18 projects
empaia-app-test-suite
The EMPAIA App Test Suite (EATS)
cc-faice
FAICE (Fair Collaboration and Experiments) is part of the Curious Containers project. It enables researchers to perform and distribute reproducible data-driven experiments defined in the RED format.
cc-agency
CC-Agency is part of the Curious Containers project. It connects to a cluster of docker-engines for the distributed execution of reproducible data-driven experiments defined in the RED format.
cc-core
CC-Core is part of the Curious Containers project. It contains shared code of the CC-FAICE and CC-Agency packages.
red-fill
RED-fill is part of the Curious Containers project. It provides functionality to resolve template keys for RED clients.
red-val
RED-val is part of the Curious Containers project. It provides functionality to implement red clients and red execution engines.
red-connector-ftp
RED Connector FTP is part of the Curious Containers project.
red-connector-ssh
RED Connector SSH is part of the Curious Containers project.
red-connector-xnat
RED Connector XNAT is part of the Curious Containers project.
red-connector-http
RED Connector HTTP is part of the Curious Containers project.
edfrd
edfrd is a Python 3 software library to read and write EDF files.
xnat-access
XNAT Access.
red-connector-httpdirfs
RED Connector HTTP is part of the Curious Containers project.
red-connector-sshfs
RED Connector SSHFS is part of the Curious Containers project.
cc-connector-cli
CC Connector CLI is part of the Curious Containers project.
faice
FAICE (Fair Collaboration and Experiments) is a tool suite, helping researchers to work with experiments published in the FAICE description format. The FAICE software is developed at CBMI (HTW Berlin - University of Applied Sciences)
stance
stance provides self-instantiating worker processes for Python 3
brocas-lm
Broca's LM is a free python library providing a probabilistic language model based on a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM). It utilizes Gensim's Word2Vec implementation to transform input word sequences into a dense vector space. The output of the model is a seqeuence of probability distributions across the given vocabulary.
