task (RRID:SCR_016018) Copy
https://github.com/ABCD-STUDY/redcap-completion
Software to measure item level completion in a large REDCap project. It provides a web-interface to review data and it is used in the ABCD project to assess data collection sites for the reached level of completion.
Proper citation: redcap-completion (RRID:SCR_016019) Copy
https://github.com/ABCD-STUDY/timeline-followback
Software to capture subject information about substance use using local copies of external files provided by the abcd-report framework of ABCD. No connection to REDCap is attempted to get events and participant names but local files are read in to supply this information.
Proper citation: timeline-followback (RRID:SCR_016017) Copy
https://github.com/ABCD-STUDY/aux-file-upload
Software application to upload functional MR imaging runs produce auxilary data that can be collected centrally. Connects to a subject database research electronic data capture (REDCap).
Proper citation: aux-file-upload (RRID:SCR_016026) Copy
https://github.com/ABCD-STUDY/FIONA-QC-PHANTOM
Software for online quality control operations performed on Phantom MRI data. It checks the accuracy and reproducibility of data.
Proper citation: FIONA-QC-PHANTOM (RRID:SCR_016024) Copy
https://github.com/ABCD-STUDY/nih-ipad-app-end-point
Data collection software for centrally and securely storing data from the NIH iPad application. It allows users to capture results from multiple iPads at a central location.
Proper citation: nih-ipad-app-end-point (RRID:SCR_016029) Copy
https://github.com/ABCD-STUDY/redcap-to-nda
Software for metadata-driven electronic data capture to export REDCap data dictionaries and data to the NIMH National Data Archive (NDA). Prepares data submissions as csv formatted spreadsheets for data dictionary spreadsheets and for data spreadsheets.
Proper citation: redcap-to-nda (RRID:SCR_016008) Copy
https://github.com/hakyimlab/PrediXcan
Software tool to detect known and novel genes associated with disease traits and provide insights into the mechanism of these associations. Used to test the molecular mechanisms through which genetic variation affects phenotype.
Proper citation: PrediXcan (RRID:SCR_016739) Copy
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