Tools   Select Another Resource Report Type

http://cnas.ucr.edu/guppy/

A project that observes the processes of adaptive evolution in nature, and tests evolutionary hypotheses, by studying populations of guppies on the Caribbean island of Trinidad. Darwin thought that evolution by natural selection occurred very slowly, over hundreds if not thousands of years. Evolutionary biologists now know that evolutionary changes in species can happen very quickly, over a relatively few generations. The National Science Foundation (NSF), through its Integrative Biological Research (FIBR) program, is funding a 5-year study by 13 biologists from colleges, universities, and research institutions throughout the United States and Canada, to study the relationship of adaptive evolution and environmental circumstances. The Trinidadian guppy (Poecilia reticulata) is an excellent species for these purposes because: * It matures rapidly (one generation = 3-4 months) * It inhabits different ecological environments that can be easily manipulated On Trinidad, guppies live in streams, or portions of streams, that can differ in the species of predators that the guppies have to contend with. Some streams are high-predation environments, others low-predation. Different predation environments are often right next to one another, separated by a waterfall (which neither guppies nor predators can cross). Guppies from high-predation environments experience much higher mortality rates than do guppies in low-predation environments. High mortality is associated with the following characteristics, all of which have a genetic basis: * Earlier maturity * Greater investment of resources in reproduction * More and smaller offspring. We have found that mortality rates can be manipulated by: * Transplanting guppies from high-predation localities into sites from which they and their predators had previously been excluded by natural waterfalls, thus lowering mortality rates; * Introducing predators into low-predation sites, thus increasing mortality rates. Such experiments have shown that species evolve as predicted by theory. We have also found that evolution by natural selection can be remarkably fast, on the order of four to seven orders of magnitude faster than had been inferred from the fossil record.

Proper citation: Guppy Project (RRID:SCR_006255) Copy   

•   Source: SciCrunch Registry
  

http://www.plantontology.org

Ontology and database that links plant anatomy, morphology and growth and development to plant genomics data.Plant Ontology Consortium develops, curates and shares controlled vocabularies (ontologies) that describe plant structures and growth and developmental stages, providing semantic framework for meaningful cross species queries across databases. PO is under active development to expand to encompass terms and annotations from all plants.

Proper citation: Plant Ontology (RRID:SCR_006494) Copy   

•   Source: SciCrunch Registry
  

http://www.rcsb.org/#Category-welcome

Collection of structural data of biological macromolecules. Database of information about 3D structures of large biological molecules, including proteins and nucleic acids. Users can perform queries on data and analyze and visualize results.

Proper citation: Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) (RRID:SCR_012820) Copy   

•   Source: SciCrunch Registry
  

http://grassius.org/

A public resource composed of a collection of databases, computational and experimental resources that relate to the control of gene expression in the grasses, and their relationship with agronomic traits. As knowledge on the interactions of transcription factors (TFs) and cis-regulatory elements in the promoters of the genes that they regulate continues to accumulate, the information is acquired by GRASSIUS, either through contributions by the community, or by literature analysis. The overarching objective of GRASSIUS is to provide a one-stop resource that will facilitate research and communication within the plant community with regards to genome-wide regulation of gene expression processes.

Proper citation: GRASSIUS (RRID:SCR_012999) Copy   

•   Source: SciCrunch Registry
  

https://www.planetmicrobe.org/

Web based platform that enables data discovery from curated historical and on going oceanographic sequencing efforts. Enables discovery and integration of oceanographic ‘omics, environmental and physiochemical data layers. Used to centralize and standardize contextual data associated with major marine 'omic datasets. Used for marine microbiology to discover and analyze interconnected 'omics and environmental data.

Proper citation: Planet Microbe (RRID:SCR_024478) Copy   

•   Source: SciCrunch Registry
  

https://github.com/katiasmirn/PERFect#perfect-permutation-filtering-package-in-r

Software R package as filtering test for microbiome data. Permutation filtering approach to address two unsolved problems in microbiome data processing: (i) define and quantify loss due to filtering by implementing thresholds and (ii) introduce and evaluate a permutation test for filtering loss to provide a measure of excessive filtering.

Proper citation: PERFect (RRID:SCR_024682) Copy   

•   Source: SciCrunch Registry
  

http://www.agic.umn.edu/

Portal as data resource, map repository, and GIS service provider for federally-funded scientists conducting research in the Arctic and Antarctic. The PGC holds an extensive collection of satellite imagery and aerial photography at varying resolutions.

Proper citation: Polar Geospatial Center (RRID:SCR_000402) Copy   

•   Source: SciCrunch Registry
  

https://plantcyc.org/content/plantcyc-15.2.0

Multi species reference database. Comprehensive plant biochemical pathway database, containing curated information from literature and computational analyses about genes, enzymes, compounds, reactions, and pathways involved in primary and secondary metabolism.

Proper citation: PlantCyc (RRID:SCR_002110) Copy   

•   Source: SciCrunch Registry
  

http://icatb.sourceforge.net/fusion/fusion_startup.php

A MATLAB toolbox which implements the joint Independent Component Analysis (ICA), parallel ICA and CCA with joint ICA methods. It is used to to extract the shared information across modalities like fMRI, EEG, sMRI and SNP data. * Environment: Win32 (MS Windows), Gnome, KDE * Operating System: MacOS, Windows, Linux * Programming Language: MATLAB * Supported Data Format: ANALYZE, NIfTI-1

Proper citation: Fusion ICA Toolbox (RRID:SCR_003494) Copy   

•   Source: SciCrunch Registry
  

http://ccr.coriell.org/Sections/Collections/IPBIR/?SsId=18

The purpose of the IPBIR - Integrated Primate Biomaterials and Information Resource is to assemble, characterize, and distribute high-quality DNA samples of known provenance with accompanying demographic, geographic, and behavioral information in order to stimulate and facilitate research in primate genetic diversity and evolution, comparative genomics, and population genetics. Further research in these areas will advance our understanding of human origins, the biological basis of cognitive processes, evolutionary history and relationships, and social structure, and will provide critical scientific information needed to facilitate conservation of biological diversity. The derived DNA will be openly available to the broad scientific community who agree to restrict use to non-commercial purposes. DNA and cell culture samples are distributed only to qualified professional persons who are associated with recognized research, medical, or educational organizations engaged in research.

Proper citation: IPBIR - Integrated Primate Biomaterials and Information Resource (RRID:SCR_004614) Copy   

•   Source: SciCrunch Registry
  

http://datalad.org/

Project to adapt model of open source software distributions to address technical limitations of data sharing and develop all components of data distribution. Builds on top of git-annex and extends it with intuitive command line interface. Enables users to operate on data using familiar concepts, such as files and directories, while transparently managing data access and authorization with underlying hosting providers. Can create DataLad datasets using any data files published on the web.

Proper citation: DataLad (RRID:SCR_003931) Copy   

•   Source: SciCrunch Registry
  

http://www.delsaglobal.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on July 1, 2022. Organization whose mission is to build and promote a sustainable ecosystem of professional societies, funding agencies, foundations, companies, and citizens together with life science researchers and innovators in computing, infrastructure and analysis with the expressed goal of translating new discoveries into tools, resources and products.

Proper citation: DELSA (RRID:SCR_006231) Copy   

•   Source: SciCrunch Registry
  

https://www.msu.edu/~brains/brains/human/index.html

A labeled three-dimensional atlas of the human brain created from MRI images. In conjunction are presented anatomically labeled stained sections that correspond to the three-dimensional MRI images. The stained sections are from a different brain than the one which was scanned for the MRI images. Also available the major anatomical features of the human hypothalamus, axial sections stained for cell bodies or for nerve fibers, at six rostro-caudal levels of the human brain stem; images and Quicktime movies. The MRI subject was a 22-year-old adult male. Differing techniques used to study the anatomy of the human brain all have their advantages and disadvantages. Magnetic resonance imaging (MRI) allows for the three-dimensional viewing of the brain and structures, precise spatial relationships and some differentiation between types of tissue, however, the image resolution is somewhat limited. Stained sections, on the other hand, offer excellent resolution and the ability to see individual nuclei (cell stain) or fiber tracts (myelin stain), however, there are often spatial distortions inherent in the staining process. The nomenclature used is from Paxinos G, and Watson C. 1998. The Rat Brain in Stereotaxic Coordinates, 4th ed. Academic Press. San Diego, CA. 256 pp

Proper citation: Human Brain Atlas (RRID:SCR_006131) 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
  

http://www.autopack.org/

A specialized version of autoPack designed to pack biological components together. The current version is optimized to pack molecules into cells with biologically relevant interactions to populate massive cell models with atomic or near-atomic details. Components of the algorithm pack transmembrane proteins and lipids into bilayers, globular molecules into compartments defined by the bilayers (or as exteriors), and fibrous components like microtubules, actin, and DNA.

Proper citation: Cellpack (RRID:SCR_006831) Copy   

•   Source: SciCrunch Registry
  

https://github.com/Benson-Genomics-Lab/TRF

Software tool to locate and display tandem repeats in DNA sequences. Program to analyze DNA sequences.

Proper citation: Tandem Repeats Finder (RRID:SCR_022193) Copy   

•   Source: SciCrunch Registry
  

https://github.com/marbl/MashMap

Software tool as fast approximate aligner for long DNA sequences. Used for computing local alignment boundaries between long DNA sequences.

Proper citation: MashMap (RRID:SCR_022194) 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.geosamples.org/

Sample Catalog and Registry for the International Geo Sample Number. SESAR catalogs and preserves sample metadata profiles, and provides access to the sample catalog via the Global Sample Search.

Proper citation: System for Earth Sample Registration (RRID:SCR_002222) Copy   

•   Source: SciCrunch Registry
  

http://nsidc.org/agdc/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 2, 2025. Archives and distributes Antarctic glaciological and cryospheric system data collected by the U.S. Antarctic Program. The Data Catalog contains data sets collected by individual investigators and products assembled from many different PI data sets, published literature, and other sources. The catalog provides useful compilations of important geophysical parameters, such as accumulation rate or ice velocity. The NSF OPP Guidelines and Award Conditions for Scientific Data state that PIs should submit data collected as a result of their OPP grant to a designated data center as soon as possible, but no later than two years after the data are collected.

Proper citation: Antarctic Glaciological Data Center (RRID:SCR_002219) Copy   

•   Source: SciCrunch Registry