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https://www.ncdc.noaa.gov/

National Data Center that accepts and makes available weather, climate, paleoclimate, meteorological data.

Proper citation: National Climatic Data Center (RRID:SCR_009427) Copy   

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https://portal.dbmi.hms.harvard.edu/projects/GRDR/

Data repository of de-identified patient data, aggregated in a standardized manner, to enable analyses across many rare diseases and to facilitate various research projects, clinical studies, and clinical trials. The aim is to facilitate drug and therapeutics development, and to improve the quality of life for the many millions of people who are suffering from rare diseases. The goal of GRDR is to enable analyses of data across many rare diseases and to facilitate clinical trials and other studies. During the two-year pilot program, a web-based template will be developed to allow any patient organization to establish a rare disease patient registry. At the conclusion of the program, guidance will be available to patient groups to establish a registry and to contribute de-identified patient data to the GRDR repository. A Request for Information (RFI) was released on February 10, 2012 requesting information from patient groups about their interest in participating in a GRDR pilot project. ORDR selected 30 patient organizations to participate in this pilot program to test the different functionalities of the GRDR. Fifteen (15) organizations with established registries and 15 organizations that do not have patient registry. The 15 patient groups, each without a registry, were selected to assist in testing the implementation of the ORDR Common Data Elements (CDEs) in the newly developed registry infrastructure. These organizations will participate in the development and promotion of a new patient registry for their rare disease. The GRDR program will fund the development and hosting of the registry during the pilot program. Thereafter, the patient registry is expected to be self-sustaining.The 15 established patient registries were selected to integrate their de-identified data into the GRDR to evaluate the data mapping and data import/export processes. The GRDR team will assist these organizations in mapping their existing registry data to the CDEs. Participating registries must have a means to export their de-identified registry data into a specified data format that will facilitate loading the data into the GRDR repository on a regular basis. The GRDR will also develop the capability to link patients'''' data and medical information to donated biospecimens by using a Voluntary Global Unique Patient Identifier (GUID). The identifier will enable the creation of an interface between the patient registries that are linked to biorepositories and the Rare Disease Human Biospecimens/Biorepositories (RD-HUB) http://biospecimens.ordr.info.nih.gov/.

Proper citation: GRDR (RRID:SCR_008978) Copy   

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http://www.jst.go.jp/csc/virtual/find/mindlab/english/base.html

Interactive laboratory of the mind composed of 4 themed sessions housing four short introductory movies and sixteen trials with those you can experience visual phenomena and illusions used for study in psychological experiments.

Proper citation: Mind Lab (RRID:SCR_009025) Copy   

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http://bioinformatics.ua.pt/software/mfcompress/

A compression tool for FASTA and multi-FASTA files.

Proper citation: MFCompress (RRID:SCR_009301) Copy   

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http://bioinformatics.research.nicta.com.au/software/rlz/

Optimized relative Lempel-Ziv compression of genomes.

Proper citation: RLZ (RRID:SCR_009420) Copy   

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http://ftp://ftp.ieeta.pt/~ap/codecs/GReEn1.tar.gz

A compression tool recently proposed for compressing genome resequencing data using a reference genome sequence., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: GReEn (RRID:SCR_009264) Copy   

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http://ageing-map.org/

Database of age-related changes covering different biological levels, including molecular, physiological, psychological and pathological age-related data, to create an interactive portal that serves as a centralized collection of human aging changes and pathologies. To facilitate integrative, system-level studies of aging, the DAA provides a centralized source for aging-related data as well as basic tools to query and visualize the data, including anatomical models. Data in the DAA is manually curated from the literature and retrieved from public databases. For more detailed analyses users are able to download the entire database. More information on how to use the DAA is available on the help page. The DAA primarily focuses on human aging, but also includes supplementary mouse data, in particular gene expression data, to enhance and expand the information on human aging. If you would like to contribute to the database yourself, for instance if you have new data on aging, please use the contribute page to submit your data.

Proper citation: Digital Ageing Atlas (RRID:SCR_009020) Copy   

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http://pir.georgetown.edu/pirwww/dbinfo/pirsf.shtml

A SuperFamily classification system, with rules for functional site and protein name, to facilitate the sensible propagation and standardization of protein annotation and the systematic detection of annotation errors. The PIRSF concept is being used as a guiding principle to provide comprehensive and non-overlapping clustering of UniProtKB sequences into a hierarchical order to reflect their evolutionary relationships. The PIRSF classification system is based on whole proteins rather than on the component domains; therefore, it allows annotation of generic biochemical and specific biological functions, as well as classification of proteins without well-defined domains. There are different PIRSF classification levels. The primary level is the homeomorphic family, whose members are both homologous (evolved from a common ancestor) and homeomorphic (sharing full-length sequence similarity and a common domain architecture). At a lower level are the subfamilies which are clusters representing functional specialization and/or domain architecture variation within the family. Above the homeomorphic level there may be parent superfamilies that connect distantly related families and orphan proteins based on common domains. Because proteins can belong to more than one domain superfamily, the PIRSF structure is formally a network. The FTP site provides free download for PIRSF.

Proper citation: PIRSF (RRID:SCR_003352) Copy   

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http://biomed.emory.edu/PROGRAM_SITES/MSP/

The Molecular and Systems Pharmacology graduate program at Emory University offers broad training in the biomedical sciences for students interested in learning how the drugs of today work and how the novel therapeutics of tomorrow can be developed. The Program offers a specialization in toxicology. Emory University was recently rated by The Scientist magazine as the number 1 university in the world in terms of impact in pharmacology and toxicology research. Particular strengths within the MSP graduate school program at Emory include neuropharmacology, cancer biology, AIDS research, cardiovascular pharmacology, toxicology, and chemical biology. Ph.D. training in the Emory MSP program provides students with an ideal preparation for successful careers in the biotechnology and pharmaceutical industries as well as in academic research, teaching, government research, patent law and other disciplines that depend upon knowledge of fundamental pharmacological principles.

Proper citation: Emory University, Molecular and Systems Pharmacology (RRID:SCR_003351) Copy   

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http://www.gwumc.edu/pharm/

The Department of Pharmacology & Physiology at the George Washington University provides unique research opportunities at the predoctoral and postdoctoral levels. They also offer courses in Pharmacology and Physiology for Medical and Health Sciences students, as well as a number of graduate-level courses. Our mission is to provide the highest quality of educational opportunities to our community, and to advance scientific knowledge and improve human health through leading-edge research in the biomedical sciences.

Proper citation: George Washington University, Department of Pharmacology and Physiology (RRID:SCR_003358) Copy   

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http://mouseNET.princeton.edu

A functional network for laboratory mouse based on integration of diverse genetic and genomic data. It allows the users to accurately predict novel functional assignments and network components. MouseNET uses a probabilistic Bayesian algorithm to identify genes that are most likely to be in the same pathway/functional neighborhood as your genes of interest. It then displays biological network for the resulting genes as a graph. The nodes in the graph are genes (clicking on each node will bring up SGD page for that gene) and edges are interactions (clicking on each edge will show evidence used to predict this interaction). Most likely, the first results to load on the results page will be a list of significant Gene Ontology terms. This list is calculated for the genes in the biological network created by the mouseNET algorithm. If a gene ontology term appears on this list with a low p-value, it is statistically significantly overrepresented in this biological network. The graph may be explored further. As you move the mouse over genes in the network, interactions involving these genes are highlighted.If you click on any of the highlighted interactions graph, evidence pop-up window will appear. The Evidence pop-up lists all evidence for this interaction, with links to the papers that produced this evidence - clicking these links will bring up the relevant source citation(s) in PubMed.

Proper citation: MouseNET (RRID:SCR_003357) Copy   

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http://niftilib.sourceforge.net

Niftilib is a set of i/o libraries for reading and writing files in the nifti-1 data format. nifti-1 is a binary file format for storing medical image data, e.g. magnetic resonance image (MRI) and functional MRI (fMRI) brain images. Niftilib currently has C, Java, MATLAB, and Python libraries; we plan to add some MATLAB/mex interfaces to the C library in the not too distant future. Niftilib has been developed by members of the NIFTI DFWG and volunteers in the neuroimaging community and serves as a reference implementation of the nifti-1 file format. In addition to being a reference implementation, we hope it is also a useful i/o library. Niftilib code is released into the public domain, developers are encouraged to incorporate niftilib code into their applications, and, to contribute changes and enhancements to niftilib. Please contact us if you would like to contribute additonal functionality to the i/o library.

Proper citation: Niftilib (RRID:SCR_003355) Copy   

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http://www.etsu.edu/com/pharmacology/default.aspx

Faculty members of our Department are actively engaged in delivering outstanding teaching to undergraduate students, graduate students, medical students, and residents. Our Doctor of Philosophy (graduate) students matriculate to Pharmacology through the Biomedical Sciences Graduate Program at the Quillen College of Medicine. Students pursuing Master of Science and Doctor of Philosophy degrees may pursue a focus in Toxicology. Our faculty members are trained in several medical disciplines and our research applies methodological approaches that span molecular biology, cellular biology, systems biology, and human biology and pathology. Through research, our department strives to understand human disease pathology and use this understanding to develop new therapeutic entities (e.g. drugs) for the treatment of major human diseases. The primary foci of department research efforts are cardiovascular and neuropsychiatric diseases, although other areas of interest and activity exist. Our laboratories are funded by the National Institutes of Health, American Heart Association, the American Foundation for Suicide Prevention and a variety of other agencies and sources.

Proper citation: East Tennessee State University, Department of Pharmacology (RRID:SCR_003350) Copy   

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

An ontology that describes the LIPIDMAPS nomenclature classification explicitly using description logics (OWL-DL). Lipid classes are organized hierarchically with the super-classes restricted by generic necessary conditions. More specific necessary conditions are used to define membership requirements for sub classes of lipid according to appropriate functional groups. Lipid research is increasingly integrated within systems level biology such as lipidomics where lipid classification is required before appropriate annotation of chemical functions can be applied.

Proper citation: Lipid Ontology (RRID:SCR_003349) Copy   

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http://unp.aas.duke.edu//

The major and minor in Neuroscience at Duke University was approved by the Arts and Sciences Council of Trinity College of Arts and Sciences on April 9, 2009. The program offers three academic plans: Bachelor of Science (B.S.) degree in Neuroscience, Bachelor of Arts (A.B.) degree in Neuroscience, and a Minor in Neuroscience. Although Neuroscience is a new major/minor, there is a rich and long-standing tradition of excellence in undergraduate neuroscience research and education at Duke. Groups of faculty in the Department of Psychology and Neuroscience and the Department of Biology, as well as the Department of Neurobiology in the Duke University School of Medicine, have been especially engaged in teaching neuroscience in undergraduate classes and hosting independent study projects in their research laboratories. Building upon this broad foundation, the new Undergraduate Studies in Neuroscience program is a truly interdisciplinary experience reflecting the diverse sources of knowledge that advance our understanding of the brain sciences. Undergraduate Studies in Neuroscience is a unique collaboration among many Departments and Schools , with administrative support provided by Trinity College of Arts and Sciences and the Duke Institute for Brain Sciences.

Proper citation: Duke University Trinity College of Arts and Sciences Undergraduate Neuroscience (RRID:SCR_003345) Copy   

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http://www.xpmutations.org

Interactive repository of mutations and other allelic variations of the genes involved in the DNA repair disorders, Xeroderma Pigmentosum (XP), Cockayne Syndrome (CS), Trichothiodystrophy (TTD), and other UV-sensitivity disorders. Any omitted data or new data may be submitted by using the on-line data submission form. There is a message board system to support discussions amongst those interested in XP and DNA Repair. RESOURCES * Educational module of the molecular biology of Nucleotide Excision Repair * Introduction to the DNA Repair disorders (XP, CS, TTD, UVs) * Background on each of the XP genes * A searchable database of mutations and sequence variations for the XP genes * Contact point for the submission of new mutation data * Discussion Forums and a Guest Book * Web Links to Additional Resources

Proper citation: Allelic Variations of The XP Genes (RRID:SCR_003376) Copy   

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https://www.fishersci.com/us/en/brands/IAOCLVV5/fisher-bioreagents.html

An Antibody supplier

Proper citation: Fisher BioReagents (RRID:SCR_003374) Copy   

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

An ontology designed to represent the variety of methods used to make qualitative and quantitative clinical and phenotype measurements both in the clinic and with model organisms.

Proper citation: Measurement Method Ontology (RRID:SCR_003373) Copy   

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http://ctrad-csi.nus.edu.sg/gbsa/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on October 28,2025. Software for analyzing whole-genome bisulfite sequencing data.

Proper citation: GBSA (RRID:SCR_003413) Copy   

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http://sig.biostr.washington.edu/projects/fm/

A domain ontology that represents a coherent body of explicit declarative knowledge about human anatomy. It is concerned with the representation of classes or types and relationships necessary for the symbolic representation of the phenotypic structure of the human body in a form that is understandable to humans and is also navigable, parseable and interpretable by machine-based systems. Its ontological framework can be applied and extended to all other species. The description of how the OWL version was generated is in Pushing the Envelope: Challenges in a Frame-Based Representation of Human Anatomy by N. F. Noy, J. L. Mejino, C. Rosse, M. A. Musen: http://bmir.stanford.edu/publications/view.php/pushing_the_envelope_challenges_in_a_frame_based_representation_of_human_anatomy The Foundational Model of Anatomy ontology has four interrelated components: # Anatomy taxonomy (At), # Anatomical Structural Abstraction (ASA), # Anatomical Transformation Abstraction (ATA), # Metaknowledge (Mk), The ontology contains approximately 75,000 classes and over 120,000 terms; over 2.1 million relationship instances from over 168 relationship types link the FMA's classes into a coherent symbolic model.

Proper citation: FMA (RRID:SCR_003379) Copy   

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