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ReScience - The ReScience journal. Reproducible Science is Good. Replicated Science is better.


ReScience is a peer-reviewed journal that targets computational research and encourages the explicit replication of already published research promoting new and open-source implementations in order to ensure the original research is reproducible. To achieve such a goal, the whole editing chain is radically different from any other traditional scientific journal. ReScience lives on github where each new implementation is made available together with the comments, explanations and tests. Each submission takes the form of a pull request that is publicly reviewed and tested in order to guarantee any researcher can re-use it. If you ever replicated computational result from the literature, ReScience is the perfect place to publish this new implementation. Reproducible Science is Good. Replicated Science is better.

open-science-resources - A publicly-editable collection of open science resources, including tools, datasets, meta-resources, etc


Scientific data and tools should, as much as possible, be free as in beer and free as in freedom. The vast majority of science today is paid for by taxpayer-funded grants; at the same time, the incredible successes of science are strong evidence for the benefit of collaboration in knowledgable pursuits. Within the scientific academy, sharing of expertise, data, tools, etc. is prolific, but only recently with the rise of the Open Access movement has this sharing come to embrace the public. Even though most research data is never shared, both the public and even scientists in their own fields are often unaware of just much data, tools, and other resources are made freely available for analysis! This list is a small attempt at bringing light to data repositories and computational science tools that are often siloed according to each scientific discipline, in the hopes of spurring along both public and professional contributions to science. These categories are very non-exclusive, as many resources could fit multiple categories. If you're interested in computational neuroscience specifically, I've made a similar repo-list of open computational neuroscience resources here.

mfem - Mirror of MFEM - a lightweight, general, scalable C++ library for finite element methods

  •    C++

Mirror of MFEM - a lightweight, general, scalable C++ library for finite element methods. Please use the official repository, https://github.com/mfem/mfem, to create issues and pull requests. See also the MFEM website:

hiperc - High Performance Computing Strategies for Boundary Value Problems

  •    C

The example codes in this repository implement the same basic algorithm using whichever of the mainstream accelerator programming methods apply. Running the code on different parallel hardware configurations — CPU threading, GPU offloading, and CPU coprocessing — provides a benchmark of these tools using common computational materials science workloads. Comparing performance against the serial baseline will help you make informed decisions about which development pathways are appropriate for your scientific computing projects. Note that the examples do not depend on a particular simulation framework: dependencies are kept minimal, and the C functions are kept as simple as possible to enhance readability for study and reusability in other codes. The goal here is to learn how to use accelerators for materials science simulations, not to enhance or promote any particular software package. Generically speaking, OpenMP and OpenACC provide low barriers for entry into acceleration; CUDA and Xeon Phi require high investments for hardware and compilers, but offer the greatest capabilities for performance and optimization of a specific application. CUDA hardware can be emulated on the CPU using the MCUDA framework. Proof-of-concept trials on GPU and KNL hardware can be run on Amazon's EC2, Rescale's ScaleX, and equivalent HPC cloud computing platforms. Most of the current generation of research supercomputers contain GPU or KNL accelerator hardware, including Argonne National Labs' Bebop, NERSC Cori, TACC Stampede2, and XSEDE.

Py4Eng - A Programming Course for MATLAB® Users

  •    Jupyter

A Python programming course for MATLAB® users. I'm Yoav Ram I develop and give Python training for engineers, scientists, and everyone else.

OpenDreamKit - Main repository for sharing files and documents about OpenDreamKit

  •    TeX

Use existing infrastructure whenever possible (e.g. Sage's trac server), and in particular when contributing to existing software. Use ODK's github organisation for most public ODK specific infrastructure.

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