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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

http://neuromorphometrics.com/?page_id=23

Collection of neuroanatomically labeled MRI brain scans, created by neuroanatomical experts. Regions of interest include the sub-cortical structures (thalamus, caudate, putamen, hippocampus, etc), along with ventricles, brain stem, cerebellum, and gray and white matter and sub-divided cortex into parcellation units that are defined by gyral and sulcal landmarks.

Proper citation: Manually Labeled MRI Brain Scan Database (RRID:SCR_009604) Copy   

•   Source: SciCrunch Registry

http://www.cbrc.jp/htbin/show_tffactor_mw

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 13, 2016.

A dataset about transcriptional regulation in eukaryotic cells, including data such as transcription factors and their binding sites and profiles. Resource is in Chinese.

Proper citation: TFFACTOR (RRID:SCR_000519) Copy   

•   Source: SciCrunch Registry

https://bioconductor.org/packages/FlowSorted.Blood.450k/

Illumina HumanMethylation data on sorted blood cell populations.

Proper citation: FlowSorted.Blood.450k R package (RRID:SCR_018003) 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://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://dsigdb.tanlab.org/DSigDBv1.0/

Online database provides collection of gene sets based on quantitative inhibition and/or drug-induced gene expression changes data of drugs and compounds. Allows users to search, view and download drugs/compounds and gene sets.

Proper citation: DSigDB (RRID:SCR_026202) Copy   

•   Source: SciCrunch Registry

https://github.com/vital-ai/vital-documentation/wiki/Vital-AI-Ontology

Ontology for the four consensus human vital signs: blood pressure, body temperature, respiration rate, pulse rate. It provides a controlled structured vocabulary for describing vital signs measurement data, the various processes of measuring vital signs, and the various devices and anatomical entities participating in such measurements.

Proper citation: Vital Signs Ontology (RRID:SCR_001422) Copy   

•   Source: SciCrunch Registry

http://berkeleybop.org/pkb/

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. This interface is for exploring data collected as part of the NIF Neurodegenerative Disease Ontology project. Not generally intended for public consumption yet, but people are welcome to look - large caveat emptor applies. Sponsors: This resource is part of the NIF project.

Proper citation: OBD-PKB Interface (RRID:SCR_002882) Copy   

•   Source: SciCrunch Registry

https://camera.niehs.nih.gov/

Interactive database and user interface providing online access to validated alternative methods for U.S. regulatory and other contexts of use. Central hub and unified resource of validated alternative methods that enhances accessibility to validation study reports, data, protocols / SOPs, and information on regulatory guidance.Users can filter searches by alternative method types, defined approaches, Test Method Endpoint, and regulatory guidance.

Proper citation: Collection of Alternative Methods for Regulatory Application (CAMERA) (RRID:SCR_027893) Copy   

•   Source: SciCrunch Registry

http://cardiogenomica.altervista.org/CARDIOGENOMICS/CardioGenomics%20Homepage.htm

The primary goal of the CardioGenomics PGA is to begin to link genes to structure, function, dysfunction and structural abnormalities of the cardiovascular system caused by clinically relevant genetic and environmental stimuli. The principal biological theme to be pursued is how the transcriptional network of the cardiovascular system responds to genetic and environmental stresses to maintain normal function and structure, and how this network is altered in disease. This PGA will generate a high quality, comprehensive data set for the functional genomics of structural and functional adaptation of the cardiovascular system by integrating expression data from animal models and human tissue samples, mutation screening of candidate genes in patients, and DNA polymorphisms in a well characterized general population. Such a data set will serve as a benchmark for future basic, clinical, and pharmacogenomic studies. Training and education are also a key focus of the CardioGenomics PGA. In addition to ongoing journal clubs and seminars, the PGA will be sponsoring symposia at major conferences, and developing workshops related to the areas of focus of this PGA. Information regarding upcoming events can be found in the Events section of this site, and information about training and education opportunities sponsored by CardioGenomics can be found on the Teaching and Education page. The CardioGenomics project came to a close in 2005. This server, cardiogenomics.med.harvard.edu, remains online in order to continue to distribute data that was generated by investigators under the auspices of the CardioGenomics Program for Genomic Applications (PGA). :Sponsors: This resource is supported by The National Heart, Lung and Blood Institute (NHLBI) of the NIH., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: CardioGenomics (RRID:SCR_007248) Copy   

•   Source: SciCrunch Registry

http://edamontology.org/

An ontology of bioinformatics operations (tool, application, or workflow functions), types of data including identifiers, topics (application domains), and data formats. The applications of EDAM are within organizing tools and data, finding suitable tools in catalogues, and integrating them into complex applications or workflows. Semantic annotations with EDAM are applicable to diverse entities such as for example Web services, databases, programmatic libraries, standalone tools and toolkits, interactive applications, data schemas, data sets, or publications within bioinformatics. Annotation with EDAM may also contribute to data provenance, and EDAM terms and synonyms can be used in text mining. EDAM - and in particular the EDAM Data sub-ontology - serves also as a markup vocabulary for bioinformatics data on the Semantic Web.

Proper citation: EDAM Ontology (RRID:SCR_006620) Copy   

•   Source: SciCrunch Registry

https://www.mousephenotype.org/imits/

This resource has been replaced by GenTaR. Software tool for the planning of all IMPC mouse production. Allows IMPC production centers to record the progress of mouse production, cre-excision and to summarise the progress of phenotype data collection and transfer to the IMPC DCC. Stores all the mutation molecular structures made for the IKMC, catalogs of all IKMC products.

Proper citation: iMITS (RRID:SCR_016552) Copy   

•   Source: SciCrunch Registry

http://www.worm.mpi-cbg.de/phenobank/cgi-bin/ProjectInfoPage.py

A database that provides primary data from two high-content screens that profile the set of ~900 essential C. elegans genes (~5% of the genome) required for embryo production and/or events during the first two embryonic divisions. Phenobank houses the movies, scored defects, and phenotypic classification data for the embryo-filming and gonad morphology screens.

Proper citation: PhenoBank (RRID:SCR_000930) Copy   

•   Source: SciCrunch Registry

http://www.brain-map.org

Seattle based independent, nonprofit medical research organization dedicated to accelerating the understanding of how human brain works. Provides free data and tools to researchers and educators and variety of unique online public resources for exploring the nervous system. Integrates gene expression data and neuroanatomy, along with data search and viewing tools, these resources are openly accessible via the Allen Brain Atlas data portal. Provides Allen Mouse Brain, Allen Spinal Cord Atlas, Allen Developing Mouse Brain Atlas, Allen Human Brain Atlas,Allen Mouse Brain Connectivity Atlas, Allen Cell Type Database, The Ivy Glioblastoma Atlas Project (Ivy GAP), The BrainSpan Atlas of the Developing Human Brain.

Proper citation: Allen Institute for Brain Science (RRID:SCR_006491) Copy   

•   Source: SciCrunch Registry

http://mousespinal.brain-map.org/about.html

Platform for exploring spinal cord at cellular and molecular levels. Map of gene expression for adult and juvenile mouse spinal cord. Provides map of normal mouse when used to compare gene expression in diseased or injury models. Interactive database of gene expression mapped across all anatomic segments of mouse spinal cord at postnatal days 4 and 56. Indexed set of images based on RNA in situ hybridization data, searchable and sortable by gene, age, expression, cervical, thoracic, lumbar, sacral, and coccygeal segments.

Proper citation: Allen Mouse Spinal Cord Atlas (RRID:SCR_007418) Copy   

•   Source: SciCrunch Registry

https://www.daqcord.org/

Software tool for practical self assessment and reporting method for clinical research studies, to capture key information about data acquisition and quality control measures. Linked to dataset so that potential research collaborators can determine if data meets their needs and expectations.

Proper citation: DAQCORD (RRID:SCR_017395) Copy   

•   Source: SciCrunch Registry