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http://www.cjdats.org

A cooperative research program to explore the issues related to the complex system of offender treatment services. Nine research centers and a Coordinating Center were created in partnership with researchers, criminal justice professionals, and drug abuse treatment practitioners to form a national research infrastructure. The establishment of CJ-DATS is an outstanding example of cooperation among Federal agencies with the research community... We need to understand how to provide better drug treatment services for criminal justice offenders to alter their drug use and criminal behavior. - Dr. Nora Volkow, Director of NIDA. CJ-DATS PHASE I In 2002, NIDA launched the National Criminal Justice����������Drug Abuse Treatment Studies (CJ-DATS). CJ-DATS is a multisite research program aimed at improving the treatment of offenders with drug use disorders and integrating criminal justice and public health responses to drug involved offenders. From 2002 through 2008, CJ-DATS researchers from 9 research centers, a coordinating center, and NIDA worked together with federal, state, and local criminal justice partners to develop and test integrated approaches to the treatment of offenders with drug use disorders. The areas that were studied included: * Assessing Offender Problems * Measuring Progress in Treatment and Recovery * Linking Criminal Justice and Drug Abuse Treatment * Adolescent Interventions * HIV and Hepatitis Risk Reduction * Understanding Systems CJ-DATS PHASE II In 2008, CJ-DATS began to focus on the problems of implementing research-based practices drug treatment practices. This research concerns the organizational and systems processes involved in implementing valid, evidence-based practices to reduce drug use and drug-related recidivism for individuals in the criminal justice system. 12 CJ-DATS Research Centers are conducting implementation research in three primary domains: * Research to improve the implementation of evidence-based assessment processes for offenders with drug problems * Implementing effective treatment for drug-involved offenders * Implementing evidence-based interventions to improve an HIV continuum-of-care for offenders

Proper citation: Criminal Justice Drug Abuse Treatment Studies (RRID:SCR_006996) Copy   

•   Source: SciCrunch Registry

https://github.com/KumarLabJax/JABS-behavior-classifier

Video based phenotyping platform for laboratory mouse. Provides complete details of software and hardware, including 3D designs used for data collection. Data acquisition system consists of video collection hardware and software, behavior labeling and active learning app, and online database for sharing classifiers. Hardware and software solution collects high quality data for behavior analysis.

Proper citation: JAX Animal Behavior System (RRID:SCR_023721) 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/ABCD-STUDY/FINDTHECAT

Software that conducts a jspsych test for response time evaluation. Used in the ABCD Study.

Proper citation: FINDTHECAT (RRID:SCR_016036) Copy   

•   Source: SciCrunch Registry

https://github.com/ABCD-STUDY/stroop-task

Software that conducts the Stroop Color Task. Used in the ABCD Study.

Proper citation: stroop-task (RRID:SCR_016033) Copy   

•   Source: SciCrunch Registry

https://github.com/compbiolabucf/omicsGAN

Software generative adversarial network to integrate two omics data and their interaction network to generate one synthetic data corresponding to each omics profile that can result in better phenotype prediction. Used to capture information from interaction network as well as two omics datasets and fuse them to generate synthetic data with better predictive signals.

Proper citation: OmicsGAN (RRID:SCR_022976) Copy   

•   Source: SciCrunch Registry

http://portal.ncibi.org/gateway/saga.html

SAGA (Substructure Index-based Approximate Graph Alignment) is a tool for querying a biological graph database to retrieve matches between subgraphs of molecular interactions and biological networks. SAGA implements an efficient approximate subgraph matching algorithm that can be used for a variety of biological graph matching problems such as the pathway matching SAGA uses to compare pathways in KEGG and Reactome. You can also use SAGA to find matches in literature databases that have been parsed into semantic graphs. In this use of SAGA, portions of PubMed have been parsed into graphs that have nodes representing gene names. A link is drawn between two genes if they are discussed in the same sentence (indicating there is potential association between the two genes). SAGA lets you match graphs between different databases even though the content is distinct and the databases organize pathways in different ways. This cross-database matching is achieved by SAGA's flexible approximate subgraph matching model that computes graph similarity, and allows for node gaps, node mismatches, and graph structural differences. Comparing pathways from different databases can be a useful precursor to pathway data integration. SAGA is very efficient for querying relatively small graphs, but becomes prohibitory expensive for querying large graphs. Large graph data sets are common in many emerging database applications, and most notably in large-scale scientific applications. To fully exploit the wealth of information encoded in graphs, effective and efficient graph matching tools are critical. Due to the noisy and incomplete nature of real graph datasets, approximate, rather than exact, graph matching is required. Furthermore, many modern applications need to query large graphs, each of which has hundreds to thousands of nodes and edges. TALE is an approximate subgraph matching tool for matching graph queries with a large number of nodes and edges. TALE employs a novel indexing technique that achieves a high pruning power and scales linearly with the database size.

Proper citation: Substructure Index-based Approximate Graph Alignment (RRID:SCR_003434) Copy   

•   Source: SciCrunch Registry

http://www.duke.edu/web/gpcr-assay/index.html

Describes data from and access to permanent cell lines containing beta-arrestin fluorescent protein biosensors. This assay Bank provides plasmids, cells lines, and resulting data to the NIDA/NIH funded research community in order to better understand and combat addiction.

Proper citation: Addiction Research GPCR Assay Bank (RRID:SCR_002895) 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://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   

•   Source: SciCrunch Registry

https://neuinfo.org/mynif/search.php?q=nlx_149462&t=indexable&list=cover&nif=nlx_144509-1

A virtual database that indexes both BioNOT for negation data, and the Resource Discovery Pipeline: an automated resource discovery and semi-automated type characterization with text-mining scripts that facilitate curation team efforts to discover, integrate and display new content. This virtual database currently indexes the following resources: * BioNOT, http://snake.ims.uwm.edu/bionot/index.php?searchterm=mecp2+autism&submit=Search * Resource Discovery Pipeline, http://lucene1.neuinfo.org/nif_resource/current/

Proper citation: Integrated Auto-Extracted Annotation (RRID:SCR_005892) Copy   

•   Source: SciCrunch Registry

http://mialab.mrn.org/data/index.html

An MRI data set that demonstrates the utility of a mega-analytic approach by identifying the effects of age and gender on the resting-state networks (RSNs) of 603 healthy adolescents and adults (mean age: 23.4 years, range: 12-71 years). Data were collected on the same scanner, preprocessed using an automated analysis pipeline based in SPM, and studied using group independent component analysis. RSNs were identified and evaluated in terms of three primary outcome measures: time course spectral power, spatial map intensity, and functional network connectivity. Results revealed robust effects of age on all three outcome measures, largely indicating decreases in network coherence and connectivity with increasing age. Gender effects were of smaller magnitude but suggested stronger intra-network connectivity in females and more inter-network connectivity in males, particularly with regard to sensorimotor networks. These findings, along with the analysis approach and statistical framework described, provide a useful baseline for future investigations of brain networks in health and disease.

Proper citation: MIALAB - Resting State Data (RRID:SCR_008914) Copy   

•   Source: SciCrunch Registry

http://openconnectomeproject.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 9, 2023. Connectomes repository to facilitate the analysis of connectome data by providing a unified front for connectomics research. With a focus on Electron Microscopy (EM) data and various forms of Magnetic Resonance (MR) data, the project aims to make state-of-the-art neuroscience open to anybody with computer access, regardless of knowledge, training, background, etc. Open science means open to view, play, analyze, contribute, anything. Access to high resolution neuroanatomical images that can be used to explore connectomes and programmatic access to this data for human and machine annotation are provided, with a long-term goal of reconstructing the neural circuits comprising an entire brain. This project aims to bring the most state-of-the-art scientific data in the world to the hands of anybody with internet access, so collectively, we can begin to unravel connectomes. Services: * Data Hosting - Their Bruster (brain-cluster) is large enough to store nearly any modern connectome data set. Contact them to make your data available to others for any purpose, including gaining access to state-of-the-art analysis and machine vision pipelines. * Web Viewing - Collaborative Annotation Toolkit for Massive Amounts of Image Data (CATMAID) is designed to navigate, share and collaboratively annotate massive image data sets of biological specimens. The interface is inspired by Google Maps, enhanced to allow the exploration of 3D image data. View the fork of the code or go directly to view the data. * Volume Cutout Service - RESTful API that enables you to select any arbitrary volume of the 3d database (3ddb), and receive a link to download an HDF5 file (for matlab, C, C++, or C#) or a NumPy pickle (for python). Use some other programming language? Just let them know. * Annotation Database - Spatially co-registered volumetric annotations are compactly stored for efficient queries such as: find all synapses, or which neurons synapse onto this one. Create your own annotations or browse others. *Sample Downloads - In addition to being able to select arbitrary downloads from the datasets, they have also collected a few choice volumes of interest. * Volume Viewer - A web and GPU enabled stand-alone app for viewing volumes at arbitrary cutting planes and zoom levels. The code and program can be downloaded. * Machine Vision Pipeline - They are building a machine vision pipeline that pulls volumes from the 3ddb and outputs neural circuits. - a work in progress. As soon as we have a stable version, it will be released. * Mr. Cap - The Magnetic Resonance Connectome Automated Pipeline (Mr. Cap) is built on JIST/MIPAV for high-throughput estimation of connectomes from diffusion and structural imaging data. * Graph Invariant Computation - Upload your graphs or streamlines, and download some invariants. * iPad App - WholeSlide is an iPad app that accesses utilizes our open data and API to serve images on the go.

Proper citation: Open Connectome Project (RRID:SCR_004232) Copy   

•   Source: SciCrunch Registry

http://brainarchitecture.org/

Evolving portal that will provide interactive tools and resources to allow researchers, clinicians, and students to discover, analyze, and visualize what is known about the brain's organization, and what the evidence is for that knowledge. This project has a current experimental focus: creating the first brainwide mesoscopic connectivity diagram in the mouse. Related efforts for the human brain currently focus on literature mining and an Online Brain Atlas Reconciliation Tool. The primary goal of the Brain Architecture Project is to assemble available knowledge about the structure of the nervous system, with an ultimate emphasis on the human CNS. Such information is currently scattered in research articles, textbooks, electronic databases and datasets, and even as samples on laboratory shelves. Pooling the knowledge across these heterogeneous materials - even simply getting to know what we know - is a complex challenge that requires an interdisciplinary approach and the contributions and support of the greater community. Their approach can be divided into 4 major thrusts: * Literature Curation and Text Mining * Computational Analysis * Resource Development * Experimental Efforts

Proper citation: Brain Architecture Project (RRID:SCR_004283) Copy   

•   Source: SciCrunch Registry

http://okcam.cbi.pku.edu.cn/ontology.php

CAMO (Cell Adhesion Molecule Ontology) is a set of standard vocabulary that provide a hierarchical description of cell adhesion molecules and their functions. We compiled a list for cell adhesion molecules by integrating Gene Ontology annotations, domain structure information, and keywords query against NCBI Entrez Gene annotations. Totally 496 unique human genes were identified to function as cell adhesion molecules, which is by far the most comprehensive dataset including cadherin, immunoglobulin/FNIII, integrin, neurexin, neuroligan, and catenin families. CAMO was constructed as a directed acyclic graph (DAG) using DAG-Edit to input, manage and update data. We annotated each term with name, definition and source references, as well as the relationship to other terms, based on manual reviews of domain architecture and functional annotations. If vertices represent terms and the relationships between terms are represented by edges, the terms in a DAG can be connected via a directed graph without cycles. CAMO thus provides a hierarchical description of functions of CAMs with five top-level categories: CAM gene families, CAM genetics, CAM regulation, CAM expression and CAM diseases. Each top-level term is further divided into several categories to describe the functions in detail.

Proper citation: CAMO - Cell Adhesion Molecule Ontology (RRID:SCR_004392) Copy   

•   Source: SciCrunch Registry

http://SchistoDB.net

SchistoDB is a genomic database for the parasitic organism Schistosoma mansoni, one of the major causative agents of schistosomiasis worldwide. It currently incorporates sequences and annotation for S. mansoni in a single user-friendly database. Several genomic scale analyses are available as well as ESTs, oligonucleotides, metabolic pathways and drugs. Make your data available: If you''d like to have your updates and/or datasets integrated in SchistoDB, drop us an email.

Proper citation: Schistosoma mansoni Database (RRID:SCR_004341) Copy   

•   Source: SciCrunch Registry

http://openneuro.org

Open platform for analyzing and sharing neuroimaging data from human brain imaging research studies. Brain Imaging Data Structure ( BIDS) compliant database. Formerly known as OpenfMRI. Data archives to hold magnetic resonance imaging data. Platform for sharing MRI, MEG, EEG, iEEG, and ECoG data.

Proper citation: OpenNeuro (RRID:SCR_005031) Copy   

•   Source: SciCrunch Registry

https://nidagenetics.org/

Site for collection and distribution of clinical data related to genetic analysis of drug abuse phenotypes. Anonymous data on family structure, age, sex, clinical status, and diagnosis, DNA samples and cell line cultures, and data derived from genotyping and other genetic analyses of these clinical data and biomaterials, are distributed to qualified researchers studying genetics of mental disorders and other complex diseases at recognized biomedical research facilities. Phenotypic and Genetic data will be made available to general public on release dates through distribution mechanisms specified on website.

Proper citation: National Institute on Drug Abuse Center for Genetic Studies (RRID:SCR_013061) Copy   

•   Source: SciCrunch Registry

https://github.com/ABCD-STUDY/numerical-fitting

Software for a numerical computation library that performs numerical calculations. Used in ABCD study.

Proper citation: numerical-fitting (RRID:SCR_016025) Copy   

•   Source: SciCrunch Registry

https://github.com/djamesbarker/pMAT

Open source software suite for analysis of fiber photometry data.

Proper citation: pMAT (RRID:SCR_022570) Copy   

•   Source: SciCrunch Registry