Reproducible Research in Computational Science

 

“It doesn't matter how beautiful your theory is, it doesn't matter how smart you are. If it doesn't agree with experiment, it's wrong” - Richard Feynman

 

"As a method for finding things out, science lives by its disdain for authority and its reliance on experimentation." - Chris Quigg

 

Welcome to this site about reproducible research in computational science (including signal processing, computer vision, machine learning and neural computation). This site is intended to share the source codes of the latest advances in various technical fields to the best of my knowledge. Only through Reproducible Research (RR), can we live up to the standard that hard-core science has established since Bacon and Newton. If you know of any release of the source codes that is missing from the list or any broken link, please kindly let me know.

Image denoising

Image coding

Image demosaicing

Image interpolation and Superresolution

RGBD image processing

Image segmentation/parsing and matting

Image deblurring

Blind image deblurring

Texture synthesis

Image inpainting

PDE-based image processing

Image quality assessment

Biometrics

Gradient-domain image processing

Video coding

Texture/Shape/Image classification

Object detection/recognition

Image registration/mosaicing/OFE

Action/event/scene recognition

Visual tracking

Saliency/Objectness Detection

 

Low rank methods

Deep Learning

Manifold learning and embedding

Wavelets and frames

Compressed Sensing

HDR imaging

Biomedical Imaging

2D Phase Unwrapping

DTI and fiber tractography

Data Clustering

Stereo matching& Multiview geometry

Graphics, Cartoons,Motion&3D

 

Machine learning&Neural Networks

Blind source separation

Camera calibration

Sampling&Physics-based Simulation

Evolutionary computing/Optimization

Networking Research

Miscellaneous

Links to other communities' reproducible research effort

Links to reproducible books/journals/tutorials

Links to other individual's reproducible research



Google Scholar is great but if most papers in computational sciences could be accessed along with their source codes (not just the citation number), the world for scientific researchers will be even better. It is easy to find papers these days but when can finding the source codes of a paper become easy too? I think Don Knuth's old-day advices on Literate Programming are still relevant to the current state of reproducible research. I believe that the time is ripe for significantly promoting experimentally reproducible research (just like mathemathetical theories - mentally reproducible research), and that we can best achieve this by considering research codes to be works of literature (so they can be easily picked up by other researchers). Only when the reproducibility of research in computational science becomes a default instead of a luxury, can we look further by standing on each other's shoulders.
 
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