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http://krasnow1.gmu.edu/cn3/hippocampus3d/

Data files for a high resolution three dimensional (3D) structure of the rat hippocampus reconstructed from histological sections. The data files (supplementary data for Ropireddy et al., Neurosci., 2012 Mar 15;205:91-111) are being shared on the Windows Live cloud space provided by Microsoft. Downloadable data files include the Nissl histological images, the hippocampus layer tracings that can be visualized alone or superimposed to the corresponding Nissl images, the voxel database coordinates, and the surface rendering VRML files. * Hippocampus Nissl Images: The high resolution histological Nissl images obtained at 16 micrometer inter-slice distance for the Long-Evans rat hippocampus can be downloaded or directly viewed in a browser. This dataset consists of 230 jpeg images that cover the hippocampus from rostral to caudal poles. This image dataset is uploaded in seven parts as rar files. * Hippocampus Layer Tracings: The seven hippocampus layers ''ML, ''GC'', ''HILUS'' in DG and ''LM'', ''RAD'', ''PC'', ''OR'' in CA were segmented (traced) using the Reconstruct tool which can be downloaded from Synapse web. This tool outputs all the tracings for each image in XML format. The XML tracing files for all these seven layers for each of the above Nissl images are zipped into one file and can be downloaded. * Hippocampus VoxelDB: The 3D hippocampus reconstructed is volumetrically transformed into 16 micrometer sized voxels for all the seven layers. Each voxel is reported according to multiple coordinate systems, namely in Cartesian, along the natural hippocampal dimensions, and in reference to the canonical brain planes. The voxel database file is created in ascii format. The single voxel database file was split into three rar archive files. Please note that the three rar archive files should be downloaded and decompressed in a single directory in order to obtain the single voxel data file (Hippocampus-VoxelDB.txt). * 3D Surface Renderings: This is a rar archive file with a single VRML file containing the surface rendering of DG and CA layers. This VRML file can be opened and visualized in any VRML viewer, e.g. the open source software view3dscene. * 3D Hippocampus Movie: This movie contains visualization of the 3D surface renderings of CA (blue) and DG (red) inner and outer boundaries; neuronal embeddings of DG granule and CA pyramidal dendritic arbors; potential synapses between CA3b interneuron axon and pyramidal dendrite, and between CA2 pyramidal axon and CA pyramidal dendrites.

Proper citation: Hippocampus 3D Model (RRID:SCR_005083) Copy   

•   Source: SciCrunch Registry

http://www.webarraydb.org/webarray/index.html

An open source integrated microarray database and analysis suite that features convenient uploading of data for storage in a MIAME (Minimal Information about a Microarray Experiment) compliant fashion. It allows data to be mined with a large variety of R-based tools, including data analysis across multiple platforms. Different methods for probe alignment, normalization and statistical analysis are included to account for systematic bias. Student's t-test, moderated t-tests, non-parametric tests and analysis of variance or covariance (ANOVA/ANCOVA) are among the choices of algorithms for differential analysis of data. Users also have the flexibility to define new factors and create new analysis models to fit complex experimental designs. All data can be queried or browsed through a web browser. The computations can be performed in parallel on symmetric multiprocessing (SMP) systems or Linux clusters.

Proper citation: WebArrayDB (RRID:SCR_005577) Copy   

•   Source: SciCrunch Registry

http://www.chori.org/

CHORI is the internationally renowned biomedical research institute of Children''s Hospital and Research Center at Oakland. With world-class scientists and research centers known both nationally and internationally in multiple fields, CHORI is 5th in the nation for National Institutes of Health pediatric research funding. Bridging basic science and clinical research in the treatment and prevention of human disease, CHORI is a leader in translational research, providing cures for blood diseases, developing new vaccines for infectious diseases, and discovering new treatment protocols for previously fatal or debilitating conditions. Striving to provide the highest standard of excellence and innovation, CHORI brings together a multidisciplinary collaborative of distinguished investigators in six different Centers of Research: The Center for Cancer Research, The Center for Genetics, The Center for Immunobiology & Vaccine Development, The Center for Nutrition & Metabolism, The Center for Prevention of Obesity, Cardiovascular Disease & Diabetes, and The Center for Sickle Cell Disease & Thalassemia. Within these major areas of focus, CHORI pushes the frontiers of science and of excellence beyond their borders. Among the leading biotech enterprises in the Bay Area, CHORI produced 25 patents in the last 5 years alone. In addition to providing world-class research, CHORI is also a teaching institute, offering unique educational opportunities to high school, college, doctoral and post-doctoral students.

Proper citation: Childrens Hospital Oakland Research Institute (RRID:SCR_005582) Copy   

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http://cs.nyu.edu/~bingsun/

NYU Bioinformatics group applies algorithmic, statistical, and mathematical techniques to solve problems of interest to biology, biotechnology and biomedicine. The group focuses on bioinformatics, computational biology and systems biology with many active projects in areas ranging from single molecules to entire populations: Analysis of Single-Molecule/Single-Cell Data, SPM-based Transcriptomic Profiling, Whole-Genome Haplotype Sequencing using SMASH (Single Molecule Approaches to Haplotype Sequencing), SUTTA (Scoring and Unfolding Trimmed Tree Assembler) assembly algorithm, Analysis of Spatio-Temporal Data, Model Checking and Model Building for Systems Biology, GOALIE-based Phenomenological Models and their Verification, Causality Analysis, Causal Models and their Verification, Analysis of EHR (Electronic Health Record Data) and Disease Models (e.g., Chronic Fatigue Syndrome, Congestive Heart Failure, Deep Vein Thrombosis, etc.), Models of Cancer, Applications to Pancreatic Cancer, Polymorphisms and Biomarkers, Strategies for Group Testing, Epidemiological and Bio-Warfare Models, Planning with Large Agent Networks against Catastrophes (PLAN C), Population Genomics, and Genome Wide Association Studies (GWAS). The group has received its funding from Air Force, Army, CCPR, DARPA, NIH, NIST, NSF, NYSTAR, etc. and various other governmental and commercial entities. Currently, the group is part of an NSF funded Expedition in Computing project (CMACS: Center for Modeling and Analysis of Complex Systems at CMU) and collaborates widely, both nationally and internationally. The group is highly multi-disciplinary, attracting researchers and students from mathematics, statistics, computer science, and biology who team up with physicians, physicists, and chemists as well as professionals in their own disciplines. This group is led by Prof. Bud Mishra, a professor of computer science and mathematics at NYU''s Courant Institute of Mathematical Sciences.

Proper citation: NYU Bioinformatics Group (RRID:SCR_005697) Copy   

•   Source: SciCrunch Registry

http://www.mouseimaging.ca/

A unique resource and comprehensive imaging facility combining the latest state-of-the-art digital medical imaging technologies for the characterization of mouse functional genomics. The goals of the Mouse Imaging Centre are: * To provide a variety of medical imaging technologies adapted to studying genetically modified mice. These technologies include magnetic resonance (MR) imaging, micro computed tomography (micro-CT), ultrasound biomicroscopy (UBM), and optical projection tomography (OPT). * To screen large numbers of mice for models of human diseases. * To image an individual mouse over time to observe development, disease progression and responses to experimental treatment. * To develop an exciting team of investigators with expertise in imaging techniques, computer science, engineering, imaging processing, developmental biology and mouse pathology. * To work by collaboration with researchers throughout the world. When we look for human diseases in the human population, we make extensive use of medical imaging. Therefore, it makes sense to have available the same imaging capabilities as we investigate mice for models of human disease. The Mouse Imaging Centre (MICe) has developed high field magnetic resonance imaging microscopy, ultrasound biomicroscopy, micro computed tomography, and optical techniques. With these imaging tools, MICe is screening randomly mutagenized mice to look for phenotypes that represent human diseases and is taking established human disease models in mice and using imaging to follow the progression of disease and response to treatment over time. It is clear that imaging has a major contribution to make to phenotyping genetic variants and to characterizing mouse models. MICe is staffed by an exciting new team of about 30 investigators with expertise in imaging techniques, computer science, engineering, imaging processing, developmental biology and mouse pathology. The Mouse Imaging Centre (MICe) is not a fee-for-service facility but works through collaborations. Services include: * Projects involving MicroCT are available as a fee for service. * We will eventually move to the same model above with MRI. * Ultrasound Biomicroscopy is used for cardiac, embryo and cancer studies and is available as fee for service at $100 per study or in some cases on a collaborative basis. * Optical Projection Tomography has only limited availability on a collaborative basis. Mouse Atlas As our images are inherently three-dimensional, we will be able to make quantitative measures of size and volume. With this in mind, we are developing a mouse atlas showing the normal deviation of organ sizes. This atlas is an important resource for biologists as it has the potential to eliminate the need to sacrifice as many controls when making comparisons with mutants. Mouse Atlas Examples: * Variational Mouse Brain Atlas * Cerebral Vascular Atlas of the CBA Mouse * Neuroanatomy Atlas of the C57Bl/6j Mouse * Vascular Atlas of the Developing Mouse Embryo * Micro-CT E15.5 Mouse Embryo Atlas

Proper citation: MICe - Mouse Imaging Centre (RRID:SCR_006145) Copy   

•   Source: SciCrunch Registry

https://panoramaweb.org/project/home/begin.view?

Repository software for targeted mass spectrometry assays from Skyline. Targeted proteomics knowledge base. Public repository for quantitative data sets processed in Skyline. Facilitates viewing, sharing, and disseminating results contained in Skyline documents.

Proper citation: PanoramaWeb (RRID:SCR_017136) Copy   

•   Source: SciCrunch Registry

https://dandiarchive.org

DANDI is a platform for publishing, sharing, and processing neurophysiology data funded by the BRAIN Initiative. The archive is not just an endpoint to dump data, it is intended as a living repository that enables collaboration within and across labs, and for others, the entry point for research.

Proper citation: Distributed Archives for Neurophysiology Data Integration (RRID:SCR_017571) Copy   

•   Source: SciCrunch Registry

https://bioinformaticshome.com/tools/rna-seq/descriptions/LIGER.html

Software R package for integrating and analyzing multiple single-cell datasets. It relies on integrative non-negative matrix factorization to identify shared and dataset-specific factors. Used for analysis of multiple scRNA-seq data sets.

Proper citation: LIGER (RRID:SCR_018100) Copy   

•   Source: SciCrunch Registry

https://nih.figshare.com/

Repository to make datasets resulting from NIH funded research more accessible, citable, shareable, and discoverable. Data submitted will be reviewed to ensure there is no personally identifiable information in data and metadata prior to being published and in line with FAIR -Findable, Accessible, Interoperable, and Reusable principles. Data published on Figshare is assigned persistent, citable DOI (Digital Object Identifier) and is discoverable in Google, Google Scholar, Google Dataset Search, and more.Complited on July,2020. Researches can continue to share NIH funded data and other research product on figshare.com.

Proper citation: NIH Figshare Archive (RRID:SCR_017580) Copy   

•   Source: SciCrunch Registry

https://amoebadb.org/amoeba/

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   

•   Source: SciCrunch Registry

https://github.com/galaxyproteomics/mvpapplication-git.git

Software tool as plugin to enable viewing of results produced from workflows integrating genomic sequencing data and mass spectrometry proteomics data. Plugin to Galaxy bioinformatics workbench which enables visualization of mass spectrometry-based proteomics data integrated with genomic and/or transcriptomic sequencing data. Useful for verifying quality of results and characterizing novel peptide sequences identified using multi-omic proteogenomic approach.

Proper citation: Multi-omics Visualization Platform (RRID:SCR_018077) Copy   

•   Source: SciCrunch Registry

https://icite.od.nih.gov/covid19/search/

NIH comprehensive, curated source for publications related to COVID-19. Includes articles from PubMed and pre-prints from arXiv, medRxiv, bioRxiv, and ChemRxiv. Updated daily. NIH Office of Portfolio Analysis has developed this resource to explore and analyze set of advances in COVID‑19 research as they accumulate in real time, and complements efforts by NLM to aggregate full text documents broadly related to COVID-19 and other outbreaks, and articles on COVID‑19 specific to PubMed database.

Proper citation: NIH COVID-19 Portfolio (RRID:SCR_018295) Copy   

•   Source: SciCrunch Registry

https://datascience.nih.gov/covid-19-open-access-resources

COVID-19 open access data and computational resources provided by federal agencies, including NIH, public consortia, and private entities. Continuously updated as more information becomes available. These resources are being shared for scientific and public health interests, and content is responsibility of resource organizers.

Proper citation: Data and Computational Resources to Address COVID-19 (RRID:SCR_018274) Copy   

•   Source: SciCrunch Registry

http://glycam.org/

Web provides tools for modeling 3D structures of molecules and complexes containing carbohydrates including oligosaccharide conformation modeling and glycoprotein 3D structure modeling. Used to simplify prediction of three dimensional structures of carbohydrates and macromolecular structures involving carbohydrates.

Proper citation: GLYCAM-Web (RRID:SCR_018260) Copy   

•   Source: SciCrunch Registry

https://ncats.nih.gov/n3c/about

Portal for centralized national data to study COVID-19 and identify potential treatments.Centralized, secure analytics platform where patient privacy is protected. Enables collection and analysis of clinical, laboratory and diagnostic data from hospitals and health care plans. Data are provided after executing data transfer agreement with National Center for Advancing Translational Sciences. N3C is partnership among NCATS supported Clinical and Translational Science Awards Program hubs and National Center for Data to Health with overall stewardship by NCATS.

Proper citation: National COVID Cohort Collaborative (RRID:SCR_018757) Copy   

•   Source: SciCrunch Registry

http://www.jobs.nih.gov/

A listing of all current openings across the NIH. You may search for NIH Jobs, browse job descriptions, view all descriptions or use the quick links.

Proper citation: Jobs(at)NIH (RRID:SCR_006471) Copy   

•   Source: SciCrunch Registry

https://www.researchmatch.org/

Free and secure registry to bring together two groups of people who are looking for one another: (1) people who are trying to find research studies, and (2) researchers who are looking for people to participate in their studies. It has been developed by major academic institutions across the country who want to involve you in the mission of helping today''''s studies make a real difference for everyone''''s health in the future. Anyone can join ResearchMatch. Many studies are looking for healthy people of all ages, while some are looking for people with specific health conditions. ResearchMatch can help ''''match'''' you with any type of research study, ranging from surveys to clinical trials, always giving you the choice to decide what studies may interest you.

Proper citation: ResearchMatch (RRID:SCR_006387) Copy   

•   Source: SciCrunch Registry

http://bioinformatics.hungry.com/clearcut/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on February 28,2023.Software as a stand-alone reference implementation for the Relaxed Neighbor Joining (RNJ) algorithm. Used in distance-based phylogenetic tree reconstruction method to process large sequence datasets., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Clearcut (RRID:SCR_016059) Copy   

•   Source: SciCrunch Registry

http://cole-trapnell-lab.github.io/monocle-release/docs/

Software package for analyzing single cell gene expression, classifying and counting cells, performing differential expression analysis between subpopulations of cells, and reconstructing cellular trajcectories. Works well with very large single-cell RNA-Seq experiments containing tens of thousands of cells or more. Used in computational analysis of gene expression data in single cell gene expression studies to profile transcriptional regulation in complex biological processes and highly heterogeneous cell populations.

Proper citation: Monocle2 (RRID:SCR_016339) Copy   

•   Source: SciCrunch Registry

https://bioconductor.org/packages/release/bioc/html/MAST.html

Software as an open source package for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data.

Proper citation: MAST (RRID:SCR_016340) Copy   

•   Source: SciCrunch Registry