Scientific Software
Free and open source software for the scientific analysis of data, including web-based applications, software for research, data processing, data analysis, visualization, etc. Includes free, publicly available software documentation and programming tutorials. Commercial software links require sponsorship.
220 listings
Submitted Dec 29, 2016 to Scientific Software An open source, distributed, deep learning library for Java.
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Submitted Dec 29, 2016 to Scientific Software Lasagne is a lightweight library to build and train neural networks in Theano.
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Submitted Dec 29, 2016 to Scientific Software Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Theano has been powering large-scale computationally intensive scientific investigations since 2007. But it is also approachable enough to be used in the classroom, such as University of Montreal’s deep learning/machine learning classes.
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Submitted Dec 29, 2016 to Scientific Software Torch is a scientific computing framework with wide support for machine learning algorithms that puts GPUs first. It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.
The goal of Torch is to have maximum flexibility and speed in building your scientific algorithms while making the process extremely simple. Torch comes with a large ecosystem of community-driven packages in machine learning, computer vision, signal processing, parallel processing, image, video, audio and networking among others, and builds on top of the Lua community. At the heart of Torch are the popular neural network and optimization libraries which are simple to use, while having maximum flexibility in implementing complex neural network topologies. You can build arbitrary graphs of neural networks, and parallelize them over CPUs and GPUs in an efficient manner. |
Submitted Dec 29, 2016 to Scientific Software Keras is a high-level neural networks library, written in Python and capable of running on top of either TensorFlow or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.
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Submitted Dec 29, 2016 to Scientific Software Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Center (BVLC) and by community contributors. Yangqing Jia created the project during his PhD at UC Berkeley. Caffe is released under the BSD 2-Clause license.
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Submitted Dec 29, 2016 to Scientific Software This page is a curated collection of Jupyter/IPython notebooks that are notable for some reason. The notebooks cover a range of topics from scientific computing to social data, physics, chemistry, biology, signal processing, natural language processing, and more.
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Submitted Dec 29, 2016 to Scientific Software Word2vec is a two-layer neural net that processes text. Its input is a text corpus and its output is a set of vectors: feature vectors for words in that corpus. Gensim's Python implementation of word2vec is easy to train and use.
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Submitted Dec 29, 2016 to Scientific Software OpenCV was designed for computational efficiency and with a strong focus on real-time applications. Usage ranges from interactive art, to mines inspection, stitching maps on the web or through advanced robotics. Written in optimized C/C++, the library can take advantage of multi-core processing. Adopted all around the world, OpenCV has more than 47 thousand people of user community and estimated number of downloads exceeding 9 million.
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Submitted Dec 28, 2016 to Scientific Software CodaLab is an open-source platform that provides an ecosystem for conducting computational research in a more efficient, reproducible, and collaborative manner. There are two aspects of CodaLab: worksheets and competitions.
Worksheets allow you to capture complex research pipelines in a reproducible way and create "executable papers" using any data format or programming language. Competitions bring together the entire community to tackle data and computational problems. Win prizes or create your own competition. |
Submitted Dec 25, 2016 to Scientific Software These tutorials by Miriam Posner at UCLA provide a basic introduction to using Cytoscape to conduct network analysis of humanistic data. They're intended to be used with the data collected about early African-American silent film.
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Submitted Dec 25, 2016 to Scientific Software Cytoscape is an open source software platform for visualizing molecular interaction networks and biological pathways and integrating these networks with annotations, gene expression profiles and other state data. Although Cytoscape was originally designed for biological research, now it is a general platform for complex network analysis and visualization. Cytoscape core distribution provides a basic set of features for data integration, analysis, and visualization. Additional features are available as Apps (formerly called Plugins). Apps are available for network and molecular profiling analyses, new layouts, additional file format support, scripting, and connection with databases. They may be developed by anyone using the Cytoscape open API based on Java™ technology and App community development is encouraged. Most of the Apps are freely available from Cytoscape App Store.
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Submitted Dec 25, 2016 (Edited Dec 29, 2016) to Scientific Software The bplib is a library implementing support for computations on groups supporting bilinear pairings, as used in modern cryptography. It is based on the OpenPairing library by Diego Aranha (https://github.com/dfaranha/OpenPairing), which is itself based on, and compatible with, OpenSSL math functions (bn and ec). The bplib is compatible with petlib types including petlib.bn and the group G1 is a petlib.ec EC group. Along with petlib, they provide easy to use support for maths and ciphers used in modern Privacy Enhancing Technologies.
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Submitted Dec 24, 2016 to Scientific Software The Astropy Project is a community effort to develop a core package for astronomy using the Python programming language and improve usability, interoperability, and collaboration between astronomy Python packages. The core astropy package contains functionality aimed at professional astronomers and astrophysicists, but may be useful to anyone developing astronomy software. The Astropy Project also includes "affiliated packages," Python packages that are not necessarily developed by the core development team, but share the goals of Astropy, and often build from the core package's code and infrastructure.
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Submitted Dec 24, 2016 to Scientific Software Phrasemachine is an R/Python library for noun phrase extraction by Brendan O'Connor. The phrasemachine library uses part-of-speech tagging to extract noun phrases, which are the phrases most likely to be of interest for analysis. This blog post by Stefan McCabe at Northeastern University describes a simple use case and links to the github for phrasemachine.
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Submitted Dec 23, 2016 to Scientific Software pyqg is a python solver for quasigeostrophic systems. Quasigeostophic equations are an approximation to the full fluid equations of motion in the limit of strong rotation and stratitifcation and are most applicable to geophysical fluid dynamics problems.
Students and researchers in ocean and atmospheric dynamics are the intended audience of pyqg. The model is simple enough to be used by students new to the field yet powerful enough for research. We strive for clear documentation and thorough testing. pyqg supports a variety of different configurations using the same computational kernel. The different configurations are evolving and are described in detail in the documentation. The kernel, implement in cython, uses a pseudo-spectral method which is heavily dependent of the fast Fourier transform. For this reason, pyqg tries to use pyfftw and the FFTW Fourier Transform library. (If pyfftw is not available, it falls back on numpy.fft) With pyfftw, the kernel is multi-threaded but does not support mpi. Optimal performance will be achieved on a single system with many cores. |
Submitted Dec 23, 2016 to Scientific Software xgcm is a python package for analyzing general circulation model (GCM) output data. xgcm is built on top of xray.
xgcm is motivated by the fact that our general circulation models are getting bigger and bigger, and we are in desperate need of some scalable analysis tools. The main functionality xgcm adds on top of xray is an more complex representation of the grids commonly used in numerical modeling and an implementation of differential and integral operators (e.g. gradients) in the specific ways appropriate to these grids. |
Submitted Dec 23, 2016 to Scientific Software The code in this repository implements an efficient generalization of the popular Convolutional Neural Networks (CNNs) to arbitrary graphs, presented in the paper: Michaël Defferrard, Xavier Bresson, Pierre Vandergheynst, Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering, Neural Information Processing Systems (NIPS), 2016.
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Submitted Dec 23, 2016 to Scientific Software WorldWatcher, a supportive scientific visualization environment for the investigation of scientific data from the GEODE Initiative project at Northwestern University, has been in use in K-12 and college classrooms since April 1996. Like its predecessor, WorldWatcher provides an accessible and supportive environment for students to explore, create, and analyze scientific data. Its goal is to provide students with access to the same features found in the powerful, general-purpose visualization environments that scientists use while providing them with the support they require to learn through the use of the tools.
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Submitted Dec 23, 2016 to Scientific Software FEAP is a general purpose finite element analysis program which is designed for research and educational use. Source code of the full program is available for compilation using Windows (Compaq or Intel compiler), LINUX or UNIX operating systems, and Mac OS X based Apple systems.
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