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https://cpndb.ca/

A curated collection of chaperonin sequence data collected from public databases or generated by a network of collaborators exploiting the cpn60 target in clinical, phylogenetic and microbial ecology studies. The database contains all available sequences for both group I and group II chaperonins. Users can search the database by Chaperonin type, group (I or II), BLAST, or other options, and can also enter and analyze FASTA sequences.

Proper citation: cpnDB: A Chaperonin Database (RRID:SCR_002263) Copy   

•   Source: SciCrunch Registry

http://cobre.mrn.org/megsim/

Realistic simulated MEG datasets ranging from basic sensory to oscillatory sets that mimic functional connectivity; as well as basic visual, auditory, and somatosensory empirical sets. The simulated sets were created for the purpose of testing analysis algorithms across the different MEG systems when the truth is known. MEG baseline recordings were obtained from 5 healthy participants, using three MEG systems: VSM/CTF Omega, Elekta Neuromag Vectorview, 4-D Magnes 3600. Simulated signals were embedded within the CTF and Neuromag 306 baseline recordings (4-D to be added). Participant MRIs are available. Averaged simulation files are available as netcdf files. Neuromag 306 averaged simulations are also available in fif format. Also available: single trials of data where the simulated signal is jittered about a mean value, continuous fif files where the simulated signal is marked by a trigger, and simulations with oscillations added to mimic functional connectivity.

Proper citation: MEGSIM (RRID:SCR_002420) Copy   

•   Source: SciCrunch Registry

http://www.udel.edu/Biology/Wags/histopage/colorpage/cne/cne.htm

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 13,2026. An image collection of tissue from the central nervous system and peripheral nervous system.

Proper citation: Nervous Tissue Color Images (RRID:SCR_002416) Copy   

•   Source: SciCrunch Registry

http://pharmacology.mc.duke.edu/

Department of Pharmacology and Cancer Biology spans two overlapping and broad disciplines, one exploring how chemical agents impact living cells and one seeking to understand how inappropriate responses to environmental molecules and internal cellular cues can lead to development of Cancer. Occupying Levine Science Research Center, Pharmacology and Cancer Biology department is dedicated to mentoring and training of graduate students and postdoctoral fellows.Innovative undergraduate program also allows students majoring in Biology or Chemistry at Duke to complete area specialization in Pharmacology, as well as offering courses in Pharmacology and Neuropharmacology for undergraduates. Department trains also students working towards Ph.D.s in Molecular Cancer Biology. Moreover, students enter our department through several university-wide multi-disciplinary programs including the Toxicology, Cell and Molecular Biology Program and the University Program in Genetics and Genomics. Our 23 faculty members are remarkably diverse and use all of the tools available to biomedical scientists to address questions critical to fundamental biology and human health. The faculty members share the common goal of exploiting cellular signaling pathways to address a myriad of important scientific questions relevant to cancer, metabolism, nervous system function, drugs of abuse and environmental toxicants.

Proper citation: Duke University, Pharmacology and Cancer Biology (RRID:SCR_003342) Copy   

•   Source: SciCrunch Registry

http://hymao.org

A structured controlled vocabulary of the anatomy of the Hymenoptera (bees, wasps, sawflies and ants)

Proper citation: Hymenoptera Anatomy Ontology (RRID:SCR_003340) Copy   

•   Source: SciCrunch Registry

http://www.patricbrc.org/portal/portal/patric/Home

A Bioinformatics Resource Center bacterial bioinformatics database and analysis resource that provides researchers with an online resource that stores and integrates a variety of data types (e.g. genomics, transcriptomics, protein-protein interactions (PPIs), three-dimensional protein structures and sequence typing data) and associated metadata. Datatypes are summarized for individual genomes and across taxonomic levels. All genomes, currently more than 10 000, are consistently annotated using RAST, the Rapid Annotations using Subsystems Technology. Summaries of different data types are also provided for individual genes, where comparisons of different annotations are available, and also include available transcriptomic data. PATRIC provides a variety of ways for researchers to find data of interest and a private workspace where they can store both genomic and gene associations, and their own private data. Both private and public data can be analyzed together using a suite of tools to perform comparative genomic or transcriptomic analysis. PATRIC also includes integrated information related to disease and PPIs. The PATRIC project includes three primary collaborators: the University of Chicago, the University of Manchester, and New City Media. The University of Chicago is providing genome annotations and a PATRIC end-user genome annotation service using their Rapid Annotation using Subsystem Technology (RAST) system. The National Centre for Text Mining (NaCTeM) at the University of Manchester is providing literature-based text mining capability and service. New City Media is providing assistance in website interface development. An FTP server and download tool are available.

Proper citation: Pathosystems Resource Integration Center (RRID:SCR_004154) Copy   

•   Source: SciCrunch Registry

http://www.imi-getreal.eu/

Consortium that aims to improve the efficiency of the medicine development process by better incorporating estimates of relative effectiveness into drug development and to enrich decision-making by regulatory authorities and health technology assessment (HTA) bodies through: * Bringing together regulators, HTA bodies, academics, companies, patients and other societal stakeholders; * Assessing existing processes, methodologies, and key research issues; * Proposing innovative (and more pragmatic) trial designs and assessing the value of information; * Proposing and testing innovative analytical and predictive modelling approaches; * Assessing operational, ethical, regulatory issues and proposing and testing solutions; * Creating new decision making frameworks, and building open tools to allow for the evaluation of development programs and use in the assessment of the value of new medicines; * Sharing and discussing deliverables with, among others, Pharmaceutical companies, regulatory authorities, HTA / reimbursement agencies, clinicians and patient organizations; * Developing training activities for researchers, decision makers and societal stakeholders in the public and private sector in order to increase knowledge about various aspects of relative effectiveness. The expected impact is that it will contribute to the knowledge base, particularly to inform clinical decision making and improve the efficiency of the R&D process. GETREAL will help to generate a consensus on best practice in the timing, performance and use of real life clinical studies in regulatory and reimbursement decision-making. It will also help to create a strong platform for the communication of results and for future discussions in this important area.

Proper citation: GetReal (RRID:SCR_003862) Copy   

•   Source: SciCrunch Registry

http://www.theseed.org/wiki/Home_of_the_SEED

The SEED is a framework to support comparative analysis and annotation of genomes. The cooperative effort focuses on the development of the comparative genomics environment and, more importantly, on the development of curated genomic data. Curation of genomic data (annotation) is done via the curation of subsystems by an expert annotator across many genomes, not on a gene by gene basis. From the curated subsystems we extract a set of freely available protein families (FIGfams). These FIGfams form the core component of our RAST automated annotation technology. Answering numerous requests for automatic Seed-Quality annotations for more or less complete bacterial and archaeal genomes, we have established the free RAST-Server (RAST=Rapid Annotation using Subsytems Technology). Using similar technology, we make the Metagenomics-RAST-Server freely available. We also provide a SEED-Viewer that allows read-only access to the latest curated data sets. We currently have 58 Archaea, 902 Bacteria, 562 Eukaryota, 1254 Plasmids and 1713 Viruses in our database. All tools and datasets that make up the SEED are in the public domain and can be downloaded at ftp://ftp.theseed.org

Proper citation: SEED (RRID:SCR_002129) Copy   

•   Source: SciCrunch Registry

http://www.med.unc.edu/bric/ideagroup/free-softwares/intergroup-image-registration

Software package that provides solutions for registering two groups of images, which are the necessary steps for many brain-related applications.

Proper citation: Inter-Group Registration Toolbox (RRID:SCR_002404) Copy   

•   Source: SciCrunch Registry

http://purl.bioontology.org/ontology/NIFCELL

Ontology for cell types from NIFSTD

Proper citation: NIF Cell Ontology (RRID:SCR_003977) Copy   

•   Source: SciCrunch Registry

http://arrowsmith.psych.uic.edu/arrowsmith_uic/

Portal for documenting the Arrowsmith project and developing text mining tools for scientific, and specifically neuroscience, literature. It also contains a search functions that identifies similar concepts between two articles.

Proper citation: Arrowsmith (RRID:SCR_002523) Copy   

•   Source: SciCrunch Registry

http://purl.bioontology.org/ontology/MS

A structured controlled vocabulary for the annotation of mass spectrometry experiments.

Proper citation: Mass Spectrometry Ontology (RRID:SCR_003579) Copy   

•   Source: SciCrunch Registry

http://www.predictprotein.org/

Web application for sequence analysis and the prediction of protein structure and function. The user interface intakes protein sequences or alignments and returned multiple sequence alignments, motifs, and nuclear localization signals., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 15,2026.

Proper citation: Predictions for Entire Proteomes (RRID:SCR_002803) Copy   

•   Source: SciCrunch Registry

http://opensim.stanford.edu

OpenSim is an open-source software system that lets users develop models of musculoskeletal structures and create dynamic simulations of movement. The software provides a platform on which the biomechanics community can build a library of simulations that can be exchanged, tested, analyzed, and improved through multi-institutional collaboration. The underlying software is written in ANSI C++, and the graphical user interface (GUI) is written in Java. OpenSim technology makes it possible to develop customized controllers, analyses, contact models, and muscle models among other things. These plugins can be shared without the need to alter or compile source code. Users can analyze existing models and simulations and develop new models and simulations from within the GUI.

Proper citation: OpenSim (RRID:SCR_002683) Copy   

•   Source: SciCrunch Registry

http://regulondb.ccg.unam.mx/

Database on transcriptional regulation in Escherichia coli K-12 containing knowledge manually curated from original scientific publications, complemented with high throughput datasets and comprehensive computational predictions. Graphic and text-integrated environment with friendly navigation where regulatory information is always at hand. They provide integrated views to understand as well as organized knowledge in computable form. Users may submit data to make it publicly available.

Proper citation: RegulonDB (RRID:SCR_003499) Copy   

•   Source: SciCrunch Registry

http://www.GeneWeaver.org

Freely accessible phenotype-centered database with integrated analysis and visualization tools. It combines diverse data sets from multiple species and experiment types, and allows data sharing across collaborative groups or to public users. It was conceived of as a tool for the integration of biological functions based on the molecular processes that subserved them. From these data, an empirically derived ontology may one day be inferred. Users have found the system valuable for a wide range of applications in the arena of functional genomic data integration.

Proper citation: Gene Weaver (RRID:SCR_003009) Copy   

•   Source: SciCrunch Registry

http://www.proteomexchange.org

A data repository for proteomic data sets. The ProteomeExchange consortium, as a whole, aims to provide a coordinated submission of MS proteomics data to the main existing proteomics repositories, as well as to encourage optimal data dissemination. ProteomeXchange provides access to a number of public databases, and users can access and submit data sets to the consortium's PRIDE database and PASSEL/PeptideAtlas.

Proper citation: ProteomeXchange (RRID:SCR_004055) Copy   

•   Source: SciCrunch Registry

http://www.carmen.org.uk/

THIS RESOURCE IS NO LONGER IN SERVICE.Documented on January 14, 2023. Infrastructure for sharing data, tools and services, this virtual research environment (VRE) supports e-Neuroscience and is designed to provide services for data and processing of that data. While the system is initially focused on electrophysiology data (neural activity recordings are the primary data types), it is equally applicable to many domains outside neuroscience. The Portal Provides: * User login and customization. * Data upload/download. * Data handling including custom permissions for public, shared or private data. * The ability to invoke custom public, shared or private services that consume and produce data. For example, it would allow spike series to be run through a sorter, producing new data representing the sorted spikes. * The ability to host services written in a number of languages including, but not limited to Matlab, R, Python, Perl, Java. * A system to support metadata for data objects, which provides extensive support for entering metadata at the point of upload, and allows the generation of metadata from services to provide provenance information. * The ability to invoke additional visualization for the data, for example, via the Signal Data Explorer. A core part is the development of: (i) minimum reporting guidelines for annotation of data and other computational resources for the purpose of sharing, and; (ii) intermediate formats and APIs for translation between proprietary and bespoke data types. These recommendations are being implemented and the global community is encouraged both to engage in their specification and make use of them for their own data sharing systems. * MINI: Minimum Information about a Neuroscience Investigation - This framework represents the formalized opinion of the CARMEN consortium and its associates, and identifies the minimum reporting information required to support the use of electrophysiology in a neuroscience study, for submission to the CARMEN system. * NDTF: Neurophysiology Data Translation Format - This framework provides a vendor-independent mechanism for translating between raw and processed neurphysiology data in the form of time and image series. They are implementing NDTF in CARMEN but it may also be useful for third party applications.

Proper citation: Code Analysis Repository and Modelling for e-Neuroscience (RRID:SCR_002795) Copy   

•   Source: SciCrunch Registry

http://ww2.sanbi.ac.za/Dbases.html

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. The STACKdb is knowledgebase generated by processing EST and mRNA sequences obtained from GenBank through a pipeline consisting of masking, clustering, alignment and variation analysis steps. The STACK project aims to generate a comprehensive representation of the sequence of each of the expressed genes in the human genome by extensive processing of gene fragments to make accurate alignments, highlight diversity and provide a carefully joined set of consensus sequences for each gene. The STACK project is comprised of the STACKdb human gene index, a database of virtual human transcripts, as well as stackPACK, the tools used to create the database. STACKdb is organized into 15 tissue-based categories and one disease category. STACK is a tool for detection and visualization of expressed transcript variation in the context of developmental and pathological states. The data system organizes and reconstructs human transcripts from available public data in the context of expression state. The expression state of a transcript can include developmental state, pathological association, site of expression and isoform of expressed transcript. STACK consensus transcripts are reconstructed from clusters that capture and reflect the growing evidence of transcript diversity. The comprehensive capture of transcript variants is achieved by the use of a novel clustering approach that is tolerant of sub-sequence diversity and does not rely on pairwise alignment. This is in contrast with other gene indexing projects. STACK is generated at least four times a year and represents the exhaustive processing of all publicly available human EST data extracted from GenBank. This processed information can be explored through 15 tissue-specific categories, a disease-related category and a whole-body index

Proper citation: Sequence Tag Alignment and Consensus Knowledgebase Database (RRID:SCR_002156) Copy   

•   Source: SciCrunch Registry

http://www.seg3d.org

A free volume processing segmenting tool that combines a flexible manual interface with powerful image processing and segmentation algorithms. Users can explore and label image volumes using slice windows and 3D volume rendering.

Proper citation: Seg3D (RRID:SCR_002552) Copy   

•   Source: SciCrunch Registry