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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.
Neurophysiology imaging core facility that provides anatomical and functional MRI scanning for researchers in the National Institute of Mental Health (NIMH), the National Eye Institute (NEI), and the National Institute for Neurological Disorders and Stroke (NINDS). The shared intramural resource centers on a cutting-edge 4.7T vertical bore scanner dedicated to imaging of nonhuman primates.
Proper citation: Neurophysiology Imaging Facility (RRID:SCR_004080) Copy
http://fcon_1000.projects.nitrc.org/indi/pro/nki.html
A phenotypically rich neuroimaging sample, consisting of data obtained from individuals between the ages of 4 and 85 years-old. All individuals included in the sample undergo semi-structured diagnostic psychiatric interviews, and complete a battery of psychiatric, cognitive and behavioral assessments in order to provide comprehensive phenotypic information for the purpose of exploring brain / behavior relationships.
Proper citation: NKI/Rockland Sample (RRID:SCR_009435) Copy
http://casestudies.brain-map.org/celltax
Cellular Taxonomy of Mouse Visual Cortex by analyzing gene expression patterns at single cell level. Construction of cellular taxonomy of one cortical region, primary visual cortex, in adult mice done on basis of single cell RNA sequencing.
Proper citation: CellTax vignette (RRID:SCR_017000) Copy
https://pypi.org/project/piano-integration/
Software novel variational autoencoder framework for inferring integrated latent space representations for single cell transcriptomics data that uses a negative binomial generalized linear model for stronger batch correction, and code compilation for ten times faster training than existing tools. Enables superior analyses of multiple atlases, solving challenging integration tasks across sequencing platforms, development, and species, while simultaneously preserving desired biological signals.
Proper citation: PIANO:Probabilistic Inference Autoencoder Networks for multi-Omics (RRID:SCR_027864) Copy
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