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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.

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On page 11 showing 201 ~ 220 out of 526 results
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  • RRID:SCR_006528

    This resource has 1+ mentions.

http://neurocritic.blogspot.com/

The Neurocritic is a blog deconstructing the most sensationalistic recent findings in Human Brain Imaging, Cognitive Neuroscience, and Psychopharmacology. Born in West Virginia in 1980, The Neurocritic embarked upon a roadtrip across America at the age of thirteen with his mother. She abandoned him when they reached San Francisco and The Neurocritic descended into a spiral of drug abuse and prostitution. At fifteen, The Neurocritic''s psychiatrist encouraged him to start writing as a form of therapy.

Proper citation: Neurocritic (RRID:SCR_006528) Copy   


  • RRID:SCR_001922

    This resource has 50+ mentions.

http://www.loni.usc.edu/

Biomedical technology resource center specializing in novel approaches and tools for neuroimaging. It develops novel strategies to investigate brain structure and function in their full multidimensional complexity. There is a rapidly growing need for brain models comprehensive enough to represent brain structure and function as they change across time in large populations, in different disease states, across imaging modalities, across age and sex, and even across species. International networks of collaborators are provided with a diverse array of tools to create, analyze, visualize, and interact with models of the brain. A major focus of these collaborations is to develop four-dimensional brain models that track and analyze complex patterns of dynamically changing brain structure in development and disease, expanding investigations of brain structure-function relations to four dimensions.

Proper citation: Laboratory of Neuro Imaging (RRID:SCR_001922) Copy   


  • RRID:SCR_008801

    This resource has 5000+ mentions.

http://aws.amazon.com/1000genomes/

A dataset containing the full genomic sequence of 1,700 individuals, freely available for research use. The 1000 Genomes Project is an international research effort coordinated by a consortium of 75 companies and organizations to establish the most detailed catalogue of human genetic variation. The project has grown to 200 terabytes of genomic data including DNA sequenced from more than 1,700 individuals that researchers can now access on AWS for use in disease research free of charge. The dataset containing the full genomic sequence of 1,700 individuals is now available to all via Amazon S3. The data can be found at: http://s3.amazonaws.com/1000genomes The 1000 Genomes Project aims to include the genomes of more than 2,662 individuals from 26 populations around the world, and the NIH will continue to add the remaining genome samples to the data collection this year. Public Data Sets on AWS provide a centralized repository of public data hosted on Amazon Simple Storage Service (Amazon S3). The data can be seamlessly accessed from AWS services such Amazon Elastic Compute Cloud (Amazon EC2) and Amazon Elastic MapReduce (Amazon EMR), which provide organizations with the highly scalable compute resources needed to take advantage of these large data collections. AWS is storing the public data sets at no charge to the community. Researchers pay only for the additional AWS resources they need for further processing or analysis of the data. All 200 TB of the latest 1000 Genomes Project data is available in a publicly available Amazon S3 bucket. You can access the data via simple HTTP requests, or take advantage of the AWS SDKs in languages such as Ruby, Java, Python, .NET and PHP. Researchers can use the Amazon EC2 utility computing service to dive into this data without the usual capital investment required to work with data at this scale. AWS also provides a number of orchestration and automation services to help teams make their research available to others to remix and reuse. Making the data available via a bucket in Amazon S3 also means that customers can crunch the information using Hadoop via Amazon Elastic MapReduce, and take advantage of the growing collection of tools for running bioinformatics job flows, such as CloudBurst and Crossbow.

Proper citation: 1000 Genomes Project and AWS (RRID:SCR_008801) Copy   


http://www.cs.tau.ac.il/~shlomito/tissue-net/

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. Network visualizations in which the expression and predicted flux data are projected over the global human network. These network visualizations are accessible through the supplemental website using the publicly available Cytoscape software (Cline, Smoot et al. 2007). Since many high degree nodes exist in the network, special layouts are required to produce network visualizations that are readily interpretable. To this end we produced network visualizations in which hub nodes are repeated multiple times and hence layouts with a small number of edge crossings can be generated. Contains entries for brain compartments and brain pathways.

Proper citation: Network-based Prediction of Human Tissue-specific Metabolism (RRID:SCR_007392) Copy   


https://confluence.crbs.ucsd.edu/display/NIF/StemCellInfo

Data tables providing an overview of information about stem cells that have been derived from mice and humans. The tables summarize published research that characterizes cells that are capable of developing into cells of multiple germ layers (i.e., multipotent or pluripotent) or that can generate the differentiated cell types of another tissue (i.e., plasticity) such as a bone marrow cell becoming a neuronal cell. The tables do not include information about cells considered progenitor or precursor cells or those that can proliferate without the demonstrated ability to generate cell types of other tissues. The tables list the tissue from which the cells were derived, the types of cells that developed, the conditions under which differentiation occurred, the methods by which the cells were characterized, and the primary references for the information.

Proper citation: National Institutes of Health Stem Cell Tables (RRID:SCR_008359) Copy   


http://harvard.eagle-i.net/i/0000012a-2518-fb6c-5617-794280000000

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on October 27, 2023. Core provides services: RT PCR service, Gene expression profiling service, Proteomics analysis service, Bioinformatics and Systems Biology analyses, Next Generation Sequencing Service, Affymetrix Human and Mouse Gene 2.0 ST Arrays and 2.1 ST Arrayplates. Core proteomics facility for the Dana-Farber/Harvard Cancer Center. Workflows and algorithms for analysis of next-generation sequencing data including RNA-Seq, ChIP-Seq, Epigenetics-Seq and DNA seq, Comprehensive workflow for analysis of Microbiome sequencing data, Integrated systems biology analysis of transcriptome, miRNA, epigenome, metabolomics and proteomics data. Pipelines: MALDI Tissue imaging and targeted quantitative proteomics.

Proper citation: Beth Israel Deaconess Medical Center Genomics Proteomics Bioinformatics and Systems Biology Center (RRID:SCR_009668) Copy   


http://cerebrovascularportal.org

Portal enables browsing, searching, and analysis of human genetic information linked to cerebrovascular disease and related traits, while protecting the integrity and confidentiality of the underlying data.

Proper citation: Cerebrovascular Disease Knowledge Portal (RRID:SCR_015628) Copy   


https://hirnetwork.org/consortium/chib

Consortium that is an independent research initiative of the Human Research Information Network (HIRN). It is combining advances in beta cell biology and cell biology with tissue engineering technologies to develop microdevices that support functional human islets.

Proper citation: HIRN Consortium on Human Islet Biomimetics (RRID:SCR_016199) Copy   


https://anvilproject.org/

Portal to facilitate integration and computing on and across large datasets generated by NHGRI programs, as well as initiatives funded by National Institutes of Health or by other agencies that support human genomics research. Resource for genomic scientific community, that leverages cloud based infrastructure for democratizing genomic data access, sharing and computing across large genomic, and genomic related data sets. Component of federated data ecosystem, and is expected to collaborate and integrate with other genomic data resources through adoption of FAIR (Findable, Accessible, Interoperable, Reusable) principles, as their specifications emerge from scientific community. Will provide collaborative environment, where datasets and analysis workflows can be shared within consortium and be prepared for public release to broad scientific community through AnVIL user interfaces.

Proper citation: Analysis, Visualization, and Informatics Lab-space (AnVIL) (RRID:SCR_017469) Copy   


https://github.com/zuoxinian/CCS

Software tool for multimodal human brain imaging data analysis. Computational pipeline for discovery science of human brain connectomes at macroscale with multimodal magnetic resonance imaging technologies.

Proper citation: Connectome Computation System (RRID:SCR_017342) Copy   


  • RRID:SCR_017567

    This resource has 50+ mentions.

https://portal.brain-map.org/atlases-and-data/rnaseq

Software tool to visualize and analyze transcriptomics data and transcriptomic cell types for mouse and human, all directly in web browser. To explore gene expression heatmap across cell types in datasets, search for genes of interest, explore tSNE visualization, colored by cell types or expression of genes of interest, visualize dataset’s sampling strategy to see how cells and nuclei were sampled across brain areas, cortical layer, and other dimensions, find cell type of interest in one visualization and see its characteristics in different visualization.Used for Allen Brain Map Cell Types Database to Browse Data: Human - Multiple Cortical Areas, and Mouse - Cortex and Hippocampus.

Proper citation: Transcriptomics Explorer (RRID:SCR_017567) Copy   


  • RRID:SCR_017612

    This resource has 1+ mentions.

https://kg.ebrains.eu/

Metadata management system built for EBRAINS. Multi modal metadata store which brings together information from different areas of Human Brain Project as well as from external partners. Graph database tracks linkage between experimental data and neuroscientific data science supporting more extensive data reuse and complex computational research.Supports rich terminologies, ontologies and controlled vocabularies. Built by design to support iterative elaborations of common standards and supports these by probabilistic suggestion and review systems.

Proper citation: EBRAINS Knowledge Graph (RRID:SCR_017612) Copy   


https://scdevdb.deepomics.org/

Database for insights into single cell gene expression profiles during human developmental processes. Interactive database provides DE gene lists in each developmental pathway, t-SNE map, and GO and KEGG enrichment analysis based on these differential genes.

Proper citation: Single Cell Developmental Database (RRID:SCR_017546) Copy   


  • RRID:SCR_018078

    This resource has 10+ mentions.

http://circadb.hogeneschlab.org/

Database of mammalian circadian gene expression profiles. Works with link outs to Wikipedia, HomoloGene, Refseq, etc.. Open source database of circadian transcriptional profiles from time course expression experiments from mice and humans.

Proper citation: CircaDB (RRID:SCR_018078) Copy   


https://omictools.com/l2l-tool

THIS RESOURCE IS NO LONGER IN SERVICE, documented May 10, 2017. A pilot effort that has developed a centralized, web-based biospecimen locator that presents biospecimens collected and stored at participating Arizona hospitals and biospecimen banks, which are available for acquisition and use by researchers. Researchers may use this site to browse, search and request biospecimens to use in qualified studies. The development of the ABL was guided by the Arizona Biospecimen Consortium (ABC), a consortium of hospitals and medical centers in the Phoenix area, and is now being piloted by this Consortium under the direction of ABRC. You may browse by type (cells, fluid, molecular, tissue) or disease. Common data elements decided by the ABC Standards Committee, based on data elements on the National Cancer Institute''s (NCI''s) Common Biorepository Model (CBM), are displayed. These describe the minimum set of data elements that the NCI determined were most important for a researcher to see about a biospecimen. The ABL currently does not display information on whether or not clinical data is available to accompany the biospecimens. However, a requester has the ability to solicit clinical data in the request. Once a request is approved, the biospecimen provider will contact the requester to discuss the request (and the requester''s questions) before finalizing the invoice and shipment. The ABL is available to the public to browse. In order to request biospecimens from the ABL, the researcher will be required to submit the requested required information. Upon submission of the information, shipment of the requested biospecimen(s) will be dependent on the scientific and institutional review approval. Account required. Registration is open to everyone.. Documented on August 26, 2019.

Database of published microarray gene expression data, and a software tool for comparing that published data to a user''''s own microarray results. It is very simple to use - all you need is a web browser and a list of the probes that went up or down in your experiment. If you find L2L useful please consider contributing your published data to the L2L Microarray Database in the form of list files. L2L finds true biological patterns in gene expression data by systematically comparing your own list of genes to lists of genes that have been experimentally determined to be co-expressed in response to a particular stimulus - in other words, published lists of microarray results. The patterns it finds can point to the underlying disease process or affected molecular function that actually generated the observed changed in gene expression. Its insights are far more systematic than critical gene analyses, and more biologically relevant than pure Gene Ontology-based analyses. The publications included in the L2L MDB initially reflected topics thought to be related to Cockayne syndrome: aging, cancer, and DNA damage. Since then, the scope of the publications included has expanded considerably, to include chromatin structure, immune and inflammatory mediators, the hypoxic response, adipogenesis, growth factors, hormones, cell cycle regulators, and others. Despite the parochial origins of the database, the wide range of topics covered will make L2L of general interest to any investigator using microarrays to study human biology. In addition to the L2L Microarray Database, L2L contains three sets of lists derived from Gene Ontology categories: Biological Process, Cellular Component, and Molecular Function. As with the L2L MDB, each GO sub-category is represented by a text file that contains annotation information and a list of the HUGO symbols of the genes assigned to that sub-category or any of its descendants. You don''''t need to download L2L to use it to analyze your microarray data. There is an easy-to-use web-based analysis tool, and you have the option of downloading your results so you can view them at any time on your own computer, using any web browser. However, if you prefer, the entire L2L project, and all of its components, can be downloaded from the download page. Platform: Online tool, Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: L2L Microarray Analysis Tool (RRID:SCR_013440) Copy   


  • RRID:SCR_014442

    This resource has 1+ mentions.

https://www.rebuildingakidney.org

A consortium of research projects working to optimize approaches for the isolation, expansion, and differentiation of appropriate kidney cell types and their integration into complex structures that replicate human kidney function. Their goal is to coordinate and integrate research to support the development and implementation of strategies such as de novo repair of nephrons, the re-generation of nephrons, and the in vitro engineering of a biological kidney to enhance renal repair and promote the generation of new nephrons in the postnatal organ. Investigators may apply for funding of a kidney-related project through the RBK Partnership Project. Funded projects would join the consortium.

Proper citation: ReBuilding a Kidney (RRID:SCR_014442) Copy   


http://www.informatics.jax.org/phenotypes.shtml

Enables comparative phenotype analysis, searches for human disease models, and hypothesis generation by providing access to spontaneous, induced, and genetically engineered mutations and their strain-specific phenotypes.

Proper citation: Phenotypes and Mutant Alleles (RRID:SCR_017523) Copy   


  • RRID:SCR_017288

    This resource has 10+ mentions.

https://www.hmtvar.uniba.it

Manually curated database offering variability and pathogenicity information about mtDNA variants. Human mitochondrial variants data of healthy and diseased subjects.Data and text mining pipeline to annotate human mitochondrial variants with functional and clinical information.

Proper citation: HmtVar (RRID:SCR_017288) Copy   


https://www.vet.k-state.edu/research/docs/BRITE-application.pdf

The BRITE Veterinary Student Program provides DVM students interested in research with a subsidized, in-depth mentored research experience. The opportunity can be used to gain research experience, to obtain an MS, or to jump-start a DVM/PhD program. The BRITE veterinary student program is designed to expose DVM students to hypothesis-driven research activities, methodologies involved in design and execution of laboratory experiments and ethical issues pertinent to biomedical research, at a formative stage of their veterinary education. BRITE veterinary students are given a unique opportunity to utilize the rigorous didactic basic science training obtained during the first two years of the professional curriculum in pursuit of a research problem relevant to human and animal health. Sponsors: The program is funded by Kansas State University.

Proper citation: Basic Research Immersion Training Experience Veterinary Student Program (RRID:SCR_008305) Copy   


http://www.vetmed.wisc.edu/ms-phd/

The Comparative Biomedical Sciences Graduate Degree program provides exceptional graduate research training in core areas of animal and human health including genomics, immunology, molecular and cellular biology, physiology, infectious disease, neuroscience, pharmacology and toxicology, and oncology. Seventy-five faculty members in a diverse number of UW departments including Bacteriology, Biochemistry, Medical Microbiology and Immunology, Medicine, Oncology, Pathology, Radiology in addition to the 4 departments of the School of Veterinary Medicine are trainers in the program. These internationally recognized professors, as well as the integrative nature of our program, provide outstanding and unique research opportunities for our students. Because the University of Wisconsin is consistently ranked as one of the best 10 graduate institutions in the nation, the strength of our program is not only due to the superb research and teaching of our faculty but also due to the University as a whole. Approximately 55 students, most of whom are Ph.D. candidates, are currently enrolled in the program. Research strategies and academic curricula are tailored to the specific needs of each individual student. Graduates from our program are highly successful in the biotechnology industry and at top-ranked research institutions in the U.S. and abroad. The Comparative Biomedical Sciences Graduate Program offers a diverse number of research opportunities in multiple fields of study. A brief description of some of the major areas of research being performed by faculty affiliated with the Comparative Biomedical Sciences Graduate Program is provided below. Use the pull down menu above or click on the heading to find faculty members doing research in these areas. Sponsors: CBMS is supported by the University of Wisconsin

Proper citation: Comparative Biomedical Sciences Graduate Program (RRID:SCR_008304) Copy   



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