Curator Dashboard

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on July 31,2025. A measure of receptive vocabulary that is administered in a computerized adaptive format. The respondent is presented with an audio recording of a word and four photographic images on the computer screen and is asked to select the picture that most closely matches the meaning of the word. This test takes approximately 4 minutes to administer and is recommended for ages 3-85.

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• SciCrunch
• 13 years ago - by Anonymous

A Matlab toolbox for the statistical analysis of fMRI data. The fMRI data was first converted to percentage of whole volume. The statistical analysis of the percentages was based on a linear model with correlated errors. The design matrix of the linear model was first convolved with a hemodynamic response function modelled as a difference of two gamma functions timed to coincide with the acquisition of each slice. Temporal drift was removed by adding a cubic spline in the frame times to the design matrix (one covariate per 2 minutes of scan time), and spatial drift was removed by adding a covariate in the whole volume average. The correlation structure was modelled as an autoregressive process of degree 1. At each voxel, the autocorrelation parameter was estimated from the least squares residuals using the Yule-Walker equations, after a bias correction for correlations induced by the linear model. The autocorrelation parameter was first regularized by spatial smoothing, then used to "whiten" the data and the design matrix. The linear model was then re-estimated using least squares on the whitened data to produce estimates of effects and their standard errors. In a second step, runs, sessions and subjects were combined using a mixed effects linear model for the effects (as data) with fixed effects standard deviations taken from the previous analysis. This was fitted using ReML implemented by the EM algorithm. A random effects analysis was performed by first estimating the the ratio of the random effects variance to the fixed effects variance, then regularizing this ratio by spatial smoothing with a Gaussian filter. The variance of the effect was then estimated by the smoothed ratio multiplied by the fixed effects variance. The amount of smoothing was chosen to achieve 100 effective degrees of freedom. The resulting T statistic images were thresholded using the minimum given by a Bonferroni correction and random field theory, taking into account the non-isotropic spatial correlation of the errors.

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• SciCrunch
• 15 years ago - by Anonymous

Core provides pathologic services ranging from animal model troubleshooting, and tissue fixation to stain slide prep and immunohistochemistry. Offers expertise in slide interpretations and quantitative histopathology in anatomic and clinical pathology. Offers molecular pathology services via Nanostring nCounter equipment.

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• SciCrunch
• 3 years ago - submitted by Edyta Vieth

A global, open, multidisciplinary, non-profit organization that has established standards to support the acquisition, exchange, submission and archive of clinical research data and metadata. Its mission is to develop and support global, platform-independent data standards that enable information system interoperability to improve medical research and related areas of healthcare. CDISC standards are vendor-neutral, platform-independent and freely available via the CDISC website.

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• SciCrunch
• 13 years ago - by Anonymous

This toolbox is an EEGLAB plugin for performing Measure Projection Analysis. Measure Projection Analysis (MPA) is a novel probabilistic multi-subject inference method that overcomes EEG Independent Component (IC) clustering issues by abandoning the notion of distinct IC clusters. Instead, it searches voxel by voxel for brain regions having event-related IC process dynamics that exhibit statistically significant consistency across subjects and/or sessions as quantified by the values of various EEG measures. Local-mean EEG measure values are then assigned to all such locations based on a probabilistic model of IC localization error and inter-subject anatomical and functional differences.

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• SciCrunch
• 14 years ago - by Anonymous

Provides services to support the development and implementation of clinical trials at UC Davis Comprehensive Cancer Center. Oversees high quality collection, processing, and analysis of clinical specimens,typically but not exclusively blood specimens, for pharmacokinetic and pharmacodynamics studies. Conducts preclinical modeling of novel anti cancer agents to test hypotheses and develop scientific rationale required for translation of laboratory concepts into clinical trials, including assessment of DM/PK/PD properties.

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• SciCrunch
• 3 years ago - submitted by Edyta Vieth

An ontology for DICOM as used in the SeDI project.

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• SciCrunch
• 13 years ago - by Anonymous

A computerized, odor delivery device that can be used for basic research applications including mapping olfactory centers, cognitive / learning research, neuro-marketing among other uses. Additional products including tests for odor threshold, odor identification, odor discrimination and odor memory.

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• SciCrunch
• 13 years ago - by Anonymous

Offers sorting and flow cytometry services. Equipment includes Cytek Aurora, BD ARIA III, SONY cell sorters and Cytek Aurora, 2 BD LSRFortessa X-20, BD FACSCelesta,BD LSR II, CANTO A analyzers. Individual mandatory training is necessary to access this facility. ARIA III cell sorter as well as SONY SH800 are equipped with negative pressure hood allowing sorting of potentially infectious samples including human cells from healthy donors.

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• SciCrunch
• 3 years ago - submitted by Edyta Vieth

Software using statistical methods and functionality to analyze flow data that is beyond the basic infrastructure provided by the flowCore package.

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• SciCrunch
• 12 years ago - by Anonymous

A viewer for medical imaging data that supports a variety of scientific file formats out-of-the-box (see https://github.com/xtk/X/wiki/X:Fileformats for a complete list). We think that the best way to render your files is without any necessary conversions. Just drop'em on a website and they are ready to render. Just drag'n'drop some medical imaging files on this website or try one of the four examples in the right corner. Then, play with the panels on the left and click, drag and rotate the 3d content. Slice:Drop uses WebGL and HTML5 Canvas to render the data in 2D and 3D. We use our own open-source toolkit to perform the rendering, called XTK ( http://goxtk.com ).

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• SciCrunch
• 13 years ago - by Anonymous

Repository for highly controlled sound presentation in freely moving rats. Provides all necessary information for building, adjusting, and using Ratphones to reliably present auditory stimulation to freely moving rats. Used for 3D printing of headphones designed for auditory psychophysics in behaving rats.

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• RRID
• 3 years ago - submitted by Jensen Palmer

A Cytoscape app designed to identify protein pathways / complexes as densely connected subnetworks from seed lists of proteins derived from pull-down assays (i.e AP-MS...).

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• SciCrunch
• 12 years ago - by Anonymous

Web applications for analysis of multimodal/multispecies neuroimaging data. Image analysis software package. Has facilities for DTI and fMRI processing. Capabilities for both neuro/cardiac and abdominal image analysis and visualization. Many packages are extensible, and provide functionality for image visualization and registration, surface editing, cardiac 4D multi-slice editing, diffusion tensor image processing, mouse segmentation and registration, and much more. Can be intergrated with other biomedical image processing software, such as FSL, AFNI, and SPM.

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• SciCrunch
• 17 years ago - by Anonymous

Tool/Resource Public Description *

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• NIF
• 3 years ago - by Anonymous

THIS RESOURCE IS NO LONGER IN SERVICE, documented on April 23, 2014. Description not available.

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• SciCrunch
• 13 years ago - by Anonymous

A software plugin for 3D Slicer that matches morphological signatures of medical images automatically. HAMMER is an acronym for Hierarchical Attribute Matching Mechanism for Elastic Registration (Dinggang Shen, Christos Davatzikos, HAMMER: Hierarchical Attribute Matching Mechanism for Elastic Registration, IEEE Trans. on Medical Imaging, 21(11):1421-1439, Nov 2002) - an elastic registration algorithm for medical images, matching morphological signatures of images in a hierarchical multi-scale regime. White matter lesion (WML) segmentation is a novel multi-spectral WML segmentation protocol via incorporating information from T1-w, T2-w, PD-w and FLAIR MR brain images. (Zhiqiang Lao, Dinggang Shen, Dengfeng Liu, Abbas F Jawad, Elias R Melhem, Lenore J Launer, Nick R Bryan, Christos Davatzikos, Computer-Assisted Segmentation of White Matter Lesions in 3D MR images, Using Pattern Recognition, Academic Radiology, 15(3):300-313, March 2008).

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• SciCrunch
• 15 years ago - by Anonymous

Software R package used for searching for optimal clustering procedure for data set.

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• RRID
• 3 years ago - by Anonymous

Tissue bank that provides access to tissue samples and an anonymized version of the tissue bank database.Bank has large collection of tumour, DNA and other tissue samples from childhood cancer patients.

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• SciCrunch
• 16 years ago - by Anonymous

A software toolbox based on pattern recognition techniques for the analysis of neuroimaging data. Statistical pattern recognition is a field within the area of machine learning which is concerned with automatic discovery of regularities in data through the use of computer algorithms, and with the use of these regularities to take actions such as classifying the data into different categories. In PRoNTo, brain scans are treated as spatial patterns and statistical learning models are used to identify statistical properties of the data that can be used to discriminate between experimental conditions or groups of subjects (classification models) or to predict a continuous measure (regression models).

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• SciCrunch
• 13 years ago - by Anonymous