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http://www.archer.edu.au/

The ARCHER project is built upon the prototype software developed by the DART (http://dart.edu.au) and ARROW (http://arrow.edu.au) projects to produce a robust set of software tools. These tools: - may be customised to suit the needs of diverse research areas - automate the collection and management of instrument generated data - enable the repository storage of research data and associated metadata - enable collection and tagging of research data in a collaborative environment, and - provide these capabilities in a secure end-to-end proces. :ARCHER developed a ''production-ready'' software tools, operating in a secure environment, to assist researchers to: - collect, capture and retain large data sets from a range of different sources including scientific instruments - deposit data files and data sets to eResearch storage repositories - populate these eResearch data repositories with associated metadata - permit data set annotation and discussion in a collaborative environment, and - support next-generation methods for research publication, dissemination and access.

Proper citation: Australian ResearCH Enabling enviRonment (RRID:SCR_008390) Copy   

•   Source: SciCrunch Registry

http://www.oas.samhsa.gov/nsduh.htm

NSDUH is the primary source of statistical information on the use of illegal drugs, alcohol, and tobacco by the U.S. civilian, noninstitutionalized population aged 12 or older. Conducted by the Federal Government since 1971, the survey collects data through face-to-face interviews with a representative sample of the population at the respondent''s place of residence. Correlates in OAS reports include the following: age, gender, pregnancy status, race / ethnicity, education, employment, geographic area, frequency of use, and association with alcohol, tobacco, & illegal drug use. NSDUH collects information from residents of households and noninstitutional group quarters (e.g., shelters, rooming houses, dormitories) and from civilians living on military bases. The survey excludes homeless persons who do not use shelters, military personnel on active duty, and residents of institutional group quarters, such as jails and hospitals. Most of the questions are administered with audio computer-assisted self-interviewing (ACASI). ACASI is designed to provide the respondent with a highly private and confidential mode for responding to questions in order to increase the level of honest reporting of illicit drug use and other sensitive behaviors. Less sensitive items are administered by interviewers using computer-assisted personal interviewing (CAPI). The 2010 NSDUH employed a State-based design with an independent, multistage area probability sample within each State and the District of Columbia. The eight States with the largest population (which together account for about half of the total U.S. population aged 12 or older) were designated as large sample States (California, Florida, Illinois, Michigan, New York, Ohio, Pennsylvania, and Texas) and had a sample size of about 3,600 each. For the remaining 42 States and the District of Columbia, the sample size was about 900 per State. The design oversampled youths and young adults; each State''s sample was approximately equally distributed among three age groups: 12 to 17 years, 18 to 25 years, and 26 years or older.

Proper citation: National Survey on Drug Use and Health (RRID:SCR_007031) Copy   

•   Source: SciCrunch Registry

http://physionet.org/physiobank/

Archive of well-characterized digital recordings of physiologic signals and related data for use by the biomedical research community. PhysioBank currently includes databases of multi-parameter cardiopulmonary, neural, and other biomedical signals from healthy subjects and patients with a variety of conditions with major public health implications, including sudden cardiac death, congestive heart failure, epilepsy, gait disorders, sleep apnea, and aging. The PhysioBank Archives now contain over 700 gigabytes of data that may be freely downloaded. PhysioNet is seeking contributions of data sets that can be made freely available in PhysioBank. Contributions of digitized and anonymized (deidentified) physiologic signals and time series of all types are welcome. If you have a data set that may be suitable, please review PhysioNet''s guidelines for contributors and contact them.

Proper citation: Physiobank (RRID:SCR_006949) Copy   

•   Source: SciCrunch Registry

http://www.biomanta.org/

This project encompasses development of novel biological network analysis methods and infrastructure for querying biological data in a semantically-enabled format, and aims to create a semantic interactome model. Research within the BioMANTA project will focus on computational modelling and analysis, primarily using Semantic Web technologies and Machine Learning methods, of large-scale protein-protein interaction and compound activity networks across a wide variety of species. A range of information such as kinetic activity, tissue expression, and subcellular localization and disease state attributes will be included in the resulting data model. Protein interactions are a fundamental component of biological processes. Many proteins are functional only in multimeric complexes, or require interaction partners to achieve their correct localisation or function. For this reason, the study of protein-protein interaction (PPI) networks has become an area of growing interest in computational biology. Through the use of Semantic Web technologies such as Resource Description Framework (RDF) and Web Ontology Language (OWL), interaction data is modelled to create a knowledge representation in which meaning is vested in the ontology rather than instances of data. Stochastic and computational intelligence methods are applied to this data to infer high coverage networks. Semantic inferencing is used to infer previously unknown and meaningful pathways. Major project components: - The BioMANTA Ontology:- An OWL DL ontology incorporating the PSI-MI Ontology, the NCBI Taxonomy, and elements of BioPax ontology and Gene Ontology (describing subcellular localisation). This allows us to re-use existing ontologies, thereby reducing overheads associated with knowledge acquisition in the ontology development process. We are able to integrate existing public data that contain annotation in these formats. - Data conversion & semantic protein integration:- A set of software components that convert protein-protein databases (DIP, MPact, IntAct, etc.) from PSI-MI XML to RDF compliant with the BioMANTA ontology. These software allow us to make these protein-protein interaction datasets (and more generally, any PSI-MI XML data) semantically available for querying and inference within BioMANTA. - A RDF triple store based on RDF Molecules and the MapReduce architecture:- A proof-of-concept RDF triple store using RDF molecules and Hadoop scale-out architectures. Regular RDF graphs are deconstructed into RDF molecules, which are distributed over distributed compute nodes in the MapReduce architecture, and are subsequently combined to form equivalent RDF graphs. Such an approach makes the distributed SPARQL querying and reasoning on RDF triple stores possible. - A quantitative framework to integrate networks extracted from independent data sources (gene expression, subcellular localization, and ortholog mapping):- The model is multi-layer, with a first layer based on Decision Trees where each Decision tree is built on each dataset independently. The tree nodes are cut using Shannon''s entropy (mutual information); the decision of these independent trees is integrated using logistic regression, and the parameters are optimised using maximum likelihood. Sponsors: This resource is supported by the Pfizer Global Research and Development, the Institute for Molecular Bioscience (IMB), and the University of Queensland, Australia.

Proper citation: BioMANTA (RRID:SCR_007177) Copy   

•   Source: SciCrunch Registry

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

Curated database of published models so that they can be openly accessed, downloaded, and tested to support computational neuroscience. Provides accessible location for storing and efficiently retrieving computational neuroscience models.Coupled with NeuronDB. Models can be coded in any language for any environment. Model code can be viewed before downloading and browsers can be set to auto-launch the models. The model source code has to be available from publicly accessible online repository or WWW site. Original source code is used to generate simulation results from which authors derived their published insights and conclusions.

Proper citation: ModelDB (RRID:SCR_007271) Copy   

•   Source: SciCrunch Registry

http://www.birncommunity.org/collaborators/function-birn/

The FBIRN Federated Informatics Research Environment (FIRE) includes tools and methods for multi-site functional neuroimaging. This includes resources for data collection, storage, sharing and management, tracking, and analysis of large fMRI datasets. fBIRN is a national initiative to advance biomedical research through data sharing and online collaboration. BIRN provides data-sharing infrastructure, software tools, strategies and advisory services - all from a single source.

Proper citation: Function BIRN (RRID:SCR_007291) Copy   

•   Source: SciCrunch Registry

http://www.fei.com/software/amira-3d-for-life-sciences/

Software tool for visualizing, manipulating, and understanding data from tomography, microscopy, MRI and other imaging processes.Used to import and export options, to processes 3D image filtering and DTI based fiber tracking to visualization, volume and surface rendering, author tools for virtual reality navigation, video generation, and more.

Proper citation: Advanced 3D Visualization and Volume Modeling (RRID:SCR_007353) Copy   

•   Source: SciCrunch Registry

http://www.ime.fraunhofer.de/en.html

Provides assistance with consulting on experimental design, training on bioinformatics tools and databases, data quality assessment, data processing, data visualization, data interpretation, data mining of published datasets, and assistance with preparation of manuscripts and grant proposals.

Proper citation: Mainz Institute of Molecular Biology Bioinformatics Core Facility (RRID:SCR_011244) Copy   

•   Source: SciCrunch Registry

https://alleninstitute.org/

Non profit bioscience research organization in Seattle, Washington dedicated to accelerating research globally and sharing that data within the science community. Allen Institute for Brain Science, Allen Institute for Cell Science, Allen Institute for Immunology, and The Paul G. Allen Frontiers Group are four divisions of this Institute with commitment to open science model within its research institutes.

Proper citation: Allen Institute (RRID:SCR_005435) Copy   

•   Source: SciCrunch Registry

https://datadryad.org

International, curated, digital repository that makes the data underlying scientific publications discoverable, freely reusable, and citable. Particularly data for which no specialized repository exists. Provides the infrastructure for, and promotes the re-use of, data underlying the scholarly literature. Governed by a nonprofit membership organization. Membership is open to any stakeholder organization, including but not limited to journals, scientific societies, publishers, research institutions, libraries, and funding organizations. Most data are associated with peer-reviewed articles, although data associated with non-peer reviewed publications from reputable academic sources, such as dissertations, are also accepted. Used to validate published findings, explore new analysis methodologies, repurpose data for research questions unanticipated by the original authors, and perform synthetic studies.UC system is member organization of Dryad general subject data repository.

Proper citation: Dryad Digital Repository (RRID:SCR_005910) Copy   

•   Source: SciCrunch Registry

https://www.mdc-berlin.de/8551903/en/research/research_teams/systems_biology_of_gene_regulatory_elements/projects/miRDeep

Software tool to identify known and novel miRNA genes in seven animal clades by analyzing sequenced RNAs. Used for discovering known and novel miRNAs from small RNA sequencing data.

Proper citation: miRDeep (RRID:SCR_010829) 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://eddylab.org/software.html

Software library containing tools for statistical manipulations of data. Tools include profile hidden Markov models for biological sequence analysis, RNA structure analysis, and a prototype noncoding RNA genefinder.

Proper citation: Eddy Lab Software (RRID:SCR_001458) Copy   

•   Source: SciCrunch Registry

https://github.com/ElementoLab/ChIPseeqer

Software that provides a comprehensive framework for the analysis of ChIP-seq data.

Proper citation: ChIPseeqer (RRID:SCR_001545) Copy   

•   Source: SciCrunch Registry

http://mobile.ebiocenter.com/ebionews/

eBioNews specializes in online information services and resource exchanges in the fields of life sciences and biotechnology. By applying its knowledge database and content management system (CMS), eBioNews offers readers and customers the organized and comprehensive information. eBioNews also provides a membership-based service to assist our customers in information and data search, processing, storage, and sharing. Generally, eBioNews covers the following areas: - life science frontiers - news and discussions - features and specials - resources and sourcing - career development - academic and industry - training and education Additionally, eBioNews information is organized into the following two clusters: - News Center: 1. Headlights 2. Research Frontiers 3. General Research 4. Clinical Development 5. Enterprise & Industry 6. Products & Services 7. Investment & Financials 8. Features 9. Newsletter The News Center consists of the elements and mechanisms that enable collecting, organizing, displaying, and delivering life science related information, data, and knowledge. - Resource Center: 1. eBioResources 2. Cooperation 3. Events 4. Human Resources 5. Intellectual Property 6. Finance & Legal 7. Operations 8. Organization 9. Publication The Resource Center is a system that hosts and facilitates the resource-related information between and among multiple parties, especially for promoting cooperation, collaboration, consortium, partnering, joint venture, licensing, out-sourcing, and trading. Sponsors: This resource is supported by eBioCenter Corporation.

Proper citation: eBioNews - A Subsidiary of eBioCenter (RRID:SCR_001717) Copy   

•   Source: SciCrunch Registry

http://neuroimage.usc.edu/brainstorm/

Software as collaborative, open source application dedicated to analysis of brain recordings: MEG, EEG, fNIRS, ECoG, depth electrodes and animal invasive neurophysiology. User-Friendly Application for MEG/EEG Analysis.

Proper citation: Brainstorm (RRID:SCR_001761) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/itk-snap/

Open source interactive software application for three dimentional medical images, manual delineation of anatomical regions of interest, and performing automatic image segmentation. Used for delineating anatomical structures and regions in MRI, CT and other 3D biomedical imaging data.WebGL-based viewer for volumetric data. It is capable of displaying arbitrary (non axis-aligned) cross-sectional views of volumetric data, as well as 3-D meshes and line-segment based models (skeletons).

Proper citation: ITK-SNAP (RRID:SCR_002010) 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

https://t1dexchange.org/pages/

Provides access to resources T1D researchers need to conduct clinical studies. Data sets from their clinic registry is openly available, as are new study results. They also offer use of T1D Discovery Tool, which allows users to search different fields from registry data, and T1D Exchange Biobank, which offers specimen types such as serum, plasma, white blood cells, DNA, and RNA.

Proper citation: T1D Exchange (RRID:SCR_014532) Copy   

•   Source: SciCrunch Registry

http://factominer.free.fr/index.html

Software R package for multivariate analysis which takes into account different types of data structure. Data can be organized in groups of variable, groups of individuals, or into hierarchy of variables.

Proper citation: FactoMineR (RRID:SCR_014602) Copy   

•   Source: SciCrunch Registry