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http://help.sap.com/saphelp_ve-author80/helpdata/en/bc/b3a5c7d8ec4dbebe54199d2ba9a58a/frameset.htm

A desktop application used to create and manage 2D and 3D animation and video and audio assets. SAP Visual Enterprise Author can be used to render visual content into common desktop documents, file formats, and business applications for secure downstream use. A variety of tools allows users to view, transform, optimize, animate, and publish in different formats.

Proper citation: SAP Visual Enterprise Author (RRID:SCR_014293) Copy   

•   Source: SciCrunch Registry

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

A collection of methods for comparing the performance of different image algorithms. These methods generate quantitative scores that measure divergences to a standard.

Proper citation: SCORE (RRID:SCR_014165) Copy   

•   Source: SciCrunch Registry

https://simthyr.sourceforge.io

Open source simulation software for thyroid homeostasis, based on published nonlinear model of pituitary thyroid feedback control. Simulates pituitary thyroid feedback control, which allows users to study relationship between structure and behaviour of thyroid homeostasis.

Proper citation: SimThyr (RRID:SCR_014351) Copy   

•   Source: SciCrunch Registry

http://www.spotimaging.com/software/spot-basic/

Image capture software which creates publication quality images. SPOT allows users to view the live image and a preview of the captured image side by side, enabling image editing before capture. Enhancements such as calibration marks and measurements can be applied after image capture. An advanced version is also available.

Proper citation: SPOT Basic (RRID:SCR_014313) Copy   

•   Source: SciCrunch Registry

http://www.aevol.fr/

Simulation software for experimental evolution of microorganisms. Aevol is a digital genetics model for the study of structural variations of the genome (e.g. number of genes, synteny, proportion of coding sequences).

Proper citation: Aevol (RRID:SCR_015966) Copy   

•   Source: SciCrunch Registry

http://abacus.gene.ucl.ac.uk/software/indelible/

Software that generates nucleotide, amino acid and codon sequence data by simulating insertions and deletions (indels) as well as substitutions. It is used for biological sequence simulation of multi-partitioned nucleotide, amino-acid, or codon data sets through the processes of insertion, deletion, and substitution in continuous time.

Proper citation: Indelible (RRID:SCR_016163) Copy   

•   Source: SciCrunch Registry

http://bids.neuroimaging.io

Standard specification for organizing and describing outputs of neuroimaging experiments. Used to organize and describe neuroimaging and behavioral data by neuroscientific community as standard to organize and share data. BIDS prescribes file naming conventions and folder structure to store data in set of already existing file formats. Provides standardized templates to store associated metadata in form of Javascript Object Notation (JSON) and tab-separated value (TSV) files. Facilitates data sharing, metadata querying, and enables automatic data analysis pipelines. System to curate, aggregate, and annotate neuroimaging databases. Intended for magnetic resonance imaging data, magnetoencephalography data, electroencephalography data, and intracranial encephalography data.

Proper citation: Brain Imaging Data Structure (BIDs) (RRID:SCR_016124) Copy   

•   Source: SciCrunch Registry

http://interlex.org

The InterLex project - a core component of SciCrunch and supported by projects such as the Neuroscience Information Framework project (NIF), the NIDDK Information Network (dkNET), and the Open Data Commons for Spinal Cord Injury - is a dynamic lexicon of biomedical terms. Unlike an encyclopedia, a lexicon provides the meaning of a term, and not all there is to know about it. InterLex is being constructed to help improve the way that biomedical scientists communicate about their data, so that information systems like NIF and dkNET can find data more easily and provide more powerful means of integrating that data across distributed resources. One of the big roadblocks to data integration in the biomedical sciences is the inconsistent use of terminology in databases and other resources such as the literature. When we use the same terms to mean different things, we cannot easily ask questions that span across multiple resources. For example, if three databases have information about what genes are expressed in cortex, but they all use different definitions of cerebral cortex, then it is hard to compare them. InterLex allows for the association of data values (i.e. the value of a field or text within a field) to terminologies enabling the crowdsourcing of data-terminology mappings. InterLex was built on the foundation of NeuroLex (see Larson and Martone 2013 Neurolex: An online framework for neuroscience knowledge. Frontiers in Neuroinformatics, 7:18) and contains all of the existing NeuroLex terms. The initial entries in NeuroLex were built from the NIFSTD ontologies. NIFSTD currently has about 60,000 concepts (includes both classes and synonyms) that span gross anatomy, cells, subcellular structures, diseases, functions and techniques. InterLex models terms using primitives of the Web Ontology Language (OWL) and can export directly to a variety of standard ontology formats. A primary goal of interlex is to provide a stable layer on top of the many other existing terminologies, lexicons, and ontologies (i.e. provide a way to federate ontologies for data applications) and to provide a set of inter-lexical and inter-data-lexical mappings. In the future, InterLex will support user specific namespaces so that users can customize the exact definitions or ontologies they source from, as well as the relationships on those terms. Importantly, however, InterLex enforces a simple rule which is that terms which represent the same concept under the same superclass will maintain the same identifier fragment (i.e. 'ilx_1234567'). However, each user will be able to 'fork' a term into their own namespace (e.g. http://uri.interlex.org/user/ilx_1234567). This enables the various perspectives on a term or concept to have equal space so that the full diversity of views on a term can be seen and expressed. Sign-up for updates to get notified about updates to InterLex and when new features are available.

Proper citation: InterLex (RRID:SCR_016178) Copy   

•   Source: SciCrunch Registry

https://pdb-dev.wwpdb.org

Data repository for integrative/hybrid structural models of macromolecules and their assemblies. This includes atomistic models as well as multi-scale models consisting of different coarse-grained representations.

Proper citation: PDB-Dev (RRID:SCR_016185) Copy   

•   Source: SciCrunch Registry

http://vensim.com/

Simulation software for improving the performance of real systems. Used for developing, analyzing, and packaging dynamic feedback models.

Proper citation: Vensim (RRID:SCR_016394) Copy   

•   Source: SciCrunch Registry

https://www.schrodinger.com/macromodel

Software package for molecular modeling. Computes free energy changes using free energy perturbation method. Used to examine molecular conformations, molecular motion, and intermolecular interactions, such as those in a ligand-receptor complex.

Proper citation: MacroModel (RRID:SCR_016747) Copy   

•   Source: SciCrunch Registry

https://www.schrodinger.com/shape-screening

Software tool for shape-based superposition and similarity searching. Identifies new compounds with shapes (and, if desired, other properties) that are similar to the known binder.

Proper citation: Shape screening (RRID:SCR_016750) Copy   

•   Source: SciCrunch Registry

https://picrust.github.io/picrust/

Software package to predict metagenome functional content from marker gene (e.g., 16S rRNA) surveys and full genomes. Used to predict which gene families are present and then combines gene families to estimate the composite metagenome.

Proper citation: PICRUSt (RRID:SCR_016855) Copy   

•   Source: SciCrunch Registry

https://biosharing.org/collection/MIBBI

A common portal for minimum information (MI) checklists to act as a one-stop shop for those exploring the range of extant projects, foster collaborative development and ultimately promote gradual integration. Goals include * To increase the visibility of projects developing guidance for the reporting of biological and biomedical science. * To encourage appropriate collaborative development between projects to avoid duplication of effort or competition. * To promote the adoption of consensus guidance on reporting by journals and funders.

Proper citation: Minimum Information for Biological and Biomedical Investigations (RRID:SCR_002042) Copy   

•   Source: SciCrunch Registry

http://www.dendrites.org/software

Dendritica is a program package for relating dendritic geometry and signal propagation. The programs are based on those used for the simulations described in the following paper: Vetter, P., Roth, A. & Husser, M. (2001). Action potential propagation in dendrites depends on dendritic morphology. Journal of Neurophysiology, 85: 926-937. Dendritica can functionally be divided into three main parts: - Interactive morphological analysis and electrophysiological simulation of single cells - Automated batch simulations across a set of morphologies using the same simulation parameters - Automated analysis of batch simulation runs Dendritica requires NEURON 4.1.1 with some modifications described in Appendix 1. It was tested for NEURON 4.1.1 on Linux and SGI IRIX. Some modifications to the Dendritica code may be necessary in order to run it on older or newer versions of NEURON. Sponsors: This work was supported by the Wellcome Trust, the European Community, the Max-Planck-Gesellschaft, the Wellcome Trust 4-year PhD Programme in Neuroscience.

Proper citation: Dendritica: Software Tools for Studying Dendritic Signaling (RRID:SCR_001865) Copy   

•   Source: SciCrunch Registry

https://medicine.stonybrookmedicine.edu/pathology/neuropathology

This is a primer of basic neuropathology- The Central Nervous System and Skeletal Muscle. It is organized in chapters by category of disease with a separate chapter for skeletal muscle. Many of the diseases could be included in more than one chapter because of overlapping pathophysiology; in each case the disorder is included in a single section in the interest of convenience. In order to recognize pathology one must have a basic foundation in normal structure, so the first chapter is an overview of basic regional central nervous system structure and anatomy. It includes an introduction to neurohistology. Other chapters address the pathophysiology of different categories of disease and provide examples of gross and microscopic pathology when they are available.

Proper citation: Stony Brook University Medical Center: Neuropathology Primer (RRID:SCR_001866) Copy   

•   Source: SciCrunch Registry

http://omlc.ogi.edu/software/mc/

MCML is a Monte Carlo simulation program for Multi-layered Turbid Media with an infinitely narrow photon beam as the light source. The simulation is specified by an input text file called, for example, sample.mci, which can be modified by any simple text editor. The output is another text file called, for example, sample.mco. (The names are arbitrary.) CONV is a convolution program which uses the MCML output file to convolve for photon beams of any size in a Gaussian or flat field shape. CONV can provide a variety of output formats (reflectance, transmission, iso-fluence contours, etc.), which are compatible with standard graphics applications.

Proper citation: MCML and CONV (RRID:SCR_002419) Copy   

•   Source: SciCrunch Registry

http://www.thevirtualbrain.org/

Simulation software for modeling the entire human brain by combining structural and functional data from empirical neuroimaging data. It can generate local field potentials, EEG, MEG and fMRI BOLD data based on neural mass models. The user can also modify the model parameters to match clinical conditions from focal lesions or degenerative disorders.

Proper citation: Virtual brain (RRID:SCR_002249) Copy   

•   Source: SciCrunch Registry

https://arvados.org/

Bioinformatics platform for storing, organizing, processing, and sharing genomic and other biomedical big data. Designed to make it easier for bioinformaticians to develop analyses, developers to create genomic web applications and IT administers to manage large-scale compute and storage genomic resources. Designed to run on top of cloud operating systems such as Amazon Web Services and OpenStack. Currently, there are implementations that work on AWS and Xen+Debian/Ubuntu. Functionally, Arvados has two major sets of capabilities: (a) data management and (b) compute management.

Proper citation: Arvados (RRID:SCR_002223) Copy   

•   Source: SciCrunch Registry

https://github.com/seqan/seqan/tree/master/apps/mason2

Collection of software tools for simulating biological sequences, including simulations of genome fragment sampling, random genomic sequences, methylation levels, and NGS reads.

Proper citation: Mason (RRID:SCR_002476) Copy   

•   Source: SciCrunch Registry