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https://github.com/wyp1125/MCScanx

Software toolkit for detection and evolutionary analysis of gene synteny and collinearity.

Proper citation: MCScanX (RRID:SCR_022067) Copy   

•   Source: SciCrunch Registry

http://www.biomart.org

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 4,2023.Platform provides free software and data services to international scientific community in order to foster scientific collaboration and facilitate scientific discovery process. Project adheres to open source philosophy that promotes collaboration and code reuse.

Proper citation: BioMart Project (RRID:SCR_002987) Copy   

•   Source: SciCrunch Registry

https://www.proteinspire.org/MOPED/

An expanding multi-omics resource that enables rapid browsing of gene and protein expression information from publicly available studies on humans and model organisms. MOPED also serves the greater research community by enabling users to visualize their own expression data, compare it with existing studies, and share it with others via private accounts. MOPED uniquely provides gene and protein level expression data, meta-analysis capabilities and quantitative data from standardized analysis utilizing SPIRE (Systematic Protein Investigative Research Environment). Data can be queried for specific genes and proteins; browsed based on organism, tissue, localization and condition; and sorted by false discovery rate and expression. MOPED links to various gene, protein, and pathway databases, including GeneCards, Entrez, UniProt, KEGG and Reactome. The current version of MOPED (MOPED 2.5) The current version of MOPED (MOPED 2.5, 2014) contains approximately 5 million total records including ~260 experiments and ~390 conditions.

Proper citation: MOPED - Model Organism Protein Expression Database (RRID:SCR_006065) Copy   

•   Source: SciCrunch Registry

http://scholarometer.indiana.edu/

Scholarometer (beta) is a social tool to facilitate citation analysis and help evaluate the impact of an author''s publications. It is a social (crowdsourcing) application that leverages the wisdom of the crowds. Scholarometer makes visualization of author and discipline networks available on the web site. It requires users to tag their queries with one or more discipline names, choosing from predefined ISI subject categories or arbitrary tags. This generates annotations that go into a database, which collects statistics about the various disciplines, such as average number of citations per paper, average number of papers per authors, etc. This data is publicly available. Scholarometer users can save the finding into formats appropriate for local reference management software (e.g., EndNote), or for social publication sharing systems (e.g., BibSonomy). Currently, our system supports the following export formats: BibTex (BIB), RefMan (RIS), EndNote (ENW), comma-separated values (CSV), tab-separated values (XLS), and BibJSON. Export data is dynamically generated in response to any filter, merge or delete actions performed by the user. Since Scholarometer is a browser extension that provides a smart interface for Google Scholar, it does not have the limitations of server based citation analysis tools that sit between the user and Google Scholar. At the same time Scholarometer is not an application, such as Publish or Perish, and therefore it is platform independent and runs on every system that supports the Firefox or the Chrome browser. Still, Scholarometer uses Google Scholar, which provides the most comprehensive source of citation data across the sciences and social sciences. Scholarometer provides a RESTful web API so that other developers can make use of our crowdsourced data. Select the method on the left panel to see corresponding documentation. The extension/add-on code is available in the Mozilla Firefox Add-ons and Google Chrome Extensions repositories. Additional server-side code is not available at this time.

Proper citation: Scholarometer (RRID:SCR_004279) Copy   

•   Source: SciCrunch Registry

http://www.antweb.org/

Database of images, specimen records, and natural history information on ants including Search Tools, Regional Lists, In Depth Information, Ant Image Comparison Tool, PDF Field Guides, Maps on AntWeb and Google Earth, and Ant Genera of the World Slideshow. It is community driven and open to contribution from anyone with specimen records, natural history comments, or images. As of February of 2013, AntWeb has 97,814 ant images, of 23,272 specimens representing over 10,549 species. AntWeb provides tools for submitting images, specimen records, annotating species pages, and managing regional species lists. AntWeb contains information on the ant faunas of several areas in the Nearctic and Malagasy biogeographic regions, and global coverage of all ant genera. AntWeb provides tools for exploring the diversity and identification of ants (Hymenoptera: Formicidae). These tools have been developed to encourage the study of ants, to facilitate the use of ants in inventory and monitoring programs, and to provide ant taxonomists with access to images of type specimens. AntWeb illustrates the diversity of ants by providing information and high quality color images of many of the approximately 10,000 known species of ants. AntWeb currently focuses on the species of the Nearctic and Malagasy biogeographic regions, and the ant genera of the world. Over time, the site will grow to describe every species of ant known.

Proper citation: AntWeb (RRID:SCR_004851) Copy   

•   Source: SciCrunch Registry

https://www.dataone.org/

A distributed framework and cyberinfrastructure for open, persistent, and secure access to Earth observational data. It ensures the preservation, access, use and reuse of multi-scale, multi-discipline, and multi-national science data via three primary cyberinfrastucture elements and a broad education and outreach program. The DataONE Investigator Toolkit is a collection of software tools for finding, using, and contributing data in DataONE. DataONE currently hosts three Coordinating Nodes that provide network-wide services to enhance interoperability of the Member Nodes and support indexing and replication services. Coordinating Nodes provide a replicated catalog of Member Node holdings and make it easy for scientists to discover data wherever they reside, also enabling data repositories to make their data and services more broadly available to the international community. DataONE Coordinating Nodes are located at the University of New Mexico, the University of California Santa Barbara and at the University of Tennessee (in collaboration with Oak Ridge National Laboratory). DataONE comprises a distributed network of data centers, science networks or organizations. These organizations can expose their data within the DataONE network through the implementation of the DataONE Member Node service interface. In addition to scientific data, Member Nodes can provide computing resources, or services such as data replication, to the DataONE community.

Proper citation: DataONE (RRID:SCR_003999) Copy   

•   Source: SciCrunch Registry

http://crcns.org

Website for brain experimental data and other resources such as stimuli and analysis tools. Provides marketplace and discussion forum for sharing tools and data in neuroscience. Data repository and collaborative tool that supports integration of theoretical and experimental neuroscience through collaborative research projects. CRCNS offers funding for new class of proposals focused on data sharing and other resources.

Proper citation: CRCNS (RRID:SCR_005608) Copy   

•   Source: SciCrunch Registry

https://obofoundry.org/ontology/cl.html

Ontology designed as a structured controlled vocabulary for cell types. It was constructed for use by the model organism and other bioinformatics databases. It includes cell types from prokaryotes, mammals, and fungi. The ontology is available in the formats adopted by the Open Biological Ontologies umbrella and is designed to be used in the context of model organism genome and other biological databases.

Proper citation: Cell Type Ontology (RRID:SCR_004251) Copy   

•   Source: SciCrunch Registry

http://www.clairlib.org

A suite of open-source Perl modules intended to simplify a number of generic tasks in natural language processing (NLP), information retrieval (IR), and network analysis (NA). Its architecture also allows for external software to be plugged in with very little effort. The latest version of clairlib is 1.06 which was released on March 2009 and includes about 130 modules implementing a wide range of functionalities. Clairlib is distributed in two forms: * Clairlib-core, which has essential functionality and minimal dependence on external software, and * Clairlib-ext, which has extended functionality that may be of interest to a smaller audience. Much can be done using Clairlib on its own. Some of the things that Clairlib can do are: Tokenization, Summarization, Document Clustering, Document Indexing, Web Graph Analysis, Network Generation, Power Law Distribution Analysis, Network Analysis, RandomWalks on Graphs, Tf-IDF, Perceptron Learning and Classification, and Phrase Based Retrieval and Fuzzy OR Queries.

Proper citation: Clair library (RRID:SCR_007019) Copy   

•   Source: SciCrunch Registry

http://pilgrm.princeton.edu

PILGRM (the platform for interactive learning by genomics results mining) puts advanced supervised analysis techniques applied to enormous gene expression compendia into the hands of bench biologists. This flexible system empowers its users to answer diverse biological questions that are often outside of the scope of common databases in a data-driven manner. This capability allows domain experts to quickly and easily generate hypotheses about biological processes, tissues or diseases of interest. Specifically PILGRM helps biologists generate these hypotheses by analyzing the expression levels of known relevant genes in large compendia of microarray data. PILGRM is for the biologist with a set of proteins relevant to a disease, biological function or tissue of interest who wants to find additional players in that process. It uses a data driven method that provides added value for literature search results by mining compendia of publicly available gene expression datasets using lists of relevant and irrelevant genes (standards). PILGRM produces publication quality PDFs usable as supplementary material to describe the computational approach, standards and datasets. Each PILGRM analysis starts with an important biological question (e.g. What genes are relevant for breast cancer but not mammary tissue in general?). For PILGRM to discover relevant genes, it needs examples of both genes that you would (positive) and would not (negative) find interesting. Lists of these genes are what we call standards and in PILGRM you can build your own standards or you can use standards from common sources that we pre-load for your convenience. PILGRM lets you build your own literature-documented standards so that processes, disease, and tissues that are not well covered in databases of tissue expression, disease, or function can still be used for an analysis.

Proper citation: PILGRM (RRID:SCR_004749) Copy   

•   Source: SciCrunch Registry

http://nbc.ece.drexel.edu/

Webserver for taxonomic classification of metagenomic reads.

Proper citation: NBC (RRID:SCR_004772) Copy   

•   Source: SciCrunch Registry

http://bioinformatics.clemson.edu/G-SESAME/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 2,2025. G-SESAME contains a set of tools. They include: tools for measuring the semantic similarity of GO terms; tools for measuring the functional similarity of genes; and tools for clustering genes based on their GO term annotation information. Platform: Online tool, THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: G-SESAME - Gene Semantic Similarity Analysis and Measurement Tools (RRID:SCR_005816) Copy   

•   Source: SciCrunch Registry

http://code.google.com/p/lapdftext/

Software that facilitates accurate extraction of text from PDF files of research articles for use in text mining applications. It is intended for both scientists and natural language processing (NLP) engineers interested in getting access to text within specific sections of research articles. The system extracts text blocks from PDF-formatted full-text research articles and classifies them into logical units based on rules that characterize specific sections. The LA-PDFText system focuses only on the textual content of the research articles. The current version of LA-PDFText is a baseline system that extracts text using a three-stage process: * identification of blocks of contiguous text * classification of these blocks into rhetorical categories * extraction of the text from blocks grouped section-wise.

Proper citation: lapdftext (RRID:SCR_006167) Copy   

•   Source: SciCrunch Registry

http://erin.sfn.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 23,2022. A database that lists, reviews, and rates resources for teaching neuroscience at the graduate and undergraduate level.

Proper citation: Educational Resources in Neuroscience (RRID:SCR_000169) Copy   

•   Source: SciCrunch Registry

http://www.pymvpa.org

A Python package intended to ease statistical learning analyses of large datasets. It offers an extensible framework with a high-level interface to a broad range of algorithms for classification, regression, feature selection, data import and export. While it is not limited to the neuroimaging domain, it is eminently suited for such datasets. PyMVPA is truly free software (in every respect) and additionally requires nothing but free-software to run. Decoding patterns of neural activity onto cognitive states is one of the central goals of functional brain imaging. Standard univariate fMRI analysis methods, which correlate cognitive and perceptual function with the blood oxygenation-level dependent (BOLD) signal, have proven successful in identifying anatomical regions based on signal increases during cognitive and perceptual tasks. Recently, researchers have begun to explore new multivariate techniques that have proven to be more flexible, more reliable, and more sensitive than standard univariate analysis. Drawing on the field of statistical learning theory, these new classifier-based analysis techniques possess explanatory power that could provide new insights into the functional properties of the brain. However, unlike the wealth of software packages for univariate analyses, there are few packages that facilitate multivariate pattern classification analyses of fMRI data. This Python-based, cross-platform, open-source software toolbox software toolbox for the application of classifier-based analysis techniques to fMRI datasets makes use of Python's ability to access libraries written in a large variety of programming languages and computing environments to interface with the wealth of existing machine learning packages.

Proper citation: PyMVPA (RRID:SCR_006099) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/gimme/

Software Matlab toolbox for directed functional connectivity analysis of fMRI BOLD signal from predefined regions of interest. It recovers true structure of connections and estimates weights attributed to each connection. Obtains patterns at group and individual levels.

Proper citation: GIMME (RRID:SCR_014115) Copy   

•   Source: SciCrunch Registry

http://www.scite.ai

Web tool to classify citation statements from scientific articles using deep learning. Used for discovering and evaluating scientific articles via Smart Citations.

Proper citation: scite (RRID:SCR_018568) Copy   

•   Source: SciCrunch Registry

https://github.com/denisecailab/minian

Software miniscope analysis pipeline that requires low memory and computational demand so it can be run without specialized hardware. Offers interactive visualization that allows users to see how parameters in each step of pipeline affect output.

Proper citation: Minian (RRID:SCR_022601) Copy   

•   Source: SciCrunch Registry

https://github.com/Cai-Lab-at-University-of-Michigan/nTracer

Software tool as plug-in for ImageJ software. Used for tracing microscopic images.

Proper citation: nTracer (RRID:SCR_023032) Copy   

•   Source: SciCrunch Registry

https://github.com/compbiolabucf/APA-Scan

Software Python tool for detection and visualization of annotated and potential alternative polyadenylation events in downstream 3'-UTR of gene among two different biological conditions. Used for detection and visualization of 3'-UTR alternative polyadenylation with RNA-seq and 3'-end-seq data.

Proper citation: APA-Scan (RRID:SCR_022974) Copy   

•   Source: SciCrunch Registry