Tools   Select Another Resource Report Type

https://www.mirri.org/

Microbial stock center. Collaborative platform for the pan-European research infrastructure for microbial resources. Provides access to microorganisms, their derivatives, associated data and services for research, development and application.

Proper citation: MIRRI (RRID:SCR_016461) Copy   

•   Source: SciCrunch Registry

http://gcm.wfcc.info/

Database and information retrieval, analysis, and visualization system for microbial resources to help culture collections to manage, disseminate and share the information related to their holdings. Provides an interface for the scientific and industrial communities to access the microbial resource information.

Proper citation: Global Catalogue of Microorganisms (RRID:SCR_016460) Copy   

•   Source: SciCrunch Registry

http://nacet.org/az-core-labs/

A core facility which provides fee-based sample imaging and analysis using analytical and microscopy equipment to technology incubator clients, university researchers, and industry.

Proper citation: AZ Core Labs (RRID:SCR_001036) Copy   

•   Source: SciCrunch Registry

http://www.biocurrents.org/

The BioCurrents Research Center (BRC) is an integrated technology resource of the NIH:NCRR. The activities of the Center focus on molecular physiology as it relates to the cell function and disease. Our particular interest is how the dynamics of cell responses are reflected in the chemical profiles of microdomains surrounding single living cells. In order to measure complex cellular boundary layers, the BRC has specialized in the development of extremely sensitive signal acquisition and processing methods along with miniaturized electrochemical sensor designs. The technique is non-invasive and termed self-referencing. Since its establishment in 1996, the BRC has directed its technological research and development to the design and application of ultra-microelectrodes (tip diameters of less than 10m) tailored for the detection of specific chemicals. These have been successfully applied to the boundary layer profiles of many different cell types, with thematic strength in diabetes research, reproductive health and development (see collaborative profiles). More recently, it is changing its focus to technical developments, enhancing the integrative approach to cell function. To understand a cell as a dynamic and integrated whole, BRC must be able to examine responses from different domains as near to real time and as synchronously as possible. To this end, it is developing imaging capabilities to work in parallel with electrochemistry and conventional electrophysiological techniques. Imaging includes a spinning disc confocal, as well as a low light/luminescent imager designed and built within the BRC. The technologies developed or under development are in high demand within the biomedical community. Over 40 investigators work with the Center each year in a collaborative or service capacity. Over 80 of our visitor pool is NIH funded, representing approximately 25 NIH divisions and institutes. As part of our training and dissemination program we host occasional workshops at major national and international meetings, train a significant number of new investigators each year and host graduate students undertaking portions of their thesis dissertation using our technologies. In dissemination we advise on, and install, electrochemical systems in off campus research endeavors, both academic and industrial.

Proper citation: BioCurrents Research Center (RRID:SCR_002020) Copy   

•   Source: SciCrunch Registry

http://www.adinstruments.com/products/software/modules/neuro_explorer.php

THIS RESOURCE IS NO LONGER IN SERVICE, documented on May 19, 2018; A provider of computer-based data acquisition and analysis systems for life science. Products enable users to record and analyze life science data quickly and efficiently. ADInstruments product range is based on the PowerLab data acquisition system with LabChart software. The PowerLab system (also MacLab) is used in universities, hospitals, research institutes, pharmaceutical companies, contract research organizations and other private industry research sectors.

Proper citation: ADInstruments - Data Acquisition Systems for Life Science (RRID:SCR_001620) Copy   

•   Source: SciCrunch Registry

http://www.immundiagnostik.com

Commercial company that develops and provides laboratory diagnostics and other medical research products.

Proper citation: Immundiagnostik (RRID:SCR_001977) Copy   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/bstp/

A free collection of MRI brain images for testing segmentation algorithms. It is available for download to assess the accuracy, reproducibility and sensitivity of MRI segmentation software. It includes data from infants and adults as well as patients with Alzheimer's disease.

Proper citation: Brain Segmentation Testing Protocol (RRID:SCR_009445) Copy   

•   Source: SciCrunch Registry

http://www.rand.org/labor/FLS/MHSS.html

A data set of the health and socioeconomic factors that affect the elderly in Matlab, a region of rural Bangladesh. The survey captures measurements and statistics such as adult survival, health status, health care utilization, resource flows between generations and the impact of community services and infrastructure on adult health care. Data was collected through surveys that touch on four topics: household and individual information; determinants of natural fertility; migration out of the community; and community and provider survey of healthcare and education infrastructure.

Proper citation: Matlab Health and Socio-Economic Survey (RRID:SCR_008942) Copy   

•   Source: SciCrunch Registry

https://www.k-inbre.org/pages/k-inbre_about_bio-core.html

The K-INBRE Bioinformatics Core collaborates with Kansas researchers to perform research on cell and developmental biology. Core aims to serve the needs of investigators engaged in computationally intensive biomedical research, and to promote education in bioinformatics to students and researchers across the state of Kansas.

Proper citation: Kansas State University - INBRE Bioinformatics Core Facility (RRID:SCR_012596) Copy   

•   Source: SciCrunch Registry

https://bils.se

A national research infrastructure that provides bioinformatics support to life science researchers in Sweden. Their work is supported by the Swedish Research Council.

Proper citation: Bioinformatics Infrastructure for Life Sciences (RRID:SCR_014723) Copy   

•   Source: SciCrunch Registry

http://www.lji.org/faculty-research/scientific-cores/dlac/#overview

The department of laboratory animal care at the La Jolla Institute for Allergy and Immunology.

Proper citation: La Jolla Institute for Allergy and Immunology Department of Laboratory Animal Care (RRID:SCR_014834) Copy   

•   Source: SciCrunch Registry

http://www.lji.org/faculty-research/scientific-cores/clinical-studies/#overview

Core facility for clinical studies carried out by the La Jolla Institute of Allergy and Immunology. It is also a non-profit research organization that focuses on studying topics lthat include pollen allergies, HIV, food allergies and tuberculosis.

Proper citation: La Jolla Institute for Immunology Clinical Studies Core Facility (RRID:SCR_014833) Copy   

•   Source: SciCrunch Registry

http://www.lji.org/faculty-research/scientific-cores/functional-genomics/#overview

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on July 5, 2024. Core facility that combines large-scale automation and high-throughput capabilities with gene disruption techniques to pinpoint the function of individual genes and find new ways to disrupt genetic triggers of disease. The research capabilities are aimed towards finding new treatments for immune-related diseases.

Proper citation: La Jolla Institute for Allergy and Immunology Functional Genomics Core Facility (RRID:SCR_014836) Copy   

•   Source: SciCrunch Registry

http://caties.cabig.upmc.edu/

The Cancer Text Information Extraction System (caTIES) provides tools for de-identification and automated coding of free-text structured pathology reports. It also has a client that can be used to search these coded reports. The client also supports Tissue Banking and Honest Broker operations. caTIES focuses on two important challenges of bioinformatics * Information extraction (IE) from free text * Access to tissue. Regarding the first challenge, information from free-text pathology documents represents a vital and often underutilized source of data for cancer researchers. Typically, extracting useful data from these documents is a slow and laborious manual process requiring significant domain expertise. Application of automated methods for IE provides a method for radically increasing the speed and scope with which this data can be accessed. Regarding the second challenge, there is a pressing need in the cancer research community to gain access to tissue specific to certain experimental criteria. Presently, there are vast quantities of frozen tissue and paraffin embedded tissue throughout the country, due to lack of annotation or lack of access to annotation these tissues are often unavailable to individual researchers. caTIES has three goals designed to solve these problems: * Extract coded information from free text Surgical Pathology Reports (SPRs), using controlled terminologies to populate caBIG-compliant data structures. * Provide researchers with the ability to query, browse and create orders for annotated tissue data and physical material across a network of federated sources. With caTIES the SPR acts as a locator to tissue resources. * Pioneer research for distributed text information extraction within the context of caBIG. caTIES focuses on IE from SPRs because they represent a high-dividend target for automated analysis. There are millions of SPRs in each major hospital system, and SPRs contain important information for researchers. SPRs act as tissue locators by indicating the presence of tissue blocks, frozen tissue and other resources, and by identifying the relationship of the tissue block to significant landmarks such as tumor margins. At present, nearly all important data within SPRs are embedded within loosely-structured free-text. For these reasons, SPRs were chosen to be coded through caTIES because facilitating access to information contained in SPRs will have a powerful impact on cancer research. Once SPR information has been run through the caTIES Pipeline, the data may be queried and inspected by the researcher. The goal of this search may be to extract and analyze data or to acquire slides of tissue for further study. caTIES provides two query interfaces, a simple query dashboard and an advanced diagram query builder. Both of these interfaces are capable of NCI Metathesaurus, concept-based searching as well as string searching. Additionally, the diagram interface is capable of advanced searching functionalities. An important aspect of the interface is the ability to manage queries and case sets. Users are able to vet query results and save them to case sets which can then be edited at a later time. These can be submitted as tissue orders or used to derive data extracts. Queries can also be saved, and modified at a later time. caTIES provides the following web services by default: MMTx Service, TIES Coder Service

Proper citation: caTIES - Cancer Text Information Extraction System (RRID:SCR_003444) Copy   

•   Source: SciCrunch Registry

http://databases.unesco.org/bioethics/biowebintro.shtml

Bioethics database comprises over 645 bioethics institutions (bioethics committees, commissions, training, research and documentation centres) in over 80 countries, including information on activities and publications. Information is based on replies obtained from a widely distributed questionnaire and has been gathered in cooperation with National Commissions and Permanent Delegations to UNESCO. The Program develops four main action areas: -Intellectual forum -Standard-setting action -Advisory role and capacity-building -Education and awareness raising The Bioethics Program is part of UNESCOs Division of the Ethics of Science and Technology in the Social and Human Sciences Sector. It is primarily responsible for the Secretariat of two advisory bodies: the International Bioethics Committee (IBC), composed of 36 independent experts, and the Intergovernmental Bioethics Committee (IGBC), composed of representatives of 36 Member States. These Committees cooperate to produce advice, recommendations and proposals that each submits to the Director-General for consideration by UNESCOs governing bodies.

Proper citation: Bioethics Institutes Database (RRID:SCR_008173) Copy   

•   Source: SciCrunch Registry

http://www.lamhdi.org/

THIS RESOURCE IS NO LONGER IN SERVICE, it has been replaced by Monarch Initiative. LAMHDI, the initiative to Link Animal Models to Human DIsease, is designed to accelerate the research process by providing biomedical researchers with a simple, comprehensive Web-based resource to find the best animal model for their research. LAMDHI is a free, Web-based, resource to help researchers bridge the gap between bench testing and human trials. It provides a free, unbiased resource that enables scientists to quickly find the best animal models for their research studies. LAMHDI includes mouse data from MGI, the Mouse Genome Informatics website; zebrafish data from ZFIN, the Zebrafish Model Organism Database; rat data from RGD, the Rat Genome Database; yeast data from SGD, the Saccharomyces Genome Database; and fly data from FlyBase. LAMHDI.org is operational today, and data is added regularly. Enhancements are planned to let researchers contribute their knowledge of the animal models available through LAMHDI. The LAMHDI goal is to allow researchers to share information about and access to animal models so they can refine research and testing, and reduce or replace the use of animal models where possible. LAMHDI Database Search: LAMHDI brings together scientifically validated information from various sources to create a composite multi-species database of animal models of human disease. To do this, the LAMHDI database is prepared from a variety of sources. The LAMHDI team takes publicly available data from OMIM, NCBI''s Entrez Gene database, Homologene, and WikiPathways, and builds a mathematical graph (think of it as a map or a web) that links these data together. OMIM is used to link human diseases with specific human genes, and Entrez provides universal identifiers for each of those genes. Human genes are linked to their counterpart genes in other species with Homologene, and those genes are linked to other genes tentatively or authoritatively using the data in WikiPathways. This preparatory work gives LAMHDI a web of human diseases linked to specific human genes, orthologous human genes, homologous genes in other species, and both human and non-human genes involved in specific metabolic pathways associated with those diseases. LAMHDI includes model data that partners provide directly from their data structures. For instance, MGI provides information about mouse models, including a disease for each model, as well as some genetic information (the ID of the model, in fact, identifies one or more genes). ZFIN provides genetic information for each zebrafish model, but no diseases, so zebrafish models are integrated by using the genes as the glue. For instance, a zebrafish model built to feature the zebrafish PKD2 gene would plug into the larger disease-gene map at the node representing the zebrafish PKD2 gene, which is connected to the node representing the human PKD2 gene, which in turn is connected to the node representing the human disease known as polycystic kidney disease. (Some of the partner data LAMHDI receives can even extend the base map. MGI provides a disease for every model, and in some cases this allows the creation of a disease-to-gene relationship in the LAMHDI database that might not already be documented in the OMIM dataset.) With curatorial and model information in hand, LAMHDI runs a lengthy automated process that exhaustively searches for every possible path between each model and each disease in the data, up to a set number of hops, producing for each disease-to-model pair a set of links from the disease to the model. The algorithm avoids circular paths and paths that include more than one disease anywhere in the middle of the path. At the end of this phase, LAMHDI has a comprehensive set of paths representing all the disease-to-model relationships in the data, varying in length from one hop to many hops. Each disease-to-model path is essentially a string of nodes in the data, where each node represents a disease, a gene, a linkage between genes (an orthologue, a homologue, or a pathway connection, referred to as a gene cluster or association), or a model. Each node has a human-friendly label, a set of terms and keywords, and - in most cases - a URL linking the node to the data source where it originated. When a researcher submits a search on the LAMHDI website, LAMHDI searches for the user''s search terms in its precomputed list of all known disease-to-model paths. It looks for the terms not only in the disease and model nodes, but also in every node along each path. The complete set of hits may include multiple paths between any given disease-to-model pair of endpoints. Each of these disease-to-model pair sets is ordered by the number of hops it involves, and the one involving the fewest hops is chosen to represent its respective disease-to-model pair in the search results presented to the user. Results are sorted by scores that represent their matches. The number of hops is one barometer of the strength of the evidence linking the model and the disease; fewer hops indicates the relationship is stronger, more hops indicates it may be weaker. This indicator works best for comparing models from a single partner dataset: MGI explicitly identifies a disease for each mouse model, so there can be disease-to-model hits for mice that involve just one hop. Because ZFIN does not explicitly identify a disease for each model, no zebrafish model will involve fewer than four hops to the nearest disease, from the zebrafish model to a zebrafish gene to a gene cluster to a human gene to a human disease.

Proper citation: LAMHDI: The Initiative to Link Animal Models to Human DIsease (RRID:SCR_008643) Copy   

•   Source: SciCrunch Registry

http://www.BioMedSearch.com

BioMedSearch is a biomedical search engine that contains NIH/PubMed documents, plus a large collection of theses, dissertations, and other publications not found anywhere else for free, making it the most comprehensive free search on the web. :Besides free-form search, users can search based on Author, Journal Title, Publication Date, the Language in which the article was published (many non-English articles have English language abstracts), MeSH (Medical Subject Headings) and more. : The goal of BioMedSearch.com is to provide free access to a massive collection of authoritative documents relating to the biomedical field. Our mission is to make these important works available to the community in a way that is fast and easy, while still offering the advanced features demanded by power users such as portfolios, collaboration features, bibliographical citation export, alerts, and more. Whether you are doctor, scientist, or someone interested in researching a medical topic out of personal interest, BioMedSearch aggregates a vast number of authoritative documents in one place to make finding medical information easy, fast and free.

Proper citation: Biomedical Search: Medical Research and Health Resources (RRID:SCR_008683) Copy   

•   Source: SciCrunch Registry

http://www.phac-aspc.gc.ca/msds-ftss/

Material Safety Data Sheets for chemical products are available to laboratory workers for most chemicals and reagents. However because many laboratory workers, whether in research, public health, teaching, etc., are exposed to not only chemicals but infectious substances as well, there was a large gap in the readily available safety literature for employees. These MSDS are produced for personnel working in the life sciences as quick safety reference material relating to infectious micro-organisms. The MSDS are organized to contain health hazard information such as infectious dose, viability (including decontamination), medical information, laboratory hazard, recommended precautions, handling information and spill procedures. The intent of these documents is to provide a safety resource for laboratory personnel working with these infectious substances. Because these workers are usually working in a scientific setting and are potentially exposed to much higher concentrations of these human pathogens than the general public, the terminology in these MSDS is technical and detailed, containing information that is relevant specifically to the laboratory setting. It is hoped along with good laboratory practices, these MSDS will help provide a safer, healthier environment for everyone working with infectious substances. The MSDS is ran by the Public Health Agency of Canada. The Public Health Agency of Canada (PHAC) is the main Government of Canada agency responsible for public health in Canada. PHACs primary goal is to strengthen Canadas capacity to protect and improve the health of Canadians and to help reduce pressures on the health-care system. To do this, the Agency is working to build an effective public health system that enables Canadians to achieve better health and well-being in their daily lives by promoting good health, helping prevent and control chronic diseases and injury, and protecting Canadians from infectious diseases and other threats to their health. PHAC is also committed to reducing health disparities between the most advantaged and disadvantaged Canadians. Because public health is a shared responsibility, the Public Health Agency of Canada works in close collaboration with all levels of government (provincial, territorial and municipal) to build on each others skills and strengths. The Agency also works closely with non-government organizations, including civil society and business, and other countries and international organizations like the World Health Organization (WHO) to share knowledge, expertise and experiences.

Proper citation: Material Safety Data Sheets for Infectious Substances of Canada (RRID:SCR_013003) Copy   

•   Source: SciCrunch Registry

http://tcm.lifescience.ntu.edu.tw/index.html

TCMGeneDIT is a database system providing association information about traditional Chinese medicines (TCMs), genes, diseases, TCM effects and TCM ingredients automatically mined from vast amount of biomedical literature. Integrated protein-protein interaction and biological pathways information collected from public databases are also available. In addition, the transitive relationships among genes, TCMs and diseases could be inferred through the shared intermediates. Furthermore, TCMGeneDIT is useful in deducing possible synergistic or antagonistic contributions of the prescription components to the overall therapeutic effects. TCMGeneDIT is a unique database of various association information about TCMs. The database integrating TCMs with life sciences and biomedical studies would facilitate the modern clinical research and the understanding of therapeutic mechanisms of TCMs and gene regulations.

Proper citation: TCMGeneDIT (RRID:SCR_013396) Copy   

•   Source: SciCrunch Registry