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http://kwanlab.bio.cuhk.edu.hk/BSRD/

A repository for bacterial small regulatory RNA. They welcome you to submit new experimental validated sRNA targets.

Proper citation: BSRD (RRID:SCR_004249) Copy   

•   Source: SciCrunch Registry
  

http://neurovault.org/

Data repository where researchers can publicly store and share unthresholded statistical brain activation maps produced by MRI and PET studies.

Proper citation: NeuroVault (RRID:SCR_003806) Copy   

•   Source: SciCrunch Registry
  

http://bioimage.ucsb.edu/bisque

Open source database for exchange and exploration of biological images. Used to store, visualize, organize and analyze images in cloud. Centered around database of images and metadata.

Proper citation: Bisque database (RRID:SCR_005559) Copy   

•   Source: SciCrunch Registry
  

http://www.chimpanzeebrain.org/

Collection and distribution of chimpanzee neuroimaging data and postmortem brain tissue. Portal to access chimpanzee brain atlas tools, data repository, bibliography of publications, educational information, and links to other chimpanzee brain resources and datasets on the Internet. Serves as repository for in vivo structural MRI scans of chimpanzee brains, in vivo and postmortem diffusion tensor images (DTI), as well as postmortem fixed and frozen brain specimens.

Proper citation: National Chimpanzee Brain Resource (RRID:SCR_019183) Copy   

•   Source: SciCrunch Registry
  

http://www.epilepsygenes.org/page/show/homepage

The Epilepsy Genetic Association Database (epiGAD) is an online repository of data relating to genetic association studies in the field of epilepsy. It summarizes the results of both published and unpublished studies, and is intended as a tool for researchers in the field to keep abreast of recent studies, providing a bird''s eye view of this research area. The goal of epiGAD is to collate all association studies in epilepsy in order to help researchers in this area identify all the available gene-disease associations. Finally, by including unpublished studies, it hopes to reduce the problem of publication bias and provide more accurate data for future meta-analyses. It is also hoped that epiGAD will foster collaboration between the different epilepsy genetics groups around the world, and faciliate formation of a network of investigators in epilepsy genetics. There are 4 databases within epiGAD: - the susceptibility genes database - the epilepsy pharmacogenetics database - the meta-analysis database - the genome-wide association studies (GWAS) database The susceptibility genes database compiles all studies related to putative epilepsy susceptibility genes (eg. interleukin-1-beta in TLE), while the pharmacogenetics studies in epilepsy (eg. ABCB1 studies) are stored in ''phamacogenetics''. The meta-analysis database compiles all existing published epilepsy genetic meta-analyses, whether for susceptibility genes, or pharmacogenetics. The GWAS database is currently empty, but will be filled once GWAS are published. Sponsors: The epiGAD website is supported by the ILAE Genetics Commission.

Proper citation: Epilepsy Genetic Association Database (RRID:SCR_006840) Copy   

•   Source: SciCrunch Registry
  

http://www.pathbase.net/

Database of histopathology photomicrographs and macroscopic images derived from mutant or genetically manipulated mice. The database currently holds more than 1000 images of lesions from mutant mice and their inbred backgrounds and further images are being added continuously. Images can be retrieved by searching for specific lesions or class of lesion, by genetic locus, or by a wide set of parameters shown on the Advanced Search Interface. Its two key aims are: * To provide a searchable database of histopathology images derived from experimental manipulation of the mouse genome or experiments conducted on genetically manipulated mice. * A reference / didactic resource covering all aspects of mouse pathology Lesions are described according to the Pathbase pathology ontology developed by the Pathbase European Consortium, and are available at the site or on the Gene Ontology Consortium site - OBO. As this is a community resource, they encourage everyone to upload their own images, contribute comments to images and send them their feedback. Please feel free to use any of the SOAP/WSDL web services. (under development)

Proper citation: Pathbase (RRID:SCR_006141) Copy   

•   Source: SciCrunch Registry
  

https://www.synapse.org/#!Synapse:syn22345748/wiki/605339

Reference dataset of multiplexed immunofluorescence microscopy images collected at HMS Laboratory of Systems Pharmacology. Includes set of images of different types for development and benchmarking of computational methods for image processing. As of 4/2/2021, EMIT comprises tissue microarray containing cores from 34 cancer, non-neoplastic diseases, and normal tissue collected from clinical discards under IRB supervised protocol. TMA was imaged using cyclic immunofluorescence method. Additional extensions of EMIT are currently in the planning stages. Long term goal is to compose ImageNet like resource for highly multiplexed images of tissues and tumors by consolidating high quality curated datasets.

Proper citation: Exemplar Microscopy Images of Tissues (RRID:SCR_021052) Copy   

•   Source: SciCrunch Registry
  

http://www.ala.org.au/

Online repository of information about Australian plants, animals, and fungi. Development started in 2006. The Commonwealth Scientific and Industrial Research Organisation is organisation significantly involved in development of ALA.

Proper citation: Atlas of Living Australia (RRID:SCR_006467) Copy   

•   Source: SciCrunch Registry
  

http://ccr.coriell.org/Sections/Collections/ADA/?SsId=12

The purpose of the American Diabetes Association (ADA), GENNID Study (Genetics of non-insulin dependent diabetes mellitus, NIDDM) is to establish a national database and cell repository consisting of information and genetic material from families with well-documented NIDDM. The GENNID Study will provide investigators with the information and samples necessary to conduct genetic linkage studies and locate the genes for NIDDM. Non-Hispanic white, Hispanic, African-American, and Japanese-American multiplex NIDDM families, with a minimum of one affected sib-pair, are being collected by the eight Harold Rifkin Family Acquisition Centers. Detailed family and medical histories are obtained from all participants. Family members with diabetes have fasting blood samples drawn, while nondiabetic family members have an oral glucose tolerance test and, when possible, insulin sensitivity and insulin secretion measurements by frequently sampled intravenous glucose tolerance testing or euglycemic insulin clamp. Lymphoblastoid cell lines are established for all participants. DNA samples and extensive phenotypic data are available from the American Diabetes Association's GENNID study (Genetics of NIDDM). GENNID has collected detailed family histories and a broad array of data on 170 large pedigrees, all of which contain at least one affected sib pair, with a total of 650 affected individuals and approximately 1,200 total subjects. Included are approximately 65 Caucasian, 60 Hispanic, 25 African American, and 20 Japanese American pedigrees. In addition, GENNID also contains DNA and data on 1,000 additional affected sib pairs in each of three groups, African American, Caucasian, and Hispanic. DNA and phenotypic data, including race, gender and age, are available for all members of the pedigrees. The data set includes multiple metabolic factors, including carbohydrate metabolism, lipid metabolism, and body size measures, as well as lifestyle variables obtained by questionnaire (e.g., employment, exercise, etc.). The GENNID resource is ideally suited for genetic linkage and association studies as well as SNP discovery and typing. Investigators interested in obtaining the DNA samples and/or data will need to submit a proposal to the Association that addresses the genetics of type 2 diabetes.

Proper citation: ADA GENNID Study (RRID:SCR_000527) Copy   

•   Source: SciCrunch Registry
  

https://knights-lab.github.io/MLRepo/

Machine learning repository for microbiome datasets.

Proper citation: ML Repo (RRID:SCR_017079) Copy   

•   Source: SciCrunch Registry
  

http://central.xnat.org

Online repository of open access images including MR Sessions, MRI, Freesurfer APARC, Freesurfer ASEGs, Clinical Assessments, Atlas Scaling Factors, and Fast Segmentations data. CENTRAL currently contains 374 Projects, 3808 Subjects, and 5174 Imaging Sessions (June 2014). Central is powered by XNAT (The Extensible Neuroimaging Archive Toolkit), an open source software platform designed to facilitate management and exploration of neuroimaging and related data. XNAT includes a secure database backend and a rich web-based user interface.

Proper citation: XNAT Central (RRID:SCR_006235) Copy   

•   Source: SciCrunch Registry
  

https://fruitdbase.csic.es

Curated, open-access repository integrating publicly available transcriptomic data for Prunus dulcis and related stone-fruit species. Transcriptomic resource for almonds (Prunus dulcis).

Proper citation: FruitDBase (RRID:SCR_029077) Copy   

•   Source: SciCrunch Registry
  

http://www.barthsyndrome.org/english/View.asp?x=1513

The Barth Syndrome Registry and Repository (BRR) at the University of Florida and Children''s Hospital Boston was created to provide additional information about this rare disorder. By collecting information directly from families and abstracting medical information from patient records, we hope to build a large anonymized database that is useful for answering scientific research questions and that is a source of medical information. Additionally, we are collecting blood samples for DNA isolation and storage and for the development of special cell lines. These tools will allow researchers to better understand the cellular and genetic nature of this disease and how these abnormalities may impact the clinical variability of Barth syndrome patients. Currently available data from the Barth Syndrome Registry and Repository includes self-reported data. This includes basic demographics, age at diagnosis of Barth Syndrome, reason for diagnosis, family history; symptoms, presence or absence of a history of cardiomyopathy, history regarding hospitalizations, cardiac transplant or listing for transplant, presence or absence of a history of neutropenia; history of infections, medical therapy and basic developmental history. We are in the process of abstracting medical data to build a more complete medical database and may be able to provide additional data with a specific request. Currently the DNA bank has data from 65 patients, genomic DNA from 50 patients, lymphoblast lines from 41 patients, fibroblast lines from 3 patients, and myocardium/autopsy from 3 patients. However, this is increasing as new patients enroll in the Registry. Please contact us with questions and specific requests about data or specimens.

Proper citation: Barth Syndrome Biorepository (RRID:SCR_004775) Copy   

•   Source: SciCrunch Registry
  

https://merritt.cdlib.org/

Data repository service from the University of California Curation Center (UC3) that lets the UC community manage, archive, and share its valuable digital content. Merritt provides long-term preservation of digital assets, allows researchers to share data with others, or meet the data sharing and preservation requirements of a grant-funded project.

Proper citation: UC3 Merritt (RRID:SCR_000642) Copy   

•   Source: SciCrunch Registry
  

http://openbiobank.com/

This is a demo store. Any orders placed through this store will not be honored or fulfilled. The Open Biobank is a network of bio-banks, collaborators, and consumers supplying or acquiring samples that are proactively acquired for future testing, analysis, and/or replenishment for human life. Our goal is to supply quality samples to clinicians and researchers with making their ordering experience convenient, accessible, and available 24/7. Suppliers that we distribute for are Bio-Banks, Tissues Banks, and Seed Banks who supply samples to the open life science community. Samples are stored in bio-repositories, physical and data storage facilities with various controlled environments normally containing associated data history on the sample(s) sourced. We have a policy to provide all consumers with documentation from suppliers on the state, procedures, protocols, and historical data on sample handling and testing analysis on samples ordered from suppliers. In choosing samples to order, suppliers categorize the usage, handling, environment, and application of samples to properly supply samples to users. However, users expect information on sample handling, collection or harvesting, storage, management, shipment, and other logistical considerations that may affect sample quality and impact sample use. The Amazon-like storefront saves lives by sourcing samples from all bio-banks around the world. This is how it works: * Choose a supplier you trust. Find the sample you need. Select samples from search results and place an order. Payment systems by Amazon are reliable. Choose your shipper and method of delivery. Track your orders from multiple vendors. Query reports of all orders place by your staff. Rate the samples you ordered and view the ratings of your peers. * Market your Biospecimen and Data on the Open Biobank

Proper citation: Open Biobank (RRID:SCR_005364) Copy   

•   Source: SciCrunch Registry
  

https://biodare2.ed.ac.uk/

Data analysis resource and repository for biological data related to circadian rhythms, sharing public data from over 10,000 experiments (in 2025). The main features of BioDare2 are: fast analysis of rhythmic data (rhythm detection and period, phase, amplitude), timeseries processing, attractive visualizations, simple data description, data sharing with permalinks, easy data uploads and a modern UI.

Proper citation: BioDare2 (RRID:SCR_027193) Copy   

•   Source: SciCrunch Registry
  

https://gitlab.gwdg.de/MedBioinf/metabolomics/metaboserv

Browser based platform for selecting, exchanging, and visualizing metabolomics data with controlled data access. Used to facilitate collaborative metabolomics research and to enable researchers to make their experimental data findable, accessible, interoperable, and re-usable as defined by the FAIR principles.

Proper citation: MetaboSERV (RRID:SCR_025496) Copy   

•   Source: SciCrunch Registry
  

https://neurobridges.org/

Platform for data discovery to enhance the reuse of clinical neuroscience and neuroimaging data. Provides NeuroBridge ontology, and combines machine learning with ontology based search of both neuroimaging repositories like XNAT databases and open access full text journals such as PubMed Central. Connects researchers with potential neuroimaging data, by searching text of papers describing neuroimaging studies that contain information on neuroimaging data.

Proper citation: NeuroBridge (RRID:SCR_023703) Copy   

•   Source: SciCrunch Registry
  

http://www.liberatingresearch.org

A repository which hosts and archives original and edited scientific papers. Scientists can post their academic manuscripts on Libre, where they can find reviewers who will formally evaluate the manuscripts. Evaluated works are posted alongside the original manuscript with reviewers' identities disclosed and the work they edit given its own DOI. Users can post additional updated versions of their work to Libre or send their papers to journals. Papers and reviews are rated by other scientists, and from these Libre creates holistic indeces listing the best rated writers and reviewers.

Proper citation: Libre (RRID:SCR_014018) Copy   

•   Source: SciCrunch Registry
  

https://mybinder.org/

Open-source web application for managing digital repositories. Allows to create custom computing environments that can be shared and used by many remote users.Turns a GitHub repository containing a collection of Jupyter Notebooks into a collection of interactive notebooks. With Binder, open those notebooks in an executable environment, making your code immediately reproducible by anyone, anywhere.

Proper citation: Binder (RRID:SCR_016437) Copy   

•   Source: SciCrunch Registry