Are you sure you want to leave this community? Leaving the community will revoke any permissions you have been granted in this community.
SciCrunch Registry is a curated repository of scientific resources, with a focus on biomedical resources, including tools, databases, and core facilities - visit SciCrunch to register your resource.
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://imputationserver.sph.umich.edu/index.html#!pages/home
Web based service for imputation that facilitates access to new reference panels and improves user experience and productivity. Server implements whole genotype imputation workflow using MapReduce programming model for efficient parallelization of computationally intensive tasks. Genotype imputation service using Minimac4.
Proper citation: Michigan Imputation Server (RRID:SCR_023554) Copy
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
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
http://prosightlite.northwestern.edu/
Software application for matching a single candidate protein sequence and its modifications against a set of mass spectrometric observations. Used to analyze top-down mass spectrometry data.
Proper citation: ProSight Lite (RRID:SCR_016908) Copy
Integrated genomic and functional genomic database for Entamoeba and Acanthamoeba parasites. Contains genomes of three Entamoeba species and microarray expression data for E. histolytica. Integrates whole genome sequence and annotation and includes experimental data and environmental isolate sequences provided by community researchers.
Proper citation: AmoebaDB (RRID:SCR_017592) Copy
http://neuroproteomics.scs.illinois.edu/microMS.htm
Software Python platform for image guided Mass Spectrometry profiling. Provides graphical user interface for automatic cell finding and point based registration from whole slide images. Simplifies single cell analysis with feature rich image processing.
Proper citation: microMS (RRID:SCR_017443) Copy
https://imputationserver.sph.umich.edu/
Web server to implement whole genotype imputation workflow for efficient parallelization of computationally intensive tasks. Service for imputation that facilitates access to new reference panels and greatly improves user experience and productivity. Used to find haplotype segments and reference panel of sequenced genomes, assign genotypes at untyped markers, improve genome coverage, facilitate comparison and combination of studies that use different marker panels, increase power to detect genetic association, and guide fine mapping.
Proper citation: Michigan Imputation Server (RRID:SCR_017579) Copy
A searchable, keyword-indexed bibliography on conditioned taste aversion learning, the avoidance of fluids and foods previously associated with the aversive effects of a variety of drugs. The database includes articles as early as 1951, and papers just published given that the database is ongoing and constantly updated. In the mid 1950''s, John Garcia and his colleagues at the Radiological Defense Laboratory at Hunters Point in San Francisco assessed the effects of ionizing radiation on a myriad of behaviors in the laboratory rat. One of their behavioral findings was that radiated rats avoided consumption of solutions that had been present during radiation, presumably due to the association of the taste of the solution with the aversive effects of the radiation. These results were published in Science and introduced to the literature the phenomenon of conditioned taste aversion learning (or the Garcia Effect). Subsequently, Garcia and his colleagues demonstrated that such learning appeared unique in a number of respects, including the fact that these aversions were acquired often in a single conditioning trial, selectively to gustatory stimuli and even when long delays were imposed between access to the solution and administration of the aversive agent. Together, these unique characteristics appeared to violate the basic tenets of traditional learning theory and along with a number of other behavioral phenomena (e.g., bird song learning, species-specific defense reactions, tonic immobility and schedule-induced polydipsia) introduced the concept of biological constraints on learning that forced a reconceptualization of the role evolution played in the acquisition of behavior (Garcia and Ervin, 1968; Revusky and Garcia, 1970; Rozin and Kalat, 1971). Although the initial investigations into conditioned taste aversion learning focused on these biological and evolutionary issues and their relation to learning, research in this area soon assessed the basic generality of the phenomenon, specifically, under what conditions such learning did or did not occur. With such research, a wide variety of gustatory stimuli were reported as effective conditioned stimuli and an extensive list of drugs with diverse consequences were reported as effective aversion-inducing agents. Aversions were established in a range of strains and species and under many experimental conditions. Research in this area continues to extend the conditions under which such learning occurs and to demonstrate its biological, neurochemical and anatomical substrates. Although the conditions under which aversion learning are reported to occur appear to generalize from the specific conditions under which they were originally reported, a number of factors including sex, age, training and testing procedures, deprivation level and drug history, all affect the rate of its acquisition and its terminal strength (Riley, 1998). In addition to these experimental demonstrations and assessments of generality, research on conditioned taste aversions has expanded to include investigations into its research and clinical applications (Braveman and Bronstein, 1985). In so doing, taste aversion learning has been applied to the characterization and classification of drug toxicity, the demonstration of the stimulus properties of abused drugs, the management of wildlife predation, the assessment of the etiology and treatment of cancer anorexia, the study of the biochemistry and molecular biology of learning, the etiology and control of alcohol use and abuse, the receptor characterization of the motivational effects of drugs, the occurrence of drug interactions, the characterization of drug withdrawal, the determination of taste psychophysics, the treatment of autoimmune diseases and the evaluation of the role of malaise in drug-induced satiety and drug-induced behavioral deficits. The speed with which aversions are acquired and the relative robustness of this preparation have made conditioned taste aversion learning a widely used, highly replicable and sensitive tool. In 1976, we published the first of three bibliographies on conditioned taste aversion learning. In this initial publication (see Riley and Baril, 1976), we listed and annotated 403 papers in this field. Subsequent lists published in 1977 (Riley and Clarke, 1977) and 1985 (Riley and Tuck, 1985) listed 632 and 1373 papers, respectively. Since that time, we have maintained a bibliography on taste aversion learning utilizing a variety of journal and on-line searches as well as benefiting from the generous contribution of preprints, reprints and pdf files from many colleagues. To date, the number of papers on conditioned taste aversion learning is approaching 3000. The present database lists these papers and provides a mechanism for searching the articles according to a number of search functions. Specifically, it was constructed to provide the reader access to these articles via a variety of search terms, including Author(s), Key Words, Date, Article Title and Journal. One can search for single or multiple items within any specific category. Further, one can search a single or combination of categories. The database is constantly being updated, and any feedback and suggestions are welcome and can be sent to CTALearning (at) american.edu.
Proper citation: Conditioned Taste Aversion: An Annotated Bibliography (RRID:SCR_005953) Copy
A clustering and visualization tool that enables the interactive exploration of genome-wide data, with a specialization in epigenomics data. Spark is also available as a service within the Epigenome toolset of the Genboree Workbench. The approach utilizes data clusters as a high-level visual guide and supports interactive inspection of individual regions within each cluster. The cluster view links to gene ontology analysis tools and the detailed region view connects to existing genome browser displays taking advantage of their wealth of annotation and functionality.
Proper citation: Spark (RRID:SCR_006207) Copy
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
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
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
Can't find your Tool?
We recommend that you click next to the search bar to check some helpful tips on searches and refine your search firstly. Alternatively, please register your tool with the SciCrunch Registry by adding a little information to a web form, logging in will enable users to create a provisional RRID, but it not required to submit.
Welcome to the T1D Resources search. From here you can search through a compilation of resources used by T1D and see how data is organized within our community.
You are currently on the Community Resources tab looking through categories and sources that T1D has compiled. You can navigate through those categories from here or change to a different tab to execute your search through. Each tab gives a different perspective on data.
If you have an account on T1D then you can log in from here to get additional features in T1D such as Collections, Saved Searches, and managing Resources.
Here is the search term that is being executed, you can type in anything you want to search for. Some tips to help searching:
You can save any searches you perform for quick access to later from here.
We recognized your search term and included synonyms and inferred terms along side your term to help get the data you are looking for.
If you are logged into T1D you can add data records to your collections to create custom spreadsheets across multiple sources of data.
Here are the sources that were queried against in your search that you can investigate further.
Here are the categories present within T1D that you can filter your data on
Here are the subcategories present within this category that you can filter your data on
If you have any further questions please check out our FAQs Page to ask questions and see our tutorials. Click this button to view this tutorial again.