Workshop materials for a course introducing digital scholarship and open research
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Updated
Nov 22, 2017 - TeX
Workshop materials for a course introducing digital scholarship and open research
⚗️ Aeromancy: A framework for performing reproducible AI and ML
Details for reproducing the experiments in our d-blink paper
Template for YODA's organigram on data analysis
Paper list and implementation (codes and results) of CNN-based single image super-resolution.
Reproducers for bugs/concepts in tox
A simple Lucene framework to get started with Information Retrieval experiments on TREC documents
Reproducible case for @std/esm + couchbase-node bug
Examples for Executable Research Compendia and compatible workspaces
A Survey on Reproducibility by Evaluating Deep Reinforcement Learning Algorithms on Real-World Robots using a fork of Kindred AI's SenseAct https://github.com/dti-research/SenseAct
Code used in the article "SATIN: A Persistent Musical Database for Music Information Retrieval" by Yann Bayle, Pierre Hanna and Matthias Robine in CBMI 2017. SATIN is a MIR dataset for reproducible research.
Print session information
Extra resources in the Collective Knowledge Format for ARM's Workload Automation Framework:
A logical and reasonably simple project structure for developing quantitative trading work like strategies and research works.
Experimental artefacts to reproduce our results published at SoCC 2017
This protocol describes the measurement of epithelial barrier permeability in real-time following pharmacologic treatment in human intestinal organoids using fluorescent microscopy and live cell microscopy
Source code and data for the paper "Data Assimilation in Large Prandtl Rayleigh-Bénard Convection from Thermal Measurements" by A. Farhat, N. E. Glatt-Holtz, V. R. Martinez, S. A. McQuarrie, and J. P. Whitehead.
Python code to reproduce our article "Toward faultless content-based playlists generation for instrumentals"
Tool demonstrating building credit risk models
Crowdsourcing video experiments (such as collaborative benchmarking and optimization of DNN algorithms) using Collective Knowledge Framework across diverse Android devices provided by volunteers. Results are continuously aggregated in the open repository:
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