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http://locustdb.genomics.org.cn/

The migratory locust (Locusta migratoria) is an orthopteran pest and a representative member of hemimetabolous insects. Its transcriptomic data provide invaluable information for molecular entomology study of the insect and pave a way for comparative studies of other medically, agronomically, and ecologically relevant insects. This first transcriptomic database of the locust (LocustDB) has been developed, building necessary infrastructures to integrate, organize, and retrieve data that are either currently available or to be acquired in the future. It currently hosts 45,474 high quality EST sequences from the locust, which were assembled into 12,161 unigenes. This database contains original sequence data, including homologous/orthologous sequences, functional annotations, pathway analysis, and codon usage, based on conserved orthologous groups (COG), gene ontology (GO), protein domain (InterPro), and functional pathways (KEGG). It also provides information from comparative analysis based on data from the migratory locust and five other invertebrate species, such as the silkworm, the honeybee, the fruitfly, the mosquito and the nematode. LocustDB also provides information from comparative analysis based on data from the migratory locust and five other invertebrate species, such as the silkworm, the honeybee, the fruitfly, the mosquito and the nematode. It starts with the first transcriptome information for an orthopteran and hemimetabolous insect and will be extended to provide a framework for incorporation of in-coming genomic data of relevant insect groups and a workbench for cross-species comparative studies.

Proper citation: Migratory Locust EST Database (RRID:SCR_008201) Copy   

•   Source: SciCrunch Registry

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   

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http://www.schematikon.org/Nh3D.html

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 17, 2013. It is freely available as a reference dataset for the statistical analysis of sequence and structure features of proteins in the PDB. It is a dataset of structurally dissimilar proteins. This dataset has been compiled by selecting well resolved representatives from the Topology level of the CATH database which hierarchically classifies all protein structures. These have been been pruned to remove: i) domains that may contain homologous elements (by pairwise sequence comparison and structural superposition of aligned residues) ii) internal duplications (by repeat detection) iii) regions with high B-Factor The statistical analysis of protein structures requires datasets in which structural features can be considered independently distributed, i.e. not related through common ancestry, and that fulfill minimal requirements regarding the experimental quality of the structures it contains. However, non-redundant datasets based on sequence similarity invariably contain distantly related homologues. Here a reference dataset of non-homologous protein domains is provided, assuming that structural dissimilarity at the topology level is incompatible with recognizable common ancestry. It contains the best refined representatives of each Topology level, validates structural dissimilarity and removes internally duplicated fragments. The compilation of Nh3D is fully scripted. The current Nh3D list contains 570 domains with a total of 90780 residues. It covers more than 70% of folds at the Topology level of the CATH database and represents more than 90% of the structures in the PDB that have been classified by CATH. Even though all protein pairs are structurally dissimilar, some pairwise sequence identities after global alignment are greater than 30%. Nh3D is freely available as a reference dataset for the statistical analysis of sequence and structure features of proteins in the PDB.

Proper citation: Nh3D: A Reference Dataset of Structures of Non-homologous Proteins (RRID:SCR_008212) Copy   

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http://www.cs.tau.ac.il/~spike/

Database of curated human signaling pathways with an associated interactive software tool for analysis and dynamic visualization of pathways. Individual pathway maps can be viewed and downloaded; the entire database may be browsed, or launched via a map viewer tool that allows dynamic visualization of the database and save networks in XGMML format that can be viewed in all generic XGMML viewers. Map Topics * Cell cycle progress and check points * DNA damage response * Programmed cell death related processes * Stress-activated transcription factors * Mitogen-activated protein kinase pathways * Immune response signaling * HEarSpike: hearing related pathways

Proper citation: SPIKE (RRID:SCR_010466) Copy   

•   Source: SciCrunch Registry

http://www.rnasoft.ca/sstrand

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on July 31,2025. A scientific community-crowdsourced database containing the RNA secondary structures of known types and organisms. It is meant to provide a simple and powerful way to analyze, search and update a shared repository of information.

Proper citation: RNA STRAND-The RNA secondary STRucture and statistical ANalysis Database (RRID:SCR_000086) Copy   

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http://neuralensemble.org/sumatra/

A software tool for managing and tracking projects based on numerical simulation or analysis to support reproducible research. It can be thought of as an automated electronic lab notebook for simulation/analysis projects. Sumatra consists of: a command-line interface, smt, for launching simulations/analyses with automatic recording of information about the context, annotating these records, linking to data files, etc.; a web interface with a built-in web-server, smtweb, for browsing and annotating simulation/analysis results; a LaTeX package and Sphinx extension for including Sumatra-tracked figures and links to provenance information in papers and other documents; and a Python API, on which smt and smtweb are based, that can be used in personalized scripts in place of using smt.

Proper citation: Sumatra (RRID:SCR_001381) Copy   

•   Source: SciCrunch Registry

http://dirt.projectbamboo.org/

Registry of digital research tools for scholarly use that makes it easy for digital humanists and others conducting digital research to find and compare resources ranging from content management systems to music OCR, statistical analysis packages to mindmapping software., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Bamboo DiRT (RRID:SCR_002556) Copy   

•   Source: SciCrunch Registry

http://viperdb.scripps.edu/

Database for icosahedral virus capsid structures. The emphasis of the resource is on providing data from structural and computational analyses on these systems, as well as high quality renderings for visual exploration. In addition, all virus capsids are placed in a single icosahedral orientation convention, facilitating comparison between different structures. The web site includes powerful search utilities , links to other relevant databases, background information on virus capsid structure, and useful database interface tools. It is an information source for the analysis of high resolution virus structures. VIPERdb is a one-stop site dedicated to helping users around the world examine the many icosahedral virus structures contained within the Protein Data Bank (PDB) by providing them with an easy to use database containing current data and a variety of analytical tools. Sponsors: VIPERdb is funded by the NIH., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: VIPERdb (RRID:SCR_002853) Copy   

•   Source: SciCrunch Registry

https://biocodecommons.org/

Platform provides resources for genomic observations from collection to analysis and publication. Works with standards community to ensure clear vocabularies and useful ontologies for biological resources and related assets. Biocode Commons is also collaborating on development of Biological Collections Ontology, working to better integrate ontologies, vocabularies, and relevant standards that are related to BCO.

Proper citation: Biocode Commons (RRID:SCR_024553) Copy   

•   Source: SciCrunch Registry

http://rfmri.org/DPARSF

A MATLAB toolbox forpipeline data analysis of resting-state fMRI that is based on Statistical Parametric Mapping (SPM) and a plug-in software within DPABI. After the user arranges the Digital Imaging and Communications in Medicine (DICOM) files and click a few buttons to set parameters, DPARSF will then give all the preprocessed (slice timing, realign, normalize, smooth) data and results for functional connectivity, regional homogeneity, amplitude of low-frequency fluctuation (ALFF), fractional ALFF, degree centrality, voxel-mirrored homotopic connectivity (VMHC) results. DPARSF can also create a report for excluding subjects with excessive head motion and generate a set of pictures for easily checking the effect of normalization. In addition, users can also use DPARSF to extract time courses from regions of interest. DPARSF basic edition is very easy to use while DPARSF advanced edition (alias: DPARSFA) is much more flexible and powerful. DPARSFA can parallel the computation for each subject, and can be used to reorient images interactively or define regions of interest interactively. Users can skip or combine the processing steps in DPARSF advanced edition freely.

Proper citation: DPARSF (RRID:SCR_002372) Copy   

•   Source: SciCrunch Registry

https://www.upf.edu/web/ntsa/downloads/-/asset_publisher/xvT6E4pczrBw/content/2012-nonrandomness-nonlinear-dependence-and-nonstationarity-of-electroencephalographic-recordings-from-epilepsy-patients

THIS RESOURCE IS NO LONGER IN SERVICE, documented November 23, 2020; EEG data set, source code, and results from 7500 signal pairs from 5 epilepsy patients analyzed in the manuscript, Andrzejak RG, Schindler K, Rummel C. Nonrandomness, nonlinear dependence, and nonstationarity of electroencephalographic recordings from epilepsy patients. Phys. Rev. E, 86, 046206, 2012. All Matlab source codes are included in the file ASR_Sources_2012_10_16.zip. The clinical purpose of these recordings was to delineate the brain areas to be surgically removed in each individual patient in order to achieve seizure control.

Proper citation: Bern-Barcelona EEG database (RRID:SCR_001582) Copy   

•   Source: SciCrunch Registry

http://www.genetrap.org/

Consortium represents all publicly available gene trap cell lines, which are available on non-collaborative basis for nominal handling fees. Researchers can search and browse IGTC database for cell lines of interest using accession numbers or IDs, keywords, sequence data, tissue expression profiles and biological pathways, can find trapped genes of interest on IGTC website, and order cell lines for generation of mutant mice through blastocyst injection. Consortium members include: BayGenomics (USA), Centre for Modelling Human Disease (Toronto, Canada), Embryonic Stem Cell Database (University of Manitoba, Canada), Exchangeable Gene Trap Clones (Kumamoto University, Japan), German Gene Trap Consortium provider (Germany), Sanger Institute Gene Trap Resource (Cambridge, UK), Soriano Lab Gene Trap Resource (Mount Sinai School of Medicine, New York, USA), Texas Institute for Genomic Medicine - TIGM (USA), TIGEM-IRBM Gene Trap (Naples, Italy).

Proper citation: International Gene Trap Consortium (RRID:SCR_002305) 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://gephi.org/

Open-source software for network visualization and analysis helping data analysts to intuitively reveal patterns and trends, highlight outliers and tells stories with their data. It uses a 3D render engine to display large graphs in real-time and to speed up the exploration. Gephi combines built-in functionalities and flexible architecture to: explore, analyze, spatialize, filter, cluterize, manipulate and export all types of networks. Gephi runs on Windows, Linux and Mac OS X. Gephi is based on a visualize-and-manipulate paradigm which allow any user to discover networks and data properties. Moreover, it is designed to follow the chain of a case study, from data file to nice printable maps. It is open-source and free (GNU General Public License). Applications: * Exploratory Data Analysis: intuition-oriented analysis by networks manipulations in real time. * Link Analysis: revealing the underlying structures of associations between objects, in particular in scale-free networks. * Social Network Analysis: easy creation of social data connectors to map community organizations and small-world networks. * Biological Network analysis: representing patterns of biological data. * Poster creation: scientific work promotion with hi-quality printable maps. Gephi 0.7 architecture is modular and therefore allows developers to add and extend functionalities with ease. New features like Metrics, Layout, Filters, Data sources and more can be easily packaged in plugins and shared. The built-in Plugins Center automatically gets the list of plugins available from the Gephi Plugin portal and takes care of all software updates. Download, comment, and rate plugins provided by community members and third-party companies, or post your own contributions!

Proper citation: Gephi (RRID:SCR_004293) Copy   

•   Source: SciCrunch Registry

http://analysis2.bio-x.cn/myAnalysis.php

A powerful web-based platform for analyses of linkage disequilibrium, haplotype construction, and genetic association at polymorphism loci.

Proper citation: SHEsis: Analysis Tools For Random Samples (RRID:SCR_002958) Copy   

•   Source: SciCrunch Registry

http://www.yeastgenome.org/

A curated database that provides comprehensive integrated biological information for Saccharomyces cerevisiae along with search and analysis tools to explore these data. SGD allows researchers to discover functional relationships between sequence and gene products in fungi and higher organisms. The SGD also maintains the S. cerevisiae Gene Name Registry, a complete list of all gene names used in S. cerevisiae which includes a set of general guidelines to gene naming. Protein Page provides basic protein information calculated from the predicted sequence and contains links to a variety of secondary structure and tertiary structure resources. Yeast Biochemical Pathways allows users to view and search for biochemical reactions and pathways that occur in S. cerevisiae as well as map expression data onto the biochemical pathways. Literature citations are provided where available.

Proper citation: SGD (RRID:SCR_004694) Copy   

•   Source: SciCrunch Registry

http://www.guardian.co.uk/science

Latest science news, comment, analysis and features from guardian.co.uk, the world''s leading liberal voice.

Proper citation: The Guardian: Science (RRID:SCR_005166) Copy   

•   Source: SciCrunch Registry

http://blogs.scientificamerican.com/observations/

From the editors and reporters of Scientific American, this blog delivers commentary, opinion and analysis on the latest developments in science and technology and their influence on society and policy. From reasoned arguments and cultural critiques to personal and skeptical takes on interesting science news, you''ll find a wide range of scientifically relevant insights here.

Proper citation: Scientific American Observations (RRID:SCR_005195) Copy   

•   Source: SciCrunch Registry

http://bioinformatics.biol.rug.nl/standalone/fiva/

Functional Information Viewer and Analyzer (FIVA) aids researchers in the prokaryotic community to quickly identify relevant biological processes following transcriptome analysis. Our software is able to assist in functional profiling of large sets of genes and generates a comprehensive overview of affected biological processes. Currently, seven different modules containing functional information have been implemented: (i) gene regulatory interactions, (ii) cluster of orthologous groups (COG) of proteins, (iii) gene ontologies (GO), (iv) metabolic pathways (v) Swiss Prot keywords, (vi) InterPro domains - and (vii) generic functional categories. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: FIVA - Functional Information Viewer and Analyzer (RRID:SCR_005776) Copy   

•   Source: SciCrunch Registry

http://ftp://ftp.geneontology.org/pub/go/www/GO.tools_by_type.term_enrichment.shtml#gobean

GoBean is a Java application for gene ontology enrichment analysis. It utilizes the NetBeans platform framework. Features * Graphical comparison of multiple enrichment analysis results * Versatile filter facility for focused analysis of enrichment results * Effective exploitation of the graphical/hierarchical structure of GO * Evidence code based association filtering * Supports local data files such as the ontology obo file and gene association files * Supports late enrichment methods and multiple testing corrections * Built-in ID conversion for common species using Ensembl biomart service Platform: Windows compatible, Mac OS X compatible, Linux compatible

Proper citation: GoBean - a Java application for Gene Ontology enrichment analysis (RRID:SCR_005808) Copy   

•   Source: SciCrunch Registry