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

Non-profit academic organization for research and services in bioinformatics. Provides freely available data from life science experiments, performs basic research in computational biology, and offers user training programme, manages databases of biological data including nucleic acid, protein sequences, and macromolecular structures. Part of EMBL.

Proper citation: European Bioinformatics Institute (RRID:SCR_004727) Copy   

•   Source: SciCrunch Registry

http://www.de-idata.com

A commercial organization that suppplies software which creates separate HIPAA-compliant repositories of de-identified patient records and reports. This software allows clinicians, researchers, and administrative leadership to safely access, search, share, and analyze de-identified patient-level data. DE-ID can be acquired as stand-alone tool or integrated with data networks or clinical information systems.

Proper citation: DE-ID Data Corp (RRID:SCR_008668) Copy   

•   Source: SciCrunch Registry

http://www.opentox.org/dev/apis/api-1.1/structure

Tools for the integration of data from various sources (public and confidential), for the generation and validation of computer models for toxic effects, libraries for the development and seamless integration of new algorithms, and scientifically sound validation routines. The goal of OpenTox is to develop an interoperable predictive toxicology framework which may be used as an enabling platform for the creation of predictive toxicology applications. OpenTox is relevent for users from a variety of research areas: Toxicological and chemical experts (e.g. risk assessors, drug designers, researchers) computer model developers and algorithm developers non specialists requiring access to Predictive Toxicology models and data OpenTox applications can combine multiple web services providing users access to distributed toxicological resources including data, computer models, validation and reporting. Applications are based on use cases that satisfy user needs in predictive toxicology. OpenTox was initiated as a collaborative project involving a combination of different enterprise, university and government research groups to design and build the initial OpenTox framework. Additionally numerous organizations with industry, regulatory or expert interests are active in providing guidance and direction. The goal is to expand OpenTox as a community project enabling additional expert and user participants to be involved in developments in as timely a manner as possible. To this end, our mission is to carry out developments in an open and transparent manner from the early days of the project, and to open up discussions and development to the global community at large, who may either participate in developments or provide user perspectives. Cooperation on data standards, data integration, ontologies, integration of algorithm predictions from different methods, and testing and validation all have significant collaboration opportunities and benefits for the community. OpenTox is working to meet the requirements of the REACH legislation using alternative testing methods to contribute to the reduction of animal experiments for toxicity testing. Relevant international authorities (e.g., ECB, ECVAM, US EPA, US FDA) and industry organizations participate actively in the advisory board of the OpenTox project and provide input for the continuing development of requirement definitions and standards for data, knowledge and model exchange. OpenTox actively supports the development and validation of in silico models and algorithms by improving the interoperability between individual systems (common standards for data and model exchange), increasing the reproducibility of in silico models (by providing a quality source of structures, toxicity data and algorithms) and by providing scientifically sound and easy-to-use validation routines. OpenTox is committed to the support and integration of alternative testing methods using in vitro assay approaches, systems biology, stem cell technology, and the mining and analysis of human epidemiological data. Hence the framework design must take into account extensibility to satisfy a broad range of scientific developments and use cases.

Proper citation: OpenTox Framework (RRID:SCR_008686) Copy   

•   Source: SciCrunch Registry

https://code.google.com/p/knime4bio/

A set of custom nodes for the KNIME (The Konstanz Information Miner) graphical workbench, for analysing next-generation sequencing (NGS) data without the requirement of programming skills.

Proper citation: Knime4Bio (RRID:SCR_005376) Copy   

•   Source: SciCrunch Registry

https://www.nimhgenetics.org/

Collaborative venture between the National Institute of Mental Health (NIMH) and several academic institutions. Repository facilitates psychiatric genetic research by providing patient and control samples and phenotypic data for wide-range of mental disorders and Stem Cells.Stores biosamples, genetic, pedigree and clinical data collected in designated NIMH-funded human subject studies. RGR database likewise links to other repositories holding data from same subjects, including dbGAP, GEO and NDAR. Allows to access these data and biospecimens (e.g., lymphoblastoid cell lines, induced pluripotent cell lines, fibroblasts) and further expand genetic and molecular characterization of patient populations with severe mental illness.

Proper citation: NIMH Repository and Genomics Resources (RRID:SCR_006698) Copy   

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http://cran.r-project.org/web/packages/gap/

GAP is designed as an integrated package for genetic data analysis of both population and family data. Currently, it contains functions for sample size calculations of both population-based and family-based designs, classic twin models, probability of familial disease aggregation, kinship calculation, some statistics in linkage analysis, and association analysis involving one or more genetic markers including haplotype analysis with or without environmental covariates.

Proper citation: Genetic Analysis Package (RRID:SCR_003006) Copy   

•   Source: SciCrunch Registry

http://graphpad.com/

A commercial graphing software company that offers scientific software for statistical analyses, curve fitting and data analysis. It offers four programs: Prism, InStat, StatMate and QuickCalcs.

Proper citation: GraphPad (RRID:SCR_000306) Copy   

•   Source: SciCrunch Registry

http://bioinformatics.mdanderson.org/main/BreakFusion

Software package written in Perl and C++ that provides a computational pipeline for identifying gene fusions from RNA-seq data.

Proper citation: BreakFusion (RRID:SCR_001102) Copy   

•   Source: SciCrunch Registry

https://www.medrxiv.org

Free online archive and distribution server for complete but unpublished manuscripts in the medical, clinical, and related health sciences. Preprints are preliminary reports of work that have not been certified by peer review. They should not be relied on to guide clinical practice or health related behavior and should not be reported in news media as established information. Research articles, systematic reviews and meta analyses, clinical research design protocols and data articles may be posted.

Proper citation: medRxiv (RRID:SCR_018222) Copy   

•   Source: SciCrunch Registry

https://www.ebi.ac.uk/chembl/

Collection of bioactive drug-like small molecules that contains 2D structures, calculated properties and abstracted bioactivities. Used for drug discovery and chemical biology research. Clinical progress of new compounds is continuously integrated into the database.

Proper citation: ChEMBL (RRID:SCR_014042) Copy   

•   Source: SciCrunch Registry

http://www.uniprot.org/

Collection of data of protein sequence and functional information. Resource for protein sequence and annotation data. Consortium for preservation of the UniProt databases: UniProt Knowledgebase (UniProtKB), UniProt Reference Clusters (UniRef), and UniProt Archive (UniParc), UniProt Proteomes. Collaboration between European Bioinformatics Institute (EMBL-EBI), SIB Swiss Institute of Bioinformatics and Protein Information Resource. Swiss-Prot is a curated subset of UniProtKB.

Proper citation: UniProt (RRID:SCR_002380) Copy   

•   Source: SciCrunch Registry

https://www.cdc.gov/fluview/?CDC_AAref_Val=https://www.cdc.gov/flu/weekly/weeklyarchives2008-2009/weekly15.htm

This page contains the Influenza Surveillance Report during 2008-2009 Influenza Season Week 15, ending April 18, 2009.

Proper citation: FluView: A Weekly Influenza Surveillance Report (RRID:SCR_001118) Copy   

•   Source: SciCrunch Registry

https://www.xsede.org/

XSEDE is a single virtual system that scientists can use to interactively share computing resources, data and expertise. People around the world use these resources and services things like supercomputers, collections of data and new tools to improve our planet. XSEDE resources may be broadly categorized as follows: High Performance Computing, High Throughput Computing, Visualization, Storage, and Data Services. Many resources provide overlapping functionality across categories. Scientists, engineers, social scientists, and humanists around the world - many of them at colleges and universities - use advanced digital resources and services every day. Things like supercomputers, collections of data, and new tools are critical to the success of those researchers, who use them to make our lives healthier, safer, and better. XSEDE integrates these resources and services, makes them easier to use, and helps more people use them. XSEDE supports 16 supercomputers and high-end visualization and data analysis resources across the country. Digital services, meanwhile, provide users with seamless integration to NSF''s high-performance computing and data resources. XSEDE''s integrated, comprehensive suite of advanced digital services will federate with other high-end facilities and with campus-based resources, serving as the foundation for a national cyberinfrastructure ecosystem. Common authentication and trust mechanisms, global namespace and filesystems, remote job submission and monitoring, and file transfer services are examples of XSEDE''s advanced digital services. XSEDE''s standards-based architecture allows open development for future digital services and enhancements. XSEDE also provides the expertise to ensure that researchers can make the most of the supercomputers and tools.

Proper citation: XSEDE - Extreme Science and Engineering Discovery Environment (RRID:SCR_006091) Copy   

•   Source: SciCrunch Registry

https://www.embrys.jp/embrys/html/About.html

Data collection of gene expression patterns mapped in whole-mount mouse embryo (ICR strain) of mid-gestational stages (Embryonic Day 9.5, 10.5, 11.5), in which most striking dynamics in pattern formation and organogenesis is observed. Collection of gene expression patterns of transcription factors (TFs) and TF-related factors such as transcription cofactors. Genes were extracted from databases including RIKEN Transcription Factor Database and Panther Classification System.

Proper citation: EMBRYS (RRID:SCR_006689) Copy   

•   Source: SciCrunch Registry

http://www.oasis-brains.org/

Project aimed at making neuroimaging data sets of brain freely available to scientific community. By compiling and freely distributing neuroimaging data sets, future discoveries in basic and clinical neuroscience are facilitated.

Proper citation: Open Access Series of Imaging Studies (RRID:SCR_007385) Copy   

•   Source: SciCrunch Registry

http://topdb.enzim.hu

Collection of transmembrane protein datasets containing experimentally derived topology information from the literature and from public databases. Web interface of TOPDB includes tools for searching, relational querying and data browsing, visualisation tools for topology data.

Proper citation: Topology Data Bank of Transmembrane Proteins (RRID:SCR_007964) Copy   

•   Source: SciCrunch Registry

http://locustdb.genomics.org.cn/

The migratory locust (Locusta migratoria) is an orthopteran pest and a representative member of hemimetabolous insects. Its transcriptomic data provide invaluable information for molecular entomology study of the insect and pave a way for comparative studies of other medically, agronomically, and ecologically relevant insects. This first transcriptomic database of the locust (LocustDB) has been developed, building necessary infrastructures to integrate, organize, and retrieve data that are either currently available or to be acquired in the future. It currently hosts 45,474 high quality EST sequences from the locust, which were assembled into 12,161 unigenes. This database contains original sequence data, including homologous/orthologous sequences, functional annotations, pathway analysis, and codon usage, based on conserved orthologous groups (COG), gene ontology (GO), protein domain (InterPro), and functional pathways (KEGG). It also provides information from comparative analysis based on data from the migratory locust and five other invertebrate species, such as the silkworm, the honeybee, the fruitfly, the mosquito and the nematode. LocustDB also provides information from comparative analysis based on data from the migratory locust and five other invertebrate species, such as the silkworm, the honeybee, the fruitfly, the mosquito and the nematode. It starts with the first transcriptome information for an orthopteran and hemimetabolous insect and will be extended to provide a framework for incorporation of in-coming genomic data of relevant insect groups and a workbench for cross-species comparative studies.

Proper citation: Migratory Locust EST Database (RRID:SCR_008201) Copy   

•   Source: SciCrunch Registry

https://epilepsy.uni-freiburg.de/freiburg-seizure-prediction-project

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on April 29,2025. Electroencephalogram (EEG) data recorded from invasive and scalp electrodes. The EEG database contains invasive EEG recordings of 21 patients suffering from medically intractable focal epilepsy. The data were recorded during an invasive pre-surgical epilepsy monitoring at the Epilepsy Center of the University Hospital of Freiburg, Germany. In eleven patients, the epileptic focus was located in neocortical brain structures, in eight patients in the hippocampus, and in two patients in both. In order to obtain a high signal-to-noise ratio, fewer artifacts, and to record directly from focal areas, intracranial grid-, strip-, and depth-electrodes were utilized. The EEG data were acquired using a Neurofile NT digital video EEG system with 128 channels, 256 Hz sampling rate, and a 16 bit analogue-to-digital converter. Notch or band pass filters have not been applied. For each of the patients, there are datasets called ictal and interictal, the former containing files with epileptic seizures and at least 50 min pre-ictal data. the latter containing approximately 24 hours of EEG-recordings without seizure activity. At least 24 h of continuous interictal recordings are available for 13 patients. For the remaining patients interictal invasive EEG data consisting of less than 24 h were joined together, to end up with at least 24 h per patient. An interdisciplinary project between: * Epilepsy Center, University Hospital Freiburg * Bernstein Center for Computational Neuroscience (BCCN), Freiburg * Freiburg Center for Data Analysis and Modeling (FDM).

Proper citation: Electroencephalogram Database: Prediction of Epileptic Seizures (RRID:SCR_008032) Copy   

•   Source: SciCrunch Registry

http://amazonia.montp.inserm.fr/

A web interface and associated tools for easy query of public human transcriptome data by keyword, through thematic pages with list annotations. Amazonia provides a thematic entry to public transcriptomes: users may for instance query a gene on a Stem Cells page, where they will see the expression of their favorite gene across selected microarray experiments related to stem cell biology. This selection of samples can be customized at will among the 6331 samples currently present in the database. Every transcriptome study results in the identification of lists of genes relevant to a given biological condition. In order to include this valuable information in any new query in the Amazonia database, they indicate for each gene in which lists it is included. This is a straightforward and efficient way to synthesize hundreds of microarray publications., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: AmaZonia: Explore the Jungle of Microarrays Results (RRID:SCR_008405) Copy   

•   Source: SciCrunch Registry

http://www.molecularbrain.org/

MolecularBrain is an attempt to collect, collates, analyze and present the microarray derived gene expression data from various brain regions side by side. Transcription Profile of any gene in Mouse (online) and Human Brain (not yet) can be accessed as a histogram along with links to access various aspects of that gene. The expression levels were calculated from microarray data deposited at GEO (Gene expression omnibus). The molecular brain database could be searched using the built in search tool with the terms Entrez GeneID, gene symbol, synonym or description. Gene information along with their expression values can be also accessed from the alphabetical list of gene symbols on the footer. The protocol and GEO sample information is available.

Proper citation: Molecular Brain: Transcription Profiles of Mouse and Human Brains (RRID:SCR_008689) Copy   

•   Source: SciCrunch Registry