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https://kidsfirstdrc.org/portal/portal-features/

Portal for analysis and interpretation of pediatric genomic and clinical data to advance personalized medicine for detection, therapy, and management of childhood cancer and structural birth defects. For patients, researchers, and clinicians to create centralized database of well curated clinical and genetic sequence data from patients with childhood cancer or structural birth defects.

Proper citation: Kids First Data Resource Portal (RRID:SCR_016493) Copy   

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https://portal.aodn.org.au/

Interoperable online network of marine and climate data resources.AODN Portal provides access to all available Australian marine and climate science data and provides the primary access to IMOS data.

Proper citation: Australian Ocean Data Network (RRID:SCR_023134) Copy   

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http://www.polardata.ca

Repository of metadata and data that describes and provides access to diverse data sets generated by Arctic and Antarctic researchers.Metadata records follow ISO 19115 and Federal Geographic Data Committee standard formats to provide exchange with other data centres.Records cover wide range of disciplines from natural sciences and policy, to health and social sciences. The PDC Geospatial Search tool is available to the public and researchers alike and allows searching data using mapping interface and other parameters.

Proper citation: Canadian Polar Data Network (RRID:SCR_023143) Copy   

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https://www.hdruk.ac.uk/covid-19/

Initiative to mobilize talent and partnerships across United Kingdom to coordinate and connect national data science driven research efforts related to COVID-19 to address wider impact of COVID-19 pandemic.National Institute for Health Data Science for England, Wales, Scotland and Northern Ireland, is championing use of health data to respond to COVID-19.

Proper citation: Health Data Research UK COVID-19 Initiative (RRID:SCR_018335) Copy   

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https://www.data.casearth.cn/

Portal built for the release and sharing of data resources of "Big Earth Data Science Engineering Program (CASEarth)" launched by the Chinese Academy of Sciences. A global raster data of land cover and land use. This data can be used for mapping and spatial modeling in Geographic Information Systems (GIS) or other computer programs. This website is not accessible from the USA.

Proper citation: Chinese CASEarth Data Sharing and Service Portal (RRID:SCR_025660) Copy   

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https://www.uio.no/english/services/it/research/sensitive-data/

Platform to collect, store and analyze sensitive research data in secure environment. It comprises secure project area and integrated solution for collecting sensitive data (Nettskjema), accessible by anywhere in the world.

Proper citation: University of Oslo Services for sensitive data (TSD) (RRID:SCR_027773) Copy   

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http://www.ndexbio.org/

Repository where scientists and organizations can share, store, manipulate, and publish biological network data. Users can also run their own copies of NDEx Server software in cases where stored networks must be kept in highly secure environment (such as for HIPAA compliance) or where high application load is incompatible with shared public resource. Open source software system that is part of Cytoscape family. Project of Cytoscape Consortium in conjunction with Ideker lab at UCSD School of Medicine. Public forum where biologists can exchange and publish computable network models in many types and formats. NDEx is based on REST web API which can be accessed by any application, including NDEx website and NDEx Cytoscape App. NDEx networks are assigned stable, globally unique URIs and so can be referenced by publications, by other networks, and by analytic applications.

Proper citation: Network Data Exchange (NDEx) (RRID:SCR_003943) Copy   

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http://www.nih.gov/science/models/mouse/deltagenlexicon/theresource.html

Repository of knockout mice that have been extensively characterized. For each mouse line, the contractors will provide not only the mouse line itself, but also detailed, objective data on the impact of the specific gene deletion on the mouse''s phenotype, which includes appearance, health, fitness, behavior, ability to reproduce, and radiological and microscopic data. Such comprehensive information on such a large group of mice has never been available to public sector researchers, and is expected to greatly accelerate efforts to explore gene functions in health and disease. This resource will give researchers unprecedented access to two private collections of knockout mice, providing valuable models for the study of human disease and laying the groundwork for a public, genome-wide library of knockout mice. The contracts also provide for the opportunity for NIH to obtain up to 1500 additional mouse lines and phenotypic data over the next three years, pending available funds. The new contracts provide NIH with irrevocable, perpetual, worldwide, royalty-free licenses to use and distribute to academic and non-profit researchers these lines of knockout mice. The mouse lines, which will be stored in the form of frozen embryos, frozen sperm and frozen embryonic stem (ES) cells, will be delivered to NIH-funded mouse repositories that supply mice to universities, medical schools and research labs all over the world. When researchers express interest in obtaining a certain knockout mouse line, the repositories will send them live mice, frozen embryos, sperm, and/or ES cells, so they can study the mice in their own labs. All data on the mice will be made available to researchers worldwide without restriction in publicly available databases on the Web. This resource will be available for a nominal fee which will be used to cover the cost of handling, shipping and replenishing the stock. Under the license agreements with Deltagen and Lexicon, researchers who receive the knockout mice lines through NIH are free to publish any results from research involving the line and also to seek patent or other intellectual property protection for any of the inventions or discoveries resulting from such research. List of Available Knockout Mice: http://www.informatics.jax.org/external/ko/

Proper citation: Deltagen and Lexicon Knockout Mice and Phenotypic Data Resource (RRID:SCR_007312) Copy   

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https://github.com/pha4ge/SARS-CoV-2-Contextual-Data-Specification

Software package as collection template and associated materials for SARS-CoV-2 metadata. Contains metadata collection template based on data standard designed to harmonize genomic surveillance contextual data (sample metadata, epidemiological/clinical/laboratory data, methods information) across datasets, laboratories and databases during pandemic, and beyond. Template contains standardized fields, terms and formats for structuring information, and is accompanied by field level reference guide which provides definitions, guidance and examples for populating fields. Fields have been prioritized using colour coding system as required (yellow), recommended (purple), and optional (white). Users can use as much or as little of specification as they wish, according to their needs, and need not share information unless permitted to do so. SOP and protocols for submitting to public repositories are also included.

Proper citation: Public Health Alliance for Genomic Epidemiology SARS-CoV-2 Contextual Data Specification (RRID:SCR_021378) Copy   

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http://www.ccdc.cam.ac.uk/Solutions/CSDSystem

It records bibliographic, chemical and crystallographic information for organic molecules and metal-organic compounds whose 3D structures have been determined using X-ray diffraction and neutron diffraction.
The CSD records results of single crystal studies and powder diffraction studies which yield 3D atomic coordinate data for at least all non-H atoms. In some cases the CCDC is unable to obtain coordinates, and incomplete entries are archived to the CSD.
The CSD includes crystal structure data arising from:
* publications in the open literature
* Private Communications to the CSD (via direct data deposition)
The Cambridge Structural Database System (CSDS) is a single product that comprises the following components: The Cambridge Structural Database (CSD); CSDS Software: search and information retrieval (ConQuest), structure visualization (Mercury), statistical analysis of retrieved data (VISTA), and software for database creation (PreQuest); Knowledge bases derived from the CSD: Mogul (intramolecular geometry) and IsoStar (intermolecular interactions, including data from the PDB).
Cambridge Structural Database (CSD) is the world repository of small-molecule crystal structures. For example, the crystal structures supported by the National Institute on Drug Abuse are deposited here.

Proper citation: Cambridge Structural Data Base (RRID:SCR_007310) Copy   

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http://scientificdatasharing.com/

While many data sharing programs exist worldwide, widespread sharing of raw data has not yet won across-the-board acceptance in the scientific community, and the very existence of all these databases makes the approach fractured at best. The Data Sharing Project, launched last year by University of California-San Francisco Professor Michael Weiner, has two goals: One is to make widespread raw data sharing a reality initially in the realm of medicine through creation of a repository system accessible to all researchers; the second goal is to foster broad scientific support for this move and its adoption in other fields of research. With major projects such as the Human Genome Project demonstrating the tremendous scientific breakthroughs made possible by data sharing and with the decline of technological barriers impeding such efforts, the time has come to work to achieve widespread sharing of raw data worldwide. The Data Sharing Project proposes to further this goal initially in the field of medicine by working to create a raw data sharing program that will serve as a model to other disciplines attempting to make their own way in this arena. The Northern California Institute for Research and Education (NCIRE) together with the University of California-San Francisco and support from the Michael J. Fox Foundation is now in the process of canvassing the scientific community to analyze the best possible data sharing program and practices to establish in the field of medicine.

Proper citation: Scientific Data Sharing Project (RRID:SCR_004481) Copy   

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https://www.i2b2.org/NLP/DataSets/Main.php

The data for the smoking challenge consisted exclusively of discharge summaries from Partners HealthCare which were preprocessed and converted into XML format, and separated into training and test sets. I2B2 is a data warehouse containing clinical data on over 150k patients, including outpatient DX, lab results, medications, and inpatient procedures. ETL processes authored to pull data from EMR and finance systems Institutional review boards of Partners HealthCare approved the challenge and the data preparation process. The data were annotated by pulmonologists and classified patients into Past Smokers, Current Smokers, Smokers, Non-smokers, and unknown. Second-hand smokers were considered non-smokers. Other institutions involved include Massachusetts Institute of Technology, and the State University of New York at Albany. i2b2 is a passionate advocate for the potential of existing clinical information to yield insights that can directly impact healthcare improvement. In our many use cases (Driving Biology Projects) it has become increasingly obvious that the value locked in unstructured text is essential to the success of our mission. In order to enhance the ability of natural language processing (NLP) tools to prise increasingly fine grained information from clinical records, i2b2 has previously provided sets of fully deidentified notes from the Research Patient Data Repository at Partners HealthCare for a series of NLP Challenges organized by Dr. Ozlem Uzuner. We are pleased to now make those notes available to the community for general research purposes. At this time we are releasing the notes (~1,000) from the first i2b2 Challenge as i2b2 NLP Research Data Set #1. A similar set of notes from the Second i2b2 Challenge will be released on the one year anniversary of that Challenge (November, 2010).

Proper citation: Smoking NLP Challenge Data (RRID:SCR_008644) Copy   

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http://www.commondataelements.ninds.nih.gov

The purpose of the NINDS Common Data Elements (CDEs) Project is to standardize the collection of investigational data in order to facilitate comparison of results across studies and more effectively aggregate information into significant metadata results. The goal of the National Institute of Neurological Disorders and Stroke (NINDS) CDE Project specifically is to develop data standards for clinical research within the neurological community. Central to this Project is the creation of common definitions and data sets so that information (data) is consistently captured and recorded across studies. To harmonize data collected from clinical studies, the NINDS Office of Clinical Research is spearheading the effort to develop CDEs in neuroscience. This Web site outlines these data standards and provides accompanying tools to help investigators and research teams collect and record standardized clinical data. The Institute still encourages creativity and uniqueness by allowing investigators to independently identify and add their own critical variables. The CDEs have been identified through review of the documentation of numerous studies funded by NINDS, review of the literature and regulatory requirements, and review of other Institute''s common data efforts. Other data standards such as those of the Clinical Data Interchange Standards Consortium (CDISC), the Clinical Data Acquisition Standards Harmonization (CDASH) Initiative, ClinicalTrials.gov, the NINDS Genetics Repository, and the NIH Roadmap efforts have also been followed to ensure that the NINDS CDEs are comprehensive and as compatible as possible with those standards. CDEs now available: * General (CDEs that cross diseases) Updated Feb. 2011! * Congenital Muscular Dystrophy * Epilepsy (Updated Sept 2011) * Friedreich''s Ataxia * Parkinson''s Disease * Spinal Cord Injury * Stroke * Traumatic Brain Injury CDEs in development: * Amyotrophic Lateral Sclerosis (Public review Sept 15 through Nov 15) * Frontotemporal Dementia * Headache * Huntington''s Disease * Multiple Sclerosis * Neuromuscular Diseases ** Adult and pediatric working groups are being finalized and these groups will focus on: Duchenne Muscular Dystrophy, Facioscapulohumeral Muscular Dystrophy, Myasthenia Gravis, Myotonic Dystrophy, and Spinal Muscular Atrophy The following tools are available through this portal: * CDE Catalog - includes the universe of all CDEs. Users are able to search the full universe to isolate a subset of the CDEs (e.g., all stroke-specific CDEs, all pediatric epilepsy CDEs, etc.) and download details about those CDEs. * CRF Library - (a.k.a., Library of Case Report Form Modules and Guidelines) contains all the CRF Modules that have been created through the NINDS CDE Project as well as various guideline documents. Users are able to search the library to find CRF Modules and Guidelines of interest. * Form Builder - enables users to start the process of assembling a CRF or form by allowing them to choose the CDEs they would like to include on the form. This tool is intended to assist data managers and database developers to create data dictionaries for their study forms.

Proper citation: NINDS Common Data Elements (RRID:SCR_006577) Copy   

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https://ida.loni.usc.edu/login.jsp

Archive used for archiving, searching, sharing, tracking and disseminating neuroimaging and related clinical data. IDA is utilized for dozens of neuroimaging research projects across North America and Europe and accommodates MRI, PET, MRA, DTI and other imaging modalities.

Proper citation: LONI Image and Data Archive (RRID:SCR_007283) Copy   

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http://www.braincode.ca

Brain-CODE is a large-scale informatics platform that manages the acquisition and storage of multidimensional data collected from participants with a variety of brain disorders.

Proper citation: Brain-CODE (RRID:SCR_015877) Copy   

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http://predictsite.com

Patient database that contains EEG data sets, executable tasks, and computational tools., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: PREDiCT (RRID:SCR_015517) Copy   

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https://publicneuro.eu/

PublicnEUro is a repository for human brain-imaging research data, including MRI, PET, EEG, MEG and related data, operated by the Neurobiology Research Unit at Rigshospitalet, Region Hovedstaden, Denmark. It supports sharing of BIDS-organised datasets through open or controlled access, with openly available catalogue metadata and a DOI for each published dataset version. Services include BIDS validation, optional curation, storage, management of access requests, machine-readable Digital Use Conditions, and browser and command-line retrieval. Controlled access uses identity verification and dataset-specific Data User Agreements to protect participants and respect data-controller requirements.

Proper citation: PublicnEUro (RRID:SCR_027770) Copy   

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http://www.ebi.ac.uk/interpro

Service providing functional analysis of proteins by classifying them into families and predicting domains and important sites. They combine protein signatures from a number of member databases into a single searchable resource, capitalizing on their individual strengths to produce a powerful integrated database and diagnostic tool. This integrated database of predictive protein signatures is used for the classification and automatic annotation of proteins and genomes. InterPro classifies sequences at superfamily, family and subfamily levels, predicting the occurrence of functional domains, repeats and important sites. InterPro adds in-depth annotation, including GO terms, to the protein signatures. You can access the data programmatically, via Web Services. The member databases use a number of approaches: # ProDom: provider of sequence-clusters built from UniProtKB using PSI-BLAST. # PROSITE patterns: provider of simple regular expressions. # PROSITE and HAMAP profiles: provide sequence matrices. # PRINTS provider of fingerprints, which are groups of aligned, un-weighted Position Specific Sequence Matrices (PSSMs). # PANTHER, PIRSF, Pfam, SMART, TIGRFAMs, Gene3D and SUPERFAMILY: are providers of hidden Markov models (HMMs). Your contributions are welcome. You are encouraged to use the ''''Add your annotation'''' button on InterPro entry pages to suggest updated or improved annotation for individual InterPro entries.

Proper citation: InterPro (RRID:SCR_006695) Copy   

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http://www.pediatricmri.nih.gov/

Data sets of clinical / behavioral and image data are available for download by qualified researchers from a seven year, multi-site, longitudinal study using magnetic resonance technologies to study brain maturation in healthy, typically-developing infants, children, and adolescents and to correlate brain development with cognitive and behavioral development. The information obtained in this study is expected to provide essential data for understanding the course of normal brain development as a basis for understanding atypical brain development associated with a variety of developmental, neurological, and neuropsychiatric disorders affecting children and adults. This study enrolled over 500 children, ranging from infancy to young adulthood. The goal was to study each participant at least three times over the course of the project at one of six Pediatric Centers across the United States. Brain MR and clinical/behavioral data have been compiled and analyzed at a Data Coordinating Center and Clinical Coordinating Center. Additionally, MR spectroscopy and DTI data are being analyzed. The study was organized around two objectives corresponding to two age ranges at the time of enrollment, each with its own protocols. * Objective 1 enrolled children ages 4 years, 6 months through 18 years (total N = 433). This sample was recruited across the six Pediatric Study Centers using community based sampling to reflect the demographics of the United States in terms of income, race, and ethnicity. The subjects were studied with both imaging and clinical/behavioral measures at two year intervals for three time points. * Objective 2 enrolled newborns, infants, toddlers, and preschoolers from birth through 4 years, 5 months, who were studied three or more times at two Pediatric Study Centers at intervals ranging from three months for the youngest subjects to one year as the children approach the Objective 1 age range. Both imaging and clinical/behavioral measures were collected at each time point. Participant recruitment used community based sampling that included hospital venues (e.g., maternity wards and nurseries, satellite physician offices, and well-child clinics), community organizations (e.g., day-care centers, schools, and churches), and siblings of children participating in other research at the Pediatric Study Centers. At timepoint 1, of those enrolled, 114 children had T1 scans that passed quality control checks. Staged data release plan: The first data release included structural MR images and clinical/behavioral data from the first assessments, Visit 1, for Objective 1. A second data release included structural MRI and clinical/behavioral data from the second visit for Objective 1. A third data release included structural MRI data for both Objective 1 and 2 and all time points, as well as preliminary spectroscopy data. A fourth data release added cortical thickness, gyrification and cortical surface data. Yet to be released are longitudinally registered anatomic MRI data and diffusion tensor data. A collaborative effort among the participating centers and NIH resulted in age-appropriate MR protocols and clinical/behavioral batteries of instruments. A summary of this protocol is available as a Protocol release document. Details of the project, such as study design, rationale, recruitment, instrument battery, MRI acquisition details, and quality controls can be found in the study protocol. Also available are the MRI procedure manual and Clinical/Behavioral procedure manuals for Objective 1 and Objective 2.

Proper citation: NIH MRI Study of Normal Brain Development (RRID:SCR_003394) Copy   

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http://www.chordomafoundation.org/research/rdrepository.aspx

U-CH1 and U-CH2 are sacral-chordoma derived cell lines created by the lab of Dr. Peter Moeller at the University of Ulm, Germany. The Chordoma Foundation maintains a repository of these cell lines and makes them available to academic and industry investigators. Additionally, Notochordal tissue is available from the Congenital Defects Lab at the University of Washington. (Contact us for more information.) The following data is available: * Xenographs: The U-CH1 cell line forms tumors that resemble chordomas in NSG mice. A protocol for creating a xenograft using U-CH1 was developed by Dr. Adrienne Flanagan. * Tissue Microarrays (sacral, spine, skull base, lumbar, cervical, mobile spine, etc.) * Gene Expression Data: EBI Array Express Experiment E-MEXP-353: transcription profiling of human mesenchymal and some possibly neural crest derived neoplasms using the Affymetrix GeneChip? Human Genome HG-U133A. This data set was generated by the University College London Cancer Institute and contains 96 tissue samples including 4 chordomas. * Comparative Genomic Hybridization Data: Gene Expression Omnibus Series GSE9023: DNA copy number analysis of 21 fresh frozen chordoma biopsies, and the respective relapse in four of them, using 32k and 1Mb array CGH. Cases 1-11 were analyzed using 32k array CGH and male genomic DNA (Promega) was used as reference. Cases 17-26, and the respective relapse in four of these tumors, were analyzed with 1 Mb array CGH, using sex matched controls. All cases showed copy number alterations and primarily deletions of chromosomal regions were found. Particularly, the CDKN2A and CDKN2B loci in 9p21 were homo- or heterozygously lost in 70% of the tumors.

Proper citation: Chordoma Foundation Biobank (RRID:SCR_006425) Copy   

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