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http://projects.kmi.open.ac.uk/scholonto/index.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on February 25,2022. Project (2001-2004) to build and deploy a prototype infrastructure for making scholarly claims about the significance of research documents. "Claims" are made by making connections between ideas. Any claim is open to counterarguments. The connections are grounded in a discourse/argumentation ontology, which makes possible innovative services for navigating, visualizing and analyzing the network as it grows. They have evolved a suite of prototype tools for modelling conceptual networks of ideas and arguments, publishing them for reuse and argumentation, and navigating, searching and filtering this repository as it grows. Some of these are freely available to play with, but WARNING! they are no longer able to support them. The system they have built is called ClaiMaker. Others include ClaimSpotter, ClaiMapper, ClaimFinder, ClaimBlogger and ClaiMaker Microsoft Word plug-in. Additionally, the ScholOnto RDF Schema is available, http://projects.kmi.open.ac.uk/scholonto/resources/Scholonto2.rdfs User Communities: They envisage such a tool as having potential not only in scholarly publishing, but for corporate knowledge analysis, and in education. Complementary work: ScholOnto's focus on net-centric representations of research is complemented by work on augmenting more conventional textual documents, with threaded commentary and debate. An open source web publishing tool is available called D3E (Digital Document Discourse Environment). Many applications are illustrated on the site, including the award-winning Journal of Interactive Media in Education.We are no longer able to support tools, so are not able to assist if you have problems or lose data.

Proper citation: Scholarly Ontologies Project (RRID:SCR_000057) Copy   

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https://www.nanomaterialregistry.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on June 9,2023. Registry that archives curated nanomaterial research data and their biological and environmental implications. The Registry provides data management plans for researchers, and accepts users' public-ready data, archive them, integrate them into the registry, allowing for the data to be shared publicly. Users can request more information on specific nanomaterial records, compare multiple nanomaterials, and export data to their desktop.

Proper citation: Nanomaterial Registry (RRID:SCR_013700) Copy   

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http://ndct.nimh.nih.gov

A database which houses human subjects clinical trial data. NDCT currently contains data on 13,409 subjects and has access to data on 100,500 subjects from the NIMH Data Archive. Users can also sign up for news updates and watch video tutorials.

Proper citation: National Database for Clinical Trials related to Mental Illness (RRID:SCR_013795) Copy   

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http://www.ctspedia.org/do/view/CTSpedia

CTSpedia is a national effort to collect wisdom, tools, educational materials, and other items useful for clinical and translational researchers and to provide timely and useful advice to clinical and translational researchers with specific problems. The CTSpedia is a collaborative vehicle for the CTSA''s Biostatistics/Epidemiology/Research/Design (BERD) Online Resources and Education taskforce to identify and share resources across the national consortium and community researchers world-wide. With the support of the national BERD consortia, the project obtained funding and support from the National Center for Research Resources (NCRR) to expand the original scope and content of CTSpedia and foster collaboration amongst CTSAs. The main goal of CTSpedia.org is to create a definable academic home on the internet for the discipline of clinical and translational sciences across the country and the world. * While the CTSA consortium serves the onsite physical level of the institutions involved, CTSpedia.org seeks to fill the gaps where the network is lacking, and to augment that network as the central hub for the peer to peer sharing of knowledge and resources. * While the CTSA national scope comes to fruition, the international scope of the consortia is more readily facilitated with an online resource like CTSpedia. * Utilizing the collaborative nature of the wiki-style website, CTSpedia.org allows for researchers anywhere in the world to ask questions and receive answers and related information in a timely and efficient manner, overcoming the logistical issues of distance and scheduling. * The streamlined availability of an online resource and knowledge repository will aid in addressing common issues that arise in clinical research, which will filter out consultation requests for minor questions, allowing for CTSA consultants to address more prevalent consultations.

Proper citation: CTSpedia (RRID:SCR_008733) Copy   

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http://neurosurgery.ucsf.edu/index.php/research_tissue_bank.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 4th,2023. Brain Tumor Research Center Tissue Bank began collecting tissue in 1978 and has established an organized repository of characterized tissues--frozen, paraffin-embedded, blood and cultures--that are maintained in a manner useful for a wide range of studies. Samples are collected only from patients who have agreed to have their tissues banked and used for future research. Consent documents are maintained in a secure area and associated clinical data are held in a double-password protected computer database. Each sample received into the Tissue Bank is non-identifying number. No protected health information (PHI) is released. To obtain samples, investigators submit a request form to the Manager. The request form requires an explanation of the tissue requested (type, number of samples, justification), description of the study, CHR approval (see new policy regarding human vs. non-human research) and Project Leader authorization. The Manager reviews each request for feasibility before presentation to the Scientific Core Committee. The UCSF Neurosurgery Tissue Bank makes its inventory of stock cell lines available to all investigators. Requested cells are grown in T-25 flasks and shipped FedEx Priority Overnight at the receipient's expense. However, if you prefer, we can ship the frozen cells, packed in dry ice. (Note: some countries restrict dry ice shipments.)

Proper citation: UCSF Brain Tumor Tissue Bank (RRID:SCR_000647) Copy   

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http://www.scienceexchange.com/facilities/institute-for-behavioral-genetics

The Institute for Behavioral Genetics (IBG) is an organized research unit of the University of Colorado Graduate School dedicated to conducting and facilitating research on the genetic and environmental bases of individual differences in behavior. Founded in 1967, IBG is one of the top research facilities in the world for genetic research on behavior. Data collection and analysis is ongoing for several internationally renowned studies including the Colorado Adoption Project, the Colorado Twin Registry, the National Youth Survey Family Study, the Colorado Learning Disabilities Research Center, and the National Longitudinal Study of Adolescent Health. IBG is home to one of the nation''''s largest DNA repositories for research on human behavior, as well as housing a wide array of behaviorally and genetically defined lines of selected, recombinant inbred, transgenic, and knockout-gene mice. Current research includes studies of aging ( The Johnson Lab, The Link Lab), psychopathology, reading disability, cognition, substance abuse, behavioral development, and evolution. In addition to our research, we provide training opportunities for graduate students in affiliated programs at the University of Colorado campuses in Boulder and Denver. IBG receives funding from several federal agencies, principally through the National Institutes of Health, as well as a broad spectrum of Associations, Foundations and Programs. Welcome to our site, please feel free to contact us with any questions about our research or programs.

Proper citation: CU Boulder Institute for Behavioral Genetics (RRID:SCR_012495) Copy   

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https://www.fdilab.org/toolsservices

Integrated data set and analytics platform combining Research Resource Identifiers (RRIDs), text mining and data aggregation.

Proper citation: Resource Information Network (RRID:SCR_023186) Copy   

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https://www.eagle-i.net/

Web application to discover resources available at participating networked universities. This distributed platform for creating and sharing semantically rich data is built around semantic web technologies and follows linked open data principles.

Proper citation: Eagle I (RRID:SCR_013153) Copy   

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http://biolit.ucsd.edu/doc/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on May 16, 2016. The establishment of open access literature makes it possible for knowledge to be extracted from scholarly articles and included in other resources. BioLit aims to extract database identifiers and rich meta-data from open access articles in the life sciences and integrate that information with existing biological databases. We have begun prototyping this effort using a clone of the RCSB Protein Data Bank, a database of macromolecular structures. Cyberinfrastructure is integral to all aspects of conducting experimental research and distributing those results. However, it has yet to make a similar impact on the way we communicate that information. Peer-reviewed publications have long been the currency of scientific research as they are the fundamental unit through which scientists communicate with and evaluate each other. However, in striking contrast to the data, publications have yet to benefit from the opportunities offered by cyberinfrastructure. While the means of distributing publications has vastly improved, publishers have done little else to capitalize on the electronic medium. In particular, semantic information describing the content of these publications is sorely lacking, as is the integration of this information with data in public repositories. This is confounding considering that many basic tools for marking-up and integrating publication content in this manner already exist, such as a centralized literature database, relevant ontologies, and machine-readable document standards. We believe that the research community is ripe for a revolution in scientific communication and that the current generation of scientists will be the one to push it forward. These scientists, generally graduate students and new post-docs and have grown up with cyberinfrastructure as a part of their daily lives, not just a specialized aspect of their profession. They have a natural ability to do science in an electronic environment without the need for printed publications or static documents and, in fact, can feel quite limited by the traditional format of a publication. Perhaps most importantly, they appreciate that the sheer amount of data and the number of publications is prohibitive to the traditional methods of keeping current with the literature. Fink, L., Bourne, P. Reinventing Scholarly Communication for the Electronic Age, CTWatch Quarterly, Volume 3, Number 3, August 2007., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: BioLit (RRID:SCR_008270) Copy   

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http://www.elsevier.com/online-tools/pathway-studio/biological-database

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 5, 2023. MedScan is a fast and flexible biomedical information extraction technology. It uses dictionaries to identify individual biomedical terms (proteins, cellular processes, small molecules, diseases, etc) referred to in literature articles, and applies advanced natural language processing techniques to detect the relationships within the article and extract these terms and the relationships; the overall process of detection, identification, extraction and assembling, is termed Information Harvesting. Information extracted by MedScan represents the multiple aspects of protein function, including protein modification, cellular localization, protein-protein interactions, gene expression regulation, molecular transport and synthesis, as well as association with diseases, and regulation of various cellular processes. This scope can be broadened by modifying information extraction rules and the dictionaries. Dictionaries can be assembled on any topic or area that is represented in the literature you wish to harvest. High-throughput data generation methodologies like microarray gene expression require new approaches for gathering information for data analysis. For the best results, computational approaches used for high-throughput data analysis require that biological information from the literature be a coherent and integrated part of the analysis software itself. Pathway Studio meets this challenge through its MedScan Technology and underlying ResNet database. All editions of Pathway Studio contain MedScan Technology to harvest information from the literature and to save this information in the Pathway Studio ResNet database ready for data analysis. MedScan is more than a web search engine. Indeed, the output of a Google search can be channeled into MedScan for example. Web searches, like Google, are excellent at finding items as a result of a query. A quick look at the output list usually locates the item for which you are looking. This approach however, is not well suited for information and knowledge gathering. Also, once information is gathered, where do you put it for later computational use? MedScan meets this challenge for the area of biomedical literature and biomedical online information. PubMed meets the needs for a central repository of biomedical literature. Researchers can go to PubMed and search for any topic and articles of interest, much like a web type of search. However, just like a web type of search, PubMed also provides a list of all the hits with a link to the articles. If a single article, or even just a few, are sought, this search approach is useful. Alternatively, MedScan will list all the articles of interest but additionally scans the text for relationships, highlights these relationships in the articles and then lists these relationships and the biological molecules and processes involved in the relationships in separate tables. The tables of relationships can be viewed graphically in Pathway Studio and can be saved into the ResNet database for use in experimental data analysis.

Proper citation: MedScan (RRID:SCR_003314) Copy   

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http://www.flymine.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 14,2026. Integrated database of genomic, expression and protein data for Drosophila, Anopheles, C. elegans and other organisms. You can run flexible queries, export results and analyze lists of data. FlyMine presents data in categories, with each providing information on a particular type of data (for example Gene Expression or Protein Interactions). Template queries, as well as the QueryBuilder itself, allow you to perform searches that span data from more than one category. Advanced users can use a flexible query interface to construct their own data mining queries across the multiple integrated data sources, to modify existing template queries or to create your own template queries. Access our FlyMine data via our Application Programming Interface (API). We provide client libraries in the following languages: Perl, Python, Ruby and & Java API

Proper citation: FlyMine (RRID:SCR_002694) Copy   

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http://dknet.org/

The NIDDK Information Network (dkNET) is a community-based network to serve needs of basic and clinical investigators that includes large pools of data and research resources relevant to mission of National Institute of Diabetes and Digestive and Kidney Disease.

Proper citation: NIDDK Information Network (dkNET) (RRID:SCR_001606) Copy   

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http://nimh-repository.rti.org/

A program that synthesizes, purifies, and distributes otherwise unavailable essential compounds to stimulate basic and clinical research in psychopharmacology relevant to mental health in areas such as the molecular pharmacology and signaling of CNS receptors, longitudinal studies to evaluate the molecular, biochemical, and behavioral actions of psychoactive compounds, and functional brain imaging in both primates and humans. WHAT IS AVAILABLE: * Ligands for CNS receptors, radiolabeled compounds for autoradiography and neuroimaging, biochemical markers, drug analogs and metabolites, and reference standards * Synthesis (including GMP) of promising compounds for mental health research, including preclinical toxicology and safety studies, especially compounds for PET neuroimaging * A listing of currently available NIMH CSDSP compounds is available online at www.nimh-repository.rti.org. RTI International scientists can provide investigators with technical assistance and additional information about the compounds on request. Data sheets containing purity, storage, and handling information are supplied with all NIMH CSDSP compounds. WHO IS ELIGIBLE: Investigators involved in basic or clinical research relevant to mental health are eligible to submit requests. To learn more about current NIMH research areas, please visit the NIMH website at www.nimh.nih.gov. NIMH CSDSP compounds are free to qualified academic investigators, but payment may be required from nonacademic requestors. Investigators interested in obtaining radiolabeled compounds but uncertain about what type of label or specific activity would work best for them may obtain help by communicating with the technical contacts listed on the website.

Proper citation: NIMH Chemical Synthesis and Drug Supply Program (RRID:SCR_004921) Copy   

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http://en.wikipedia.org/wiki/Gene_Wiki

The Gene Wiki is a project that facilitates transferring information on human genes to Wikipedia article stubs with the goal of promoting collaboration and expansion of the articles. Number of gene articles The human genome contains an estimated 20,00025,000 protein-coding genes. The goal of the Gene Wiki project is to create seed articles for every notable human gene, that is, every gene whose function has been assigned in the peer-reviewed scientific literature. Approximately half of human genes have assigned function, therefore the total number of articles seeded by the Gene Wiki project would be expected to be in the range of 10,000 - 15,000. To date, approximately 10,271 articles have been created or augmented to include Gene Wiki project content. Expansion Once seed articles have been established, the hope and expectation is that these will be annotated and expanded by editors ranging in experience from the lay audience to students to professionals and academics. Proteins encoded by genes The majority of genes encode proteins hence understanding the function of a gene generally requires understanding of the function of the corresponding protein. In addition to including basic information about the gene, the project therefore also includes information about the protein encoded by the gene. Stubs for the Gene Wiki project are created by a bot and contain links to the following primary gene/protein databases * HUGO Gene Nomenclature Committee official gene name * Entrez Gene database * OMIM (Mendelian Inheritance in Man) database that catalogues all the known diseases with a genetic component * Amigo Gene Ontology * HomoloGene gene homologs in other species * SymAtlasRNA gene expression pattern in tissues * Protein Data Bank 3D structure of protein encoded by the gene * Uniprot (universal protein resource) a central repository of protein data

Proper citation: Gene Wiki (RRID:SCR_005317) Copy   

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http://syndb.cbi.pku.edu.cn

SynDB is an online resource of proteins known or predicted to be related to the synapse or synaptic activity, and extensive information on the proteins'' functions, sequences, structures, expression, pathways, interactions, and disease associations. It is intended to be a repository of current knowledge and data as well as a starting point for future proteomics research in neurobiology. SynDB is the first focused database of the molecular biology of the synapse proteome. It contains the most comprehensive collection of proteins (13809 unique proteins spanning 1979 species and 104 protein domains, Aug 2006) that are known or predicted to be associated with synaptic activities. It integrates extensive information on protein functions, sequences, structures, expression, pathways, interactions, and disease associations. SynDB was generated using a combination of automated approaches, including keyword- and domain-based searches, and manual curation. It serves as a starting point for future neurobiology, neuropharmacology, and neuroinformatics research. Synapse ontology is a set of standard vocabulary which help to describe all synaptic gene products in a consistant way. As in common ontology, synapse ontolgy is composed of all the terms in a hierarchical structure, but specifically restricted to the function and structure annotation of synapse related gene products. Synapse ontology is a callaborative fruit of bioinformatists and neural biologists. Synapse ontolgy is aimed to describe all the synaptic molecules in terms of structure/biochemistry of synapse and physiology/function at synapse in a specied-independent manner. The controled vocabularies are hierarchically structured, so you can browser the related gene products in different levels: for example, you can find all the gene products of synaptic vesicle cycling or ion channels and receptors, or you can zoom in on all the gene products playing roles in the priming step of synaptic vesicle cycling.

Proper citation: SynDB: Synapse DataBase (RRID:SCR_005918) Copy   

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http://trans.nih.gov/bmap/index.htm

The Brain Molecular Anatomy Project is a trans-NIH project aimed at understanding gene expression and function in the nervous system. BMAP has two major scientific goals: # Gene discovery: to catalog of all the genes expressed in the nervous system, under both normal and abnormal conditions. # Gene expression analysis: to monitor gene expression patterns in the nervous system as a function of cell type, anatomical location, developmental stage, and physiological state, and thus gain insight into gene function. In pursuit of these goals, BMAP has launched several initiatives to provide resources and funding opportunities for the scientific community. These include several Requests for Applications and Requests for Proposals, descriptions of which can be found in this Web site. BMAP is also in the process of establishing physical and electronic resources for the community, including repositories of cDNA clones for nervous system genes, and databases of gene expression information for the nervous system. Most of the BMAP initiatives so far have focused on the mouse as a model species because of the ease of experimental and genetic manipulation of this organism, and because many models of human disease are available in the mouse. However, research in humans, other mammalian species, non-mammalian vertebrates, and invertebrates is also being funded through BMAP. For the convenience of interested investigators, we have established this Web site as a central information resource, focusing on major NIH-sponsored funding opportunities, initiatives, genomic resources available to the research community, courses and scientific meetings related to BMAP initiatives, and selected reports and publications. When appropriate, we will also post initiatives not directly sponsored by BMAP, but which are deemed relevant to its goals. Posting decisions are made by the Trans-NIH BMAP Committee

Proper citation: BMAP - Brain Molecular Anatomy Project (RRID:SCR_008852) Copy   

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http://operons.ibt.unam.mx/OperonPredictor/

The Prokaryotic Operon DataBase (ProOpDB) constitutes one of the most precise and complete repository of operon predictions in our days. Using our novel and highly accurate operon algorithm, we have predicted the operon structures of more than 1,200 prokaryotic genomes. ProOpDB offers diverse alternatives by which a set of operon predictions can be retrieved including: i) organism name, ii) metabolic pathways, as defined by the KEGG database, iii) gene orthology, as defined by the COG database, iv) conserved protein motifs, as defined by the Pfam database, v) reference gene, vi) reference operon, among others. In order to limit the operon output to non-redundant organisms, ProOpDB offers an efficient protocol to select the more representative organisms based on a precompiled phylogenetic distances matrix. In addition, the ProOpDB operon predictions are used directly as the input data of our Gene Context Tool (GeConT) to visualize their genomic context and retrieve the sequence of their corresponding 5�� regulatory regions, as well as the nucleotide or amino acid sequences of their genes. The prediction algorithm The algorithm is a multilayer perceptron neural network (MLP) classifier, that used as input the intergenic distances of contiguous genes and the functional relationship scores of the STRING database between the different groups of orthologous proteins, as defined in the COG database. Nevertheless, the operon prediction of our method is not restricted to only those genes with a COG assignation, since we successfully defined new groups of orthologous genes and obtained, by extrapolation, a set of equivalent STRING-like scores based on conserved gene pairs on different genomes. Since the STRING functional relationships scores are determined in an un-bias manner and efficiently integrates a large amount of information coming from different sources and kind of evidences, the prediction made by our MLP are considerably less influenced by the bias imposed in the training procedure using one specific organism.

Proper citation: ProOpDB (RRID:SCR_006111) Copy   

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http://simbios.stanford.edu/index.html

Simbios is the NIH Center for physics-based Simulation of Biological Structures. Simbios provides infrastructure, software, and training to help biomedical researchers understand biological form and function as they create novel drugs, synthetic tissues, medical devices, and surgical interventions. Simbios is investigating a wide scale of biological structures - from molecules to organisms. Driving biological problems include RNA folding, protein folding, myosin dynamics, cardiovascular dynamics, and neuromuscular biomechanics. Investigators interested in collaborating with Simbios can apply for NIH funding. To encourage collaboration in building accurate biological models and simulations, Simbios also provides the biomedical community with https://simtk.org, a free, secure, distributed, development system for projects. Projects may include models, software, data, documentation, publications, and graphics and have automatic backups and off-site storage. Projects may be public or private and have project-specific mailing lists, forums, bug & feature databases, news, blogs, and source-code repositories. Simbios is developing and disseminating the SimTK core simulation toolkit, (simtk.org/home/simtkcore). SimTK core is open-source software developed by experienced professionals. The software includes advanced capabilities for modeling the geometry and physics of biological systems. To ensure utility and accuracy, the software and training material is being developed and tested in close collaboration with biomedical scientists. Simbios has developed OpenSim, an application for advanced neuromuscular modeling that uses the SimTK toolkit, and is making it openly available at simtk.org/home/opensim. Simbios also publishes the Biomedical Computation Review, a magazine devoted to the science and tools in biocomputation, aimed at the community which encompasses the diverse biocomputation disciplines. To help researchers find high quality software and tools Simbios has also establishes the Simbiome an inventory of high-quality commercial and academic bio-simulation tools. Simbios has recurring openings for postdoctoral researchers.

Proper citation: Simbios (RRID:SCR_004320) Copy   

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http://www.nescent.org/

The National Evolutionary Synthesis Center (NESCent) is a nonprofit science center dedicated to cross-disciplinary research in evolution. NESCent promotes the synthesis of information, concepts and knowledge to address significant, emerging, or novel questions in evolutionary science and its applications. NESCent achieves this by supporting research and education across disciplinary, institutional, geographic, and demographic boundaries. Synthetic research in evolutionary science takes many forms but includes integrating novel data sets and models to address important problems within a discipline, developing new analytical approaches and tools, and combining methods and perspectives from multiple disciplines to answer and even create new fundamental scientific questions. NESCent facilitates such synthetic research by providing an environment for fertile interactions among scientists. Our Science and Synthesis program sponsors postdoctoral fellows and sabbatical scholars as resident scientists, and two kinds of meetings, working groups and catalysis meetings. Catalysis meetings provide a novel mechanism for bringing together diverse research communities and cultures to identify common interests, while working groups provide an opportunity for scientists to work together intensively on fundamental synthetic questions over a several-year period. These activities are community driven through our application process and evaluated by an external advisory board. Our Informatics program provides state of the art informatics tools to visiting and in-house scientists and aims to take the lead in assembling novel databases and developing new analytical tools for evolutionary biology. Finally it is sponsoring a major initiative to provide a digital data repository for work in evolutionary biology. NESCent''s Education and Outreach group communicates the results of evolutionary biology research to the general public and scientific community, provides outreach to groups who are underrepresented in evolutionary biology and works to improve evolution education.

Proper citation: NESCent - National Evolutionary Synthesis Center (RRID:SCR_005911) Copy   

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http://www.ppmi-info.org/

An observational longitudinal clinical study partnership to identify and validate biomarkers of Parkinson disease (PD) progression and provide easy and open web-based access to the comprehensive set of correlated clinical data and biospecimens, information, and biosamples acquired from PD and age and gender matched healthy control subjects to the research community. The data and specimens have been collected in a standardized manner under strict protocols and includes clinical (demographic, motor and non-motor, cognitive and neurobehavioral), imaging (raw and processed MRI, SPECT and DAT), and blood chemistry and hematology subject assessments and biospecimen inventories (serum, plasma, whole blood, CSF, DNA, RNA and urine). All data are de-identified to protect patient privacy. PPMI will be carried out over five years at 21 clinical sites in the United States and Europe and requires the participation of 400 Parkinson's patients and 200 control participants. The PPMI database provides researchers with access to correlated clinical and imaging data, along with annotated biospecimens, all available within an open access system that encourages data sharing (http://www.ppmi-info.org/access-data-specimens/). The website hosts an Ongoing Analysis section to keep the scientific community apprised of analyses being completed, in hopes of stimulating collaborations between researchers who are using PPMI data and specimens.

Proper citation: Parkinson's Progression Markers Initiative (RRID:SCR_006431) Copy   

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