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https://www.axionbiosystems.com/products/software/neural-module

Neural Module by Axion BioSystems Inc, provides simplified approach to set up, execution and analysis of neural experiments. Enables Maestro Pro and Edge to record and analyze key parameters of neural network function, including activity, synchrony, and network oscillations.

Proper citation: Neural Module (RRID:SCR_019270) Copy   

•   Source: SciCrunch Registry

http://incelldownload.gehealthcare.com/bin/download_data/SoftWoRx/7.0.0/SoftWoRx.htm

Software for acquisition, deconvolution, processing, analysis, and display of DeltaVision images. Used in DeltaVision OMX SR imaging system., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: SoftWoRx software (RRID:SCR_019157) Copy   

•   Source: SciCrunch Registry

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

Software tool as deep collaborative learning with application to study of multimodal brain development. Uses deep network to represent original data and then seeks their correlations, while also linking data representation with phenotypical information.

Proper citation: Deep Collaborative Learning (RRID:SCR_019258) Copy   

•   Source: SciCrunch Registry

https://github.com/constantAmateur/SoupX

Software R package for estimation and removal of cell free mRNA contamination in droplet based single cell RNA-seq data.

Proper citation: SoupX (RRID:SCR_019193) Copy   

•   Source: SciCrunch Registry

https://github.com/datatagsuite

Software suite to enable discoverability of datasets. Enables submission of metadata on datasets to DataMed. Has core set of elements, which are generic and applicable to any type of dataset, and extended set that can accommodate more specialized data types. Platform independent model developed by NIH BD2K bioCADDIE project for DataMed Data Discovery Index prototype being developed. Also available as annotated serialization in schema.org, which in turn is widely used by major search engines like Google, Microsoft, Yahoo and Yandex.

Proper citation: DatA Tag Suite (RRID:SCR_019236) Copy   

•   Source: SciCrunch Registry

http://www.bionet.nsw.gov.au/

Repository for biodiversity data products managed by Department of Planning, Industry and Environment, New South Wales, Australia. It stores species sightings, systematic surveys, threatened biodiversity records and species names.

Proper citation: NSW BioNet (RRID:SCR_019162) Copy   

•   Source: SciCrunch Registry

https://www.phantomhighspeed.com/resourcesandsupport/phantomresources/pccsoftware

Software application for Phantom cameras. Controls every camera function on every Phantom camera model. Fine-tune resolution, frame rate, exposure, memory segmentation, trigger modes and automatic functions prior to recording. PCC also makes it easy to work with synchronized cameras, manage Phantom Cine raw files and convert files to format for final project.Software is compatible with Windows 7 Pro and Windows 8.1 and Windows 10, for both 32 and 64-bit operating systems.

Proper citation: Phantom Camera Control (RRID:SCR_021080) Copy   

•   Source: SciCrunch Registry

https://wiki.med.harvard.edu/SysBio/Megason/GoFigure

GoFigure is a software platform for quantitating complex 4d in vivo microscopy based data in high-throughput at the level of the cell. A prime goal of GoFigure is the automatic segmentation of nuclei and cell membranes and in temporally tracking them across cell migration and division to create cell lineages. GoFigure v2.0 is a major new release of our software package for quantitative analysis of image data. The research focuses on analyzing cells in intact, whole zebrafish embryos using 4d (xyzt) imaging which tends to make automatic segmentation more difficult than with 2d or 2d+time imaging of cells in culture. This resource has developed an automatic segmentation pipeline that includes ICA based channel unmixing, membrane nuclear channel subtraction, Gaussian correlation, shape models, and level set based variational active contours. GoFigure was designed to meet the challenging requirements of in toto imaging. In toto imaging is a technology that we are developing in which we seek to track all the cell movements and divisions that form structures during embryonic development of zebrafish and to quantitate protein expression and localization on top of this digital lineage. For in toto imaging, GoFigure uses zebrafish embryos in which the nuclei and cell membranes have been marked with 2 different color fluorescent proteins to allow cells to be segmented and tracked. A transgenic line in a third color can be used to mark protein expression and localization using a genetic approach that this resource developed called FlipTraps or using traditional transgenic approaches. Embryos are imaged using confocal or 2-photon microscopy to capture high-resolution xyzt image sets used for cell tracking. The GoFigure GUI will provide many tools for visualization and analysis of bioimages. Since fully automatic segmentation of cells is never perfect, GoFigure will provide easy to use tools for semi-automatically and manually adding, deleting, and editing traces in 2d (figures-xy, xz, or yz), 3d (meshes- xyz), 4d (tracks- xyzt) and 4d+cell division (lineages). GoFigure will also provide a number of views into complex image data sets including 3d XYZ and XYT image views, tabular list views of traces, histograms, and scattergrams. Importantly, all these views will be linked together to allow the user to explore their data from multiple angles. Data will be easily sorted and color-coded in many ways to explore correlations in higher dimensional data. The GoFigure architecture is designed to allow additional segmentation, visualization, and analysis filters to be plugged in. Sponsors: GoFigure is developed by Harvard University., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Harvard Medical School, Department of Systems Biology: The Megason Lab -GoFigure Software (RRID:SCR_008037) Copy   

•   Source: SciCrunch Registry

http://connectomics.org/viewer

Extensible, scriptable, pythonic software tool for visualization and analysis in structural neuroimaging research on many spatial scales. Employing the Connectome File Format, diverse data such as networks, surfaces, volumes, tracks and metadata are handled and integrated. The field of Connectomics research benefits from recent advances in structural neuroimaging technologies on all spatial scales. The need for software tools to visualize and analyze the emerging data is urgent. The ConnectomeViewer application was developed to meet the needs of basic and clinical neuroscientists, as well as complex network scientists, providing an integrative, extensible platform to visualize and analyze Connectomics data. With the Connectome File Format, interlinking different datatypes such as hierarchical networks, surface data, volumetric data is easy and might provide new ways of analyzing and interacting with data. Furthermore, ConnectomeViewer readily integrates with: * ConnectomeWiki: a semantic knowledge base representing connectomics data at a mesoscale level across various species, allowing easy access to relevant literature and databases. * ConnectomeDatabase: a repository to store and disseminate Connectome files.

Proper citation: ConnectomeViewer: Multi-Modal Multi-Level Network Visualization and Analysis (RRID:SCR_008312) Copy   

•   Source: SciCrunch Registry

http://www.broad.mit.edu/cancer/software/genecluster2/gc2.html

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 17, 2013. A software package for analyzing gene expression and other bioarray data, giving users a variety of methods to build and evaluate class predictors, visualize marker lists, cluster data and validate results. GeneCluster 2.0 greatly expands the data analysis capabilities of GeneCluster 1.0 by adding supervised classification, gene selection, class discovery and permutation test methods. It includes algorithms for building and testing supervised models using weighted voting (WV) and k-nearest neighbor (KNN) algorithms, a module for systematically finding and evaluating clustering via self-organizing maps, and modules for marker gene selection and heat map visualization that allow users to view and sort samples and genes by many criteria. It enhances the clustering capabilities of GeneCluster 1.0 by adding a module for batch SOM clustering, and also includes a marker gene finder based on a KNN analysis and a visualization module. GeneCluster 2.0 is a stand-alone Java application and runs on any platform that supports the Java Runtime Environment version 1.3.1 or greater.

Proper citation: GeneCluster 2: An Advanced Toolset for Bioarray Analysis (RRID:SCR_008446) Copy   

•   Source: SciCrunch Registry

http://www.biobankcentral.org/resource/wwibb.php

THIS RESOURCE IS NO LONGER IN SERVICE, documented on March 27, 2013. Web-based portal to connect all the constituencies in the global biobank community. The project seeks to increase the transparency and accessibility of the scientific research process by connecting researchers with an additional source of funding - microinvestments received from the broader online community. In exchange for these public investments, researchers will maintain research logs detailing the play-by-play progress made in their project, as well as publishing all of their data in a public database under a science commons license. These research projects, in turn, will serve to continually update a research-based neuroscience-based human brain & body curriculum. Biobanks are the meeting point of two major information trends in biomedical research: the generation of huge amounts of genomic and other laboratory data, and the electronic capture and integration of patient clinical records. They are comprised of large numbers of human biospecimens supplemented with clinical data. Biobanks when implemented effectively can harness the power of both genomic and clinical data and serve as a critical bridge between basic and applied research, linking laboratory to patient and getting to cures faster. As science and technology leaders work to address the many challenges facing U.S. biobanks logistical, technical, ethical, financial, intellectual property, and IT BioBank Central will serve as an accurate and timely source of knowledge and news about biorepositories and their role in research and drug development. The Web site also provides a working group venue, patient and public education programs, and a forum for international collaboration and harmonization of best practices.

Proper citation: BioBank Central (RRID:SCR_008645) Copy   

•   Source: SciCrunch Registry

http://rgd.mcw.edu/rgdCuration/?module=portal&func=show&name=nuro

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 12,2023. Portal that provides researchers with easy access to data on rat genes, QTLs, strain models, biological processes and pathways related to neurological diseases. This resource also includes dynamic data analysis tools.

Proper citation: Rat Genome Database: Neurological Disease Portal (RRID:SCR_008685) Copy   

•   Source: SciCrunch Registry

http://www.blogmydata.org/

Software tool for data sharing, incorporating blogs and spatial registration of data. Mainly used in geological data sets. A Virtual Research Environment (VRE) aims to combine the capabilities of two existing technologies that have already seen wide adoption among scientists: - The Godiva2 data visualization system provides a means for scientists to browse interactively in a ''Google Maps-like'' fashion through large environmental datasets, including numerical model outputs and high-resolution satellite imagery, using only a web browser. - The LabBlog is a web-based blogging tool specifically designed for the practising scientist to record, disseminate and evaluate their research. The Blog can also be used as a collaboration tool that allows secure discussion between colleagues. Although initially designed for the use of laboratory chemists, the LabBlog is being adapted in this project to meet the needs of environmental scientists. The BlogMyData VRE will allow scientists to explore data visually using Godiva2, then make comments about features in the data on a blog. Colleagues will discover these blog entries and offer further information, providing answers to research questions through comments. Through RSS and GeoRSS feeds, colleagues, investigators and other interested parties can be notified of research activity, and scientists can discover hitherto-unknown colleagues working with similar data in similar geographic regions. Sponsors: BlogMyData is a collaboration between the Reading e-Science Centre and the University of Southampton and is one of the JISC VRERI projects.

Proper citation: BlogMyData (RRID:SCR_008697) Copy   

•   Source: SciCrunch Registry

http://neuro-information.org/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 16, 2013. The International Observatory on Neuro-Information is the central source of knowledge, research and data on all skills and issues for Neuroscience applied in Information Sciences. It is an initiative of the Documentation Sciences Foundation, from Spain, which aims to gather information, express opinions, prepare documents, make comparative research, support and promote policy-making, evaluate trends, and take other appropriate action relating to the Neuroscience and its application to the Information Sciences (Libraries, Archives, Documentation centers), and how the traditional knowledge of Information Sciences can bring expertise in data visualization and retrieval techniques, records management, quality assurance and usability in Neuroscience. The Observatory may work together, or in agreement with other national or international organizations pursuing similar or compatible aims.

Proper citation: International Observatory on Neuro-Information (RRID:SCR_008690) Copy   

•   Source: SciCrunch Registry

https://www.brainproducts.com/

Commercial organization for hardware and software for neurophysiological research. Provides EEG and ERP amplifier systems, EEG recording caps, Data recording and analysis software, TMS Stimulator for combined EEG/TMS coregistrations and more.

Proper citation: Brain Products (RRID:SCR_009443) Copy   

•   Source: SciCrunch Registry

http://wbiomed.curtin.edu.au/genepop/

Population genetic data analysis software package. Used to perform exact Hardy Weinberg Equilibrium test. Used for population differentiation and for genotypic disequilibrium among pairs of loci. Computes estimates of F-statistics, null allele frequencies, allele size-based statistics for microsatellites, etc. and performs analyses of isolation by distance from pairwise comparisons of individuals or population samples., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: GENEPOP (RRID:SCR_009194) Copy   

•   Source: SciCrunch Registry

https://github.com/ABCD-STUDY/pearson-central-end-point

Data collection software as an end-point for centrally storing data from the Pearsons Q-Interactive.

Proper citation: pearson-central-end-point (RRID:SCR_016034) Copy   

•   Source: SciCrunch Registry

https://clue.io

Dataset of cellular signatures that catalogs transcriptional responses of human cells to chemical and genetic perturbation. CMap contains perturbagens, expression signatures, and small molecules from cell lines.

Proper citation: CMap (RRID:SCR_016204) Copy   

•   Source: SciCrunch Registry

http://ikrsrv1.medma.uni-heidelberg.de/redmine/projects/ummperfusion

Analysis software for dynamic contrast enhanced magnetic resonance images with implementation of a pixel-by-pixel deconvolution approach. It quantifies T1-weighted contrast-enhanced dynamic MR imaging (DCE-MRI) perfusion data as an OsiriX plug-in.

Proper citation: UMMPerfusion (RRID:SCR_015970) Copy   

•   Source: SciCrunch Registry

https://github.com/ABCD-STUDY/redcap-hook-framework

Software tool to organize and deploy custom hooks in a single project or across the entire instance. It features multi-language support for data entry and survey pages, a bar-code for text fields, and highlighting of rows on data entry and survey pages that have been filled out.

Proper citation: redcap-hook-framework (RRID:SCR_016028) Copy   

•   Source: SciCrunch Registry